Easier and Faster Is Not Always Better: Grounded Theory of the Impact of Large-Scale System Transformation on the Clinical Work of Emergency Medicine Nurses and Physicians (Preprint)
Bibliographic record
Abstract
BACKGROUND The effectiveness of Lean Thinking as a quality improvement method for health care has been contested due, in part, to our limited contextual understanding of how it affects the working conditions and clinical workflow of nurses and physicians. Although there are some initial indications, arising from prevalence surveys and interviews, that Lean may intensify work performed within medical environments, the evidence base still requires detailed descriptions of the changes that were actually introduced to individuals’ clinical workflow and how these changes impacted health care professionals. OBJECTIVE The aim of this study was to explore ways in which a Lean intervention may impact the clinical work of emergency medicine nurses and physicians. METHODS We used a realist grounded theory approach to explore the clinical work of nurses and physicians practicing in 2 emergency medicine departments from a single teaching hospital in Canada. The hospital has 1000 beds with 128,000 emergency department (ED) visits annually. In 2013, both sites began a large-scale, Lean-driven system transformation of their practice environments. In-person interviews were iteratively conducted with health care professionals from July to December 2017. Information from transcripts was coded into categories and compared with existing codes. With repeated review of transcripts and evolving coding, we organized categories into themes. Data collection continued to theoretical sufficiency. RESULTS A total of 15 emergency medicine nurses and 5 physicians were interviewed. Of these, 18 individuals had practiced for at least 10 years. Our grounded theory involved 3 themes: (1) organization of our clinical work, (2) pushed pace in the front cell, and (3) the toll this all takes on us. Although the intervention was supposed to make the EDs work easier, faster, and better, the participants in our study indicated that the changes made had the opposite impact. Nurses and physicians described ways in which the reconfigured EDs disrupted their established practice routines and resulted in the intensification of their work. Participants also identified indications of deskilling of nurses’ work and how the new push-forward model of patient care had detrimental impacts on their physical, cognitive, and emotional well-being. CONCLUSIONS To our knowledge, this is the first study to describe the impact of Lean health care on the working conditions and actual work of emergency medicine nurses and physicians. We theorize that rather than support health care professionals in their management of the complexities that characterize emergency medicine, the physical and process-based changes introduced by the Lean intervention acted to further complicate their working environment. We have illuminated some unintended consequences associated with accelerating patient flow on the clinical workflow and perceived well-being of health care professionals. We identify some areas for reconsideration by the departments and put forward ideas for future research.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | high |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | high |
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 | 0.050 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.007 | 0.037 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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, unvalidatedLabeled directly by 2 models reading the full record.
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".