Modeling healthcare processes as service orchestrations and choreographies
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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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".