Learning to Prescribe Interventions for Tuberculosis Patients Using Digital Adherence Data

Digital Adherence Technologies (DATs) are an increasingly popular method for verifying patient adherence to many medications. We analyze data from one city served by 99DOTS, a phone-call-based DAT deployed for Tuberculosis (TB) treatment in India where nearly 3 million people are afflicted with the disease each year. The data contains nearly 17,000 patients and 2.1M dose records. We lay the groundwork for learning from this real-world data, including a method for avoiding the effects of unobserved interventions in training data used for machine learning. We then construct a deep learning model, demonstrate its interpretability, and show how it can be adapted and trained in different clinical scenarios to better target and improve patient care. In the real-time risk prediction setting our model could be used to proactively intervene with 21% more patients and before 76% more missed doses than current heuristic baselines. For outcome prediction, our model performs 40% better than baseline methods, allowing cities to target more resources to clinics with a heavier burden of patients at risk of failure. Finally, we present a case study demonstrating how our model can be trained in an end-to-end decision focused learning setting to achieve 15% better solution quality in an example decision problem faced by health workers.Comment: 10 pages, 6 figure

The roadmap to healthcare digitalization: factors that affect the Portuguese approach to ehealth

Healthcare systems worldwide need to improve health outcomes while reducing costs. Increasing demand for chronic disease management, such as pulmonary disease, is driving digital transformation in healthcare. The Kata® inhalation app aims to improve patients' inhalation techniques through a data-based algorithm. This study aims to classify Portugal’s proneness to healthcare digitalization. Specifically, it investigates to which extent Portugal is lagging compared to digital pioneers and how it can keep up with advancements in a rapidly evolving technological world. The results suggest that Portugal is a positive example of eHealth but its main challenges are patients’ low digital trust and inoperability across infrastructures and health subsystems

How 5G wireless (and concomitant technologies) will revolutionize healthcare?

The need to have equitable access to quality healthcare is enshrined in the United Nations (UN) Sustainable Development Goals (SDGs), which defines the developmental agenda of the UN for the next 15 years. In particular, the third SDG focuses on the need to “ensure healthy lives and promote well-being for all at all ages”. In this paper, we build the case that 5G wireless technology, along with concomitant emerging technologies (such as IoT, big data, artificial intelligence and machine learning), will transform global healthcare systems in the near future. Our optimism around 5G-enabled healthcare stems from a confluence of significant technical pushes that are already at play: apart from the availability of high-throughput low-latency wireless connectivity, other significant factors include the democratization of computing through cloud computing; the democratization of Artificial Intelligence (AI) and cognitive computing (e.g., IBM Watson); and the commoditization of data through crowdsourcing and digital exhaust. These technologies together can finally crack a dysfunctional healthcare system that has largely been impervious to technological innovations. We highlight the persistent deficiencies of the current healthcare system and then demonstrate how the 5G-enabled healthcare revolution can fix these deficiencies. We also highlight open technical research challenges, and potential pitfalls, that may hinder the development of such a 5G-enabled health revolution

Jefferson Digital Commons quarterly report: January-March 2020

This quarterly report includes: New Look for the Jefferson Digital Commons Articles COVID-19 Working Papers Educational Materials From the Archives Grand Rounds and Lectures JeffMD Scholarly Inquiry Abstracts Journals and Newsletters Master of Public Health Capstones Oral Histories Posters and Conference Presentations What People are Saying About the Jefferson the Digital Common

Artificial intelligence for social impact: Learning and planning in the data-to-deployment pipeline

With the maturing of artificial intelligence (AI) and multiagent systems research, we have a tremendous opportunity to direct these advances toward addressing complex societal problems. In pursuit of this goal of AI for social impact, we as AI researchers must go beyond improvements in computational methodology; it is important to step out in the field to demonstrate social impact. To this end, we focus on the problems of public safety and security, wildlife conservation, and public health in low-resource communities, and present research advances in multiagent systems to address one key cross-cutting challenge: how to effectively deploy our limited intervention resources in these problem domains. We present case studies from our deployments around the world as well as lessons learned that we hope are of use to researchers who are interested in AI for social impact. In pushing this research agenda, we believe AI can indeed play an important role in fighting social injustice and improving society

AI for Social Impact: Learning and Planning in the Data-to-Deployment Pipeline

With the maturing of AI and multiagent systems research, we have a tremendous opportunity to direct these advances towards addressing complex societal problems. In pursuit of this goal of AI for Social Impact, we as AI researchers must go beyond improvements in computational methodology; it is important to step out in the field to demonstrate social impact. To this end, we focus on the problems of public safety and security, wildlife conservation, and public health in low-resource communities, and present research advances in multiagent systems to address one key cross-cutting challenge: how to effectively deploy our limited intervention resources in these problem domains. We present case studies from our deployments around the world as well as lessons learned that we hope are of use to researchers who are interested in AI for Social Impact. In pushing this research agenda, we believe AI can indeed play an important role in fighting social injustice and improving society.Comment: To appear, AI Magazin

How primary healthcare workers obtain information during consultations to aid safe prescribing in low-income and lower middle-income countries: a systematic review.

Background: We systematically reviewed the evidence on how primary healthcare workers obtain information during consultations to support decision-making for prescribing in low and lower middle-income countries. Methods: We searched electronic databases, consulted the Healthcare Information For All network, hand searched reference lists, ran citation searches of included studies and emailed authors of identified papers. Two reviewers extracted data and appraised quality with relevant tools. Results: Of 60 497 records found, 23 studies met our inclusion criteria. Fourteen studies were observational and nine were interventional. Frequently mentioned sources of information were books, leaflets, guidelines, aids and the internet. These sources were sometimes out of date and health workers reported being confused which to use. Internet access varied and even when it was available, use was limited by technical issues. Of the five electronic tools that were assessed, four had positive outcomes. Tools assisted prescribers with medicine selection and dosage calculations, which increased prescribing accuracy. The quality of reporting varied but was overall low. Discussion: Studies indicated a lack of up-to-date and relevant medicine information in low and lower middle-income settings. Internet-based sources appeared to be useful when it is possible to download content for offline use and to update when there is internet access. Electronic tools showed promise, but their accuracy needs to be validated and they should focus on giving actionable advice to guide prescribers. PROSPERO registration number: CRD42018091088