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Record W2965858466 · doi:10.1109/seh.2019.00014

Lean Healthcare Processes: Effective Technology Integration and Comprehensive Decision Support Using Requirements Engineering Methods

2019· article· en· W2965858466 on OpenAlexaff
Malak Baslyman, Daniel Amyot, Yasser Alshalahi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldHealth Professions
TopicQuality and Safety in Healthcare
Canadian institutionsInstitut du Savoir MontfortUniversity of Ottawa
Fundersnot available
KeywordsHealth careComputer scienceDecision support systemSystems engineeringRisk analysis (engineering)Process managementEngineeringBusinessArtificial intelligence

Abstract

fetched live from OpenAlex

Healthcare faces many challenges in delivering better service quality and fulfilling, rapidly, changing needs. Lean management approaches, widely adopted in healthcare, provide methods and tools for process improvement. However, the main pitfall of the Lean approach in healthcare is a narrow focus on patient values and needs that often excludes those of caregivers, among others. Caregivers in hospitals are a special type of employees, different from those of most other organizations. They are intensively immersed in a dynamic context in which they treat end-users (patients) with many variables to consider, and multiple critical procedures to follow. It is essential to consider their needs and goals, in addition to organizational goals and values, in any change management process, especially if changes involve new technology. In this paper, we propose a Lean-AbPI model that combines the main concepts of Lean management with the Activity-based Process Integration approach (AbPI). AbPI provides several integration alternatives of new processes into existing ones while analyzing the impact of the changes on stakeholder/user needs, organizational goals, and performance objectives. The use of the Lean-AbPI model is demonstrated through a real hospital case study. The results show that using the model, a comprehensive analysis is provided that leads to improved decision support and implementation-related rationales traceability.

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.531
Threshold uncertainty score0.792

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.140
GPT teacher head0.534
Teacher spread0.394 · 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 designOther design
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

Citations3
Published2019
Admission routes1
Has abstractyes

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