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Navale SM, Koroukian S, Cook N, Templeton A, McGrath BM, Crocker L, Bensken WP, Quiñones AR, Schiltz NK, Wei MY, Stange KC. Capturing the care of complex community-based health center patients: A comparison of multimorbidity indices and clinical classification software. Health Serv Res 2024. [PMID: 39212052 DOI: 10.1111/1475-6773.14378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/04/2024] Open
Abstract
OBJECTIVE To compare morbidity burden captured from multimorbidity indices and aggregated measures of clinically meaningful categories captured in primary care community-based health center (CBHC) patients. DATA SOURCES AND STUDY SETTING Electronic health records of patients seen in 2019 in OCHIN's national network of CBHCs serving patients in rural and underserved communities. STUDY DESIGN Age-stratified analyses comparing the most common conditions captured by the Charlson, Elixhauser, and Multimorbidity Weighted (MWI) indices, and Classification Software Refined (CCSR) and Chronic Condition Indicator (CCI) algorithms. DATA COLLECTION/EXTRACTION METHODS Active ICD-10 conditions on patients' problem list in 2019. PRINCIPAL FINDINGS Approximately 35%-56% of patients with at least one condition are not captured by the Charlson, Elixhauser, and MWI indices. When stratified by age, this range broadens to 9%-90% with higher percentages in younger patients. The CCSR and CCI reflect a broader range of acute and chronic conditions prevalent among CBHC patients. CONCLUSION Three commonly used indices to capture morbidity burden reflect conditions most prevalent among older adults, but do not capture those on problem lists for younger CBHC patients. An index with an expanded range of care conditions is needed to understand the complex care provided to primary care populations across the lifespan.
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Affiliation(s)
| | - Siran Koroukian
- Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, Ohio, USA
| | | | | | | | | | | | - Ana R Quiñones
- Department of Family Medicine, and OHSU-PSU School of Public Health, Oregon Health & Science University, Portland, Oregon, USA
| | - Nicholas K Schiltz
- Frances Payne Bolton School of Nursing, Case Western Reserve University, Cleveland, Ohio, USA
| | - Melissa Y Wei
- Division of General Internal Medicine & Health Services Research, David Geffen School of Medicine at University of California, Los Angeles, Los Angeles, California, USA
| | - Kurt C Stange
- Center for Community Health Integration, Case Western Reserve University, Cleveland, Ohio, USA
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Huang Y, Gao Y, Quan S, Pan H, Wang Y, Dong Y, Ye L, Wu M, Zhou A, Ruan X, Wang B, Chen J, Zheng C, Xu H, Lu Y, Pan J. DEVELOPMENT AND INTERNAL-EXTERNAL VALIDATION OF THE ACCI-SOFA MODEL FOR PREDICTING IN-HOSPITAL MORTALITY OF PATIENTS WITH SEPSIS-3 IN THE ICU: A MULTICENTER RETROSPECTIVE COHORT STUDY. Shock 2024; 61:367-374. [PMID: 38407987 DOI: 10.1097/shk.0000000000002311] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/28/2024]
Abstract
ABSTRACT Objective: To achieve a better prediction of in-hospital mortality, the Sequential Organ Failure Assessment (SOFA) score needs to be adjusted and combined with comorbidities. This study aims to enhance the prediction of SOFA score for in-hospital mortality in patients with Sepsis-3. Methods: This study adjusted the maximum SOFA score within the first 3 days (Max Day3 SOFA) in relation to in-hospital mortality using logistic regression and incorporated the age-adjusted Charlson Comorbidity Index (aCCI) as a continuous variable to build the age-adjusted Charlson Comorbidity Index-Sequential Organ Failure Assessment (aCCI-SOFA) model. The outcome was in-hospital mortality. We developed, internally validated, and externally validated the aCCI-SOFA model using cohorts of Sepsis-3 patients from the MIMIC-IV, MIMIC-III (CareVue), and the FAHWMU cohort. The predictive performance of the model was assessed through discrimination and calibration, which was assessed using the area under the receiver operating characteristic and calibration curves, respectively. The overall predictive effect was evaluated using the Brier score. Measurements and main results: Compared with the Max Day3 SOFA, the aCCI-SOFA model showed significant improvement in area under the receiver operating characteristic with all cohorts: development cohort (0.81 vs 0.75, P < 0.001), internal validation cohort (0.81 vs 0.76, P < 0.001), MIMIC-III (CareVue) cohort (0.75 vs 0.68, P < 0.001), and FAHWMU cohort (0.72 vs 0.67, P = 0.001). In sensitivity analysis, it was suggested that the application of aCCI-SOFA in early nonseptic shock patients had greater clinical value, with significant differences compared with the original SOFA scores in all cohorts ( P < 0.05). Conclusion: For septic patients in intensive care unit, the aCCI-SOFA model exhibited superior predictive performance. The application of aCCI-SOFA in early nonseptic shock patients had greater clinical value.
