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Tyack Z, Carter H, Allen M, Senanayake S, Warhurst K, Naicker S, Abell B, McPhail SM. Multicomponent processes to identify and prioritise low-value care in hospital settings: a scoping review. BMJ Open 2024; 14:e078761. [PMID: 38604625 PMCID: PMC11015208 DOI: 10.1136/bmjopen-2023-078761] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Accepted: 01/15/2024] [Indexed: 04/13/2024] Open
Abstract
OBJECTIVES This scoping review mapped and synthesised original research that identified low-value care in hospital settings as part of multicomponent processes. DESIGN Scoping review. DATA SOURCES Electronic databases (EMBASE, PubMed, CINAHL, PsycINFO and Cochrane CENTRAL) and grey literature were last searched 11 July and 3 June 2022, respectively, with no language or date restrictions. ELIGIBILITY CRITERIA We included original research targeting the identification and prioritisation of low-value care as part of a multicomponent process in hospital settings. DATA EXTRACTION AND SYNTHESIS Screening was conducted in duplicate. Data were extracted by one of six authors and checked by another author. A framework synthesis was conducted using seven areas of focus for the review and an overuse framework. RESULTS Twenty-seven records were included (21 original studies, 4 abstracts and 2 reviews), originating from high-income countries. Benefit or value (11 records), risk or harm (10 records) were common concepts referred to in records that explicitly defined low-value care (25 records). Evidence of contextualisation including barriers and enablers of low-value care identification processes were identified (25 records). Common components of these processes included initial consensus, consultation, ranking exercise or list development (16 records), and reviews of evidence (16 records). Two records involved engagement of patients and three evaluated the outcomes of multicomponent processes. Five records referenced a theory, model or framework. CONCLUSIONS Gaps identified included applying systematic efforts to contextualise the identification of low-value care, involving people with lived experience of hospital care and initiatives in resource poor contexts. Insights were obtained regarding the theories, models and frameworks used to guide initiatives and ways in which the concept 'low-value care' had been used and reported. A priority for further research is evaluating the effect of initiatives that identify low-value care using contextualisation as part of multicomponent processes.
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Affiliation(s)
- Zephanie Tyack
- Queensland University of Technology, Brisbane, Queensland, Australia
| | - Hannah Carter
- Australian Centre for Health Services Innovation (AusHSI) and Centre for Healthcare Transformation, School of Public Health and Social Work, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Michelle Allen
- Queensland University of Technology, Brisbane, Queensland, Australia
| | - Sameera Senanayake
- Australian Centre for Health Services Innovation (AusHSI) and Centre for Healthcare Transformation, School of Public Health and Social Work, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Kym Warhurst
- Australian Centre for Health Services Innovation (AusHSI) and Centre for Healthcare Transformation, School of Public Health and Social Work, Queensland University of Technology, Brisbane, Queensland, Australia
- Mater Misericordiae Ltd, South Brisbane, Queensland, Australia
| | - Sundresan Naicker
- Australian Centre for Health Services Innovation (AusHSI) and Centre for Healthcare Transformation, School of Public Health and Social Work, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Bridget Abell
- Australian Centre for Health Services Innovation (AusHSI) and Centre for Healthcare Transformation, School of Public Health and Social Work, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Steven M McPhail
- Australian Centre for Health Services Innovation (AusHSI) and Centre for Healthcare Transformation, School of Public Health and Social Work, Queensland University of Technology, Brisbane, Queensland, Australia
- Metro South Health, Brisbane, Queensland, Australia
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Liu S, McCoy AB, Peterson JF, Lasko TA, Sittig DF, Nelson SD, Andrews J, Patterson L, Cobb CM, Mulherin D, Morton CT, Wright A. Leveraging explainable artificial intelligence to optimize clinical decision support. J Am Med Inform Assoc 2024; 31:968-974. [PMID: 38383050 PMCID: PMC10990514 DOI: 10.1093/jamia/ocae019] [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: 09/11/2023] [Revised: 01/02/2024] [Accepted: 01/22/2024] [Indexed: 02/23/2024] Open
Abstract
OBJECTIVE To develop and evaluate a data-driven process to generate suggestions for improving alert criteria using explainable artificial intelligence (XAI) approaches. METHODS We extracted data on alerts generated from January 1, 2019 to December 31, 2020, at Vanderbilt University Medical Center. We developed machine learning models to predict user responses to alerts. We applied XAI techniques to generate global explanations and local explanations. We evaluated the generated suggestions by comparing with alert's historical change logs and stakeholder interviews. Suggestions that either matched (or partially matched) changes already made to the alert or were considered clinically correct were classified as helpful. RESULTS The final dataset included 2 991 823 firings with 2689 features. Among the 5 machine learning models, the LightGBM model achieved the highest Area under the ROC Curve: 0.919 [0.918, 0.920]. We identified 96 helpful suggestions. A total of 278 807 firings (9.3%) could have been eliminated. Some of the suggestions also revealed workflow and education issues. CONCLUSION We developed a data-driven process to generate suggestions for improving alert criteria using XAI techniques. Our approach could identify improvements regarding clinical decision support (CDS) that might be overlooked or delayed in manual reviews. It also unveils a secondary purpose for the XAI: to improve quality by discovering scenarios where CDS alerts are not accepted due to workflow, education, or staffing issues.
