Evidence map›Paper›PMID 31315828›Full record

SynthesisBMJ (Clinical research ed.)2019

Prevalence, severity, and nature of preventable patient harm across medical care settings: systematic review and meta-analysis.

Maria Panagioti, Kanza Khan, Richard N Keers, Aseel Abuzour, Denham Phipps, Evangelos Kontopantelis, Peter Bower, Stephen Campbell, Razaan Haneef, Anthony J Avery and 1 more

2 registry-linked trialsAbstract readMeta-AnalysisSystematic Review
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in BMJ (Clinical research ed.), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 358 papers, 15 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
358citing papers in PubMed, 15 pooled it
–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.

NCT05247801 nacompletednot on this mapstarted 2022, after this paper: background citation

Safe Use of Medication at Home by Caregivers. Experimental Study

TypeinterventionalSponsorUniversidad Miguel Hernandez de ElcheRan2022 to 2024Enrolled142ConditionsCaregivers of Multi-pathological and Polypharmacy AdultsArmsPsychoeducational intervention for safe medication use at home + Medication dosing device, Medication dosing device
NCT07358793 narecruitingnot on this mapstarted 2026, after this paper: background citation

Effectiveness of In Situ and Off-site Simulation on A-C-L-S Teamwork Model Implementation for Adult Non-trauma Resuscitation: A Two-group Pre-post Study

TypeinterventionalSponsorNational Taiwan University HospitalRan2026 to 2027Enrolled100ConditionsAdult Non-trauma Cardiac Arrest Resuscitation, Effectiveness of In-situ Simulation (ISS) Training, Effectiveness of Off-site Simulation (OSS) Training, Improvement of Teamwork in Emergency Care SettingsArmsIn-situ Simulation (ISS) Training, Off-site Simulation (OSS) Training
3 · Its place in the literature

Who cites it

358 citing papers in PubMed, 15 syntheses or guidelines pooled it.

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  16. Promoting Patient Safety Through Patient Engagement at the Organisational Level: A Delphi-Based Needs Assessment Among Patient and Family Advisory Councils.Health expectations : an international journal of public participation in health care and health policy · 2025
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298 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors.

Maria PanagiotiNIHR Greater Manchester Patient Safety Translational Research Centre, NIHR School for Primary Care Research, Division of Population Health, Health Services Research and Primary Care, University of Manchester, Manchester M13 9PL, UK maria.panagioti@manchester.ac.uk.ORCID 0000-0002-7153-5745
Kanza KhanNIHR Greater Manchester Patient Safety Translational Research Centre, NIHR School for Primary Care Research, Division of Population Health, Health Services Research and Primary Care, University of Manchester, Manchester M13 9PL, UK.
Richard N KeersCentre for Pharmacoepidemiology and Drug Safety, Division of Pharmacy and Optometry, University of Manchester, Manchester, UK.
Aseel AbuzourCentre for Pharmacoepidemiology and Drug Safety, Division of Pharmacy and Optometry, University of Manchester, Manchester, UK.
Denham PhippsCentre for Pharmacoepidemiology and Drug Safety, Division of Pharmacy and Optometry, University of Manchester, Manchester, UK.
Evangelos KontopantelisNIHR Greater Manchester Patient Safety Translational Research Centre, NIHR School for Primary Care Research, Division of Population Health, Health Services Research and Primary Care, University of Manchester, Manchester M13 9PL, UK.
Peter BowerNIHR Greater Manchester Patient Safety Translational Research Centre, NIHR School for Primary Care Research, Division of Population Health, Health Services Research and Primary Care, University of Manchester, Manchester M13 9PL, UK.
Stephen CampbellNIHR Greater Manchester Patient Safety Translational Research Centre, NIHR School for Primary Care Research, Division of Population Health, Health Services Research and Primary Care, University of Manchester, Manchester M13 9PL, UK.
Razaan HaneefLancashire Teaching Hospitals NHS Foundation Trust, Manchester, UK.
Anthony J AveryDivision of Primary Care, School of Medicine, University of Nottingham, Nottingham, UK.
Darren M AshcroftNIHR Greater Manchester Patient Safety Translational Research Centre, NIHR School for Primary Care Research, Division of Population Health, Health Services Research and Primary Care, University of Manchester, Manchester M13 9PL, UK.

Funding

Department of Health
6 · The paper itself

Abstract

objectiveTo systematically quantify the prevalence, severity, and nature of preventable patient harm across a range of medical settings globally.

designSystematic review and meta-analysis. DATA SOURCES: Medline, PubMed, PsycINFO, Cinahl and Embase, WHOLIS, Google Scholar, and SIGLE from January 2000 to January 2019. The reference lists of eligible studies and other relevant systematic reviews were also searched. REVIEW

methodsObservational studies reporting preventable patient harm in medical care. The core outcomes were the prevalence, severity, and types of preventable patient harm reported as percentages and their 95% confidence intervals. Data extraction and critical appraisal were undertaken by two reviewers working independently. Random effects meta-analysis was employed followed by univariable and multivariable meta regression. Heterogeneity was quantified by using the I

resultsOf the 7313 records identified, 70 studies involving 337 025 patients were included in the meta-analysis. The pooled prevalence for preventable patient harm was 6% (95% confidence interval 5% to 7%). A pooled proportion of 12% (9% to 15%) of preventable patient harm was severe or led to death. Incidents related to drugs (25%, 95% confidence interval 16% to 34%) and other treatments (24%, 21% to 30%) accounted for the largest proportion of preventable patient harm. Compared with general hospitals (where most evidence originated), preventable patient harm was more prevalent in advanced specialties (intensive care or surgery; regression coefficient b=0.07, 95% confidence interval 0.04 to 0.10).

conclusionsAround one in 20 patients are exposed to preventable harm in medical care. Although a focus on preventable patient harm has been encouraged by the international patient safety policy agenda, there are limited quality improvement practices specifically targeting incidents of preventable patient harm rather than overall patient harm (preventable and non-preventable). Developing and implementing evidence-based mitigation strategies specifically targeting preventable patient harm could lead to major service quality improvements in medical care which could also be more cost effective.

Indexed as

Cross-Sectional StudiesEvidence-Based PracticeHumansObservational Studies as TopicPatient HarmPatient SafetyPrevalenceQuality ImprovementRetrospective StudiesSeverity of Illness Index

Identifiers

PMID31315828
PMCPMC6939648

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

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.