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Affiliation(s)
| | | | | | - Hao Pan
- Department of Orthopaedics, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, PR China
| | | | | | | | | | | | | | | | | | - Chenfei Zheng
- Department of Nephrology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, PR China
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3
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Smith WP. Negative Lifestyle Factors Specific to Aging Persons Living with HIV and Multimorbidity. J Int Assoc Provid AIDS Care 2024; 23:23259582241245228. [PMID: 39051608 PMCID: PMC11273731 DOI: 10.1177/23259582241245228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 02/28/2024] [Accepted: 03/18/2024] [Indexed: 07/27/2024] Open
Abstract
The primary goal of medical care during the pre-antiretroviral therapy (ART) era was to keep persons living with human immunodeficiency virus (HIV) alive, whereas since the advent of ART, the treatment objective has shifted to decreasing viral loads and infectiousness while increasing CD4+ T-cell counts and longevity. The health crisis, however, is in preventing and managing multimorbidity (ie, type 2 diabetes), which develops at a more accelerated or accentuated pace among aging persons living with HIV. Relative to the general population and age-matched uninfected adults, it may be more difficult for aging HIV-positive persons who also suffer from multimorbidity to improve negative lifestyle factors to the extent that their behaviors could support the prevention and management of diseases. With recommendations and a viable solution, this article explores the impact of negative lifestyle factors (ie, poor mental health, suboptimal nutrition, physical inactivity, alcohol use) on the health of aging individuals living with HIV.
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Klappe ES, Heijmans J, Groen K, Ter Schure J, Cornet R, de Keizer NF. Correctly structured problem lists lead to better and faster clinical decision-making in electronic health records compared to non-curated problem lists: A single-blinded crossover randomized controlled trial. Int J Med Inform 2023; 180:105264. [PMID: 37890203 DOI: 10.1016/j.ijmedinf.2023.105264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Revised: 10/08/2023] [Accepted: 10/15/2023] [Indexed: 10/29/2023]
Abstract
BACKGROUND Correctly structured problem lists in electronic health records (EHRs) offer major benefits to patient care. Without structured lists, diagnosis information is often scatteredly documented in free text, which may contribute to errors and inefficient information retrieval. This study aims to assess whether EHRs with correctly structured problem lists result in better and faster clinical decision-making compared to non-curated problem lists. METHODS Two versions of two patient records (A and B) were created in an EHR training environment: one version included diagnosis information structured and coded on the problem list ("correctly structured problem list"), the other version had missing problem list diagnoses and diagnosis information partly documented in free text ("non-curated problem list"). In this single-blinded crossover randomized controlled trial, healthcare providers, who can prescribe medications, from two Dutch university medical center locations first evaluated a randomized version of patient A, then B. Participants were asked to motivate their answer to two medication prescription questions. One (test) question required information similarly presented in both record versions. The second (comparison) question required information documented on problem lists and/or in notes. The primary outcome measure was the correctness of the motivated answer to the comparison question. Secondary outcome measure was the time to answer and motivate both questions correctly. RESULTS As planned, 160 participants enrolled. Two were excluded for not meeting inclusion criteria. Correctly structured problem lists increased providers' ability to answer the comparison question correctly (56.3 % versus 33.5 %, McNemar odds ratio 2.80 (1.65-4.93) 95 %-CI). Median time to answer both questions correctly was significantly lower for EHRs with correctly structured problem lists (Wilcoxon-signed-rank test p = 0.00002, with incorrect answers coded equally at slowest time). CONCLUSIONS Correctly structured problem lists lead to better and faster clinical decision-making. Increased structured problem lists usage may be warranted for which implementation policies should be developed.
