Analysis of user interactions with a digital health wallet for enabling care continuity in the context of an ongoing pandemic.

BACKGROUND: The onset of COVID-19 and related policy responses made it difficult to study interactive health informatics solutions in clinical study settings. Instrumented log and event data from interactive systems capture temporal details that can be used to generate insights about care continuity during ongoing pandemics. OBJECTIVE: To investigate user interactions with a digital health wallet (DHW) system for addressing care continuity challenges in chronic disease management in the context of an ongoing pandemic. MATERIALS AND METHODS: We analyzed user interaction log data generated by clinicians, nurses, and patients from the deployment of a DHW in a feasibility study conducted during the COVID-19 pandemic in Kenya. We used the Hamming distance from Information Theory to quantify deviations of usage patterns extracted from the events data from predetermined workflow sequences supported by the platform. RESULTS: Nurses interacted with all the user interface elements relevant to triage. Clinicians interacted with only 43% of elements relevant to consultation, while patients interacted with 67% of the relevant user interface elements. Nurses and clinicians deviated from the predetermined workflow sequences by 42% and 36%, respectively. Most deviations pertained to users going back to previous steps in their usage workflow. CONCLUSIONS: User interaction log analysis is a valuable alternative method for generating and quantifying user experiences in the context of ongoing pandemics. However, researchers should mitigate the potential disruptions of the actual use of the studied technologies as well as use multiple approaches to investigate user experiences of health technology during pandemics.

Quantifying the impact of COVID-19 on essential health services: a comparison of interrupted time series analysis using Prophet and Poisson regression models.

BACKGROUND: Coronavirus disease 2019 (COVID-19) altered healthcare utilization patterns. However, there is a dearth of literature comparing methods for quantifying the extent to which the pandemic disrupted healthcare service provision in sub-Saharan African countries. OBJECTIVE: To compare interrupted time series analysis using Prophet and Poisson regression models in evaluating the impact of COVID-19 on essential health services. METHODS: We used reported data from Uganda's Health Management Information System from February 2018 to December 2020. We compared Prophet and Poisson models in evaluating the impact of COVID-19 on new clinic visits, diabetes clinic visits, and in-hospital deliveries between March 2020 to December 2020 and across the Central, Eastern, Northern, and Western regions of Uganda. RESULTS: The models generated similar estimates of the impact of COVID-19 in 10 of the 12 outcome-region pairs evaluated. Both models estimated declines in new clinic visits in the Central, Northern, and Western regions, and an increase in the Eastern Region. Both models estimated declines in diabetes clinic visits in the Central and Western regions, with no significant changes in the Eastern and Northern regions. For in-hospital deliveries, the models estimated a decline in the Western Region, no changes in the Central Region, and had different estimates in the Eastern and Northern regions. CONCLUSIONS: The Prophet and Poisson models are useful in quantifying the impact of interruptions on essential health services during pandemics but may result in different measures of effect. Rigor and multimethod triangulation are necessary to study the true effect of pandemics on essential health services.

AI-assisted tracking of worldwide non-pharmaceutical interventions for COVID-19.

The Coronavirus disease 2019 (COVID-19) global pandemic has transformed almost every facet of human society throughout the world. Against an emerging, highly transmissible disease, governments worldwide have implemented non-pharmaceutical interventions (NPIs) to slow the spread of the virus. Examples of such interventions include community actions, such as school closures or restrictions on mass gatherings, individual actions including mask wearing and self-quarantine, and environmental actions such as cleaning public facilities. We present the Worldwide Non-pharmaceutical Interventions Tracker for COVID-19 (WNTRAC), a comprehensive dataset consisting of over 6,000 NPIs implemented worldwide since the start of the pandemic. WNTRAC covers NPIs implemented across 261 countries and territories, and classifies NPIs into a taxonomy of 16 NPI types. NPIs are automatically extracted daily from Wikipedia articles using natural language processing techniques and then manually validated to ensure accuracy and veracity. We hope that the dataset will prove valuable for policymakers, public health leaders, and researchers in modeling and analysis efforts to control the spread of COVID-19.

A Framework for Inferring Epidemiological Model Parameters using Bayesian Nonparametrics.

The use of epidemiological models for decision-making has been prominent during the COVID-19 pandemic. Our work presents the application of nonparametric Bayesian techniques for inferring epidemiological model parameters based on available data sets published during the pandemic, towards enabling predictions under uncertainty during emerging pandemics. We present a methodology and framework that allows epidemiological model drivers to be integrated as input into the model calibration process. We demonstrate our methodology using the stringency index and mobility data for COVID-19 on an SEIRD compartmental model for selected US states. Our results directly compare the use of Bayesian nonparametrics for model predictions based on best parameter estimates with results of inference of parameter values across the US states. The proposed methodology provides a framework for What-If analysis and sequential decision-making methods for disease intervention planning and is demonstrated for COVID-19, while also applicable to other infectious disease models.

Enhancing Guideline-Based Prescribing and Personalized Medication Scheduling.

Poor communication of medication administration instructions is a preventable cause of medication nonadherence. The Universal Medication Schedule (UMS) framework improves adherence by providing a simplified set of dose timing rules. However, this framework does not readily generalize to individuals with varying daily routines. We propose a point-of-care solution for enhancing guideline-based electronic prescribing and personalizing dose schedules. We describe a JSON-based approach to encode and execute standard treatment guidelines to support electronic prescribing as well as an algorithm for optimizing medication administration schedules based on a patient's daily routine. We evaluated the structure and accuracy of our JavaScript Object Notation (JSON) formalism focusing on Kenya's hypertension treatment guidelines. Our experiments compare the medication schedules generated by our algorithm with those generated by pharmacists. Our findings show that treatment guidelines can be efficiently represented and executed using the JSON formalism, and that different medication administration schedules can be generated automatically and optimized for patients' daily routines.