Using Electronic Health Record Data to Measure Care Quality for Individuals with Multiple Chronic Medical Conditions
Bibliographic record
Abstract
OBJECTIVES: To inform the development of a data-driven measure of quality care for individuals with multiple chronic conditions (MCCs) derived from an electronic health record (EHR). DESIGN: Qualitative study using focus groups, interactive webinars, and a modified Delphi process. SETTING: Research department within an integrated delivery system. PARTICIPANTS: The webinars and Delphi process included 17 experts in clinical geriatrics and primary care, health policy, quality assessment, health technology, and health system operations. The focus group included 10 individuals aged 70-87 with three to six chronic conditions selected from a random sample of individuals aged 65 and older with three or more chronic medical conditions. MEASUREMENTS: Through webinars and the focus group, input was solicited on constructs representing high-quality care for individuals with MCCs. A working list was created of potential measures representing these constructs. Using a modified Delphi process, experts rated the importance of each possible measure and the feasibility of implementing each measure using EHR data. RESULTS: High-priority constructs reflected processes rather than outcomes of care. High-priority constructs that were potentially feasible to measure included assessing physical function, depression screening, medication reconciliation, annual influenza vaccination, outreach after hospital admission, and documented advance directives. High-priority constructs that were less feasible to measure included goal setting and shared decision-making, identifying drug-drug interactions, assessing social support, timely communication with patients, and other aspects of good customer service. Lower-priority domains included pain assessment, continuity of care, and overuse of screening or laboratory testing. CONCLUSION: High-quality MCC care should be measured using meaningful process measures rather than outcomes. Although some care processes are currently extractable from electronic data, capturing others will require adapting and applying technology to encourage holistic, person-centered care.
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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 | 0.069 | 0.180 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".