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Eguale T, Bastardot F, Song W, Motta-Calderon D, Elsobky Y, Rui A, Marceau M, Davis C, Ganesan S, Alsubai A, Matthews M, Volk LA, Bates DW, Rozenblum R. A Machine Learning Application to Classify Patients at Differing Levels of Risk of Opioid Use Disorder: Clinician-Based Validation Study. JMIR Med Inform 2024; 12:e53625. [PMID: 38842167 DOI: 10.2196/53625] [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: 10/20/2023] [Revised: 03/15/2024] [Accepted: 04/20/2024] [Indexed: 06/07/2024] Open
Abstract
Background Despite restrictive opioid management guidelines, opioid use disorder (OUD) remains a major public health concern. Machine learning (ML) offers a promising avenue for identifying and alerting clinicians about OUD, thus supporting better clinical decision-making regarding treatment. Objective This study aimed to assess the clinical validity of an ML application designed to identify and alert clinicians of different levels of OUD risk by comparing it to a structured review of medical records by clinicians. Methods The ML application generated OUD risk alerts on outpatient data for 649,504 patients from 2 medical centers between 2010 and 2013. A random sample of 60 patients was selected from 3 OUD risk level categories (n=180). An OUD risk classification scheme and standardized data extraction tool were developed to evaluate the validity of the alerts. Clinicians independently conducted a systematic and structured review of medical records and reached a consensus on a patient's OUD risk level, which was then compared to the ML application's risk assignments. Results A total of 78,587 patients without cancer with at least 1 opioid prescription were identified as follows: not high risk (n=50,405, 64.1%), high risk (n=16,636, 21.2%), and suspected OUD or OUD (n=11,546, 14.7%). The sample of 180 patients was representative of the total population in terms of age, sex, and race. The interrater reliability between the ML application and clinicians had a weighted kappa coefficient of 0.62 (95% CI 0.53-0.71), indicating good agreement. Combining the high risk and suspected OUD or OUD categories and using the review of medical records as a gold standard, the ML application had a corrected sensitivity of 56.6% (95% CI 48.7%-64.5%) and a corrected specificity of 94.2% (95% CI 90.3%-98.1%). The positive and negative predictive values were 93.3% (95% CI 88.2%-96.3%) and 60.0% (95% CI 50.4%-68.9%), respectively. Key themes for disagreements between the ML application and clinician reviews were identified. Conclusions A systematic comparison was conducted between an ML application and clinicians for identifying OUD risk. The ML application generated clinically valid and useful alerts about patients' different OUD risk levels. ML applications hold promise for identifying patients at differing levels of OUD risk and will likely complement traditional rule-based approaches to generating alerts about opioid safety issues.
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Affiliation(s)
- Tewodros Eguale
- School of Pharmacy, Massachusetts College of Pharmacy and Health Sciences, Boston, MA, United States
- Division of General Internal Medicine, Brigham and Women's Hospital, Boston, MA, United States
| | - François Bastardot
- Innovation and Clinical Research Directorate, Lausanne University Hospital (CHUV), Lausanne, Switzerland
- Medical Directorate, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Wenyu Song
- Division of General Internal Medicine, Brigham and Women's Hospital, Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
| | | | - Yasmin Elsobky
- Division of General Internal Medicine, Brigham and Women's Hospital, Boston, MA, United States
- Alexandria University, Alexandria, Egypt
| | - Angela Rui
- Division of General Internal Medicine, Brigham and Women's Hospital, Boston, MA, United States
| | - Marlika Marceau
- Clinical Quality and IS Analysis, Mass General Brigham, Somerville, MA, United States
| | - Clark Davis
- Division of General Internal Medicine, Brigham and Women's Hospital, Boston, MA, United States
| | - Sandya Ganesan
- Division of General Internal Medicine, Brigham and Women's Hospital, Boston, MA, United States
| | - Ava Alsubai
- Division of General Internal Medicine, Brigham and Women's Hospital, Boston, MA, United States
| | - Michele Matthews
- School of Pharmacy, Massachusetts College of Pharmacy and Health Sciences, Boston, MA, United States
- Department of Pharmacy, Brigham and Women's Hospital, Boston, MA, United States
| | - Lynn A Volk
- Clinical Quality and IS Analysis, Mass General Brigham, Somerville, MA, United States
| | - David W Bates
- Division of General Internal Medicine, Brigham and Women's Hospital, Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
- Clinical Quality and IS Analysis, Mass General Brigham, Somerville, MA, United States
- Harvard TH Chan School of Public Health, Boston, MA, United States
| | - Ronen Rozenblum
- Division of General Internal Medicine, Brigham and Women's Hospital, Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