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Affiliation(s)
- Siru Liu
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, United States
- Department of Computer Science, Vanderbilt University, Nashville, TN 37212, United States
| | - Allison B McCoy
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | - Josh F Peterson
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, United States
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | - Thomas A Lasko
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, United States
- Department of Computer Science, Vanderbilt University, Nashville, TN 37212, United States
| | - Dean F Sittig
- School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX 77030, United States
| | - Scott D Nelson
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | - Jennifer Andrews
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN 37203, United States
- Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | - Lorraine Patterson
- HeathIT, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | - Cheryl M Cobb
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | - David Mulherin
- HeathIT, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | - Colleen T Morton
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | - Adam Wright
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, United States
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37203, United States
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3
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Liu S, Wright AP, Patterson BL, Wanderer JP, Turer RW, Nelson SD, McCoy AB, Sittig DF, Wright A. Using AI-generated suggestions from ChatGPT to optimize clinical decision support. J Am Med Inform Assoc 2023:7136722. [PMID: 37087108 DOI: 10.1093/jamia/ocad072] [Citation(s) in RCA: 85] [Impact Index Per Article: 85.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 03/28/2023] [Accepted: 04/11/2023] [Indexed: 04/24/2023] Open
Abstract
OBJECTIVE To determine if ChatGPT can generate useful suggestions for improving clinical decision support (CDS) logic and to assess noninferiority compared to human-generated suggestions. METHODS We supplied summaries of CDS logic to ChatGPT, an artificial intelligence (AI) tool for question answering that uses a large language model, and asked it to generate suggestions. We asked human clinician reviewers to review the AI-generated suggestions as well as human-generated suggestions for improving the same CDS alerts, and rate the suggestions for their usefulness, acceptance, relevance, understanding, workflow, bias, inversion, and redundancy. RESULTS Five clinicians analyzed 36 AI-generated suggestions and 29 human-generated suggestions for 7 alerts. Of the 20 suggestions that scored highest in the survey, 9 were generated by ChatGPT. The suggestions generated by AI were found to offer unique perspectives and were evaluated as highly understandable and relevant, with moderate usefulness, low acceptance, bias, inversion, redundancy. CONCLUSION AI-generated suggestions could be an important complementary part of optimizing CDS alerts, can identify potential improvements to alert logic and support their implementation, and may even be able to assist experts in formulating their own suggestions for CDS improvement. ChatGPT shows great potential for using large language models and reinforcement learning from human feedback to improve CDS alert logic and potentially other medical areas involving complex, clinical logic, a key step in the development of an advanced learning health system.
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Affiliation(s)
- Siru Liu
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Aileen P Wright
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Barron L Patterson
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Jonathan P Wanderer
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Anesthesiology, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Robert W Turer
- Department of Emergency Medicine, University of Texas Southwestern Medical Center, Dallas, Texas, USA
- Clinical Informatics Center, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Scott D Nelson
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Allison B McCoy
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Dean F Sittig
- School of Biomedical Informatics, University of Texas Health Science Center, Houston, Texas, USA
| | - Adam Wright
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
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4
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Liu S, Wright AP, Patterson BL, Wanderer JP, Turer RW, Nelson SD, McCoy AB, Sittig DF, Wright A. Assessing the Value of ChatGPT for Clinical Decision Support Optimization. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.02.21.23286254. [PMID: 36865144 PMCID: PMC9980251 DOI: 10.1101/2023.02.21.23286254] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/25/2023]
Abstract
Objective To determine if ChatGPT can generate useful suggestions for improving clinical decision support (CDS) logic and to assess noninferiority compared to human-generated suggestions. Methods We supplied summaries of CDS logic to ChatGPT, an artificial intelligence (AI) tool for question answering that uses a large language model, and asked it to generate suggestions. We asked human clinician reviewers to review the AI-generated suggestions as well as human-generated suggestions for improving the same CDS alerts, and rate the suggestions for their usefulness, acceptance, relevance, understanding, workflow, bias, inversion, and redundancy. Results Five clinicians analyzed 36 AI-generated suggestions and 29 human-generated suggestions for 7 alerts. Of the 20 suggestions that scored highest in the survey, 9 were generated by ChatGPT. The suggestions generated by AI were found to offer unique perspectives and were evaluated as highly understandable and relevant, with moderate usefulness, low acceptance, bias, inversion, redundancy. Conclusion AI-generated suggestions could be an important complementary part of optimizing CDS alerts, can identify potential improvements to alert logic and support their implementation, and may even be able to assist experts in formulating their own suggestions for CDS improvement. ChatGPT shows great potential for using large language models and reinforcement learning from human feedback to improve CDS alert logic and potentially other medical areas involving complex, clinical logic, a key step in the development of an advanced learning health system.