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Affiliation(s)
- Eva S Klappe
- Amsterdam UMC - University of Amsterdam, Medical Informatics & Amsterdam Public Health, Digital Health & Methodology, Meibergdreef 9, Amsterdam, the Netherlands.
| | - Jarom Heijmans
- Department of Haematology, Amsterdam UMC, Vrije Universiteit Amsterdam, University of Amsterdam, Amsterdam, the Netherlands; Department of general internal medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Kaz Groen
- Department of Haematology, Amsterdam UMC, Vrije Universiteit Amsterdam, University of Amsterdam, Amsterdam, the Netherlands
| | - Judith Ter Schure
- Department of Epidemiology & Data Science, Amsterdam UMC, Meibergdreef 9, 1105AZ, Amsterdam the Netherlands
| | - Ronald Cornet
- Amsterdam UMC - University of Amsterdam, Medical Informatics & Amsterdam Public Health, Digital Health & Methodology, Meibergdreef 9, Amsterdam, the Netherlands
| | - Nicolette F de Keizer
- Amsterdam UMC - University of Amsterdam, Medical Informatics & Amsterdam Public Health, Digital Health & Quality of Care, Meibergdreef 9, Amsterdam, the Netherlands
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5
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Huguet N, Hodes T, Liu S, Marino M, Schmidt TD, Voss RW, Peak KD, Quiñones AR. Impact of Health Insurance Patterns on Chronic Health Conditions Among Older Patients. J Am Board Fam Med 2023; 36:839-850. [PMID: 37704394 PMCID: PMC10662026 DOI: 10.3122/jabfm.2023.230106r1] [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: 03/17/2023] [Revised: 05/25/2023] [Accepted: 06/05/2023] [Indexed: 09/15/2023] Open
Abstract
BACKGROUND Patients have varying levels of chronic conditions and health insurance patterns as they become Medicare age-eligible. Understanding these dynamics will inform policies and reforms that direct capacity and resources for primary care clinics to care for these aging patients. This study 1) determined changes in chronic condition rates following Medicare age eligibility among patients with different insurance patterns and 2) estimated the number of chronically ill patients who remain inadequately insured post-Medicare eligibility among patients receiving care in community health centers. METHOD We used retrospective electronic health record data from 45,527 patients aged 62 to 68 from 990 community health centers in 25 states in 2014 to 2019. Insurance patterns (continuously insured, continuously uninsured, uninsured/discontinuously insured who gained insurance after age 65, lost insurance after age 65, discontinuously insured) and diagnosis of chronic conditions were defined at each visit pre- and post-Medicare eligibility. Difference-in-differences Poisson GEE models estimated changes of chronic condition rates by insurance groups pre- to post-Medicare age eligibility. RESULTS Post-Medicare eligibility, 72% patients were continuously insured, 14% gained insurance; and 14% were uninsured or discontinuously insured. The prevalence of multimorbidity (≥2 chronic conditions) was 77%. Those who gained insurance had a significantly larger increase in the rate of documented chronic conditions from pre- to post-Medicare (DID: 1.06, 95%CI:1.05-1.07) compared with the continuously insured group. CONCLUSIONS Post-Medicare age eligibility, a significant proportion of patients were diagnosed with new conditions leading to high burden of disease. One in 4 older adults continue to have inadequate health care coverage in their older age.
