A multisectoral and multidisciplinary endeavor: a review of diabetes self-management apps in China.

BACKGROUND: While the use of self-management apps has considerable promise to efficiently reduce the diabetes burden that disproportionally affects low- and middle-income countries (LMICs), and the multisectoral and multidisciplinary approaches have been encouraged to be used in diabetes management, little is known about the status of the integration of these approaches in the existing diabetes self-management apps. This review examines the diabetes apps in China as an indication of the current status of integrating multisectoral and multidisciplinary approaches in diabetes mHealth care in LMICs. METHODS: Eligible diabetes apps were searched on major Chinese app stores up to December 23, 2022. The app comprehensiveness index (ranging 0-80) regarding the app functions and diabetes management domains was created. The multisectoral and multidisciplinary features were summarized using indices derived from current guidance. RESULTS: Sixty-six apps were reviewed, all developed by private companies. The average comprehensiveness score was 16, with many major self-management domains and functions not represented among the reviewed apps. Forty apps (61%) involved multiple sectoral entities, with public/private and private/private collaborations being the most common collaborative combinations. Thirty-seven apps (56%) involved multiple disciplines, among which endocrinology/metabolism, nutrition, and cardiovascular medicine were the top three most common disciplines. Compared to non-multidisciplinary apps, multidisciplinary apps tended to provide more comprehensive services in apps (6.14 vs. 5.18, p = 0.0345). Different sectors and disciplines tended to work independently, without robust interactions, in providing diabetes management services in the reviewed apps. CONCLUSION: Multisectoral and multidisciplinary features has presented in the current diabetes self-management apps in China; however, it is still in its infancy and significant limitations existed. More engagement of civil society organizations and community groups and innovative collaborations between sectors and disciplines are needed to provide comprehensive, continuous, and patient-centered mHealth care for patients with diabetes in LMICs like China. Clear guidance for integrating and evaluating the multisectoral and multidisciplinary efforts in self-management apps is necessary to ensure the effective use of mHealth solutions for diabetes management in LMICs.

Contextual bias, the democratization of healthcare, and medical artificial intelligence in low- and middle-income countries.

Medical artificial intelligence (MAI) creates an opportunity to radically expand access to healthcare across the globe by allowing us to overcome the persistent labor shortages that limit healthcare access. This democratization of healthcare is the greatest moral promise of MAI. Whatever comes of the enthusiastic discourse about the ability of MAI to improve the state-of-the-art in high-income countries (HICs), it will be far less impactful than improving the desperate state-of-the-actual in low- and middle-income countries (LMICs). However, the almost exclusive development of MAI in HICs risks this promise being thwarted by contextual bias, an algorithmic bias that arises when the context of the training data is significantly dissimilar from potential contexts of application, which makes the unreflective application of HIC-based MAI in LMIC contexts dangerous. The use of MAI in LMICs demands careful attention to context. In this paper, I aim to provide that attention. First, I illustrate the dire state of healthcare in LMICs and the hope that MAI may help us to improve this state. Next, I show that the radical differences between the health contexts of HICs and those of LMICs create an extraordinary risk of contextual bias. Then, I explore ethical challenges raised by this risk, and propose policies that will help to overcome those challenges. Finally, I sketch a wide range of related issues that need to be addressed to ensure that MAI has a positive impact on LMICs-and is able to improve, rather than worsen, global health equity.

Impact of federalization for health financing and workforce in Nepal.

The adoption of its 2015 constitution has converted Nepal to a federal government while simultaneously resulted in significant reforms of the health system in Nepal in terms of both structure and commitment. In this commentary, we review evidence ranging from health financing to health workforce development to show that the impact of federalization on Nepal's health system and its efforts to achieve equitable and affordable universal health care have been mixed. On the one hand, careful efforts of the federal government to support subnational governments during the transition appears to have avoided serious disruption, subnational governments have successfully taken on the financial burden of the health system, and increase subnational control has allowed more flexible adaptation to changing needs than might have otherwise been possible. On the other hand, financing resource and ability disparities across subnational governments contributes to significant disparities in workforce development, and subnational authorities appear to have underestimated significant health issues (e.g. NCDs) in their budgets. We then provide three recommendations to improve the success of the Nepalese system: (1) to assess whether the services covered by health financing and insurance schemes like the National Health Insurance Program adequately address the needs of the rising burden of NCDs in Nepal, (2) to set clear minimum requirements on key metrics for subnational health systems, and (3) to extend grant programs to address resource disparities.