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5
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Nguyen JQ, Crews KR, Moore BT, Kornegay NM, Baker DK, Hasan M, Campbell PK, Dean SM, Relling MV, Hoffman JM, Haidar CE. Clinician adherence to pharmacogenomics prescribing recommendations in clinical decision support alerts. J Am Med Inform Assoc 2022; 30:132-138. [PMID: 36228116 PMCID: PMC9748527 DOI: 10.1093/jamia/ocac187] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 09/26/2022] [Accepted: 10/02/2022] [Indexed: 12/15/2022] Open
Abstract
Thoughtful integration of interruptive clinical decision support (CDS) alerts within the electronic health record is essential to guide clinicians on the application of pharmacogenomic results at point of care. St. Jude Children's Research Hospital implemented a preemptive pharmacogenomic testing program in 2011 in a multidisciplinary effort involving extensive education to clinicians about pharmacogenomic implications. We conducted a retrospective analysis of clinicians' adherence to 4783 pharmacogenomically guided CDS alerts that triggered for 12 genes and 60 drugs. Clinicians adhered to the therapeutic recommendations provided in 4392 alerts (92%). In our population of pediatric patients with catastrophic illnesses, the most frequently presented gene/drug CDS alerts were TPMT/NUDT15 and thiopurines (n = 3850), CYP2D6 and ondansetron (n = 667), CYP2D6 and oxycodone (n = 99), G6PD and G6PD high-risk medications (n = 51), and CYP2C19 and proton pump inhibitors (omeprazole and pantoprazole; n = 50). The high adherence rate was facilitated by our team approach to prescribing and our collaborative CDS design and delivery.
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Affiliation(s)
- Jenny Q Nguyen
- Department of Pharmacy and Pharmaceutical Sciences, St. Jude Children’s Research Hospital, Memphis, Tennessee, USA
| | - Kristine R Crews
- Department of Pharmacy and Pharmaceutical Sciences, St. Jude Children’s Research Hospital, Memphis, Tennessee, USA
| | - Ben T Moore
- Department of Pharmacy and Pharmaceutical Sciences, St. Jude Children’s Research Hospital, Memphis, Tennessee, USA
| | - Nancy M Kornegay
- Department of Pharmacy and Pharmaceutical Sciences, St. Jude Children’s Research Hospital, Memphis, Tennessee, USA
| | - Donald K Baker
- Department of Information Services, St. Jude Children’s Research Hospital, Memphis, Tennessee, USA
| | - Murad Hasan
- Department of Pharmacy and Pharmaceutical Sciences, St. Jude Children’s Research Hospital, Memphis, Tennessee, USA
| | - Patrick K Campbell
- Department of Information Services, St. Jude Children’s Research Hospital, Memphis, Tennessee, USA
- Department of Oncology, St. Jude Children’s Research Hospital, Memphis, Tennessee, USA
| | - Shannon M Dean
- Department of Information Services, St. Jude Children’s Research Hospital, Memphis, Tennessee, USA
- Department of Pediatrics, St. Jude Children’s Research Hospital, Memphis, Tennesse, USA
| | - Mary V Relling
- Department of Pharmacy and Pharmaceutical Sciences, St. Jude Children’s Research Hospital, Memphis, Tennessee, USA
| | - James M Hoffman
- Department of Pharmacy and Pharmaceutical Sciences, St. Jude Children’s Research Hospital, Memphis, Tennessee, USA
- Department of the Office of Quality and Patient Safety, St. Jude Children’s Research Hospital, Memphis, Tennesse, USA
| | - Cyrine E Haidar
- Department of Pharmacy and Pharmaceutical Sciences, St. Jude Children’s Research Hospital, Memphis, Tennessee, USA
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Baysari MT, Dort BAV, Zheng WY, Li L, Hilmer S, Westbrook J, Day R. Prescribers’ reported acceptance and use of drug-drug interaction alerts: An Australian survey. Health Informatics J 2022; 28:14604582221100678. [DOI: 10.1177/14604582221100678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Drug-drug interaction (DDI) alerts are frequently included in electronic medical record (eMR) systems to provide users with relevant information and guidance at the point of care. In this study, we aimed to examine views of DDI alerts among prescribers, including junior doctors, registrars and senior doctors, across Australia. A validated survey for assessing prescribers’ reported acceptance and use of DDI alerts was distributed among researcher networks and in newsletters. Fifty useable responses were received, more than half ( n = 28) from senior doctors. Prescribers at all levels expected DDI alerts to improve performance but junior doctors reported that this was at a high cost, with respect to time and effort. Senior doctors and registrars reported rarely reading alerts and rarely changing prescribing decisions based on alerts. Respondents identified a number of problems with current alerts including limited relevance, repetition, and poor design, highlighting some clear areas for alert improvement.
