The Value of Electronic Medical Record Implementation in Mental Health Care: A Case Study
Bibliographic record
Abstract
BACKGROUND: Electronic medical records (EMR) have been implemented in many organizations to improve the quality of care. Evidence supporting the value added to a recovery-oriented mental health facility is lacking. OBJECTIVE: The goal of this project was to implement and customize a fully integrated EMR system in a specialized, recovery-oriented mental health care facility. This evaluation examined the outcomes of quality improvement initiatives driven by the EMR to determine the value that the EMR brought to the organization. METHODS: The setting was a tertiary-level mental health facility in Ontario, Canada. Clinical informatics and decision support worked closely with point-of-care staff to develop workflows and documentation tools in the EMR. The primary initiatives were implementation of modules for closed loop medication administration, collaborative plan of care, clinical practice guidelines for schizophrenia, restraint minimization, the infection prevention and control surveillance status board, drug of abuse screening, and business intelligence. RESULTS: Medication and patient scan rates have been greater than 95% since April 2014, mitigating the adverse effects of medication errors. Specifically, between April 2014 and March 2015, only 1 moderately severe and 0 severe adverse drug events occurred. The number of restraint incidents decreased 19.7%, which resulted in cost savings of more than Can $1.4 million (US $1.0 million) over 2 years. Implementation of clinical practice guidelines for schizophrenia increased adherence to evidence-based practices, standardizing care across the facility. Improved infection prevention and control surveillance reduced the number of outbreak days from 47 in the year preceding implementation of the status board to 7 days in the year following. Decision support to encourage preferential use of the cost-effective drug of abuse screen when clinically indicated resulted in organizational cost savings. CONCLUSIONS: EMR implementation allowed Ontario Shores Centre for Mental Health Sciences to use data analytics to identify and select appropriate quality improvement initiatives, supporting patient-centered, recovery-oriented practices and providing value at the clinical, organizational, and societal levels.
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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 | 0.018 | 0.087 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".