1
|
Janiak E, Rexrode K, Santacroce L, Johns SL, Behn M, Braaten KP, Feldman CH. Screening to understand pregnancy preferences and offer referrals and treatment (SUPPORT): Results of a pilot quality improvement initiative. PLoS One 2024; 19:e0303930. [PMID: 39074124 DOI: 10.1371/journal.pone.0303930] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Accepted: 05/03/2024] [Indexed: 07/31/2024] Open
Abstract
OBJECTIVE To assess the feasibility of integrating a pregnancy intention assessment screening algorithm into the electronic medical record (EMR) at a multispecialty clinic focused on the health of women and people assigned female at birth (AFAB). STUDY DESIGN This pilot quality improvement project implemented a series of clinician reminders, new data fields in the patient record, and templated clinical notes to prompt care providers across specialties to ask AFAB reproductive age individuals about their desire for future pregnancies. Investigators created a novel screening question based on prior literature and expert input. Prospective observational study of one year of during-intervention EMR data on screening uptake and documentation, contraceptive use, and referrals to obstetrics and gynecology (OBGYN) for preconception care, contraceptive care, and related services. RESULTS SUPPORT launched in February 2020 and was paused for 6 months due to the COVID-19 pandemic. During the intervention period through July 2021, 18% of patients for whom the automated screening reminder was activated had a documented pregnancy intention. Patients were screened in OBGYN, internal medicine, and eight subspecialty medical clinics. Among those screened, individuals who reported they did not desire pregnancy in the next year were more likely to use contraception (aOR 1.8, 95% CI 1.1, 3.1). Individuals that did desire pregnancy in the next year were more likely to be subsequently referred to OBGYN (aOR 2.7, 95% CI 1.2, 6.0). CONCLUSIONS Despite the competing demands of the COVID-19 pandemic, the SUPPORT intervention was utilized at higher rates than prior similar interventions and across multiple disease specialties. IMPLICATIONS Results from the SUPPORT pilot suggest that pregnancy intention screening of reproductive age AFAB individuals with an EMR-based screening prompt is feasible at scale.
Collapse
Affiliation(s)
- Elizabeth Janiak
- Department of Obstetrics and Gynecology, Brigham and Women's Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
- Harvard TH Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Kathryn Rexrode
- Harvard Medical School, Boston, Massachusetts, United States of America
- Department of Medicine, Division of Women's Health, Brigham and Women's Hospital, Boston, Massachusetts, United States of America
| | - Leah Santacroce
- Department of Medicine, Division of Rheumatology, Inflammation, and Immunity, Brigham and Women's Hospital, Boston, Massachusetts, United States of America
| | - Sarah L Johns
- Department of Obstetrics and Gynecology, Brigham and Women's Hospital, Boston, Massachusetts, United States of America
- Harvard TH Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Maya Behn
- Harvard Medical School, Boston, Massachusetts, United States of America
- Department of Medicine, Division of Women's Health, Brigham and Women's Hospital, Boston, Massachusetts, United States of America
| | - Kari P Braaten
- Department of Obstetrics and Gynecology, Brigham and Women's Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
- Department of Medicine, Division of Women's Health, Brigham and Women's Hospital, Boston, Massachusetts, United States of America
| | - Candace H Feldman
- Harvard Medical School, Boston, Massachusetts, United States of America
- Department of Medicine, Division of Rheumatology, Inflammation, and Immunity, Brigham and Women's Hospital, Boston, Massachusetts, United States of America
| |
Collapse
|
2
|
Flores E, Martínez-Racaj L, Torreblanca R, Blasco A, Lopez-Garrigós M, Gutiérrez I, Salinas M. Clinical Decision Support System in laboratory medicine. Clin Chem Lab Med 2024; 62:1277-1282. [PMID: 38044692 DOI: 10.1515/cclm-2023-1239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Accepted: 11/24/2023] [Indexed: 12/05/2023]
Abstract
Clinical Decision Support Systems (CDSS) have been implemented in almost all healthcare settings. Laboratory medicine (LM), is one of the most important structured health data stores, but efforts are still needed to clarify the use and scope of these tools, especially in the laboratory setting. The aim is to clarify CDSS concept in LM, in the last decade. There is no consensus on the definition of CDSS in LM. A theoretical definition of CDSS in LM should capture the aim of driving significant improvements in LM mission, prevention, diagnosis, monitoring, and disease treatment. We identified the types, workflow and data sources of CDSS. The main applications of CDSS in LM were diagnostic support and clinical management, patient safety, workflow improvements, and cost containment. Laboratory professionals, with their expertise in quality improvement and quality assurance, have a chance to be leaders in CDSS.
Collapse
Affiliation(s)
- Emilio Flores
- Clinical Laboratory, University Hospital Sant Joan d'Alacant, San Juan de Alicante, Spain
- Clinical Medicine Department, Universidad Miguel Hernandez, San Juan de Alicante, Spain
| | - Laura Martínez-Racaj
- Clinical Laboratory, University Hospital Sant Joan d'Alacant, San Juan de Alicante, Spain
- Fundación para el Fomento de la Investigación Sanitaria y Biomédica de la Comunitat Valenciana (FISABIO), Valencia, Spain
| | - Ruth Torreblanca
- Clinical Laboratory, University Hospital Sant Joan d'Alacant, San Juan de Alicante, Spain
| | - Alvaro Blasco
- Clinical Laboratory, University Hospital Sant Joan d'Alacant, San Juan de Alicante, Spain
| | - Maite Lopez-Garrigós
- Clinical Laboratory, University Hospital Sant Joan d'Alacant, San Juan de Alicante, Spain
- Department of Biochemistry and Molecular Pathology, Universidad Miguel Hernandez, Elche, Spain
| | - Irene Gutiérrez
- Clinical Laboratory, University Hospital Sant Joan d'Alacant, San Juan de Alicante, Spain
| | - Maria Salinas
- Clinical Laboratory, University Hospital Sant Joan d'Alacant, San Juan de Alicante, Spain
| |
Collapse
|
3
|
Soames J, Pettigrew LM. Electronic health record-based behaviour change interventions aimed at general practitioners in the UK: a mixed methods systematic review using behaviour change theory. BMJ Open 2024; 14:e080546. [PMID: 38816046 PMCID: PMC11141199 DOI: 10.1136/bmjopen-2023-080546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Accepted: 03/24/2024] [Indexed: 06/01/2024] Open
Abstract
OBJECTIVES Electronic health record (EHR) systems are used extensively in healthcare; their design can influence clinicians' behaviour. We conducted a systematic review of EHR-based interventions aimed at changing the clinical practice of general practitioners in the UK, assessed their effectiveness and applied behaviour change theory to identify lessons for other settings. DESIGN Mixed methods systematic review. DATA SOURCES MEDLINE, EMBASE, CENTRAL and APA PsycINFO were searched up to March 2023. ELIGIBILITY CRITERIA Quantitative and qualitative findings from randomised controlled trials (RCTs) controlled before-and-after studies and interrupted time series of EHR-based interventions in UK general practice were included. DATA EXTRACTION AND SYNTHESIS Quantitative synthesis was based on Cochrane's Synthesis without Meta-analysis. Interventions were categorised using the Behaviour Change Wheel and MINDSPACE frameworks and effectiveness determined by vote-counting using direction of effect. Inductive thematic synthesis was used for qualitative studies. RESULTS Database searching identified 3824 unique articles; 10 were included (from 2002 to 2021), comprising eight RCTs and two associated qualitative studies. Four of seven quantitative studies showed a positive effect on clinician behaviour and three on patient-level outcomes. Behaviour change techniques that may trigger emotions and required less cognitive engagement appeared to have positive effects. Qualitative findings indicated that interventions reassured clinicians of their decisions but were sometimes ignored. CONCLUSION Despite widespread use, there is little high quality, up-to-date experimental evidence evaluating the effectiveness of EHR-based interventions in UK general practice. The evidence suggested EHR-based interventions may be effective at changing behaviour. Persistent, simple action-oriented prompts appeared more effective than complex interventions requiring greater cognitive engagement. However, studies lacked detail in intervention design and theory behind design choices. Future research should seek to optimise EHR-based behaviour change intervention design and delineate limitations, providing theory-based justification for interventions. This will be of increasing importance with the growing use of EHRs to influence clinicians' decisions. PROSPERO REGISTRATION NUMBER CRD42022341009.
Collapse
Affiliation(s)
- Jamie Soames
- Department of Health Services Research and Policy, Faculty of Public Health and Policy, London School of Hygiene & Tropical Medicine, London, UK
| | - Luisa M Pettigrew
- Department of Health Services Research and Policy, Faculty of Public Health and Policy, London School of Hygiene & Tropical Medicine, London, UK
| |
Collapse
|
4
|
De Coninck L, Declercq A, Bouckaert L, Döpp C, Graff MJL, Aertgeerts B. Promoting meaningful activities by occupational therapy in elderly care in Belgium: the ProMOTE intervention. BMC Geriatr 2024; 24:275. [PMID: 38509458 PMCID: PMC10953191 DOI: 10.1186/s12877-024-04797-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 02/08/2024] [Indexed: 03/22/2024] Open
Abstract
BACKGROUND Older people want to age in place. Despite advancing functional limitations and their desire of aging in place, they are not always faithful to therapy that maintains independence and promotes safety. Occupational therapists can facilitate aging in place. Occupational therapy is defined as the therapeutic use of everyday life occupations with persons, groups, or populations for the purpose of enhancing or enabling participation. AIM To describe the content a high-adherence-to-therapy and evidence-based occupational therapy intervention to optimize functional performance and social participation of home-based physically frail older adults and wellbeing of their informal caregiver, and the research activities undertaken to design this intervention. METHODS A roadmap was created to develop the occupational therapy intervention. This roadmap is based on the Medical Research Council (MRC) framework and is supplemented with elements of the Intervention Mapping approach. The TIDieR checklist is applied to describe the intervention in detail. A systematic review and two qualitative studies substantiated the content of the intervention scientifically. RESULTS The application of the first two phases of the MRC framework resulted in the ProMOTE intervention (Promoting Meaningful activities by Occupational Therapy in Elderly). The ProMOTE intervention is a high-adherence-to-therapy occupational therapy intervention that consists of six steps and describes in detail the evidence-based components that are required to obtain an operational intervention for occupational therapy practice. CONCLUSION This study transparently reflects on the process of a high-quality occupational therapy intervention to optimize the functional performance and social participation of the home-based physically frail older adult and describes the ProMOTE intervention in detail. The ProMOTE intervention contributes to safely aging in place and to maintaining social participation. The designed intervention goes beyond a description of the 'what'. The added value lies in the interweaving of the 'why' and 'how'. By describing the 'how', our study makes the concept of 'therapeutic use-of-self' operational throughout the six steps of the occupational therapy intervention. A further rigorous study of the effect of the ProMOTE intervention on adherence, functional performance and social participation is recommended based to facilitate the implementation of this intervention on a national level in Belgium.
Collapse
Affiliation(s)
- Leen De Coninck
- Academic Center for General Practice, Department of Public Health and Primary Care, KU Leuven, Kapucijnenvoer 7, Leuven, 3000, Belgium.
- CEBAM Belgian Center for Evidence-based Medicine vzw, Kapucijnenvoer 7, Leuven, 3000, Belgium.
| | - Anja Declercq
- LUCAS Center for Care Research and Consultancy & CESO Center for Sociological Research, KU Leuven, Minderbroedersstraat 8, Leuven, 3000, Belgium
| | - Leen Bouckaert
- Department of Occupational Therapy, Artevelde University of Applied Sciences, Voetweg 66, Ghent, 9000, Belgium
| | - Carola Döpp
- Scientific Institute for Quality of Health Care, Department of Rehabilitation, Radboudumc Research Institute, Radboud University Medical Center, Houtlaan 4, Nijmegen, 6525 XZ, The Netherlands
| | - Maud J L Graff
- Scientific Institute for Quality of Health Care, Department of Rehabilitation, Radboudumc Research Institute, Radboud University Medical Center, Houtlaan 4, Nijmegen, 6525 XZ, The Netherlands
| | - Bert Aertgeerts
- Academic Center for General Practice, Department of Public Health and Primary Care, KU Leuven, Kapucijnenvoer 7, Leuven, 3000, Belgium.
- CEBAM Belgian Center for Evidence-based Medicine vzw, Kapucijnenvoer 7, Leuven, 3000, Belgium.
| |
Collapse
|
5
|
García-García JA, Carrero M, Escalona MJ, Lizcano D. Evaluation of clinical practice guideline-derived clinical decision support systems using a novel quality model. J Biomed Inform 2024; 149:104573. [PMID: 38081565 DOI: 10.1016/j.jbi.2023.104573] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Revised: 11/26/2023] [Accepted: 12/08/2023] [Indexed: 01/22/2024]
Abstract
Over the last decade, clinical practice guidelines (CPGs) have become an important asset for daily life in healthcare organizations. Efficient management and digitization of CPGs help achieve organizational objectives and improve patient care and healthcare quality by reducing variability. However, digitizing CPGs is a difficult, complex task because they are usually expressed as text, and this often leads to the development of partial software solutions. At present, different research proposals and CPG-derived CDSS (clinical decision support system) do exist for managing CPG digitalization lifecycles (from modeling to deployment and execution), but they do not all provide full lifecycle support, making it more difficult to choose solutions or proposals that fully meet the needs of a healthcare organization. This paper proposes a method based on quality models to uniformly compare and evaluate technological tools, providing a rigorous method that uses qualitative and quantitative analysis of technological aspects. In addition, this paper also presents how this method has been instantiated to evaluate and compare CPG-derived CDSS by highlighting each phase of the CPG digitization lifecycle. Finally, discussion and analysis of currently available tools are presented, identifying gaps and limitations.
Collapse
Affiliation(s)
- Julián Alberto García-García
- ES3 Group (Engineering and Science for Software Systems group). Computer Languages and Systems Department. Escuela Técnica Superior de Ingeniería Informática., Avda. Reina Mercedes s/n. 41012 Seville, Spain.
| | - Manuel Carrero
- ES3 Group (Engineering and Science for Software Systems group). Computer Languages and Systems Department. Escuela Técnica Superior de Ingeniería Informática., Avda. Reina Mercedes s/n. 41012 Seville, Spain.
| | - María José Escalona
- ES3 Group (Engineering and Science for Software Systems group). Computer Languages and Systems Department. Escuela Técnica Superior de Ingeniería Informática., Avda. Reina Mercedes s/n. 41012 Seville, Spain.
| | - David Lizcano
- School of Computer Science, Madrid Open University, UDIMA., Campus Collado Villalba, Madrid, Spain.
| |
Collapse
|
6
|
Cornell S, Doust J, Morgan M, Greaves K, Hawkes AL, de Wet C, O'Connor D, Bonner C. Implementing patient decision aids into general practice clinical decision support systems: Feasibility study in cardiovascular disease prevention. PEC INNOVATION 2023; 2:100140. [PMID: 37214489 PMCID: PMC10194094 DOI: 10.1016/j.pecinn.2023.100140] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 02/18/2023] [Accepted: 02/18/2023] [Indexed: 05/24/2023]
Abstract
Objective Patient decision aids (DA) facilitate shared decision making, but implementation remains a challenge. This study tested the feasibility of integrating a cardiovascular disease (CVD) prevention DA into general practice software. Methods We developed a desktop computer application (app) to auto-populate a CVD prevention DA from general practice medical records. 4 practices received monthly practice reports from July-Nov 2021, and 2 practices use the app with limited engagement. CVD risk assessment data and app use were monitored. Results The proportion of eligible patients with complete CVD risk assessment data ranged from 59 to 94%. Monthly app use ranged from 0 to 285 sessions by 13 individual practice staff including GPs and nurses, with staff using the app an average of 67 sessions during the study period. High users in the 5-month study period continued to use the app for 10 months. Low use was attributed to reduced staff capacity during COVID-19 and technical issues. Conclusion High users sustained interest in the app, but additional strategies are required for low users. The study will inform implementation plans for new guidelines. Innovation This study showed it is feasible to integrate patient decision aids with Australian general practice software, despite the challenges of COVID-19 at the time of the study.
Collapse
Affiliation(s)
- Samuel Cornell
- Faculty of Medicine and Health, School of Public Health, University of Sydney, New South Wales, Australia
| | - Jenny Doust
- Australian Women and Girls' Health Research (AWaGHR) Centre, School of Public Health, Faculty of Medicine, University of Queensland, Queensland, Australia
| | - Mark Morgan
- Faculty of Health Sciences & Medicine, Bond University, Queensland, Australia
| | - Kim Greaves
- Department of Cardiology, Sunshine Coast University Hospital, University of the Sunshine Coast, Queensland, Australia
| | | | - Carl de Wet
- Gold Coast Hospital and Health Service, Queensland, Australia
| | - Denise O'Connor
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Victoria, Australia
- Monash-Cabrini Department of Musculoskeletal Health and Clinical Epidemiology, Cabrini Health, Victoria, Australia
| | - Carissa Bonner
- Faculty of Medicine and Health, School of Public Health, University of Sydney, New South Wales, Australia
| |
Collapse
|
7
|
Mazurenko O, McCord E, McDonnell C, Apathy NC, Sanner L, Adams MCB, Mamlin BW, Vest JR, Hurley RW, Harle CA. Examining primary care provider experiences with using a clinical decision support tool for pain management. JAMIA Open 2023; 6:ooad063. [PMID: 37575955 PMCID: PMC10412405 DOI: 10.1093/jamiaopen/ooad063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Revised: 06/22/2023] [Accepted: 07/25/2023] [Indexed: 08/15/2023] Open
Abstract
Objective To evaluate primary care provider (PCP) experiences using a clinical decision support (CDS) tool over 16 months following a user-centered design process and implementation. Materials and Methods We conducted a qualitative evaluation of the Chronic Pain OneSheet (OneSheet), a chronic pain CDS tool. OneSheet provides pain- and opioid-related risks, benefits, and treatment information for patients with chronic pain to PCPs. Using the 5 Rights of CDS framework, we conducted and analyzed semi-structured interviews with 19 PCPs across 2 academic health systems. Results PCPs stated that OneSheet mostly contained the right information required to treat patients with chronic pain and was correctly located in the electronic health record. PCPs used OneSheet for distinct subgroups of patients with chronic pain, including patients prescribed opioids, with poorly controlled pain, or new to a provider or clinic. PCPs reported variable workflow integration and selective use of certain OneSheet features driven by their preferences and patient population. PCPs recommended broadening OneSheet access to clinical staff and patients for data entry to address clinician time constraints. Discussion Differences in patient subpopulations and workflow preferences had an outsized effect on CDS tool use even when the CDS contained the right information identified in a user-centered design process. Conclusions To increase adoption and use, CDS design and implementation processes may benefit from increased tailoring that accommodates variation and dynamics among patients, visits, and providers.
