Team Collaboration Networks and Multidisciplinarity in Diabetes Care: Implications for Patient Outcomes

Efficacy of a Digital Educational Intervention for Patients With Type 2 Diabetes Mellitus: Multicenter, Randomized, Prospective, 6-Month Follow-Up Study

BACKGROUND

Adherence to therapies and metabolic control among patients with type 2 diabetes mellitus (T2DM) remain challenging. The use of new technologies, such as telemedicine, digitalized systems, and social networks, could improve self-management and disease control.

OBJECTIVE

We evaluated the efficacy of a digital educational intervention for patients with T2DM, expressed as changes in glycated hemoglobin (HbA1c) and body composition and evaluation of the response using validated questionnaires of satisfaction with health care professionals (Instrument for Evaluation of the Experience of Chronic Patients), Diabetes Knowledge Scale (ECODI), and adherence to treatment over 6 months of follow-up (Morisky, Green, Levine Medication Assessment Questionnaire).

METHODS

This multicenter, randomized, prospective study included adults with T2DM with poor metabolic control who started treatment with glucagon-like peptide-1 receptor agonists. Patients were randomized to digital intervention or usual care. The intervention group received education through social networks and digital tools in a structured program of healthy lifestyle changes. This was provided by a "Digital Coach" for weekly and on-demand advice and individualized support. Baseline and follow-up demographic, clinical parameter, adherence, and quality of life data were collected.

RESULTS

We included 85 patients (control: n=41; intervention: n=44). Both groups were matched regarding demographics, physical examination, insulin, and biochemical parameters. We observed a reduction in body weight (intervention: -8.7, SD 6.1 kg vs control: -4.9, SD 5.0 kg; t83=-3.13; P=.002), BMI (intervention: -3.0, SD 2.1 kg/m2 vs control: -1.8, SD 1.8 kg/m2; t83=-2.82; P=.006), and fast mass in both groups but greater in the intervention group. There were greater reductions in fasting plasma glucose (intervention: 122.6, SD 81.5 mg/dL vs control: 70.5, SD 72.9 mg/dL; t83=3.10; P=.004) and HbA1c (intervention: 3.7%, SD 1.9% vs control: 2.6%, SD 2.1%; t83=2.54; P=.006) in the intervention group. Although there was no significant change in the Spanish version of the Diabetes Quality of Life Questionnaire (EsDQOL) satisfaction score in the control group after 6 months of follow-up (0.7, SD 19.8), there was a marked reduction in EsDQOL satisfaction score in the intervention group (-13.7, SD 23.1; t83=-3.08; P=.02). According to the ECODI scale, knowledge about diabetes increased more in the intervention group (intervention: 0.3, SD 1.8 vs control: 1.5, SD 1.5; t83=-3.33; P=.001). Although the medication adherence score worsened in the control group after 6 months, it significantly improved with the intervention (control: -8% vs intervention: 13.8%; χ21=0.35; P=.01). Patients' health care experiences improved with the intervention but not with the control.

CONCLUSIONS

The digital educational intervention was effective at improving glycemic control, body composition, adherence, and patient satisfaction compared with usual care in patients with T2DM. The implementation of digital tools and social media could highly improve the multidisciplinary approach to the management of this population.

TRIAL REGISTRATION

ClinicalTrials.gov NCT06850129; https://clinicaltrials.gov/study/NCT06850129.

Narrative review of comprehensive management strategies for diabetic retinopathy: interdisciplinary approaches and future perspectives

This review examines the epidemiological trends, pathophysiologic mechanisms, and current and future therapeutic strategies for diabetic retinopathy (DR), focusing on innovative management countermeasures in the face of this global public health challenge. As the number of patients with diabetes continues to increase, DR, as one of its major complications, poses a significant threat to global visual health. This review not only summarises the latest advances in personalised treatment and emerging therapeutic modalities (such as anti-vascular endothelial growth factor therapy, laser treatment, surgical procedures and cutting-edge gene and stem cell therapies) but also emphasises the revolutionary potential of telemedicine technologies and digital health platforms to improve DR screening and adherence among people with diabetes. We show how these technological innovations, especially in resource-limited settings, can achieve early diagnosis and effective treatment, thereby significantly reducing the public health burden of DR. In addition, this article highlights the critical role of interdisciplinary teamwork in optimising the comprehensive management of DR, involving close collaboration among physicians, researchers, patient education specialists and policy-makers, as well as the importance of implementing these innovative solutions through societal engagement and policy support. By highlighting these innovative strategies and their specific impact on improving public health practices, this review offers new perspectives and strategies for the future management of DR, with the goal of promoting the prevention, diagnosis and treatment of DR worldwide, improving patient prognosis and enhancing quality of life.

Variation in patient-sharing network characteristics of health care professionals treating different mental and substance use disorder patient sub-groups in primary care

BACKGROUND

Providing efficient and targeted services for patients with mental health problems requires efficient collaboration and coordination within healthcare providers, but measuring collaboration using traditional methods is challenging.

AIMS

To explore the patient-sharing networks of professionals taking care of different groups of patients with mental or substance use disorders.

METHOD

We used data that covered adult patients' visits to the primary care service providers of seven municipalities in Finland during year 2021. Data included 8,217 patients (147,430 visits) with mental or substance use disorders who were treated by 1,566 health care professionals. We calculated descriptive network metrics to examine the connectivity of professionals in three different patient groups (patients with substance use disorders, psychotic disorders, and depressive disorders) and compared these characteristics to a network based on all patients. We also analyzed whether patient sharing was associated with the health care professionals' attributes (occupational group, municipality) using Exponential Random Graph Models (ERGM).

RESULTS

Diagnosis-specific networks were denser and more connected compared to the all-patients network. Nurses were the most central occupation in all the diagnosis-specific networks and especially in the substance use disorder patients network. When examining all patients, two professionals were more likely to share patients when they belonged to the same occupational group. However, in the network with depressive disorder patients we found the opposite: professionals were more likely to share patients if they were of different occupational groups.

CONCLUSIONS

Patient-sharing networks within patients with a specific mental or substance use disorders are denser and more connected than networks based on all patients with mental or substance use disorders. In the substance use disorder patients network particularly, nurses were the most central occupation. Multi-professional connections were more likely in depressive disorder networks than in the all-patients network.

Analysis of the relationship between treatment networks and the evolution of patients with Type 2 Diabetes Mellitus

Type 2 Diabetes Mellitus (T2DM) is a chronic disease that has been increasing in prevalence in recent years and that can cause severe complications. To ensure patient care is administered correctly, it is necessary for medical treatment teams to be both multidisciplinary and cohesive. The analysis of health processes is a constant challenge due to their high variability and complexity. This paper proposes a method based on the analysis of social networks to detect treatment networks, and to identify a relationship between these networks and patient evolution, as measured by glycated hemoglobin (HbA1c) levels. The networks were segmented based on patient adherence to their medical appointments and their mean time of delay. We applied this method on a sample of 1574 patients diagnosed with T2DM. Results show that participatory treatment -in which a patient sees a particular group of professionals on a recurrent basis - together with high levels of adherence are associated to those patients who improve their HbA1c levels in the case of high levels of adherence, while those who continually experience referrals to different professionals, remain unstable and, in some cases, get worse. On the other hand, in order to maintain a patient as stable, continuous control of the patient is enough, regardless of the recurrence to the same professionals.