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Affiliation(s)
- Melissa T Baysari
- Biomedical Informatics and Digital Health, School of Medical Sciences, Faculty of Medicine and Health, Charles Perkins Centre, The University of Sydney, NSW, Australia
| | - Bethany A Van Dort
- Biomedical Informatics and Digital Health, School of Medical Sciences, Faculty of Medicine and Health, Charles Perkins Centre, The University of Sydney, NSW, Australia
| | - Wu Yi Zheng
- Biomedical Informatics and Digital Health, School of Medical Sciences, Faculty of Medicine and Health, Charles Perkins Centre, The University of Sydney, NSW, Australia
- Black Dog Institute, NSW Australia
| | - Ling Li
- Centre for Health Systems and Safety Research, Australian Institute of Health Innovation, Macquarie University, Sydney, NSW, Australia
| | - Sarah Hilmer
- Kolling Institute of Medical Research, Faculty of Medicine and Health, The University of Sydney, NSW, Australia
- Departments of Clinical Pharmacology and Aged Care, Royal North Shore Hospital, Sydney, NSW, Australia
| | - Johanna Westbrook
- Centre for Health Systems and Safety Research, Australian Institute of Health Innovation, Macquarie University, Sydney, NSW, Australia
| | - Richard Day
- Department of Clinical Pharmacology and Toxicology, St Vincent’s Hospital, Sydney, NSW, Australia
- St Vincent’s Clinical School, Faculty of Medicine, UNSW, Sydney, NSW, Australia
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Wasylewicz ATM, van de Burgt BWM, Manten T, Kerskes M, Compagner WN, Korsten EHM, Egberts TCG, Grouls RJE. Contextualized Drug-Drug Interaction Management Improves Clinical Utility Compared With Basic Drug-Drug Interaction Management in Hospitalized Patients. Clin Pharmacol Ther 2022; 112:382-390. [PMID: 35486411 PMCID: PMC9540177 DOI: 10.1002/cpt.2624] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Accepted: 04/07/2022] [Indexed: 11/23/2022]
Abstract
Drug–drug interactions (DDIs) frequently trigger adverse drug events or reduced efficacy. Most DDI alerts, however, are overridden because of irrelevance for the specific patient. Basic DDI clinical decision support (CDS) systems offer limited possibilities for decreasing the number of irrelevant DDI alerts without missing relevant ones. Computerized decision tree rules were designed to context‐dependently suppress irrelevant DDI alerts. A crossover study was performed to compare the clinical utility of contextualized and basic DDI management in hospitalized patients. First, a basic DDI‐CDS system was used in clinical practice while contextualized DDI alerts were collected in the background. Next, this process was reversed. All medication orders (MOs) from hospitalized patients with at least one DDI alert were included. The following outcome measures were used to assess clinical utility: positive predictive value (PPV), negative predictive value (NPV), number of pharmacy interventions (PIs)/1,000 MOs, and the median time spent on DDI management/1,000 MOs. During the basic DDI management phase 1,919 MOs/day were included, triggering 220 DDI alerts/1,000 MOs; showing 57 basic DDI alerts/1,000 MOs to pharmacy staff; PPV was 2.8% with 1.6 PIs/1,000 MOs costing 37.2 minutes/1,000 MOs. No DDIs were missed by the contextualized CDS system (NPV 100%). During the contextualized DDI management phase 1,853 MOs/day were included, triggering 244 basic DDI alerts/1,000 MOs, showing 9.6 contextualized DDIs/1,000 MOs to pharmacy staff; PPV was 41.4% (P < 0.01), with 4.0 PIs/1,000 MOs (P < 0.01) and 13.7 minutes/1,000 MOs. The clinical utility of contextualized DDI management exceeds that of basic DDI management.