Collapse
Affiliation(s)
- Olena Mazurenko
- Department of Health Policy and Management, Indiana University Richard M. Fairbanks School of Public Health, Indianapolis, Indiana, USA
- Clem McDonald Center for Biomedical Informatics, Regenstrief Institute, Indianapolis, Indiana, USA
| | - Emma McCord
- Department of Health Policy and Management, Indiana University Richard M. Fairbanks School of Public Health, Indianapolis, Indiana, USA
| | - Cara McDonnell
- Anesthesiology, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA
| | - Nate C Apathy
- Clem McDonald Center for Biomedical Informatics, Regenstrief Institute, Indianapolis, Indiana, USA
- MedStar Health Research Institute
| | - Lindsey Sanner
- Department of Health Policy and Management, Indiana University Richard M. Fairbanks School of Public Health, Indianapolis, Indiana, USA
| | - Meredith C B Adams
- Anesthesiology, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA
| | - Burke W Mamlin
- Clem McDonald Center for Biomedical Informatics, Regenstrief Institute, Indianapolis, Indiana, USA
- School of Medicine, Indiana University, Indianapolis, Indiana, USA
| | - Joshua R Vest
- Department of Health Policy and Management, Indiana University Richard M. Fairbanks School of Public Health, Indianapolis, Indiana, USA
- Clem McDonald Center for Biomedical Informatics, Regenstrief Institute, Indianapolis, Indiana, USA
| | - Robert W Hurley
- Anesthesiology, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA
| | - Christopher A Harle
- Department of Health Policy and Management, Indiana University Richard M. Fairbanks School of Public Health, Indianapolis, Indiana, USA
- Clem McDonald Center for Biomedical Informatics, Regenstrief Institute, Indianapolis, Indiana, USA
| |
Collapse
|
8
|
Parra-Calderón CL, Román-Villarán E, Alvarez-Romero C, Escobar-Rodríguez GA, Martínez-Brocca MA, Martínez-García A, García-García JA, Escalona-Cuaresma MJ. A prospective observational concordance study to evaluate computational model-driven clinical practice guidelines for Type 2 diabetes mellitus. Int J Med Inform 2023; 178:105208. [PMID: 37703798 DOI: 10.1016/j.ijmedinf.2023.105208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2023] [Revised: 08/18/2023] [Accepted: 08/30/2023] [Indexed: 09/15/2023]
Abstract
BACKGROUND Clinical Practice Guidelines (CPGs) provide healthcare professionals with performance and decision-making support during the treatment of patients. Sometimes, however, they are poorly implemented. The IDE4ICDS platform was developed and validated with CPGs for type 2 diabetes mellitus (T2DM). OBJECTIVE The main objective of this paper is to present the results of the clinical validation of the IDE4ICDS platform in a real clinical environment at two health clinics in the Andalusian Public Health System (SSPA) in the southern Spanish region of Andalusia. METHODS National and international knowledge sources on T2DM were selected and reviewed and used to define a diabetes CPG model on the IDE4ICDS platform. Once the diabetes CPG was configured and deployed, it was validated. A total of 506 patients were identified as meeting the inclusion criteria, of whom 130 could be recruited and 89 attended the appointment. RESULTS A concordance analysis was performed with the kappa value. Overall agreement between the recommendations provided by the system and those recorded in each patient's EHR was good (0.61 - 0.80) with a total kappa index of 0.701, leading to the conclusion that the system provided appropriate recommendations for each patient and was therefore well-functioning. CONCLUSIONS A series of possible improvements were identified based on the limitations for the recovery of variables related to the quality of these recolected variables, the detection of duplicate recommendations based on different input variables for the same patient, and clinical usability, such as the capacity to generate reports based on the recommendations generated. Nevertheless, the project resulted in the IDE4ICDS platform: a Clinical Decision Support System (CDSS) capable of providing appropriate recommendations for improving the management and quality of patient care and optimizing health outcomes. The result of this validation is a safe and effective pathway for developing and adopting digital transformation at the regional scale of the use of biomedical knowledge in real healthcare.
Collapse
Affiliation(s)
- Carlos Luis Parra-Calderón
- Computational Health Informatics' Group. Seville Institute of Biomedicine (IbiS)/"Virgen del Rocío" University Hospital/CSIC/University of Seville, Avenida Manuel Siurot, 41013 Seville, Spain.
| | - Esther Román-Villarán
- Computational Health Informatics' Group. Seville Institute of Biomedicine (IbiS)/"Virgen del Rocío" University Hospital/CSIC/University of Seville, Avenida Manuel Siurot, 41013 Seville, Spain.
| | - Celia Alvarez-Romero
- Computational Health Informatics' Group. Seville Institute of Biomedicine (IbiS)/"Virgen del Rocío" University Hospital/CSIC/University of Seville, Avenida Manuel Siurot, 41013 Seville, Spain.
| | - Germán Antonio Escobar-Rodríguez
- Computational Health Informatics' Group. Seville Institute of Biomedicine (IbiS)/"Virgen del Rocío" University Hospital/CSIC/University of Seville, Avenida Manuel Siurot, 41013 Seville, Spain.
| | - Maria Asunción Martínez-Brocca
- Virgen Macarena" University Hospital, Seville, Spain; Comprehensive Plan for Diabetes in Andalusia, Andalusian Health Service, Calle Doctor Fedriani, 3, 41009 Seville, Spain.
| | - Alicia Martínez-García
- Computational Health Informatics' Group. Seville Institute of Biomedicine (IbiS)/"Virgen del Rocío" University Hospital/CSIC/University of Seville, Avenida Manuel Siurot, 41013 Seville, Spain.
| | - Julián Alberto García-García
- Computer Languages and Systems Department, Escuela Técnica Superior de Ingeniería Informática, Avda. Reina Mercedes s/n. 41012 Seville, Spain.
| | - María José Escalona-Cuaresma
- Computer Languages and Systems Department, Escuela Técnica Superior de Ingeniería Informática, Avda. Reina Mercedes s/n. 41012 Seville, Spain.
| |
Collapse
|
9
|
Söling S, Demirer I, Köberlein-Neu J, Hower KI, Müller BS, Pfaff H, Karbach U. Complex implementation mechanisms in primary care: do physicians' beliefs about the effectiveness of innovation play a mediating role? Applying a realist inquiry and structural equation modeling approach in a formative evaluation study. BMC PRIMARY CARE 2023; 24:131. [PMID: 37369994 DOI: 10.1186/s12875-023-02081-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/18/2023] [Accepted: 06/13/2023] [Indexed: 06/29/2023]
Abstract
BACKGROUND The adoption of digital health technologies can improve the quality of care for polypharmacy patients, if the underlying complex implementation mechanisms are better understood. Context effects play a critical role in relation to implementation mechanisms. In primary care research, evidence on the effects of context in the adoption of digital innovation for polypharmacy management is lacking. STUDY AIM This study aims to identify contextual factors relevant to physician behavior and how they might mediate the adoption process. METHODS The physicians who participated in this formative evaluation study (n = 218) were part of the intervention group in a cluster-randomized controlled trial (AdAM). The intervention group implemented a digital innovation for clinical decision making in polypharmacy. A three-step methodological approach was used: (1) a realist inquiry approach, which involves the description of a context-mechanism-outcome configuration for the primary care setting; (2) a belief elicitation approach, which involves qualitative content analysis and the development of a quantitative latent contextualized scale; and (3) a mediation analysis using structural equation modeling (SEM) based on quantitative survey data from physicians to assess the mediating role of the contextualized scale (n = 179). RESULTS The key dimensions of a (1) context-mechanism-outcome model were mapped and refined. A (2) latent construct of the physicians' innovation beliefs related to the effectiveness of polypharmacy management practices was identified. Innovation beliefs play a (3) mediating role between the organizational readiness to implement change (p < 0.01) and the desired behavioral intent of physicians to adopt digital innovation (p < 0.01; R2 = 0.645). Our contextualized model estimated significant mediation, with a relative size of 38% for the mediation effect. Overall, the model demonstrated good fit indices (CFI = 0.985, RMSEA = 0.034). CONCLUSION Physician adoption is directly affected by the readiness of primary care organizations for the implementation of change. In addition, the mediation analysis revealed that this relationship is indirectly influenced by primary care physicians' beliefs regarding the effectiveness of digital innovation. Both individual physician beliefs and practice organizational capacity could be equally prioritized in developing implementation strategies. The methodological approach used is suitable for the evaluation of complex implementation mechanisms. It has been proven to be an advantageous approach for formative evaluation. TRIAL REGISTRATION NCT03430336 . First registration: 12/02/2018. CLINICALTRIALS gov.
Collapse
Affiliation(s)
- Sara Söling
- Institute of Medical Sociology, Health Services Research, and Rehabilitation Sciences, Faculty of Human Sciences & Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany.
- Center for Health Economics and Health Services Research, Schumpeter School of Business and Economics, University of Wuppertal, Wuppertal, Germany.
| | - Ibrahim Demirer
- Institute of Medical Sociology, Health Services Research, and Rehabilitation Sciences, Faculty of Human Sciences & Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Juliane Köberlein-Neu
- Center for Health Economics and Health Services Research, Schumpeter School of Business and Economics, University of Wuppertal, Wuppertal, Germany
| | - Kira Isabel Hower
- Institute of Medical Sociology, Health Services Research, and Rehabilitation Sciences, Faculty of Human Sciences & Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Beate Sigrid Müller
- Institute for General Medicine, Faculty of Medicine, University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Holger Pfaff
- Institute of Medical Sociology, Health Services Research, and Rehabilitation Sciences, Faculty of Human Sciences & Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Ute Karbach
- Institute of Medical Sociology, Health Services Research, and Rehabilitation Sciences, Faculty of Human Sciences & Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| |
Collapse
|
10
|
Thompson C, Mebrahtu T, Skyrme S, Bloor K, Andre D, Keenan AM, Ledward A, Yang H, Randell R. The effects of computerised decision support systems on nursing and allied health professional performance and patient outcomes: a systematic review and user contextualisation. HEALTH AND SOCIAL CARE DELIVERY RESEARCH 2023:1-85. [PMID: 37470324 DOI: 10.3310/grnm5147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/21/2023]
Abstract
Background Computerised decision support systems (CDSS) are widely used by nurses and allied health professionals but their effect on clinical performance and patient outcomes is uncertain. Objectives Evaluate the effects of clinical decision support systems use on nurses', midwives' and allied health professionals' performance and patient outcomes and sense-check the results with developers and users. Eligibility criteria Comparative studies (randomised controlled trials (RCTs), non-randomised trials, controlled before-and-after (CBA) studies, interrupted time series (ITS) and repeated measures studies comparing) of CDSS versus usual care from nurses, midwives or other allied health professionals. Information sources Nineteen bibliographic databases searched October 2019 and February 2021. Risk of bias Assessed using structured risk of bias guidelines; almost all included studies were at high risk of bias. Synthesis of results Heterogeneity between interventions and outcomes necessitated narrative synthesis and grouping by: similarity in focus or CDSS-type, targeted health professionals, patient group, outcomes reported and study design. Included studies Of 36,106 initial records, 262 studies were assessed for eligibility, with 35 included: 28 RCTs (80%), 3 CBA studies (8.6%), 3 ITS (8.6%) and 1 non-randomised trial, a total of 1318 health professionals and 67,595 patient participants. Few studies were multi-site and most focused on decision-making by nurses (71%) or paramedics (5.7%). Standalone, computer-based CDSS featured in 88.7% of the studies; only 8.6% of the studies involved 'smart' mobile or handheld technology. Care processes - including adherence to guidance - were positively influenced in 47% of the measures adopted. For example, nurses' adherence to hand disinfection guidance, insulin dosing, on-time blood sampling, and documenting care were improved if they used CDSS. Patient care outcomes were statistically - if not always clinically - significantly improved in 40.7% of indicators. For example, lower numbers of falls and pressure ulcers, better glycaemic control, screening of malnutrition and obesity, and accurate triaging were features of professionals using CDSS compared to those who were not. Evidence limitations Allied health professionals (AHPs) were underrepresented compared to nurses; systems, studies and outcomes were heterogeneous, preventing statistical aggregation; very wide confidence intervals around effects meant clinical significance was questionable; decision and implementation theory that would have helped interpret effects - including null effects - was largely absent; economic data were scant and diverse, preventing estimation of overall cost-effectiveness. Interpretation CDSS can positively influence selected aspects of nurses', midwives' and AHPs' performance and care outcomes. Comparative research is generally of low quality and outcomes wide ranging and heterogeneous. After more than a decade of synthesised research into CDSS in healthcare professions other than medicine, the effect on processes and outcomes remains uncertain. Higher-quality, theoretically informed, evaluative research that addresses the economics of CDSS development and implementation is still required. Future work Developing nursing CDSS and primary research evaluation. Funding This project was funded by the National Institute for Health and Care Research (NIHR) Health and Social Care Delivery Research programme and will be published in Health and Social Care Delivery Research; 2023. See the NIHR Journals Library website for further project information. Registration PROSPERO [number: CRD42019147773].
Collapse
Affiliation(s)
- Carl Thompson
- School of Healthcare, University of Leeds, Leeds, UK
| | | | - Sarah Skyrme
- School of Healthcare, University of Leeds, Leeds, UK
| | - Karen Bloor
- Department of Health Sciences, University of York, York, UK
| | - Deidre Andre
- Library Services, University of Leeds, Leeds, UK
| | | | | | - Huiqin Yang
- School of Healthcare, University of Leeds, Leeds, UK
| | - Rebecca Randell
- Faculty of Health Studies, University of Bradford, Bradford, UK
| |
Collapse
|
11
|
Gruss CL, Kappen TH, Fowler LC, Li G, Freundlich RE, McEvoy MD, Rothman BS, Sandberg WS, Wanderer JP. Automated feedback modestly improves perioperative treatment adherence of postoperative nausea and vomiting. J Clin Anesth 2023; 86:111081. [PMID: 36812833 PMCID: PMC10148234 DOI: 10.1016/j.jclinane.2023.111081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 02/03/2023] [Accepted: 02/12/2023] [Indexed: 02/22/2023]
Abstract
STUDY OBJECTIVE Extensive evidence demonstrates that medical record modernization and a vast amount of available data have not overcome the gap between recommended and delivered care. This study aimed to evaluate the use of clinical decision support (CDS) in conjunction with feedback (post-hoc reporting) to improve PONV medication administration compliance and postoperative nausea and vomiting (PONV) outcomes. DESIGN Single center, prospective observational study between January 1, 2015, and June 30, 2017. SETTING Perioperative care at a university-affiliated tertiary care center. PATIENTS 57,401 adult patients who received general anesthesia in a non-emergency setting. INTERVENTION A multi-phased intervention that consisted of post-hoc reporting for individual providers by email about PONV occurrences in their patients, followed by directive CDS through preoperative daily case emails that provided therapeutic PONV prophylaxis recommendations based on patients' PONV risk scores. MEASUREMENT Compliance with PONV medication recommendations, as well as hospital rates of PONV were measured. MAIN RESULT Over the study period, there was a 5.5% (95% CI, 4.2% to 6.4%; p < 0.001) improvement in the compliance of PONV medication administration along with an 8.7% (95% CI, 7.1% to 10.2%, p < 0.001) reduction in PONV rescue medication administration in the PACU. However, there was no statistically or clinically significant reduction in the prevalence of PONV in the PACU. The prevalence of PONV rescue medication administration decreased during the Intervention Rollout Period (odds ratio 0.95 [per month]; 95% CI, 0.91 to 0.99; p = 0.017), and during the Feedback with CDS Recommendation Period (odds ratio, 0.96 [per month]; 95% CI, 0.94 to 0.99; p = 0.013). CONCLUSION PONV medication administration compliance modestly improves with CDS in conjunction with post-hoc reporting; however, no improvement in PACU rates of PONV occurred.
Collapse
Affiliation(s)
- Calvin L Gruss
- Department of Anesthesiology, Vanderbilt University Medical Center, United States of America.
| | - Teus H Kappen
- Department of Anesthesiology, Intensive Care and Emergency Medicine, University Medical Center Utrecht, the Netherlands
| | - Leslie C Fowler
- Department of Anesthesiology, Vanderbilt University Medical Center, United States of America
| | - Gen Li
- Department of Anesthesiology, Vanderbilt University Medical Center, United States of America
| | - Robert E Freundlich
- Department of Anesthesiology, Vanderbilt University Medical Center, United States of America; Department of Biomedical Informatics, Vanderbilt University, United States of America
| | - Matthew D McEvoy
- Department of Anesthesiology, Vanderbilt University Medical Center, United States of America; Department of Surgery, Vanderbilt University Medical Center, United States of America
| | - Brian S Rothman
- Department of Anesthesiology, Vanderbilt University Medical Center, United States of America; Department of Biomedical Informatics, Vanderbilt University, United States of America; Department of Surgery, Vanderbilt University Medical Center, United States of America
| | - Warren S Sandberg
- Department of Anesthesiology, Vanderbilt University Medical Center, United States of America
| | - Jonathan P Wanderer
- Department of Anesthesiology, Vanderbilt University Medical Center, United States of America; Department of Biomedical Informatics, Vanderbilt University, United States of America
| |
Collapse
|
12
|
White NM, Carter HE, Kularatna S, Borg DN, Brain DC, Tariq A, Abell B, Blythe R, McPhail SM. Evaluating the costs and consequences of computerized clinical decision support systems in hospitals: a scoping review and recommendations for future practice. J Am Med Inform Assoc 2023; 30:1205-1218. [PMID: 36972263 PMCID: PMC10198542 DOI: 10.1093/jamia/ocad040] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 02/23/2023] [Accepted: 03/03/2023] [Indexed: 11/14/2023] Open
Abstract
OBJECTIVE Sustainable investment in computerized decision support systems (CDSS) requires robust evaluation of their economic impacts compared with current clinical workflows. We reviewed current approaches used to evaluate the costs and consequences of CDSS in hospital settings and presented recommendations to improve the generalizability of future evaluations. MATERIALS AND METHODS A scoping review of peer-reviewed research articles published since 2010. Searches were completed in the PubMed, Ovid Medline, Embase, and Scopus databases (last searched February 14, 2023). All studies reported the costs and consequences of a CDSS-based intervention compared with current hospital workflows. Findings were summarized using narrative synthesis. Individual studies were further appraised against the Consolidated Health Economic Evaluation and Reporting (CHEERS) 2022 checklist. RESULTS Twenty-nine studies published since 2010 were included. Studies evaluated CDSS for adverse event surveillance (5 studies), antimicrobial stewardship (4 studies), blood product management (8 studies), laboratory testing (7 studies), and medication safety (5 studies). All studies evaluated costs from a hospital perspective but varied based on the valuation of resources affected by CDSS implementation, and the measurement of consequences. We recommend future studies follow guidance from the CHEERS checklist; use study designs that adjust for confounders; consider both the costs of CDSS implementation and adherence; evaluate consequences that are directly or indirectly affected by CDSS-initiated behavior change; examine the impacts of uncertainty and differences in outcomes across patient subgroups. DISCUSSION AND CONCLUSION Improving consistency in the conduct and reporting of evaluations will enable detailed comparisons between promising initiatives, and their subsequent uptake by decision-makers.
