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Davids J, Bohlken N, Brown M, Murphy M. What can be done about workplace wellbeing in emergency departments? 'There's no petrol for this Ferrari'. Int Emerg Nurs 2024; 75:101487. [PMID: 38936273 DOI: 10.1016/j.ienj.2024.101487] [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: 01/17/2024] [Revised: 05/27/2024] [Accepted: 06/12/2024] [Indexed: 06/29/2024]
Abstract
Workplace wellbeing encompasses all aspects of working life. Peak health organisations recognise that poor workplace wellbeing is costly, both to individuals and to the organisation, and the value in promoting healthy workplaces. Workplace wellbeing improves when its barriers are acknowledged and addressed, and protective factors are promoted. The Emergency Department (ED) is a place of intense and challenging activity, exacerbated by high workloads and overcrowding. This impacts negatively on patient care, staff safety and wellbeing. We held focus groups across four EDs to discuss barriers and enablers to wellbeing and found four core themes: Workplace Satisfaction; Barriers to Wellbeing; Organisational Culture that Prioritises Staff Wellbeing; Self-care and Self Compassion. From this, and existing literature, we collaboratively developed a contextualised staff wellbeing framework titled: 'Staff Wellbeing Good Practice Framework: From Surviving to Thriving, How to Protect your Wellbeing in the Emergency Department' that emphasises their values of Competence, Connection and Control.
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Affiliation(s)
- Jennifer Davids
- Western Sydney Local Health District, NSW Health, Australia.
| | - Nicole Bohlken
- Western Sydney Local Health District, NSW Health, Australia
| | | | - Margaret Murphy
- Western Sydney Local Health District, NSW Health, Australia; University of Sydney, Australia
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2
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Horvat CM, Suresh S, James N, Aneja RK, Au AK, Berry S, Blumer A, Bricker K, Clark RSB, Dolinich H, Hahner S, Jockel C, Kalivoda J, Loar I, Marasco D, Marcinick A, Marroquin O, O'brien J, Pelletier J, Ramgopal S, Venkataraman S, Angus DC, Butler G. A randomized, embedded, pragmatic, Bayesian clinical trial examining clinical decision support for high flow nasal cannula management in children with bronchiolitis: design and statistical analysis plan. Trials 2024; 25:484. [PMID: 39014495 PMCID: PMC11253479 DOI: 10.1186/s13063-024-08327-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2024] [Accepted: 07/08/2024] [Indexed: 07/18/2024] Open
Abstract
BACKGROUND High flow nasal cannula (HFNC) has been increasingly adopted in the past 2 decades as a mode of respiratory support for children hospitalized with bronchiolitis. The growing use of HFNC despite a paucity of high-quality data regarding the therapy's efficacy has led to concerns about overutilization. We developed an electronic health record (EHR) embedded, quality improvement (QI) oriented clinical trial to determine whether standardized management of HFNC weaning guided by clinical decision support (CDS) results in a reduction in the duration of HFNC compared to usual care for children with bronchiolitis. METHODS The design and summary of the statistical analysis plan for the REspiratory SupporT for Efficient and cost-Effective Care (REST EEC; "rest easy") trial are presented. The investigators hypothesize that CDS-coupled, standardized HFNC weaning will reduce the duration of HFNC, the trial's primary endpoint, for children with bronchiolitis compared to usual care. Data supporting trial design and eventual analyses are collected from the EHR and other real world data sources using existing informatics infrastructure and QI data sources. The trial workflow, including randomization and deployment of the intervention, is embedded within the EHR of a large children's hospital using existing vendor features. Trial simulations indicate that by assuming a true hazard ratio effect size of 1.27, equivalent to a 6-h reduction in the median duration of HFNC, and enrolling a maximum of 350 children, there will be a > 0.75 probability of declaring superiority (interim analysis posterior probability of intervention effect > 0.99 or final analysis posterior probability of intervention effect > 0.9) and a > 0.85 probability of declaring superiority or the CDS intervention showing promise (final analysis posterior probability of intervention effect > 0.8). Iterative plan-do-study-act cycles are used to monitor the trial and provide targeted education to the workforce. DISCUSSION Through incorporation of the trial into usual care workflows, relying on QI tools and resources to support trial conduct, and relying on Bayesian inference to determine whether the intervention is superior to usual care, REST EEC is a learning health system intervention that blends health system operations with active evidence generation to optimize the use of HFNC and associated patient outcomes. TRIAL REGISTRATION ClinicalTrials.gov NCT05909566. Registered on June 18, 2023.
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Affiliation(s)
- Christopher M Horvat
- UPMC Children's Hospital of Pittsburgh, Pittsburgh, PA, USA.
- Division of Health Informatics, Department of Pediatrics, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
- Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
- UPMC, Pittsburgh, PA, USA.
| | - Srinivasan Suresh
- UPMC Children's Hospital of Pittsburgh, Pittsburgh, PA, USA
- Division of Health Informatics, Department of Pediatrics, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
- UPMC, Pittsburgh, PA, USA
| | | | - Rajesh K Aneja
- UPMC Children's Hospital of Pittsburgh, Pittsburgh, PA, USA
- Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
- UPMC, Pittsburgh, PA, USA
| | - Alicia K Au
- UPMC Children's Hospital of Pittsburgh, Pittsburgh, PA, USA
- Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
- UPMC, Pittsburgh, PA, USA
| | - Scott Berry
- Berry Statistical Consultants, Austin, TX, USA
| | - Arthur Blumer
- UPMC Children's Hospital of Pittsburgh, Pittsburgh, PA, USA
- UPMC, Pittsburgh, PA, USA
| | - Kelly Bricker
- UPMC Children's Hospital of Pittsburgh, Pittsburgh, PA, USA
- UPMC, Pittsburgh, PA, USA
| | - Robert S B Clark
- UPMC Children's Hospital of Pittsburgh, Pittsburgh, PA, USA
- Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
- UPMC, Pittsburgh, PA, USA
| | - Heidilyn Dolinich
- UPMC Children's Hospital of Pittsburgh, Pittsburgh, PA, USA
- UPMC, Pittsburgh, PA, USA
| | - Sheila Hahner
- UPMC Children's Hospital of Pittsburgh, Pittsburgh, PA, USA
- UPMC, Pittsburgh, PA, USA
| | - Christina Jockel
- UPMC Children's Hospital of Pittsburgh, Pittsburgh, PA, USA
- UPMC, Pittsburgh, PA, USA
| | - Jordan Kalivoda
- UPMC Children's Hospital of Pittsburgh, Pittsburgh, PA, USA
- UPMC, Pittsburgh, PA, USA
| | - India Loar
- Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
- UPMC, Pittsburgh, PA, USA
| | - Denee Marasco
- UPMC Children's Hospital of Pittsburgh, Pittsburgh, PA, USA
- UPMC, Pittsburgh, PA, USA
| | - Adrienne Marcinick
- UPMC Children's Hospital of Pittsburgh, Pittsburgh, PA, USA
- UPMC, Pittsburgh, PA, USA
| | | | | | - Jonathan Pelletier
- Division of Pediatric Critical Care Medicine, Department of Pediatrics, Akron Children's Hospital, Akron, OH, USA
| | - Sriram Ramgopal
- Division of Emergency Medicine, Department of Pediatrics, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, USA
| | - Shekhar Venkataraman
- UPMC Children's Hospital of Pittsburgh, Pittsburgh, PA, USA
- Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
- UPMC, Pittsburgh, PA, USA
| | - Derek C Angus
- Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
- UPMC, Pittsburgh, PA, USA
| | - Gabriella Butler
- UPMC Children's Hospital of Pittsburgh, Pittsburgh, PA, USA
- Division of Health Informatics, Department of Pediatrics, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
- UPMC, Pittsburgh, PA, USA
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Grammatikopoulou M, Zachariadou M, Zande M, Giannios G, Chytas A, Karanikas H, Georgakopoulos S, Karanikas D, Nikolaidis G, Natsiavas P, Stavropoulos TG, Nikolopoulos S, Kompatsiaris I. Evaluation of an electronic prescription platform: Clinicians' feedback on three distinct services aiming to facilitate clinical decision and safer e-prescription. Res Social Adm Pharm 2024; 20:640-647. [PMID: 38653646 DOI: 10.1016/j.sapharm.2024.04.004] [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: 01/19/2024] [Revised: 04/02/2024] [Accepted: 04/03/2024] [Indexed: 04/25/2024]
Abstract
BACKGROUND Health Care Professionals (HCPs) are the main end-users of digital clinical tools such as electronic prescription systems. For this reason, it is of high importance to include HCPs throughout the design, development and evaluation of a newly introduced system to ensure its usefulness, as well as confirm that it tends to their needs and can be integrated in their everyday clinical practice. METHODS In the context of the PrescIT project, an electronic prescription platform with three services was developed (i.e., Prescription Check, Prescription Suggestion, Therapeutic Prescription Monitoring). To allow an iterative process of discovery through user feedback, design and implementation, a two-phase evaluation was carried out, with the participation of HCPs from three hospitals in Northern Greece. The two-phase evaluation included presentations of the platform, followed by think-aloud sessions, individual platform testing and the collection of qualitative as well as quantitative feedback, through standard questionnaires (e.g., SUS, PSSUQ). RESULTS Twenty one HCPs (8 in the first, 18 in the second phase, and five present in both) participated in the two-phase evaluation. HCPs comprised clinicians varying in their specialty and one pharmacist. Clinicians' feedback during the first evaluation phase already deemed usability as "excellent" (with SUS scores ranging from 75 to 95/100, showing a mean value of 86.6 and SD of 9.2) but also provided additional user requirements, which further shaped and improved the services. In the second evaluation phase, clinicians explored the system's usability, and identified the services' strengths and weaknesses. Clinicians perceived the platform as useful, as it provides information on potential adverse drug reactions, drug-to-drug interactions and suggests medications that are compatible with patients' comorbidities and current medication. CONCLUSIONS The developed PrescIT platform aims to increase overall safety and effectiveness of healthcare services. Therefore, including clinicians in a two-phase evaluation confirmed that the introduced system is useful, tends to the users' needs, does not create fatigue and can be integrated in their everyday clinical practice to support clinical decision and e-prescribing.
