Best of Both Worlds: Bridging One Model for All and Group-Specific Model Approaches using Ensemble-based Subpopulation Modeling.

Subpopulation models have become of increasing interest in prediction of clinical outcomes because they promise to perform better for underrepresented patient subgroups. However, the personalization benefits gained from these models tradeoff their statistical power, and can be impractical when the subpopulation's sample size is small. We hypothesize that a hierarchical model in which population information is integrated into subpopulation models would preserve the personalization benefits and offset the loss of power. In this work, we integrate ideas from ensemble modeling, personalization, and hierarchical modeling and build ensemble-based subpopulation models in which specialization relies on whole group samples. This approach significantly improves the precision of the positive class, especially for the underrepresented subgroups, with minimal cost to the recall. It consistently outperforms one model for all and one model for each subgroup approaches, especially in the presence of a high class-imbalance, for subgroups with at least 380 training samples.

Closing the Digital Divide in Interventions for Substance Use Disorder.

Digital health interventions are exploding in today's medical practice and have tremendous potential to support the treatment of substance use disorders (SUD). Developers and healthcare providers alike must be cognizant of the potential for digital interventions to exacerbate existing inequities in SUD treatment, particularly as they relate to Social Determinants of Health (SDoH). To explore this evolving area of study, this manuscript will review the existing concepts of the digital divide and digital inequities, and the role SDoH play as drivers of digital inequities. We will then explore how the data used and modeling strategies can create bias in digital health tools for SUD. Finally, we will discuss potential solutions and future directions to bridge these gaps including smartphone ownership, Wi-Fi access, digital literacy, and mitigation of historical, algorithmic, and measurement bias. Thoughtful design of digital interventions is quintessential to reduce the risk of bias, decrease the digital divide, and create equitable health outcomes for individuals with SUD.

A Qualitative Evaluation of a Decision Support System for District-Level Disease Surveillance in Sierra Leone.

A decision support system for district-level disease surveillance was piloted with the Port Loko District Health Management Team in Sierra Leone. Through a qualitative evaluation, the study explores the impact of the system on disease surveillance workflows. Results indicate that the system aided decision making for operational tasks, and reduced the time taken to analyze and report surveillance data. In addition, the study discusses the challenges of deploying a pilot system during the Ebola recovery in Sierra Leone, and proposes a high-level architecture for a modular, interoperable decision support system for disease surveillance for public health decision makers in low-resource health systems.

Design and evaluation of a web-based decision support tool for district-level disease surveillance in a low-resource setting.

During the 2014 West African Ebola Virus outbreak it became apparent that the initial response to the outbreak was hampered by limitations in the collection, aggregation, analysis and use of data for intervention planning. As part of the post-Ebola recovery phase, IBM Research Africa partnered with the Port Loko District Health Management Team (DHMT) in Sierra Leone and GOAL Global, to design, implement and deploy a web-based decision support tool for district-level disease surveillance. This paper discusses the design process and the functionality of the first version of the system. The paper presents evaluation results prior to a pilot deployment and identifies features for future iterations. A qualitative assessment of the tool prior to pilot deployment indicates that it improves the timeliness and ease of using data for making decisions at the DHMT level.