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Peters AE, Jones WS, Anderson B, Bramante CT, Broedl U, Hornik CP, Kehoe L, Knowlton KU, Krofah E, Landray M, Locke T, Patel MR, Psotka M, Rockhold FW, Roessig L, Rothman RL, Schofield L, Stockbridge N, Trontell A, Curtis LH, Tenaerts P, Hernandez AF. Framework of the strengths and challenges of clinically integrated trials: An expert panel report. Am Heart J 2024; 275:62-73. [PMID: 38795793 DOI: 10.1016/j.ahj.2024.05.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/13/2024] [Accepted: 05/15/2024] [Indexed: 05/28/2024]
Abstract
The limitations of the explanatory clinical trial framework include the high expense of implementing explanatory trials, restrictive entry criteria for participants, and redundant logistical processes. These limitations can result in slow evidence generation that is not responsive to population health needs, yielding evidence that is not generalizable. Clinically integrated trials, which integrate clinical research into routine care, represent a potential solution to this challenge and an opportunity to support learning health systems. The operational and design features of clinically integrated trials include a focused scope, simplicity in design and requirements, the leveraging of existing data structures, and patient participation in the entire trial process. These features are designed to minimize barriers to participation and trial execution and reduce additional research burdens for participants and clinicians alike. Broad adoption and scalability of clinically integrated trials are dependent, in part, on continuing regulatory, healthcare system, and payer support. This analysis presents a framework of the strengths and challenges of clinically integrated trials and is based on a multidisciplinary expert "Think Tank" panel discussion that included representatives from patient populations, academia, non-profit funding agencies, the U.S. Food and Drug Administration, and industry.
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Affiliation(s)
- Anthony E Peters
- Division of Cardiology, Department of Medicine, Duke University School of Medicine, Durham, NC
| | - W Schuyler Jones
- Division of Cardiology, Department of Medicine, Duke University School of Medicine, Durham, NC; Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC
| | | | - Carolyn T Bramante
- Departmentd of Medicine, University of Minnesota Medical School, Minneapolis, MN
| | | | - Christoph P Hornik
- Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC; Department of Pediatrics, Duke University School of Medicine, Durham, NC
| | - Lindsay Kehoe
- Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC
| | - Kirk U Knowlton
- Intermountain Medical Center Heart Institute, Salt Lake City, UT
| | | | | | - Trevan Locke
- Margolis Institute for Health Policy, Duke University, Durham, NC
| | - Manesh R Patel
- Division of Cardiology, Department of Medicine, Duke University School of Medicine, Durham, NC; Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC
| | | | - Frank W Rockhold
- Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC; Department of Biostatistics & Bioinformatics, Duke University School of Medicine, Durham, NC
| | | | | | | | - Norman Stockbridge
- Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD
| | - Anne Trontell
- Patient-Centered Outcomes Research Institute (PCORI), Washington, DC
| | - Lesley H Curtis
- Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC
| | | | - Adrian F Hernandez
- Division of Cardiology, Department of Medicine, Duke University School of Medicine, Durham, NC; Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC.
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Riffin C, Mei L, Brody L, Herr K, Pillemer KA, Reid MC. Program of All-Inclusive Care for the Elderly: an untapped setting for research to advance pain care in older persons. FRONTIERS IN PAIN RESEARCH 2024; 5:1347473. [PMID: 38712020 PMCID: PMC11070459 DOI: 10.3389/fpain.2024.1347473] [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: 11/30/2023] [Accepted: 04/10/2024] [Indexed: 05/08/2024] Open
Abstract
The Program of All-Inclusive Care for the Elderly (PACE) is a community-based care model in the United States that provides comprehensive health and social services to frail, nursing home-eligible adults aged 55 years and older. PACE organizations aim to support adequate pain control in their participants, yet few evidence-based pain interventions have been adopted or integrated into this setting. This article provides a roadmap for researchers who are interested in collaborating with PACE organizations to embed and evaluate evidence-based pain tools and interventions. We situate our discussion within the Consolidated Framework for Implementation Research (CFIR), a meta-theoretical framework that considers multi-level influences to implementation and evaluation of evidence-based programs. Within each CFIR domain, we identify key factors informed by our own work that merit consideration by research teams and PACE collaborators. Inner setting components pertain to the organizational culture of each PACE organization, the type and quality of electronic health record data, and availability of staff to assist with data abstraction. Outer setting components include external policies and regulations by the National PACE Association and audits conducted by the Centers for Medicare and Medicaid Services, which have implications for research participant recruitment and enrollment. Individual-level characteristics of PACE organization leaders include their receptivity toward new innovations and perceived ability to implement them. Forming and sustaining research-PACE partnerships to deliver evidence-based pain interventions pain will require attention to multi-level factors that may influence future uptake and provides a way to improve the health and well-being of patients served by these programs.