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Affiliation(s)
- Nathalie Huguet
- Department of Family Medicine, Oregon Health & Science University, Portland, OR
| | - Tahlia Hodes
- Department of Family Medicine, Oregon Health & Science University, Portland, OR
| | - Shuling Liu
- Department of Family Medicine, Oregon Health & Science University, Portland, OR
| | - Miguel Marino
- Department of Family Medicine, Oregon Health & Science University, Portland, OR
- OHSU-PSU School of Public Health, Oregon Health & Science University, Portland, OR
| | | | | | - Katherine D. Peak
- Department of Family Medicine, Oregon Health & Science University, Portland, OR
| | - Ana R. Quiñones
- Department of Family Medicine, Oregon Health & Science University, Portland, OR
- OHSU-PSU School of Public Health, Oregon Health & Science University, Portland, OR
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MacRae C, Morales D, Mercer SW, Lone N, Lawson A, Jefferson E, McAllister D, van den Akker M, Marshall A, Seth S, Rawlings A, Lyons J, Lyons RA, Mizen A, Abubakar E, Dibben C, Guthrie B. Impact of data source choice on multimorbidity measurement: a comparison study of 2.3 million individuals in the Welsh National Health Service. BMC Med 2023; 21:309. [PMID: 37582755 PMCID: PMC10426056 DOI: 10.1186/s12916-023-02970-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: 04/11/2023] [Accepted: 07/03/2023] [Indexed: 08/17/2023] Open
Abstract
BACKGROUND Measurement of multimorbidity in research is variable, including the choice of the data source used to ascertain conditions. We compared the estimated prevalence of multimorbidity and associations with mortality using different data sources. METHODS A cross-sectional study of SAIL Databank data including 2,340,027 individuals of all ages living in Wales on 01 January 2019. Comparison of prevalence of multimorbidity and constituent 47 conditions using data from primary care (PC), hospital inpatient (HI), and linked PC-HI data sources and examination of associations between condition count and 12-month mortality. RESULTS Using linked PC-HI compared with only HI data, multimorbidity was more prevalent (32.2% versus 16.5%), and the population of people identified as having multimorbidity was younger (mean age 62.5 versus 66.8 years) and included more women (54.2% versus 52.6%). Individuals with multimorbidity in both PC and HI data had stronger associations with mortality than those with multimorbidity only in HI data (adjusted odds ratio 8.34 [95% CI 8.02-8.68] versus 6.95 (95%CI 6.79-7.12] in people with ≥ 4 conditions). The prevalence of conditions identified using only PC versus only HI data was significantly higher for 37/47 and significantly lower for 10/47: the highest PC/HI ratio was for depression (14.2 [95% CI 14.1-14.4]) and the lowest for aneurysm (0.51 [95% CI 0.5-0.5]). Agreement in ascertainment of conditions between the two data sources varied considerably, being slight for five (kappa < 0.20), fair for 12 (kappa 0.21-0.40), moderate for 16 (kappa 0.41-0.60), and substantial for 12 (kappa 0.61-0.80) conditions, and by body system was lowest for mental and behavioural disorders. The percentage agreement, individuals with a condition identified in both PC and HI data, was lowest in anxiety (4.6%) and highest in coronary artery disease (62.9%). CONCLUSIONS The use of single data sources may underestimate prevalence when measuring multimorbidity and many important conditions (especially mental and behavioural disorders). Caution should be used when interpreting findings of research examining individual and multiple long-term conditions using single data sources. Where available, researchers using electronic health data should link primary care and hospital inpatient data to generate more robust evidence to support evidence-based healthcare planning decisions for people with multimorbidity.
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Affiliation(s)
- Clare MacRae
- Advanced Care Research Centre, University of Edinburgh, Bio Cube 1, Edinburgh BioQuarter, 13 Little France Road, Edinburgh, UK.
- Usher Institute, College of Medicine and Veterinary Medicine, University of Edinburgh, Edinburgh, UK.
