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Hatef E, Chang HY, Richards TM, Kitchen C, Budaraju J, Foroughmand I, Lasser EC, Weiner JP. Development of a Social Risk Score in the Electronic Health Record to Identify Social Needs Among Underserved Populations: Retrospective Study. JMIR Form Res 2024; 8:e54732. [PMID: 38470477 DOI: 10.2196/54732] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 02/02/2024] [Accepted: 02/08/2024] [Indexed: 03/13/2024] Open
Abstract
BACKGROUND Patients with unmet social needs and social determinants of health (SDOH) challenges continue to face a disproportionate risk of increased prevalence of disease, health care use, higher health care costs, and worse outcomes. Some existing predictive models have used the available data on social needs and SDOH challenges to predict health-related social needs or the need for various social service referrals. Despite these one-off efforts, the work to date suggests that many technical and organizational challenges must be surmounted before SDOH-integrated solutions can be implemented on an ongoing, wide-scale basis within most US-based health care organizations. OBJECTIVE We aimed to retrieve available information in the electronic health record (EHR) relevant to the identification of persons with social needs and to develop a social risk score for use within clinical practice to better identify patients at risk of having future social needs. METHODS We conducted a retrospective study using EHR data (2016-2021) and data from the US Census American Community Survey. We developed a prospective model using current year-1 risk factors to predict future year-2 outcomes within four 2-year cohorts. Predictors of interest included demographics, previous health care use, comorbidity, previously identified social needs, and neighborhood characteristics as reflected by the area deprivation index. The outcome variable was a binary indicator reflecting the likelihood of the presence of a patient with social needs. We applied a generalized estimating equation approach, adjusting for patient-level risk factors, the possible effect of geographically clustered data, and the effect of multiple visits for each patient. RESULTS The study population of 1,852,228 patients included middle-aged (mean age range 53.76-55.95 years), White (range 324,279/510,770, 63.49% to 290,688/488,666, 64.79%), and female (range 314,741/510,770, 61.62% to 278,488/448,666, 62.07%) patients from neighborhoods with high socioeconomic status (mean area deprivation index percentile range 28.76-30.31). Between 8.28% (37,137/448,666) and 11.55% (52,037/450,426) of patients across the study cohorts had at least 1 social need documented in their EHR, with safety issues and economic challenges (ie, financial resource strain, employment, and food insecurity) being the most common documented social needs (87,152/1,852,228, 4.71% and 58,242/1,852,228, 3.14% of overall patients, respectively). The model had an area under the curve of 0.702 (95% CI 0.699-0.705) in predicting prospective social needs in the overall study population. Previous social needs (odds ratio 3.285, 95% CI 3.237-3.335) and emergency department visits (odds ratio 1.659, 95% CI 1.634-1.684) were the strongest predictors of future social needs. CONCLUSIONS Our model provides an opportunity to make use of available EHR data to help identify patients with high social needs. Our proposed social risk score could help identify the subset of patients who would most benefit from further social needs screening and data collection to avoid potentially more burdensome primary data collection on all patients in a target population of interest.
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Affiliation(s)
- Elham Hatef
- Division of General Internal Medicine, Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, United States
- Center for Population Health Information Technology, Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
| | - Hsien-Yen Chang
- Center for Population Health Information Technology, Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
| | - Thomas M Richards
- Center for Population Health Information Technology, Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
| | - Christopher Kitchen
- Center for Population Health Information Technology, Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
| | - Janya Budaraju
- Center for Population Health Information Technology, Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
| | - Iman Foroughmand
- Center for Population Health Information Technology, Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
| | - Elyse C Lasser
- Center for Population Health Information Technology, Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
| | - Jonathan P Weiner
- Center for Population Health Information Technology, Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
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Guo K, McCoy AB, Reese TJ, Wright A, Rosenbloom ST, Liu S, Russo EM, Steitz BD. POINT: Pipeline for Offline Conversion and Integration of Geocodes and Neighborhood Data. Appl Clin Inform 2023; 14:833-842. [PMID: 37541656 PMCID: PMC10584391 DOI: 10.1055/a-2148-6414] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Accepted: 08/03/2023] [Indexed: 08/06/2023] Open
Abstract
OBJECTIVES Geocoding, the process of converting addresses into precise geographic coordinates, allows researchers and health systems to obtain neighborhood-level estimates of social determinants of health. This information supports opportunities to personalize care and interventions for individual patients based on the environments where they live. We developed an integrated offline geocoding pipeline to streamline the process of obtaining address-based variables, which can be integrated into existing data processing pipelines. METHODS POINT is a web-based, containerized, application for geocoding addresses that can be deployed offline and made available to multiple users across an organization. Our application supports use through both a graphical user interface and application programming interface to query geographic variables, by census tract, without exposing sensitive patient data. We evaluated our application's performance using two datasets: one consisting of 1 million nationally representative addresses sampled from Open Addresses, and the other consisting of 3,096 previously geocoded patient addresses. RESULTS A total of 99.4 and 99.8% of addresses in the Open Addresses and patient addresses datasets, respectively, were geocoded successfully. Census tract assignment was concordant with reference in greater than 90% of addresses for both datasets. Among successful geocodes, median (interquartile range) distances from reference coordinates were 52.5 (26.5-119.4) and 14.5 (10.9-24.6) m for the two datasets. CONCLUSION POINT successfully geocodes more addresses and yields similar accuracy to existing solutions, including the U.S. Census Bureau's official geocoder. Addresses are considered protected health information and cannot be shared with common online geocoding services. POINT is an offline solution that enables scalability to multiple users and integrates downstream mapping to neighborhood-level variables with a pipeline that allows users to incorporate additional datasets as they become available. As health systems and researchers continue to explore and improve health equity, it is essential to quickly and accurately obtain neighborhood variables in a Health Insurance Portability and Accountability Act (HIPAA)-compliant way.