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Affiliation(s)
- Arthur T M Wasylewicz
- Department of Healthcare Intelligence, Catharina Hospital, Eindhoven, The Netherlands.,Department of Signal Processing Systems, Faculty of Electronic Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | | | - Thomas Manten
- Department of Clinical Pharmacy, Catharina Hospital, Eindhoven, The Netherlands
| | - Marieke Kerskes
- Department of Clinical Pharmacy, Catharina Hospital, Eindhoven, The Netherlands
| | - Wilma N Compagner
- Department of Healthcare Intelligence, Catharina Hospital, Eindhoven, The Netherlands
| | - Erik H M Korsten
- Department of Healthcare Intelligence, Catharina Hospital, Eindhoven, The Netherlands.,Department of Signal Processing Systems, Faculty of Electronic Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Toine C G Egberts
- Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Utrecht, The Netherlands.,Department of Clinical Pharmacy, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Rene J E Grouls
- Department of Clinical Pharmacy, Catharina Hospital, Eindhoven, The Netherlands
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8
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Williams J, Malden S, Heeney C, Bouamrane M, Holder M, Perera U, Bates DW, Sheikh A. Optimizing Hospital Electronic Prescribing Systems: A Systematic Scoping Review. J Patient Saf 2022; 18:e547-e562. [PMID: 35188939 PMCID: PMC8855945 DOI: 10.1097/pts.0000000000000867] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
OBJECTIVE Considerable international investment in hospital electronic prescribing (ePrescribing) systems has been made, but despite this, it is proving difficult for most organizations to realize safety, quality, and efficiency gains in prescribing. The objective of this work was to develop policy-relevant insights into the optimization of hospital ePrescribing systems to maximize the benefits and minimize the risks of these expensive digital health infrastructures. METHODS We undertook a systematic scoping review of the literature by searching MEDLINE, Embase, and CINAHL databases. We searched for primary studies reporting on ePrescribing optimization strategies and independently screened and abstracted data until saturation was achieved. Findings were theoretically and thematically synthesized taking a medicine life-cycle perspective, incorporating consultative phases with domain experts. RESULTS We identified 23,609 potentially eligible studies from which 1367 satisfied our inclusion criteria. Thematic synthesis was conducted on a data set of 76 studies, of which 48 were based in the United States. Key approaches to optimization included the following: stakeholder engagement, system or process redesign, technological innovations, and education and training packages. Single-component interventions (n = 26) described technological optimization strategies focusing on a single, specific step in the prescribing process. Multicomponent interventions (n = 50) used a combination of optimization strategies, typically targeting multiple steps in the medicines management process. DISCUSSION We identified numerous optimization strategies for enhancing the performance of ePrescribing systems. Key considerations for ePrescribing optimization include meaningful stakeholder engagement to reconceptualize the service delivery model and implementing technological innovations with supporting training packages to simultaneously impact on different facets of the medicines management process.
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Affiliation(s)
- Jac Williams
- From the Usher Institute, University of Edinburgh, Edinburgh, United Kingdom
| | - Stephen Malden
- From the Usher Institute, University of Edinburgh, Edinburgh, United Kingdom
| | - Catherine Heeney
- From the Usher Institute, University of Edinburgh, Edinburgh, United Kingdom
| | - Matt Bouamrane
- From the Usher Institute, University of Edinburgh, Edinburgh, United Kingdom
| | - Mike Holder
- From the Usher Institute, University of Edinburgh, Edinburgh, United Kingdom
| | - Uditha Perera
- From the Usher Institute, University of Edinburgh, Edinburgh, United Kingdom
| | - David W. Bates
- Division of General Internal Medicine and Primary Care, Brigham and Women’s Hospital, Boston, Massachusetts
| | - Aziz Sheikh
- From the Usher Institute, University of Edinburgh, Edinburgh, United Kingdom
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Liu S, Kawamoto K, Del Fiol G, Weir C, Malone DC, Reese TJ, Morgan K, ElHalta D, Abdelrahman S. The potential for leveraging machine learning to filter medication alerts. J Am Med Inform Assoc 2022; 29:891-899. [PMID: 34990507 PMCID: PMC9006688 DOI: 10.1093/jamia/ocab292] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2021] [Revised: 12/03/2021] [Accepted: 12/23/2021] [Indexed: 01/07/2023] Open
Abstract
OBJECTIVE To evaluate the potential for machine learning to predict medication alerts that might be ignored by a user, and intelligently filter out those alerts from the user's view. MATERIALS AND METHODS We identified features (eg, patient and provider characteristics) proposed to modulate user responses to medication alerts through the literature; these features were then refined through expert review. Models were developed using rule-based and machine learning techniques (logistic regression, random forest, support vector machine, neural network, and LightGBM). We collected log data on alerts shown to users throughout 2019 at University of Utah Health. We sought to maximize precision while maintaining a false-negative rate <0.01, a threshold predefined through discussion with physicians and pharmacists. We developed models while maintaining a sensitivity of 0.99. Two null hypotheses were developed: H1-there is no difference in precision among prediction models; and H2-the removal of any feature category does not change precision. RESULTS A total of 3,481,634 medication alerts with 751 features were evaluated. With sensitivity fixed at 0.99, LightGBM achieved the highest precision of 0.192 and less than 0.01 for the pre-defined maximal false-negative rate by subject-matter experts (H1) (P < 0.001). This model could reduce alert volume by 54.1%. We removed different combinations of features (H2) and found that not all features significantly contributed to precision. Removing medication order features (eg, dosage) most significantly decreased precision (-0.147, P = 0.001). CONCLUSIONS Machine learning potentially enables the intelligent filtering of medication alerts.
