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Huang D, Whitehead C, Kuper A. Competing discourses, contested roles: Electronic health records in medical education. MEDICAL EDUCATION 2024. [PMID: 38764398 DOI: 10.1111/medu.15428] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 04/06/2024] [Accepted: 04/29/2024] [Indexed: 05/21/2024]
Abstract
INTRODUCTION The integration of electronic health records (EHRs) into medical education remains contested despite their widespread use in clinical practice. For medical trainees, this has resulted in idiosyncratic and often ad hoc methods of instruction on EHR use. The purpose of this study was to understand the currently fragmented nature of EHR instruction by examining discourses of EHR use within the medical education literature. METHODS We conducted a Foucauldian critical discourse analysis to identify discourses of EHRs in the medical education literature. We found our texts through a systematic search of widely cited medical education journals from 2013-2023. Each text was analysed for recurring truth statements-claims framed as self-evidently true and thus not needing supporting evidence-about the role of EHRs in medical education. RESULTS We identified three major discourses: (1) EHRs as a clinical skill and competency, emphasising training of physical interactions between learners, patients and computers; (2) EHRs as a system, emphasising the creation and facilitation of networks of people, technologies, institutions and standards; and (3) EHRs as a cognitive process, framed as a method to shape processes like clinical reasoning and bias. Each discourse privileged certain stakeholders over others and served to rationalise educational interventions that could be seen as beneficial in isolation yet were often disjointed in combination. CONCLUSIONS Competing discourses of EHR use in medical education produce divergent interventions that exacerbate their contested role in contemporary medical education. Identifying different claims for the benefits of EHR use in these settings allows educators to make rational choices between competing educational directions.
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Affiliation(s)
- Daniel Huang
- St. Michael's Hospital, Department of Medicine, University of Toronto, Toronto, Canada
| | - Cynthia Whitehead
- Women's College Hospital, Department of Family and Community Medicine, University of Toronto, Toronto, Canada
- The Wilson Centre, University Health Network and University of Toronto, Toronto, Canada
| | - Ayelet Kuper
- Sunnybrook Health Sciences Centre, Division of General Internal Medicine, Department of Medicine, University of Toronto, Toronto, Canada
- The Wilson Centre, University Health Network and University of Toronto, Toronto, Canada
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Li C, Mowery DL, Ma X, Yang R, Vurgun U, Hwang S, Donnelly HK, Bandhey H, Akhtar Z, Senathirajah Y, Sadhu EM, Getzen E, Freda PJ, Long Q, Becich MJ. Realizing the Potential of Social Determinants Data: A Scoping Review of Approaches for Screening, Linkage, Extraction, Analysis and Interventions. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.02.04.24302242. [PMID: 38370703 PMCID: PMC10871446 DOI: 10.1101/2024.02.04.24302242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
Background Social determinants of health (SDoH) like socioeconomics and neighborhoods strongly influence outcomes, yet standardized SDoH data is lacking in electronic health records (EHR), limiting research and care quality. Methods We searched PubMed using keywords "SDOH" and "EHR", underwent title/abstract and full-text screening. Included records were analyzed under five domains: 1) SDoH screening and assessment approaches, 2) SDoH data collection and documentation, 3) Use of natural language processing (NLP) for extracting SDoH, 4) SDoH data and health outcomes, and 5) SDoH-driven interventions. Results We identified 685 articles, of which 324 underwent full review. Key findings include tailored screening instruments implemented across settings, census and claims data linkage providing contextual SDoH profiles, rule-based and neural network systems extracting SDoH from notes using NLP, connections found between SDoH data and healthcare utilization/chronic disease control, and integrated care management programs executed. However, considerable variability persists across data sources, tools, and outcomes. Discussion Despite progress identifying patient social needs, further development of standards, predictive models, and coordinated interventions is critical to fulfill the potential of SDoH-EHR integration. Additional database searches could strengthen this scoping review. Ultimately widespread capture, analysis, and translation of multidimensional SDoH data into clinical care is essential for promoting health equity.