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Affiliation(s)
- Kevin Guo
- School of Medicine, Vanderbilt University, Nashville, Tennessee, United States
| | - Allison B. McCoy
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, United States
| | - Thomas J. Reese
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, United States
| | - Adam Wright
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, United States
| | - Samuel Trent Rosenbloom
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, United States
| | - Siru Liu
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, United States
| | - Elise M. Russo
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, United States
| | - Bryan D. Steitz
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, United States
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Canfell OJ, Kodiyattu Z, Eakin E, Burton-Jones A, Wong I, Macaulay C, Sullivan C. Real-world data for precision public health of noncommunicable diseases: a scoping review. BMC Public Health 2022; 22:2166. [PMID: 36434553 PMCID: PMC9694563 DOI: 10.1186/s12889-022-14452-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Accepted: 10/25/2022] [Indexed: 11/27/2022] Open
Abstract
BACKGROUND Global public health action to address noncommunicable diseases (NCDs) requires new approaches. NCDs are primarily prevented and managed in the community where there is little investment in digital health systems and analytics; this has created a data chasm and relatively silent burden of disease. The nascent but rapidly emerging area of precision public health offers exciting new opportunities to transform our approach to NCD prevention. Precision public health uses routinely collected real-world data on determinants of health (social, environmental, behavioural, biomedical and commercial) to inform precision decision-making, interventions and policy based on social position, equity and disease risk, and continuously monitors outcomes - the right intervention for the right population at the right time. This scoping review aims to identify global exemplars of precision public health and the data sources and methods of their aggregation/application to NCD prevention. METHODS The Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews (PRISMA-ScR) was followed. Six databases were systematically searched for articles published until February 2021. Articles were included if they described digital aggregation of real-world data and 'traditional' data for applied community, population or public health management of NCDs. Real-world data was defined as routinely collected (1) Clinical, Medication and Family History (2) Claims/Billing (3) Mobile Health (4) Environmental (5) Social media (6) Molecular profiling (7) Patient-centred (e.g., personal health record). Results were analysed descriptively and mapped according to the three horizons framework for digital health transformation. RESULTS Six studies were included. Studies developed population health surveillance methods and tools using diverse real-world data (e.g., electronic health records and health insurance providers) and traditional data (e.g., Census and administrative databases) for precision surveillance of 28 NCDs. Population health analytics were applied consistently with descriptive, geospatial and temporal functions. Evidence of using surveillance tools to create precision public health models of care or improve policy and practice decisions was unclear. CONCLUSIONS Applications of real-world data and designed data to address NCDs are emerging with greater precision. Digital transformation of the public health sector must be accelerated to create an efficient and sustainable predict-prevent healthcare system.