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Affiliation(s)
| | | | | | - Georgios Giannios
- Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Greece.
| | - Achilleas Chytas
- Institute of Applied Biosciences, Centre for Research and Technology Hellas, Thessaloniki, Greece.
| | - Haralampos Karanikas
- Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece.
| | - Spiros Georgakopoulos
- Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece.
| | - Dimitrios Karanikas
- Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece
| | | | - Pantelis Natsiavas
- Institute of Applied Biosciences, Centre for Research and Technology Hellas, Thessaloniki, Greece.
| | - Thanos G Stavropoulos
- Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Greece.
| | - Spiros Nikolopoulos
- Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Greece.
| | - Ioannis Kompatsiaris
- Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki, Greece.
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Wu Y, Wu M, Wang C, Lin J, Liu J, Liu S. Evaluating the Prevalence of Burnout Among Health Care Professionals Related to Electronic Health Record Use: Systematic Review and Meta-Analysis. JMIR Med Inform 2024; 12:e54811. [PMID: 38865188 PMCID: PMC11208837 DOI: 10.2196/54811] [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: 11/27/2023] [Revised: 02/23/2024] [Accepted: 04/17/2024] [Indexed: 06/13/2024] Open
Abstract
BACKGROUND Burnout among health care professionals is a significant concern, with detrimental effects on health care service quality and patient outcomes. The use of the electronic health record (EHR) system has been identified as a significant contributor to burnout among health care professionals. OBJECTIVE This systematic review and meta-analysis aims to assess the prevalence of burnout among health care professionals associated with the use of the EHR system, thereby providing evidence to improve health information systems and develop strategies to measure and mitigate burnout. METHODS We conducted a comprehensive search of the PubMed, Embase, and Web of Science databases for English-language peer-reviewed articles published between January 1, 2009, and December 31, 2022. Two independent reviewers applied inclusion and exclusion criteria, and study quality was assessed using the Joanna Briggs Institute checklist and the Newcastle-Ottawa Scale. Meta-analyses were performed using R (version 4.1.3; R Foundation for Statistical Computing), with EndNote X7 (Clarivate) for reference management. RESULTS The review included 32 cross-sectional studies and 5 case-control studies with a total of 66,556 participants, mainly physicians and registered nurses. The pooled prevalence of burnout among health care professionals in cross-sectional studies was 40.4% (95% CI 37.5%-43.2%). Case-control studies indicated a higher likelihood of burnout among health care professionals who spent more time on EHR-related tasks outside work (odds ratio 2.43, 95% CI 2.31-2.57). CONCLUSIONS The findings highlight the association between the increased use of the EHR system and burnout among health care professionals. Potential solutions include optimizing EHR systems, implementing automated dictation or note-taking, employing scribes to reduce documentation burden, and leveraging artificial intelligence to enhance EHR system efficiency and reduce the risk of burnout. TRIAL REGISTRATION PROSPERO International Prospective Register of Systematic Reviews CRD42021281173; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021281173.
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Affiliation(s)
- Yuxuan Wu
- Department of Medical Informatics, West China Hospital, Sichuan University, Chengdu, China
| | - Mingyue Wu
- Information Center, West China Hospital, Sichuan University, Chengdu, China
| | - Changyu Wang
- West China College of Stomatology, Sichuan University, Chengdu, China
| | - Jie Lin
- Department of Oral Implantology, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Jialin Liu
- Department of Medical Informatics, West China Hospital, Sichuan University, Chengdu, China
- Information Center, West China Hospital, Sichuan University, Chengdu, China
| | - Siru Liu
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States
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Sova C, Poon E, Musser RC, Chowdhury A. Social Media's Lessons for Clinical Decision Support: Strategies to Improve Engagement and Acceptance. Appl Clin Inform 2024; 15:528-532. [PMID: 38960377 PMCID: PMC11221992 DOI: 10.1055/s-0044-1787648] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/05/2024] Open
Affiliation(s)
- Christopher Sova
- Infection Prevention and Hospital Epidemiology, Duke University Hospital, Durham, North Carolina, United States
| | - Eric Poon
- Duke Health Technology Services, Durham, North Carolina, United States
| | - Robert Clayton Musser
- Department of Medicine, Duke University Health System, Durham, North Carolina, United States
| | - Anand Chowdhury
- Pulmonary, Allergy and Critical Care Medicine, Duke University School of Medicine, Durham, North Carolina, United States
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6
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Gao E, Radpavar I, Clark EJ, Ryan GW, Ross MK. Application of a user experience design approach for an EHR-based clinical decision support system. JAMIA Open 2024; 7:ooae019. [PMID: 38646110 PMCID: PMC11032728 DOI: 10.1093/jamiaopen/ooae019] [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: 05/08/2023] [Revised: 01/17/2024] [Accepted: 03/09/2024] [Indexed: 04/23/2024] Open
Abstract
Objective We applied a user experience (UX) design approach to clinical decision support (CDS) tool development for the specific use case of pediatric asthma. Our objective was to understand physicians' workflows, decision-making processes, barriers (ie, pain points), and facilitators to increase usability of the tool. Materials and methods We used a mixed-methods approach with semi-structured interviews and surveys. The coded interviews were synthesized into physician-user journey maps (ie, visualization of a process to accomplish goals) and personas (ie, user types). Interviews were conducted via video. We developed physician journey maps and user personas informed by their goals, systems interactions, and experiences with pediatric asthma management. Results The physician end-user personas identified were: efficiency, relationship, and learning. Features of a potential asthma CDS tool sought varied by physician practice type and persona. It was important to the physician end-user that the asthma CDS tool demonstrate value by lowering workflow friction (ie, difficulty or obstacles), improving the environment surrounding physicians and patients, and using it as a teaching tool. Customizability versus standardization were important considerations for uptake. Discussion Different values and motivations of physicians influence their use and interaction with the EHR and CDS tools. These different perspectives can be captured by applying a UX design approach to the development process. For example, with the importance of customizability, one approach may be to build a core module with variations depending on end-user preference. Conclusion A UX approach can drive design to help understand physician-users and meet their needs; ultimately with the goal of increased uptake.