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Affiliation(s)
- Chenyu Li
- University of Pittsburgh School of Medicine Department of Biomedical Informatics
| | - Danielle L. Mowery
- University of Pennsylvania, Institute for Biomedical Informatics
- University of Pennsylvania, Department of Biostatistics, Epidemiology and Informatics
| | - Xiaomeng Ma
- University of Toronto, Institute of Health Policy Management and Evaluations
| | - Rui Yang
- Duke-NUS Medical School, Centre for Quantitative Medicine
| | - Ugurcan Vurgun
- University of Pennsylvania, Institute for Biomedical Informatics
| | - Sy Hwang
- University of Pennsylvania, Institute for Biomedical Informatics
| | | | - Harsh Bandhey
- Cedars-Sinai Medical Center, Department of Computational Biomedicine
| | - Zohaib Akhtar
- Northwestern University, Kellogg School of Management
| | - Yalini Senathirajah
- University of Pittsburgh School of Medicine Department of Biomedical Informatics
| | - Eugene Mathew Sadhu
- University of Pittsburgh School of Medicine Department of Biomedical Informatics
| | - Emily Getzen
- University of Pennsylvania, Department of Biostatistics, Epidemiology and Informatics
| | - Philip J Freda
- Cedars-Sinai Medical Center, Department of Computational Biomedicine
| | - Qi Long
- University of Pennsylvania, Institute for Biomedical Informatics
- University of Pennsylvania, Department of Biostatistics, Epidemiology and Informatics
| | - Michael J. Becich
- University of Pittsburgh School of Medicine Department of Biomedical Informatics
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Davis VH, Dainty KN, Dhalla IA, Sheehan KA, Wong BM, Pinto AD. "Addressing the bigger picture": A qualitative study of internal medicine patients' perspectives on social needs data collection and use. PLoS One 2023; 18:e0285795. [PMID: 37285324 DOI: 10.1371/journal.pone.0285795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Accepted: 04/29/2023] [Indexed: 06/09/2023] Open
Abstract
BACKGROUND There is increasing interest in collecting sociodemographic and social needs data in hospital settings to inform patient care and health equity. However, few studies have examined inpatients' views on this data collection and what should be done to address social needs. This study describes internal medicine inpatients' perspectives on the collection and use of sociodemographic and social needs information. METHODS A qualitative interpretive description methodology was used. Semi-structured interviews were conducted with 18 patients admitted to a large academic hospital in Toronto, Canada. Participants were recruited using maximum variation sampling for diverse genders, races, and those with and without social needs. Interviews were coded using a predominantly inductive approach and a thematic analysis was conducted. RESULTS Patients expressed that sociodemographic and social needs data collection is important to offer actionable solutions to address their needs. Patients described a gap between their ideal care which would attend to social needs, versus the reality that hospital-based teams are faced with competing priorities and pressures that make it unfeasible to provide such care. They also believed that this data collection could facilitate more holistic, integrated care. Patients conveyed a need to have a trusting and transparent relationship with their provider to alleviate concerns surrounding bias, discrimination, and confidentiality. Lastly, they indicated that sociodemographic and social needs data could be useful to inform care, support research to inspire social change, and assist them with navigating community resources or creating in-hospital programs to address unmet social needs. CONCLUSIONS While the collection of sociodemographic and social needs information in hospital settings is generally acceptable, there were varied views on whether hospital staff should intervene, as their priority is medical care. The results can inform the implementation of social data collection and interventions in hospital settings.