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Affiliation(s)
- Siru Liu
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA
| | - Kensaku Kawamoto
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA
| | - Guilherme Del Fiol
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA
| | - Charlene Weir
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA
| | - Daniel C Malone
- Department of Pharmacotherapy, Skaggs College of Pharmacy, University of Utah, Salt Lake City, Utah, USA
| | - Thomas J Reese
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA,Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Keaton Morgan
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA
| | - David ElHalta
- Pharmacy Services, University of Utah, Salt Lake City, Utah, USA
| | - Samir Abdelrahman
- Corresponding Author: Samir Abdelrahman, MS, PhD, Department of Biomedical Informatics, University of Utah, 421 Wakara Way, Salt Lake City, UT 84108, USA;
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10
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Chou E, Boyce RD, Balkan B, Subbian V, Romero A, Hansten PD, Horn JR, Gephart S, Malone DC. Designing and evaluating contextualized drug-drug interaction algorithms. JAMIA Open 2021; 4:ooab023. [PMID: 33763631 PMCID: PMC7976224 DOI: 10.1093/jamiaopen/ooab023] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2020] [Revised: 01/28/2021] [Accepted: 03/09/2021] [Indexed: 11/12/2022] Open
Abstract
Objective Alert fatigue is a common issue with off-the-shelf clinical decision support. Most warnings for drug-drug interactions (DDIs) are overridden or ignored, likely because they lack relevance to the patient's clinical situation. Existing alerting systems for DDIs are often simplistic in nature or do not take the specific patient context into consideration, leading to overly sensitive alerts. The objective of this study is to develop, validate, and test DDI alert algorithms that take advantage of patient context available in electronic health records (EHRs) data. Methods Data on the rate at which DDI alerts were triggered but for which no action was taken over a 3-month period (override rates) from a single tertiary care facility were used to identify DDIs that were considered a high-priority for contextualized alerting. A panel of DDI experts developed algorithms that incorporate drug and patient characteristics that affect the relevance of such warnings. The algorithms were then implemented as computable artifacts, validated using a synthetic health records data, and tested over retrospective data from a single urban hospital. Results Algorithms and computable knowledge artifacts were developed and validated for a total of 8 high priority DDIs. Testing on retrospective real-world data showed the potential for the algorithms to reduce alerts that interrupt clinician workflow by more than 50%. Two algorithms (citalopram/QT interval prolonging agents, and fluconazole/opioid) showed potential to filter nearly all interruptive alerts for these combinations. Conclusion The 8 DDI algorithms are a step toward addressing a critical need for DDI alerts that are more specific to patient context than current commercial alerting systems. Data commonly available in EHRs can improve DDI alert specificity.
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Affiliation(s)
- Eric Chou
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Richard D Boyce
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Baran Balkan
- College of Engineering, University of Arizona, Tucson, Arizona, USA
| | - Vignesh Subbian
- College of Engineering, University of Arizona, Tucson, Arizona, USA
| | - Andrew Romero
- Banner University Medical Center, Tucson, Arizona, USA
| | - Philip D Hansten
- Department of Pharmacy, University of Washington, Seattle, Washington, USA
| | - John R Horn
- Department of Pharmacy, University of Washington, Seattle, Washington, USA
| | - Sheila Gephart
- College of Nursing, University of Arizona, Tucson, Arizona, USA
| | - Daniel C Malone
- Department of Pharmacotherapy, University of Utah, Salt Lake City, Utah, USA
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11
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Edrees H, Amato MG, Wong A, Seger DL, Bates DW. High-priority drug-drug interaction clinical decision support overrides in a newly implemented commercial computerized provider order-entry system: Override appropriateness and adverse drug events. J Am Med Inform Assoc 2021; 27:893-900. [PMID: 32337561 DOI: 10.1093/jamia/ocaa034] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2020] [Revised: 02/21/2020] [Accepted: 03/12/2020] [Indexed: 01/01/2023] Open
Abstract
OBJECTIVE The study sought to determine frequency and appropriateness of overrides of high-priority drug-drug interaction (DDI) alerts and whether adverse drug events (ADEs) were associated with overrides in a newly implemented electronic health record. MATERIALS AND METHODS We conducted a retrospective study of overridden high-priority DDI alerts occurring from April 1, 2016, to March 31, 2017, from inpatient and outpatient settings at an academic health center. We studied highest-severity DDIs that were previously designated as "hard stops" and additional high-priority DDIs identified from clinical experience and literature review. All highest-severity alert overrides (n = 193) plus a stratified random sample of additional overrides (n = 371) were evaluated for override appropriateness, using predetermined criteria. Charts were reviewed to identify ADEs for overrides that resulted in medication administration. A chi-square test was used to compare ADE rate by override appropriateness. RESULTS Of 16 011 alerts presented to providers, 15 318 (95.7%) were overridden, including 193 (87.3%) of the highest-severity DDIs and 15 125 (95.8%) of additional DDIs. Override appropriateness was 45.4% overall, 0.5% for highest-severity DDIs and 68.7% for additional DDIs. For alerts that resulted in medication administration (n = 423, 75.0%), 29 ADEs were identified (6.9%, 5.1 per 100 overrides). The rate of ADEs was higher with inappropriate vs appropriate overrides (9.4% vs 4.3%; P = .038). CONCLUSIONS The override rate was nearly 90% for even the highest-severity DDI alerts, indicating that stronger suggestions should be made for these alerts, while other alerts should be evaluated for potential suppression.