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Fleurence RL, Kent S, Adamson B, Tcheng J, Balicer R, Ross JS, Haynes K, Muller P, Campbell J, Bouée-Benhamiche E, García Martí S, Ramsey S. Assessing Real-World Data From Electronic Health Records for Health Technology Assessment: The SUITABILITY Checklist: A Good Practices Report of an ISPOR Task Force. VALUE IN HEALTH : THE JOURNAL OF THE INTERNATIONAL SOCIETY FOR PHARMACOECONOMICS AND OUTCOMES RESEARCH 2024; 27:692-701. [PMID: 38871437 DOI: 10.1016/j.jval.2024.01.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Accepted: 01/23/2024] [Indexed: 06/15/2024]
Abstract
This ISPOR Good Practices report provides a framework for assessing the suitability of electronic health records data for use in health technology assessments (HTAs). Although electronic health record (EHR) data can fill evidence gaps and improve decisions, several important limitations can affect its validity and relevance. The ISPOR framework includes 2 components: data delineation and data fitness for purpose. Data delineation provides a complete understanding of the data and an assessment of its trustworthiness by describing (1) data characteristics; (2) data provenance; and (3) data governance. Fitness for purpose comprises (1) data reliability items, ie, how accurate and complete the estimates are for answering the question at hand and (2) data relevance items, which assess how well the data are suited to answer the particular question from a decision-making perspective. The report includes a checklist specific to EHR data reporting: the ISPOR SUITABILITY Checklist. It also provides recommendations for HTA agencies and policy makers to improve the use of EHR-derived data over time. The report concludes with a discussion of limitations and future directions in the field, including the potential impact from the substantial and rapid advances in the diffusion and capabilities of large language models and generative artificial intelligence. The report's immediate audiences are HTA evidence developers and users. We anticipate that it will also be useful to other stakeholders, particularly regulators and manufacturers, in the future.
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Affiliation(s)
| | - Seamus Kent
- Erasmus School of Health & Policy Management, Erasmus University, Rotterdam, The Netherlands
| | | | | | | | - Joseph S Ross
- Yale School of Medicine, Yale University, New Haven, CT, USA
| | - Kevin Haynes
- Janssen Research and Development, Titusville, NJ, USA
| | - Patrick Muller
- Centre for Guidelines, National Institute for Health and Care Excellence, Manchester or London, England, UK
| | - Jon Campbell
- National Pharmaceutical Council, Washington, DC, USA
| | - Elsa Bouée-Benhamiche
- Public Health and Healthcare Division, Institut National du Cancer, Boulogne-Billancourt, France
| | - Sebastián García Martí
- Health Technology Assessment and Health Economics Department, Institute for Clinical Effectiveness and Health Policy, Buenos Aires, Argentina
| | - Scott Ramsey
- Hutchinson Institute for Cancer Outcomes Research, Fred Hutchinson Cancer Center, Seattle, WA, USA.
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Yurkovich JT, Evans SJ, Rappaport N, Boore JL, Lovejoy JC, Price ND, Hood LE. The transition from genomics to phenomics in personalized population health. Nat Rev Genet 2024; 25:286-302. [PMID: 38093095 DOI: 10.1038/s41576-023-00674-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/03/2023] [Indexed: 03/21/2024]
Abstract
Modern health care faces several serious challenges, including an ageing population and its inherent burden of chronic diseases, rising costs and marginal quality metrics. By assessing and optimizing the health trajectory of each individual using a data-driven personalized approach that reflects their genetics, behaviour and environment, we can start to address these challenges. This assessment includes longitudinal phenome measures, such as the blood proteome and metabolome, gut microbiome composition and function, and lifestyle and behaviour through wearables and questionnaires. Here, we review ongoing large-scale genomics and longitudinal phenomics efforts and the powerful insights they provide into wellness. We describe our vision for the transformation of the current health care from disease-oriented to data-driven, wellness-oriented and personalized population health.
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Affiliation(s)
- James T Yurkovich
- Phenome Health, Seattle, WA, USA
- Center for Phenomic Health, The Buck Institute for Research on Aging, Novato, CA, USA
- Department of Bioengineering, University of Texas at Dallas, Richardson, TX, USA
| | - Simon J Evans
- Phenome Health, Seattle, WA, USA
- Center for Phenomic Health, The Buck Institute for Research on Aging, Novato, CA, USA
| | - Noa Rappaport
- Center for Phenomic Health, The Buck Institute for Research on Aging, Novato, CA, USA
- Institute for Systems Biology, Seattle, WA, USA
| | - Jeffrey L Boore
- Phenome Health, Seattle, WA, USA
- Center for Phenomic Health, The Buck Institute for Research on Aging, Novato, CA, USA
| | - Jennifer C Lovejoy
- Phenome Health, Seattle, WA, USA
- Center for Phenomic Health, The Buck Institute for Research on Aging, Novato, CA, USA
- Institute for Systems Biology, Seattle, WA, USA
| | - Nathan D Price
- Institute for Systems Biology, Seattle, WA, USA
- Thorne HealthTech, New York, NY, USA
- Department of Bioengineering, University of Washington, Seattle, WA, USA
- Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, WA, USA
| | - Leroy E Hood
- Phenome Health, Seattle, WA, USA.
