Enhancing healthcare process analysis through object-centric process mining: Transforming OMOP common data models into object-centric event logs

Gyunam Park, Yaejin Lee, Minsu Cho
January, 2024
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Abstract

Objectives. This study aims to enhance the analysis of healthcare processes by introducing Object-Centric Process Mining (OCPM). By offering a holistic perspective that accounts for the interactions among various objects, OCPM transcends the constraints of conventional patient-centric process mining approaches, ensuring a more detailed and inclusive understanding of healthcare dynamics. Methods. We develop a novel method to transform the Observational Medical Outcomes Partnership Common Data Models (OMOP CDM) into Object-Centric Event Logs (OCELs). First, an OMOP CDM4PM is created from the standard OMOP CDM, focusing on data relevant to generating OCEL and addressing healthcare data’s heterogeneity and standardization challenges. Second, this subset is transformed into OCEL based on specified healthcare criteria, including identifying various object types, clinical activities, and their relationships. The methodology is tested on the MIMIC-IV database to evaluate its effectiveness and utility. Results. The application of our proposed method to the MIMIC-IV dataset effectively produces OCELs, allowing for the implementation of OCPM in the healthcare industry. We rigorously evaluate the comprehensiveness and level of abstraction to validate our approach’s effectiveness. Additionally, we create diverse object-centric process models intricately designed to navigate the complexities inherent in healthcare processes. Conclusion. Our approach introduces a novel perspective by integrating multiple viewpoints simultaneously. To the best of our knowledge, this is the inaugural application of OCPM within the healthcare sector, marking a significant advancement in the field.

Type
Journal article
Publication
Journal of Biomedical Informatics

Practical problem

Hospitals use process mining to see how patients actually move through care, but these analyses take the patient or the visit as the only case. That hides the parallel workflows of physicians, nurses, pharmacists and medical devices and how they lock together in the same clinical event, such as an X-ray involving a patient, a technologist and a machine at once. Object-centric process mining can show all of these perspectives together, but it needs object-centric event logs, and no one has specified which object types, activity granularities and relationships a clinical log should contain. On top of that, hospital information systems differ in data structure and vocabulary, so results are hard to standardize or reproduce across institutions.

Research questions

  1. How can a standard clinical data model, the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM), be transformed systematically into an object-centric event log?
  2. Which object types, levels of clinical activity abstraction and event-to-object relationships should a healthcare object-centric event log contain?
  3. Does the transformation preserve data quality and produce object-centric process models that are informative on real hospital data?

Contributions

  • OMOP CDM4PM, a formally defined subset of the OMOP CDM that organizes event tables, object tables, event-object relationships and concept relationships for process mining.
  • A formal transformation from OMOP CDM4PM to an object-centric event log (OCEL) built from identifier, timestamp, event type and object type assignment functions.
  • A three-level event type assignment (domain, concept class, concept name) and a two-level object type assignment derived from the OMOP concept table and the SNOMED CT hierarchy.
  • An evaluation protocol that applies the Kahn et al. data quality framework (completeness, conformance) together with abstraction and utility analyses on three MIMIC-IV sub-datasets.
  • An open-source implementation and manual, omop2ocel, released on GitHub (gyunamister/omop2ocel).

Key findings

  • On three MIMIC-IV subsets converted to OMOP CDM (100 patients across all visit types, 1,088 inpatients, 913 emergency patients), the transformation keeps every event (364,532; 1,650,306; 1,580,208), object, patient, visit and length-of-stay value unchanged.
  • Abstracting from low-level to high-level activities cuts the number of events by 85.71%, 95.50% and 95.51% on the three datasets, and the number of event types in the 100-patient set from 2,393 to 25 (middle level) to 7 (high level).
  • The resulting logs contain eight object types in three groups: patient-related (patients, visits), provider-related (physicians, advanced health professionals, pharmacists) and device-related (dermal drug delivery, injection, diagnostic and supplements).
  • Object-centric directly-follows graphs over all object types are highly complex, largely because of device types; filtering to providers shows pharmacists handle only drug activities, physicians only condition activities, and advanced health professionals measurements, procedures and observations.
  • Limitations: no domain experts reviewed the models, the method gives no guidance on choosing an abstraction level, and scalability beyond MIMIC-IV to other institutions remains untested.