scholarly journals Processing of Electronic Medical Records for Health Services Research in an Academic Medical Center: Methods and Validation (Preprint)

2018 ◽  
Author(s):  
Nabilah Rahman ◽  
Debby D Wang ◽  
Sheryl Hui-Xian Ng ◽  
Sravan Ramachandran ◽  
Srinath Sridharan ◽  
...  
Keyword(s):  
Health Care ◽  
Health Services Research ◽  
Health Services ◽  
International Classification Of Diseases ◽  
Care Utilization ◽  
Housing Type ◽  
Services Research ◽  
Diagnosis Codes ◽  
Classification Of Diseases

BACKGROUND Electronic medical records (EMRs) contain a wealth of information that can support data-driven decision making in health care policy design and service planning. Although research using EMRs has become increasingly prevalent, challenges such as coding inconsistency, data validity, and lack of suitable measures in important domains still hinder the progress. OBJECTIVE The objective of this study was to design a structured way to process records in administrative EMR systems for health services research and assess validity in selected areas. METHODS On the basis of a local hospital EMR system in Singapore, we developed a structured framework for EMR data processing, including standardization and phenotyping of diagnosis codes, construction of cohort with multilevel views, and generation of variables and proxy measures to supplement primary data. Disease complexity was estimated by Charlson Comorbidity Index (CCI) and Polypharmacy Score (PPS), whereas socioeconomic status (SES) was estimated by housing type. Validity of modified diagnosis codes and derived measures were investigated. RESULTS Visit-level (N=7,778,761) and patient-level records (n=549,109) were generated. The International Classification of Diseases, Tenth Revision, Australian Modification (ICD-10-AM) codes were standardized to the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) with a mapping rate of 87.1%. In all, 97.4% of the ICD-9-CM codes were phenotyped successfully using Clinical Classification Software by Agency for Healthcare Research and Quality. Diagnosis codes that underwent modification (truncation or zero addition) in standardization and phenotyping procedures had the modification validated by physicians, with validity rates of more than 90%. Disease complexity measures (CCI and PPS) and SES were found to be valid and robust after a correlation analysis and a multivariate regression analysis. CCI and PPS were correlated with each other and positively correlated with health care utilization measures. Larger housing type was associated with lower government subsidies received, suggesting association with higher SES. Profile of constructed cohorts showed differences in disease prevalence, disease complexity, and health care utilization in those aged above 65 years and those aged 65 years or younger. CONCLUSIONS The framework proposed in this study would be useful for other researchers working with EMR data for health services research. Further analyses would be needed to better understand differences observed in the cohorts.

Health Services Research at United HealthCare Corporation: The Role of the Center for Health Care Policy and Evaluation

1996 ◽  
Vol 53 (1_suppl) ◽  
pp. 65-76 ◽  
Author(s):  
Eileen Peterson ◽  
Deborah Shatin ◽  
Douglas Mccarthy
Keyword(s):  
Health Care ◽  
Managed Care ◽  
Health Services Research ◽  
Health Services ◽  
Health Care Policy ◽  
Research Priorities ◽  
Research Capacity ◽  
Lessons Learned ◽  
Services Research ◽  
And Performance

This article describes collaborative health services research and performance evaluation activities at United HealthCare Corporation, a national health care management services company. We outline the development of a research capacity within our company, the principal data sources used, and the types of research conducted. The importance of health services research within a managed care system is illustrated using two projects as examples. finally, we discuss issues faced by organizations such as ours in defining appropriate research priorities, ensuring health plan participation, and disseminating research findings. Lessons learned should be of interest to health services researchers working in or collaborating with managed care organizations as well as others seeking to understand the dynamics of research in private-sector health care companies.

scholarly journals Qualitative Research Methodology and its Scope in Health Services Research

2020 ◽  
Vol 3 (2) ◽  
pp. 18-21
Author(s):  
Naiya Patel
Keyword(s):  
Health Care ◽  
Qualitative Research ◽  
Clinical Trials ◽  
Health Services Research ◽  
Health Services ◽  
Health Care Providers ◽  
Research Methodology ◽  
Care Providers ◽  
Services Research ◽  
Qualitative Research Methodology

Health services research is a multidisciplinary field which involves policy makers, health care providers, as well as quality outcomes professionals of the health services provided in an organizational setting to name some. Using qualitative research methodology to get insights of both the provider and patient experience down the pipeline can help strengthen what is lacking. Bridging the gap of translation research by not just surveys 1 might be an appropriate research methodology, however, inclusion of case studies, ethnographies might help stakeholders in the field, to visualize in depth phenomenon occurring in health services research field. Telly medicine, commercial digital health status trackr might be some of the inetrventions to improvise health care services, however, knowing what are the actual needs at individual level might efficiently help in redistribution of resources or policy laws. Recruiting for clinical trials through story telling communication technology2,3, might help in recruitment for novel drug therapies to explore possibilities, however, exploring the barriers to enroll for the clinical trials, or why the drug might work effectively in some cultural population and why not on others, can only be efficiently explored through qualitative research methodologies.

scholarly journals Association Between Diagnosis Code Expansion and Changes in 30‐Day Risk‐Adjusted Outcomes for Cardiovascular Diseases

2021 ◽  
Author(s):  
Lauren Gilstrap ◽  
Rishi K. Wadhera ◽  
Andrea M. Austin ◽  
Stephen Kearing ◽  
Karen E. Joynt Maddox ◽  
...  
Keyword(s):  
Logistic Regression ◽  
International Classification Of Diseases ◽  
Fee For Service ◽  
Readmission Rates ◽  
Difference In Differences ◽  
Diagnosis Codes ◽  
Classification Of Diseases ◽  
Changes In Risk ◽  
The Impact

