Multidisciplinary Collaboration in the Treatment of Patients With Type 2 Diabetes in Primary Care: Analysis Using Process Mining

Abstract

BackgroundPublic health in several countries is characterized by a shortage of professionals and a lack of economic resources. Monitoring and redesigning processes can foster the success of health care institutions, enabling them to provide a quality service while simultaneously reducing costs. Process mining, a discipline that extracts knowledge from information system data to analyze operational processes, affords an opportunity to understand health care processes.ObjectiveHealth care processes are highly flexible and multidisciplinary, and health care professionals are able to coordinate in a variety of different ways to treat a diagnosis. The aim of this work was to understand whether the ways in which professionals coordinate their work affect the clinical outcome of patients.MethodsThis paper proposes a method based on the use of process mining to identify patterns of collaboration between physician, nurse, and dietitian in the treatment of patients with type 2 diabetes mellitus and to compare these patterns with the clinical evolution of the patients within the context of primary care. Clustering is used as part of the preprocessing of data to manage the variability, and then process mining is used to identify patterns that may arise.ResultsThe method is applied in three primary health care centers in Santiago, Chile. A total of seven collaboration patterns were identified, which differed primarily in terms of the number of disciplines present, the participation intensity of each discipline, and the referrals between disciplines. The pattern in which the three disciplines participated in the most equitable and comprehensive manner had a lower proportion of highly decompensated patients compared with those patterns in which the three disciplines participated in an unbalanced manner.ConclusionsBy discovering which collaboration patterns lead to improved outcomes, health care centers can promote the most successful patterns among their professionals so as to improve the treatment of patients. Process mining techniques are useful for discovering those collaborations patterns in flexible and unstructured health care processes.

Dynamic Models Supporting Personalised Chronic Disease Management through Healthcare Sensors with Interactive Process Mining

“…PALIA has been widely tested in real healthcare scenarios. It has been applied to the analysis of follow up protocols of patients with diabetes [ 35 , 53 ]; to measure and discover the individualised behaviour of older adults at risk of dementia [ 33 ]; for the characterisation of emergency flows, measuring organisational changes effects [ 46 ], for discovering surgery department flow [ 52 ], malnutrition assessment [ 49 ] or obesity characterisation [ 54 ]. PMApp also enables the creation of interactive dashboards that respond to the selection of arrows and nodes by capturing GUI events and it also allows the user to create custom forms and algorithms for discovery, filters, enhancement maps, and so forth [ 46 ].…”

Analysis of the Relationship between the Referral and Evolution of Patients with Type 2 Diabetes Mellitus

“…Only patients with T2DM who had their cardiovascular periodic appointments (CVPA) or check-ups were considered. These appointments or check-ups were carried out by any of the three professionals relevant to this study: doctor, nurse, or nutritionist [ 26 ]. Each time one of the three aforementioned professionals completed a CVPA, they must indicate which professional the patient should visit during his/her next check-up, as well as an approximate date of that appointment.…”

Individual Behavior Modeling with Sensors Using Process Mining

“…Process mining techniques are used to provide comprehensive models and information about human behaviors using data from the system. The data extracted from these devices are used to build an event log which contains the relevant information (activities completed, timestamps, case IDs and the resources that performed those activities), to be used in the available tools such as ProM [45], PALIA Suite [46] and DISCO [47]. These tools provide an output of different models that allow the detailed visualizations of the processes.…”

“…A discovery algorithm in process mining is applied to identify trajectories, and the process followed in the indoor scenario [27]. The PALIA Suite has an implementation of quality threshold clustering technique [46]. It enables to group human behaviors by a predefined similarity ratio using a workflow distance based on the error correcting method [48].…”