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Affiliation(s)
- Victoria H Davis
- Upstream Lab, MAP Centre for Urban Health Solutions, Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Ontario, Canada
| | - Katie N Dainty
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada
- Department of Research and Innovation, North York General Hospital, Toronto, Ontario, Canada
| | - Irfan A Dhalla
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada
- Department of Medicine, St. Michael's Hospital, Toronto, Ontario, Canada
- Department of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Kathleen A Sheehan
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
- Centre for Mental Health, University Health Network, Toronto, Ontario, Canada
| | - Brian M Wong
- Department of Medicine, University of Toronto, Toronto, Ontario, Canada
- Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
- Centre for Quality Improvement and Patient Safety, University of Toronto, Toronto, Ontario, Canada
| | - Andrew D Pinto
- Upstream Lab, MAP Centre for Urban Health Solutions, Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Ontario, Canada
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada
- Department of Family and Community Medicine, St. Michael's Hospital, Toronto, Ontario, Canada
- Department of Family & Community Medicine, University of Toronto, Toronto, Ontario, Canada
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Davis VH, Rodger L, Pinto AD. Collection and Use of Social Determinants of Health Data in Inpatient General Internal Medicine Wards: A Scoping Review. J Gen Intern Med 2023; 38:480-489. [PMID: 36471193 PMCID: PMC9905340 DOI: 10.1007/s11606-022-07937-z] [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: 03/11/2022] [Accepted: 11/04/2022] [Indexed: 12/12/2022]
Abstract
BACKGROUND There is growing interest in incorporating social determinants of health (SDoH) data collection in inpatient hospital settings to inform patient care. However, there is limited information on this data collection and its use in inpatient general internal medicine (GIM). This scoping review sought to describe the current state of the literature on SDoH data collection and its application to patient care in inpatient GIM settings. METHODS English-language searches on MedLine, Embase, Web of Science, CINAHL, Cochrane, and PsycINFO were conducted from 2000 to April 2021. Studies reporting systematic data collection or use of at least three SDoH, sociodemographic, or social needs variables in inpatient hospital GIM settings were included. Four independent reviewers screened abstracts, and two reviewers screened full-text articles. RESULTS A total of 8190 articles underwent abstract screening and eight were included. A range of SDoH tools were used, such as THRIVE, PRAPARE, WHO-Quality of Life, Measuring Health Equity, and a biopsychosocial framework. The most common SDoH were food security or malnutrition (n=7), followed by housing, transportation, employment, education, income, functional status and disability, and social support (n=5 each). Four of the eight studies applied the data to inform patient care, and three provided community resource referrals. DISCUSSION There is limited evidence to guide the collection and use of SDoH data in inpatient GIM settings. This review highlights the need for integrated care, the role of the electronic health record, and social history taking, all of which may benefit from more robust SDoH data collection. Future research should examine the feasibility and acceptability of SDoH integration in inpatient GIM settings.
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Affiliation(s)
- Victoria H Davis
- Upstream Lab, MAP Centre for Urban Health Solutions, Li Ka Shing Knowledge Institute, Unity Health Toronto, Toronto, ON, Canada.
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada.
| | - Laura Rodger
- Upstream Lab, MAP Centre for Urban Health Solutions, Li Ka Shing Knowledge Institute, Unity Health Toronto, Toronto, ON, Canada
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada
- Department of Medicine, St. Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada
| | - Andrew D Pinto
- Upstream Lab, MAP Centre for Urban Health Solutions, Li Ka Shing Knowledge Institute, Unity Health Toronto, Toronto, ON, Canada
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada
- Department of Family and Community Medicine, St. Michael's Hospital, Toronto, ON, Canada
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5
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Lasser EC, Gudzune KA, Lehman H, Kharrazi H, Weiner JP. Trends and Patterns of Social History Data Collection Within an Electronic Health Record. Popul Health Manag 2023; 26:13-21. [PMID: 36607903 DOI: 10.1089/pop.2022.0209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023] Open
Abstract
There is increased acceptance that social and behavioral determinants of health (SBDH) impact health outcomes, but electronic health records (EHRs) are not always set up to capture the full range of SBDH variables in a systematic manner. The purpose of this study was to explore rates and trends of social history (SH) data collection-1 element of SBDH-in a structured portion of an EHR within a large academic integrated delivery system. EHR data for individuals with at least 1 visit in 2017 were included in this study. Completeness rates were calculated for how often SBDH variable was assessed and documented. Logistic regressions identified factors associated with assessment rates for each variable. A total of 44,166 study patients had at least 1 SH variable present. Tobacco use and alcohol use were the most frequently captured SH variables. Black individuals were more likely to have their alcohol use assessed (odds ratio [OR] 1.21) compared with White individuals, whereas White individuals were more likely to have their "smokeless tobacco use" assessed (OR 0.92). There were also differences between insurance types. Drug use was more likely to be assessed in the Medicaid population for individuals who were single (OR 0.95) compared with the commercial population (OR 1.05). SH variable assessment is inconsistent, which makes use of EHR data difficult to gain better understanding of the impact of SBDH on health outcomes. Standards and guidelines on how and why to collect SBDH information within the EHR are needed.