Collapse
Affiliation(s)
- Nicole M White
- Australian Centre for Health Services Innovation and Centre for Healthcare Transformation, School of Public Health and Social Work, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Hannah E Carter
- Australian Centre for Health Services Innovation and Centre for Healthcare Transformation, School of Public Health and Social Work, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Sanjeewa Kularatna
- Australian Centre for Health Services Innovation and Centre for Healthcare Transformation, School of Public Health and Social Work, Queensland University of Technology, Brisbane, Queensland, Australia
| | - David N Borg
- Australian Centre for Health Services Innovation and Centre for Healthcare Transformation, School of Public Health and Social Work, Queensland University of Technology, Brisbane, Queensland, Australia
| | - David C Brain
- Australian Centre for Health Services Innovation and Centre for Healthcare Transformation, School of Public Health and Social Work, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Amina Tariq
- Australian Centre for Health Services Innovation and Centre for Healthcare Transformation, School of Public Health and Social Work, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Bridget Abell
- Australian Centre for Health Services Innovation and Centre for Healthcare Transformation, School of Public Health and Social Work, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Robin Blythe
- Australian Centre for Health Services Innovation and Centre for Healthcare Transformation, School of Public Health and Social Work, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Steven M McPhail
- Australian Centre for Health Services Innovation and Centre for Healthcare Transformation, School of Public Health and Social Work, Queensland University of Technology, Brisbane, Queensland, Australia
- Digital Health and Informatics Directorate, Metro South Health, Brisbane, Queensland, Australia
| |
Collapse
|
13
|
Walraven JEW, Verhoeven RHA, van der Hoeven JJM, van der Meulen R, Lemmens VEPP, Hesselink G, Desar IME. Pros and cons of streamlining and use of computerised clinical decision support systems to future-proof oncological multidisciplinary team meetings. Front Oncol 2023; 13:1178165. [PMID: 37274246 PMCID: PMC10233094 DOI: 10.3389/fonc.2023.1178165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Accepted: 04/27/2023] [Indexed: 06/06/2023] Open
Abstract
Introduction Nowadays nearly every patient with cancer is discussed in a multidisciplinary team meeting (MDTM) to determine an optimal treatment plan. The growth in the number of patients to be discussed is unsustainable. Streamlining and use of computerised clinical decision support systems (CCDSSs) are two major ways to restructure MDTMs. Streamlining is the process of selecting the patients who need to be discussed and in which type of MDTM. Using CCDSSs, patient data is automatically loaded into the minutes and a guideline-based treatment proposal is generated. We aimed to identify the pros and cons of streamlining and CCDSSs. Methods Semi-structured interviews were conducted with Dutch MDTM participants. With purposive sampling we maximised variation in participants' characteristics. Interview data were thematically analysed. Results Thirty-five interviews were analysed. All interviewees agreed on the need to change the current MDTM workflow. Streamlining suggestions were thematised based on standard and complex cases and the location of the MDTM (i.e. local, regional or nationwide). Interviewees suggested easing the pressure on MDTMs by discussing standard cases briefly, not at all, or outside the MDTM with only two to three specialists. Complex cases should be discussed in tumour-type-specific regional MDTMs and highly complex cases by regional/nationwide expert teams. Categorizing patients as standard or complex was found to be the greatest challenge of streamlining. CCDSSs were recognised as promising, although none of the interviewees had made use of them. The assumed advantage was their capacity to generate protocolised treatment proposals based on automatically uploaded patient data, to unify treatment proposals and to facilitate research. However, they were thought to limit the freedom to deviate from the treatment advice. Conclusion To make oncological MDTMs sustainable, methods of streamlining should be developed and introduced. Physicians still have doubts about the value of CCDSSs.
Collapse
Affiliation(s)
- Janneke E. W. Walraven
- Department of Medical Oncology, Radboud University Medical Center, Nijmegen, Netherlands
- Department of Research, Netherlands Comprehensive Cancer Organization (IKNL), Utrecht, Netherlands
| | - Rob H. A. Verhoeven
- Department of Research, Netherlands Comprehensive Cancer Organization (IKNL), Utrecht, Netherlands
- Department of Medical Oncology, Amsterdam University Medical Center location University of Amsterdam, Amsterdam, Netherlands
| | | | - Renske van der Meulen
- Department of Medical Oncology, Radboud University Medical Center, Nijmegen, Netherlands
| | - Valery E. P. P. Lemmens
- Department of Research, Netherlands Comprehensive Cancer Organization (IKNL), Utrecht, Netherlands
| | - Gijs Hesselink
- Department of Intensive Care, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, Netherlands
| | - Ingrid M. E. Desar
- Department of Medical Oncology, Radboud University Medical Center, Nijmegen, Netherlands
| |
Collapse
|
14
|
Fonseca SF, Ribeiro ALP, Cimini CCR, Soares TBDC, Delfino-Pereira P, Nogueira LT, Moura RMF, Motta-Santos D, Ribeiro LB, Camargos MCS, Paixão MC, Pires MC, Batchelor J, Marcolino MS. Scale up of implementation of a multidimensional intervention to enhance hypertension and diabetes care at the primary care setting: A protocol for a cluster-randomized study in Brazil. Am Heart J 2023; 262:119-130. [PMID: 37044364 DOI: 10.1016/j.ahj.2023.04.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 04/04/2023] [Accepted: 04/06/2023] [Indexed: 05/21/2023]
Abstract
BACKGROUND Hypertension and diabetes mellitus (DM) are highly prevalent in low and middle-income countries (LMICs), and the proportion of patients with uncontrolled diseases is higher than in high-income countries. Innovative strategies are required to surpass barriers of low sources, distance and quality of health care. Our aim is to assess the uptake and effectiveness of the implementation of an integrated multidimensional strategy in the primary care setting, for the management of people with hypertension and diabetes mellitus in Brazil. METHODS This scale up implementation study called Control of Hypertension and diAbetes in MINas Gerais (CHArMING) Project has mixed-methods, and comprehends 4 steps: (1) needs assessment, including a standardized structured questionnaire and focus groups with health care practitioners; (2) baseline period, 3 months before the implementation of the intervention; (3) cluster randomized controlled trial (RCT) with a 12-months follow-up period; and (4) a qualitative study after the end of follow-up. The cluster RCT will randomize 35 centers to intervention (n = 18) or usual care (n = 17). Patients ≥18 years old, with diagnosis of hypertension and/or DM, of 5 Brazilian cities in a resource-constrained area will be enrolled. The intervention consists of a multifaceted strategy, with a multidisciplinary approach, including telehealth tools (decision support systems, short message service, telediagnosis), continued education with an approach to issues related to the care of people with hypertension and diabetes in primary care, including pharmacological and non-pharmacological treatment and behavioral change. The project has actions focused on professionals and patients. CONCLUSIONS This study consists of a multidimensional strategy with multidisciplinary approach using digital health to improve the control of hypertension and/or DM in the primary health care setting. We expect to provide the basis for implementing an innovative management program for hypertension and DM in Brazil, aiming to reduce the present and future burden of these diseases in Brazil and other LMICs. CLINICAL TRIAL IDENTIFIER This study was registered in ClinicalTrials.gov. (NCT05660928).
Collapse
Affiliation(s)
- Sueli Ferreira Fonseca
- Telehealth Center, University Hospital, Universidade Federal de Minas Gerais. Belo Horizonte, Brazil; Department of Physiotherapy, Universidade Federal dos Vales do Jequitinhonha e Mucuri. Diamantina, Brazil
| | - Antonio Luiz Pinho Ribeiro
- Telehealth Center, University Hospital, Universidade Federal de Minas Gerais. Belo Horizonte, Brazil; Cardiology Service, University Hospital, Universidade Federal de Minas Gerais. Belo Horizonte, Brazil.; Department of Internal Medicine, Medical School and University Hospital, Universidade Federal de Minas Gerais. Belo Horizonte, Brazil; Institute for Health Technology Assessment (IATS). Porto Alegre, Brazil
| | - Christiane Correa Rodrigues Cimini
- Telehealth Center, University Hospital, Universidade Federal de Minas Gerais. Belo Horizonte, Brazil; Medical School, Universidade Federal dos Vales do Jequitinhonha e Mucuri. Teófilo Otoni, Brazil
| | | | - Polianna Delfino-Pereira
- Telehealth Center, University Hospital, Universidade Federal de Minas Gerais. Belo Horizonte, Brazil; Department of Internal Medicine, Medical School and University Hospital, Universidade Federal de Minas Gerais. Belo Horizonte, Brazil; Institute for Health Technology Assessment (IATS). Porto Alegre, Brazil
| | - Lucas Tavares Nogueira
- Telehealth Center, University Hospital, Universidade Federal de Minas Gerais. Belo Horizonte, Brazil
| | - Regina Marcia Faria Moura
- Telehealth Center, University Hospital, Universidade Federal de Minas Gerais. Belo Horizonte, Brazil
| | - Daisy Motta-Santos
- Telehealth Center, University Hospital, Universidade Federal de Minas Gerais. Belo Horizonte, Brazil
| | - Leonardo Bonisson Ribeiro
- Telehealth Center, University Hospital, Universidade Federal de Minas Gerais. Belo Horizonte, Brazil
| | | | - Maria Cristina Paixão
- Telehealth Center, University Hospital, Universidade Federal de Minas Gerais. Belo Horizonte, Brazil
| | - Magda Carvalho Pires
- Department of Statistics, Universidade Federal de Minas Gerais. Belo Horizonte, Brazil
| | - James Batchelor
- Clinical Informatics Research Unit, University of Southampton, Southampton United Kingdom
| | - Milena Soriano Marcolino
- Telehealth Center, University Hospital, Universidade Federal de Minas Gerais. Belo Horizonte, Brazil; Department of Internal Medicine, Medical School and University Hospital, Universidade Federal de Minas Gerais. Belo Horizonte, Brazil; Institute for Health Technology Assessment (IATS). Porto Alegre, Brazil.
| |
Collapse
|
15
|
Walraven JEW, Verhoeven RHA, van der Meulen R, van der Hoeven JJM, Lemmens VEPP, Hesselink G, Desar IME. Facilitators and barriers to conducting an efficient, competent and high-quality oncological multidisciplinary team meeting. BMJ Open Qual 2023; 12:bmjoq-2022-002130. [PMID: 36759037 PMCID: PMC9923284 DOI: 10.1136/bmjoq-2022-002130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 02/01/2023] [Indexed: 02/11/2023] Open
Abstract
BACKGROUND Optimal oncological care nowadays requires discussing every patient in a multidisciplinary team meeting (MDTM). The number of patients to be discussed is rising rapidly due to the increasing incidence and prevalence of cancer and the emergence of new multidisciplinary treatment options. This puts MDTMs under considerable time pressure. The aim of this study is therefore to identify the facilitators and barriers with regard to performing an efficient, competent and high-quality MDTM. METHODS Semistructured interviews were conducted with Dutch medical specialists and residents participating in oncological MDTMs. Purposive sampling was used to maximise variation in participants' professional and demographic characteristics (eg, sex, medical specialist vs resident, specialty, type and location of affiliated hospital). Interview data were systematically analysed according to the principles of thematic content analysis. RESULTS Sixteen medical specialists and 19 residents were interviewed. All interviewees agreed that attending and preparing MDTMs is time-consuming and indicated the need for optimal execution in order to ensure that MDTMs remain feasible in the near future. Four themes emerged that are relevant to achieving an optimal MDTM: (1) organisational aspects; (2) participants' responsibilities and requirements; (3) competences, behaviour and team dynamics and (4) meeting content. Good organisation, a sound structure and functioning information and communication technology facilitate high-quality MDTMs. Multidisciplinary collaboration and adequate communication are essential competences for participants; a lack thereof and the existence of a hierarchy are hindering factors. CONCLUSION Conducting an efficient, competent and high-quality oncological MDTM is facilitated and hindered by many factors. Being aware of these factors provides opportunities for optimising MDTMs, which are under pressure due to the increase in the number of patients to discuss.
Collapse
Affiliation(s)
- Janneke E W Walraven
- Department of Medical Oncology, Radboudumc, Nijmegen, The Netherlands .,Department of Research & Development, IKNL, Utrecht, The Netherlands
| | - Rob H A Verhoeven
- Department of Research & Development, IKNL, Utrecht, The Netherlands,Department of Medical Oncology, University of Amsterdam, Amsterdam, The Netherlands
| | | | | | | | - Gijs Hesselink
- Department of Intensive Care, Radboudumc, Nijmegen, The Netherlands,Department of IQ healthcare, Radboud Institute for Health Sciences, Nijmegen, The Netherlands
| | - Ingrid M E Desar
- Department of Medical Oncology, Radboudumc, Nijmegen, The Netherlands
| |
Collapse
|
16
|
van Staa TP, Pirmohamed M, Sharma A, Buchan I, Ashcroft DM. Clinical Relevance of Drug-Drug Interactions With Antibiotics as Listed in a National Medication Formulary: Results From Two Large Population-Based Case-Control Studies in Patients Aged 65-100 Years Using Linked English Primary Care and Hospital Data. Clin Pharmacol Ther 2023; 113:423-434. [PMID: 36448824 PMCID: PMC10107602 DOI: 10.1002/cpt.2807] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 11/23/2022] [Indexed: 12/05/2022]
Abstract
This study evaluated drug-drug interactions (DDIs) between antibiotic and nonantibiotic drugs listed with warnings of severe outcomes in the British National Formulary based on adverse drug reaction (ADR) detectable with routine International Classification of Diseases, Tenth Revision coding. Data sources were Clinical Practice Research Databank GOLD and Aurum anonymized electronic health records from English general practices linked to hospital admission records. In propensity-matched case-control study, outcomes were ADR or emergency admissions. Analyzed were 121,546 ADR-related admission cases matched to 638,238 controls. For most antibiotics, adjusted odds ratios (aORs) for ADR-related hospital admission were large (aOR for trimethoprim 4.13; 95% confidence interval (CI), 3.97-4.30). Of the 51 DDIs evaluated for ADR-related admissions, 38 DDIs (74.5%) had statistically increased aORs of concomitant exposure compared with nonexposure (mean aOR 3.96; range 1.59-11.42); for the 89 DDIs for emergency hospital admission, the results were 75 (84.3%) and mean aOR 2.40; range 1.43-4.17. Changing reference group to single antibiotic exposure reduced aORs for concomitant exposure by 76.5% and 83.0%, respectively. Medicines listed to cause nephrotoxicity substantially increased risks that were related to number of medicines (aOR was 2.55 (95% CI, 2.46-2.64) for current use of 1 and 10.44 (95% CI, 7.36-14.81) for 3 or more medicines). In conclusion, no evidence of substantial risk was found for multiple DDIs with antibiotics despite warnings of severe outcomes in a national formulary and flagging in electronic health record software. It is proposed that the evidence base for inclusion of DDIs in national formularies be strengthened and made publicly accessible and indiscriminate flagging, which compounds alert fatigue, be reduced.
Collapse
Affiliation(s)
- Tjeerd Pieter van Staa
- Centre for Health Informatics & Health Data Research UK North, Division of Informatics, Imaging and Data Science, School of Health Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK
| | - Munir Pirmohamed
- Centre for Drug Safety Science, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK
| | - Anita Sharma
- Chadderton South Health Centre, Eaves Lane, Chadderton, Oldham, UK
| | - Iain Buchan
- Institute of Population Health, NIHR Applied Research Collaboration North West Coast, University of Liverpool, Liverpool, UK
| | - Darren M Ashcroft
- Centre for Pharmacoepidemiology and Drug Safety, National Institute for Health Research Greater Manchester Patient Safety Translational Research Centre, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
| |
Collapse
|
17
|
Ullah A, Whittaker W, Bradley F, Nelson PA, Dowding D, Morciano M, Cullum N. The use and impact of digital COVID-19 tracking in adult social care: a prospective cohort study of care homes in Greater Manchester. BMC Infect Dis 2023; 23:47. [PMID: 36690927 PMCID: PMC9869837 DOI: 10.1186/s12879-022-07939-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 12/10/2022] [Indexed: 01/24/2023] Open
Abstract
BACKGROUND To support proactive care during the coronavirus pandemic, a digital COVID-19 symptom tracker was deployed in Greater Manchester (UK) care homes. This study aimed to understand what factors were associated with the post-uptake use of the tracker and whether the tracker had any effects in controlling the spread of COVID-19. METHODS Daily data on COVID-19, tracker uptake and use, and other key indicators such as staffing levels, the number of staff self-isolating, availability of personal protective equipment, bed occupancy levels, and any problems in accepting new residents were analysed for 547 care homes across Greater Manchester for the period April 2020 to April 2021. Differences in tracker use across local authorities, types of care homes, and over time were assessed using correlated effects logistic regressions. Differences in numbers of COVID-19 cases in homes adopting versus not adopting the tracker were compared via event design difference-in-difference estimations. RESULTS Homes adopting the tracker used it on 44% of days post-adoption. Use decreased by 88% after one year of uptake (odds ratio 0.12; 95% confidence interval 0.06-0.28). Use was highest in the locality initiating the project (odds ratio 31.73; 95% CI 3.76-268.05). Care homes owned by a chain had lower use (odds ratio 0.30; 95% CI 0.14-0.63 versus single ownership care homes), and use was not associated with COVID-19 or staffing levels. Tracker uptake had no impact on controlling COVID-19 spread. Staff self-isolating and local area COVID-19 cases were positively associated with lagged COVID-19 spread in care homes (relative risks 1.29; 1.2-1.4 and 1.05; 1.0-1.1, respectively). CONCLUSIONS The use of the COVID-19 symptom tracker in care homes was not maintained except in Locality 1 and did not appear to reduce the COVID-19 spread. COVID-19 cases in care homes were mainly driven by care home local-area COVID-19 cases and infections among the staff members. Digital deterioration trackers should be co-produced with care home staff, and local authorities should provide long-term support in their adoption and use.