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Affiliation(s)
- Oliver J. Canfell
- grid.1003.20000 0000 9320 7537Centre for Health Services Research, Faculty of Medicine, The University of Queensland, St Lucia, QLD Australia ,grid.1003.20000 0000 9320 7537UQ Business School, Faculty of Business, Economics and Law, The University of Queensland, St Lucia, QLD Australia ,grid.450426.10000 0001 0124 2253Digital Health Cooperative Research Centre, Australian Government, Sydney, NSW Australia ,grid.453171.50000 0004 0380 0628Health and Wellbeing Queensland, Queensland Government, The State of Queensland, Milton, QLD Australia ,grid.1003.20000 0000 9320 7537Queensland Digital Health Centre, Faculty of Medicine, The University of Queensland, Herston, QLD Australia
| | - Zack Kodiyattu
- grid.1003.20000 0000 9320 7537School of Clinical Medicine, Faculty of Medicine, The University of Queensland, St Lucia, QLD Australia
| | - Elizabeth Eakin
- grid.1003.20000 0000 9320 7537School of Public Health, Faculty of Medicine, The University of Queensland, St Lucia, QLD Australia
| | - Andrew Burton-Jones
- grid.1003.20000 0000 9320 7537UQ Business School, Faculty of Business, Economics and Law, The University of Queensland, St Lucia, QLD Australia
| | - Ides Wong
- grid.453171.50000 0004 0380 0628Department of Health, Office of the Chief Clinical Information Officer, Clinical Excellence Queensland, Queensland Government, Brisbane, QLD Australia
| | - Caroline Macaulay
- grid.453171.50000 0004 0380 0628Health and Wellbeing Queensland, Queensland Government, The State of Queensland, Milton, QLD Australia
| | - Clair Sullivan
- grid.1003.20000 0000 9320 7537Centre for Health Services Research, Faculty of Medicine, The University of Queensland, St Lucia, QLD Australia ,grid.453171.50000 0004 0380 0628Health and Wellbeing Queensland, Queensland Government, The State of Queensland, Milton, QLD Australia ,grid.1003.20000 0000 9320 7537Queensland Digital Health Centre, Faculty of Medicine, The University of Queensland, Herston, QLD Australia ,grid.453171.50000 0004 0380 0628Department of Health, Metro North Hospital and Health Service, Queensland Government, Herston, QLD Australia
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Szefler SJ, Cicutto L, Brewer SE, Gleason M, McFarlane A, DeCamp LR, Brinton JT, Huebschmann AG. Applying dissemination and implementation research methods to translate a school-based asthma program. J Allergy Clin Immunol 2022; 150:535-548. [PMID: 35569568 PMCID: PMC9590448 DOI: 10.1016/j.jaci.2022.04.029] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Revised: 04/15/2022] [Accepted: 04/20/2022] [Indexed: 11/23/2022]
Abstract
Our school-based asthma program has reduced asthma exacerbations for youth with health disparities in the Denver metropolitan area, due partly to addressing social determinants of health, such as access to health care and medications. Dissemination and implementation (D&I) science approaches accelerate the translation of evidence-based programs into routine practice. D&I methods are being applied more commonly to improve health equity. The purpose of this publication was to give an overview of D&I research methods, using our school-based asthma program as an example. To successfully scale out our program across the state of Colorado, we are applying a D&I framework that guides the adaptation of our existing implementation approach to better meet our stakeholders' local context-the Exploration, Preparation, Implementation, Sustainment framework. In a pragmatic trial design, we will evaluate the outcomes of implementing the program across 5 Colorado regions, with attention to health equity, using a second commonly used D&I framework-Reach, Effectiveness, Adoption, Implementation, and Maintenance. Our central hypothesis is that our program will have broad and equitable reach to eligible students (primary outcome) and will reduce asthma attacks and symptoms. This D&I approach accelerates dissemination of our program and is an applicable process for translating other effective allergy/asthma programs to address asthma and allergy-related disparities.
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Affiliation(s)
- Stanley J Szefler
- Department of Pediatrics, University of Colorado, Anschutz Medical Campus, Aurora, Colo; Children's Hospital Colorado Breathing Institute, Aurora, Colo; Adult and Child Consortium for Outcomes Research and Delivery Science (ACCORDS), University of Colorado, Anschutz Medical Campus, Aurora, Colo.
| | - Lisa Cicutto
- National Jewish Health, Aurora, Colo; Clinical Science and College of Nursing, University of Colorado, Anschutz Medical Campus, Aurora, Colo
| | - Sarah E Brewer
- Adult and Child Consortium for Outcomes Research and Delivery Science (ACCORDS), University of Colorado, Anschutz Medical Campus, Aurora, Colo; Department of Family Medicine, University of Colorado, Anschutz Medical Campus, Aurora, Colo
| | - Melanie Gleason
- Department of Pediatrics, University of Colorado, Anschutz Medical Campus, Aurora, Colo; Children's Hospital Colorado Breathing Institute, Aurora, Colo
| | | | - Lisa Ross DeCamp
- Department of Pediatrics, University of Colorado, Anschutz Medical Campus, Aurora, Colo; Adult and Child Consortium for Outcomes Research and Delivery Science (ACCORDS), University of Colorado, Anschutz Medical Campus, Aurora, Colo; Latino Research and Policy Center, Colorado School of Public Health, Aurora, Colo
| | - John T Brinton
- Department of Pediatrics, University of Colorado, Anschutz Medical Campus, Aurora, Colo; Children's Hospital Colorado Breathing Institute, Aurora, Colo
| | - Amy G Huebschmann
- Adult and Child Consortium for Outcomes Research and Delivery Science (ACCORDS), University of Colorado, Anschutz Medical Campus, Aurora, Colo; Department of Medicine, Division of General Internal Medicine, University of Colorado, Aurora, Colo; Ludeman Family Center for Women's Health Research, Aurora, Colo
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Canfell OJ, Davidson K, Woods L, Sullivan C, Cocoros NM, Klompas M, Zambarano B, Eakin E, Littlewood R, Burton-Jones A. Precision Public Health for Non-communicable Diseases: An Emerging Strategic Roadmap and Multinational Use Cases. Front Public Health 2022; 10:854525. [PMID: 35462850 PMCID: PMC9024120 DOI: 10.3389/fpubh.2022.854525] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Accepted: 02/18/2022] [Indexed: 12/14/2022] Open
Abstract
Non-communicable diseases (NCDs) remain the largest global public health threat. The emerging field of precision public health (PPH) offers a transformative opportunity to capitalize on digital health data to create an agile, responsive and data-driven public health system to actively prevent NCDs. Using learnings from digital health, our aim is to propose a vision toward PPH for NCDs across three horizons of digital health transformation: Horizon 1—digital public health workflows; Horizon 2—population health data and analytics; Horizon 3—precision public health. This perspective provides a high-level strategic roadmap for public health practitioners and policymakers, health system stakeholders and researchers to achieving PPH for NCDs. Two multinational use cases are presented to contextualize our roadmap in pragmatic action: ESP and RiskScape (USA), a mature PPH platform for multiple NCDs, and PopHQ (Australia), a proof-of-concept population health informatics tool to monitor and prevent obesity. Our intent is to provide a strategic foundation to guide new health policy, investment and research in the rapidly emerging but nascent area of PPH to reduce the public health burden of NCDs.
