Indicators of missing Electronic Medical Record (EMR) discharge summaries: A retrospective study on data from a large Canadian cohort
Bibliographic record
Abstract
Abstract Background: Healthcare systems worldwide have adopted the electronic medical record (EMR). EMRs are an efficient method of interprofessional communication, and can improve data availability for secondary research purposes. The discharge summary (DS) is a crucial document for both interprofessional communication, and coding of data for research purposes. We aimed to assess the completeness of our EMRs by assessing the presence of a DS in the EMR. Additionally, we evaluated the presence of indicators for a missing DS. Method: A retrospective chart review was conducted on 3,011 inpatient charts from 3 hospitals in Calgary, Alberta Canada. 893 charts were missing an electronic DS. A 10% sample was drawn to assess for presence of a paper DS. A list of variables was compiled to assess for association between patient and hospital characteristics, patient comorbidities, and the absence of an electronic DS. A Chi-square test, Fisher’s test and logistic regression were conducted to assess for associations. 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. The multivariate logistic regression showed that those that were middle aged, surgical patients, or with 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. Conclusion: Our study is the first to describe indicators associated with missing electronic discharge summaries. There is a modern day propensity for adoption of the EMR across healthcare systems worldwide. The EMR, especially the DS, is used for the improvement of interprofessional communication, patient outcomes, and data quality. Therefore, the implications of an incomplete EMR 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. Additionally, our study highlights the need for further investigation into the lack of discharge summaries in surgical units.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".