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Patrickson B, Shams L, Fouyaxis J, Strobel J, Schubert KO, Musker M, Bidargaddi N. Evolving Adult ADHD Care: Preparatory Evaluation of a Prototype Digital Service Model Innovation for ADHD Care. Int J Environ Res Public Health 2024; 21:582. [PMID: 38791796 DOI: 10.3390/ijerph21050582] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Revised: 04/10/2024] [Accepted: 04/14/2024] [Indexed: 05/26/2024]
Abstract
BACKGROUND Given the prevalence of ADHD and the gaps in ADHD care in Australia, this study investigates the critical barriers and driving forces for innovation. It does so by conducting a preparatory evaluation of an ADHD prototype digital service innovation designed to help streamline ADHD care and empower individual self-management. METHODS Semi-structured interviews with ADHD care consumers/participants and practitioners explored their experiences and provided feedback on a mobile self-monitoring app and related service innovations. Interview transcripts were double coded to explore thematic barriers and the enablers for better ADHD care. RESULTS Fifteen interviews (9 consumers, 6 practitioners) revealed barriers to better ADHD care for consumers (ignorance and prejudice, trust, impatience) and for practitioners (complexity, sustainability). Enablers for consumers included validation/empowerment, privacy, and security frameworks, tailoring, and access. Practitioners highlighted the value of transparency, privacy and security frameworks, streamlined content, connected care between services, and the tailoring of broader metrics. CONCLUSIONS A consumer-centred approach to digital health service innovation, featuring streamlined, private, and secure solutions with enhanced mobile tools proves instrumental in bridging gaps in ADHD care in Australia. These innovations should help to address the gaps in ADHD care in Australia. These innovations should encompass integrated care, targeted treatment outcome data, and additional lifestyle support, whilst recognising the tensions between customised functionalities and streamlined displays.
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Affiliation(s)
- Bronwin Patrickson
- Digital Health Research Lab, College of Medicine and Public Health, Flinders University, Adelaide 5042, Australia
| | - Lida Shams
- Digital Health Research Lab, College of Medicine and Public Health, Flinders University, Adelaide 5042, Australia
| | - John Fouyaxis
- Digital Health Research Lab, College of Medicine and Public Health, Flinders University, Adelaide 5042, Australia
| | - Jörg Strobel
- Digital Health Research Lab, College of Medicine and Public Health, Flinders University, Adelaide 5042, Australia
- Division of Mental Health, Barossa Hills Fleurieu Local Health Network, 29 North St, Angaston 5353, Australia
| | - Klaus Oliver Schubert
- Division of Mental Health, Northern Adelaide Local Health Network, 7-9 Park Terrace, Salisbury 5108, Australia
- Discipline of Psychiatry, Adelaide Medical School, The University of Adelaide, North Terrace, Adelaide 5005, Australia
- The Headspace Adelaide Early Psychosis, Sonder, 173 Wakefield St, Adelaide 5000, Australia
| | - Mike Musker
- Clinical Health Sciences, Mental Health and Suicide Prevention Research and Education Group, University of South Australia, City East, Centenary Building, North Terrace, Adelaide 5000, Australia
| | - Niranjan Bidargaddi
- Digital Health Research Lab, College of Medicine and Public Health, Flinders University, Adelaide 5042, Australia
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Tsaftaridis N, Goldin M, Spyropoulos AC. System-Wide Thromboprophylaxis Interventions for Hospitalized Patients at Risk of Venous Thromboembolism: Focus on Cross-Platform Clinical Decision Support. J Clin Med 2024; 13:2133. [PMID: 38610898 PMCID: PMC11013003 DOI: 10.3390/jcm13072133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Revised: 03/23/2024] [Accepted: 04/02/2024] [Indexed: 04/14/2024] Open
Abstract
Thromboprophylaxis of hospitalized patients at risk of venous thromboembolism (VTE) presents challenges owing to patient heterogeneity and lack of adoption of evidence-based methods. Intuitive practices for thromboprophylaxis have resulted in many patients being inappropriately prophylaxed. We conducted a narrative review summarizing system-wide thromboprophylaxis interventions in hospitalized patients. Multiple interventions for thromboprophylaxis have been tested, including multifaceted approaches such as national VTE prevention programs with audits, pre-printed order entry, passive alerts (either human or electronic), and more recently, the use of active clinical decision support (CDS) tools incorporated into electronic health records (EHRs). Multifaceted health-system and order entry interventions have shown mixed results in their ability to increase appropriate thromboprophylaxis and reduce VTE unless mandated through a national VTE prevention program, though the latter approach is potentially costly and effort- and time-dependent. Studies utilizing passive human or electronic alerts have also shown mixed results in increasing appropriate thromboprophylaxis and reducing VTE. Recently, a universal cloud-based and EHR-agnostic CDS VTE tool incorporating a validated VTE risk score revealed high adoption and effectiveness in increasing appropriate thromboprophylaxis and reducing major thromboembolism. Active CDS tools hold promise in improving appropriate thromboprophylaxis, especially with further refinement and widespread implementation within various EHRs and clinical workflows.
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Affiliation(s)
- Nikolaos Tsaftaridis
- Institute of Health System Science, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY 11030, USA; (N.T.); (M.G.)
- Anticoagulation and Clinical Thrombosis Services, Northwell Health at Lenox Hill Hospital, New York, NY 10075, USA
| | - Mark Goldin
- Institute of Health System Science, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY 11030, USA; (N.T.); (M.G.)
- Anticoagulation and Clinical Thrombosis Services, Northwell Health at Lenox Hill Hospital, New York, NY 10075, USA
- The Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY 11549, USA
| | - Alex C. Spyropoulos
- Institute of Health System Science, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY 11030, USA; (N.T.); (M.G.)
