dfgcompare: a library to support process variant analysis through Markov models.

BACKGROUND: Data-driven process analysis is an important area that relies on software support. Process variant analysis is a sort of analysis technique in which analysts compare executed process variants, a.k.a. process cohorts. This comparison can help to identify insights for improving processes. There are a few software supports to enable process cohort comparison based on the frequencies of process activities and performance metrics. These metrics are effective in cohort analysis, but they cannot support cohort comparison based on the probability of transitions among states, which is an important enabler for cohort analysis in healthcare. RESULTS: This paper defines an approach to compare process cohorts using Markov models. The approach is formalized, and it is implemented as an open-source python library, named dfgcompare. This library can be used by other researchers to compare process cohorts. The implementation is also used to compare caregivers' behavior when prescribing drugs in the Stockholm Region. The result shows that the approach enables the comparison of process cohorts in practice. CONCLUSIONS: We conclude that dfgcompare supports identifying differences among process cohorts.

Evaluating a Clinical Decision Support System for Drug-Drug Interactions.

Janusmed is a clinical decision support system, developed by the Stockholm County Council that supports physicians in identifying drug-drug interactions. To determine how Janusmed is used in and affects the clinical practice, an evaluation study is currently being carried out that analyzes multiple data sources through descriptive statistics. The study focuses on how Janusmed affects the behavior of the physicians, in particular, to what extent physicians reconsider their prescription decisions based on warnings from Janusmed.

The coordination hub: Toward patient-centered and collaborative care processes.

The organization and processes of today's health and social care are becoming ever more complex as a consequence of societal trends, including an aging population and an increased reliance on care at home. One aspect of the increased complexity is that a single patient may receive care from several care providers, which easily results in situations with potentially incoherent, uncoordinated, and interfering care processes. In order to describe and analyze such situations, the article introduces the notion of a process conglomeration. This is defined as a set of patient-care processes that all concern the same patient, that are overlapping in time, and that all are sharing the overall goal of improving or maintaining the health and social well-being of the patient. Problems and challenges of process conglomerations are investigated using coordination theory and models for continuous process improvement. In order to address the challenges, a solution is proposed in the form of a Coordination Hub, being an integrated software service that offers a number of information services for coordinating the activities of the processes in a process conglomeration.