Investigating a Work System Approach to Implement an Emergency Department Surge Management System: Case Study
Bibliographic record
Abstract
BACKGROUND: Emergency department (ED) crowding is a global health care issue. eHealth systems have the potential to reduce crowding; however, the true benefits are seldom realized because the systems are not integrated into clinicians' work. We sought a deep understanding of how an eHealth system implementation can be structured to truly integrate the system into the workflow. OBJECTIVE: The specific objectives of this study were to examine whether work system theory (WST) is a good approach to structure the implementation of an eHealth system by incorporating the entire work system, and not just the eHealth system, in the implementation framework; identify the role that specific elements of WST's static framework and dynamic work system life cycle model play in the implementation; and demonstrate how WST can be applied in the health care setting to guide the implementation of an eHealth system. METHODS: Through a case study of an ED in a rural hospital, we used a mixed methods approach to examine the implementation of a surge management system through the lens of WST. We conducted 14 hours of observation in the ED; 20 interviews with clinicians, management, and members of the implementation team; and a survey of 23 clinicians; reviewed related documentation; and analyzed ED data to measure wait times. We used template analysis based on WST to structure our analysis of qualitative data and descriptive statistics for quantitative data. RESULTS: The surge management system helped to reduce crowding in the ED, staff was satisfied with the implementation, and wait time improvements have been maintained for several years. Although study participants indicated changes to their workflow, 72% (13/18) of survey participants were satisfied with their use of the system, and 82% (14/17) indicated that it was integrated with their workflow. Examining the implementation through the lens of WST enabled us to identify the aspects of the implementation that made it so successful. By applying the WST static framework, we saw how the implementation team incorporated the elements of the ED work system, assessed their alignment, and designed interventions to address areas of misalignment. The dynamic work system life cycle model captured how planned and unplanned changes were managed throughout the iterative implementation cycle-83% (15/18) of participants indicated that there was sufficient management support for the changes and 80% (16/20) indicated the change served an important purpose. CONCLUSIONS: The broad scope and holistic approach of WST is well suited to guide eHealth system implementations as it focuses efforts on the entire work system and not just the IT artifact. We broaden the focus of WST by applying it to the implementation of an ED surge management system. These findings will guide further studies and implementations of eHealth systems using WST.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".