A holonic approach to clinical pathway data analysis.

Background: Healthcare faces challenges due to the advancements of Industry 4.0 as large volumes of data are generated within healthcare facilities that, combined with the complex nature of healthcare environments, make it difficult to utilise and interpret this data effectively.

Purpose: A novel holonic approach to clinical pathway data analysis is presented and implemented as a clinical pathway digital twin. A holon is here taken to be an autonomous and co-operative building block of a software system for transforming, transporting, storing and/or validating information. The digital twin's aim is to ingest, structure and analyse the information associated with a clinical pathway to support healthcare professionals in making informed decisions, for example monitoring and predicting the duration from admission to discharge for individual patients.

Method: Real world observations and a review of literature led to the identification of a generic set of clinical pathway analysis needs and, derived therefrom, a set of design requirements. A proof-of-concept clinical pathway analysis digital twin was implemented using a holonic approach derived from the ARTI reference architecture. The holonic approach is evaluated in a hip and knee replacement pathway case study. The evaluation includes automated statistical analyses and machine learning predictions.

Results: The evaluation demonstrates that the holonic approach provides an intuitive and extensible means to aggregate and disaggregate information tactically, and to derive context-tailored analysis features. The holonic approach enhances checking for data completion and handling data anomalies. The evaluation also demonstrates on-demand report generation, which reduces repetitive manual tasks for healthcare professionals.

Conclusion: The novel holonic data analysis approach facilitates context-rich analyses tailored to specific clinical pathway activities, with effective tailoring of data ingestion and analysis. Healthcare professionals can use the data analysis approach to extract valuable insights for decision-making related to clinical pathways.