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

Abstract

Background: Public 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. Objective: Health 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. Methods: This 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. Results: The 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. Conclusions: By 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.

Health professionals' views and experiences around the dietary and lifestyle management of gestational diabetes in New Zealand

“…Internationally, there have also been various initiatives exemplifying a shift toward integrative primary care services that include dietary and lifestyle education. [30][31][32][33][34] Better access to nutrition professionals in primary care has the potential to ease the burden from other registered health professionals such as nurse practitioners, general practitioners and midwives who act as a first point of contact for women in need of dietary advice. 35 Health coaches have also recently been upheld in primary care settings to perform various health educational roles and facilitate behaviour change.…”

“…Guidance in pregnancy and GDM was one of several roles which, by improving dietary behaviours, could save $210 million per year in cardiovascular disease and diabetes care. Internationally, there have also been various initiatives exemplifying a shift toward integrative primary care services that include dietary and lifestyle education 30–34 . Better access to nutrition professionals in primary care has the potential to ease the burden from other registered health professionals such as nurse practitioners, general practitioners and midwives who act as a first point of contact for women in need of dietary advice 35 …”

Clinical and operational insights from data-driven care pathway mapping: a systematic review

“…A number of more recent papers follow Garg et al [84] in using metrics of resource usage Statistical analysis of associations within a symptom-diagnosis-treatment model [109]; and between derived pathways and treatments using probabilistic topic models [21] Ibanez-Sanchez et al [110] Fernandez-Llatas et al [111] Statistical analysis of admission times for different groups of patient pathways, extended to show significant effect of departmental reorganisation Vogt et al [112] Outcome analysis including odds of hospitalisation for a very large and disparate set of pathways Findlay et al [113] Extensive analysis of varied care pathways and outcomes populating a pre-defined pathway model Yu et al [114] "Care Pathway Workbench", facilitating guideline and statistical outcome analysis of patient pathways Hierarchical clustering [116] and social network modelling [117][118][119]. Filtering from the departmental perspective allows insights on strategic departments and seasonal variation [118], while associations are found between biomarkers, patterns of collaboration, and outcomes in [119] Table 20 Examples of publications considering pathways from a physical position perspective or cost as enhancing information [11,86,87,95,140,141]; combined with the continuing interest in simulations modelled from derived care pathways described in the "Optimisation and simulation" section, this comprehensive use of data in resource planning and service redesign should find increasing application in health systems under continual pressure to maximise efficiency. In the broader context, clinical pathway redesign is increasingly data-facilitated if not always data-driven; a good example is the recent well publicised report of Connell et al [142], where DeepMind (a subsidiary of Alphabet Inc.) essentially generated a portable implementation of a real-time updated electronic care record to facilitate a streamlined Acute Kidney Injury care pathway.…”

Modelagem, Mineração e Análise de Jornadas / Trajetórias de Pacientes

“…Cada vez mais os pacientes têm sido acompanhados por times formados por diferentes especialidades e, possivelmente, distribuídos entre múltiplas unidades de saúde [Rebuge and Ferreira 2012]. Além da variabilidade advinda das decisões de múltiplos especialistas, percebe-se que a constituição dos times de cuidado varia para cada paciente [Conca et al 2018].…”