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Affiliation(s)
- Catherine Riffin
- Department of Medicine, Weill Cornell Medicine, New York, NY, United States
| | - Lauren Mei
- Department of Medicine, Weill Cornell Medicine, New York, NY, United States
| | - Lilla Brody
- Department of Medicine, Weill Cornell Medicine, New York, NY, United States
| | - Keela Herr
- College of Nursing, University of Iowa, Iowa City, IA, United States
| | - Karl A. Pillemer
- Department of Medicine, Weill Cornell Medicine, New York, NY, United States
- College of Human Ecology, Cornell University, Ithaca, NY, United States
| | - M. Carrington Reid
- Department of Medicine, Weill Cornell Medicine, New York, NY, United States
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Grzenda A, Widge AS. Electronic health records and stratified psychiatry: bridge to precision treatment? Neuropsychopharmacology 2024; 49:285-290. [PMID: 37667021 PMCID: PMC10700348 DOI: 10.1038/s41386-023-01724-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 08/24/2023] [Accepted: 08/27/2023] [Indexed: 09/06/2023]
Abstract
The use of a stratified psychiatry approach that combines electronic health records (EHR) data with machine learning (ML) is one potentially fruitful path toward rapidly improving precision treatment in clinical practice. This strategy, however, requires confronting pervasive methodological flaws as well as deficiencies in transparency and reporting in the current conduct of ML-based studies for treatment prediction. EHR data shares many of the same data quality issues as other types of data used in ML prediction, plus some unique challenges. To fully leverage EHR data's power for patient stratification, increased attention to data quality and collection of patient-reported outcome data is needed.
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Affiliation(s)
- Adrienne Grzenda
- Department of Psychiatry & Biobehavioral Sciences, David Geffen School of Medicine, University of California-Los Angeles, Los Angeles, CA, USA.
- Olive View-UCLA Medical Center, Sylmar, CA, USA.
| | - Alik S Widge
- Department of Psychiatry & Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA
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Levenson M, He W, Chen L, Dharmarajan S, Izem R, Meng Z, Pang H, Rockhold F. Statistical consideration for fit-for-use real-world data to support regulatory decision making in drug development. Stat Biopharm Res 2022. [DOI: 10.1080/19466315.2022.2120533] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
Affiliation(s)
| | - Weili He
- Global Medical Affairs Statistics, Data and Statistical Sciences, AbbVie, North Chicago, IL
| | - Li Chen
- Global Medical Affairs Statistics, Data and Statistical Sciences, AbbVie, North Chicago, IL
| | | | - Rima Izem
- Novartis Institutes for BioMedical Research Basel, Basel, Basel-Stadt, CH
| | | | | | - Frank Rockhold
- Department of Biostatistics & Bioinformatics, Duke University, Durham, NC
- Duke Clinical Research Institute, Duke University, Durham, NC
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Schwartz C, Winchester DE. Discrepancy between patient-reported and clinician-documented symptoms for myocardial perfusion imaging: initial findings from a prospective registry. Int J Qual Health Care 2021; 33:6258102. [PMID: 33913488 DOI: 10.1093/intqhc/mzab076] [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: 01/15/2021] [Revised: 04/05/2021] [Accepted: 04/28/2021] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND Occasionally, the symptoms reported by patients disagree with those documented in the medical record. We designed the Patient Centered Assessment of Symptoms (PCAS) registry to measure discrepancies between patient-reported and clinician-documented symptoms. OBJECTIVE Use patient-derived symptoms data to measure discrepancies with clinical documentation. METHODS The PCAS registry captured data from a prospective cohort of patients undergoing myocardial perfusion imaging (MPI) and includes free response and structured questions to gauge symptoms. Clinician-documented symptoms were extracted from the patients' medical records. The appropriateness of testing was determined twice: once using the patient-reported symptoms and once using the clinician-documented symptoms. RESULTS A total of 90 subjects were enrolled, among whom diabetes (36.7%), prior coronary disease (28.9%), hypertension (80.0%) and hyperlipidemia (85.6%) were common. Percentage of patient-reported symptoms compared to clinician-documented symptoms and agreement were as follows: chest pain (patient 29.0%, clinician 36.6%, moderate [kappa = 0.54]), chest pressure (patient 18.3%, clinician 10.8%, fair [kappa = 0.27]), dyspnea (patient 41.0%, clinician 36.6%, fair [kappa = 0.28]), onset with exertion (patient 61.7%, clinician 59.6%, slight [kappa = 0.17]), symptoms same as prior coronary artery disease (patient 46.2%, clinician 15.3%, slight [kappa = 0.01]). As a result of these inconsistencies, appropriateness ratings were different for 13.3% (n = 12) subjects. CONCLUSION In this prospective registry of patients undergoing MPI, we observed substantial disagreements between patient-reported and clinician-documented symptoms. Disagreement resulted in a considerable proportion of MPI appropriateness ratings also being incongruous.
