Clinical pathway efficiency for elective joint replacement surgeries: a case study
Bibliographic record
Abstract
PURPOSE: The purpose of this paper is to define a clinical pathway for total joint replacement (TJR) surgery, estimate the effect of delays between steps of the pathway on wait time for surgery and to identify factors contributing to more efficient operations and challenges to their implementation. DESIGN/METHODOLOGY/APPROACH: This is a case study with a mixed methods approach. The authors conducted interviews with hospital staff. Data collected in the interviews and through on-site observation were analyzed to map the TJR process and identify the steps of the care pathway. The authors extracted and analyzed data (time stamps) from 60 hospital patient records for each step in the pathway and ran a regression on the duration of the whole trajectory. FINDINGS: There were wide variations in the delays observed between the seven steps identified. The delay between Step 1 and Step 2 was the only significant variable in predicting the total wait time to surgery. In one hospital, one delay explained 50 percent of the variation. There was misalignment between findings from the qualitative data in terms of strategies implemented to increase efficiency of the clinical pathway to the quantitative data on delays between the steps. RESEARCH LIMITATIONS/IMPLICATIONS: The study identified the clinical pathway from the consultation with an orthopaedic surgeon to the surgery. However, it did not go beyond the surgery. Future research could investigate the relationship between specific processes and delays between steps of the process and patient outcomes, including length of stay, mobilization and functionality in activities of daily living, as well as potential complications from surgery, readmission and the services required after the patient was discharged. PRACTICAL IMPLICATIONS: Wait times can be addressed by implementing strategies at the health system level or at the organizational level. The authors found and discuss areas where there could be efficiency gains for health care organizations. SOCIAL IMPLICATIONS: Stakeholders in care processes are diverse and they each have their preferences in how they practice (in the case of providers) and how they perceive and wish to respond adequately to patients' needs in contexts that have different norms and approaches. The approach in this study enables a better understanding of the processes, the organizational culture and how these may affect each other. ORIGINALITY/VALUE: Our mixed methods enabled a process mapping and the identification of factors that significantly affected the efficiency of the TJR surgery process. It combines methods from process engineering with health services and management research. To some extent, this study demonstrates that although managers can define and enforce processes, organizational culture and practices are harder to influence.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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, 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".