MétaCan
Menu
Back to cohort

The epistemology of patient safety research

2008· article· en· W4241319293 on OpenAlexaff
W. B. Runciman, G. Ross Baker, Philippe Michel, Itziar Larizgoitia Jauregui, Richard Lilford, Anne Andermann, Rhona Flin, William B. Weeks

Bibliographic record

VenueInternational Journal of Evidence-Based Healthcare · 2008
Typearticle
Languageen
FieldHealth Professions
TopicPatient Safety and Medication Errors
Canadian institutionsMcGill UniversityUniversity of Toronto
Fundersnot available
KeywordsPatient safetyAuditRisk analysis (engineering)Context (archaeology)Scope (computer science)Risk managementProcess (computing)Health careQuality (philosophy)Observational studyBusinessKnowledge managementProcess managementComputer scienceMedicinePolitical science

Abstract

fetched live from OpenAlex

Patient safety has only recently been subjected to wide-spread systematic study. Healthcare differs from other high risk industries in being more diverse and multi-contextual, and less certain and regulated. Also many patient safety problems are low-frequency events associated with many, varied contributing factors. The subject of this paper is the epistemology of patient safety (the science of the method of finding out about patient safety). Patient safety research is considered here on the background of a risk management framework which requires researchers to: Understand the context – as a subset of healthcare quality, services and systems research, with technical and human behavioural (cultural) components and a range of external and internal organisational influences, a wide range of research disciplines is necessary Identify the risks – identify the things that go wrong and the frequency and nature of different types of incidents from sources such as medical record review, observational studies, audit, incident and medico-legal reports Analyse the risks – deconstruct the things that go wrong, identifying contributing factors and trying to detect trends and patterns in contributing factors, detection, mitigation factors, ameliorating factors and actions taken to reduce risk Evaluate the risks – decide on priorities, identifying preventive and corrective strategies and judging the risk- and cost-benefit of potential corrective strategies such as standardisation or simplification of a process or device Manage the risk – evaluate and scope preventive and/or corrective strategies and then implement these, or place the problem on a risk register pending solution, or accept that what is needed is unaffordable Communicate and consult – use interactive sessions, audit, on-going feedback, reminders and patient mediated prompts Monitor and review the state of the problem – get baseline trends and patterns so that changes can be tracked and properly attributed to an intervention A hierarchy of levels of evidence has been proposed for clinical research and we argue that insufficient weighting has been given to lower ranked levels of research and to qualitative research, although critical interpretive synthesis is now gaining acceptance in mainstream thinking (e.g. by the Cochrane Collaboration). Fundamental challenges remain including how to grasp the elusive concept of patient safety, how to quantify, characterise and cost the problems, how to judge the extent to which harm can be attributed to errors, violations or system failures, how to identify contributing factors and the extent to which they can be implicated, how to judge whether incidents or their precursors are preventable, how to generate strong evidence to make healthcare safer and how to translate research into practice. Future directions include addressing the mundane as well as rare, dramatic events, and developing further research in non-hospital settings and in developing countries. In summary, a mixture of qualitative and quantitative methods, using information from all available data sources and combining retrospective, real time and prospective study designs, is necessary to address some of the more difficult patient safety problems.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.623
Threshold uncertainty score0.839

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.560
GPT teacher head0.571
Teacher spread0.012 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Evidence-Based HealthcareSame topicPatient Safety and Medication ErrorsFrench-language works237,207