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Affiliation(s)
- Cody Schwartz
- Department of Medicine, University of Florida, 1600 SW Archer Road Gainesville, Gainesville, FL 32610, USA
| | - David E Winchester
- Medical Service, Malcom Randall VA Medical Center, 1601 SW Archer Road, Box 111-D, Gainesville, FL 32608, USA.,Division of Cardiology, University of Florida, 1600 SW Archer Road Gainesville, Gainesville, FL 32610, USA
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He W, Fang Y, Wang H, Chan I. Applying Quantitative Approaches in the Use of RWE in Clinical Development and Life-Cycle Management. Stat Biopharm Res 2021. [DOI: 10.1080/19466315.2021.1927827] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Affiliation(s)
- Weili He
- Global Medical Affairs Statistics, Data and Statistical Sciences, AbbVie, North Chicago, IL
| | - Yixin Fang
- Global Medical Affairs Statistics, Data and Statistical Sciences, AbbVie, North Chicago, IL
| | - Hongwei Wang
- Global Medical Affairs Statistics, Data and Statistical Sciences, AbbVie, North Chicago, IL
| | - Ivan Chan
- Global Biometrics & Data Sciences, Bristol Myers Squibb, Berkeley Heights, NJ
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Grape A, Wicks M, Tumiel-Berhalter L, Sloand E, Rhee H. Enhanced access to healthcare utilization data through medical record review: Lessons learned from a community-based, multi-site project. Res Nurs Health 2021; 44:724-731. [PMID: 34114246 DOI: 10.1002/nur.22160] [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: 12/10/2020] [Revised: 05/08/2021] [Accepted: 05/31/2021] [Indexed: 11/07/2022]
Abstract
Collecting accurate healthcare utilization (HCU) data on community-based interventions is essential to establishing their clinical effectiveness and cost-related impact. Strategies used to enhance receiving medical records for HCU data extraction in a multi-site longitudinal randomized control trial with urban adolescents are presented. Successful strategies included timely assessment of procedures and practice preferences for access to electronic health records and hardcopy medical charts. Repeated outreach to clinical practice sites to identify and accommodate their preferred procedure for medical record release and flexibility in obtaining chart information helped achieve a 75% success rate in this study. Maintaining participant contact, updating provider information, and continuously evaluating site-specific personnel needs are recommended.
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Affiliation(s)
- Annette Grape
- Department of Social Work, SUNY Brockport, Brockport, New York, USA
| | - Mona Wicks
- College of Nursing, University of Tennessee Health Science Center, Memphis, Tennessee, USA
| | | | - Elizabeth Sloand
- School of Nursing, Johns Hopkins University, Baltimore, Maryland, USA
| | - Hyekyun Rhee
- School of Nursing, University of Rochester, Rochester, New York, USA
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Gallis JA, Kusibab K, Egger JR, Olsen MK, Askew S, Steinberg DM, Bennett GG. Can Electronic Health Records Validly Estimate the Effects of Health System Interventions Aimed at Controlling Body Weight? Obesity (Silver Spring) 2020; 28:2107-2115. [PMID: 32985131 PMCID: PMC8351620 DOI: 10.1002/oby.22958] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Revised: 06/08/2020] [Accepted: 06/26/2020] [Indexed: 11/12/2022]
Abstract
OBJECTIVE This study aimed to compare weight collected at clinics and recorded in the electronic health record (EHR) with primary study-collected trial weights to assess the validity of using EHR data in future pragmatic weight loss or weight gain prevention trials. METHODS For both the Track and Shape obesity intervention randomized trials, clinic EHR weight data were compared with primary trial weight data over the same time period. In analyzing the EHR weights, intervention effects were estimated on the primary outcome of weight (in kilograms) with EHR data, using linear mixed effects models. RESULTS EHR weight measurements were higher on average and more variable than trial weight measurements. The mean difference and 95% CI were similar at all time points between the estimates using EHR and study-collected weights. CONCLUSIONS The results of this study can be used to help guide the planning of future pragmatic weight-related trials. This study provides evidence that body weight measurements abstracted from the EHR can provide valid, efficient, and cost-effective data to estimate treatment effects from randomized clinical weight loss and weight management trials. However, care should be taken to properly understand the data-generating process and any mechanisms that may affect the validity of these estimates.
