Holding Hospitals Accountable for Improved Patient Safety: Confidential Reporting of Major Incidents
Bibliographic record
Abstract
Context: Evidence that the quality of medical care is improved by adverse event reporting is growing, but systemic improvement in patient safety remains uncoordinated and is driven by organizations and states, rather than by any unified plan. Massachusetts requires hospitals to report several types of major incidents to two different agencies, one set of reports are available to the public, the other kept strictly confidential. We analyzed the effect of the confidential reporting and feedback system on the number of reports, their type and whether they contained corrective actions, before and after a letter notifying hospitals of their failure to comply with the reporting statutes was sent in August 2003. This notification clearly held hospitals accountable for improved patient safety.Objective: To assess the changes in the number of reports filed for major incidents and the presence in the reports of corrective actions planned by the hospitals for the year before and two years after the compliance was sent.Design, Setting and Participants: All 1,317 major incident reports (MIRs) submitted in fiscal years 2003, 2004 and 2005 to the Patient Care Assessment Division (PCA) of the Board of Registration in Medicine of Massachusetts from all 94 acute care hospitals in Massachusetts were analyzed, including the presence of corrective actions included in their report to the PCA. MIRs were grouped by fiscal year, because the compliance letter holding hospitals accountable for improved patient safety was sent in FY 2003; thus improvements were expected to be seen from the baseline period of FY 2003, to the two subsequent fiscal years.Main Outcome Measures: Changes in number of reports of serious major incident reports and presence of hospitals' corrective actions included in reports.Results: Immediately following the baseline fiscal year of 2003, there was a large and significant increase in both the number of hospitals submitting at least one report and the mean number of reports submitted per hospital, and both numbers remained at this higher level in the third year. There was a significant increase in reports for fatal/life-threatening incidents as measured by the presence of corrective actions submitted by the hospitals in their reports, but this improvement lagged one year behind the increase in number of reports.Conclusions: The purpose of a mandatory adverse reporting system, holding hospitals accountable for improved patient safety and quality, is demonstrated by significant improvements in reporting compliance and the presence of corrective actions to the PCA during the three-year period examined.
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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 | 0.025 | 0.138 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".