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Affiliation(s)
- Oliver J. Canfell
- Centre for Health Services Research, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
- UQ Business School, Faculty of Business, Economics and Law, The University of Queensland, Brisbane, QLD, Australia
- Digital Health Cooperative Research Centre, Australian Government, Sydney, NSW, Australia
- Health and Wellbeing Queensland, Queensland Government, The State of Queensland, Brisbane, QLD, Australia
- *Correspondence: Oliver J. Canfell
| | - Kamila Davidson
- UQ Business School, Faculty of Business, Economics and Law, The University of Queensland, Brisbane, QLD, Australia
| | - Leanna Woods
- Centre for Health Services Research, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
- Digital Health Cooperative Research Centre, Australian Government, Sydney, NSW, Australia
| | - Clair Sullivan
- Centre for Health Services Research, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
- Health and Wellbeing Queensland, Queensland Government, The State of Queensland, Brisbane, QLD, Australia
- Metro North Hospital and Health Service, Department of Health, Queensland Government, Herston, QLD, Australia
| | - Noelle M. Cocoros
- Department of Population Medicine at Harvard Medical School and the Harvard Pilgrim Health Care Institute, Boston, MA, United States
| | - Michael Klompas
- Department of Population Medicine at Harvard Medical School and the Harvard Pilgrim Health Care Institute, Boston, MA, United States
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, United States
| | - Bob Zambarano
- Commonwealth Informatics Inc., Waltham, MA, United States
| | - Elizabeth Eakin
- School of Public Health, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
| | - Robyn Littlewood
- Health and Wellbeing Queensland, Queensland Government, The State of Queensland, Brisbane, QLD, Australia
| | - Andrew Burton-Jones
- UQ Business School, Faculty of Business, Economics and Law, The University of Queensland, Brisbane, QLD, Australia
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van der Velde LA, Steyerberg EW, Numans ME, Kiefte-de Jong JC. Food insecurity status is of added value in explaining poor health: a cross-sectional study among parents living in disadvantaged neighbourhoods in the Netherlands. BMJ Open 2022; 12:e052827. [PMID: 35140151 PMCID: PMC8830251 DOI: 10.1136/bmjopen-2021-052827] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
OBJECTIVES The aim of this study was to examine the added value of food insecurity in explaining poor physical and mental health beyond other socioeconomic risk factors. DESIGN, SETTING, PARTICIPANTS AND OUTCOME MEASURES Data for this cross-sectional study were collected using questionnaires with validated measures for food insecurity status and health status, including 199 adult participants with at least 1 child living at home, living in or near disadvantaged neighbourhoods in The Hague, the Netherlands. To assess the added value of food insecurity, optimism-corrected goodness-of-fit statistics of multivariate regression models with and without food insecurity status as a covariate were compared. RESULTS In the multivariable models explaining poor physical health (Physical Component Summary: PCS) and mental health (Mental Component Summary: MCS), from all included socioeconomic risk factors, food insecurity score was the most important covariate. Including food insecurity score in those models led to an improvement of explained variance from 6.3% to 9.2% for PCS, and from 5.8% to 11.0% for MCS, and a slightly lower root mean square error. Further analyses showed that including food insecurity score improved the discriminative ability between those individuals most at risk of poor health, reflected by an improvement in C-statistic from 0.64 (95% CI 0.59 to 0.71) to 0.69 (95% CI 0.62 to 0.73) for PCS and from 0.65 (95% CI 0.55 to 0.68) to 0.70 (95% CI 0.61 to 0.73) for MCS. Further, explained variance in these models improved with approximately one-half for PCS and doubled for MCS. CONCLUSIONS From these results it follows that food insecurity score is of added value in explaining poor physical and mental health beyond traditionally used socioeconomic risk factors (ie, age, educational level, income, living situation, employment status and migration background) in disadvantaged communities. Therefore, routine food insecurity screening may be important for effective risk stratification to identify populations at increased risk of poor health and provide targeted interventions.