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Affiliation(s)
- Elyse C Lasser
- Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.,Johns Hopkins Center for Population Health IT, Baltimore, Maryland, USA
| | - Kimberly A Gudzune
- Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.,Department of General Internal Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
| | - Harold Lehman
- Pediatrics Department, Johns Hopkins School of Medicine, Baltimore, Maryland, USA.,Johns Hopkins Biomedical Informatics and Data Sciences, Baltimore, Maryland, USA
| | - Hadi Kharrazi
- Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.,Johns Hopkins Center for Population Health IT, Baltimore, Maryland, USA.,Johns Hopkins Biomedical Informatics and Data Sciences, Baltimore, Maryland, USA
| | - Jonathan P Weiner
- Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.,Johns Hopkins Center for Population Health IT, Baltimore, Maryland, USA
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Stewart de Ramirez S, Shallat J, McClure K, Foulger R, Barenblat L. Screening for Social Determinants of Health: Active and Passive Information Retrieval Methods. Popul Health Manag 2022; 25:781-788. [PMID: 36454231 DOI: 10.1089/pop.2022.0228] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022] Open
Abstract
Screening for social determinants of health (SDOH) is recommended, but numerous barriers exist to implementing SDOH screening in clinical spaces. In this study, the authors identified how both active and passive information retrieval methods may be used in clinical spaces to screen for SDOH and meet patient needs. The authors conducted a retrospective sequential cohort analysis comparing the active identification of SDOH through a patient-led digital manual screening process completed in primary care offices from September 2019 to January 2020 and passive identification of SDOH through natural language processing (NLP) from September 2016 to August 2018, among 1735 patients at a large midwestern tertiary referral hospital system and its associated outlying primary care and outpatient facilities. The percent of patients identified by both the passive and active identification methods as experiencing SDOH varied from 0.3% to 4.7%. The active identification method identified social integration, domestic safety, financial resources, food insecurity, transportation, housing, and stress in proportions ranging from 5% to 36%. The passive method contributed to the identification of financial resource issues and stress, identifying 9.6% and 3% of patients to be experiencing these issues, respectively. SDOH documentation varied by provider type. The combination of passive and active SDOH screening methods can provide a more comprehensive picture by leveraging historic patient interactions, while also eliciting current patient needs. Using passive, NLP-based methods to screen for SDOH will also help providers overcome barriers that have historically prevented screening.
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Affiliation(s)
- Sarah Stewart de Ramirez
- Department of Population Health Services, OSF HealthCare System, Peoria, Illinois, USA.,Department of Emergency Medicine, University of Illinois College of Medicine at Peoria, Peoria, Illinois, USA
| | - Jaclyn Shallat
- Department of Epidemiology and Biostatistics, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Keaton McClure
- University of Illinois College of Medicine at Peoria, Peoria, Illinois, USA
| | - Roopa Foulger
- Department of Health Care Analytics, OSF HealthCare System, Peoria, Illinois, USA.,Department of OSF OnCall, OSF Healthcare System, Peoria, Illinois, USA
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Hall AG, Davlyatov GK, Orewa GN, Mehta TS, Feldman SS. Multiple Electronic Health Record-Based Measures of Social Determinants of Health to Predict Return to the Emergency Department Following Discharge. Popul Health Manag 2022; 25:771-780. [PMID: 36315199 DOI: 10.1089/pop.2022.0088] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
Health care systems continue to struggle with preventing 30-day readmissions to their institutions. Social determinants of health (SDOH) are important predictors of repeat visits to the hospital. In many health systems, SDOH data are limited to those variables that are most relevant to care delivery or payment (eg, race, gender, insurance status). Despite calls for integrating a more robust set of measures (eg, measures of health behaviors and living conditions) into the electronic health record (EHR), these data often have missing values necessitating the use of imputation to build a comprehensive picture of patients who are likely to return to the health system. Using logistic regression analyses and imputation of missing data from 2017 to 2018, this study uses measures found in the EHR (eg, tobacco use, living situation, problems at home, education) to assess those SDOH that might predict a return to the emergency department within 30 days of discharge from a health system. In both imputed and raw data, the total number of recorded health conditions was the most important predictor and collectively SDOH variables made a relatively small contributions in determining the likelihood of a return to the hospital. Although SDOH variables might be important in the design of programs aimed at preventing readmissions, they may not be useful in readmission predictive models.