Collapse
Affiliation(s)
- Akbar Ullah
- grid.5379.80000000121662407Manchester Centre for Health Economics, Faculty of Biology Medicine and Health, The University of Manchester, Jean McFarlane Building, Oxford Road, Manchester, M13 9PL UK
| | - William Whittaker
- grid.5379.80000000121662407Manchester Centre for Health Economics, Faculty of Biology Medicine and Health, The University of Manchester, Jean McFarlane Building, Oxford Road, Manchester, M13 9PL UK
| | - Fay Bradley
- grid.5379.80000000121662407Division of Nursing, Midwifery and Social Work, School of Health Sciences, Faculty of Biology Medicine and Health, The University of Manchester, Jean McFarlane Building, Oxford Road, Manchester, M13 9PL UK
| | - Pauline A. Nelson
- grid.5379.80000000121662407Division of Nursing, Midwifery and Social Work, School of Health Sciences, Faculty of Biology Medicine and Health, The University of Manchester, Jean McFarlane Building, Oxford Road, Manchester, M13 9PL UK
| | - Dawn Dowding
- grid.5379.80000000121662407Division of Nursing, Midwifery and Social Work, School of Health Sciences, Faculty of Biology Medicine and Health, The University of Manchester, Jean McFarlane Building, Oxford Road, Manchester, M13 9PL UK
| | - Marcello Morciano
- grid.7548.e0000000121697570Department of Economics, University of Modena and Reggio Emilia, Via Università, 4, 41121 Modena, MO Italy
| | - Nicky Cullum
- grid.5379.80000000121662407Division of Nursing, Midwifery and Social Work, School of Health Sciences, Faculty of Biology Medicine and Health, The University of Manchester, Jean McFarlane Building, Oxford Road, Manchester, M13 9PL UK
| |
Collapse
|
18
|
Haselager P, Schraffenberger H, Thill S, Fischer S, Lanillos P, van de Groes S, van Hooff M. Reflection Machines: Supporting Effective Human Oversight Over Medical Decision Support Systems. Camb Q Healthc Ethics 2023:1-10. [PMID: 36624620 DOI: 10.1017/s0963180122000718] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Human decisions are increasingly supported by decision support systems (DSS). Humans are required to remain "on the loop," by monitoring and approving/rejecting machine recommendations. However, use of DSS can lead to overreliance on machines, reducing human oversight. This paper proposes "reflection machines" (RM) to increase meaningful human control. An RM provides a medical expert not with suggestions for a decision, but with questions that stimulate reflection about decisions. It can refer to data points or suggest counterarguments that are less compatible with the planned decision. RMs think against the proposed decision in order to increase human resistance against automation complacency. Building on preliminary research, this paper will (1) make a case for deriving a set of design requirements for RMs from EU regulations, (2) suggest a way how RMs could support decision-making, (3) describe the possibility of how a prototype of an RM could apply to the medical domain of chronic low back pain, and (4) highlight the importance of exploring an RM's functionality and the experiences of users working with it.
Collapse
Affiliation(s)
- Pim Haselager
- Donders Institute for Brain, Cognition and Behaviour, Department of AI, Radboud University, Nijmegen, The Netherlands
| | | | - Serge Thill
- Donders Institute for Brain, Cognition and Behaviour, Department of AI, Radboud University, Nijmegen, The Netherlands
| | - Simon Fischer
- Donders Institute for Brain, Cognition and Behaviour, Department of AI, Radboud University, Nijmegen, The Netherlands
| | - Pablo Lanillos
- Donders Institute for Brain, Cognition and Behaviour, Department of AI, Radboud University, Nijmegen, The Netherlands
| | | | - Miranda van Hooff
- Health Sciences, Radboud UMC, Nijmegen, The Netherlands
- St Maartenskliniek, Nijmegen, The Netherlands
| |
Collapse
|
19
|
Chomutare T, Tejedor M, Svenning TO, Marco-Ruiz L, Tayefi M, Lind K, Godtliebsen F, Moen A, Ismail L, Makhlysheva A, Ngo PD. Artificial Intelligence Implementation in Healthcare: A Theory-Based Scoping Review of Barriers and Facilitators. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph192316359. [PMID: 36498432 PMCID: PMC9738234 DOI: 10.3390/ijerph192316359] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 12/01/2022] [Accepted: 12/02/2022] [Indexed: 05/09/2023]
Abstract
There is a large proliferation of complex data-driven artificial intelligence (AI) applications in many aspects of our daily lives, but their implementation in healthcare is still limited. This scoping review takes a theoretical approach to examine the barriers and facilitators based on empirical data from existing implementations. We searched the major databases of relevant scientific publications for articles related to AI in clinical settings, published between 2015 and 2021. Based on the theoretical constructs of the Consolidated Framework for Implementation Research (CFIR), we used a deductive, followed by an inductive, approach to extract facilitators and barriers. After screening 2784 studies, 19 studies were included in this review. Most of the cited facilitators were related to engagement with and management of the implementation process, while the most cited barriers dealt with the intervention's generalizability and interoperability with existing systems, as well as the inner settings' data quality and availability. We noted per-study imbalances related to the reporting of the theoretic domains. Our findings suggest a greater need for implementation science expertise in AI implementation projects, to improve both the implementation process and the quality of scientific reporting.
Collapse
Affiliation(s)
- Taridzo Chomutare
- Norwegian Centre for E-Health Research, 9019 Tromsø, Norway
- Correspondence:
| | - Miguel Tejedor
- Norwegian Centre for E-Health Research, 9019 Tromsø, Norway
| | | | | | - Maryam Tayefi
- Norwegian Centre for E-Health Research, 9019 Tromsø, Norway
| | - Karianne Lind
- Norwegian Centre for E-Health Research, 9019 Tromsø, Norway
| | - Fred Godtliebsen
- Norwegian Centre for E-Health Research, 9019 Tromsø, Norway
- Department of Mathematics and Statistics, Faculty of Science and Technology, UiT The Arctic University of Norway, 9037 Tromsø, Norway
| | - Anne Moen
- Norwegian Centre for E-Health Research, 9019 Tromsø, Norway
- Institute for Health and Society, Faculty of Medicine, University of Oslo, 0318 Oslo, Norway
| | - Leila Ismail
- Department of Computer Science and Software Engineering, College of Information Technology, United Arab Emirates University, Al Ain 15551, United Arab Emirates
- National Water and Energy Center, United Arab Emirates University, Al Ain 15551, United Arab Emirates
- School of Computing and Information Systems, Faculty of Engineering and Information Technology, The University of Melbourne, Parkville, VIC 3010, Australia
| | | | | |
Collapse
|
20
|
Harnessing Electronic Medical Records in Cardiovascular Clinical Practice and Research. J Cardiovasc Transl Res 2022:10.1007/s12265-022-10313-1. [DOI: 10.1007/s12265-022-10313-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Accepted: 08/29/2022] [Indexed: 10/14/2022]
|
21
|
Román-Villarán E, Alvarez-Romero C, Martínez-García A, Escobar-Rodríguez GA, García-Lozano MJ, Barón-Franco B, Moreno-Gaviño L, Moreno-Conde J, Rivas-González JA, Parra-Calderón CL. A Personalized Ontology-Based Decision Support System for Complex Chronic Patients: Retrospective Observational Study. JMIR Form Res 2022; 6:e27990. [PMID: 35916719 PMCID: PMC9382545 DOI: 10.2196/27990] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Revised: 05/24/2021] [Accepted: 03/29/2022] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND Due to an increase in life expectancy, the prevalence of chronic diseases is also on the rise. Clinical practice guidelines (CPGs) provide recommendations for suitable interventions regarding different chronic diseases, but a deficiency in the implementation of these CPGs has been identified. The PITeS-TiiSS (Telemedicine and eHealth Innovation Platform: Information Communications Technology for Research and Information Challenges in Health Services) tool, a personalized ontology-based clinical decision support system (CDSS), aims to reduce variability, prevent errors, and consider interactions between different CPG recommendations, among other benefits. OBJECTIVE The aim of this study is to design, develop, and validate an ontology-based CDSS that provides personalized recommendations related to drug prescription. The target population is older adult patients with chronic diseases and polypharmacy, and the goal is to reduce complications related to these types of conditions while offering integrated care. METHODS A study scenario about atrial fibrillation and treatment with anticoagulants was selected to validate the tool. After this, a series of knowledge sources were identified, including CPGs, PROFUND index, LESS/CHRON criteria, and STOPP/START criteria, to extract the information. Modeling was carried out using an ontology, and mapping was done with Health Level 7 Fast Healthcare Interoperability Resources (HL7 FHIR) and Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT; International Health Terminology Standards Development Organisation). Once the CDSS was developed, validation was carried out by using a retrospective case study. RESULTS This project was funded in January 2015 and approved by the Virgen del Rocio University Hospital ethics committee on November 24, 2015. Two different tasks were carried out to test the functioning of the tool. First, retrospective data from a real patient who met the inclusion criteria were used. Second, the analysis of an adoption model was performed through the study of the requirements and characteristics that a CDSS must meet in order to be well accepted and used by health professionals. The results are favorable and allow the proposed research to continue to the next phase. CONCLUSIONS An ontology-based CDSS was successfully designed, developed, and validated. However, in future work, validation in a real environment should be performed to ensure the tool is usable and reliable.
Collapse
Affiliation(s)
- Esther Román-Villarán
- Computational Health Informatics Group, Institute of Biomedicine of Seville, Virgen del Rocío University Hospital, Consejo Superior de Investigaciones Científicas, University of Seville, Seville, Spain
| | - Celia Alvarez-Romero
- Computational Health Informatics Group, Institute of Biomedicine of Seville, Virgen del Rocío University Hospital, Consejo Superior de Investigaciones Científicas, University of Seville, Seville, Spain
| | - Alicia Martínez-García
- Computational Health Informatics Group, Institute of Biomedicine of Seville, Virgen del Rocío University Hospital, Consejo Superior de Investigaciones Científicas, University of Seville, Seville, Spain
| | - German Antonio Escobar-Rodríguez
- Computational Health Informatics Group, Institute of Biomedicine of Seville, Virgen del Rocío University Hospital, Consejo Superior de Investigaciones Científicas, University of Seville, Seville, Spain
| | | | - Bosco Barón-Franco
- Internal Medicine Department, Virgen del Rocío University Hospital, Seville, Spain
| | | | - Jesús Moreno-Conde
- Computational Health Informatics Group, Institute of Biomedicine of Seville, Virgen del Rocío University Hospital, Consejo Superior de Investigaciones Científicas, University of Seville, Seville, Spain
| | - José Antonio Rivas-González
- Computational Health Informatics Group, Institute of Biomedicine of Seville, Virgen del Rocío University Hospital, Consejo Superior de Investigaciones Científicas, University of Seville, Seville, Spain
| | - Carlos Luis Parra-Calderón
- Computational Health Informatics Group, Institute of Biomedicine of Seville, Virgen del Rocío University Hospital, Consejo Superior de Investigaciones Científicas, University of Seville, Seville, Spain
| |
Collapse
|
22
|
Heider AK, Mang H. Integration of Risk Scores and Integration Capability in Electronic Patient Records. Appl Clin Inform 2022; 13:828-835. [PMID: 36070800 PMCID: PMC9451952 DOI: 10.1055/s-0042-1756367] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Accepted: 07/13/2022] [Indexed: 11/02/2022] Open
Abstract
BACKGROUND Digital availability of patient data is continuously improving with the increasing implementation of electronic patient records in physician practices. The emergence of digital health data defines new fields of application for data analytics applications, which in turn offer extensive options of using data. Common areas of data analytics applications include decision support, administration, and fraud detection. Risk scores play an important role in compiling algorithms that underlay tools for decision support. OBJECTIVES This study aims to identify the current state of risk score integration and integration capability in electronic patient records for cardiovascular disease and diabetes in German primary care practices. METHODS We developed an evaluation framework to determine the current state of risk score integration and future integration options for four cardiovascular disease risk scores (arriba, Pooled Cohort Equations, QRISK3, and Systematic Coronary Risk Evaluation) and two diabetes risk scores (Finnish Diabetes Risk Score and German Diabetes Risk Score). We then used this framework to evaluate the integration of risk scores in common practice software solutions by examining the software and inquiring the respective software contact person. RESULTS Our evaluation showed that the most widely integrated risk score is arriba, as recommended by German medical guidelines. Every software version in our sample provided either an interface to arriba or the option to implement one. Our assessment of integration capability revealed a more nuanced picture. Results on data availability were mixed. Each score contains at least one variable, which requires laboratory diagnostics. Our analysis of data standardization showed that only one score documented all variables in a standardized way. CONCLUSION Our assessment revealed that the current state of risk score integration in physician practice software is rather low. Integration capability currently faces some obstacles. Future research should develop a comprehensive framework that considers the reasonable integration of risk scores into practice workflows, disease prevention programs, and the awareness of physicians and patients.
Collapse
Affiliation(s)
- Ann-Kathrin Heider
- Faculty of Medicine, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Harald Mang
- Universitätsklinikum Erlangen, Erlangen, Germany
| |
Collapse
|
23
|
Chen W, O’Bryan CM, Gorham G, Howard K, Balasubramanya B, Coffey P, Abeyaratne A, Cass A. Barriers and enablers to implementing and using clinical decision support systems for chronic diseases: a qualitative systematic review and meta-aggregation. Implement Sci Commun 2022; 3:81. [PMID: 35902894 PMCID: PMC9330991 DOI: 10.1186/s43058-022-00326-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 07/10/2022] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND Clinical decision support (CDS) is increasingly used to facilitate chronic disease care. Despite increased availability of electronic health records and the ongoing development of new CDS technologies, uptake of CDS into routine clinical settings is inconsistent. This qualitative systematic review seeks to synthesise healthcare provider experiences of CDS-exploring the barriers and enablers to implementing, using, evaluating, and sustaining chronic disease CDS systems. METHODS A search was conducted in Medline, CINAHL, APA PsychInfo, EconLit, and Web of Science from 2011 to 2021. Primary research studies incorporating qualitative findings were included if they targeted healthcare providers and studied a relevant chronic disease CDS intervention. Relevant CDS interventions were electronic health record-based and addressed one or more of the following chronic diseases: cardiovascular disease, diabetes, chronic kidney disease, hypertension, and hypercholesterolaemia. Qualitative findings were synthesised using a meta-aggregative approach. RESULTS Thirty-three primary research articles were included in this qualitative systematic review. Meta-aggregation of qualitative data revealed 177 findings and 29 categories, which were aggregated into 8 synthesised findings. The synthesised findings related to clinical context, user, external context, and technical factors affecting CDS uptake. Key barriers to uptake included CDS systems that were simplistic, had limited clinical applicability in multimorbidity, and integrated poorly into existing workflows. Enablers to successful CDS interventions included perceived usefulness in providing relevant clinical knowledge and structured chronic disease care; user confidence gained through training and post training follow-up; external contexts comprised of strong clinical champions, allocated personnel, and technical support; and CDS technical features that are both highly functional, and attractive. CONCLUSION This systematic review explored healthcare provider experiences, focussing on barriers and enablers to CDS use for chronic diseases. The results provide an evidence-base for designing, implementing, and sustaining future CDS systems. Based on the findings from this review, we highlight actionable steps for practice and future research. TRIAL REGISTRATION PROSPERO CRD42020203716.
Collapse
Affiliation(s)
- Winnie Chen
- Menzies School of Health Research, Charles Darwin University, PO Box 41096, Casuarina, NT 0811 Australia
| | - Claire Maree O’Bryan
- Menzies School of Health Research, Charles Darwin University, PO Box 41096, Casuarina, NT 0811 Australia
| | - Gillian Gorham
- Menzies School of Health Research, Charles Darwin University, PO Box 41096, Casuarina, NT 0811 Australia
| | - Kirsten Howard
- School of Public Health, Faculty of Medicine and Health, University of Sydney, Sydney, NSW Australia
| | - Bhavya Balasubramanya
- Menzies School of Health Research, Charles Darwin University, PO Box 41096, Casuarina, NT 0811 Australia
| | - Patrick Coffey
- Menzies School of Health Research, Charles Darwin University, PO Box 41096, Casuarina, NT 0811 Australia
| | - Asanga Abeyaratne
- Menzies School of Health Research, Charles Darwin University, PO Box 41096, Casuarina, NT 0811 Australia
| | - Alan Cass
- Menzies School of Health Research, Charles Darwin University, PO Box 41096, Casuarina, NT 0811 Australia
| |
Collapse
|
24
|
Graber J, Kittelson A, Juarez-Colunga E, Jin X, Bade M, Stevens-Lapsley J. Comparing "people-like-me" and linear mixed model predictions of functional recovery following knee arthroplasty. J Am Med Inform Assoc 2022; 29:1899-1907. [PMID: 35903035 PMCID: PMC10161535 DOI: 10.1093/jamia/ocac123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 06/21/2022] [Accepted: 07/12/2022] [Indexed: 11/13/2022] Open
Abstract
OBJECTIVE Prediction models can be useful tools for monitoring patient status and personalizing treatment in health care. The goal of this study was to compare the relative strengths and weaknesses of 2 different approaches for predicting functional recovery after knee arthroplasty: a neighbors-based "people-like-me" (PLM) approach and a linear mixed model (LMM) approach. MATERIALS AND METHODS We used 2 distinct datasets to train and then test PLM and LMM prediction approaches for functional recovery following knee arthroplasty. We used the Timed Up and Go (TUG)-a common test of mobility-to operationalize physical function. Both approaches used patient characteristics and baseline postoperative TUG values to predict TUG recovery from days 1-425 following surgery. We then compared the accuracy and precision of PLM and LMM predictions. RESULTS A total of 317 patient records with 1379 TUG observations were used to train PLM and LMM approaches, and 456 patient records with 1244 TUG observations were used to test the predictions. The approaches performed similarly in terms of mean squared error and bias, but the PLM approach provided more accurate and precise estimates of prediction uncertainty. DISCUSSION AND CONCLUSION Overall, the PLM approach more accurately and precisely predicted TUG recovery following knee arthroplasty. These results suggest PLM predictions may be more clinically useful for monitoring recovery and personalizing care following knee arthroplasty. However, clinicians and organizations seeking to use predictions in practice should consider additional factors (eg, resource requirements) when selecting a prediction approach.