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Affiliation(s)
- Laura A van der Velde
- Department of Public Health and Primary Care/ LUMC-Campus The Hague, Leiden University Medical Centre, Leiden, The Netherlands
| | - Ewout W Steyerberg
- Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, The Netherlands
| | - Mattijs E Numans
- Department of Public Health and Primary Care/ LUMC-Campus The Hague, Leiden University Medical Centre, Leiden, The Netherlands
| | - Jessica C Kiefte-de Jong
- Department of Public Health and Primary Care/ LUMC-Campus The Hague, Leiden University Medical Centre, Leiden, The Netherlands
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Canfell OJ, Littlewood R, Burton-Jones A, Sullivan C. Digital health and precision prevention: shifting from disease-centred care to consumer-centred health. AUST HEALTH REV 2021; 46:279-283. [PMID: 34882538 DOI: 10.1071/ah21063] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Accepted: 08/04/2021] [Indexed: 11/23/2022]
Abstract
Digital disruption and transformation of health care is occurring rapidly. Concurrently, a global syndemic of preventable chronic disease is crippling healthcare systems and accelerating the effect of the COVID-19 pandemic. Healthcare investment is paradoxical; it prioritises disease treatment over prevention. This is an inefficient break-fix model versus a person-centred predict-prevent model. It is easy to reward and invest in acute health systems because activity is easily measured and therefore funded. Social, environmental and behavioural health determinants explain ~70% of health variance; yet, we cannot measure these community data contemporaneously or at population scale. The dawn of digital health and the digital citizen can initiate a precision prevention era, where consumer-centred, real-time data enables a new ability to count and fund population health, making disease prevention 'matter'. Then, precision decision making, intervention and policy to target preventable chronic disease (e.g. obesity) can be realised. We argue for, identify barriers to, and propose three horizons for digital health transformation of population health towards precision prevention of chronic disease, demonstrating childhood obesity as a use case. Clinicians, researchers and policymakers can commence strategic planning and investment for precision prevention of chronic disease to advance a mature, value-based model that will ensure healthcare sustainability in Australia and globally.
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Affiliation(s)
- Oliver J Canfell
- Centre for Health Services Research, Faculty of Medicine, The University of Queensland, Brisbane, Qld, Australia
| | - Robyn Littlewood
- Health and Wellbeing Queensland, Queensland Government, Brisbane, Qld, Australia
| | - Andrew Burton-Jones
- UQ Business School, Faculty of Business, Economics and Law, The University of Queensland, Brisbane, Qld, Australia
| | - Clair Sullivan
- Centre for Health Services Research, Faculty of Medicine, The University of Queensland, Brisbane, Qld, Australia
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Lasser EC, Kim JM, Hatef E, Kharrazi H, Marsteller JA, DeCamp LR. Social and Behavioral Variables in the Electronic Health Record: A Path Forward to Increase Data Quality and Utility. ACADEMIC MEDICINE : JOURNAL OF THE ASSOCIATION OF AMERICAN MEDICAL COLLEGES 2021; 96:1050-1056. [PMID: 33735133 PMCID: PMC8243784 DOI: 10.1097/acm.0000000000004071] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2023]
Abstract
PURPOSE Social and behavioral determinants of health (SBDH) are important factors that affect the health of individuals but are not routinely captured in a structured and systematic manner in electronic health records (EHRs). The purpose of this study is to generate recommendations for systematic implementation of SBDH data collection in EHRs through (1) reviewing SBDH conceptual and theoretical frameworks and (2) eliciting stakeholder perspectives on barriers to and facilitators of using SBDH information in the EHR and priorities for data collection. METHOD The authors reviewed SBDH frameworks to identify key social and behavioral variables and conducted focus groups and interviews with 17 clinicians and researchers at Johns Hopkins Health System between March and May 2018. Transcripts were coded and common themes were extracted to understand the barriers to and facilitators of accessing SBDH information. RESULTS The authors found that although the frameworks agreed that SBDH affect health outcomes, the lack of model consensus complicates the development of specific recommendations for the prioritization of SBDH data collection. Study participants recognized the importance of SBDH information and individual health and agreed that patient-reported information should be captured, but clinicians and researchers cited different priorities for which variables are most important. For the few SBDH variables that are captured, participants reported that data were often incomplete, unclear, or inconsistent, affecting both researcher and clinician responses to SBDH barriers to health. CONCLUSIONS Health systems need to identify and prioritize the systematic implementation of collection of a high-impact but limited list of SBDH variables in the EHR. These variables should affect care and be amenable to change and collection should be integrated into clinical workflows. Improved data collection of SBDH variables can lead to a better understanding of how SBDH affect health outcomes and ways to better address underlying health disparities that need urgent action.