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Affiliation(s)
- Heba Edrees
- Department of Pharmacy Practice, Massachusetts College of Pharmacy and Health Sciences, Boston, Massachusetts, USA
| | - Mary G Amato
- Department of Pharmacy Practice, Massachusetts College of Pharmacy and Health Sciences, Boston, Massachusetts, USA.,Center for Patient Safety Research and Practice, Division of General Internal Medicine and Primary Care; Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Adrian Wong
- Department of Pharmacy Practice, Massachusetts College of Pharmacy and Health Sciences, Boston, Massachusetts, USA.,Center for Patient Safety Research and Practice, Division of General Internal Medicine and Primary Care; Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Diane L Seger
- Center for Patient Safety Research and Practice, Division of General Internal Medicine and Primary Care; Brigham and Women's Hospital, Boston, Massachusetts, USA.,Clinical and Quality Analysis, Information Systems, Partners HealthCare, Somerville, Massachusetts, USA
| | - David W Bates
- Center for Patient Safety Research and Practice, Division of General Internal Medicine and Primary Care; Brigham and Women's Hospital, Boston, Massachusetts, USA.,Clinical and Quality Analysis, Information Systems, Partners HealthCare, Somerville, Massachusetts, USA.,Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA
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12
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Hochheiser H, Jing X, Garcia EA, Ayvaz S, Sahay R, Dumontier M, Banda JM, Beyan O, Brochhausen M, Draper E, Habiel S, Hassanzadeh O, Herrero-Zazo M, Hocum B, Horn J, LeBaron B, Malone DC, Nytrø Ø, Reese T, Romagnoli K, Schneider J, Zhang L(Y, Boyce RD. A Minimal Information Model for Potential Drug-Drug Interactions. Front Pharmacol 2021; 11:608068. [PMID: 33762928 PMCID: PMC7982727 DOI: 10.3389/fphar.2020.608068] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Accepted: 12/29/2020] [Indexed: 01/22/2023] Open
Abstract
Despite the significant health impacts of adverse events associated with drug-drug interactions, no standard models exist for managing and sharing evidence describing potential interactions between medications. Minimal information models have been used in other communities to establish community consensus around simple models capable of communicating useful information. This paper reports on a new minimal information model for describing potential drug-drug interactions. A task force of the Semantic Web in Health Care and Life Sciences Community Group of the World-Wide Web consortium engaged informaticians and drug-drug interaction experts in in-depth examination of recent literature and specific potential interactions. A consensus set of information items was identified, along with example descriptions of selected potential drug-drug interactions (PDDIs). User profiles and use cases were developed to demonstrate the applicability of the model. Ten core information items were identified: drugs involved, clinical consequences, seriousness, operational classification statement, recommended action, mechanism of interaction, contextual information/modifying factors, evidence about a suspected drug-drug interaction, frequency of exposure, and frequency of harm to exposed persons. Eight best practice recommendations suggest how PDDI knowledge artifact creators can best use the 10 information items when synthesizing drug interaction evidence into artifacts intended to aid clinicians. This model has been included in a proposed implementation guide developed by the HL7 Clinical Decision Support Workgroup and in PDDIs published in the CDS Connect repository. The complete description of the model can be found at https://w3id.org/hclscg/pddi.
