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Record W2901935916 · doi:10.2196/11714

A Qualitative Study of the Theory Behind the Chairs: Balancing Lean-Accelerated Patient Flow With the Need for Privacy and Confidentiality in an Emergency Medicine Setting

2018· article· en· W2901935916 on OpenAlexaffvenueabout
Elaine Zibrowski, Lisa Shepherd, Richard Booth, Kamran Sedig, Candace J. Gibson

Bibliographic record

VenueJMIR Human Factors · 2018
Typearticle
Languageen
FieldMedicine
TopicPatient Dignity and Privacy
Canadian institutionsWestern University
Fundersnot available
KeywordsConfidentialityInternet privacyLean manufacturingQualitative researchComputer securityComputer scienceBusinessPsychologyMedical emergencyMedicineSociologyMarketing

Abstract

fetched live from OpenAlex

BACKGROUND: Many emergency departments (EDs) have used the Lean methodology to guide the restructuring of their practice environments and patient care processes. Despite research cautioning that the layout and design of treatment areas can increase patients' vulnerability to privacy breaches, evaluations of Lean interventions have ignored the potential impact of these on patients' informational and physical privacy. If professional regulatory organizations are going to require that nurses and physicians interact with their patients privately and confidentially, we need to examine the degrees to which their practice environment supports them to do so. OBJECTIVE: This study explored how a Lean intervention impacted the ability of emergency medicine physicians and nurses to optimize conditions of privacy and confidentiality for patients under their care. METHODS: From July to December 2017, semistructured interviews were iteratively conducted with health care professionals practicing emergency medicine at a single teaching hospital in Ontario, Canada. The hospital has 1000 beds, and approximately 128,000 patients visit its 2 EDs annually. In response to poor wait times, in 2013, the hospital's 2 EDs underwent a Lean redesign. As the interviews proceeded, information from their transcripts was first coded into topics and then organized into themes. Data collection continued to theoretical sufficiency. RESULTS: Overall, 15 nurses and 5 physicians were interviewed. A major component of the Lean intervention was the construction of a three-zone front cell at both sites. Each zone was outfitted with a set of chairs in an open concept configuration. Although, in theory, professionals perceived value in having the chairs, in practice, these served multiple, and often, competing uses by patients, family members, and visitors. In an attempt to work around limitations they encountered and keep patients flowing, professionals often needed to move a patient out from a front chair and actively search for another location that better protected individuals' informational and physical privacy. CONCLUSIONS: To our knowledge, this is the first qualitative study of the impact of a Lean intervention on patient privacy and confidentiality. The physical configuration of the front cell often intensified the clinical work of professionals because they needed to actively search for spaces better affording privacy and confidentiality for patient encounters. These searches likely increased clinical time and added to these patients' length of stay. We advocate that the physical structure and configuration of the front cell should be re-examined under the lens of Lean's principle of value-added activities. Future exploration of the perspectives of patients, family members, and visitors regarding the relative importance of privacy and confidentiality during emergency care is warranted.

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)0.028
metaresearch head score (Gemma)0.031
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.146

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0280.031
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0160.020
Scholarly communication0.0060.006
Open science0.0030.006
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0030.000

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 head0.101
GPT teacher head0.409
Teacher spread0.308 · 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 designQualitative
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

Citations11
Published2018
Admission routes3
Has abstractyes

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