- Anticoagulation and Clinical Thrombosis Services, Northwell Health at Lenox Hill Hospital, New York, NY 10075, USA
- The Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY 11549, USA
- Elmezzi Graduate School of Molecular Medicine, Manhasset, NY 11030, USA
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3
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Rajabzadeh-Oghaz H, Kumar V, Berry DB, Singh A, Schoch BS, Aibinder WR, Gobbato B, Polakovic S, Elwell J, Roche CP. Impact of Deltoid Computer Tomography Image Data on the Accuracy of Machine Learning Predictions of Clinical Outcomes after Anatomic and Reverse Total Shoulder Arthroplasty. J Clin Med 2024; 13:1273. [PMID: 38592118 PMCID: PMC10931952 DOI: 10.3390/jcm13051273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Revised: 02/05/2024] [Accepted: 02/20/2024] [Indexed: 04/10/2024] Open
Abstract
Background: Despite the importance of the deltoid to shoulder biomechanics, very few studies have quantified the three-dimensional shape, size, or quality of the deltoid muscle, and no studies have correlated these measurements to clinical outcomes after anatomic (aTSA) and/or reverse (rTSA) total shoulder arthroplasty in any statistically/scientifically relevant manner. Methods: Preoperative computer tomography (CT) images from 1057 patients (585 female, 469 male; 799 primary rTSA and 258 primary aTSA) of a single platform shoulder arthroplasty prosthesis (Equinoxe; Exactech, Inc., Gainesville, FL) were analyzed in this study. A machine learning (ML) framework was used to segment the deltoid muscle for 1057 patients and quantify 15 different muscle characteristics, including volumetric (size, shape, etc.) and intensity-based Hounsfield (HU) measurements. These deltoid measurements were correlated to postoperative clinical outcomes and utilized as inputs to train/test ML algorithms used to predict postoperative outcomes at multiple postoperative timepoints (1 year, 2-3 years, and 3-5 years) for aTSA and rTSA. Results: Numerous deltoid muscle measurements were demonstrated to significantly vary with age, gender, prosthesis type, and CT image kernel; notably, normalized deltoid volume and deltoid fatty infiltration were demonstrated to be relevant to preoperative and postoperative clinical outcomes after aTSA and rTSA. Incorporating deltoid image data into the ML models improved clinical outcome prediction accuracy relative to ML algorithms without image data, particularly for the prediction of abduction and forward elevation after aTSA and rTSA. Analyzing ML feature importance facilitated rank-ordering of the deltoid image measurements relevant to aTSA and rTSA clinical outcomes. Specifically, we identified that deltoid shape flatness, normalized deltoid volume, deltoid voxel skewness, and deltoid shape sphericity were the most predictive image-based features used to predict clinical outcomes after aTSA and rTSA. Many of these deltoid measurements were found to be more predictive of aTSA and rTSA postoperative outcomes than patient demographic data, comorbidity data, and diagnosis data. Conclusions: While future work is required to further refine the ML models, which include additional shoulder muscles, like the rotator cuff, our results show promise that the developed ML framework can be used to evolve traditional CT-based preoperative planning software into an evidence-based ML clinical decision support tool.
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Affiliation(s)
| | - Vikas Kumar
- Exactech, Inc., Gainesville, FL 32653, USA; (H.R.-O.); (V.K.); (S.P.); (J.E.)
| | - David B. Berry
- Department of Orthopedic Surgery, University of California San Diego, San Diego, CA 92093, USA; (D.B.B.); (A.S.)
| | - Anshu Singh
- Department of Orthopedic Surgery, University of California San Diego, San Diego, CA 92093, USA; (D.B.B.); (A.S.)
| | | | - William R. Aibinder
- Department of Orthopedic Surgery, University of Michigan, Ann Arbor, MI 48109, USA;
| | - Bruno Gobbato
- R. José Emmendoerfer, 1449—Nova Brasília, Jaraguá do Sul 89252-278, SC, Brazil;
| | - Sandrine Polakovic
- Exactech, Inc., Gainesville, FL 32653, USA; (H.R.-O.); (V.K.); (S.P.); (J.E.)
| | - Josie Elwell
- Exactech, Inc., Gainesville, FL 32653, USA; (H.R.-O.); (V.K.); (S.P.); (J.E.)
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Alexiuk M, Elgubtan H, Tangri N. Clinical Decision Support Tools in the Electronic Medical Record. Kidney Int Rep 2024; 9:29-38. [PMID: 38312784 PMCID: PMC10831391 DOI: 10.1016/j.ekir.2023.10.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Accepted: 10/23/2023] [Indexed: 02/06/2024] Open
Abstract
The integration of clinical decision support (CDS) tools into electronic medical record (EMR) systems has become common. Although there are many benefits for both patients and providers from successful integration, barriers exist that prevent consistent and effective use of these tools. Such barriers include tool alert fatigue, lack of interoperability between tools and medical record systems, and poor acceptance of tools by care providers. However, successful integration of CDS tools into EMR systems have been reported; examples of these include the Statin Choice Decision Aid, and the Kidney Failure Risk Equation (KFRE). This article reviews the history of EMR systems and its integration with CDS tools, the barriers preventing successful integration, and the benefits reported from successful integration. This article also provides suggestions and strategies for improving successful integration, making these tools easier to use and more effective for care providers.