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Affiliation(s)
- Emily Gao
- College of Letters and Sciences, University of California Los Angeles, Los Angeles, CA 90095, United States
| | - Ilana Radpavar
- College of Letters and Sciences, University of California Los Angeles, Los Angeles, CA 90095, United States
| | - Emma J Clark
- Department of Pediatrics, University of California Los Angeles, David Geffen School of Medicine, Los Angeles, CA 90095, United States
| | - Gery W Ryan
- Department of Health Systems Science, Kaiser Permanente, Bernard J. Tyson School of Medicine, Pasadena, CA 91101, United States
| | - Mindy K Ross
- Department of Pediatrics, University of California Los Angeles, David Geffen School of Medicine, Los Angeles, CA 90095, United States
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7
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Wright AP, Embi PJ, Nelson SD, Smith JC, Turchin A, Mize DE. Development and Validation of Inpatient Hypoglycemia Models Centered Around the Insulin Ordering Process. J Diabetes Sci Technol 2024; 18:423-429. [PMID: 36047538 PMCID: PMC10973866 DOI: 10.1177/19322968221119788] [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] [Indexed: 11/17/2022]
Abstract
BACKGROUND The insulin ordering process is an opportunity to provide clinicians with hypoglycemia risk predictions, but few hypoglycemia models centered around the insulin ordering process exist. METHODS We used data on adult patients, admitted in 2019 to non-ICU floors of a large teaching hospital, who had orders for subcutaneous insulin. Our outcome was hypoglycemia, defined as a blood glucose (BG) <70 mg/dL within 24 hours after ordering insulin. We trained and evaluated models to predict hypoglycemia at the time of placing an insulin order, using logistic regression, random forest, and extreme gradient boosting (XGBoost). We compared performance using area under the receiver operating characteristic curve (AUCs) and precision-recall curves. We determined recall at our goal precision of 0.30. RESULTS Of 21 052 included insulin orders, 1839 (9%) were followed by a hypoglycemic event within 24 hours. Logistic regression, random forest, and XGBoost models had AUCs of 0.81, 0.80, and 0.79, and recall of 0.44, 0.49, and 0.32, respectively. The most significant predictor was the lowest BG value in the 24 hours preceding the order. Predictors related to the insulin order being placed at the time of the prediction were useful to the model but less important than the patient's history of BG values over time. CONCLUSIONS Hypoglycemia within the next 24 hours can be predicted at the time an insulin order is placed, providing an opportunity to integrate decision support into the medication ordering process to make insulin therapy safer.
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Affiliation(s)
- Aileen P. Wright
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Peter J. Embi
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Scott D. Nelson
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Joshua C. Smith
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Alexander Turchin
- Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Dara E. Mize
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
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Sheehan KN, Cioci AL, Lucioni TM, Hernandez SM. Resident-Driven Clinical Decision Support Governance to Improve the Utility of Clinical Decision Support. Appl Clin Inform 2024; 15:335-341. [PMID: 38692282 PMCID: PMC11062759 DOI: 10.1055/s-0044-1786682] [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: 10/16/2023] [Accepted: 03/12/2024] [Indexed: 05/03/2024] Open
Abstract
OBJECTIVES This resident-driven quality improvement project aimed to better understand the known problem of a misaligned clinical decision support (CDS) strategy and improve CDS utilization. METHODS An internal survey was sent to all internal medicine (IM) residents to identify the most bothersome CDS alerts. Survey results were supported by electronic health record (EHR) data of CDS firing rates and response rates which were collected for each of the three most bothersome CDS tools. Changes to firing criteria were created to increase utilization and to better align with the five rights of CDS. Findings and proposed changes were presented to our institution's CDS Governance Committee. Changes were approved and implemented. Postintervention firing rates were then collected for 1 week. RESULTS Twenty nine residents participated in the CDS survey and identified sepsis alerts, lipid profile reminders, and telemetry renewals to be the most bothersome alerts. EHR data showed action rates for these CDS as low as 1%. We implemented changes to focus emergency department (ED)-based sepsis alerts to the right provider, better address the right information for lipid profile reminders, and select the right time in workflow for telemetry renewals to be most effective. With these changes we successfully eliminated ED-based sepsis CDS reminders for IM providers, saw a 97% reduction in firing rates for the lipid profile CDS, and noted a 55% reduction in firing rates for telemetry CDS. CONCLUSION This project highlighted that alert improvements spearheaded by resident teams can be completed successfully using robust CDS governance strategies and can effectively optimize interruptive alerts.
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Affiliation(s)
- Kristin N. Sheehan
- Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States
| | - Anthony L. Cioci
- Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States
| | - Tomas M. Lucioni
- Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States
| | - Sean M. Hernandez
- Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States
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Tse G, Algaze C, Pageler N, Wood M, Chadwick W. Using Clinical Decision Support Systems to Decrease Intravenous Acetaminophen Use: Implementation and Lessons Learned. Appl Clin Inform 2024; 15:64-74. [PMID: 37995743 PMCID: PMC10807987 DOI: 10.1055/a-2216-5775] [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: 07/20/2023] [Accepted: 11/22/2023] [Indexed: 11/25/2023] Open
Abstract
BACKGROUND Clinical decision support systems (CDSS) can enhance medical decision-making by providing targeted information to providers. While they have the potential to improve quality of care and reduce costs, they are not universally effective and can lead to unintended harm. OBJECTIVES To describe the implementation of an unsuccessful interruptive CDSS that aimed to promote appropriate use of intravenous (IV) acetaminophen at an academic pediatric hospital, with an emphasis on lessons learned. METHODS Quality improvement methodology was used to study the effect of an interruptive CDSS, which set a mandatory expiry time of 24 hours for all IV acetaminophen orders. This CDSS was implemented on April 5, 2021. The primary outcome measure was number of IV acetaminophen administrations per 1,000 patient days, measured pre- and postimplementation. Process measures were the number of IV acetaminophen orders placed per 1,000 patient days. Balancing measures were collected via survey data and included provider and nursing acceptability and unintended consequences of the CDSS. RESULTS There was no special cause variation in hospital-wide IV acetaminophen administrations and orders after CDSS implementation, nor when the CDSS was removed. A total of 88 participants completed the survey. Nearly half (40/88) of respondents reported negative issues with the CDSS, with the majority stating that this affected patient care (39/40). Respondents cited delays in patient care and reduced efficiency as the most common negative effects. CONCLUSION This study underscores the significance of monitoring CDSS implementations and including end user acceptability as an outcome measure. Teams should be prepared to modify or remove CDSS that do not achieve their intended goal or are associated with low end user acceptability. CDSS holds promise for improving clinical practice, but careful implementation and ongoing evaluation are crucial for maximizing their benefits and minimizing potential harm.
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Affiliation(s)
- Gabriel Tse
- Department of Pediatrics, Division of Pediatric Hospital Medicine, Stanford University School of Medicine, Stanford, California, United States
| | - Claudia Algaze
- Department of Pediatrics, Division of Pediatric Cardiology, Stanford University School of Medicine, Stanford, California, United States
| | - Natalie Pageler
- Department of Pediatrics, Division of Pediatric Critical Care Medicine, Stanford University School of Medicine, Stanford, California, United States
| | - Matthew Wood
- Center for Pediatric and Maternal Value, Lucile Packard Children's Hospital, Palo Alto, California, United States
| | - Whitney Chadwick
- Department of Pediatrics, Division of Pediatric Hospital Medicine, Stanford University School of Medicine, Stanford, California, United States
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10
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Kerr WT, McFarlane KN. Machine Learning and Artificial Intelligence Applications to Epilepsy: a Review for the Practicing Epileptologist. Curr Neurol Neurosci Rep 2023; 23:869-879. [PMID: 38060133 DOI: 10.1007/s11910-023-01318-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/24/2023] [Indexed: 12/08/2023]
Abstract
PURPOSE OF REVIEW Machine Learning (ML) and Artificial Intelligence (AI) are data-driven techniques to translate raw data into applicable and interpretable insights that can assist in clinical decision making. Some of these tools have extremely promising initial results, earning both great excitement and creating hype. This non-technical article reviews recent developments in ML/AI in epilepsy to assist the current practicing epileptologist in understanding both the benefits and limitations of integrating ML/AI tools into their clinical practice. RECENT FINDINGS ML/AI tools have been developed to assist clinicians in almost every clinical decision including (1) predicting future epilepsy in people at risk, (2) detecting and monitoring for seizures, (3) differentiating epilepsy from mimics, (4) using data to improve neuroanatomic localization and lateralization, and (5) tracking and predicting response to medical and surgical treatments. We also discuss practical, ethical, and equity considerations in the development and application of ML/AI tools including chatbots based on Large Language Models (e.g., ChatGPT). ML/AI tools will change how clinical medicine is practiced, but, with rare exceptions, the transferability to other centers, effectiveness, and safety of these approaches have not yet been established rigorously. In the future, ML/AI will not replace epileptologists, but epileptologists with ML/AI will replace epileptologists without ML/AI.
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Affiliation(s)
- Wesley T Kerr
- Department of Neurology, University of Pittsburgh, 3471 Fifth Ave, Kaufmann 811.22, Pittsburgh, PA, 15213, USA.
- Department of Biomedical Informatics, University of Pittsburgh, 3471 Fifth Ave, Kaufmann 811.22, Pittsburgh, PA, 15213, USA.
