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Availability of information on functional limitations in structured electronic health records data

Data from electronic health records (EHR) are increasingly used for research, to inform clinical care decisions, assess quality of care, and identify patients at high-risk of poor outcomes. Functional status— including mobility and the ability to perform activities of daily living (ADL)—are key indicators associated with mortality, healthcare use, ability to self-manage disease, and health-related quality of life of older adults. However, EHR documentation of function is not standardized across vendors and health systems, limiting interoperability and information sharing. The lack of standardization means that important measures of function are likely absent from EHRintegrated software and apps which often rely exclusively on standardized structured data fields. The goal of this analysis was to quantify the extent that functional limitations are captured in a national pool of structured EHR data.


INTRODUCTION
Data from electronic health records (EHR) are increasingly used for research, to inform clinical care decisions, assess quality of care, and identify patients at high-risk of poor outcomes. 1 Functional statusincluding mobility and the ability to perform activities of daily living (ADL)-are key indicators associated with mortality, healthcare use, ability to self-manage disease, and health-related quality of life of older adults. 2 However, EHR documentation of function is not standardized across vendors and health systems, limiting interoperability and information sharing. 3,4 The lack of standardization means that important measures of function are likely absent from EHRintegrated software and apps which often rely exclusively on standardized structured data fields. 5 The goal of this analysis was to quantify the extent that functional limitations are captured in a national pool of structured EHR data.

METHODS
This was a cross-sectional study using IBM Watson Health Explorys, a network of EHR data from 26 health systems comprising 360 hospitals and 64 million unique patients in the United States. 6 Explorys maps the standard elements of structured EHR data from each health system to SNOMED-CT concept codes, which were used to identify functional limitations into five categories: mobility and gross motor, fine motor, large muscle, ADL, and instrumental activities of daily living (IADL) ( Table S1). The study sample included 7,662,030 adults age ≥65 with at least one healthcare encounter between 02/14/2017 and 02/14/2022. For benchmarking, the rates identified in EHR data were compared with the national prevalence rates of functional limitations among older adults in the community using the 2016 Health & Retirement Study (Text S1). 7 The study was deemed exempt by the authors' IRB.

RESULTS
Functional limitations were captured in just 11.5% of the EHR study population compared to 71.5% of the community-based survey population. The prevalence in EHRs was lower across all five categories of function with differences ranging from 9.6 percentage points (IADL limitations) to 62.7 percentage points (large muscle group limitations). The most common underrepresented functional limitation categories captured in structured EHR data were limitations in mobility (9.3%), followed by ADL limitations (3.5%), and IADL limitations (1.0%) ( Table 1). Fine motor impairment and limitations in large muscle function were captured in ≤0.01% of patients in the structured data. Most ADL limitations captured in the data were related to difficulty walking (Table S1). Nearly all IADLs captured are due to non-compliance with medication, which could be for reasons other than functional limitation. in problem lists and discharge diagnoses lack specificity to record detailed functional limitations and care dependence. Third, the assessment of functional limitations itself may vary across care settings and health systems.
The implication is that functional limitation measures are likely missing from health systems' initiatives to improve quality, safety, and value through "meaningful use" of EHR data, especially in smaller hospitals and clinics that do not have resources to develop custom software or collect additional data. For example, functional limitations are important predictors of hospital readmission, but these measures are likely absent from research and software/apps designed to identify high-risk patients prior to discharge. 8 Likewise, function is likely missing from population health management and learning health system efforts that rely on routinely collected structured EHR data.
Possible solutions include modifying EHRs to include a standardized place to document functional limitations and other disabilities such as the one recommended by HL7 International, 3 and/or allowing more detailed functional concepts as valid entries in the diagnosis or problem lists. Advances in natural language processing may allow for better use of clinical notes data in the future. 9 Adopting age-friendly models of care that incorporate routine functional assessment for older adults may improve documentation. 10 Study limitations: the Health & Retirement Study collects self-reported measures in the community, while Explorys data are from EHR documentation in care settings, so some differences are expected. However, the rates are so low in the Explorys data that under-capture, rather than a healthier population, is the more likely explanation. Our study may miss functional limitations documented in non-standardized