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Affiliation(s)
- Mackenzie Alexiuk
- Chronic Disease Innovation Centre, Winnipeg, Manitoba, Canada
- Community Health Sciences, Max Rady College of Medicine, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Heba Elgubtan
- Chronic Disease Innovation Centre, Winnipeg, Manitoba, Canada
- Community Health Sciences, Max Rady College of Medicine, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Navdeep Tangri
- Chronic Disease Innovation Centre, Winnipeg, Manitoba, Canada
- Department of Internal Medicine, Max Rady College of Medicine, University of Manitoba, Winnipeg, Manitoba, Canada
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Talekar MK, Painter JL, Elizalde MA, Thomas M, Stein HK. Semi-automation of keratopathy visual acuity grading of corneal events in belantamab mafodotin clinical trials: clinical decision support software. Front Digit Health 2023; 5:1138453. [PMID: 37881364 PMCID: PMC10597720 DOI: 10.3389/fdgth.2023.1138453] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 09/19/2023] [Indexed: 10/27/2023] Open
Abstract
Background Belantamab mafodotin (belamaf) has demonstrated clinically meaningful antimyeloma activity in patients with heavily pretreated multiple myeloma. However, it is highly active against dividing cells, contributing to off-target adverse events, particularly ocular toxicity. Changes in best corrected visual acuity (BCVA) and corneal examination findings are routinely monitored to determine Keratopathy Visual Acuity (KVA) grade to inform belamaf dose modification. Objective We aimed to develop a semiautomated mobile app to facilitate the grading of ocular events in clinical trials involving belamaf. Methods The paper process was semiautomated by creating a library of finite-state automaton (FSA) models to represent all permutations of KVA grade changes from baseline BCVA readings. The transition states in the FSA models operated independently of eye measurement units (e.g., Snellen, logMAR, decimal) and provided a uniform approach to determining KVA grade changes. Together with the FSA, the complex decision tree for determining the grade change based on corneal examination findings was converted into logical statements for accurate and efficient overall KVA grade computation. First, a web-based user interface, conforming to clinical practice settings, was developed to simplify the input of key KVA grading criteria. Subsequently, a mobile app was developed that included additional guided steps to assist in clinical decision-making. Results The app underwent a robust Good Clinical Practice validation process. Outcomes were reviewed by key stakeholders, our belamaf medical lead, and the systems integration team. The time to compute a patient's overall KVA grade using the Belamaf Eye Exam (BEE) app was reduced from a 20- to 30-min process to <1-2 min. The BEE app was well received, with most investigators surveyed selecting "satisfied" or "highly satisfied" for its accuracy and time efficiency. Conclusions Our semiautomated approach provides for an accurate, simplified method of assessment of patients' corneal status that reduces errors and quickly delivers information critical for potential belamaf dose modifications. The app is currently available on the Apple iOS and Android platforms for use by investigators of the DREAMM clinical trials, and its use could easily be extended to the clinic to support healthcare providers who need to make informed belamaf treatment decisions.
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Affiliation(s)
- Mala K. Talekar
- Oncology Clinical Development GSK, Collegeville, PA, United States
| | | | - Mica A. Elizalde
- Regulatory Affairs, Precision Medicine and Digital Health, GSK, Rockville, MD, United States
| | - Michele Thomas
- Oncology Clinical Development GSK, Collegeville, PA, United States
| | - Heather K. Stein
- Oncology Clinical Development GSK, Collegeville, PA, United States
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Hogg HDJ, Al-Zubaidy M, Keane PA, Hughes G, Beyer FR, Maniatopoulos G. Evaluating the translation of implementation science to clinical artificial intelligence: a bibliometric study of qualitative research. Front Health Serv 2023; 3:1161822. [PMID: 37492632 PMCID: PMC10364639 DOI: 10.3389/frhs.2023.1161822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Accepted: 06/26/2023] [Indexed: 07/27/2023]
Abstract
Introduction Whilst a theoretical basis for implementation research is seen as advantageous, there is little clarity over if and how the application of theories, models or frameworks (TMF) impact implementation outcomes. Clinical artificial intelligence (AI) continues to receive multi-stakeholder interest and investment, yet a significant implementation gap remains. This bibliometric study aims to measure and characterize TMF application in qualitative clinical AI research to identify opportunities to improve research practice and its impact on clinical AI implementation. Methods Qualitative research of stakeholder perspectives on clinical AI published between January 2014 and October 2022 was systematically identified. Eligible studies were characterized by their publication type, clinical and geographical context, type of clinical AI studied, data collection method, participants and application of any TMF. Each TMF applied by eligible studies, its justification and mode of application was characterized. Results Of 202 eligible studies, 70 (34.7%) applied a TMF. There was an 8-fold increase in the number of publications between 2014 and 2022 but no significant increase in the proportion applying TMFs. Of the 50 TMFs applied, 40 (80%) were only applied once, with the Technology Acceptance Model applied most frequently (n = 9). Seven TMFs were novel contributions embedded within an eligible study. A minority of studies justified TMF application (n = 51,58.6%) and it was uncommon to discuss an alternative TMF or the limitations of the one selected (n = 11,12.6%). The most common way in which a TMF was applied in eligible studies was data analysis (n = 44,50.6%). Implementation guidelines or tools were explicitly referenced by 2 reports (1.0%). Conclusion TMFs have not been commonly applied in qualitative research of clinical AI. When TMFs have been applied there has been (i) little consensus on TMF selection (ii) limited description of selection rationale and (iii) lack of clarity over how TMFs inform research. We consider this to represent an opportunity to improve implementation science's translation to clinical AI research and clinical AI into practice by promoting the rigor and frequency of TMF application. We recommend that the finite resources of the implementation science community are diverted toward increasing accessibility and engagement with theory informed practices. The considered application of theories, models and frameworks (TMF) are thought to contribute to the impact of implementation science on the translation of innovations into real-world care. The frequency and nature of TMF use are yet to be described within digital health innovations, including the prominent field of clinical AI. A well-known implementation gap, coined as the "AI chasm" continues to limit the impact of clinical AI on real-world care. From this bibliometric study of the frequency and quality of TMF use within qualitative clinical AI research, we found that TMFs are usually not applied, their selection is highly varied between studies and there is not often a convincing rationale for their selection. Promoting the rigor and frequency of TMF use appears to present an opportunity to improve the translation of clinical AI into practice.