- Department of Neurology, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA.
| | - Katherine N McFarlane
- Department of Neurology, University of Pittsburgh, 3471 Fifth Ave, Kaufmann 811.22, Pittsburgh, PA, 15213, USA
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Colicchio TK, Cimino JJ. Beyond the override: Using evidence of previous drug tolerance to suppress drug allergy alerts; a retrospective study of opioid alerts. J Biomed Inform 2023; 147:104508. [PMID: 37748541 DOI: 10.1016/j.jbi.2023.104508] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 08/29/2023] [Accepted: 09/22/2023] [Indexed: 09/27/2023]
Abstract
OBJECTIVE Despite the extensive literature exploring alert fatigue, most studies have focused on describing the phenomenon, but not on fixing it. The authors aimed to identify data useful to avert clinically irrelevant alerts to inform future research on clinical decision support (CDS) design. METHODS We conducted a retrospective observational study of opioid drug allergy alert (DAA) overrides for the calendar year of 2019 at a large academic medical center, to identify data elements useful to find irrelevant alerts to be averted. RESULTS Overall, 227,815 DAAs were fired in 2019, with an override rate of 91 % (n = 208196). Opioids represented nearly two-thirds of these overrides (n = 129063; 62 %) and were the drug class with the highest override rate (96 %). On average, 29 opioid DAAs were overridden per patient. While most opioid alerts (97.1 %) are fired for a possible match (the drug class of the allergen matches the drug class of the prescribed drug), they are overridden significantly less frequently for definite match (exact match between allergen and prescribed drug) (88 % vs. 95.9 %, p < 0.001). When comparing the triggering drug with previously administered drugs, override rates were equally high for both definite match (95.9 %), no match (95.5 %), and possible match (95.1 %). Likewise, when comparing to home medications, overrides were excessively high for possible match (96.3 %), no match (96 %), and definite match (94.4 %). CONCLUSION We estimate that 74.5% of opioid DAAs (46.4% of all DAAs) at our institution could be relatively safely averted, since they either have a definite match for previous inpatient administrations suggesting drug tolerance or are fired as possible match with low risk of cross-sensitivity. Future research should focus on identifying other relevant data elements ideally with automated methods and use of emerging standards to empower CDS systems to suppress false-positive alerts while avoiding safety hazards.
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Affiliation(s)
- Tiago K Colicchio
- Informatics Institute, University of Alabama at Birmingham, AL, USA.
| | - James J Cimino
- Informatics Institute, University of Alabama at Birmingham, AL, USA
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Wang SM, Hogg HDJ, Sangvai D, Patel MR, Weissler EH, Kellogg KC, Ratliff W, Balu S, Sendak M. Development and Integration of Machine Learning Algorithm to Identify Peripheral Arterial Disease: Multistakeholder Qualitative Study. JMIR Form Res 2023; 7:e43963. [PMID: 37733427 PMCID: PMC10557008 DOI: 10.2196/43963] [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: 11/02/2022] [Revised: 01/20/2023] [Accepted: 04/30/2023] [Indexed: 09/22/2023] Open
Abstract
BACKGROUND Machine learning (ML)-driven clinical decision support (CDS) continues to draw wide interest and investment as a means of improving care quality and value, despite mixed real-world implementation outcomes. OBJECTIVE This study aimed to explore the factors that influence the integration of a peripheral arterial disease (PAD) identification algorithm to implement timely guideline-based care. METHODS A total of 12 semistructured interviews were conducted with individuals from 3 stakeholder groups during the first 4 weeks of integration of an ML-driven CDS. The stakeholder groups included technical, administrative, and clinical members of the team interacting with the ML-driven CDS. The ML-driven CDS identified patients with a high probability of having PAD, and these patients were then reviewed by an interdisciplinary team that developed a recommended action plan and sent recommendations to the patient's primary care provider. Pseudonymized transcripts were coded, and thematic analysis was conducted by a multidisciplinary research team. RESULTS Three themes were identified: positive factors translating in silico performance to real-world efficacy, organizational factors and data structure factors affecting clinical impact, and potential challenges to advancing equity. Our study found that the factors that led to successful translation of in silico algorithm performance to real-world impact were largely nontechnical, given adequate efficacy in retrospective validation, including strong clinical leadership, trustworthy workflows, early consideration of end-user needs, and ensuring that the CDS addresses an actionable problem. Negative factors of integration included failure to incorporate the on-the-ground context, the lack of feedback loops, and data silos limiting the ML-driven CDS. The success criteria for each stakeholder group were also characterized to better understand how teams work together to integrate ML-driven CDS and to understand the varying needs across stakeholder groups. CONCLUSIONS Longitudinal and multidisciplinary stakeholder engagement in the development and integration of ML-driven CDS underpins its effective translation into real-world care. Although previous studies have focused on the technical elements of ML-driven CDS, our study demonstrates the importance of including administrative and operational leaders as well as an early consideration of clinicians' needs. Seeing how different stakeholder groups have this more holistic perspective also permits more effective detection of context-driven health care inequities, which are uncovered or exacerbated via ML-driven CDS integration through structural and organizational challenges. Many of the solutions to these inequities lie outside the scope of ML and require coordinated systematic solutions for mitigation to help reduce disparities in the care of patients with PAD.
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Affiliation(s)
- Sabrina M Wang
- Duke University School of Medicine, Durham, NC, United States
| | - H D Jeffry Hogg
- Population Health Science Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
- Newcastle Eye Centre, Royal Victoria Infirmary, Newcastle upon Tyne, United Kingdom
| | - Devdutta Sangvai
- Population Health Management, Duke Health, Durham, NC, United States
| | - Manesh R Patel
- Department of Cardiology, Duke University, Durham, NC, United States
| | - E Hope Weissler
- Department of Vascular Surgery, Duke University, Durham, NC, United States
| | | | - William Ratliff
- Duke Institute for Health Innovation, Durham, NC, United States
| | - Suresh Balu
- Duke Institute for Health Innovation, Durham, NC, United States
| | - Mark Sendak
- Duke Institute for Health Innovation, Durham, NC, United States
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Douthit BJ, McCoy AB, Nelson SD. The Impact of Clinical Decision Support on Health Disparities and the Digital Divide. Yearb Med Inform 2023; 32:169-178. [PMID: 37414030 PMCID: PMC10751127 DOI: 10.1055/s-0043-1768722] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/08/2023] Open
Abstract
OBJECTIVES This literature review summarizes relevant studies from the last three years (2020-2022) related to clinical decision support (CDS) and CDS impact on health disparities and the digital divide. This survey identifies current trends and synthesizes evidence-based recommendations and considerations for future development and implementation of CDS tools. METHODS We conducted a search in PubMed for literature published between 2020 and 2022. Our search strategy was constructed as a combination of the MEDLINE®/PubMed® Health Disparities and Minority Health Search Strategy and relevant CDS MeSH terms and phrases. We then extracted relevant data from the studies, including priority population when applicable, domain of influence on the disparity being addressed, and the type of CDS being used. We also made note of when a study discussed the digital divide in some capacity and organized the comments into general themes through group discussion. RESULTS Our search yielded 520 studies, with 45 included at the conclusion of screening. The most frequent CDS type in this review was point-of-care alerts/reminders (33.3%). Health Care System was the most frequent domain of influence (71.1%), and Blacks/African Americans were the most frequently included priority population (42.2%). Throughout the literature, we found four general themes related to the technology divide: inaccessibility of technology, access to care, trust of technology, and technology literacy.This survey revealed the diversity of CDS being used to address health disparities and several barriers which may make CDS less effective or potentially harmful to certain populations. Regular examinations of literature that feature CDS and address health disparities can help to reveal new strategies and patterns for improving healthcare.
