Modeling healthcare processes as service orchestrations and choreographies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose Service‐oriented architecture is becoming increasingly important for healthcare delivery as it assures seamless integration internally between various teams and departments, and externally between healthcare organizations and their partners. In order to make healthcare more efficient and effective, we need to understand and evaluate its processes, and one way of achieving that is through process modeling. Modeling healthcare processes within a service‐oriented environment opens up new perspectives and raises challenging questions. The purpose of this paper is to investigate one of these questions, namely the suitability of web service orchestration and choreography, two closely related but fundamentally different methodologies for modeling web service‐based healthcare processes. Design/methodology/approach The authors use a case‐based approach that first developed a set of 12 features for modeling healthcare processes and then used the features to compare orchestration and choreography for modeling part of the scheduled workflow. Findings The findings show that neither methodology can, by itself, meet all healthcare modeling requirements in the context of the case study. The appropriate methodology must be selected after consideration of the specific modeling needs. The authors identified usability, capabilities, and evolution as three key considerations to assist with selection of a methodology for healthcare process modeling. Further, sometimes one method will not meet all modeling needs and hence the authors recommend combining the two methodologies in order to harness the benefits of modeling healthcare processes in a service‐oriented environment. Originality/value Although literature exists on process modeling of web services for healthcare, there are no criteria describing necessary features for micro‐level modeling, nor is there a comparison of the two leading service composition methodologies within the healthcare context. This paper provides some necessary formalization for process modeling in healthcare.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it