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Affiliation(s)
- H. D. J. Hogg
- Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, United Kingdom
- The Royal Victoria Infirmary, Newcastle Upon Tyne Hospitals NHS Foundation Trust, Newcastle Upon Tyne, United Kingdom
- Moorfields Eye Hospital NHS Foundation Trust, London, United Kingdom
| | - M. Al-Zubaidy
- The Royal Victoria Infirmary, Newcastle Upon Tyne Hospitals NHS Foundation Trust, Newcastle Upon Tyne, United Kingdom
| | - P. A. Keane
- Moorfields Eye Hospital NHS Foundation Trust, London, United Kingdom
- Institute of Ophthalmology, University College London, London, United Kingdom
| | - G. Hughes
- Nuffield Department of Primary Care Health Sciences, Oxford University, Oxford, United Kingdom
- University ofLeicester School of Business, University of Leicester, Leicester, United Kingdom
| | - F. R. Beyer
- Evidence Synthesis Group, Population Health Sciences Institute, Newcastle University, Newcastle Upon Tyne, United Kingdom
| | - G. Maniatopoulos
- Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, United Kingdom
- University ofLeicester School of Business, University of Leicester, Leicester, United Kingdom
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Sakagianni A, Koufopoulou C, Feretzakis G, Kalles D, Verykios VS, Myrianthefs P, Fildisis G. Using Machine Learning to Predict Antimicrobial Resistance-A Literature Review. Antibiotics (Basel) 2023; 12:antibiotics12030452. [PMID: 36978319 PMCID: PMC10044642 DOI: 10.3390/antibiotics12030452] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 02/21/2023] [Accepted: 02/22/2023] [Indexed: 03/30/2023] Open
Abstract
Machine learning (ML) algorithms are increasingly applied in medical research and in healthcare, gradually improving clinical practice. Among various applications of these novel methods, their usage in the combat against antimicrobial resistance (AMR) is one of the most crucial areas of interest, as increasing resistance to antibiotics and management of difficult-to-treat multidrug-resistant infections are significant challenges for most countries worldwide, with life-threatening consequences. As antibiotic efficacy and treatment options decrease, the need for implementation of multimodal antibiotic stewardship programs is of utmost importance in order to restrict antibiotic misuse and prevent further aggravation of the AMR problem. Both supervised and unsupervised machine learning tools have been successfully used to predict early antibiotic resistance, and thus support clinicians in selecting appropriate therapy. In this paper, we reviewed the existing literature on machine learning and artificial intelligence (AI) in general in conjunction with antimicrobial resistance prediction. This is a narrative review, where we discuss the applications of ML methods in the field of AMR and their value as a complementary tool in the antibiotic stewardship practice, mainly from the clinician's point of view.
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Affiliation(s)
| | - Christina Koufopoulou
- 1st Anesthesiology Department, Aretaieio Hospital, National and Kapodistrian University of Athens Medical School, 11528 Athens, Greece
| | - Georgios Feretzakis
- School of Science and Technology, Hellenic Open University, 26335 Patras, Greece
- Department of Quality Control, Research and Continuing Education, Sismanogleio General Hospital, 15126 Marousi, Greece
| | - Dimitris Kalles
- School of Science and Technology, Hellenic Open University, 26335 Patras, Greece
| | - Vassilios S Verykios
- School of Science and Technology, Hellenic Open University, 26335 Patras, Greece
| | - Pavlos Myrianthefs
- Faculty of Nursing, School of Health Sciences, National and Kapodistrian University of Athens, 11527 Athens, Greece
| | - Georgios Fildisis
- Faculty of Nursing, School of Health Sciences, National and Kapodistrian University of Athens, 11527 Athens, Greece
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Greenberg JK, Olsen MA, Johnson GW, Ahluwalia R, Hill M, Hale AT, Belal A, Baygani S, Foraker RE, Carpenter CR, Ackerman LL, Noje C, Jackson EM, Burns E, Sayama CM, Selden NR, Vachhrajani S, Shannon CN, Kuppermann N, Limbrick DD. Measures of Intracranial Injury Size Do Not Improve Clinical Decision Making for Children With Mild Traumatic Brain Injuries and Intracranial Injuries. Neurosurgery 2022; 90:691-699. [PMID: 35285454 PMCID: PMC9117421 DOI: 10.1227/neu.0000000000001895] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Accepted: 12/05/2021] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND When evaluating children with mild traumatic brain injuries (mTBIs) and intracranial injuries (ICIs), neurosurgeons intuitively consider injury size. However, the extent to which such measures (eg, hematoma size) improve risk prediction compared with the kids intracranial injury decision support tool for traumatic brain injury (KIIDS-TBI) model, which only includes the presence/absence of imaging findings, remains unknown. OBJECTIVE To determine the extent to which measures of injury size improve risk prediction for children with mild traumatic brain injuries and ICIs. METHODS We included children ≤18 years who presented to 1 of the 5 centers within 24 hours of TBI, had Glasgow Coma Scale scores of 13 to 15, and had ICI on neuroimaging. The data set was split into training (n = 1126) and testing (n = 374) cohorts. We used generalized linear modeling (GLM) and recursive partitioning (RP) to predict the composite of neurosurgery, intubation >24 hours, or death because of TBI. Each model's sensitivity/specificity was compared with the validated KIIDS-TBI model across 3 decision-making risk cutoffs (<1%, <3%, and <5% predicted risk). RESULTS The GLM and RP models included similar imaging variables (eg, epidural hematoma size) while the GLM model incorporated additional clinical predictors (eg, Glasgow Coma Scale score). The GLM (76%-90%) and RP (79%-87%) models showed similar specificity across all risk cutoffs, but the GLM model had higher sensitivity (89%-96% for GLM; 89% for RP). By comparison, the KIIDS-TBI model had slightly higher sensitivity (93%-100%) but lower specificity (27%-82%). CONCLUSION Although measures of ICI size have clear intuitive value, the tradeoff between higher specificity and lower sensitivity does not support the addition of such information to the KIIDS-TBI model.