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Affiliation(s)
- Brian J. Douthit
- Post-Doctoral Research Fellow: United States Department of Veterans Affairs, Vanderbilt University, Nashville, TN, USA
| | - Allison B. McCoy
- Assistant Professor: Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA
- Director: Clinical Informatics Core, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Scott D. Nelson
- Associate Professor: Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA
- Program Director: MS in Applied Clinical Informatics Program (MS-ACI), Vanderbilt University, Nashville, TN, USA
- Clinical Director: HealthIT, Vanderbilt University Medical Center, Nashville, TN, USA
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Roberts MM, Marino M, Wells R, Atem FD, Balasubramanian BA. Differences in Use of Clinical Decision Support Tools and Implementation of Aspirin, Blood Pressure Control, Cholesterol Management, and Smoking Cessation Quality Metrics in Small Practices by Race and Sex. JAMA Netw Open 2023; 6:e2326905. [PMID: 37531106 PMCID: PMC10398408 DOI: 10.1001/jamanetworkopen.2023.26905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Accepted: 06/22/2023] [Indexed: 08/03/2023] Open
Abstract
Importance Practice-level evidence is needed to clarify the value of population-based clinical decision support (CDS) tools in reducing racial and sex disparities in cardiovascular care. Objective To evaluate the association between CDS tools and racial and sex disparities in the aspirin use, blood pressure control, cholesterol management, and smoking cessation (ABCS) care quality metrics among smaller primary care practices. Design, Setting, and Participants This cross-sectional study used practice-level data from the Agency for Healthcare Research and Quality-funded EvidenceNOW initiative. The national initiative from May 1, 2015, to April 30, 2021, spanned 12 US states and focused on improving cardiovascular preventive care by providing quality improvement support to smaller primary care practices. A total of 576 primary care practices in EvidenceNOW submitted both survey data and electronic health record (EHR)-derived ABCS data stratified by race and sex. Main Outcomes and Measures Practice-level estimates of disparities between Black and White patients and between male and female patients were calculated as the difference in proportions of eligible patients within each practice meeting ABCS care quality metrics. The association between CDS tools (EHR prompts, standing orders, and clinical registries) and disparities was evaluated by multiply imputed multivariable models for each CDS tool, adjusted for practice rurality, ownership, and size. Results Across the 576 practices included in the analysis, 219 (38.0%) had patient panels that were more than half White and 327 (56.8%) had panels that were more than half women. The proportion of White compared with Black patients meeting metrics for blood pressure (difference, 5.16% [95% CI, 4.29%-6.02%]; P < .001) and cholesterol management (difference, 1.49% [95% CI, 0.04%-2.93%] P = .04) was higher; the proportion of men meeting metrics for aspirin use (difference, 4.36% [95% CI, 3.34%-5.38%]; P < .001) and cholesterol management (difference, 3.88% [95% CI, 3.14%-4.63%]; P < .001) was higher compared with women. Conversely, the proportion of women meeting practice blood pressure control (difference, -1.80% [95% CI, -2.32% to -1.28%]; P < .001) and smoking cessation counseling (difference, -1.67% [95% CI, -2.38% to -0.95%]; P < .001) metrics was higher compared with men. Use of CDS tools was not associated with differences in race or sex disparities except for the smoking metric. Practices using CDS tools showed a higher proportion of men meeting the smoking counseling metric than women (coefficient, 3.82 [95% CI, 0.95-6.68]; P = .009). Conclusions and Relevance The findings of this cross-sectional study suggest that practices using CDS tools had small disparities that were not statistically significant, but CDS tools were not associated with reductions in disparities. More research is needed on effective practice-level interventions to mitigate disparities.
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Affiliation(s)
- Madeline M. Roberts
- Department of Epidemiology, Human Genetics, and Environmental Sciences, University of Texas Health Science Center at Houston (UTHealth Houston) School of Public Health, Dallas
| | - Miguel Marino
- Department of Family Medicine, Oregon Health & Science University, Portland
- School of Public Health, Oregon Health & Science University, Portland
| | - Rebecca Wells
- Department of Management, Policy, & Community Health, UTHealth Houston School of Public Health, Houston
| | - Folefac D. Atem
- Department of Biostatistics and Data Science, UTHealth Houston School of Public Health, Dallas
| | - Bijal A. Balasubramanian
- Department of Epidemiology, Human Genetics, and Environmental Sciences, University of Texas Health Science Center at Houston (UTHealth Houston) School of Public Health, Dallas
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Jennings LK, Ward R, Pekar E, Szwast E, Sox L, Hying J, Mccauley J, Obeid JS, Lenert LA. The effectiveness of a noninterruptive alert to increase prescription of take-home naloxone in emergency departments. J Am Med Inform Assoc 2023; 30:683-691. [PMID: 36718091 PMCID: PMC10018256 DOI: 10.1093/jamia/ocac257] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 12/21/2022] [Accepted: 12/31/2022] [Indexed: 02/01/2023] Open
Abstract
OBJECTIVE Opioid-related overdose (OD) deaths continue to increase. Take-home naloxone (THN), after treatment for an OD in an emergency department (ED), is a recommended but under-utilized practice. To promote THN prescription, we developed a noninterruptive decision support intervention that combined a detailed OD documentation template with a reminder to use the template that is automatically inserted into a provider's note by decision rules. We studied the impact of the combined intervention on THN prescribing in a longitudinal observational study. METHODS ED encounters involving an OD were reviewed before and after implementation of the reminder embedded in the physicians' note to use an advanced OD documentation template for changes in: (1) use of the template and (2) prescription of THN. Chi square tests and interrupted time series analyses were used to assess the impact. Usability and satisfaction were measured using the System Usability Scale (SUS) and the Net Promoter Score. RESULTS In 736 OD cases defined by International Classification of Disease version 10 diagnosis codes (247 prereminder and 489 postreminder), the documentation template was used in 0.0% and 21.3%, respectively (P < .0001). The sensitivity and specificity of the reminder for OD cases were 95.9% and 99.8%, respectively. Use of the documentation template led to twice the rate of prescribing of THN (25.7% vs 50.0%, P < .001). Of 19 providers responding to the survey, 74% of SUS responses were in the good-to-excellent range and 53% of providers were Net Promoters. CONCLUSIONS A noninterruptive decision support intervention was associated with higher THN prescribing in a pre-post study across a multiinstitution health system.
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Affiliation(s)
- Lindsey K Jennings
- Department of Emergency Medicine, Medical University of South Carolina, Charleston, South Carolina, USA
| | - Ralph Ward
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, South Carolina, USA
| | - Ekaterina Pekar
- Biomedical Informatics Center, Medical University of South Carolina, Charleston, South Carolina, USA
| | - Elizabeth Szwast
- Biomedical Informatics Center, Medical University of South Carolina, Charleston, South Carolina, USA
| | - Luke Sox
- Biomedical Informatics Center, Medical University of South Carolina, Charleston, South Carolina, USA
| | - Joseph Hying
- Biomedical Informatics Center, Medical University of South Carolina, Charleston, South Carolina, USA
| | - Jenna Mccauley
- Department of Psychiatry and Behavioral Science, Addiction Sciences Division, Medical University of South Carolina, Charleston, South Carolina, USA
| | - Jihad S Obeid
- Biomedical Informatics Center, Medical University of South Carolina, Charleston, South Carolina, USA
| | - Leslie A Lenert
- Corresponding Author: Leslie A. Lenert, MD, Biomedical Informatics Center, Medical University of South Carolina, 22 West Edge Suite 13, Charleston, SC 29425, USA;
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Harper J, Hunt T, Choudry M, Kapron AL, Cooney KA, Martin C, Ambrose J, O'Neil B. Clinician interest in clinical decision support for PSA-based prostate cancer screening. Urol Oncol 2023; 41:145.e17-145.e23. [PMID: 36610816 PMCID: PMC9992103 DOI: 10.1016/j.urolonc.2022.11.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2022] [Revised: 11/13/2022] [Accepted: 11/21/2022] [Indexed: 01/07/2023]
Abstract
OBJECTIVE To evaluate the interest of primary care clinicians in utilizing CDS for PSA screening. Evidence suggests that electronic clinical decision support (CDS) may decrease low-value prostate-specific antigen (PSA) testing. However, physician attitudes towards CDS for PSA screening are largely unknown. METHODS A survey was sent to 201 primary care clinicians, including both physicians and Advanced Practice Providers (APP), within a large academic health system. Eligible clinicians cared for male patients aged 40 to 80 years and ordered ≥5 PSA tests in the past year. Respondents were stratified into 3 groups, appropriate screeners, low-value screeners, or rare-screeners, based on responses to survey questions assessing PSA screening practices. The degree of interest in electronic CDS was determined via a composite Likert score comprising relevant survey items. RESULTS Survey response rate was 29% (59/201) consisting of 85% MD/DO and 15% APP respondents. All clinicians surveyed were interested in CDS (P < 0.001) without significant difference between screener groups. Clinicians agreed most uniformly that CDS be evidence-based. Clinicians disagreed on whether CDS would decrease professional discretion over patient decisions. CONCLUSIONS Primary care clinicians are interested in CDS for PSA screening regardless of their current screening practices. Prioritizing CDS features that clinicians value, such as ensuring CDS recommendations are evidence-based, may increase the likelihood of successful implementation, whereas perceived threat to autonomy may be a hinderance to utilization.
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Affiliation(s)
- Jonathan Harper
- Division of Urology, Department of Surgery, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT
| | - Trevor Hunt
- Division of Urology, Department of Surgery, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT; Department of Urology, University of Rochester Medical Center, Rochester, NY
| | - Mouneeb Choudry
- Division of Urology, Department of Surgery, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT
| | - Ashley L Kapron
- Utah Clinical & Translational Science Institute, University of Utah Health, Salt Lake City, UT
| | - Kathleen A Cooney
- Department of Medicine, Duke Cancer Institute, Duke University School of Medicine, Durham, NC
| | - Christopher Martin
- Division of Urology, Department of Surgery, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT
| | - Jacob Ambrose
- Division of Urology, Department of Surgery, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT
| | - Brock O'Neil
- Division of Urology, Department of Surgery, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT.