BACKGROUND In January 2011, Centers for Medicare and Medicaid Services expanded the number of inpatient diagnosis codes from 9 to 25, which may influence comorbidity counts and risk‐adjusted outcome rates for studies spanning January 2011. This study examines the association between (1) limiting versus not limiting diagnosis codes after 2011, (2) using inpatient‐only versus inpatient and outpatient data, and (3) using logistic regression versus the Centers for Medicare and Medicaid Services risk‐standardized methodology and changes in risk‐adjusted outcomes. METHODS AND RESULTS Using 100% Medicare inpatient and outpatient files between January 2009 and December 2013, we created 2 cohorts of fee‐for‐service beneficiaries aged ≥65 years. The acute myocardial infarction cohort and the heart failure cohort had 578 728 and 1 595 069 hospitalizations, respectively. We calculate comorbidities using (1) inpatient‐only limited diagnoses, (2) inpatient‐only unlimited diagnoses, (3) inpatient and outpatient limited diagnoses, and (4) inpatient and outpatient unlimited diagnoses. Across both cohorts, International Classification of Diseases, Ninth Revision ( ICD‐9 ) diagnoses and hierarchical condition categories increased after 2011. When outpatient data were included, there were no significant differences in risk‐adjusted readmission rates using logistic regression or the Centers for Medicare and Medicaid Services risk standardization. A difference‐in‐differences analysis of risk‐adjusted readmission trends before versus after 2011 found that no significant differences between limited and unlimited models for either cohort. CONCLUSIONS For studies that span 2011, researchers should consider limiting the number of inpatient diagnosis codes to 9 and/or including outpatient data to minimize the impact of the code expansion on comorbidity counts. However, the 2011 code expansion does not appear to significantly affect risk‐adjusted readmission rate estimates using either logistic or risk‐standardization models or when using or excluding outpatient data.

scholarly journals Assigning Resources to Health Care Use for Health Services Research

Medical Care ◽  
2009 ◽  
Vol 47 (Supplement) ◽  
pp. S70-S75 ◽  
Author(s):  
Paul A. Fishman ◽  
Mark C. Hornbrook
Keyword(s):  
Health Care ◽  
Health Services Research ◽  
Health Services ◽  
Health Care Use ◽  
Services Research

Interactive visualization applications in population health and health services research: a systematic scoping review (Preprint)

2021 ◽  
Author(s):  
Jawad Chishtie ◽  
Iwona Anna Bielska ◽  
Aldo Barrera ◽  
Jean-Sebastien Marchand ◽  
Muhammad Imran ◽  
...  
Keyword(s):  
Health Care ◽  
Decision Support ◽  
Health Services Research ◽  
Health Services ◽  
Population Health ◽  
Scoping Review ◽  
Interactive Visualization ◽  
Resource Planning ◽  
Services Research ◽  
Epidemiologic Surveillance

BACKGROUND Simple visualizations in health research data, such as scatter plots, heat maps and bar charts typically present relationships between two variables. Interactive visualization methods allow for multiple related facets, such as multiple risk factors, to be studied simultaneously, leading to data insights through exploring trends and patterns from complex big healthcare data. The technique presents a powerful tool that can be used in combination with statistical analysis for knowledge discovery, hypothesis generation and testing, and decision support. OBJECTIVE The primary objective of this scoping review is to describe and summarize the evidence of interactive visualization applications, methods and tools being employed in population health and HSR, and their sub-domains in the last 15 years, from 1 January 2005 to 30 March 2019. Our secondary objective is to describe the use cases, metrics, frameworks used, settings, target audience, goals and co-design of applications. METHODS We adapted standard scoping review guidelines, with a peer reviewed search strategy, two independent researchers at each stage of screening and abstraction, with a third independent researcher to arbitrate conflicts and validate findings. A comprehensive abstraction platform was built to capture the data from diverse bodies of literature, primarily from the computer science and health care sector. After screening 11,310 articles, we present findings from 56 applications from interrelated areas of population health and health services research, and their sub-domains such as epidemiologic surveillance, health resource planning, access, utilization and costs, among diverse clinical and demographic populations. RESULTS As a companion review to our earlier systematic synthesis of literature on visual analytic applications, we present findings in six major themes of interactive visualization applications developed for eight major problem categories. We found a wide application of interactive visualization methods, the major being epidemiologic surveillance for infectious disease, resource planning, health service monitoring and quality and studying medication use patterns. Data sources included mostly secondary administrative and electronic medical record data. Additionally, at least two-third applications involved participatory co-design approaches, while introducing a distinct category ‘embedded research’ within co-design initiatives. These applications were in response to an identified need for data-driven insights towards knowledge generation and decision support. We further discuss the opportunities from the use of interactive visualization methods towards studying global health, inequities including social determinants of health, and other related areas. We also allude to the challenges in the uptake of these methods. CONCLUSIONS Visualization in health has strong historical roots, with an upward trend in the use of these methods in population health and health services research. Such applications are being fast utilized by academic and health care agencies for knowledge discovery, hypotheses generation and decision support. CLINICALTRIAL Protocol registration: RR1-10.2196/14019 Related first review: RR2-10.2196/14019 INTERNATIONAL REGISTERED REPORT RR2-10.2196/14019

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