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Affiliation(s)
- Jacob K. Greenberg
- Department of Neurological Surgery, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA;
| | - Margaret A. Olsen
- Department of Medicine, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA;
| | - Gabrielle W. Johnson
- Department of Neurological Surgery, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA;
| | - Ranbir Ahluwalia
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, Tennessee, USA;
| | - Madelyn Hill
- Division of Neurosurgery, Dayton Children's Hospital, Dayton, Ohio, USA;
| | - Andrew T. Hale
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, Tennessee, USA;
| | - Ahmed Belal
- Department of Neurological Surgery, Indiana University School of Medicine, Indianapolis, Indiana, USA;
| | - Shawyon Baygani
- Department of Neurological Surgery, Indiana University School of Medicine, Indianapolis, Indiana, USA;
| | - Randi E. Foraker
- Department of Medicine, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA;
| | - Christopher R. Carpenter
- Department of Emergency Medicine, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA;
| | - Laurie L. Ackerman
- Department of Neurological Surgery, Indiana University School of Medicine, Indianapolis, Indiana, USA;
| | - Corina Noje
- Department of Anesthesiology and Critical Care Medicine, Division of Pediatric Critical Care Medicine, The Charlotte R. Bloomberg Children's Center, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA;
| | - Eric M. Jackson
- Neurological Surgery, Johns Hopkins School of Medicine, Baltimore, Maryland, USA;
| | - Erin Burns
- Department of Pediatrics, Oregon Health and Science University, Portland, Oregon, USA;
| | - Christina M. Sayama
- Department of Pediatrics, Oregon Health and Science University, Portland, Oregon, USA;
- Department of Neurological Surgery, Oregon Health and Science University, Portland, Oregon, USA;
| | - Nathan R. Selden
- Department of Pediatrics, Oregon Health and Science University, Portland, Oregon, USA;
- Department of Neurological Surgery, Oregon Health and Science University, Portland, Oregon, USA;
| | - Shobhan Vachhrajani
- Division of Neurosurgery, Dayton Children's Hospital, Dayton, Ohio, USA;
- Department of Pediatrics, Wright State University, Dayton, Ohio, USA;
| | - Chevis N. Shannon
- Division of Neurosurgery, Dayton Children's Hospital, Dayton, Ohio, USA;
| | - Nathan Kuppermann
- Department of Emergency Medicine, University of California Davis, School of Medicine, Sacramento, California, USA;
- Department of Pediatrics, University of California Davis, School of Medicine, Sacramento, California, USA
| | - David D. Limbrick
- Department of Neurological Surgery, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA;
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Al-Zubaidy M, Hogg HDJ, Maniatopoulos G, Talks J, Teare MD, Keane PA, R Beyer F. Stakeholder Perspectives on Clinical Decision Support Tools to Inform Clinical Artificial Intelligence Implementation: Protocol for a Framework Synthesis for Qualitative Evidence. JMIR Res Protoc 2022; 11:e33145. [PMID: 35363141 PMCID: PMC9015736 DOI: 10.2196/33145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 12/12/2021] [Accepted: 02/09/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Quantitative systematic reviews have identified clinical artificial intelligence (AI)-enabled tools with adequate performance for real-world implementation. To our knowledge, no published report or protocol synthesizes the full breadth of stakeholder perspectives. The absence of such a rigorous foundation perpetuates the "AI chasm," which continues to delay patient benefit. OBJECTIVE The aim of this research is to synthesize stakeholder perspectives of computerized clinical decision support tools in any health care setting. Synthesized findings will inform future research and the implementation of AI into health care services. METHODS The search strategy will use MEDLINE (Ovid), Scopus, CINAHL (EBSCO), ACM Digital Library, and Science Citation Index (Web of Science). Following deduplication, title, abstract, and full text screening will be performed by 2 independent reviewers with a third topic expert arbitrating. The quality of included studies will be appraised to support interpretation. Best-fit framework synthesis will be performed, with line-by-line coding completed by 2 independent reviewers. Where appropriate, these findings will be assigned to 1 of 22 a priori themes defined by the Nonadoption, Abandonment, Scale-up, Spread, and Sustainability framework. New domains will be inductively generated for outlying findings. The placement of findings within themes will be reviewed iteratively by a study advisory group including patient and lay representatives. RESULTS Study registration was obtained from PROSPERO (CRD42021256005) in May 2021. Final searches were executed in April, and screening is ongoing at the time of writing. Full text data analysis is due to be completed in October 2021. We anticipate that the study will be submitted for open-access publication in late 2021. CONCLUSIONS This paper describes the protocol for a qualitative evidence synthesis aiming to define barriers and facilitators to the implementation of computerized clinical decision support tools from all relevant stakeholders. The results of this study are intended to expedite the delivery of patient benefit from AI-enabled clinical tools. TRIAL REGISTRATION PROSPERO CRD42021256005; https://tinyurl.com/r4x3thvp. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) DERR1-10.2196/33145.
