Clusters of medical specialties around patients with multimorbidity - employing fuzzy c-means clustering to explore multidisciplinary collaboration

BACKGROUND

Hospital care organization, structured around medical specialties and focused on the separate treatment of individual organ systems, is challenged by the increasing prevalence of multimorbidity. To support the hospitals' realization of multidisciplinary care, we hypothesized that using machine learning on clinical data helps to identify groups of medical specialties who are simultaneously involved in hospital care for patients with multimorbidity.

METHODS

We conducted a cross-sectional study of patients in a Dutch general hospital and used a fuzzy c-means clustering algorithm for the analysis. We explored the patients' membership degrees in each cluster to identify subgroups of medical specialties that provide care to the same patients with multimorbidity. We used retrospectively collected electronic health record data from 2017. We extracted data from 22,133 patients aged ≥18 years who had received outpatient clinical care for two or more chronic and/ or oncological diagnoses.

RESULTS

We found six clusters of medical specialties and identified 22 subgroups. The clusters were labeled based on the specialties that most characterized them: 1. dermatology/ plastic surgery, 2. six specialties (gynecology/ rheumatology/ orthopedic surgery/ urology/ gastroenterology/ otorhinolaryngology), 3. pulmonology, 4. internal medicine/ cardiology/ geriatrics, 5. neurology/ physiatry (rehabilitation)/ anesthesiology, and 6. internal medicine. Most patients had a full or dominant membership to one of these clusters of medical specialties (11 subgroups), whereas fewer patients had a membership to two clusters. The prevalence of specific diagnosis groups, patient characteristics, and healthcare utilization differed between subgroups.

CONCLUSION

Our study shows that clusters and subgroups of medical specialties simultaneously involved in hospital care for patients with multimorbidity can be identified with fuzzy c-means cluster analysis using clinical data. Clusters and subgroups differed regarding the involved medical specialties, diagnoses, patient characteristics, and healthcare utilization. With this strategy, hospitals and medical specialists can further analyze which subgroups are target populations that might benefit from improved multidisciplinary collaboration.

A hospital care coordination team intervention for patients with multimorbidity: A practice-based, participatory pilot study

OBJECTIVES

This study aims to develop and pilot a hospital care coordination team intervention for patients with multimorbidity and identify key uncertainties.

METHODS

Practice-based, participatory pilot study with mixed methods in a middle-large teaching hospital. We included adult patients who had visited seven or more outpatient specialist clinics in 2018. The intervention consisted of an intake, a comprehensive review by a dedicated care coordination team, a consultation to discuss results and two follow-up appointments. We collected both quantitative and qualitative data.

RESULTS

Out of 131 invited patients, 28 participants received the intake and comprehensive review. The intervention resulted in mixed outputs and short-term outcomes. Among the 28 participants, 21 received recommendations for at least two out of three categories (medication, involved medical specialists, other). Patients' experienced effects ranged from no to very large effects. Key uncertainties were how to identify patients with a need for care coordination and the minimum of required data that can be collected during regular clinical care with feasible effort.

DISCUSSION

Recruitment and selection for hospital care coordination should be refined to include patients with multimorbidity who might benefit most. Outcomes of research and clinical care should align and first focus on evaluating the results of care coordination before evaluating health-related outcomes.

Secondary care experiences of patients with multiple chronic conditions

BACKGROUND

This study aimed to investigate patients' experiences, beliefs and understandings of the current secondary care of patients with multiple chronic conditions (MCC) in the Netherlands.

METHODS

A qualitative, interpretative description design was used. We conducted semi-structured, in-depth interviews with patients with MCC, who visited at least two physicians in Gelre Hospitals for at least two appointments in the previous year. After eight interviews data saturation was achieved.

RESULTS

Being a patient with MCC in the hospital can be complex and keeping an overview required effort, according to the participants. Most participants would appreciate more coordination and communication. However, the exact needs seemed to differ. The multiple visits transformed them into experienced patients: based on their experiences and observations they developed strategies to sustain themselves in the hospital. Different types of communication (an important, overarching theme) evoked specific feelings and expectations that were important for the patients' care experiences as well.

CONCLUSION

An overview of patient care seems an essential element for a more coordinated, individualised approach to care. Future research might focus on ways to engage both healthcare professionals and patients in the improvement of care. It could aim to find ways to create an overview and coordination, and define responsibilities, but also to clarify which groups of patients need assistance. It might also investigate the effect of good and clear communication on reducing obstacles that patients perceive when dealing with healthcare situations. Overall, also in the future, patients' care experiences could play an important role in determining the direction of new interventions.