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Record W2056199401 · doi:10.12927/hcq.2012.22845

From Discovery to Design: The Evolution of Human Factors in Healthcare

2012· article· en· W2056199401 on OpenAlexafffund
Joseph A Cafazzo, Olivier St-Cyr

Bibliographic record

VenueHealthcare Quarterly · 2012
Typearticle
Languageen
FieldHealth Professions
TopicPatient Safety and Medication Errors
Canadian institutionsToronto Rehabilitation Institute
FundersUniversity Health Network
KeywordsHealth careBest practiceHealth administrationBusinessNursingMedicinePolitical sciencePublic health

Abstract

fetched live from OpenAlex

I n some respects, the use of human factors engineering (HFE) methods in healthcare has been transformative.However, as measurable progress has been limited in patient safety, these challenges continue.Where is HFE headed to address these challenges?We look at the progress and difficulties HFE faces in today's complex healthcare environment. Human factors in Safety ResearchApplications of HFE in system design can be traced back to the mid-1900s.Post-World War II, many engineers were preoccupied with the safety of transportation systems.As a result, human factors became prominent in domains such as aviation and the military.Applying HFE principles in these domains gave rise to fundamental conceptual frameworks to understand the relationships between humans and complex technology.For example, in the aviation industry, human factors specialists studied a series of elements influencing flight deck design, contributing to a culture shift that promoted the importance of safety in such complex socio-technical systems.From the mid-1970s to mid-1980s, much of the HFE attention focused on process control systems.Industrial events such as the Three Mile Island nuclear incident and the Bhopal industrial disaster reminded us that despite the successful HFE integration in some domains, the process control industry had partially failed to implement HFE methodologies.A series of inquiries followed these incidents, resulting in several regulatory documents outlining how HFE must systematically be incorporated in system design (Meister 1999). Human factors and HealthcareApplications of HFE principles to the domain of healthcare are relatively new.Although some references to human factors in healthcare date back to the 1980s and early 1990s (Leape 2004), it was the work of James Reason in 1995 that contributed to increasing the scope of HFE practices to healthcare systems.Reason reviewed HFE contributions to the domain of healthcare and introduced the concepts of active and latent failures.The former are errors committed by users of the systems (e.g., a doctor administers the wrong medication).The latter are errors created at the organizational level of the design (e.g., inadequate procedures, incomplete training, poor labelling choices etc.) and may lie dormant for several days, months or even years until triggered by a collection of local factors.Among other things, Reason's work emphasized the importance of considering team and organizational factors in the design of safety critical systems as well as avoiding the culture of "blaming the users" and embracing a culture of understanding the root causes of adverse events (Reason 1995).One of the early empirical demonstrations of the significant impact of using HFE design techniques on medical systems was shown in a redesign of the user interface of a commercially available patient-controlled analgesia (PCA) pump (Lin et al. 1998).The authors conducted an evaluation comparing their redesign to the original design.The results showed a significant decrease in programming time, a lower mental workload and fewer errors.These findings clearly demonstrated the applicability From Discovery to Design: The Evolution of Human Factors in Healthcare

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.049
metaresearch head score (Gemma)0.087
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.049
Threshold uncertainty score0.257

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0490.087
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.002
Science and technology studies0.0030.038
Scholarly communication0.0160.016
Open science0.0040.005
Research integrity0.0060.006
Insufficient payload (model declined to judge)0.0070.001

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 head0.113
GPT teacher head0.430
Teacher spread0.317 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations138
Published2012
Admission routes2
Has abstractno

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