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Affiliation(s)
- Mohaimen Al-Zubaidy
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
- The Royal Victoria Infirmary, Newcastle Upon Tyne, United Kingdom
| | - H D Jeffry Hogg
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
- The Royal Victoria Infirmary, Newcastle Upon Tyne, United Kingdom
| | - Gregory Maniatopoulos
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
- Faculty of Business and Law, Northumbria University, Newcastle Upon Tyne, United Kingdom
| | - James Talks
- The Royal Victoria Infirmary, Newcastle Upon Tyne, United Kingdom
| | - Marion Dawn Teare
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Pearse A Keane
- Moorfields Eye Hospital National Health Service Foundation Trust, London, United Kingdom
- Faculty of Medicine, University College London, London, United Kingdom
| | - Fiona R Beyer
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
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10
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Gardner C, Halligan J, Fontana G, Fernandez Crespo R, Prime M, Guo C, Ekinci O, Ghafur S, Darzi A. Evaluation of a clinical decision support tool for matching cancer patients to clinical trials using simulation-based research. Health Informatics J 2022; 28:14604582221087890. [PMID: 35450483 DOI: 10.1177/14604582221087890] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
There is a growing need for alternative methodologies to evaluate digital health solutions in a short timeframe and at relatively low cost. Simulation-based research (SBR) methods have been proposed as an alternative methodology for evaluating digital health solutions; however, few studies have described the applicability of SBR methods to evaluate such solutions. This study used SBR to evaluate the feasibility and user experience of a clinical decision support (CDS) tool used for matching cancer patients to clinical trials. Twenty-five clinicians and research staff were recruited to match 10 synthetic patient cases to clinical trials using both the CDS tool and publicly available online trial databases. Participants were significantly more likely to report having sufficient time (p = 0.020) and to require less mental effort (p = 0.001) to complete trial matching with the CDS tool. Participants required less time for trial matching using the CDS tool, but the difference was not significant (p = 0.093). Most participants reported that they had sufficient guidance to participate in the simulations (96%). This study demonstrates the use of SBR methods is a feasible approach to evaluate digital health solutions and to collect valuable user feedback without the need for implementation in clinical practice. Further research is required to demonstrate the feasibility of using SBR to conduct remote evaluations of digital health solutions.
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Affiliation(s)
- Clarissa Gardner
- Institute of Global Health Innovation, 4615Imperial College London, London, UK
| | - Jack Halligan
- Institute of Global Health Innovation, 4615Imperial College London, London, UK
| | - Gianluca Fontana
- Institute of Global Health Innovation, 4615Imperial College London, London, UK
| | | | - Matthew Prime
- Roche Information Solutions, 1529F. Hoffmann-La Roche AG, Basel, Switzerland
| | - Chaohui Guo
- Roche Information Solutions, 1529F. Hoffmann-La Roche AG, Basel, Switzerland
| | - Okan Ekinci
- Roche Information Solutions, 1529F. Hoffmann-La Roche AG, Basel, Switzerland
| | - Saira Ghafur
- Institute of Global Health Innovation, 4615Imperial College London, London, UK
| | - Ara Darzi
- Institute of Global Health Innovation, 4615Imperial College London, London, UK
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11
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Laka M, Milazzo A, Merlin T. Factors That Impact the Adoption of Clinical Decision Support Systems (CDSS) for Antibiotic Management. Int J Environ Res Public Health 2021; 18:ijerph18041901. [PMID: 33669353 PMCID: PMC7920296 DOI: 10.3390/ijerph18041901] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Revised: 02/08/2021] [Accepted: 02/11/2021] [Indexed: 01/22/2023]
Abstract
The study evaluated individual and setting-specific factors that moderate clinicians’ perception regarding use of clinical decision support systems (CDSS) for antibiotic management. A cross-sectional online survey examined clinicians’ perceptions about CDSS implementation for antibiotic management in Australia. Multivariable logistic regression determined the association between drivers of CDSS adoption and different moderators. Clinical experience, CDSS use and care setting were important predictors of clinicians’ perception concerning CDSS adoption. Compared to nonusers, CDSS users were less likely to lack confidence in CDSS (OR = 0.63, 95%, CI = 0.32, 0.94) and consider it a threat to professional autonomy (OR = 0.47, 95%, CI = 0.08, 0.83). Conversely, there was higher likelihood in experienced clinicians (>20 years) to distrust CDSS (OR = 1.58, 95%, CI = 1.08, 2.23) due to fear of comprising their clinical judgement (OR = 1.68, 95%, CI = 1.27, 2.85). In primary care, clinicians were more likely to perceive time constraints (OR = 1.96, 95%, CI = 1.04, 3.70) and patient preference (OR = 1.84, 95%, CI = 1.19, 2.78) as barriers to CDSS adoption for antibiotic prescribing. Our findings provide differentiated understanding of the CDSS implementation landscape by identifying different individual, organisational and system-level factors that influence system adoption. The individual and setting characteristics can help understand the variability in CDSS adoption for antibiotic management in different clinicians.
