ArticleJournal of the American Medical Informatics Association : JAMIA2017
Navigating a ship with a broken compass: evaluating standard algorithms to measure patient safety.
Article in Journal of the American Medical Informatics Association : JAMIA, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed, 8 citations in OpenAlex.
- Between-Hospital Variation in Failure to Rescue After Major Surgery.JAMA network open · 2026Article
- Patient Safety Indicator 04 Does Not Consistently Identify Failure to Rescue in the Neurosurgical Population.Neurosurgery · 2023Article
- A comparison of two structured taxonomic strategies in capturing adverse events in U.S. hospitals.Health services research · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: Agency for Healthcare Research and Quality (AHRQ) software applies standardized algorithms to hospital administrative data to identify patient safety indicators (PSIs). The objective of this study was to assess the validity of PSI flags and report reasons for invalid flagging. Material and Methods: At a 6-hospital academic medical center, a retrospective analysis was conducted of all PSIs flagged in fiscal year 2014. A multidisciplinary PSI Quality Team reviewed each flagged PSI based on quarterly reports. The positive predictive value (PPV, the percent of clinically validated cases) was calculated for 12 PSI categories. The documentation for each reversed case was reviewed to determine the reasons for PSI reversal. Results: Of 657 PSI flags, 185 were reversed. Seven PSI categories had a PPV below 75%. Four broad categories of reasons for reversal were AHRQ algorithm limitations (38%), coding misinterpretations (45%), present upon admission (10%), and documentation insufficiency (7%). AHRQ algorithm limitations included 2 subcategories: an "incident" was inherent to the procedure, or highly likely (eg, vascular tumor bleed), or an "incident" was nonsignificant, easily controlled, and/or no intervention was needed. Discussion: These findings support previous research highlighting administrative data problems. Additionally, AHRQ algorithm limitations was an emergent category not considered in previous research. Herein we present potential solutions to address these issues. Conclusions: If, despite poor validity, US policy continues to rely on PSIs for incentive and penalty programs, improvements are needed in the quality of administrative data and the standardized PSI algorithms. These solutions require national motivation, research attention, and dissemination support.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.