Evidence map›Paper›PMID 35617626›Full record

SynthesisJournal of patient safety2022

Toward Constructive Change After Making a Medical Error: Recovery From Situations of Error Theory as a Psychosocial Model for Clinician Recovery.

Reema Harrison, Judith Johnson, Ryan D McMullan, Maha Pervaz-Iqbal, Upma Chitkara, Steve Mears, Jo Shapiro, Rebecca Lawton

Open access · hybridAbstract readSystematic Review
In one paragraph

Synthesis in Journal of patient safety, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
2.7field-weighted citation impact, top 10% of its field
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

6 citing papers in PubMed, 1 synthesis or guideline pooled it, 10 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Review
  6. 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

8 authors at 6 institutions in 3 countries.

Reema HarrisonFrom the Australian Institute of Health Innovation, Macquarie University, Sydney, Australia.ORCID 0000-0002-8609-9827
Ryan D McMullanFrom the Australian Institute of Health Innovation, Macquarie University, Sydney, Australia.ORCID 0000-0003-4992-3626
Maha Pervaz-IqbalFrom the Australian Institute of Health Innovation, Macquarie University, Sydney, Australia.
Upma ChitkaraHunter New England Medical Libraya, New Lambton, Australia.
Steve MearsSchool of Population Health, University of New South Wales, Sydney, Australia.
Jo ShapiroDepartment of Anesthesia, Pain and Critical Care, Massachusetts General Hospital, Boston, Massachusetts.
Rebecca LawtonInstitute of Psychological Sciences, University of Leeds, Leeds, United Kingdom.ORCID 0000-0002-5832-402
Rex Medical (United States) · USUniversity of Leeds · GBHunter Medical Research Institute · AUMacquarie University · AUMassachusetts General Hospital · USUNSW Sydney · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMaking a medical error is a uniquely challenging psychosocial experience for clinicians. Feelings of personal responsibility, coupled with distress regarding potential or actual patient harm resulting from a mistake, create a dual burden. Over the past 20 years, experiential accounts of making an error have provided evidence of the associated distress and impacts. However, theory-based psychosocial support interventions to improve both individual outcomes for the involved clinicians and system-level outcomes, such as patient safety and workforce retention, are lacking. There is a need for evidence-based ways to both structure and evaluate interventions to decrease the distress of making a medical error and its impacts. Such interventions play a role within wider programs of health professional support. We sought to address this by developing a testable, psychosocial model of clinician recovery after error based on recent evidence.

methodsSystematic review methodology was used to identify studies published between January 2010 and June 2021 reporting experiences of direct involvement in medical errors and/or subsequent recovery. A narrative synthesis was produced from the resulting articles and used as the basis for a team-based qualitative approach to model building.

resultsWe identified 25 studies eligible for inclusion, reporting evidence primarily from experiences of doctors and nurses. The identified evidence indicates that coping approach, conversations (whether they occur and whether they are perceived to be helpful or unhelpful), and learning or development activities (helpful, unhelpful or absent) may influence the relationship between making an error and both individual clinician outcomes of emotional impact and resultant practice change. Our findings led to the development of the Recovery from Situations of Error Theory model, which provides a preliminary theoretical basis for intervention development and testing.

conclusionsThe Recovery from Situations of Error Theory model is the first testable psychosocial model of clinician recovery after making a medical error. Applying this model provides a basis to both structure and evaluate interventions to decrease the distress of making a medical error and its impacts and to support the replication of interventions that work across services and health systems toward constructive change. Such interventions may be embedded into the growing body of peer support and employee support programs internationally that address a diverse range of stressful workplace experiences.

Indexed as

Adaptation, PsychologicalMedical ErrorsHealth PersonnelHumansLearningWorkplace

Identifiers

PMID35617626
PMCPMC9422758
OpenAlexW4281561839

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.