Missing clinical and behavioral health data in a large electronic health record (EHR) system

Madden, Jeanne M.; Lakoma, Matthew D.; Rusinak, Donna; Lu, Christine Y.; Soumerai, Stephen B.

doi:10.1093/jamia/ocw021

Cited by 128 publications

(104 citation statements)

References 56 publications

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“…Because care at the university (a tertiary care center) can be fragmented and EHRs may not be interoperable, providers at the university may assume screening laboratory studies were done at an outside hospital while providers referring from the community may assume these tests were performed at the university. [35] These findings suggest that improvements in workflows and EHR systems need to occur and be studied in tandem. [36] …”

Section: Discussionmentioning

confidence: 99%

Using health-system-wide data to understand hepatitis B virus prophylaxis and reactivation outcomes in patients receiving rituximab

et al. 2017

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Section: Discussionmentioning

confidence: 99%

Using health-system-wide data to understand hepatitis B virus prophylaxis and reactivation outcomes in patients receiving rituximab

et al. 2017

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“…32 Examples of basic patient information that is commonly difficult to access, or missing from the provider's own records, in the outpatient setting include prior encounters, past medical history, laboratory results, and clinical notes. 31,33,34 Additionally, traditional paper-based and fax sharing information between providers is not timely and is often incomplete. Both difficulty in accessing information and inefficient methods of information sharing contribute to the challenge of information deficits.…”

Section: Backg Rou N Dmentioning

confidence: 99%

“…Information deficits arising from patient information that is unavailable or difficult to access are common and are often the product of patient transitions across fragmented systems of care . Examples of basic patient information that is commonly difficult to access, or missing from the provider's own records, in the outpatient setting include prior encounters, past medical history, laboratory results, and clinical notes . Additionally, traditional paper‐based and fax sharing information between providers is not timely and is often incomplete.…”

Section: Introductionmentioning

confidence: 99%

The associations between query‐based and directed health information exchange with potentially avoidable use of health care services

Vest

Unruh

Shapiro

et al. 2019

Health Services Research

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Objective To quantify the impact of two approaches (directed and query‐based) to health information exchange (HIE) on potentially avoidable use of health care services. Data Sources/Study Setting Data on ambulatory care providers’ adoption of HIE were merged with Medicare fee‐for‐service claims from 2008 to 2014. Providers were from 13 counties in New York served by the Rochester Regional Health Information Organization (RHIO). Study Design Linear regression models with provider and year fixed effects were used to estimate changes in the probability of utilization outcomes for Medicare beneficiaries attributed to providers adopting directed and/or query‐based HIE compared with beneficiaries attributed to providers who had not adopted HIE. Data Collection Providers’ HIE adoption status was determined through Rochester RHIO registration records. RHIO and claims data were linked via National Provider Identifiers. Principal Findings Query‐based HIE adoption was associated with a 0.2 percentage point reduction in the probability of an ambulatory care sensitive hospitalization and a 1.1 percentage point decrease in the likelihood of an unplanned readmission. Directed HIE adoption was not associated with any outcome. Conclusions The Centers for Medicare & Medicaid Services’ (CMS) EHR certification criteria includes requirements for directed HIE, but not query‐based HIE. Pending further research, certification criteria should place equal weight on facilitating query‐based and directed exchange.

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“…Depression is a particularly difficult phenotype to define and studies often use heterogeneous criteria when utilizing EHR data to identify patients with depression (Anderson et al, 2015;Huang et al, 2014;Madden et al, 2016;Mayer et al, 2017;Pathak et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Defining Major Depressive Disorder Cohorts Using the EHR: Multiple Phenotypes Based on ICD-9 Codes and Medication Orders

Ingram

Baker

Bauer

et al. 2017

Preprint

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The importance of methodology when conducting high-quality health behavior research cannot be overstated. Electronic Health Records (EHR) allow researchers to conduct unprecedented large-scale studies. However, careful consideration must be given to how to define patient cohorts, specifically when utilizing EHR for examining patients with depression. Because depression has been linked to increased morbidity and mortality in many disease groups and leads to higher health care utilization, better methods for identifying patients with depression must be developed in order to rigorously study the impact of depression on these outcomes. Identifying patients using only ICD9 codes for depression may result in inclusion of clinically depressed patients in comparison groups. Thus, more nuanced electronic phenotypes may better delineate patients that are receiving treatment for depression. We demonstrate the utility of a new method involving multiple depression phenotypes on a 10.75-year cohort from an integrated health system (n=287,281). Here we recommend a novel and easily adaptable method of categorizing patients. In this method, four groups are identified using ICD-9 codes and medication orders from an EHR which have varying levels of depression likelihood and severity: Dep ICD9, Rx no ICD9, Rx non-dep, and No Dep. We then measure a variety of EHR-based features including utilization patterns, medication orders, comorbidities, mortality data and symptom assessment scores to establish convergent validity of these groups. This superior and simple method allows for large scale studies of depressed patients, while accounting for the limitations associated with using specific electronic phenotypes for analysis of data from the EHR.

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Missing clinical and behavioral health data in a large electronic health record (EHR) system

Cited by 128 publications

References 56 publications

Using health-system-wide data to understand hepatitis B virus prophylaxis and reactivation outcomes in patients receiving rituximab

Using health-system-wide data to understand hepatitis B virus prophylaxis and reactivation outcomes in patients receiving rituximab

The associations between query‐based and directed health information exchange with potentially avoidable use of health care services

Defining Major Depressive Disorder Cohorts Using the EHR: Multiple Phenotypes Based on ICD-9 Codes and Medication Orders

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