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Affiliation(s)
- Harry Hochheiser
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, United States
- Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, United States
| | - Xia Jing
- Department of Public Health Sciences, Clemson University, Clemson, SC, United States
| | | | - Serkan Ayvaz
- Department of Software Engineering, Bahçeşehir University, Istanbul, Turkey
| | - Ratnesh Sahay
- Clinical Data Science, AstraZeneca, Cambridge, United Kingdom
| | - Michel Dumontier
- Institute of Data Science, Maastricht University, Maastricht, Netherlands
| | - Juan M. Banda
- Department of Computer Science, Georgia State University, Atlanta, GA, United States
| | - Oya Beyan
- Fraunhofer Institute for Applied Information Technology, RWTH Aachen University, Aachen, Germany
| | - Mathias Brochhausen
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, United States
| | | | - Sam Habiel
- Open Source Electronic Health Record Alliance, Washington, DC, United States
| | | | - Maria Herrero-Zazo
- The European Bioinformatics Institute, Birney Research Group, London, United Kingdom
| | - Brian Hocum
- Genelex Corporation, Seattle, WA, United States
| | - John Horn
- School of Pharmacy, University of Washington, Seattle, WA, United States
| | - Brian LeBaron
- Southeast Louisiana Veterans Health Care System, New Orleans, LA, United States
| | - Daniel C. Malone
- Department of Pharmacotherapy, University of Utah, Salt Lake City, UT, United States
| | - Øystein Nytrø
- Department of Computer Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - Thomas Reese
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, United States
| | - Katrina Romagnoli
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, United States
| | - Jodi Schneider
- School of Information Science, University of Illinois, Champaign, IL, United States
| | - Louisa (Yu) Zhang
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, United States
| | - Richard D. Boyce
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, United States
- Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, United States
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13
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Vest TA, Gazda NP, Schenkat DH, Eckel SF. Practice-enhancing publications about the medication-use process in 2019. Am J Health Syst Pharm 2021; 78:141-153. [PMID: 33119100 DOI: 10.1093/ajhp/zxaa355] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
PURPOSE This article identifies, prioritizes, and summarizes published literature on the medication-use process (MUP) from calendar year 2019 that can impact health-system pharmacy daily practice. The MUP is the foundational system that provides the framework for safe medication utilization within the healthcare environment. The MUP is defined in this article as having the following components: prescribing/transcribing, dispensing, administration, and monitoring. Articles that evaluated one of the steps were gauged for their usefulness in promoting daily practice change. SUMMARY A PubMed search was conducted in January 2020 for calendar year 2019 using targeted Medical Subject Headings keywords; in addition, searches of the table of contents of selected pharmacy journals were conducted. A total of 4,317 articles were identified. A thorough review identified 66 potentially practice-enhancing articles: 17 for prescribing/transcribing, 17 for dispensing, 7 for administration, and 25 for monitoring. Ranking of the articles for importance by peers led to the selection of key articles from each category. The highest-ranked articles are briefly summarized, with a mention of why each article is important within health-system pharmacy. The other articles are listed for further review and evaluation. CONCLUSION It is important to routinely review the published literature and to incorporate significant findings into daily practice; this article assists in identifying and summarizing the most impactful recently published literature in this area. Health-system pharmacists have an active role in improving the MUP in their institution, and awareness of the significant published studies can assist in changing practice at the institutional level.
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Affiliation(s)
- Tyler A Vest
- Duke University Hospital, Durham, NC.,University of North Carolina at Chapel Hill Eshelman School of Pharmacy, Chapel Hill, NC
| | | | | | - Stephen F Eckel
- University of North Carolina at Chapel Hill Eshelman School of Pharmacy, Chapel Hill, NC.,University of North Carolina Medical Center, Chapel Hill, NC
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14
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Hoffman JM, Flynn AJ, Juskewitch JE, Freimuth RR. Biomedical Data Science and Informatics Challenges to Implementing Pharmacogenomics with Electronic Health Records. Annu Rev Biomed Data Sci 2020. [DOI: 10.1146/annurev-biodatasci-020320-093614] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Pharmacogenomic information must be incorporated into electronic health records (EHRs) with clinical decision support in order to fully realize its potential to improve drug therapy. Supported by various clinical knowledge resources, pharmacogenomic workflows have been implemented in several healthcare systems. Little standardization exists across these efforts, however, which limits scalability both within and across clinical sites. Limitations in information standards, knowledge management, and the capabilities of modern EHRs remain challenges for the widespread use of pharmacogenomics in the clinic, but ongoing efforts are addressing these challenges. Although much work remains to use pharmacogenomic information more effectively within clinical systems, the experiences of pioneering sites and lessons learned from those programs may be instructive for other clinical areas beyond genomics. We present a vision of what can be achieved as informatics and data science converge to enable further adoption of pharmacogenomics in the clinic.
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Affiliation(s)
- James M. Hoffman
- Department of Pharmaceutical Sciences and the Office of Quality and Patient Care, St. Jude Children's Research Hospital, Memphis, Tennessee 38105, USA
| | - Allen J. Flynn
- Department of Learning Health Sciences, Medical School, University of Michigan, Ann Arbor, Michigan 48109, USA
| | - Justin E. Juskewitch
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota 55905, USA
| | - Robert R. Freimuth
- Division of Digital Health Sciences, Department of Health Sciences Research, Center for Individualized Medicine, and Information and Knowledge Management, Mayo Clinic, Rochester, Minnesota 55905, USA
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