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Affiliation(s)
- Mah Laka
- School of Public Health, University of Adelaide, Adelaide 5005, Australia; (M.L.); (A.M.)
| | - Adriana Milazzo
- School of Public Health, University of Adelaide, Adelaide 5005, Australia; (M.L.); (A.M.)
| | - Tracy Merlin
- Adelaide Health Technology Assessment (AHTA), School of Public Health, University of Adelaide, Adelaide 5005, Australia
- Correspondence: ; Tel.: +61-(8)-8313-3575
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12
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Vani A, Kan K, Iturrate E, Levy-Lambert D, Smilowitz NR, Saxena A, Radford MJ, Gianos E. Leveraging clinical decision support tools to improve guideline-directed medical therapy in patients with atherosclerotic cardiovascular disease at hospital discharge. Cardiol J 2020; 29:791-797. [PMID: 32986236 PMCID: PMC9550339 DOI: 10.5603/cj.a2020.0126] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Revised: 07/21/2020] [Accepted: 09/03/2020] [Indexed: 11/25/2022] Open
Abstract
BACKGROUND Guidelines recommend moderate to high-intensity statins and antithrombotic agents in patients with atherosclerotic cardiovascular disease (ASCVD). However, guideline-directed medical therapy (GDMT) remains suboptimal. METHODS In this quality initiative, best practice alerts (BPA) in the electronic health record (EHR) were utilized to alert providers to prescribe to GDMT upon hospital discharge in ASCVD patients. Rates of GDMT were compared for 5 months pre- and post-BPA implementation. Multivariable regression was used to identify predictors of GDMT. RESULTS In 5985 pre- and 5568 post-BPA patients, the average age was 69.1 ± 12.8 years and 58.5% were male. There was a 4.0% increase in statin use from 67.3% to 71.3% and a 3.1% increase in antithrombotic use from 75.3% to 78.4% in the post-BPA cohort. CONCLUSIONS This simple EHR-based initiative was associated with a modest increase in ASCVD patients being discharged on GDMT. Leveraging clinical decision support tools provides an opportunity to influence provider behavior and improve care for ASCVD patients, and warrants further investigation.
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Affiliation(s)
- Anish Vani
- NYU Langone Health, 550 1st Avenue, 10016 New York, United States
| | - Karen Kan
- NYU Langone Health, 550 1st Avenue, 10016 New York, United States
| | - Eduardo Iturrate
- NYU Langone Health, 550 1st Avenue, 10016 New York, United States
| | | | | | - Archana Saxena
- NYU Langone Health, 550 1st Avenue, 10016 New York, United States
| | - Martha J Radford
- NYU Langone Health, 550 1st Avenue, 10016 New York, United States
| | - Eugenia Gianos
- Northwell Health, Lenox Hill Hospital, 110 E 59th St - Suite 8A, 10020 New York, United States.
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13
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Angehrn Z, Haldna L, Zandvliet AS, Gil Berglund E, Zeeuw J, Amzal B, Cheung SYA, Polasek TM, Pfister M, Kerbusch T, Heckman NM. Artificial Intelligence and Machine Learning Applied at the Point of Care. Front Pharmacol 2020; 11:759. [PMID: 32625083 PMCID: PMC7314939 DOI: 10.3389/fphar.2020.00759] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2019] [Accepted: 05/06/2020] [Indexed: 12/17/2022] Open
Abstract
Introduction The increasing availability of healthcare data and rapid development of big data analytic methods has opened new avenues for use of Artificial Intelligence (AI)- and Machine Learning (ML)-based technology in medical practice. However, applications at the point of care are still scarce. Objective Review and discuss case studies to understand current capabilities for applying AI/ML in the healthcare setting, and regulatory requirements in the US, Europe and China. Methods A targeted narrative literature review of AI/ML based digital tools was performed. Scientific publications (identified in PubMed) and grey literature (identified on the websites of regulatory agencies) were reviewed and analyzed. Results From the regulatory perspective, AI/ML-based solutions can be considered medical devices (i.e., Software as Medical Device, SaMD). A case series of SaMD is presented. First, tools for monitoring and remote management of chronic diseases are presented. Second, imaging applications for diagnostic support are discussed. Finally, clinical decision support tools to facilitate the choice of treatment and precision dosing are reviewed. While tested and validated algorithms for precision dosing exist, their implementation at the point of care is limited, and their regulatory and commercialization pathway is not clear. Regulatory requirements depend on the level of risk associated with the use of the device in medical practice, and can be classified into administrative (manufacturing and quality control), software-related (design, specification, hazard analysis, architecture, traceability, software risk analysis, cybersecurity, etc.), clinical evidence (including patient perspectives in some cases), non-clinical evidence (dosing validation and biocompatibility/toxicology) and other, such as e.g. benefit-to-risk determination, risk assessment and mitigation. There generally is an alignment between the US and Europe. China additionally requires that the clinical evidence is applicable to the Chinese population and recommends that a third-party central laboratory evaluates the clinical trial results. Conclusions The number of promising AI/ML-based technologies is increasing, but few have been implemented widely at the point of care. The need for external validation, implementation logistics, and data exchange and privacy remain the main obstacles.
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Affiliation(s)
| | | | | | | | | | | | | | - Thomas M Polasek
- Certara, Princeton, NJ, United States.,Department of Clinical Pharmacology, Royal Adelaide Hospital, Adelaide, SA, Australia.,Centre for Medicines Use and Safety, Monash University, Melbourne, VIC, Australia
| | - Marc Pfister
- Certara, Princeton, NJ, United States.,Department of Pharmacology and Pharmacometrics, Children's University Hospital Basel, Basel, Switzerland
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14
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Giannopoulou E, Katsila T, Mitropoulou C, Tsermpini EE, Patrinos GP. Integrating Next-Generation Sequencing in the Clinical Pharmacogenomics Workflow. Front Pharmacol 2019; 10:384. [PMID: 31024324 PMCID: PMC6460422 DOI: 10.3389/fphar.2019.00384] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2019] [Accepted: 03/27/2019] [Indexed: 12/12/2022] Open
Abstract
Pharmacogenomics has been recognized as a fundamental tool in the era of personalized medicine with up to 266 drug labels, approved by major regulatory bodies, currently containing pharmacogenomics information. Next-generation sequencing analysis assumes a critical role in personalized medicine, providing a comprehensive profile of an individual's variome, particularly that of clinical relevance, comprising of pathogenic variants and pharmacogenomic biomarkers. Here, we propose a strategy to integrate next-generation sequencing into the current clinical pharmacogenomics workflow from deep resequencing to pharmacogenomics consultation, according to the existing guidelines and recommendations.
