Predictive monitoring of clinical pathways

We propose a learning framework for predictive monitoring of clinical pathways.Both offline analysis and online monitoring services are provided.A probabilistic topic model is developed to describe essential behaviors of CPs.Two predictive monitoring services are presented to illustrate the potential of our framework.Evaluation is done using a real clinical datasetfrom a Chinese hospital. ObjectiveAccurate and timely monitoring, as a key aspect of clinical pathway management, provides crucial information to medical staff and hospital managers for determining the efficient medical service delivered to individual patients, and for promptly handling unusual treatment behaviors in clinical pathways (CPs). In many applications, CP monitoring is performed in a reactive manner, e.g., variant treatment events are detected only after they have occurred in CPs. Alternatively, this article presents an intelligent learning system for predictive monitoring of CPs and from a large volume of electronic medical records (EMRs). MethodsThe proposed system is composed of both offline analysis and online monitoring phases. In the offline phase, a particular probabilistic topic model, i.e., treatment pattern model (TPM), is generated from electronic medical records to describe essential/critical medical behaviors of CPs. Using TPM-based measures as a descriptive vocabulary, online monitoring of CPs can be provided for ongoing patient-care journeys. Specifically, this article presents two typical predictive monitoring services, i.e., unusual treatment event prediction and clinical outcome prediction, to illustrate how the potential of the proposed system can be exploited to provide online monitoring services from both internal and external perspectives of CPs. ResultsThe proposed monitoring services have been evaluated using a real clinical dataset pertaining to the unstable angina CP and collected from a large hospital in China. In terms of unusual treatment event prediction, the overall precision and recall of our system are 0.834, and 0.96, respectively, which is comparable to identify unusual treatment events in CPs in comparison with human evaluation. In terms of clinical outcome prediction, the stable model was characterized by 0.849 accuracy, 0.064 hamming-loss and 0.053 one-loss, which outperforms the benchmark multi-label classification algorithms on clinical outcome prediction. ConclusionExtensive evaluations on a real clinical data-set, typically missing from other work, demonstrate that the proposed system, as a crucial advantage over traditional expert systems for CP management, not only provides an efficient and general surveillance of CPs, but also empowers clinicians with the capability to look insights into CPs to gain a deeper understanding of the situations in which the proposed prediction technique performs well.