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Affiliation(s)
- Elyse C Lasser
- E.C. Lasser is research associate, Johns Hopkins Center for Population Health Information Technology, Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland; ORCID: https://orcid.org/0000-0002-1758-9822
| | - Julia M Kim
- J.M. Kim is assistant professor, Department of Pediatrics, and faculty, Armstrong Institute for Patient Safety and Quality, Johns Hopkins University School of Medicine, Baltimore, Maryland; ORCID: https://orcid.org/0000-0001-5678-6629
| | - Elham Hatef
- E. Hatef is assistant scientist, Johns Hopkins Center for Population Health Information Technology and Center for Health Disparities Solutions, Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland; ORCID: https://orcid.org/0000-0003-2535-8191
| | - Hadi Kharrazi
- H. Kharrazi is associate professor, Johns Hopkins Center for Population Health Information Technology, Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland; ORCID: https://orcid.org/0000-0003-1481-4323
| | - Jill A Marsteller
- J.A. Marsteller is professor, Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health and Armstrong Institute for Patient Safety and Quality, Johns Hopkins University School of Medicine, Baltimore, Maryland; ORCID: https://orcid.org/0000-0002-8458-954X
| | - Lisa Ross DeCamp
- L.R. DeCamp is associate professor, ACCORDS (Adult and Child Consortium for Health Outcomes Research and Delivery Science), Department of Pediatrics, University of Colorado School of Medicine and Children's Hospital Colorado, Denver, Colorado; ORCID: https://orcid.org/0000-0002-5210-4675
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Tan M, Hatef E, Taghipour D, Vyas K, Kharrazi H, Gottlieb L, Weiner J. Including Social and Behavioral Determinants in Predictive Models: Trends, Challenges, and Opportunities. JMIR Med Inform 2020; 8:e18084. [PMID: 32897240 PMCID: PMC7509627 DOI: 10.2196/18084] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2020] [Revised: 06/17/2020] [Accepted: 07/20/2020] [Indexed: 12/18/2022] Open
Abstract
In an era of accelerated health information technology capability, health care organizations increasingly use digital data to predict outcomes such as emergency department use, hospitalizations, and health care costs. This trend occurs alongside a growing recognition that social and behavioral determinants of health (SBDH) influence health and medical care use. Consequently, health providers and insurers are starting to incorporate new SBDH data sources into a wide range of health care prediction models, although existing models that use SBDH variables have not been shown to improve health care predictions more than models that use exclusively clinical variables. In this viewpoint, we review the rationale behind the push to integrate SBDH data into health care predictive models and explore the technical, strategic, and ethical challenges faced as this process unfolds across the United States. We also offer several recommendations to overcome these challenges to reach the promise of SBDH predictive analytics to improve health and reduce health care disparities.
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Affiliation(s)
- Marissa Tan
- General Preventive Medicine Residency Program, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
| | - Elham Hatef
- General Preventive Medicine Residency Program, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
- Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Center for Population Health Information Technology, Baltimore, MD, United States
- Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
| | - Delaram Taghipour
- General Preventive Medicine Residency Program, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
| | - Kinjel Vyas
- Division of Health Sciences Informatics, Johns Hopkins School of Medicine, Baltimore, MD, United States
| | - Hadi Kharrazi
- Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Center for Population Health Information Technology, Baltimore, MD, United States
- Division of Health Sciences Informatics, Johns Hopkins School of Medicine, Baltimore, MD, United States
| | - Laura Gottlieb
- Social Interventions Research and Evaluation Network, Center for Health & Community, University of California, San Francisco, CA, United States
| | - Jonathan Weiner
- Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Center for Population Health Information Technology, Baltimore, MD, United States
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10
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Henderson S, Wagner JL, Gosdin MM, Hoeft TJ, Unützer J, Rath L, Hinton L. Complexity in partnerships: A qualitative examination of collaborative depression care in primary care clinics and community-based organisations in California, United States. HEALTH & SOCIAL CARE IN THE COMMUNITY 2020; 28:1199-1208. [PMID: 32052531 PMCID: PMC7318288 DOI: 10.1111/hsc.12953] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/09/2019] [Revised: 12/02/2019] [Accepted: 01/14/2020] [Indexed: 05/09/2023]
Abstract
Partnering across health clinics and community organisations, while worthwhile for improving health and well-being, is challenging and time consuming. Even partnerships that have essential elements for success in place face inevitable challenges. To better understand how cross-organisational partnerships work in practice, this paper examines collaborations between six primary care clinics and community-based organisations in the United States that were part of an initiative to address late-life depression using an enhanced collaborative care model (Archstone Foundation Care Partners Project). As part of an evaluation of the Care Partners Project, 54 key informant interviews and 10 focus groups were conducted from 2015 to 2017. Additionally, more than 80 project-related documents were reviewed. Qualitative thematic analysis was used to code the transcripts and identify prominent themes in the data. Examining clinic and community organisation partnerships in practice highlighted their inherent complexity. The partnerships were fluid and constantly evolving, shaped by a multiplicity of perspectives and values, and vulnerable to unpredictability. Care Partners sites negotiated the complexity of their partnerships drawing upon three main strategies: adaptation (allowing for flexibility and rapid change); integration (providing opportunities for multi-level partnerships within and across organisations) and cultivation (fostering a commitment to the partnership and its value). These strategies provided opportunities for Care Partners collaborators to work with the inherent complexity of partnering. Intentionally acknowledging and embracing such complexity rather than trying to reduce or avoid it, may allow clinic and community collaborators to strengthen and sustain their partnerships.