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Affiliation(s)
- Allyson G Hall
- Department of Health Services Administration, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Ganisher K Davlyatov
- Department of Health Administration and Policy, University of Oklahoma Health Sciences Center, Norman, Oklahoma, USA
| | - Gregory N Orewa
- Department of Health Services Administration, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Tapan S Mehta
- Department of Family and Community Medicine, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Sue S Feldman
- Department of Health Services Administration, University of Alabama at Birmingham, Birmingham, Alabama, USA
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Sun CA, Perrin N, Maruthur N, Renda S, Levin S, Han HR. Predictors of Follow-Up Appointment No-Shows Before and During COVID Among Adults with Type 2 Diabetes. Telemed J E Health 2022. [DOI: 10.1089/tmj.2022.0377] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
Affiliation(s)
- Chun-An Sun
- Johns Hopkins School of Nursing, Baltimore, Maryland, USA
| | - Nancy Perrin
- Johns Hopkins School of Nursing, Baltimore, Maryland, USA
| | - Nisa Maruthur
- Johns Hopkins School of Nursing, Baltimore, Maryland, USA
- Division of General Internal Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Susan Renda
- Johns Hopkins School of Nursing, Baltimore, Maryland, USA
| | - Scott Levin
- Division of General Internal Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
- Department of Emergency Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
| | - Hae-Ra Han
- Johns Hopkins School of Nursing, Baltimore, Maryland, USA
- Department of Health, Behavior, and Society, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
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Hollin IL, Bonilla B, Bagley A, Tucker CA. Social and environmental determinants of health among children with long-term movement impairment. FRONTIERS IN REHABILITATION SCIENCES 2022; 3:831070. [PMID: 36188898 PMCID: PMC9397841 DOI: 10.3389/fresc.2022.831070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Accepted: 07/13/2022] [Indexed: 11/13/2022]
Abstract
The healthcare research community increasingly recognizes the need to address social (SDOH) and environmental determinants of health (EDOH) to optimize health and healthcare. This is particularly relevant to disability and functioning and to those with child onset conditions that impair mobility and impact functioning and participation. Using the World Health Organization (WHO)'s International Classification of Functioning, Disability, and Health (ICF) as a comprehensive framework, this paper aims to discuss our understanding of the relationships between social and EDOH and outcomes among people with impaired mobility that impacts functioning. This paper offers suggestions for future developments and guidance to use SDOH and EDOH in research and clinical practice.
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Affiliation(s)
- Ilene L. Hollin
- Department of Health Services Administration and Policy, Temple University College of Public Health, Philadelphia, PA, United States
- *Correspondence: Ilene L. Hollin
| | - Bethney Bonilla
- Center for Healthcare Policy and Research, University of California, Davis, Davis, CA, United States
- Bethney Bonilla
| | - Anita Bagley
- Clinical Research, Shriners Hospitals for Children, Northern California, Sacramento, CA, United States
| | - Carole A. Tucker
- Department of Nutrition, Metabolic and Rehabilitation Sciences, University of Texas Medical Branch, School of Health Professions, Galveston, TX, United States
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Dorr DA, Quiñones AR, King T, Wei MY, White K, Bejan CA. Prediction of Future Health Care Utilization Through Note-extracted Psychosocial Factors. Med Care 2022; 60:570-578. [PMID: 35658116 PMCID: PMC9262845 DOI: 10.1097/mlr.0000000000001742] [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] [Indexed: 12/22/2022]
Abstract
BACKGROUND Persons with multimorbidity (≥2 chronic conditions) face an increased risk of poor health outcomes, especially as they age. Psychosocial factors such as social isolation, chronic stress, housing insecurity, and financial insecurity have been shown to exacerbate these outcomes, but are not routinely assessed during the clinical encounter. Our objective was to extract these concepts from chart notes using natural language processing and predict their impact on health care utilization for patients with multimorbidity. METHODS A cohort study to predict the 1-year likelihood of hospitalizations and emergency department visits for patients 65+ with multimorbidity with and without psychosocial factors. Psychosocial factors were extracted from narrative notes; all other covariates were extracted from electronic health record data from a large academic medical center using validated algorithms and concept sets. Logistic regression was performed to predict the likelihood of hospitalization and emergency department visit in the next year. RESULTS In all, 76,479 patients were eligible; the majority were White (89%), 54% were female, with mean age 73. Those with psychosocial factors were older, had higher baseline utilization, and more chronic illnesses. The 4 psychosocial factors all independently predicted future utilization (odds ratio=1.27-2.77, C -statistic=0.63). Accounting for demographics, specific conditions, and previous utilization, 3 of 4 of the extracted factors remained predictive (odds ratio=1.13-1.86) for future utilization. Compared with models with no psychosocial factors, they had improved discrimination. Individual predictions were mixed, with social isolation predicting depression and morbidity; stress predicting atherosclerotic cardiovascular disease onset; and housing insecurity predicting substance use disorder morbidity. DISCUSSION Psychosocial factors are known to have adverse health impacts, but are rarely measured; using natural language processing, we extracted factors that identified a higher risk segment of older adults with multimorbidity. Combining these extraction techniques with other measures of social determinants may help catalyze population health efforts to address psychosocial factors to mitigate their health impacts.