| | - Daniel Morales
- Division of Population Health and Genomics, University of Dundee, Dundee, UK
- Department of Public Health, University of Southern Denmark, Odense, Denmark
| | - Stewart W Mercer
- Advanced Care Research Centre, University of Edinburgh, Bio Cube 1, Edinburgh BioQuarter, 13 Little France Road, Edinburgh, UK
- Usher Institute, College of Medicine and Veterinary Medicine, University of Edinburgh, Edinburgh, UK
| | - Nazir Lone
- Usher Institute, College of Medicine and Veterinary Medicine, University of Edinburgh, Edinburgh, UK
| | - Andrew Lawson
- Usher Institute, College of Medicine and Veterinary Medicine, University of Edinburgh, Edinburgh, UK
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, USA
| | - Emily Jefferson
- Division of Population Health and Genomics, University of Dundee, Dundee, UK
| | - David McAllister
- Public Health, Institute of Health and Wellbeing, University of Glasgow, Glasgow, G12 9LX, UK
| | - Marjan van den Akker
- Institute of General Practice, Goethe University Frankfurt, Frankfurt Am Main, Germany
- Department of Public Health and Primary Care, Academic Center for General Practice, KU Leuven, Louvain, Belgium
- Department of Family Medicine, School CAPHRI, Maastricht University, Maastricht, The Netherlands
| | - Alan Marshall
- School of Social and Political Science, University of Edinburgh, Chrystal Macmillan Building, Edinburgh, EH8 9LD, UK
| | - Sohan Seth
- School of Informatics, The University of Edinburgh, Edinburgh, UK
| | - Anna Rawlings
- Swansea University Medical School, Data Science Building, Singleton Campus, Swansea, UK
| | - Jane Lyons
- Swansea University Medical School, Data Science Building, Singleton Campus, Swansea, UK
| | - Ronan A Lyons
- Swansea University Medical School, Data Science Building, Singleton Campus, Swansea, UK
| | - Amy Mizen
- Swansea University Medical School, Data Science Building, Singleton Campus, Swansea, UK
| | - Eleojo Abubakar
- Public Health, Institute of Health and Wellbeing, University of Glasgow, Glasgow, G12 9LX, UK
| | - Chris Dibben
- University of Edinburgh Institute of Geography, Institute of Geography Edinburgh, Edinburgh, UK
| | - Bruce Guthrie
- Advanced Care Research Centre, University of Edinburgh, Bio Cube 1, Edinburgh BioQuarter, 13 Little France Road, Edinburgh, UK
- Usher Institute, College of Medicine and Veterinary Medicine, University of Edinburgh, Edinburgh, UK
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Rao V, Lanni S, Yule AM, DiSalvo M, Stone M, Berger AF, Wilens TE. Diagnosing major depressive disorder and substance use disorder using the electronic health record: A preliminary validation study. JOURNAL OF MOOD AND ANXIETY DISORDERS 2023; 2:100007. [PMID: 37693103 PMCID: PMC10486184 DOI: 10.1016/j.xjmad.2023.100007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
Background One mechanism to examine if major depressive disorder (MDD) is related to the development of substance use disorder (SUD) is by leveraging naturalistic data available in the electronic health record (EHR). Rules for data extraction and variable construction linked to psychometrics validating their use are needed to extract data accurately. Objective We propose and validate a methodologic framework for using EHR variables to identify patients with MDD and non-nicotine SUD. Methods Proxy diagnoses and index dates of MDD and/or SUD were established using billing codes, problem lists, patient-reported outcome measures, and prescriptions. Manual chart reviews were conducted for the 1-year period surrounding each index date to determine (1) if proxy diagnoses were supported by chart notes and (2) if the index dates accurately captured disorder onset. Results The results demonstrated 100% positive predictive value for proxy diagnoses of MDD. The proxy diagnoses for SUD exhibited strong agreement (Cohen's kappa of 0.84) compared to manual chart review and 92% sensitivity, specificity, positive predictive value, and negative predictive value. Sixteen percent of patients showed inaccurate SUD index dates generated by EHR extraction with discrepancies of over 6 months compared to SUD onset identified through chart review. Conclusions Our methodology was very effective in identifying patients with MDD with or without SUD and moderately effective in identifying SUD onset date. These findings support the use of EHR data to make proxy diagnoses of MDD with or without SUD.
