Evaluation of interventions to improve inpatient hospital documentation within electronic health records: A Systematic Review
Bibliographic record
Abstract
IntroductionDespite increased use of electronic health records (EHRs), EHR documentation quality remains poor. Consequently, EHR data quality is also negatively affected. Many services, including disease surveillance and health services research, utilize EHR data. Accordingly, several studies have attempted to improve EHR documentation quality in the inpatient setting using various interventions. Objectives and ApproachThe purpose of this systematic review was to synthesize the literature, and assess the effectiveness of interventions seeking to improve inpatient EHR documentation quality. To identify relevant experimental, quasi-experimental and observational studies, a search strategy was developed based on elaborate inclusion/exclusion criteria using four main themes: EHR, documentation, interventions, and type of study. Four databases, Cochrane, Medline, EMBASE, and CINAHL, were searched. Study quality assessment and data extraction from selected studies were performed using a Downs and Black and Newcastle-Ottawa Scale hybrid tool, and a REDCap form, respectively. Data was then analyzed and synthesized in a narrative semi-quantitative manner. ResultsAn in-depth search of the identified databases, grey literature and reference lists, revealed a final 20 studies for inclusion in this systematic review. Due to high heterogeneity in study design, population, interventions, comparators, document types and outcomes, data could not be standardized for a quantitative comparison. However, statistically significant results in interventions and affected outcomes were further presented and discussed. A higher number of studies reported significantly improved EHR documentation when using the interventions: ‘Education’ and ‘Implementing a new EHR Reporting System’. When implementing two or more interventions, more outcome measures were affected. There was no association between study quality or study design and number of interventions used. Only one of the 20 studies found EHR documentation worsened with the interventions used. Conclusion/ImplicationsInterventions implemented to enhance EHR documentation are highly variable and require standardization. Emphasis should be placed on this novel area of research to improve communication between healthcare providers, enhance continuity of care, reduce the burden in health information management, and to facilitate data sharing between centers, provinces, and countries.
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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 | 0.035 | 0.089 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.010 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".