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Affiliation(s)
| | - Theodora Katsila
- Department of Pharmacy, School of Health Sciences, University of Patras, Patras, Greece
| | | | | | - George P Patrinos
- Department of Pharmacy, School of Health Sciences, University of Patras, Patras, Greece.,Department of Pathology, College of Medicine and Health Sciences, United Arab Emirates University, Al-Ain, United Arab Emirates.,Zayed Center of Health Sciences, United Arab Emirates University, Al-Ain, United Arab Emirates
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15
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McGinn T. Putting Meaning into Meaningful Use: A Roadmap to Successful Integration of Evidence at the Point of Care. JMIR Med Inform 2016; 4:e16. [PMID: 27199223 PMCID: PMC4891572 DOI: 10.2196/medinform.4553] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2015] [Revised: 08/26/2015] [Accepted: 09/22/2015] [Indexed: 11/13/2022] Open
Abstract
Pressures to contain health care costs, personalize patient care, use big data, and to enhance health care quality have highlighted the need for integration of evidence at the point of care. The application of evidence-based medicine (EBM) has great promise in the era of electronic health records (EHRs) and health technology. The most successful integration of evidence into EHRs has been complex decision tools that trigger at a critical point of the clinical visit and include patient specific recommendations.
The objective of this viewpoint paper is to investigate why the incorporation of complex CDS tools into the EMR is equally complex and continues to challenge health service researchers and implementation scientists. Poor adoption and sustainability of EBM guidelines and CDS tools at the point of care have persisted and continue to document low rates of usage. The barriers cited by physicians include efficiency, perception of usefulness, information content, user interface, and over-triggering.
Building on the traditional EHR implementation frameworks, we review keys strategies for successful CDSs: (1) the quality of the evidence, (2) the potential to reduce unnecessary care, (3) ease of integrating evidence at the point of care, (4) the evidence’s consistency with clinician perceptions and preferences, (5) incorporating bundled sets or automated documentation, and (6) shared decision making tools.
As EHRs become commonplace and insurers demand higher quality and evidence-based care, better methods for integrating evidence into everyday care are warranted. We have outlined basic criteria that should be considered before attempting to integrate evidenced-based decision support tools into the EHR.
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Affiliation(s)
- Thomas McGinn
- Hofstra North Shore LII School of Medicine, Manhasset, NY, United States.
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16
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van Vliet LM, Harding R, Bausewein C, Payne S, Higginson IJ. How should we manage information needs, family anxiety, depression, and breathlessness for those affected by advanced disease: development of a Clinical Decision Support Tool using a Delphi design. BMC Med 2015; 13:263. [PMID: 26464185 PMCID: PMC4604738 DOI: 10.1186/s12916-015-0449-6] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/18/2015] [Accepted: 08/12/2015] [Indexed: 01/12/2023] Open
Abstract
BACKGROUND Clinicians request guidance to aid the routine use and interpretation of Patient Reported Outcome Measures (PROMs), but tools are lacking. We aimed to develop a Clinical Decision Support Tool (CDST) focused on information needs, family anxiety, depression, and breathlessness (measured using the Palliative care Outcome Scale (POS)) and related PROM implementation guidance. METHODS We drafted recommendations based on findings from systematic literature searches. In a modified online Delphi study, 38 experts from 12 countries with different professional backgrounds, including four patient/carer representatives, were invited to rate the appropriateness of these recommendations for problems of varying severity in the CDST. The quality of evidence was added for each recommendation, and the final draft CDST reappraised by the experts. The accompanying implementation guidance was built on data from literature scoping with expert revision (n = 11 invited experts). RESULTS The systematic literature searches identified over 560 potential references, of which 43 met the inclusion criteria. Two Delphi rounds (response rate 66% and 62%; n = 25 and 23) found that good patient care, psychosocial support and empathy, and open communication were central to supporting patients and families affected by all POS concerns as a core requirement. Assessment was recommended for increasing problems (i.e. scores), followed by non-pharmacological interventions and for breathlessness and depression, pharmacological interventions. Accompanying PROM implementation guidance was built based on the 8-step International Society for Quality of Life Research framework, as revised by nine (response rate 82%) experts. CONCLUSIONS This CDST provides a straightforward guide to help support clinical care and improve evidence-based outcomes for patients with progressive illness and their families, addressing four areas of clinical uncertainty. Recommendations should be used flexibly, alongside skilled individual clinical assessment and knowledge, taking into account patients' and families' individual preferences, circumstances, and resources. The CDST is provided with accompanying implementation guidance to facilitate PROM use and is ready for further development and evaluation.
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Affiliation(s)
- Liesbeth M van Vliet
- Department of Palliative Care, Policy and Rehabilitation, Cicely Saunders Institute, King's College London, Bessemer Road, London, SE5 9PJ, UK.
| | - Richard Harding
- Department of Palliative Care, Policy and Rehabilitation, Cicely Saunders Institute, King's College London, Bessemer Road, London, SE5 9PJ, UK.
| | - Claudia Bausewein
- Department of Palliative Medicine, Munich University Hospital, Munich, Germany.
| | - Sheila Payne
- International Observatory on End of Life Care, Division of Health Research, Lancaster University, Lancaster, UK.
| | - Irene J Higginson
- Department of Palliative Care, Policy and Rehabilitation, Cicely Saunders Institute, King's College London, Bessemer Road, London, SE5 9PJ, UK.
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