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Affiliation(s)
- David A. Dorr
- Department of Medical Informatics & Clinical Epidemiology; Oregon Health & Science University; Portland, OR
| | - Ana R. Quiñones
- Department of Family Medicine; Oregon Health & Science University; Portland, OR
| | - Taylor King
- Department of Medical Informatics & Clinical Epidemiology; Oregon Health & Science University; Portland, OR
| | | | - Kellee White
- Department of Health Policy and Management; University of Maryland; College Park, MD
| | - Cosmin A. Bejan
- Department of Biomedical Informatics; Vanderbilt University Medical Center; Nashville, TN, USA
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11
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Nair SS, Li C, Doijad R, Nagy P, Lehmann H, Kharrazi H. A scoping review of knowledge authoring tools used for developing computerized clinical decision support systems. JAMIA Open 2021; 4:ooab106. [PMID: 34927003 PMCID: PMC8677433 DOI: 10.1093/jamiaopen/ooab106] [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: 07/21/2021] [Accepted: 11/30/2021] [Indexed: 11/20/2022] Open
Abstract
Objective Clinical Knowledge Authoring Tools (CKATs) are integral to the computerized Clinical Decision Support (CDS) development life cycle. CKATs enable authors to generate accurate, complete, and reliable digital knowledge artifacts in a relatively efficient and affordable manner. This scoping review aims to compare knowledge authoring tools and derive the common features of CKATs. Materials and Methods We performed a keyword-based literature search, followed by a snowball search, to identify peer-reviewed publications describing the development or use of CKATs. We used PubMed and Embase search engines to perform the initial search (n = 1579). After removing duplicate articles, nonrelevant manuscripts, and not peer-reviewed publication, we identified 47 eligible studies describing 33 unique CKATs. The reviewed CKATs were further assessed, and salient characteristics were extracted and grouped as common CKAT features. Results Among the identified CKATs, 55% use an open source platform, 70% provide an application programming interface for CDS system integration, and 79% provide features to validate/test the knowledge. The majority of the reviewed CKATs describe the flow of information, offer a graphical user interface for knowledge authors, and provide intellisense coding features (94%, 97%, and 97%, respectively). The composed list of criteria for CKAT included topics such as simulating the clinical setting, validating the knowledge, standardized clinical models and vocabulary, and domain independence. None of the reviewed CKATs met all common criteria. Conclusion Our scoping review highlights the key specifications for a CKAT. The CKAT specification proposed in this review can guide CDS authors in developing more targeted CKATs.
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Affiliation(s)
- Sujith Surendran Nair
- Division of General Internal Medicine, Section of Biomedical Informatics and Data Science, Johns Hopkins School of Medicine, Baltimore, Maryland, USA.,Informatics, American College of Radiology, Virginia, USA
| | - Chenyu Li
- Division of General Internal Medicine, Section of Biomedical Informatics and Data Science, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
| | - Ritu Doijad
- Division of General Internal Medicine, Section of Biomedical Informatics and Data Science, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
| | - Paul Nagy
- Division of General Internal Medicine, Section of Biomedical Informatics and Data Science, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
| | - Harold Lehmann
- Division of General Internal Medicine, Section of Biomedical Informatics and Data Science, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
| | - Hadi Kharrazi
- Division of General Internal Medicine, Section of Biomedical Informatics and Data Science, Johns Hopkins School of Medicine, Baltimore, Maryland, USA.,Department of Health Policy and Management, Johns Hopkins School of Public Health, Baltimore, Maryland, USA
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12
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Kharrazi H, Chang HY, Weiner JP, Gudzune KA. Assessing the Added Value of Blood Pressure Information Derived from Electronic Health Records in Predicting Health Care Cost and Utilization. Popul Health Manag 2021; 25:323-334. [PMID: 34847729 DOI: 10.1089/pop.2021.0250] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
Health care providers are increasingly using clinical measures derived from electronic health records (EHRs) for risk stratification and predictive modeling. EHR-specific data elements such as prescriptions, laboratory results, and vital signs have been shown to improve risk prediction models. In this study, the value of EHR-based blood pressure (BP) values was assessed in predicting health care costs (ie, total, medical, and pharmacy) and key utilization end points (ie, hospitalization, emergency department use, and being among the highest utilizers). The study population included 37,451 patients of a large integrated delivery system in the mid-western United States with complete EHR data files, who were 18-64 years old, had continuous insurance at an affiliated health plan, and had eligible BP records. Both EHRs and insurance claims of the study population were used to extract the