Indicators of missing Electronic Medical Record (EMR) discharge summaries: A retrospective study on data from a large Canadian cohort
Bibliographic record
Abstract
INTRODUCTION AND OBJECTIVES: Electronic medical records (EMRs), specifically the discharge summary (DS), can improve secondary use data availability and interprofessional communication. We aimed to describe the completeness of our EMRs by assessing the presence of a DS in the EMR. Additionally, we assessed for indicators of a missing DS. METHODS: A chart review was conducted on 3,011 non-obstetric adult inpatient charts in Calgary, Alberta. 893 charts were missing an electronic DS. A 10% sample was drawn to evaluate the presence of a paper DS. A Chi-square test, Fisher's test and logistic regression measured the associations between electronic DS absence and i) patient and hospital characteristics, and ii) patient comorbidities. RESULTS: The univariate analyses showed that age, being a surgical patient, a Charlson Comorbidity Index (CCI) of </1, as well as patients with myocardial infarctions, congestive heart failure, cerebrovascular disease, dementia, chronic pulmonary disease, diabetes, and renal disease were associated with a missing DS. Those that were middle aged, surgical patients, or had fewer comorbidities were more likely to have a missing DS. Within the 10% sample, approximately 50% of all patients were from a surgical department, all of which were missing both electronic and paper discharge summaries. CONCLUSIONS: Our study describes indicators of missing electronic DS. The DS impacts interprofessional communication, patient outcomes, and data quality. Therefore, the implications of an incomplete DS are widespread. Our findings will caution future researchers using EMR data about the potential for incomplete data, particularly for patients who are surgical, middle aged, and have fewer comorbidities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".