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Ramgopal S, Sanchez-Pinto LN, Horvat CM, Carroll MS, Luo Y, Florin TA. Artificial intelligence-based clinical decision support in pediatrics. Pediatr Res 2023; 93:334-341. [PMID: 35906317 PMCID: PMC9668209 DOI: 10.1038/s41390-022-02226-1] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 06/29/2022] [Accepted: 07/18/2022] [Indexed: 11/24/2022]
Abstract
Machine learning models may be integrated into clinical decision support (CDS) systems to identify children at risk of specific diagnoses or clinical deterioration to provide evidence-based recommendations. This use of artificial intelligence models in clinical decision support (AI-CDS) may have several advantages over traditional "rule-based" CDS models in pediatric care through increased model accuracy, with fewer false alerts and missed patients. AI-CDS tools must be appropriately developed, provide insight into the rationale behind decisions, be seamlessly integrated into care pathways, be intuitive to use, answer clinically relevant questions, respect the content expertise of the healthcare provider, and be scientifically sound. While numerous machine learning models have been reported in pediatric care, their integration into AI-CDS remains incompletely realized to date. Important challenges in the application of AI models in pediatric care include the relatively lower rates of clinically significant outcomes compared to adults, and the lack of sufficiently large datasets available necessary for the development of machine learning models. In this review article, we summarize key concepts related to AI-CDS, its current application to pediatric care, and its potential benefits and risks. IMPACT: The performance of clinical decision support may be enhanced by the utilization of machine learning-based algorithms to improve the predictive performance of underlying models. Artificial intelligence-based clinical decision support (AI-CDS) uses models that are experientially improved through training and are particularly well suited toward high-dimensional data. The application of AI-CDS toward pediatric care remains limited currently but represents an important area of future research.
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Affiliation(s)
- Sriram Ramgopal
- Division of Emergency Medicine, Ann & Robert H. Lurie Children's Hospital of Chicago, Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
| | - L. Nelson Sanchez-Pinto
- grid.16753.360000 0001 2299 3507Division of Critical Care Medicine, Ann & Robert H. Lurie Children’s Hospital of Chicago, Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, IL USA ,grid.16753.360000 0001 2299 3507Department of Preventive Medicine (Health and Biomedical Informatics), Feinberg School of Medicine, Northwestern University, Chicago, IL USA
| | - Christopher M. Horvat
- grid.21925.3d0000 0004 1936 9000Department of Critical Care Medicine, UPMC Children’s Hospital of Pittsburgh, University of Pittsburgh School of Medicine, Pittsburgh, PA USA
| | - Michael S. Carroll
- grid.16753.360000 0001 2299 3507Data Analytics and Reporting, Ann & Robert H. Lurie Children’s Hospital of Chicago, Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, IL USA
| | - Yuan Luo
- grid.16753.360000 0001 2299 3507Department of Preventive Medicine (Health and Biomedical Informatics), Feinberg School of Medicine, Northwestern University, Chicago, IL USA
| | - Todd A. Florin
- grid.16753.360000 0001 2299 3507Division of Emergency Medicine, Ann & Robert H. Lurie Children’s Hospital of Chicago, Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, IL USA
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Erickson JL, Wu J, Fertel BS, Pallotta AM, Englund K, Shrestha NK, Lehman B. Multidisciplinary Approach to Improve Human Immunodeficiency Virus and Syphilis Testing Rates in Emergency Departments. Open Forum Infect Dis 2022; 9:ofac601. [PMID: 36540389 PMCID: PMC9757684 DOI: 10.1093/ofid/ofac601] [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: 07/28/2022] [Accepted: 11/15/2022] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Best practice guidelines recommend that patients at risk for sexually transmitted infections (STIs), such as gonorrhea (GC) and chlamydia, should also be tested for human immunodeficiency virus (HIV) and syphilis. This prospective quality assurance study aimed to increase HIV and syphilis testing rates in emergency departments (EDs) across the Cleveland Clinic Health System from January 1, 2020 through January 1, 2022. METHODS A multidisciplinary team of emergency medicine, infectious diseases, pharmacy, and microbiology personnel convened to identify barriers to HIV and syphilis testing during ED encounters at which GC/chlamydia were tested. The following interventions were implemented in response: rapid HIV testing with new a workflow for results follow-up, a standardized STI-screening order panel, and feedback to clinicians about ordering patterns. RESULTS There were 57 797 ED visits with GC/chlamydia testing completed during the study period. Human immunodeficiency virus testing was ordered at 5% of these encounters before the interventions were implemented and increased to 8%, 23%, and 36% after each successive intervention. Syphilis testing increased from 9% before the interventions to 12%, 28%, and 39% after each successive intervention. In multivariable analyses adjusted for age, gender, and location, the odds ratio for HIV and syphilis testing after all interventions was 11.72 (95% confidence interval [CI], 10.82-12.71; P ≤.001) and 6.79 (95% CI, 6.34-7.27; P ≤.001), respectively. CONCLUSIONS The multidisciplinary intervention resulted in improved testing rates for HIV and syphilis.
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Affiliation(s)
- Jessica L Erickson
- Correspondence: Jessica Erickson, MD, Cleveland Clinic Health System, Infectious Disease Department, 9500 Euclid Avenue, Cleveland, Ohio 44195 ()
| | - Janet Wu
- Cleveland Clinic Health System, Cleveland, Ohio, USA
| | - Baruch S Fertel
- Quality & Patient Safety New York - Presbyterian Hospital; Department of Emergency Medicine, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA
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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: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [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 Supplementary Information The online version contains supplementary material available at 10.1186/s43058-022-00326-x.
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Causal deep learning reveals the comparative effectiveness of antihyperglycemic treatments in poorly controlled diabetes. Nat Commun 2022; 13:6921. [PMID: 36376286 PMCID: PMC9663714 DOI: 10.1038/s41467-022-33732-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Accepted: 09/30/2022] [Indexed: 11/16/2022] Open
Abstract
Type-2 diabetes is associated with severe health outcomes, the effects of which are responsible for approximately 1/4th of the total healthcare spending in the United States (US). Current treatment guidelines endorse a massive number of potential anti-hyperglycemic treatment options in various combinations. Strategies for optimizing treatment selection are lacking. Real-world data from a nationwide population of over one million high-risk diabetic patients (HbA1c ≥ 9%) in the US is analyzed to evaluate the comparative effectiveness for HbA1c reduction in this population of more than 80 different treatment strategies ranging from monotherapy up to combinations of five concomitant classes of drugs across each of 10 clinical cohorts defined by age, insulin dependence, and a number of other chronic conditions. A causal deep learning approach developed on such data allows for more personalized evaluation of treatment selection. An average confounder-adjusted reduction in HbA1c of 0.69% [-0.75, -0.65] is observed between patients receiving high vs low ranked treatments across cohorts for which the difference was significant. This method can be extended to explore treatment optimization for other chronic conditions.