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Affiliation(s)
- Stuart Henderson
- School of MedicineOffice of Research Evaluation UnitUniversity of CaliforniaDavis, SacramentoCAUSA
| | - Jenny L. Wagner
- School of MedicineOffice of Research Evaluation UnitUniversity of CaliforniaDavis, SacramentoCAUSA
| | - Melissa M. Gosdin
- Center for Healthcare Policy and ResearchUniversity of CaliforniaDavis, SacramentoCAUSA
| | - Theresa J. Hoeft
- Department of Psychiatry and Behavioral SciencesUniversity of WashingtonSeattleWAUSA
| | - Jürgen Unützer
- Department of Psychiatry and Behavioral SciencesUniversity of WashingtonSeattleWAUSA
| | | | - Ladson Hinton
- Department of Psychiatry and Behavioral SciencesUniversity of CaliforniaDavis, SacramentoCAUSA
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11
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Opportunities and Challenges for Developing Syndromic Surveillance Systems for the Detection of Social Epidemics. Online J Public Health Inform 2020; 12:e6. [PMID: 32742556 DOI: 10.5210/ojphi.v12i1.10579] [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: 11/24/2022] Open
Abstract
This commentary explores the potential and challenges of developing syndromic surveillance systems with the ability to more rapidly detect epidemics of addiction, poverty, housing instability, food insecurity, social isolation and other social determinants of health (SDoH). Epidemiologists tracking SDoH heavily rely on expensive government surveys released annually, delaying for months if not years the timely detection of social epidemics, defined as sudden, rapid or unexpected changes in social determinants of population health. Conversely, infectious disease syndromic surveillance is an effective early warning tool for epidemic diseases using various types of non-traditional epidemiological data from emergency room chief complaints to search query data. Based on such experience, novel social syndromic surveillance systems for early detection of social epidemics with health implications are not only possible but necessary. Challenges to their widespread implementation include incorporating disparate proprietary data sources and database integration. Significantly more resources are critically needed to address these barriers to allow for accessing, integrating and rapidly analyzing appropriate data streams to make syndromic surveillance for social determinants of health widely available to public health professionals.
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12
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Hatef E, Rouhizadeh M, Tia I, Lasser E, Hill-Briggs F, Marsteller J, Kharrazi H. Assessing the Availability of Data on Social and Behavioral Determinants in Structured and Unstructured Electronic Health Records: A Retrospective Analysis of a Multilevel Health Care System. JMIR Med Inform 2019; 7:e13802. [PMID: 31376277 PMCID: PMC6696855 DOI: 10.2196/13802] [Citation(s) in RCA: 63] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2019] [Revised: 05/03/2019] [Accepted: 05/30/2019] [Indexed: 02/02/2023] Open
Abstract
Background Most US health care providers have adopted electronic health records (EHRs) that facilitate the uniform collection of clinical information. However, standardized data formats to capture social and behavioral determinants of health (SBDH) in structured EHR fields are still evolving and not adopted widely. Consequently, at the point of care, SBDH data are often documented within unstructured EHR fields that require time-consuming and subjective methods to retrieve. Meanwhile, collecting SBDH data using traditional surveys on a large sample of patients is infeasible for health care providers attempting to rapidly incorporate SBDH data in their population health management efforts. A potential approach to facilitate targeted SBDH data collection is applying information extraction methods to EHR data to prescreen the population for identification of immediate social needs. Objective Our aim was to examine the availability and characteristics of SBDH data captured in the EHR of a multilevel academic health care system that provides both inpatient and outpatient care to patients with varying SBDH across Maryland. Methods We measured the availability of selected patient-level SBDH in both structured and unstructured EHR data. We assessed various SBDH including demographics, preferred language, alcohol use, smoking status, social connection and/or isolation, housing issues, financial resource strains, and availability of a home address. EHR’s structured data were represented by information collected between January 2003 and June 2018 from 5,401,324 patients. EHR’s unstructured data represented information captured for 1,188,202 patients between July 2016 and May 2018 (a shorter time frame because of limited availability of consistent unstructured data). We used text-mining techniques to extract a subset of SBDH factors from EHR’s unstructured data. Results We identified a valid address or zip code for 5.2 million (95.00%) of approximately 5.4 million patients. Ethnicity was captured for 2.7 million (50.00%), whereas race was documented for 4.9 million (90.00%) and a preferred language for 2.7 million (49.00%) patients. Information regarding alcohol use and smoking status was coded for 490,348 (9.08%) and 1,728,749 (32.01%) patients, respectively. Using the International Classification of Diseases–10th Revision diagnoses codes, we identified 35,171 (0.65%) patients with information related to social connection/isolation, 10,433 (0.19%) patients with housing issues, and 3543 (0.07%) patients with income/financial resource strain. Of approximately 1.2 million unique patients with unstructured data, 30,893 (2.60%) had at least one clinical note containing phrases referring to social connection/isolation, 35,646 (3.00%) included housing issues, and 11,882 (1.00%) had mentions of financial resource strain. Conclusions Apart from demographics, SBDH data are not regularly collected for patients. Health care providers should assess the availability and characteristics of SBDH data in EHRs. Evaluating the quality of SBDH data can potentially enable health care providers to modify underlying workflows to improve the documentation, collection, and extraction of SBDH data from EHRs.
