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Record W2790257511 · doi:10.1016/j.ajic.2017.12.018

Effect of electronic real-time prompting on hand hygiene behaviors in health care workers

2018· article· en· W2790257511 on OpenAlexafffund
Steven Pong, P. J. Holliday, Geoff Fernie

Bibliographic record

VenueAmerican Journal of Infection Control · 2018
Typearticle
Languageen
FieldMedicine
TopicInfection Control in Healthcare
Canadian institutionsToronto Rehabilitation InstituteUniversity of TorontoUniversity Health Network
FundersCanadian Institutes of Health ResearchToronto Rehabilitation InstituteOntario Ministry of Health and Long-Term Care
KeywordsMedicineHygieneOdds ratioConfidence intervalNursingNursing staffRandomized controlled trialDuration (music)Infection controlPhysical therapySurgeryInternal medicine

Abstract

fetched live from OpenAlex

•Real-time prompting when opportunities are missed approximately doubles handwash dispenser use.•Changes to prompt duration affect hand hygiene performance.•Staff members perform hand hygiene sooner after entering a patient room when they are prompted.•Increased hand hygiene performance with system use is maintained over a year but user participation rate drops. BackgroundPoor hand hygiene by health care workers is a major cause of nosocomial infections. This research evaluated the ability of an electronic monitoring system with real-time prompting capability to change hand hygiene behaviors.MethodsHandwashing activity was measured by counting dispenser activations on a single nursing unit before, during, and after installation of the system. The effect of changing the prompt duration on hand hygiene performance was determined by a cluster-randomized trial on 3 nursing units with 1 acting as control. Sustainability of performance and participation was observed on 4 nursing units over a year. All staff were eligible to participate.ResultsBetween June 2015 and December 2016, a total of 459,376 hand hygiene opportunities and 330,740 handwashing events from 511 staff members were recorded. Dispenser activation counts were significantly influenced by use of the system (χ2[3] = 75.76; P < .0001). Hand hygiene performance dropped from 62.61% to 24.94% (odds ratio, 0.36; 95% confidence interval, 0.34-0.38) when the prompting feature was removed. Staff participation had a negative trajectory of –0.72% (P < .001), whereas change in average performance was –0.18% (P < .001) per week for the year.ConclusionsUse of electronic monitoring with real-time prompts of 20 seconds' duration nearly doubles handwashing activity and causes handwashing to occur sooner after entering a patient room. These improvements are sustainable over a year. Poor hand hygiene by health care workers is a major cause of nosocomial infections. This research evaluated the ability of an electronic monitoring system with real-time prompting capability to change hand hygiene behaviors. Handwashing activity was measured by counting dispenser activations on a single nursing unit before, during, and after installation of the system. The effect of changing the prompt duration on hand hygiene performance was determined by a cluster-randomized trial on 3 nursing units with 1 acting as control. Sustainability of performance and participation was observed on 4 nursing units over a year. All staff were eligible to participate. Between June 2015 and December 2016, a total of 459,376 hand hygiene opportunities and 330,740 handwashing events from 511 staff members were recorded. Dispenser activation counts were significantly influenced by use of the system (χ2[3] = 75.76; P < .0001). Hand hygiene performance dropped from 62.61% to 24.94% (odds ratio, 0.36; 95% confidence interval, 0.34-0.38) when the prompting feature was removed. Staff participation had a negative trajectory of –0.72% (P < .001), whereas change in average performance was –0.18% (P < .001) per week for the year. Use of electronic monitoring with real-time prompts of 20 seconds' duration nearly doubles handwashing activity and causes handwashing to occur sooner after entering a patient room. These improvements are sustainable over a year.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.316
Threshold uncertainty score0.773

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.005
GPT teacher head0.317
Teacher spread0.313 · 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

Citations28
Published2018
Admission routes2
Has abstractyes

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