Collapse
Affiliation(s)
- Jeremy Graber
- VA Eastern Colorado Geriatric Research, Education, and Clinical Center (GRECC), VA Eastern Colorado Health Care System, Aurora, Colorado, USA.,Physical Therapy Program, Department of Physical Medicine and Rehabilitation, University of Colorado, Aurora, Colorado, USA
| | - Andrew Kittelson
- School of Physical Therapy and Rehabilitation Science, University of Montana, Missoula, Montana, USA
| | - Elizabeth Juarez-Colunga
- VA Eastern Colorado Geriatric Research, Education, and Clinical Center (GRECC), VA Eastern Colorado Health Care System, Aurora, Colorado, USA.,Department of Biostatistics and Informatics, University of Colorado, Aurora, Colorado, USA
| | - Xin Jin
- Department of Biostatistics and Informatics, University of Colorado, Aurora, Colorado, USA
| | - Michael Bade
- VA Eastern Colorado Geriatric Research, Education, and Clinical Center (GRECC), VA Eastern Colorado Health Care System, Aurora, Colorado, USA.,Physical Therapy Program, Department of Physical Medicine and Rehabilitation, University of Colorado, Aurora, Colorado, USA
| | - Jennifer Stevens-Lapsley
- VA Eastern Colorado Geriatric Research, Education, and Clinical Center (GRECC), VA Eastern Colorado Health Care System, Aurora, Colorado, USA.,Physical Therapy Program, Department of Physical Medicine and Rehabilitation, University of Colorado, Aurora, Colorado, USA
| |
Collapse
|
25
|
Chen W, Howard K, Gorham G, O'Bryan CM, Coffey P, Balasubramanya B, Abeyaratne A, Cass A. Design, effectiveness, and economic outcomes of contemporary chronic disease clinical decision support systems: a systematic review and meta-analysis. J Am Med Inform Assoc 2022; 29:1757-1772. [PMID: 35818299 PMCID: PMC9471723 DOI: 10.1093/jamia/ocac110] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 06/21/2022] [Accepted: 06/25/2022] [Indexed: 01/10/2023] Open
Abstract
Objectives Electronic health record-based clinical decision support (CDS) has the potential to improve health outcomes. This systematic review investigates the design, effectiveness, and economic outcomes of CDS targeting several common chronic diseases. Material and Methods We conducted a search in PubMed (Medline), EBSCOHOST (CINAHL, APA PsychInfo, EconLit), and Web of Science. We limited the search to studies from 2011 to 2021. Studies were included if the CDS was electronic health record-based and targeted one or more of the following chronic diseases: cardiovascular disease, diabetes, chronic kidney disease, hypertension, and hypercholesterolemia. Studies with effectiveness or economic outcomes were considered for inclusion, and a meta-analysis was conducted. Results The review included 76 studies with effectiveness outcomes and 9 with economic outcomes. Of the effectiveness studies, 63% described a positive outcome that favored the CDS intervention group. However, meta-analysis demonstrated that effect sizes were heterogenous and small, with limited clinical and statistical significance. Of the economic studies, most full economic evaluations (n = 5) used a modeled analysis approach. Cost-effectiveness of CDS varied widely between studies, with an estimated incremental cost-effectiveness ratio ranging between USD$2192 to USD$151 955 per QALY. Conclusion We summarize contemporary chronic disease CDS designs and evaluation results. The effectiveness and cost-effectiveness results for CDS interventions are highly heterogeneous, likely due to differences in implementation context and evaluation methodology. Improved quality of reporting, particularly from modeled economic evaluations, would assist decision makers to better interpret and utilize results from these primary research studies. Registration PROSPERO (CRD42020203716)
Collapse
Affiliation(s)
- Winnie Chen
- Menzies School of Health Research, Charles Darwin University, Casuarina, Northern Territory, Australia
| | - Kirsten Howard
- School of Public Health, Faculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia
| | - Gillian Gorham
- Menzies School of Health Research, Charles Darwin University, Casuarina, Northern Territory, Australia
| | - Claire Maree O'Bryan
- Menzies School of Health Research, Charles Darwin University, Casuarina, Northern Territory, Australia
| | - Patrick Coffey
- Menzies School of Health Research, Charles Darwin University, Casuarina, Northern Territory, Australia
| | - Bhavya Balasubramanya
- Menzies School of Health Research, Charles Darwin University, Casuarina, Northern Territory, Australia
| | - Asanga Abeyaratne
- Menzies School of Health Research, Charles Darwin University, Casuarina, Northern Territory, Australia
| | - Alan Cass
- Menzies School of Health Research, Charles Darwin University, Casuarina, Northern Territory, Australia
| |
Collapse
|
26
|
Akhloufi H, van der Sijs H, Melles DC, van der Hoeven CP, Vogel M, Mouton JW, Verbon A. The development and implementation of a guideline-based clinical decision support system to improve empirical antibiotic prescribing. BMC Med Inform Decis Mak 2022; 22:127. [PMID: 35538525 PMCID: PMC9087957 DOI: 10.1186/s12911-022-01860-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Accepted: 01/17/2022] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND To describe and evaluate a clinical decision support system (CDSS) for empirical antibiotic therapy using a systematic framework. METHODS A reporting framework for behavior change intervention implementation was used, which includes several domains: development, evaluation and implementation. Within the development domain a description is given of the engagement of stakeholders, a rationale for how the CDSS may influence antibiotic prescribing and a detailed outline of how the system was developed. Within the evaluation domain a technical validation is performed and the interaction between potential users and the CDSS is analyzed. Within the domain of implementation a description is given on how the CDSS was tested in the real world and the strategies that were used for implementation and adoption of the CDSS. RESULTS Development: a CDSS was developed, with the involvement of stakeholders, to assist empirical antibiotic prescribing by physicians. EVALUATION Technical problems were determined during the validation process and corrected in a new CDSS version. A usability study was performed to assess problems in the system-user interaction. IMPLEMENTATION In 114 patients the antibiotic advice that was generated by the CDSS was followed. For 54 patients the recommendations were not adhered to. CONCLUSIONS This study describes the development and validation of a CDSS for empirical antibiotic therapy and shows the usefulness of the systematic framework for reporting CDSS interventions. In addition it shows that CDSS recommendations are not always adhered to which is associated with incorrect use of the system.
Collapse
Affiliation(s)
- H Akhloufi
- Department of Medical Microbiology and Infectious Diseases, Erasmus MC University Medical Center, PO Box 2040, 3000 CA, Rotterdam, The Netherlands.
- Department of Internal Medicine, Division of Infectious Diseases, Erasmus MC University Medical Center, Rotterdam, The Netherlands.
| | - H van der Sijs
- Department of Hospital Pharmacy, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - D C Melles
- Department of Medical Microbiology and Infectious Diseases, Erasmus MC University Medical Center, PO Box 2040, 3000 CA, Rotterdam, The Netherlands
| | - C P van der Hoeven
- Department of Medical Microbiology and Infectious Diseases, Erasmus MC University Medical Center, PO Box 2040, 3000 CA, Rotterdam, The Netherlands
| | - M Vogel
- Department of Medical Microbiology and Infectious Diseases, Erasmus MC University Medical Center, PO Box 2040, 3000 CA, Rotterdam, The Netherlands
| | - J W Mouton
- Department of Medical Microbiology and Infectious Diseases, Erasmus MC University Medical Center, PO Box 2040, 3000 CA, Rotterdam, The Netherlands
| | - A Verbon
- Department of Medical Microbiology and Infectious Diseases, Erasmus MC University Medical Center, PO Box 2040, 3000 CA, Rotterdam, The Netherlands
- Department of Internal Medicine, Division of Infectious Diseases, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| |
Collapse
|
27
|
Persson Lindell O, Karlsson LO, Nilsson S, Charitakis E, Hagström E, Muhr T, Nilsson L, Henriksson M, Janzon M. Clinical decision support for familial hypercholesterolemia (CDS-FH): Rationale and design of a cluster randomized trial in primary care. Am Heart J 2022; 247:132-148. [PMID: 35181275 DOI: 10.1016/j.ahj.2022.02.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Revised: 01/21/2022] [Accepted: 02/10/2022] [Indexed: 12/24/2022]
Abstract
BACKGROUND Familial hypercholesterolemia (FH) is an underdiagnosed and undertreated genetic disorder with high risk of premature atherosclerotic cardiovascular disease and death. Clinical decision support (CDS) systems have the potential to aid in the identification and management of patients with FH. Prior studies using computer-based systems to screen patients for FH have shown promising results, but there has been no randomized controlled trial conducted. The aim of the current cluster randomized study is to evaluate if a CDS can increase the identification of FH. METHODS We have developed a CDS integrated in the electronic health records that will be activated in patients with elevated cholesterol levels (total cholesterol >8 mmol/L or low-density lipoprotein-cholesterol >5.5 mmol/L, adjusted for age, ongoing lipid lowering therapy and presence of premature coronary artery disease) at increased risk for FH. When activated, the CDS will urge the physician to send an automatically generated referral to the local lipid clinic for further evaluation. To evaluate the effects of the CDS, all primary care clinics will be cluster randomized 1:1 to either CDS intervention or standard care in a Swedish region with almost 500,000 inhabitants. The primary endpoint will be the number of patients diagnosed with FH at 30 months. Resource use and long-term health consequences will be estimated to assess the cost-effectiveness of the intervention. CONCLUSION Despite increasing awareness of FH, the condition remains underdiagnosed and undertreated. The present study will investigate whether a CDS can increase the number of patients being diagnosed with FH.
Collapse
Affiliation(s)
- Olof Persson Lindell
- Department of Cardiology, University Hospital, Linköping, Sweden; Department of Health, Medicine and Caring Sciences, Linköping University, Linköping Sweden.
| | - Lars O Karlsson
- Department of Cardiology, University Hospital, Linköping, Sweden; Department of Health, Medicine and Caring Sciences, Linköping University, Linköping Sweden
| | - Staffan Nilsson
- Department of Health, Medicine and Caring Sciences, Linköping University, Linköping Sweden; Division of Primary Health Care, Region Östergötland, Linköping, Sweden
| | - Emmanouil Charitakis
- Department of Cardiology, University Hospital, Linköping, Sweden; Department of Health, Medicine and Caring Sciences, Linköping University, Linköping Sweden
| | - Emil Hagström
- Department of Medical Sciences, Uppsala University, Uppsala, Sweden; Uppsala Clinical Research Center, Uppsala University, Uppsala, Sweden
| | - Thomas Muhr
- Department of Cardiology, University Hospital, Linköping, Sweden
| | - Lennart Nilsson
- Department of Health, Medicine and Caring Sciences, Linköping University, Linköping Sweden; Department of Medicine, County Hospital Ryhov, Jönköping, Sweden
| | - Martin Henriksson
- Center for Medical Technology Assessment, Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
| | - Magnus Janzon
- Department of Cardiology, University Hospital, Linköping, Sweden; Department of Health, Medicine and Caring Sciences, Linköping University, Linköping Sweden
| |
Collapse
|
28
|
DiMartino L, Miano T, Wessell K, Bohac B, Hanson LC. Identification of Uncontrolled Symptoms in Cancer Patients Using Natural Language Processing. J Pain Symptom Manage 2022; 63:610-617. [PMID: 34743011 PMCID: PMC8930509 DOI: 10.1016/j.jpainsymman.2021.10.014] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Revised: 10/22/2021] [Accepted: 10/24/2021] [Indexed: 12/25/2022]
Abstract
CONTEXT For patients with cancer, uncontrolled pain and other symptoms are the leading cause of unplanned hospitalizations. Early access to specialty palliative care (PC) is effective to reduce symptom burden, but more efficient approaches are needed for rapid identification and referral. Information on symptom burden largely exists in free-text notes, limiting its utility as a trigger for best practice alerts or automated referrals. OBJECTIVES To evaluate whether natural language processing (NLP) can be used to identify uncontrolled symptoms (pain, dyspnea, or nausea/vomiting) in the electronic health record (EHR) among hospitalized cancer patients with advanced disease. METHODS The dataset included 1,644 hospitalization encounters for cancer patients admitted from 1/2017 -6/2019. We randomly sampled 296 encounters, which included 15,580 clinical notes. We manually reviewed the notes and recorded symptom severity. The primary endpoint was an indicator for whether a symptom was labeled as "controlled" (none, mild, not reported) or as "uncontrolled" (moderate or severe). We randomly split the data into training and test sets and used the Random Forest algorithm to evaluate final model performance. RESULTS Our models predicted presence of an uncontrolled symptom with the following performance: pain with 61% accuracy, 69% sensitivity, and 46% specificity (F1: 69.5); nausea/vomiting with 68% accuracy, 21% sensitivity, and 90% specificity (F1: 29.4); and dyspnea with 80% accuracy, 22% sensitivity, and 88% specificity (F1: 21.1). CONCLUSION This study demonstrated initial feasibility of using NLP to identify hospitalized cancer patients with uncontrolled symptoms. Further model development is needed before these algorithms could be implemented to trigger early access to PC.
Collapse
Affiliation(s)
- Lisa DiMartino
- RTI International, Translational Health Sciences Division (L.D.), Research Triangle Park, NC, USA.
| | - Thomas Miano
- RTI International, Center for Data Science (T.M.), Research Triangle Park, NC, USA
| | - Kathryn Wessell
- Cecil G. Sheps Center for Health Services Research, University of North Carolina at Chapel Hill (K.W., L.C.H.), Chapel Hill, NC, USA
| | - Buck Bohac
- North Carolina Translational and Clinical Sciences Institute, University of North Carolina at Chapel Hill (B.B.), Chapel Hill, NC, USA
| | - Laura C Hanson
- Cecil G. Sheps Center for Health Services Research, University of North Carolina at Chapel Hill (K.W., L.C.H.), Chapel Hill, NC, USA; Division of Geriatric Medicine, University of North Carolina at Chapel Hill (L.C.H.), Chapel Hill, NC, USA
| |
Collapse
|
29
|
Kouri A, Yamada J, Lam Shin Cheung J, Van de Velde S, Gupta S. Do providers use computerized clinical decision support systems? A systematic review and meta-regression of clinical decision support uptake. Implement Sci 2022; 17:21. [PMID: 35272667 PMCID: PMC8908582 DOI: 10.1186/s13012-022-01199-3] [Citation(s) in RCA: 37] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Accepted: 02/28/2022] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Computerized clinical decision support systems (CDSSs) are a promising knowledge translation tool, but often fail to meaningfully influence the outcomes they target. Low CDSS provider uptake is a potential contributor to this problem but has not been systematically studied. The objective of this systematic review and meta-regression was to determine reported CDSS uptake and identify which CDSS features may influence uptake. METHODS Medline, Embase, CINAHL, and the Cochrane Database of Controlled Trials were searched from January 2000 to August 2020. Randomized, non-randomized, and quasi-experimental trials reporting CDSS uptake in any patient population or setting were included. The main outcome extracted was CDSS uptake, reported as a raw proportion, and representing the number of times the CDSS was used or accessed over the total number of times it could have been interacted with. We also extracted context, content, system, and implementation features that might influence uptake, for each CDSS. Overall weighted uptake was calculated using random-effects meta-analysis and determinants of uptake were investigated using multivariable meta-regression. RESULTS Among 7995 citations screened, 55 studies involving 373,608 patients and 3607 providers met full inclusion criteria. Meta-analysis revealed that overall CDSS uptake was 34.2% (95% CI 23.2 to 47.1%). Uptake was only reported in 12.4% of studies that otherwise met inclusion criteria. Multivariable meta-regression revealed the following factors significantly associated with uptake: (1) formally evaluating the availability and quality of the patient data needed to inform CDSS advice; and (2) identifying and addressing other barriers to the behaviour change targeted by the CDSS. CONCLUSIONS AND RELEVANCE System uptake was seldom reported in CDSS trials. When reported, uptake was low. This represents a major and potentially modifiable barrier to overall CDSS effectiveness. We found that features relating to CDSS context and implementation strategy best predicted uptake. Future studies should measure the impact of addressing these features as part of the CDSS implementation strategy. Uptake reporting must also become standard in future studies reporting CDSS intervention effects. REGISTRATION Pre-registered on PROSPERO, CRD42018092337.
Collapse
Affiliation(s)
- Andrew Kouri
- Division of Respirology, Department of Medicine, St. Michael's Hospital, Unity Health Toronto, 6 PGT, 30 Bond St, Toronto, ON, Canada
| | - Janet Yamada
- Daphne Cockwell School of Nursing, Faculty of Community Services, Ryerson University, Toronto, ON, Canada
| | - Jeffrey Lam Shin Cheung
- Keenan Research Center, Li Ka Shing Knowledge Institute, St. Michael's Hospital, University of Toronto, Toronto, Ontario, Canada
| | - Stijn Van de Velde
- Division for Health Services, Norwegian Institute of Public Health, Oslo, Norway
| | - Samir Gupta
- Division of Respirology, Department of Medicine, St. Michael's Hospital, Unity Health Toronto, 6 PGT, 30 Bond St, Toronto, ON, Canada.