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Affiliation(s)
- Elham Hatef
- Center for Population Health IT, Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States.,Johns Hopkins Center for Health Disparities Solutions, Baltimore, MD, United States
| | - Masoud Rouhizadeh
- Center for Clinical Data Analysis, Institute for Clinical and Translational Research, Johns Hopkins School of Medicine, Baltimore, MD, United States
| | - Iddrisu Tia
- Division of Health Sciences Informatics, Johns Hopkins School of Medicine, Baltimore, MD, United States
| | - Elyse Lasser
- Center for Population Health IT, Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
| | - Felicia Hill-Briggs
- Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, United States.,Department of Health, Behavior, and Society, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States.,Department of Acute and Chronic Care, Johns Hopkins School of Nursing, Baltimore, MD, United States.,Welch Center for Prevention, Epidemiology & Clinical Research, Johns Hopkins University, Baltimore, MD, United States.,Behavioral, Social and Systems Sciences Translational Research Community, Institute for Clinical and Translational Research, Johns Hopkins School of Medicine, Baltimore, MD, United States
| | - Jill Marsteller
- Welch Center for Prevention, Epidemiology & Clinical Research, Johns Hopkins University, Baltimore, MD, United States.,Behavioral, Social and Systems Sciences Translational Research Community, Institute for Clinical and Translational Research, Johns Hopkins School of Medicine, Baltimore, MD, United States.,Center for Health Services and Outcomes Research, Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States.,Armstrong Institute for Patient Safety and Quality, Johns Hopkins School of Medicine, Baltimore, MD, United States
| | - Hadi Kharrazi
- Center for Population Health IT, Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States.,Division of Health Sciences Informatics, Johns Hopkins School of Medicine, Baltimore, MD, United States.,Behavioral, Social and Systems Sciences Translational Research Community, Institute for Clinical and Translational Research, Johns Hopkins School of Medicine, Baltimore, MD, United States.,Center for Health Services and Outcomes Research, Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States.,Armstrong Institute for Patient Safety and Quality, Johns Hopkins School of Medicine, Baltimore, MD, United States
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13
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Hatef E, Predmore Z, Lasser EC, Kharrazi H, Nelson K, Curtis I, Fihn S, Weiner JP. Integrating social and behavioral determinants of health into patient care and population health at Veterans Health Administration: a conceptual framework and an assessment of available individual and population level data sources and evidence-based measurements. AIMS Public Health 2019; 6:209-224. [PMID: 31637271 PMCID: PMC6779595 DOI: 10.3934/publichealth.2019.3.209] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2019] [Accepted: 06/24/2019] [Indexed: 12/28/2022] Open
Abstract
The premise of this project was that social and behavioral determinants of health (SBDH) affect the use of healthcare services and outcomes for patients in an integrated healthcare system such as the Veterans Health Administration (VHA), and thus individual patient level socio-behavioral factors in addition to the neighborhood characteristics and geographically linked factors could add information beyond medical factors mostly considered in clinical decision making, patient care, and population health. To help VHA better address SBDH risk factors for the veterans it cares for within its primary care clinics, we proposed a conceptual and analytic framework, a set of evidence-based measures, and their data source. The framework and recommended SBDH metrics can provide a road map for other primary care-centric healthcare organizations wishing to use health analytic tools to better understand how SBDH affect health outcomes.
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Affiliation(s)
- Elham Hatef
- Center for Population Health IT, Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Zachary Predmore
- Center for Population Health IT, Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Elyse C. Lasser
- Center for Population Health IT, Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Hadi Kharrazi
- Center for Population Health IT, Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Karin Nelson
- Veterans Affairs Puget Sound Health Care System, Seattle, WA, USA
- Department of Medicine, University of Washington School of Medicine, Seattle, WA, USA
| | - Idamay Curtis
- Veterans Affairs Puget Sound Health Care System, Seattle, WA, USA
| | - Stephan Fihn
- Veterans Affairs Puget Sound Health Care System, Seattle, WA, USA
- Department of Medicine, University of Washington School of Medicine, Seattle, WA, USA
| | - Jonathan P. Weiner
- Center for Population Health IT, Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
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