Unlocking EMR Data to Track Physical Function Across the Continuum of Care
Bibliographic record
Abstract
ABSTRACT Introduction Physical function (PF) is a critical contributor to quality of life and healthcare value, especially for older adults at risk for functional decline following hospitalization. Tracking PF is essential for monitoring recovery, preventing adverse events, and improving care transitions. Despite the potential of electronic health records (EHRs) to enable tracking of PF, it is rarely tracked systematically. We present a case study on the extraction of PF data from EHRs for patients transitioning from hospital to homecare in a large health system, highlighting challenges and offering recommendations. Methods An expert consensus group identified data elements important to the measurement of PF. We then assessed the feasibility of extracting those elements from a single healthcare system. Working with Johns Hopkins Health System (JHHS) informatics and homecare leaders, we determined which elements were captured in the EHR and which were not feasible to extract within our resource constraints. We then requested a refined data list for adult patients during the project period. After validation, data were securely transferred to University of Utah Health (UUH). Results Data from 21,702 patients were included. Of 27 desired elements, 17 were successfully extracted. Elements were marked ‘present’ if documented at least once during admission, or ‘missing’ if absent. Administrative data had low missingness, while missingness for assessments of cognition and mobility performance in hospital were over 65% and assessments of PF capacity in home health were missing in over 80% of patients. However, 81.7% of those receiving home health rehabilitation had the expected mobility measure. Overall, 73% of patients had at least 75% of the extracted data elements. Conclusions To track PF effectively, begin with clear definitions, a targeted cohort, and relevant data elements. Collaboration with EHR, clinical, and billing experts is essential, as is upfront assessment of data availability and alignment with project resources.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".