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Yan X, Husby H, Mudiganti S, Gbotoe M, Delatorre-Reimer J, Knobel K, Hudnut A, Jones JB. Evaluating the Impact of a Point-of-Care Cardiometabolic Clinical Decision Support Tool on Clinical Efficiency Using Electronic Health Record Audit Log Data: Algorithm Development and Validation. JMIR Med Inform 2022; 10:e38385. [PMID: 36066940 PMCID: PMC9490545 DOI: 10.2196/38385] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 07/10/2022] [Accepted: 07/26/2022] [Indexed: 11/13/2022] Open
Abstract
Background Electronic health record (EHR) systems are becoming increasingly complicated, leading to concerns about rising physician burnout, particularly for primary care physicians (PCPs). Managing the most common cardiometabolic chronic conditions by PCPs during a limited clinical time with a patient is challenging. Objective This study aimed to evaluate a Cardiometabolic Sutter Health Advanced Reengineered Encounter (CM-SHARE), a web-based application to visualize key EHR data, on the EHR use efficiency. Methods We developed algorithms to identify key clinic workflow measures (eg, total encounter time, total physician time in the examination room, and physician EHR time in the examination room) using audit data, and we validated and calibrated the measures with time-motion data. We used a pre-post parallel design to identify propensity score–matched CM-SHARE users (cases), nonusers (controls), and nested-matched patients. Cardiometabolic encounters from matched case and control patients were used for the workflow evaluation. Outcome measures were compared between the cases and controls. We applied this approach separately to both the CM-SHARE pilot and spread phases. Results Time-motion observation was conducted on 101 primary care encounters for 9 PCPs in 3 clinics. There was little difference (<0.8 minutes) between the audit data–derived workflow measures and the time-motion observation. Two key unobservable times from audit data, physician entry into and exiting the examination room, were imputed based on time-motion studies. CM-SHARE was launched with 6 pilot PCPs in April 2016. During the prestudy period (April 1, 2015, to April 1, 2016), 870 control patients with 2845 encounters were matched with 870 case patients and encounters, and 727 case patients with 852 encounters were matched with 727 control patients and 3754 encounters in the poststudy period (June 1, 2016, to June 30, 2017). Total encounter time was slightly shorter (mean −2.7, SD 1.4 minutes, 95% CI −4.7 to −0.9; mean –1.6, SD 1.1 minutes, 95% CI −3.2 to −0.1) for cases than controls for both periods. CM-SHARE saves physicians approximately 2 minutes EHR time in the examination room (mean −2.0, SD 1.3, 95% CI −3.4 to −0.9) compared with prestudy period and poststudy period controls (mean −1.9, SD 0.9, 95% CI −3.8 to −0.5). In the spread phase, 48 CM-SHARE spread PCPs were matched with 84 control PCPs and 1272 cases with 3412 control patients, having 1119 and 4240 encounters, respectively. A significant reduction in total encounter time for the CM-SHARE group was observed for short appointments (≤20 minutes; 5.3-minute reduction on average) only. Total physician EHR time was significantly reduced for both longer and shorter appointments (17%-33% reductions). Conclusions Combining EHR audit log files and clinical information, our approach offers an innovative and scalable method and new measures that can be used to evaluate clinical EHR efficiency of digital tools used in clinical settings.
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Affiliation(s)
- Xiaowei Yan
- Center for Health Systems Research, Sutter Health, Walnut Creek, CA, United States
| | - Hannah Husby
- Center for Health Systems Research, Sutter Health, Walnut Creek, CA, United States
| | - Satish Mudiganti
- Center for Health Systems Research, Sutter Health, Walnut Creek, CA, United States
| | - Madina Gbotoe
- Center for Health Systems Research, Sutter Health, Walnut Creek, CA, United States
| | - Jake Delatorre-Reimer
- Department of Clinical Informatics, NorthBay Healthcare, Fairfield, CA, United States
| | - Kevin Knobel
- Sutter Gould Medical Foundation, Sutter Health, Modesto, CA, United States
| | - Andrew Hudnut
- Sutter Medical Group, Sutter Health, Sacramento, CA, United States
| | - J B Jones
- Center for Health Systems Research, Sutter Health, Walnut Creek, CA, United States
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22
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Garabedian PM, Gannon MP, Aaron S, Wu E, Burns Z, Samal L. Human-centered design of clinical decision support for management of hypertension with chronic kidney disease. BMC Med Inform Decis Mak 2022; 22:217. [PMID: 35964083 PMCID: PMC9375189 DOI: 10.1186/s12911-022-01962-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Accepted: 08/02/2022] [Indexed: 11/16/2022] Open
Abstract
Background Primary care providers face challenges in recognizing and controlling hypertension in patients with chronic kidney disease (CKD). Clinical decision support (CDS) has the potential to aid clinicians in identifying patients who could benefit from medication changes. This study designed an alert to control hypertension in CKD patients using an iterative human-centered design process. Methods In this study, we present a human-centered design process employing multiple methods for gathering user requirements and feedback on design and usability. Initially, we conducted contextual inquiry sessions to gather user requirements for the CDS. This was followed by group design sessions and one-on-one formative think-aloud sessions to validate requirements, obtain feedback on the design and layout, uncover usability issues, and validate changes. Results This study included 20 participants. The contextual inquiry produced 10 user requirements which influenced the initial alert design. The group design sessions revealed issues related to several themes, including recommendations and clinical content that did not match providers' expectations and extraneous information on the alerts that did not provide value. Findings from the individual think-aloud sessions revealed that participants disagreed with some recommended clinical actions, requested additional information, and had concerns about the placement in their workflow. Following each step, iterative changes were made to the alert content and design. Discussion This study showed that participation from users throughout the design process can lead to a better understanding of user requirements and optimal design, even within the constraints of an EHR alerting system. While raising awareness of design needs, it also revealed concerns related to workflow, understandability, and relevance. Conclusion The human-centered design framework using multiple methods for CDS development informed the creation of an alert to assist in the treatment and recognition of hypertension in patients with CKD. Supplementary Information The online version contains supplementary material available at 10.1186/s12911-022-01962-y.
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Affiliation(s)
- Pamela M Garabedian
- Mass General Brigham, 399 Revolution Drive, Somerville, MA, 857-282-4091, USA.
| | - Michael P Gannon
- Division of General Internal Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Skye Aaron
- Division of General Internal Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Edward Wu
- Alabama College of Osteopathic Medicine, Dothan, AL, USA
| | - Zoe Burns
- Division of General Internal Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Lipika Samal
- Division of General Internal Medicine, Brigham and Women's Hospital, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA
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23
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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)
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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
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24
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Duwel V, de Kort JML, Jacobs SS, Dennert RM, Busari JO. Managing the Mental Health of Healthcare Professionals in Times of Crisis: The Aruban COVID-19 Experience. Healthcare (Basel) 2022; 10:healthcare10071263. [PMID: 35885790 PMCID: PMC9318673 DOI: 10.3390/healthcare10071263] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 07/03/2022] [Accepted: 07/04/2022] [Indexed: 12/21/2022] Open
Abstract
Hospital workers in Aruba have been facing an increased demand for healthcare in the unique setting of a Small Island Developing State (SIDS). This study assessed the impact of the first wave of the SARS-CoV-2 pandemic on the mental health of staff at the major hospital in Aruba, examining the differences between employee groups, with the goal of providing recommendations for targeted support and coping strategies in future crises in a small island setting. Patients and methods: In a mixed-method cohort design, Dr. Horacio E. Oduber Hospital staff were asked to complete a 25-item questionnaire about their concerns and worries, organization of work, and general wellbeing; 24% of the hospital staff filled in the questionnaire (mean age 41 ± 11 years, 79% female). Alongside the needs assessment questionnaire, six focus groups were established to explore staff feelings on specific measures taken by hospital management during the COVID-19 crisis. Results: Questionnaire analysis (n = 231) revealed employees’ concerns about infecting their relatives and their financial stability. In particular, nurses were significantly more concerned than other staff groups. In the wellbeing section of the questionnaire, items regarding future security scored poorest, alongside increased levels of tiredness and nervousness. Focus groups discussions revealed frustrations of the hospital staff with the foreign staff brought in to help during the crisis and a need for better leadership and communication practices from hospital management. Conclusions: Comprehensive and holistic approaches should be implemented by the hospital management to prevent occupational burnout and demoralized work ethics and further emotional exhaustion.
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Affiliation(s)
- Veronika Duwel
- Faculty of Health and Life Sciences, Maastricht University, P.O. Box 616, 6200 MD Maastricht, The Netherlands;
| | | | - Shailing S. Jacobs
- Department of General Medicine, Horacio E. Oduber Hospital, Oranjestad, Aruba;
| | - Robert M. Dennert
- Department of Cardiology, Horacio E. Oduber Hospital, Oranjestad, Aruba;
| | - Jamiu O. Busari
- Department of Educational Development and Research, Faculty of Health, Medicine and Life Sciences, Maastricht University, P.O. Box 616, 6200 MD Maastricht, The Netherlands
- Department of Paediatrics, Horacio Oduber Hospital, Oranjestad, Aruba
- Correspondence: ; Tel.: +297-597-4440; Fax: +297-587-0220
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25
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Sangal RB, Liu RB, Cole KO, Rothenberg C, Ulrich A, Rhodes D, Venkatesh AK. Implementation of an Electronic Health Record Integrated Clinical Pathway Improves Adherence to COVID-19 Hospital Care Guidelines. Am J Med Qual 2022; 37:335-341. [PMID: 35026785 PMCID: PMC9241559 DOI: 10.1097/jmq.0000000000000036] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
BACKGROUND During the COVID-19 pandemic, frequently changing guidelines presented challenges to emergency department (ED) clinicians. The authors implemented an electronic health record (EHR)-integrated clinical pathway that could be accessed by clinicians within existing workflows when caring for patients under investigation (PUI) for COVID-19. The objective was to examine the association between clinical pathway utilization and adherence to institutional best practice treatment recommendations for COVID-19. METHODS The authors conducted an observational analysis of all ED patients seen in a health system inclusive of seven EDs between March 18, 2020, and April 20, 2021. They implemented the pathway as an interactive flow chart that allowed clinicians to place orders while viewing the most up-to-date institutional guidance. Primary outcomes were proportion of admitted PUIs receiving dexamethasone and aspirin in the ED, and secondary outcome was time to delivering treatment. RESULTS A total of 13 269 patients were admitted PUIs. The pathway was used by 40.6% of ED clinicians. When clinicians used the pathway, patients were more likely to be prescribed aspirin (OR, 7.15; 95% CI, 6.2-8.26) and dexamethasone (10.4; 8.85-12.2). For secondary outcomes, clinicians using the pathway had statistically significant ( P < 0.0001) improvement in timeliness of ordering medications and admission to the hospital. Aspirin, dexamethasone, and admission order time were improved by 103.89, 94.34, and 121.94 minutes, respectively. CONCLUSIONS The use of an EHR-integrated clinical pathway improved clinician adherence to changing COVID-19 treatment guidelines and timeliness to associated medication administration. As pathways continue to be implemented, their effects on improving patient outcomes and decreasing disparities in patient care should be further examined.