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Affiliation(s)
- Vinod Rao
- Department of Psychiatry, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
| | - Sylvia Lanni
- Department of Psychiatry, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
| | - Amy M. Yule
- Department of Psychiatry, Boston Medical Center, 801 Massachusetts Avenue, Boston, MA 02118, USA
| | - Maura DiSalvo
- Department of Psychiatry, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
| | - Mira Stone
- Department of Psychiatry, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
| | - Amy F. Berger
- Department of Psychiatry, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
| | - Timothy E. Wilens
- Department of Psychiatry, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
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Leese P, Anand A, Girvin A, Manna A, Patel S, Yoo YJ, Wong R, Haendel M, Chute CG, Bennett T, Hajagos J, Pfaff E, Moffitt R. Clinical encounter heterogeneity and methods for resolving in networked EHR data: a study from N3C and RECOVER programs. J Am Med Inform Assoc 2023; 30:1125-1136. [PMID: 37087110 PMCID: PMC10198518 DOI: 10.1093/jamia/ocad057] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 01/31/2023] [Accepted: 03/22/2023] [Indexed: 04/24/2023] Open
Abstract
OBJECTIVE Clinical encounter data are heterogeneous and vary greatly from institution to institution. These problems of variance affect interpretability and usability of clinical encounter data for analysis. These problems are magnified when multisite electronic health record (EHR) data are networked together. This article presents a novel, generalizable method for resolving encounter heterogeneity for analysis by combining related atomic encounters into composite "macrovisits." MATERIALS AND METHODS Encounters were composed of data from 75 partner sites harmonized to a common data model as part of the NIH Researching COVID to Enhance Recovery Initiative, a project of the National Covid Cohort Collaborative. Summary statistics were computed for overall and site-level data to assess issues and identify modifications. Two algorithms were developed to refine atomic encounters into cleaner, analyzable longitudinal clinical visits. RESULTS Atomic inpatient encounters data were found to be widely disparate between sites in terms of length-of-stay (LOS) and numbers of OMOP CDM measurements per encounter. After aggregating encounters to macrovisits, LOS and measurement variance decreased. A subsequent algorithm to identify hospitalized macrovisits further reduced data variability. DISCUSSION Encounters are a complex and heterogeneous component of EHR data and native data issues are not addressed by existing methods. These types of complex and poorly studied issues contribute to the difficulty of deriving value from EHR data, and these types of foundational, large-scale explorations, and developments are necessary to realize the full potential of modern real-world data. CONCLUSION This article presents method developments to manipulate and resolve EHR encounter data issues in a generalizable way as a foundation for future research and analysis.
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Affiliation(s)
- Peter Leese
- NC TraCS Institute, UNC-School of Medicine, Chapel Hill, North Carolina, USA
| | - Adit Anand
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
| | | | - Amin Manna
- Palantir Technologies, Denver, Colorado, USA
| | - Saaya Patel
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
| | - Yun Jae Yoo
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
| | - Rachel Wong
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
| | - Melissa Haendel
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Denver, Colorado, USA
| | - Christopher G Chute
- Schools of Medicine, Public Health, and Nursing, Johns Hopkins University, Baltimore, Maryland, USA
| | - Tellen Bennett
- Department of Pediatrics, University of Colorado Anschutz Medical Campus, Denver, Colorado, USA
| | - Janos Hajagos
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
| | - Emily Pfaff
- Department of Medicine, UNC Chapel Hill, Chapel Hill, North Carolina, USA
| | - Richard Moffitt
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
- Department of Biomedical Informatics, Emory University, Atlanta, Georgia, USA
- Department of Hematology and Medical Oncology, Emory University, Atlanta, Georgia, USA
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9
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Tierney AA, Payán DD, Brown TT, Aguilera A, Shortell SM, Rodriguez HP. Telehealth Use, Care Continuity, and Quality: Diabetes and Hypertension Care in Community Health Centers Before and During the COVID-19 Pandemic. Med Care 2023; 61:S62-S69. [PMID: 36893420 PMCID: PMC9994572 DOI: 10.1097/mlr.0000000000001811] [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] [Indexed: 03/11/2023]
Abstract
BACKGROUND Community health centers (CHCs) pivoted to using telehealth to deliver chronic care during the coronavirus COVID-19 pandemic. While care continuity can improve care quality and patients' experiences, it is unclear whether telehealth supported this relationship. OBJECTIVE We examine the association of care continuity with diabetes and hypertension care quality in CHCs before and during COVID-19 and the mediating effect of telehealth. RESEARCH DESIGN This was a cohort study. PARTICIPANTS Electronic health record data from 166 CHCs with n=20,792 patients with diabetes and/or hypertension with ≥2 encounters/year during 2019 and 2020. METHODS Multivariable logistic regression models estimated the association of care continuity (Modified Modified Continuity Index; MMCI) with telehealth use and care processes. Generalized linear regression models estimated the association of MMCI and intermediate outcomes. Formal mediation analyses assessed whether telehealth mediated the association of MMCI with A1c testing during 2020. RESULTS MMCI [2019: odds ratio (OR)=1.98, marginal effect=0.69, z=165.50, P<0.001; 2020: OR=1.50, marginal effect=0.63, z=147.73, P<0.001] and telehealth use (2019: OR=1.50, marginal effect=0.85, z=122.87, P<0.001; 2020: OR=10.00, marginal effect=0.90, z=155.57, P<0.001) were associated with higher odds of A1c testing. MMCI was associated with lower systolic (β=-2.90, P<0.001) and diastolic blood pressure (β=-1.44, P<0.001) in 2020, and lower A1c values (2019: β=-0.57, P=0.007; 2020: β=-0.45, P=0.008) in both years. In 2020, telehealth use mediated 38.7% of the relationship between MMCI and A1c testing. CONCLUSIONS Higher care continuity is associated with telehealth use and A1c testing, and lower A1c and blood pressure. Telehealth use mediates the association of care continuity and A1c testing. Care continuity may facilitate telehealth use and resilient performance on process measures.