- Center for Phenomic Health, The Buck Institute for Research on Aging, Novato, CA, USA.
- Institute for Systems Biology, Seattle, WA, USA.
- Department of Bioengineering, University of Washington, Seattle, WA, USA.
- Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, WA, USA.
- Department of Immunology, University of Washington, Seattle, WA, USA.
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Esteban S, Szmulewicz A. Making causal inferences from transactional data: A narrative review of opportunities and challenges when implementing the target trial framework. J Int Med Res 2024; 52:3000605241241920. [PMID: 38548473 PMCID: PMC10981242 DOI: 10.1177/03000605241241920] [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: 09/14/2023] [Accepted: 03/10/2024] [Indexed: 04/01/2024] Open
Abstract
The target trial framework has emerged as a powerful tool for addressing causal questions in clinical practice and in public health. In the healthcare sector, where decision-making is increasingly data-driven, transactional databases, such as electronic health records (EHR) and insurance claims, present an untapped potential for answering complex causal questions. This narrative review explores the potential of the integration of the target trial framework with real-world data to enhance healthcare decision-making processes. We outline essential elements of the target trial framework, and identify pertinent challenges in data quality, privacy concerns, and methodological limitations, proposing solutions to overcome these obstacles and optimize the framework's application.
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Affiliation(s)
- Santiago Esteban
- Instituto de Efectividad Clínica y Sanitaria, Centro de Implementación e Innovación en Políticas de Salud, Buenos Aires, Argentina
- Hospital Italiano de Buenos Aires, Family and Community Medicine Division Buenos Aires, Buenos Aires, Argentina
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Qaurooni D, Herr BW, Zappone SR, Wojciechowska K, Börner K, Schleyer T. Visual Analytics for Data-Driven Understanding of the Substance Use Disorder Epidemic. INQUIRY : A JOURNAL OF MEDICAL CARE ORGANIZATION, PROVISION AND FINANCING 2024; 61:469580241227020. [PMID: 38281107 PMCID: PMC10823843 DOI: 10.1177/00469580241227020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 12/15/2023] [Accepted: 01/02/2024] [Indexed: 01/29/2024]
Abstract
The substance use disorder epidemic has emerged as a serious public health crisis, presenting complex challenges. Visual analytics offers a unique approach to address this complexity and facilitate effective interventions. This paper details the development of an innovative visual analytics dashboard, aimed at enhancing our understanding of the substance use disorder epidemic. By employing record linkage techniques, we integrate diverse data sources to provide a comprehensive view of the epidemic. Adherence to responsive, open, and user-centered design principles ensures the dashboard's usefulness and usability. Our approach to data and design encourages collaboration among various stakeholders, including researchers, politicians, and healthcare practitioners. Through illustrative outputs, we demonstrate how the dashboard can deepen our understanding of the epidemic, support intervention strategies, and evaluate the effectiveness of implemented measures. The paper concludes with a discussion of the dashboard's use cases and limitations.
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Affiliation(s)
| | - Bruce W. Herr
- Indiana University Bloomington, Bloomington, IN, USA
| | | | | | - Katy Börner
- Indiana University Bloomington, Bloomington, IN, USA
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Hendricks-Sturrup RM, Edgar LM, Lu CY. Leveraging stories of cardiac amyloidosis patients of African ancestry or descent to support patient-derived data elements for efficient diagnosis and treatment. Front Pharmacol 2023; 14:1276396. [PMID: 38074115 PMCID: PMC10704161 DOI: 10.3389/fphar.2023.1276396] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Accepted: 10/31/2023] [Indexed: 02/12/2024] Open
Affiliation(s)
- Rachele M. Hendricks-Sturrup
- National Alliance Against Disparities in Patient Health, Woodbridge, VA, United States
- Department of Population Medicine, Harvard Pilgrim Healthcare Institute and Harvard Medical School, Boston, MA, United States
- Duke-Margolis Center for Health Policy, Washington, DC, United States
| | - Lauren M. Edgar
- Southern Nevada Black Nurses Association, Las Vegas, NV, United States
| | - Christine Y. Lu
- Department of Population Medicine, Harvard Pilgrim Healthcare Institute and Harvard Medical School, Boston, MA, United States
- School of Pharmacy, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia
- Kolling Institute, Faculty of Medicine and Health, The University of Sydney and the Northern Sydney Local Health District, Sydney, NSW, Australia
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