predictors (ie, demographics, diagnosis, and BP values) and outcomes (ie, costs and utilizations). Predictors were extracted from 2012 data, whereas concurrent and prospective outcomes were extracted from 2012 to 2013 data. Three base models (BMs) were constructed to predict each of the outcomes. The first BM no. 1 used demographics. The second BM no. 2 added the Charlson comorbidity index to BM no. 1, whereas the third BM no. 3 added the Adjusted Clinical Group Dx-PM case-mix score to BM no. 1. BP was specified as means, ranges, and classes. Adding BP ranges to BM no. 1 and BM no. 2 showed the greatest improvements when predicting costs and utilization. More specifically, adjusted R2 and area under the curve of BM no. 2 improved by 32.9% and 14.1% when BP ranges were added to predict concurrent total cost and hospitalization, respectively. The effect of BP measures on improving the risk stratification models was diminished when predicting prospective outcomes after adding the measures to BM no. 3 (ie, the more comprehensive diagnostic model), specifically when represented as BP means. Given the increasing availability of BP information, this research suggests that these data should be integrated into provider-based population health analytic activities. Future research should focus on subpopulations that benefit the most from incorporating vital signs such as BP measures in risk stratification models.
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Affiliation(s)
- Hadi Kharrazi
- Center for Population Health Information Technology, Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.,Division of General Internal Medicine, Department of Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
| | - Hsien-Yen Chang
- Center for Population Health Information Technology, Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Jonathan P Weiner
- Center for Population Health Information Technology, Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Kimberly A Gudzune
- Division of General Internal Medicine, Department of Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland, USA.,Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins Medical Institution, Baltimore, Maryland, USA
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13
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Hatef E, Singh Deol G, Rouhizadeh M, Li A, Eibensteiner K, Monsen CB, Bratslaver R, Senese M, Kharrazi H. Measuring the Value of a Practical Text Mining Approach to Identify Patients With Housing Issues in the Free-Text Notes in Electronic Health Record: Findings of a Retrospective Cohort Study. Front Public Health 2021; 9:697501. [PMID: 34513783 PMCID: PMC8429931 DOI: 10.3389/fpubh.2021.697501] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Accepted: 07/28/2021] [Indexed: 11/13/2022] Open
Abstract
Introduction: Despite the growing efforts to standardize coding for social determinants of health (SDOH), they are infrequently captured in electronic health records (EHRs). Most SDOH variables are still captured in the unstructured fields (i.e., free-text) of EHRs. In this study we attempt to evaluate a practical text mining approach (i.e., advanced pattern matching techniques) in identifying phrases referring to housing issues, an important SDOH domain affecting value-based healthcare providers, using EHR of a large multispecialty medical group in the New England region, United States. To present how this approach would help the health systems to address the SDOH challenges of their patients we assess the demographic and clinical characteristics of patients with and without housing issues and briefly look into the patterns of healthcare utilization among the study population and for those with and without housing challenges. Methods: We identified five categories of housing issues [i.e., homelessness current (HC), homelessness history (HH), homelessness addressed (HA), housing instability (HI), and building quality (BQ)] and developed several phrases addressing each one through collaboration with SDOH experts, consulting the literature, and reviewing existing coding standards. We developed pattern-matching algorithms (i.e., advanced regular expressions), and then applied them in the selected EHR. We assessed the text mining approach for recall (sensitivity) and precision (positive predictive value) after comparing the identified phrases with manually annotated free-text for different housing issues. Results: The study dataset included EHR structured data for a total of 20,342 patients and 2,564,344 free-text clinical notes. The mean (SD) age in the study population was 75.96 (7.51). Additionally, 58.78% of the cohort were female. BQ and HI were the most frequent housing issues documented in EHR free-text notes and HH was the least frequent one. The regular expression methodology, when compared to manual annotation, had a high level of precision (positive predictive value) at phrase, note, and patient levels (96.36, 95.00, and 94.44%, respectively) across different categories of housing issues, but the recall (sensitivity) rate was relatively low (30.11, 32.20, and 41.46%, respectively). Conclusion: Results of this study can be used to advance the research in this domain, to assess the potential value of EHR's free-text in identifying patients with a high risk of housing issues, to improve patient care and outcomes, and to eventually mitigate socioeconomic disparities across individuals and communities.