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Affiliation(s)
- John A. Gallis
- Department of Biostatistics & Bioinformatics, Duke University, Durham, NC, United States
- Duke Global Health Institute, Duke University, Durham, NC, United States
| | - Kristie Kusibab
- During the study, Ms. Kusibab was a Master of Science student in the Department of Biostatistics & Bioinformatics at Duke University
- PharPoint Research, Inc., Durham, NC, United States
| | - Joseph R. Egger
- Duke Global Health Institute, Duke University, Durham, NC, United States
| | - Maren K. Olsen
- Department of Biostatistics & Bioinformatics, Duke University, Durham, NC, United States
- Center for Health Services Research in Primary Care, Durham VA Medical Center, Durham, NC, United States
| | - Sandy Askew
- Duke Global Health Institute, Duke University, Durham, NC, United States
- Duke Global Digital Health Science Center, Duke University, Durham, NC, United States
| | - Dori M. Steinberg
- Duke Global Health Institute, Duke University, Durham, NC, United States
- Duke Global Digital Health Science Center, Duke University, Durham, NC, United States
- Duke School of Nursing, Duke University, Durham, NC, United States
| | - Gary G. Bennett
- Duke Global Health Institute, Duke University, Durham, NC, United States
- Duke Global Digital Health Science Center, Duke University, Durham, NC, United States
- Department of Psychology and Neuroscience, Duke University, Durham, NC, United States
- Corresponding Author Contact Info: Gary G. Bennett, ; 919-668-3420; 116 Allen Building, Box 90024, Durham NC 27708
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Bennett AV, Jonsson M, Chen RC, Al-Khatib SM, Weinfurt KP, Curtis LH. Applying patient-reported outcome methodology to capture patient-reported health data: Report from an NIH Collaboratory roundtable. HEALTHCARE-THE JOURNAL OF DELIVERY SCIENCE AND INNOVATION 2020; 8:100442. [PMID: 32919581 DOI: 10.1016/j.hjdsi.2020.100442] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Accepted: 05/31/2020] [Indexed: 11/28/2022]
Abstract
Patient-reported health data provide information for pragmatic clinical trials that may not be readily available from electronic health records or administrative claims data. In this report, we present key considerations for collecting patient-reported health information in pragmatic clinical trials, which are informed by best practices from patient-reported outcome research. We focus on question design and administration via electronic data collection platforms with respect to 3 types of patient-reported health data: medication use, utilization of health care services, and comorbid conditions. We summarize key scientific literature on the accuracy of these patient-reported data compared with electronic health record data. We discuss question design in detail, specifically defining the concept to be measured, patient understanding of the concept, recall periods of the question, and patient willingness to report. In addition, we discuss approaches for question administration and data collection platforms, which are key aspects of successful patient-reported data collection.
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Affiliation(s)
- Antonia V Bennett
- Department of Health Policy and Management, UNC Gillings School of Global Public Health, USA; Patient-Reported Outcomes Core, Cancer Outcomes Research Program, Lineberger Comprehensive Cancer Center, USA
| | - Mattias Jonsson
- Patient-Reported Outcomes Core, Cancer Outcomes Research Program, Lineberger Comprehensive Cancer Center, USA
| | - Ronald C Chen
- Department of Radiation Oncology, UNC School of Medicine, USA
| | - Sana M Al-Khatib
- University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Duke Clinical Research Institute, USA; Departments of Medicine, USA
| | - Kevin P Weinfurt
- University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Duke Clinical Research Institute, USA; Population Health Sciences, USA
| | - Lesley H Curtis
- University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Duke Clinical Research Institute, USA; Population Health Sciences, USA; Duke University School of Medicine, Durham, NC, USA.
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