Evidence map›Paper›PMID 15805457›Full record

ArticleQuality & safety in health care2005

Relationship between tort claims and patient incident reports in the Veterans Health Administration.

J M Schmidek, W B Weeks

Abstract read
In one paragraph

Article in Quality & safety in health care, 2005. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

J M SchmidekField Office of VA's National Center for Patient Safety, White River Junction, VT 05009, USA. jared.schmidek@dartmouth.edu
W B Weeks

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveThe Veterans Health Administration's patient incident reporting system was established to obtain comprehensive data on adverse events that affect patients and to act as a harbinger for risk management. It maintains a dataset of tort claims that are made against Veterans Administration's employees acting within the scope of employment. In an effort to understand the thoroughness of reporting, we examined the relationship between tort claims and patient incident reports (PIRs).

methodsUsing social security and record numbers, we matched 8260 tort claims and 32 207 PIRs from fiscal years 1993-2000. Tort claims and PIRs were considered to be related if the recorded dates of incident were within 1 month of each other. Descriptive statistics, odds ratios, and two sample t tests with unequal variances were used to determine the relationship between PIRs and tort claims.

results4.15% of claims had a related PIR. Claim payment (either settlement or judgment for plaintiff) was more likely when associated with a PIR (OR 3.62; 95% CI 2.87 to 4.60). Payment was most likely for medication errors (OR 8.37; 95% CI 2.05 to 73.25) and least likely for suicides (OR 0.25; 95% CI 0.11 to 0.55).

conclusionsAlthough few tort claims had a related PIR, if a PIR was present the tort claim was more likely to result in a payment; moreover, the payment was likely to be higher. Underreporting of patient incidents that developed into tort claims was evident. Our findings suggest that, in the Veterans Health Administration, there is a higher propensity to both report and settle PIRs with bad outcomes.

Indexed as

Liability, LegalMandatory ReportingDatabases, FactualHealth Care SurveysHospitals, VeteransHumansIatrogenic DiseaseRisk ManagementUnited StatesUnited States Department of Veterans Affairs

Identifiers

PMID15805457
PMCPMC1743988

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.