- Keenan Research Center, Li Ka Shing Knowledge Institute, St. Michael's Hospital, University of Toronto, Toronto, Ontario, Canada.
- Department of Medicine, University of Toronto, Toronto, ON, Canada.
| |
Collapse
|
30
|
Herrman H, Patel V, Kieling C, Berk M, Buchweitz C, Cuijpers P, Furukawa TA, Kessler RC, Kohrt BA, Maj M, McGorry P, Reynolds CF, Weissman MM, Chibanda D, Dowrick C, Howard LM, Hoven CW, Knapp M, Mayberg HS, Penninx BWJH, Xiao S, Trivedi M, Uher R, Vijayakumar L, Wolpert M. Time for united action on depression: a Lancet-World Psychiatric Association Commission. Lancet 2022; 399:957-1022. [PMID: 35180424 DOI: 10.1016/s0140-6736(21)02141-3] [Citation(s) in RCA: 318] [Impact Index Per Article: 159.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/19/2020] [Revised: 09/15/2021] [Accepted: 09/21/2021] [Indexed: 12/12/2022]
Affiliation(s)
- Helen Herrman
- Orygen, The National Centre of Excellence in Youth Mental Health, Parkville, VIC, Australia; Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia.
| | - Vikram Patel
- Department of Global Health and Social Medicine, Harvard Medical School, Boston, MA, USA; Sangath, Goa, India; Department of Global Health and Population, Harvard T H Chan School of Public Health, Boston, MA, USA
| | - Christian Kieling
- Department of Psychiatry, School of Medicine, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil; Child & Adolescent Psychiatry Division, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil
| | - Michael Berk
- Deakin University, IMPACT Institute, Geelong, VIC, Australia
| | - Claudia Buchweitz
- Graduate Program in Psychiatry, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Pim Cuijpers
- Department of Clinical, Neuro and Developmental Psychology, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Toshiaki A Furukawa
- Department of Health Promotion and Human Behavior, Kyoto University Graduate School of Medicine/School of Public Health, Kyoto, Japan
| | - Ronald C Kessler
- Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
| | - Brandon A Kohrt
- Department of Psychiatry and Behavioral Sciences, George Washington University, Washington, DC, USA
| | - Mario Maj
- Department of Psychiatry, University of Campania L Vanvitelli, Naples, Italy
| | - Patrick McGorry
- Orygen, The National Centre of Excellence in Youth Mental Health, Parkville, VIC, Australia; Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
| | - Charles F Reynolds
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Myrna M Weissman
- Columbia University Mailman School of Public Health, New York, NY, USA; Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA; New York State Psychiatric Institute, New York, NY, USA
| | - Dixon Chibanda
- Department of Psychiatry, University of Zimbabwe, Harare, Zimbabwe; Centre for Global Mental Health, The London School of Hygiene and Tropical Medicine, London, UK
| | - Christopher Dowrick
- Department of Primary Care and Mental Health, University of Liverpool, Liverpool, UK
| | - Louise M Howard
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Christina W Hoven
- Columbia University Mailman School of Public Health, New York, NY, USA; Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA; New York State Psychiatric Institute, New York, NY, USA
| | - Martin Knapp
- Care Policy and Evaluation Centre, London School of Economics and Political Science, London, UK
| | - Helen S Mayberg
- Departments of Neurology, Neurosurgery, Psychiatry and Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Brenda W J H Penninx
- Department of Psychiatry, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Shuiyuan Xiao
- Central South University Xiangya School of Public Health, Changsha, China
| | - Madhukar Trivedi
- Peter O'Donnell Jr Brain Institute and the Department of Psychiatry, The University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Rudolf Uher
- Department of Psychiatry, Dalhousie University, Halifax, Canada
| | - Lakshmi Vijayakumar
- Sneha, Suicide Prevention Centre and Voluntary Health Services, Chennai, India
| | | |
Collapse
|
31
|
Venkateswaran M, Ghanem B, Abbas E, Khader KA, Ward IA, Awwad T, Baniode M, Frost MJ, Hijaz T, Isbeih M, Mørkrid K, Rose CJ, Frøen JF. A digital health registry with clinical decision support for improving quality of antenatal care in Palestine (eRegQual): a pragmatic, cluster-randomised, controlled, superiority trial. THE LANCET DIGITAL HEALTH 2022; 4:e126-e136. [PMID: 35090675 PMCID: PMC8811715 DOI: 10.1016/s2589-7500(21)00269-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 10/15/2021] [Accepted: 11/19/2021] [Indexed: 02/04/2023]
Affiliation(s)
- Mahima Venkateswaran
- Global Health Cluster, Division for Health Services, Norwegian Institute of Public Health, Oslo, Norway; Centre for Intervention Science in Maternal and Child Health (CISMAC), Centre for International Health, Department of Global Health and Primary Care, University of Bergen, Bergen, Norway
| | - Buthaina Ghanem
- Palestinian National Institute of Public Health, Ramallah, Palestine
| | - Eatimad Abbas
- Palestinian National Institute of Public Health, Ramallah, Palestine
| | | | - Itimad Abu Ward
- Palestinian National Institute of Public Health, Ramallah, Palestine
| | - Tamara Awwad
- Palestinian National Institute of Public Health, Ramallah, Palestine
| | - Mohammad Baniode
- Palestinian National Institute of Public Health, Ramallah, Palestine
| | - Michael James Frost
- Health Information Systems Programme, Department of Informatics, University of Oslo, Oslo, Norway
| | - Taghreed Hijaz
- Palestinian National Institute of Public Health, Ramallah, Palestine
| | - Mervett Isbeih
- Palestinian National Institute of Public Health, Ramallah, Palestine
| | - Kjersti Mørkrid
- Global Health Cluster, Division for Health Services, Norwegian Institute of Public Health, Oslo, Norway
| | - Christopher J Rose
- Global Health Cluster, Division for Health Services, Norwegian Institute of Public Health, Oslo, Norway
| | - J Frederik Frøen
- Global Health Cluster, Division for Health Services, Norwegian Institute of Public Health, Oslo, Norway; Centre for Intervention Science in Maternal and Child Health (CISMAC), Centre for International Health, Department of Global Health and Primary Care, University of Bergen, Bergen, Norway.
| |
Collapse
|
32
|
Brunner J, Farmer MM, Bean-Mayberry B, Chanfreau-Coffinier C, Than CT, Hamilton AB, Finley EP. Implementing clinical decision support for reducing women Veterans' cardiovascular risk in VA: A mixed-method, longitudinal study of context, adaptation, and uptake. FRONTIERS IN HEALTH SERVICES 2022; 2:946802. [PMID: 36925876 PMCID: PMC10012802 DOI: 10.3389/frhs.2022.946802] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Accepted: 08/29/2022] [Indexed: 11/13/2022]
Abstract
Evaluations of clinical decision support (CDS) implementation often struggle to measure and explain heterogeneity in uptake over time and across settings, and to account for the impact of context and adaptation on implementation success. In 2017-2020, the EMPOWER QUERI implemented a cardiovascular toolkit using a computerized template aimed at reducing women Veterans' cardiovascular risk across five Veterans Healthcare Administration (VA) sites, using an enhanced Replicating Effective Programs (REP) implementation approach. In this study, we used longitudinal joint displays of qualitative and quantitative findings to explore (1) how contextual factors emerged across sites, (2) how the template and implementation strategies were adapted in response to contextual factors, and (3) how contextual factors and adaptations coincided with template uptake across sites and over time. We identified site structure, staffing changes, relational authority of champions, and external leadership as important contextual factors. These factors gave rise to adaptations such as splitting the template into multiple parts, pairing the template with a computerized reminder, conducting academic detailing, creating cheat sheets, and using small-scale pilot testing. All five sites exhibited variability in utilization over the months of implementation, though later sites exhibited higher template utilization immediately post-launch, possibly reflecting a "preloading" of adaptations from previous sites. These findings underscore the importance of adaptive approaches to implementation, with intentional shifts in intervention and strategy to meet the needs of individual sites, as well as the value of integrating mixed-method data sources in conducting longitudinal evaluation of implementation efforts.
Collapse
Affiliation(s)
- Julian Brunner
- Center for the Study of Healthcare Innovation, Implementation, and Policy, VA Greater Los Angeles Healthcare System, Los Angeles, CA, United States
| | - Melissa M Farmer
- Center for the Study of Healthcare Innovation, Implementation, and Policy, VA Greater Los Angeles Healthcare System, Los Angeles, CA, United States
| | - Bevanne Bean-Mayberry
- Center for the Study of Healthcare Innovation, Implementation, and Policy, VA Greater Los Angeles Healthcare System, Los Angeles, CA, United States.,Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | | | - Claire T Than
- Center for the Study of Healthcare Innovation, Implementation, and Policy, VA Greater Los Angeles Healthcare System, Los Angeles, CA, United States
| | - Alison B Hamilton
- Center for the Study of Healthcare Innovation, Implementation, and Policy, VA Greater Los Angeles Healthcare System, Los Angeles, CA, United States.,Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Erin P Finley
- Center for the Study of Healthcare Innovation, Implementation, and Policy, VA Greater Los Angeles Healthcare System, Los Angeles, CA, United States.,Long School of Medicine, University of Texas Health Science Center San Antonio, San Antonio, TX, United States
| |
Collapse
|
33
|
Oluoch T, Cornet R, Muthusi J, Katana A, Kimanga D, Kwaro D, Okeyo N, Abu-Hanna A, de Keizer N. A clinical decision support system is associated with reduced loss to follow-up among patients receiving HIV treatment in Kenya: a cluster randomized trial. BMC Med Inform Decis Mak 2021; 21:357. [PMID: 34930228 PMCID: PMC8686234 DOI: 10.1186/s12911-021-01718-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Accepted: 12/12/2021] [Indexed: 11/13/2022] Open
Abstract
Background Loss to follow-up (LFTU) among HIV patients remains a major obstacle to achieving treatment goals with the risk of failure to achieve viral suppression and thereby increased HIV transmission. Although use of clinical decision support systems (CDSS) has been shown to improve adherence to HIV clinical guidance, to our knowledge, this is among the first studies conducted to show its effect on LTFU in low-resource settings. Methods We analyzed data from a cluster randomized controlled trial in adults and children (aged ≥ 18 months) who were receiving antiretroviral therapy at 20 HIV clinics in western Kenya between Sept 1, 2012 and Jan 31, 2014. Participating clinics were randomly assigned, via block randomization. Clinics in the control arm had electronic health records (EHR) only while the intervention arm had an EHR with CDSS. The study objectives were to assess the effects of a CDSS, implemented as alerts on an EHR system, on: (1) the proportion of patients that were LTFU, (2) LTFU patients traced and successfully linked back to treatment, and (3) time from enrollment on the study to documentation of LTFU. Results Among 5901 eligible patients receiving ART, 40.6% (n = 2396) were LTFU during the study period. CDSS was associated with lower LTFU among the patients (Adjusted Odds Ratio—aOR 0.70 (95% CI 0.65–0.77)). The proportions of patients linked back to treatment were 25.8% (95% CI 21.5–25.0) and 30.6% (95% CI 27.9–33.4)) in EHR only and EHR with CDSS sites respectively. CDSS was marginally associated with reduced time from enrollment on the study to first documentation of LTFU (adjusted Hazard Ratio—aHR 0.85 (95% CI 0.78–0.92)). Conclusion A CDSS can potentially improve quality of care through reduction and early detection of defaulting and LTFU among HIV patients and their re-engagement in care in a resource-limited country. Future research is needed on how CDSS can best be combined with other interventions to reduce LTFU. Trial registration NCT01634802. Registered at www.clinicaltrials.gov on 12-Jul-2012. Registered prospectively.
Collapse
Affiliation(s)
- Tom Oluoch
- Division of Global HIV and TB, US Centers for Disease Control and Prevention, 1600 Clifton Road NE, GA, 30329, Atlanta, USA.
| | - Ronald Cornet
- Department of Medical Informatics, Amsterdam Public Health Research Institute, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Jacques Muthusi
- Division of Global HIV and TB, US Centers for Disease Control and Prevention, Nairobi, Kenya
| | - Abraham Katana
- Division of Global HIV and TB, US Centers for Disease Control and Prevention, Nairobi, Kenya
| | - Davies Kimanga
- Division of Global HIV and TB, US Centers for Disease Control and Prevention, Nairobi, Kenya
| | - Daniel Kwaro
- Kenya Medical Research Institute - CDC Collaborative Program, Kisumu, Kenya
| | - Nicky Okeyo
- Kenya Medical Research Institute - CDC Collaborative Program, Kisumu, Kenya
| | - Ameen Abu-Hanna
- Department of Medical Informatics, Amsterdam Public Health Research Institute, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Nicolette de Keizer
- Department of Medical Informatics, Amsterdam Public Health Research Institute, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| |
Collapse
|
34
|
Hughes AEO, Jackups R. Clinical Decision Support for Laboratory Testing. Clin Chem 2021; 68:402-412. [PMID: 34871351 DOI: 10.1093/clinchem/hvab201] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Accepted: 08/24/2021] [Indexed: 01/16/2023]
Abstract
BACKGROUND As technology enables new and increasingly complex laboratory tests, test utilization presents a growing challenge for healthcare systems. Clinical decision support (CDS) refers to digital tools that present providers with clinically relevant information and recommendations, which have been shown to improve test utilization. Nevertheless, individual CDS applications often fail, and implementation remains challenging. CONTENT We review common classes of CDS tools grounded in examples from the literature as well as our own institutional experience. In addition, we present a practical framework and specific recommendations for effective CDS implementation. SUMMARY CDS encompasses a rich set of tools that have the potential to drive significant improvements in laboratory testing, especially with respect to test utilization. Deploying CDS effectively requires thoughtful design and careful maintenance, and structured processes focused on quality improvement and change management play an important role in achieving these goals.
Collapse
Affiliation(s)
- Andrew E O Hughes
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, USA
| | - Ronald Jackups
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, USA
| |
Collapse
|
35
|
Ellertsson S, Loftsson H, Sigurdsson EL. Artificial intelligence in the GPs office: a retrospective study on diagnostic accuracy. Scand J Prim Health Care 2021; 39:448-458. [PMID: 34585629 PMCID: PMC8725823 DOI: 10.1080/02813432.2021.1973255] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Abstract
OBJECTIVE Machine learning (ML) is expected to play an increasing role within primary health care (PHC) in coming years. No peer-reviewed studies exist that evaluate the diagnostic accuracy of ML models compared to general practitioners (GPs). The aim of this study was to evaluate the diagnostic accuracy of an ML classifier on primary headache diagnoses in PHC, compare its performance to GPs, and examine the most impactful signs and symptoms when making a prediction. DESIGN A retrospective study on diagnostic accuracy, using electronic health records from the database of the Primary Health Care Service of the Capital Area (PHCCA) in Iceland. SETTING Fifteen primary health care centers of the PHCCA. SUBJECTS All patients that consulted a physician, from 1 January 2006 to 30 April 2020, and received one of the selected diagnoses. MAIN OUTCOME MEASURES Sensitivity, Specificity, Positive Predictive Value, Matthews Correlation Coefficient, Receiver Operating Characteristic (ROC) curve, and Area under the ROC curve (AUROC) score for primary headache diagnoses, as well as Shapley Additive Explanations (SHAP) values of the ML classifier. RESULTS The classifier outperformed the GPs on all metrics except specificity. The SHAP values indicate that the classifier uses the same signs and symptoms (features) as a physician would, when distinguishing between headache diagnoses. CONCLUSION In a retrospective comparison, the diagnostic accuracy of the ML classifier for primary headache diagnoses is superior to GPs. According to SHAP values, the ML classifier relies on the same signs and symptoms as a physician when making a diagnostic prediction.KeypointsLittle is known about the diagnostic accuracy of machine learning (ML) in the context of primary health care, despite its considerable potential to aid in clinical work. This novel research sheds light on the diagnostic accuracy of ML in a clinical context, as well as the interpretation of its predictions. If the vast potential of ML is to be utilized in primary health care, its performance, safety, and inner workings need to be understood by clinicians.
Collapse
Affiliation(s)
- Steindor Ellertsson
- Primary Health Care Service of the Capital Area, Reykjavik, Iceland
- CONTACT Steindor Ellertsson Primary Health Care Service of the Capital Area, Grenimelur 44, 107, Reykjavik, Iceland
| | - Hrafn Loftsson
- Department of Computer Science, Reykjavik University, Reykjavik, Iceland
| | - Emil L. Sigurdsson
- Primary Health Care Service of the Capital Area, Reykjavik, Iceland
- Development Centre for Primary Health Care in Iceland, Reykjavik, Iceland
- Department of Family Medicine, University of Iceland, Reykjavik, Iceland
| |
Collapse
|
36
|
Elchynski AL, Desai N, D’Silva D, Hall B, Marks Y, Wiisanen K, Cicali EJ, Cavallari LH, Nguyen KA. Utilizing a Human-Computer Interaction Approach to Evaluate the Design of Current Pharmacogenomics Clinical Decision Support. J Pers Med 2021; 11:jpm11111227. [PMID: 34834578 PMCID: PMC8618963 DOI: 10.3390/jpm11111227] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Revised: 11/09/2021] [Accepted: 11/12/2021] [Indexed: 01/14/2023] Open
Abstract
A formal assessment of pharmacogenomics clinical decision support (PGx-CDS) by providers is lacking in the literature. The objective of this study was to evaluate the usability of PGx-CDS tools that have been implemented in a healthcare setting. We enrolled ten prescribing healthcare providers and had them complete a 60-min usability session, which included interacting with two PGx-CDS scenarios using the “Think Aloud” technique, as well as completing the Computer System Usability Questionnaire (CSUQ). Providers reported positive comments, negative comments, and suggestions for the two PGx-CDS during the usability testing. Most provider comments were in favor of the current PGx-CDS design, with the exception of how the genotype and phenotype information is displayed. The mean CSUQ score for the PGx-CDS overall satisfaction was 6.3 ± 0.95, with seven strongly agreeing and one strongly disagreeing for overall satisfaction. The implemented PGx-CDS at our institution was well received by prescribing healthcare providers. The feedback collected from the session will guide future PGx-CDS designs for our healthcare system and provide a framework for other institutions implementing PGx-CDS.