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Affiliation(s)
- Aaron A. Tierney
- Department of Health Policy and Management, University of California, Berkeley
| | - Denise D. Payán
- Department of Health, Society, and Behavior, University of California, Irvine
| | - Timothy T. Brown
- Department of Health Policy and Management, University of California, Berkeley
| | - Adrian Aguilera
- Department of Health Policy and Management, University of California, Berkeley
| | - Stephen M. Shortell
- Department of Health Policy and Management, University of California, Berkeley
| | - Hector P. Rodriguez
- Department of Health Policy and Management, University of California, Berkeley
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10
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Arnold CG, Sonn B, Meyers FJ, Vest A, Puls R, Zirkler E, Edelmann M, Brooks IM, Monte AA. Accessing and utilizing clinical and genomic data from an electronic health record data warehouse. TRANSLATIONAL MEDICINE COMMUNICATIONS 2023; 8:7. [PMID: 38223535 PMCID: PMC10786622 DOI: 10.1186/s41231-023-00140-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Accepted: 02/20/2023] [Indexed: 01/16/2024]
Abstract
Electronic health records (EHRs) and linked biobanks have tremendous potential to advance biomedical research and ultimately improve the health of future generations. Repurposing EHR data for research is not without challenges, however. In this paper, we describe the processes and considerations necessary to successfully access and utilize a data warehouse for research. Although imperfect, data warehouses are a powerful tool for harnessing a large amount of data to phenotype disease. They will have increasing relevance and applications in clinical research with growing sophistication in processes for EHR data abstraction, biobank integration, and cross-institutional linkage.
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Affiliation(s)
- Cosby G. Arnold
- Department of Emergency Medicine, School of Medicine, University of California, Davis, 4150 V Street #2100, Sacramento, CA 95817, USA
| | - Brandon Sonn
- Department of Emergency Medicine, University of Colorado Denver-Anschutz Medical Center, University of Colorado School of Medicine, Mail Stop B-215, 12401 East 17th Avenue, Aurora, CO 80045, USA
| | - Frederick J. Meyers
- Department of Internal Medicine, University of California, Davis, School of Medicine, 4150 V Street #3100, Sacramento, CA 95817, USA
| | - Alexis Vest
- Department of Emergency Medicine, University of Colorado Denver-Anschutz Medical Center, University of Colorado School of Medicine, Mail Stop B-215, 12401 East 17th Avenue, Aurora, CO 80045, USA
| | - Richie Puls
- Department of Emergency Medicine, University of Colorado Denver-Anschutz Medical Center, University of Colorado School of Medicine, Mail Stop B-215, 12401 East 17th Avenue, Aurora, CO 80045, USA
| | - Estelle Zirkler
- Department of Biomedical Informatics, University of Colorado School of Medicine, Anschutz Health Sciences Building, 1890 N. Revere Court, Mailstop F600, Aurora, CO 80045, USA
| | - Michelle Edelmann
- Department of Biomedical Informatics, University of Colorado School of Medicine, Anschutz Health Sciences Building, 1890 N. Revere Court, Mailstop F600, Aurora, CO 80045, USA
| | - Ian M. Brooks
- Department of Biomedical Informatics, University of Colorado School of Medicine, Anschutz Health Sciences Building, 1890 N. Revere Court, Mailstop F600, Aurora, CO 80045, USA
| | - Andrew A. Monte
- Department of Emergency Medicine, School of Medicine, University of California, Davis, 4150 V Street #2100, Sacramento, CA 95817, USA
- Rocky Mountain Poison & Drug Center, Denver Health and Hospital Authority, 1391 Speer Blvd Unit 600, Denver, CO 80204, USA
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