Healthcare pathway discovery and probabilistic machine learning. (May 2020)

• Record Type: Journal Article
• Authors: Kempa-Liehr, Andreas W.; Lin, Christina Yin-Chieh; Britten, Randall; Armstrong, Delwyn; Wallace, Jonathan; Mordaunt, Dylan; O'Sullivan, Michael
• Is Part Of: International journal of medical informatics. Volume 137(2020)
• Publication Date: 2020-05
• Subjects: Healthcare pathway -- Process mining -- Electronic health record -- Probabilistic programming; Medical informatics -- Periodicals; Information science -- Periodicals; Computers -- Periodicals; Medical technology -- Periodicals; Medical Informatics -- Periodicals; Technology, Medical -- Periodicals; Computers; Information science; Medical informatics; Medical technology; Electronic journals; Periodicals; Electronic journals; 610.285
• DOI: 10.1016/j.ijmedinf.2020.104087
• Languages: English
• ISSNs: 1386-5056

Abstract

Graphical abstract: Highlights: Pathway variants and other features from event sequences can be used as co-variates for probabilistic machine learning models in order to explain the observed stochastic volatility of pathway performances. Geometric regression can generate posterior predictive distributions of individualized recovery times. Probabilistic machine learning models enable the prediction of patient specific discharge probabilities, which lead to patient specific postoperative length of stay distributions. Abstract: Background and purpose: Healthcare pathways define the execution sequence of clinical activities as patients move through a treatment process, and they are critical for maintaining quality of care. The aim of this study is to combine healthcare pathway discovery with predictive models of individualized recovery times. The pathway discovery has a particular emphasis on producing pathway models that are easy to interpret for clinicians without a sufficient background in process mining. The predictive model takes the stochastic volatility of pathway performance indicators into account. Method: This study utilizes the business process-mining software ProM to design a process mining pipeline for healthcare pathway discovery and enrichment using hospital records. The efficacy of combining learned healthcare pathways with probabilistic machine learning models is demonstrated via a case study that applies the proposed process mining pipeline to discover appendicitis pathways from hospital records. Machine learning methodologies based on probabilistic programming are utilized to explore pathway features that influence patient recovery time. Results: The produced appendicitis pathway models are easy for clinical interpretation and provide an unbiased overview of patient movements through the treatment process. Analysis of the discovered pathway model enables reasons for longer than usual treatment times to be explored and deviations from standard treatment pathways to be identified. A probabilistic regression model that estimates patient recovery time based on the information extracted by the process mining pipeline is developed and has the potential to be very useful for hospital scheduling purposes. Conclusion: This study establishes the application of the business process modelling tool ProM for the improvement of healthcare pathway mining methods. The proposed pipeline for healthcare pathway discovery has the potential to support the development of probabilistic machine learning models to further relate healthcare pathways to performance indicators such as patient recovery time.