Collapse
Affiliation(s)
- Amanda L. Elchynski
- Department of Pharmacotherapy and Translational Research, College of Pharmacy, University of Florida, Gainesville, FL 32611, USA; (A.L.E.); (N.D.); (D.D.); (B.H.); (Y.M.); (K.W.); (E.J.C.); (L.H.C.)
- Center for Pharmacogenomics and Precision Medicine, University of Florida, Gainesville, FL 32611, USA
- Arkansas Children’s Hospital, Little Rock, AR 72202, USA
| | - Nina Desai
- Department of Pharmacotherapy and Translational Research, College of Pharmacy, University of Florida, Gainesville, FL 32611, USA; (A.L.E.); (N.D.); (D.D.); (B.H.); (Y.M.); (K.W.); (E.J.C.); (L.H.C.)
| | - Danielle D’Silva
- Department of Pharmacotherapy and Translational Research, College of Pharmacy, University of Florida, Gainesville, FL 32611, USA; (A.L.E.); (N.D.); (D.D.); (B.H.); (Y.M.); (K.W.); (E.J.C.); (L.H.C.)
| | - Bradley Hall
- Department of Pharmacotherapy and Translational Research, College of Pharmacy, University of Florida, Gainesville, FL 32611, USA; (A.L.E.); (N.D.); (D.D.); (B.H.); (Y.M.); (K.W.); (E.J.C.); (L.H.C.)
| | - Yael Marks
- Department of Pharmacotherapy and Translational Research, College of Pharmacy, University of Florida, Gainesville, FL 32611, USA; (A.L.E.); (N.D.); (D.D.); (B.H.); (Y.M.); (K.W.); (E.J.C.); (L.H.C.)
| | - Kristin Wiisanen
- Department of Pharmacotherapy and Translational Research, College of Pharmacy, University of Florida, Gainesville, FL 32611, USA; (A.L.E.); (N.D.); (D.D.); (B.H.); (Y.M.); (K.W.); (E.J.C.); (L.H.C.)
- Center for Pharmacogenomics and Precision Medicine, University of Florida, Gainesville, FL 32611, USA
| | - Emily J. Cicali
- Department of Pharmacotherapy and Translational Research, College of Pharmacy, University of Florida, Gainesville, FL 32611, USA; (A.L.E.); (N.D.); (D.D.); (B.H.); (Y.M.); (K.W.); (E.J.C.); (L.H.C.)
- Center for Pharmacogenomics and Precision Medicine, University of Florida, Gainesville, FL 32611, USA
| | - Larisa H. Cavallari
- Department of Pharmacotherapy and Translational Research, College of Pharmacy, University of Florida, Gainesville, FL 32611, USA; (A.L.E.); (N.D.); (D.D.); (B.H.); (Y.M.); (K.W.); (E.J.C.); (L.H.C.)
- Center for Pharmacogenomics and Precision Medicine, University of Florida, Gainesville, FL 32611, USA
| | - Khoa A. Nguyen
- Department of Pharmacotherapy and Translational Research, College of Pharmacy, University of Florida, Gainesville, FL 32611, USA; (A.L.E.); (N.D.); (D.D.); (B.H.); (Y.M.); (K.W.); (E.J.C.); (L.H.C.)
- Center for Pharmacogenomics and Precision Medicine, University of Florida, Gainesville, FL 32611, USA
- Correspondence:
| |
Collapse
|
37
|
Liu S, Reese TJ, Kawamoto K, Del Fiol G, Weir C. A theory-based meta-regression of factors influencing clinical decision support adoption and implementation. J Am Med Inform Assoc 2021; 28:2514-2522. [PMID: 34387686 DOI: 10.1093/jamia/ocab160] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 07/09/2021] [Accepted: 07/14/2021] [Indexed: 11/13/2022] Open
Abstract
OBJECTIVE The purpose of the study was to explore the theoretical underpinnings of effective clinical decision support (CDS) factors using the comparative effectiveness results. MATERIALS AND METHODS We leveraged search results from a previous systematic literature review and updated the search to screen articles published from January 2017 to January 2020. We included randomized controlled trials and cluster randomized controlled trials that compared a CDS intervention with and without specific factors. We used random effects meta-regression procedures to analyze clinician behavior for the aggregate effects. The theoretical model was the Unified Theory of Acceptance and Use of Technology (UTAUT) model with motivational control. RESULTS Thirty-four studies were included. The meta-regression models identified the importance of effort expectancy (estimated coefficient = -0.162; P = .0003); facilitating conditions (estimated coefficient = 0.094; P = .013); and performance expectancy with motivational control (estimated coefficient = 1.029; P = .022). Each of these factors created a significant impact on clinician behavior. The meta-regression model with the multivariate analysis explained a large amount of the heterogeneity across studies (R2 = 88.32%). DISCUSSION Three positive factors were identified: low effort to use, low controllability, and providing more infrastructure and implementation strategies to support the CDS. The multivariate analysis suggests that passive CDS could be effective if users believe the CDS is useful and/or social expectations to use the CDS intervention exist. CONCLUSIONS Overall, a modified UTAUT model that includes motivational control is an appropriate model to understand psychological factors associated with CDS effectiveness and to guide CDS design, implementation, and optimization.
Collapse
Affiliation(s)
- Siru Liu
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA
| | - Thomas J Reese
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Kensaku Kawamoto
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA
| | - Guilherme Del Fiol
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA
| | - Charlene Weir
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA
| |
Collapse
|
38
|
Souza-Pereira L, Ouhbi S, Pombo N. Quality-in-use characteristics for clinical decision support system assessment. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 207:106169. [PMID: 34062492 DOI: 10.1016/j.cmpb.2021.106169] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Accepted: 05/04/2021] [Indexed: 06/12/2023]
Abstract
BACKGROUND Clinical decision support systems (CDSSs) are developed to support healthcare practitioners with decision-making about therapy and diagnosis' confirmation, among others. Although there are many advantages of using CDSSs, there are still many challenges in their adoption. Therefore, it is essential to ensure the quality of the system, so that it can be used confidently and securely. OBJECTIVE This study aims to propose a set of (sub)characteristics which should be considered in evaluating the quality-in-use of CDSSs, based on the ISO/IEC 25010 standard and on existing literature. METHODS We reviewed the existing literature on CDSS assessment and presented a list of quality characteristics evaluated. RESULTS Ten quality characteristics and 56 sub-characteristics were identified and selected from the literature, in which usability was evaluated the most. An example of a scenario has been presented to illustrate our assessment approach of satisfaction and efficiency as important quality-in-use characteristics to be applied in the evaluation of a CDSS. CONCLUSION The proposed approach will contribute in bridging the gap between the quality of CDSSs and their adoption.
Collapse
Affiliation(s)
- Leonice Souza-Pereira
- Instituto Federal do Triângulo Mineiro - Campus Uberlândia Centro, Brasil; Instituto de Telecomunicações, Lisboa, Portugal; Universidade da Beira Interior, Covilhã, Portugal.
| | - Sofia Ouhbi
- Computer Science and Software Engineering Department, CIT, UAE University, UAE
| | - Nuno Pombo
- Instituto de Telecomunicações, Lisboa, Portugal; Universidade da Beira Interior, Covilhã, Portugal
| |
Collapse
|
39
|
Damoiseaux-Volman BA, van der Velde N, Ruige SG, Romijn JA, Abu-Hanna A, Medlock S. Effect of Interventions With a Clinical Decision Support System for Hospitalized Older Patients: Systematic Review Mapping Implementation and Design Factors. JMIR Med Inform 2021; 9:e28023. [PMID: 34269682 PMCID: PMC8325084 DOI: 10.2196/28023] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Revised: 05/10/2021] [Accepted: 05/17/2021] [Indexed: 01/25/2023] Open
Abstract
Background Clinical decision support systems (CDSSs) form an implementation strategy that can facilitate and support health care professionals in the care of older hospitalized patients. Objective Our study aims to systematically review the effects of CDSS interventions in older hospitalized patients. As a secondary aim, we aim to summarize the implementation and design factors described in effective and ineffective interventions and identify gaps in the current literature. Methods We conducted a systematic review with a search strategy combining the categories older patients, geriatric topic, hospital, CDSS, and intervention in the databases MEDLINE, Embase, and SCOPUS. We included controlled studies, extracted data of all reported outcomes, and potentially beneficial design and implementation factors. We structured these factors using the Grol and Wensing Implementation of Change model, the GUIDES (Guideline Implementation with Decision Support) checklist, and the two-stream model. The risk of bias of the included studies was assessed using the Cochrane Collaboration’s Effective Practice and Organisation of Care risk of bias approach. Results Our systematic review included 18 interventions, of which 13 (72%) were effective in improving care. Among these interventions, 8 (6 effective) focused on medication review, 8 (6 effective) on delirium, 7 (4 effective) on falls, 5 (4 effective) on functional decline, 4 (3 effective) on discharge or aftercare, and 2 (0 effective) on pressure ulcers. In 77% (10/13) effective interventions, the effect was based on process-related outcomes, in 15% (2/13) interventions on both process- and patient-related outcomes, and in 8% (1/13) interventions on patient-related outcomes. The following implementation and design factors were potentially associated with effectiveness: a priori problem or performance analyses (described in 9/13, 69% effective vs 0/5, 0% ineffective interventions), multifaceted interventions (8/13, 62% vs 1/5, 20%), and consideration of the workflow (9/13, 69% vs 1/5, 20%). Conclusions CDSS interventions can improve the hospital care of older patients, mostly on process-related outcomes. We identified 2 implementation factors and 1 design factor that were reported more frequently in articles on effective interventions. More studies with strong designs are needed to measure the effect of CDSS on relevant patient-related outcomes, investigate personalized (data-driven) interventions, and quantify the impact of implementation and design factors on CDSS effectiveness. Trial Registration PROSPERO (International Prospective Register of Systematic Reviews): CRD42019124470; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=124470.
Collapse
Affiliation(s)
- Birgit A Damoiseaux-Volman
- Department of Medical Informatics, Amsterdam Public Health Research Institute, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands
| | - Nathalie van der Velde
- Section of Geriatric Medicine, Amsterdam Public Health Research Institute, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands
| | - Sil G Ruige
- Department of Medical Informatics, Amsterdam Public Health Research Institute, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands
| | - Johannes A Romijn
- Department of Medicine, Amsterdam Public Health Research Institute, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands
| | - Ameen Abu-Hanna
- Department of Medical Informatics, Amsterdam Public Health Research Institute, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands
| | - Stephanie Medlock
- Department of Medical Informatics, Amsterdam Public Health Research Institute, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands
| |
Collapse
|
40
|
Saman DM, Chrenka EA, Harry ML, Allen CI, Freitag LA, Asche SE, Truitt AR, Ekstrom HL, O'Connor PJ, Sperl-Hillen JM, Ziegenfuss JY, Elliott TE. The impact of personalized clinical decision support on primary care patients' views of cancer prevention and screening: a cross-sectional survey. BMC Health Serv Res 2021; 21:592. [PMID: 34154588 PMCID: PMC8215810 DOI: 10.1186/s12913-021-06551-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Accepted: 05/18/2021] [Indexed: 11/24/2022] Open
Abstract
BACKGROUND Few studies have assessed the impact of clinical decision support (CDS), with or without shared decision-making tools (SDMTs), on patients' perceptions of cancer screening or prevention in primary care settings. This cross-sectional survey was conducted to understand primary care patient's perceptions on cancer screening or prevention. METHODS We mailed surveys (10/2018-1/2019) to 749 patients aged 18 to 75 years within 15 days after an index clinical encounter at 36 primary care clinics participating in a clinic-randomized control trial of a CDS system for cancer prevention. All patients were overdue for cancer screening or human papillomavirus vaccination. The survey compared respondents' answers by study arm: usual care; CDS; or CDS + SDMT. RESULTS Of 387 respondents (52% response rate), 73% reported having enough time to discuss cancer prevention options with their primary care provider (PCP), 64% reported their PCP explained the benefits of the cancer screening choice very well, and 32% of obese patients reported discussing weight management, with two-thirds reporting selecting a weight management intervention. Usual care respondents were significantly more likely to decide on colorectal cancer screening than CDS respondents (p < 0.01), and on tobacco cessation than CDS + SDMT respondents (p = 0.02) and both CDS and CDS + SDMT respondents (p < 0.001). CONCLUSIONS Most patients reported discussing cancer prevention needs with PCPs, with few significant differences between the three study arms in patient-reported cancer prevention care. Upcoming research will assess differences in screening and vaccination rates between study arms during the post-intervention follow-up period. TRIAL REGISTRATION clinicaltrials.gov , NCT02986230 , December 6, 2016.
Collapse
Affiliation(s)
- Daniel M Saman
- Nicklaus Children's Health System, 3601 NW 107th Ave, Doral, FL, 33178, USA
- Essentia Institute of Rural Health, 502 E. Second Street, Duluth, MN, 55805, USA
| | - Ella A Chrenka
- HealthPartners Institute, 3311 E. Old Shakopee Road, Bloomington, MN, 55425, USA
| | - Melissa L Harry
- Essentia Institute of Rural Health, 502 E. Second Street, Duluth, MN, 55805, USA.
| | - Clayton I Allen
- Essentia Institute of Rural Health, 502 E. Second Street, Duluth, MN, 55805, USA
| | - Laura A Freitag
- Essentia Institute of Rural Health, 502 E. Second Street, Duluth, MN, 55805, USA
| | - Stephen E Asche
- HealthPartners Institute, 3311 E. Old Shakopee Road, Bloomington, MN, 55425, USA
| | - Anjali R Truitt
- HealthPartners Institute, 3311 E. Old Shakopee Road, Bloomington, MN, 55425, USA
| | - Heidi L Ekstrom
- HealthPartners Institute, 3311 E. Old Shakopee Road, Bloomington, MN, 55425, USA
| | - Patrick J O'Connor
- HealthPartners Institute, 3311 E. Old Shakopee Road, Bloomington, MN, 55425, USA
| | - JoAnn M Sperl-Hillen
- HealthPartners Institute, 3311 E. Old Shakopee Road, Bloomington, MN, 55425, USA
| | | | - Thomas E Elliott
- HealthPartners Institute, 3311 E. Old Shakopee Road, Bloomington, MN, 55425, USA
| |
Collapse
|
41
|
Leitch S, Zeng J, Smith A, Stokes T. Medication risk management and health equity in New Zealand general practice: a retrospective cross-sectional study. Int J Equity Health 2021; 20:119. [PMID: 33975606 PMCID: PMC8111894 DOI: 10.1186/s12939-021-01461-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Accepted: 04/29/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Despite an overt commitment to equity, health inequities are evident throughout Aotearoa New Zealand. A general practice electronic alert system was developed to notify clinicians about their patient's risk of harm due to their pre-existing medical conditions or current medication. We aimed to determine whether there were any disparities in clinician action taken on the alert based on patient ethnicity or other demographic factors. METHODS Sixty-six New Zealand general practices from throughout New Zealand participated. Data were available for 1611 alerts detected for 1582 patients between 1 and 2018 and 1 July 2019. The primary outcome was whether action was taken following an alert or not. Logistic regression was used to assess if patients of one ethnicity group were more or less likely to have action taken. Potential confounders considered in the analyses include patient age, gender, ethnicity, socio-economic deprivation, number of long term diagnoses and number of long term medications. RESULTS No evidence of a difference was found in the odds of having action taken amongst ethnicity groups, however the estimated odds for Māori and Pasifika patients were lower compared to the European group (Māori OR 0.88, 95 %CI 0.63-1.22; Pasifika OR 0.88, 95 %CI 0.52-1.49). Females had significantly lower odds of having action taken compared to males (OR 0.76, 95 %CI 0.59-0.96). CONCLUSIONS This analysis of data arising from a general practice electronic alert system in New Zealand found clinicians typically took action on those alerts. However, clinicians appear to take less action for women and Māori and Pasifika patients. Use of a targeted alert system has the potential to mitigate risk from medication-related harm. Recognising clinician biases may improve the equitability of health care provision.
Collapse
Affiliation(s)
- Sharon Leitch
- Department of General Practice and Rural Health, Otago Medical School - Dunedin Campus, University of Otago, Dunedin, New Zealand.
| | - Jiaxu Zeng
- Department of Preventive and Social Medicine, Otago Medical School - Dunedin Campus, University of Otago, Dunedin, New Zealand
| | - Alesha Smith
- School of Pharmacy, University of Otago, Dunedin, New Zealand
| | - Tim Stokes
- Department of General Practice and Rural Health, Otago Medical School - Dunedin Campus, University of Otago, Dunedin, New Zealand
| |
Collapse
|
42
|
The implementation, use and sustainability of a clinical decision support system for medication optimisation in primary care: A qualitative evaluation. PLoS One 2021; 16:e0250946. [PMID: 33939750 PMCID: PMC8092789 DOI: 10.1371/journal.pone.0250946] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Accepted: 04/17/2021] [Indexed: 11/19/2022] Open
Abstract
Background The quality and safety of prescribing in general practice is important, Clinical decision support (CDS) systems can be used which present alerts to health professionals when prescribing in order to identify patients at risk of potentially hazardous prescribing. It is known that such computerised alerts may improve the safety of prescribing in hospitals but their implementation and sustainable use in general practice is less well understood. We aimed to understand the factors that influenced the successful implementation and sustained use in primary care of a CDS system. Methods Participants were purposively recruited from Clinical Commissioning Groups (CCGs) and general practices in the North West and East Midlands regions of England and from the CDS developers. We conducted face-to-face and telephone-based semi-structured qualitative interviews with staff stakeholders. A selection of participants was interviewed longitudinally to explore the further sustainability 1–2 years after implementation of the CDS system. The analysis, informed by Normalisation Process Theory (NPT), was thematic, iterative and conducted alongside data collection. Results Thirty-nine interviews were conducted either individually or in groups, with 33 stakeholders, including 11 follow-up interviews. Eight themes were interpreted in alignment with the four NPT constructs: Coherence (The purpose of the CDS: Enhancing medication safety and improving cost effectiveness; Relationship of users to the technology; Engagement and communication between different stakeholders); Cognitive Participation (Management of the profile of alerts); Collective Action (Prescribing in general practice, patient and population characteristics and engagement with patients; Knowledge);and Reflexive Monitoring (Sustaining the use of the CDS through maintenance and customisation; Learning and behaviour change. Participants saw that the CDS could have a role in enhancing medication safety and in the quality of care. Engagement through communication and support for local primary care providers and management leaders was considered important for successful implementation. Management of prescribing alert profiles for general practices was a dynamic process evolving over time. At regional management levels, work was required to adapt, and modify the system to optimise its use in practice and fulfil local priorities. Contextual factors, including patient and population characteristics, could impact upon the decision-making processes of prescribers influencing the response to alerts. The CDS could operate as a knowledge base allowing prescribers access to evidence-based information that they otherwise would not have. Conclusions This qualitative evaluation utilised NPT to understand the implementation, use and sustainability of a widely deployed CDS system offering prescribing alerts in general practice. The system was understood as having a role in medication safety in providing relevant patient specific information to prescribers in a timely manner. Engagement between stakeholders was considered important for the intervention in ensuring prescribers continued to utilise its functionality. Sustained implementation might be enhanced by careful profile management of the suite of alerts in the system. Our findings suggest that the use and sustainability of the CDS was related to prescribers’ perceptions of the relevance of alerts. Shared understanding of the purpose of the CDS between CCGS and general practices particularly in balancing cost saving and safety messages could be beneficial.