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Affiliation(s)
- Elham Hatef
- Center for Population Health IT, Johns Hopkins School of Public Health, Baltimore, MD, United States
| | - Gurmehar Singh Deol
- Center for Population Health IT, Johns Hopkins School of Public Health, Baltimore, MD, United States
| | - Masoud Rouhizadeh
- The Institute for Clinical and Translational Research, Johns Hopkins School of Medicine, Baltimore, MD, United States
| | - Ashley Li
- Department of Biomedical Engineering, Johns Hopkins Whiting School of Engineering, Baltimore, MD, United States
| | | | | | | | | | - Hadi Kharrazi
- Center for Population Health IT, Johns Hopkins School of Public Health, Baltimore, MD, United States
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14
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Reeves RM, Christensen L, Brown JR, Conway M, Levis M, Gobbel GT, Shah RU, Goodrich C, Ricket I, Minter F, Bohm A, Bray BE, Matheny ME, Chapman W. Adaptation of an NLP system to a new healthcare environment to identify social determinants of health. J Biomed Inform 2021; 120:103851. [PMID: 34174396 DOI: 10.1016/j.jbi.2021.103851] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 06/16/2021] [Accepted: 06/21/2021] [Indexed: 11/18/2022]
Abstract
Social determinants of health (SDoH) are increasingly important factors for population health, healthcare outcomes, and care delivery. However, many of these factors are not reliably captured within structured electronic health record (EHR) data. In this work, we evaluated and adapted a previously published NLP tool to include additional social risk factors for deployment at Vanderbilt University Medical Center in an Acute Myocardial Infarction cohort. We developed a transformation of the SDoH outputs of the tool into the OMOP common data model (CDM) for re-use across many potential use cases, yielding performance measures across 8 SDoH classes of precision 0.83 recall 0.74 and F-measure of 0.78.
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Affiliation(s)
- Ruth M Reeves
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States; Geriatric Research Education and Clinical Care Center, Tennessee Valley Healthcare System VA, Nashville, TN, United States.
| | - Lee Christensen
- Department of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, UT, United States
| | - Jeremiah R Brown
- Department of Epidemiology and Biomedical Data Science, Dartmouth Geisel School of Medicine, Hanover, NH, United States
| | - Michael Conway
- Department of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, UT, United States
| | - Maxwell Levis
- Department of Epidemiology and Biomedical Data Science, Dartmouth Geisel School of Medicine, Hanover, NH, United States
| | - Glenn T Gobbel
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States; Division of General Internal Medicine, Vanderbilt University Medical Center, Nashville, TN, United States; Geriatric Research Education and Clinical Care Center, Tennessee Valley Healthcare System VA, Nashville, TN, United States
| | - Rashmee U Shah
- Division of Cardiovascular Medicine, University of Utah School of Medicine, Salt Lake City, UT, United States
| | - Christine Goodrich
- Department of Epidemiology and Biomedical Data Science, Dartmouth Geisel School of Medicine, Hanover, NH, United States
| | - Iben Ricket
- Department of Epidemiology and Biomedical Data Science, Dartmouth Geisel School of Medicine, Hanover, NH, United States
| | - Freneka Minter
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Andrew Bohm
- Department of Epidemiology and Biomedical Data Science, Dartmouth Geisel School of Medicine, Hanover, NH, United States
| | - Bruce E Bray
- Division of Cardiovascular Medicine, University of Utah School of Medicine, Salt Lake City, UT, United States; Department of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, UT, United States
| | - Michael E Matheny
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States; Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, United States; Division of General Internal Medicine, Vanderbilt University Medical Center, Nashville, TN, United States; Geriatric Research Education and Clinical Care Center, Tennessee Valley Healthcare System VA, Nashville, TN, United States
| | - Wendy Chapman
- Department of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, UT, United States; Centre for Clinical and Public Health Informatics, University of Melbourne, Melbourne, Australia
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