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Affiliation(s)
- Rohit B. Sangal
- Department of Emergency Medicine, Yale University School of Medicine, New Haven, CT
| | - Rachel B. Liu
- Department of Emergency Medicine, Yale University School of Medicine, New Haven, CT
| | | | - Craig Rothenberg
- Department of Emergency Medicine, Yale University School of Medicine, New Haven, CT
| | - Andrew Ulrich
- Department of Emergency Medicine, Yale University School of Medicine, New Haven, CT
| | | | - Arjun K. Venkatesh
- Department of Emergency Medicine, Yale University School of Medicine, New Haven, CT
- Center for Outcomes Research, Yale University School of Medicine, New Haven, CT
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26
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Adler-Milstein J, Chen J, Dhaliwal G. Supporting Diagnosis With Next-Generation Artificial Intelligence-Reply. JAMA 2022; 327:1400-1401. [PMID: 35412568 DOI: 10.1001/jama.2022.2306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Affiliation(s)
| | - Jonathan Chen
- Center for Biomedical Informatics Research, Stanford University, Stanford, California
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27
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Kushniruk A, Banks A, Melton GB, Porta CM, Tignanelli CJ. Barriers to and Facilitators for Acceptance of Comprehensive Clinical Decision Support System-Driven Care Maps for Patients With Thoracic Trauma: Interview Study Among Health Care Providers and Nurses. JMIR Hum Factors 2022; 9:e29019. [PMID: 35293873 PMCID: PMC8968578 DOI: 10.2196/29019] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Revised: 11/04/2021] [Accepted: 12/19/2021] [Indexed: 01/21/2023] Open
Abstract
BACKGROUND Comprehensive clinical decision support (CDS) care maps can improve the delivery of care and clinical outcomes. However, they are frequently plagued by usability problems and poor user acceptance. OBJECTIVE This study aims to characterize factors influencing successful design and use of comprehensive CDS care maps and identify themes associated with end-user acceptance of a thoracic trauma CDS care map earlier in the process than has traditionally been done. This was a planned adaptive redesign stage of a User Acceptance and System Adaptation Design development and implementation strategy for a CDS care map. This stage was based on a previously developed prototype CDS care map guided by the Unified Theory of Acceptance and Use of Technology. METHODS A total of 22 multidisciplinary end users (physicians, advanced practice providers, and nurses) were identified and recruited using snowball sampling. Qualitative interviews were conducted, audio-recorded, and transcribed verbatim. Generation of prespecified codes and the interview guide was informed by the Unified Theory of Acceptance and Use of Technology constructs and investigative team experience. Interviews were blinded and double-coded. Thematic analysis of interview scripts was conducted and yielded descriptive themes about factors influencing the construction and potential use of an acceptable CDS care map. RESULTS A total of eight dominant themes were identified: alert fatigue (theme 1), automation (theme 2), redundancy (theme 3), minimalistic design (theme 4), evidence based (theme 5), prevent errors (theme 6), comprehensive across the spectrum of disease (theme 7), and malleability (theme 8). Themes 1 to 4 addressed factors directly affecting end users, and themes 5 to 8 addressed factors affecting patient outcomes. More experienced providers prioritized a system that is easy to use. Nurses prioritized a system that incorporated evidence into decision support. Clinicians across specialties, roles, and ages agreed that the amount of extra work generated should be minimal and that the system should help them administer optimal care efficiently. CONCLUSIONS End user feedback reinforces attention toward factors that improve the acceptance and use of a CDS care map for patients with thoracic trauma. Common themes focused on system complexity, the ability of the system to fit different populations and settings, and optimal care provision. Identifying these factors early in the development and implementation process may facilitate user-centered design and improve adoption.
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Affiliation(s)
| | - Alyssa Banks
- University of Minnesota, Minneapolis, MN, United States
| | - Genevieve B Melton
- Department of Surgery, University of Minnesota, Minneapolis, MN, United States
| | - Carolyn M Porta
- School of Nursing, University of Minnesota, Minneapolis, MN, United States
| | - Christopher J Tignanelli
- Department of Surgery, University of Minnesota, Minneapolis, MN, United States.,Department of Surgery, North Memorial Health Hospital, Robbinsdale, MN, United States
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28
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Ackermann K, Baker J, Festa M, McMullan B, Westbrook J, Li L. Computerized Clinical Decision Support Systems for Early Detection of Sepsis Among Pediatric, Neonatal, and Maternal Inpatients: A Scoping Review (Preprint). JMIR Med Inform 2021; 10:e35061. [PMID: 35522467 PMCID: PMC9123549 DOI: 10.2196/35061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Revised: 02/27/2022] [Accepted: 03/19/2022] [Indexed: 11/13/2022] Open
Affiliation(s)
- Khalia Ackermann
- Centre for Health Systems and Safety Research, Australian Institute of Health Innovation, Macquarie University, Australia
| | - Jannah Baker
- Centre for Health Systems and Safety Research, Australian Institute of Health Innovation, Macquarie University, Australia
| | - Marino Festa
- Kids Critical Care Research, Department of Paediatric Intensive Care, Children's Hospital at Westmead, Sydney, Australia
| | - Brendan McMullan
- Department of Immunology and Infectious Diseases, Sydney Children's Hospital, Randwick, Sydney, Australia
- Faculty of Medicine & Health, University of New South Wales, Sydney, Australia
| | - Johanna Westbrook
- Centre for Health Systems and Safety Research, Australian Institute of Health Innovation, Macquarie University, Australia
| | - Ling Li
- Centre for Health Systems and Safety Research, Australian Institute of Health Innovation, Macquarie University, Australia
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29
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Duclos C, Bouaud J. Pragmatic Considerations on Clinical Decision Support from the 2019 Literature. Yearb Med Inform 2020; 29:155-158. [PMID: 32823309 PMCID: PMC7442518 DOI: 10.1055/s-0040-1702016] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2022] Open
Abstract
Objectives
: To summarize significant research contributions published in 2019 in the field of computerized clinical decision support and select the best papers for the Decision Support section of the International Medical Informatics Association (IMIA) Yearbook.
Methods
: Two bibliographic databases were searched for papers referring to clinical decision support systems (CDSSs) and computerized provider order entry (CPOE) systems. From search results, section editors established a list of candidate best papers, which were then peer-reviewed by external reviewers. The IMIA Yearbook editorial committee finally selected the best papers on the basis of all reviews including the section editors’ evaluation.
Results
: A total of 1,378 articles were retrieved. Fifteen best paper candidates were selected, the reviews of which resulted in the selection of three best papers. One paper reports on a guideline modeling approach based on clinical decision trees, both clinically interpretable and suitable for implementation in CDSSs. In another paper, authors promote the use of extended Timed Transition Diagrams in CDSSs to formalize consistently recurrent medical processes for chronic diseases management. The third paper proposes a conceptual framework and a grid for assessing the performance of predictive tools based on the critical appraisal of published evidence.
Conclusions
: As showed by the number and the variety of works related to decision support, research in the field is very active. This year’s selection highlighted pragmatic works that promote transparency and trust required by decision support tools.
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Affiliation(s)
- C Duclos
- Université Sorbonne Paris Nord, Sorbonne Université, INSERM, UMR_S 1142, LIMICS, Paris, France.,AP-HP, Hôpital Avicenne, Bobigny, France
| | - J Bouaud
- AP-HP, Delegation for Clinical Research and Innovation, Paris, France.,Université Sorbonne Paris Nord, Sorbonne Université, INSERM, UMR_S 1142, LIMICS, Paris, France
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