Collapse
|
43
|
Mebrahtu TF, Bloor K, Ledward A, Keenan AM, Andre D, Randell R, Skyrme S, Yang H, King H, Thompson CA. Effects of computerised clinical decision support systems (CDSS) on nursing and allied health professional performance and patient outcomes. Hippokratia 2021. [DOI: 10.1002/14651858.cd014699] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
| | - Karen Bloor
- Department of Health Sciences; University of York; York UK
| | | | | | - Deirdre Andre
- University of Leeds Library; University of Leeds; Leeds UK
| | - Rebecca Randell
- Faculty of Health Studies; University of Bradford; Bradford UK
| | - Sarah Skyrme
- School of Healthcare; University of Leeds; Leeds UK
| | - Huiqin Yang
- School of Healthcare; University of Leeds; Leeds UK
| | - Henry King
- School of Healthcare; University of Leeds; Leeds UK
| | | |
Collapse
|
44
|
Liu S, Reese TJ, Kawamoto K, Del Fiol G, Weir C. A systematic review of theoretical constructs in CDS literature. BMC Med Inform Decis Mak 2021; 21:102. [PMID: 33731089 PMCID: PMC7968272 DOI: 10.1186/s12911-021-01465-2] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Accepted: 03/02/2021] [Indexed: 01/06/2023] Open
Abstract
Background Studies that examine the adoption of clinical decision support (CDS) by healthcare providers have generally lacked a theoretical underpinning. The Unified Theory of Acceptance and Use of Technology (UTAUT) model may provide such a theory-based explanation; however, it is unknown if the model can be applied to the CDS literature. Objective Our overall goal was to develop a taxonomy based on UTAUT constructs that could reliably characterize CDS interventions. Methods We used a two-step process: (1) identified randomized controlled trials meeting comparative effectiveness criteria, e.g., evaluating the impact of CDS interventions with and without specific features or implementation strategies; (2) iteratively developed and validated a taxonomy for characterizing differential CDS features or implementation strategies using three raters. Results Twenty-five studies with 48 comparison arms were identified. We applied three constructs from the UTAUT model and added motivational control to characterize CDS interventions. Inter-rater reliability was as follows for model constructs: performance expectancy (κ = 0.79), effort expectancy (κ = 0.85), social influence (κ = 0.71), and motivational control (κ = 0.87). Conclusion We found that constructs from the UTAUT model and motivational control can reliably characterize features and associated implementation strategies. Our next step is to examine the quantitative relationships between constructs and CDS adoption.
Collapse
Affiliation(s)
- Siru Liu
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA.
| | - Thomas J Reese
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA
| | - Kensaku Kawamoto
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA
| | - Guilherme Del Fiol
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA
| | - Charlene Weir
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA
| |
Collapse
|
45
|
Stonesifer C, Crusco S, Rajupet S. Improving smoking cessation referrals among elective surgery clinics through electronic clinical decision support. Tob Prev Cessat 2021; 7:14. [PMID: 33644496 PMCID: PMC7896627 DOI: 10.18332/tpc/131823] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Revised: 10/28/2020] [Accepted: 12/19/2020] [Indexed: 11/24/2022]
Abstract
INTRODUCTION Preoperative visits are an exceptional opportunity to encourage smoking cessation, as studies demonstrate the experience of scheduling elective surgery produces an actionable incentive to quit. However, studies suggest surgeons do not regularly assess smoking behavior or offer cessation therapies. Clinical decision support (CDS) is a system in which providers are presented with clinically integrated tools to enhance decision-making. METHODS A CDS tool was designed to facilitate treatment referrals for smoking cessation services among patients seeking elective surgery. Two clinics were selected: the plastic and vascular surgeries. The study objectives were to assess the utilization rate and effectiveness of this system. RESULTS No smoking cessation referrals had been submitted by the plastic surgery or vascular surgery clinics in the year before CDS tool implementation. Providers at the plastic surgery clinic utilized the CDS tool in 95.0% (191 of 201) eligible patient encounters. Of these patients, 16.3% were identified as active smokers, and 16.1% of these smokers accepted treatment referrals. Providers at the vascular surgery clinic utilized the CDS tool in 50.3% (98 of 195) eligible patient encounters. Of these patients, 10.2% were identified as active smokers, and 30.0% of these smokers accepted treatment referrals. CONCLUSIONS The CDS tool improved the incidence of smoking cessation referrals in two surgical clinics from pretest baselines and achieved satisfactory utilization rates. This report demonstrates the feasibility of CDS tools to actualize the preoperative visit as an opportunity to promote smoking cessation.
Collapse
Affiliation(s)
- Connor Stonesifer
- Vagelos College of Physicians and Surgeons, Columbia University, New York, United States
| | - Salvatore Crusco
- Icahn School of Medicine at Mount Sinai, New York, United States.,James J. Peters VA Medical Center, Bronx, United States
| | - Sritha Rajupet
- Vagelos College of Physicians and Surgeons, Columbia University, New York, United States.,Icahn School of Medicine at Mount Sinai, New York, United States.,James J. Peters VA Medical Center, Bronx, United States
| |
Collapse
|
46
|
Stagg B, Stein JD, Medeiros FA, Cummins M, Kawamoto K, Hess R. Interests and needs of eye care providers in clinical decision support for glaucoma. BMJ Open Ophthalmol 2021; 6:e000639. [PMID: 33501378 PMCID: PMC7813287 DOI: 10.1136/bmjophth-2020-000639] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2020] [Revised: 12/21/2020] [Accepted: 12/31/2020] [Indexed: 01/30/2023] Open
Abstract
Objective To study whether clinicians who treat glaucoma are interested in using clinical decision support (CDS) tools for glaucoma, what glaucoma clinical decisions they feel would benefit from CDS, and what characteristics of CDS design they feel would be important in glaucoma clinical practice. Methods and analysis Working with the American Glaucoma Society, the Utah Ophthalmology Society and the Utah Optometric Association, we identified a group of clinicians who care for patients with glaucoma. We asked these clinicians about interest in CDS, what glaucoma clinical decisions would benefit from CDS, and what characteristics of CDS tool design would be important in glaucoma clinical practice. Results Of the 105 clinicians (31 optometrists, 10 general ophthalmologists and 64 glaucoma specialists), 93 (88.6%) were either ‘definitely’ or ‘probably’ interested in using CDS for glaucoma. There were no statistically significant differences in interest between clinical specialties (p=0.12), years in practice (p=0.85) or numbers of patients seen daily (p=0.99). Identifying progression of glaucoma was the clinical decision the largest number of clinicians felt would benefit from CDS (104/105, 99.1%). An easy to use interface was the CDS characteristic the largest number of clinicians felt would be ‘very important’ (93/105, 88.6%). Conclusion Of this group of clinicians who treat glaucoma, 88.6% were interested in using CDS for glaucoma and 99.1% felt that identification of glaucomatous progression could benefit from CDS. This level of interest supports future work to develop CDS for glaucoma.
Collapse
Affiliation(s)
- Brian Stagg
- Ophthalmology and Visual Sciences, University of Utah Health John A Moran Eye Center, Salt Lake City, Utah, USA.,Population Health Sciences, University of Utah Health, Salt Lake City, Utah, USA
| | - Joshua D Stein
- Ophthalmology and Visual Sciences, University of Michigan, Ann Arbor, Michigan, USA.,Institute for Healthcare Policty and Innovation, University of Michigan, Ann Arbor, Michigan, USA.,Health Management and Policy, University of Michigan School of Public Health, Ann Arbor, Michigan, USA
| | | | - Mollie Cummins
- College of Nursing, University of Utah Health, Salt Lake City, Utah, USA
| | - Kensaku Kawamoto
- Biomedical Informatics, University of Utah Health, Salt Lake City, Utah, USA
| | - Rachel Hess
- Population Health Sciences, University of Utah Health, Salt Lake City, Utah, USA.,Internal Medicine, University of Utah, Salt Lake City, Utah, USA
| |
Collapse
|
47
|
Muylle KM, Gentens K, Dupont AG, Cornu P. Evaluation of an optimized context-aware clinical decision support system for drug-drug interaction screening. Int J Med Inform 2021; 148:104393. [PMID: 33486355 DOI: 10.1016/j.ijmedinf.2021.104393] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Revised: 11/06/2020] [Accepted: 01/08/2021] [Indexed: 12/01/2022]
Abstract
OBJECTIVE Evaluation of the effect of six optimization strategies in a clinical decision support system (CDSS) for drug-drug interaction (DDI) screening on alert burden and alert acceptance and description of clinical pharmacist intervention acceptance. METHODS Optimizations in the new CDSS were the customization of the knowledge base (with addition of 67 extra DDIs and changes in severity classification), a new alert design, required override reasons for the most serious alerts, the creation of DDI-specific screening intervals, patient-specific alerting, and a real-time follow-up system of all alerts by clinical pharmacists with interventions by telephone was introduced. The alert acceptance was evaluated both at the prescription level (i.e. prescription acceptance, was the DDI prescribed?) and at the administration level (i.e. administration acceptance, did the DDI actually take place?). Finally, the new follow-up system was evaluated by assessing the acceptance of clinical pharmacist's interventions. RESULTS In the pre-intervention period, 1087 alerts (92.0 % level 1 alerts) were triggered, accounting for 19 different DDIs. In the post-intervention period, 2630 alerts (38.4 % level 1 alerts) were triggered, representing 86 different DDIs. The relative risk forprescription acceptance in the post-intervention period compared to the pre-intervention period was 4.02 (95 % confidence interval (CI) 3.17-5.10; 25.5 % versus 6.3 %). The relative risk for administration acceptance was 1.16 (95 % CI 1.08-1.25; 54.4 % versus 46.7 %). Finally, 86.9 % of the clinical pharmacist interventions were accepted. CONCLUSION Six concurrently implemented CDSS optimization strategies resulted in a high alert acceptance and clinical pharmacist intervention acceptance. Administration acceptance was remarkably higher than prescription acceptance.
Collapse
Affiliation(s)
- Katoo M Muylle
- Research Group Clinical Pharmacology & Clinical Pharmacy (KFAR), Faculty of Medicine and Pharmacy, Vrije Universiteit Brussel, Brussels, Laarbeeklaan 103, 1090 Brussels, Belgium.
| | - Kristof Gentens
- Department of Medical Informatics, UZ Brussel, Laarbeeklaan 101, 1090 Brussels, Belgium.
| | - Alain G Dupont
- Research Group Clinical Pharmacology & Clinical Pharmacy (KFAR), Faculty of Medicine and Pharmacy, Vrije Universiteit Brussel, Brussels, Laarbeeklaan 103, 1090 Brussels, Belgium.
| | - Pieter Cornu
- Research Group Clinical Pharmacology & Clinical Pharmacy (KFAR), Faculty of Medicine and Pharmacy, Vrije Universiteit Brussel, Brussels, Laarbeeklaan 103, 1090 Brussels, Belgium; Department of Medical Informatics, UZ Brussel, Laarbeeklaan 101, 1090 Brussels, Belgium.
| |
Collapse
|
48
|
Wales A. Decision support for Scotland's health and social care: learning from an outcomes-focused approach. BMJ Health Care Inform 2021; 27:bmjhci-2019-100124. [PMID: 32723853 PMCID: PMC7388875 DOI: 10.1136/bmjhci-2019-100124] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2019] [Revised: 04/05/2020] [Accepted: 04/28/2020] [Indexed: 11/04/2022] Open
Abstract
This short report shares learning from the research and development phase of the national decision support programme in NHS Scotland. It outlines how the programme has adopted an outcomes-focused approach which has guided critical decisions on solution design, engagement of policy sponsors, clinical and management leaders, implementation and evaluation approach, technical architecture and technology development. It discusses how this outcomes-led approach positions decision support as catalyst for a learning health and care system that continuously refreshes the healthcare knowledge base through new insights generated by evaluating impact and outcomes.
Collapse
Affiliation(s)
- Ann Wales
- Digital Health and Care Directorate, Scottish Government, Edinburgh, UK .,Digital Health and Care Institute, University of Strathclyde, Glasgow, UK
| |
Collapse
|
49
|
Stagg BC, Stein JD, Medeiros FA, Wirostko B, Crandall A, Hartnett ME, Cummins M, Morris A, Hess R, Kawamoto K. Special Commentary: Using Clinical Decision Support Systems to Bring Predictive Models to the Glaucoma Clinic. Ophthalmol Glaucoma 2021; 4:5-9. [PMID: 32810611 PMCID: PMC7854795 DOI: 10.1016/j.ogla.2020.08.006] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2020] [Revised: 08/12/2020] [Accepted: 08/12/2020] [Indexed: 01/29/2023]
Abstract
Advances in the field of predictive modeling using artificial intelligence and machine learning have the potential to improve clinical care and outcomes, but only if the results of these models are presented appropriately to clinicians at the time they make decisions for individual patients. Clinical decision support (CDS) systems could be used to accomplish this. Modern CDS systems are computer-based tools designed to improve clinician decision making for individual patients. However, not all CDS systems are effective. Four principles that have been shown in other medical fields to be important for successful CDS system implementation are (1) integration into clinician workflow, (2) user-centered interface design, (3) evaluation of CDS systems and rules, and (4) standards-based development so the tools can be deployed across health systems.
Collapse
Affiliation(s)
- Brian C Stagg
- John Moran Eye Center, Department of Ophthalmology and Visual Sciences, University of Utah, Salt Lake City, Utah; Department of Population Health Sciences, University of Utah, Salt Lake City, Utah.
| | - Joshua D Stein
- Center for Eye Policy & Innovation, Kellogg Eye Center, Department of Opthalmology and Visual Sciences, University of Michigan, Ann Arbor, Michigan; Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, Michigan; Department of Health Management and Policy, University of Michigan School of Public Health, Ann Arbor, Michigan
| | | | - Barbara Wirostko
- John Moran Eye Center, Department of Ophthalmology and Visual Sciences, University of Utah, Salt Lake City, Utah
| | - Alan Crandall
- John Moran Eye Center, Department of Ophthalmology and Visual Sciences, University of Utah, Salt Lake City, Utah
| | - M Elizabeth Hartnett
- John Moran Eye Center, Department of Ophthalmology and Visual Sciences, University of Utah, Salt Lake City, Utah
| | - Mollie Cummins
- College of Nursing, University of Utah, Salt Lake City, Utah
| | - Alan Morris
- Division of Respiratory, Critical Care and Occupational Pulmonary Medicine, Department of Internal Medicine, University of Utah, Salt Lake City, Utah
| | - Rachel Hess
- Department of Population Health Sciences, University of Utah, Salt Lake City, Utah; Department of Internal Medicine, University of Utah, Salt Lake City, Utah
| | - Kensaku Kawamoto
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah
| |
Collapse
|
50
|
Barriers and Facilitators for Implementation of a Computerized Clinical Decision Support System in Lung Cancer Multidisciplinary Team Meetings-A Qualitative Assessment. BIOLOGY 2020; 10:biology10010009. [PMID: 33375573 PMCID: PMC7830066 DOI: 10.3390/biology10010009] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Revised: 12/18/2020] [Accepted: 12/21/2020] [Indexed: 12/24/2022]
Abstract
Simple Summary Oncological computerized clinical decision support systems (CCDSSs) are currently being developed to facilitate workflows of multidisciplinary team meetings (MDTMs). To successfully implement these systems in MDTMs, the aim of this qualitative assessment was to identify barriers and facilitators for implementation and to provide actionable findings for an implementation strategy. The main facilitators for implementation of the CCDSS were considered to be easy access to well-structured data, and reducing time spent by clinicians on MDTM preparation and duration of the MDTMs. Main barriers for adoption were seen in incomplete or non-trustworthy output generated by the system and insufficient adaptability of the system to local and contextual needs. Actionable findings for an implementation strategy were a usability test and validation study involving key users in the organization’s real-life setting. Given the growing interest in CCDSSs in oncology care, insight in barriers and facilitators for successful implementation seems highly relevant. Abstract Background: Oncological computerized clinical decision support systems (CCDSSs) to facilitate workflows of multidisciplinary team meetings (MDTMs) are currently being developed. To successfully implement these CCDSSs in MDTMs, this study aims to: (a) identify barriers and facilitators for implementation for the use case of lung cancer; and (b) provide actionable findings for an implementation strategy. Methods: The Consolidated Framework for Implementation Science was used to create an interview protocol and to analyze the results. Semi-structured interviews were conducted among various health care professionals involved in MDTMs. The transcripts were analyzed using a thematic analysis following a deductive approach. Results: Twenty-six professionals participated in the interviews. The main facilitators for implementation of the CCDSS were considered to be easy access to well-structured patient data, and the resulting reduction of MDTM preparation time and of duration of MDTMs. Main barriers for adoption were seen in incomplete or non-trustworthy output generated by the system and insufficient adaptability of the system to local and contextual needs. Conclusion: Using a CCDSS in lung cancer MDTMs was expected to increase efficiency of workflows. Successful implementation was seen as dependent on the reliability and adaptability of the CCDSS and involvement of key users in the implementation process.
Collapse
|