Cost-effectiveness analysis protocol of the Smart Triage program: A point-of-care digital triage platform for pediatric sepsis in Eastern Uganda

Abstract

Background Sepsis is a clinical syndrome characterized by organ dysfunction due to presumed or proven infection. Severe cases can have case fatality ratio 25% or higher in low-middle income countries, but early diagnosis and timely treatment have a proven benefit. The Smart Triage program in Jinja Regional Referral Hospital in Uganda will provide expedited sepsis treatment in children through a data-driven electronic patient triage system. To complement the ongoing Smart Triage interventional trial, we propose methods for a concurrent cost-effectiveness analysis of the Smart Triage platform. Methods We will use a decision-analytic model taking a societal perspective, combining government and out-of-pocket costs, as patients bear a sizeable portion of healthcare costs in Uganda due to the lack of universal health coverage. Previously published secondary data will be used to link healthcare utilization with costs and intermediate outcomes with mortality. We will model uncertainty via probabilistic sensitivity analysis and present findings at various willingness-to-pay thresholds using a cost-effectiveness acceptability curve. Discussion Our proposed analysis represents a first step in evaluating the cost-effectiveness of an innovative digital triage platform designed to improve clinical outcomes in pediatric sepsis through expediting care in low-resource settings. Our use of a decision analytic model to link secondary costing data, incorporate post-discharge healthcare utilization, and model clinical endpoints is also novel in the pediatric sepsis triage literature for low-middle income countries. Our analysis, together with subsequent analyses modelling budget impact and scale up, will inform future modifications to the Smart Triage platform, as well as motivate scale-up to the district and national levels. Trial registration Trial registration of parent clinical trial: NCT04304235, https://clinicaltrials.gov/ct2/show/NCT04304235. Registered 11 March 2020.

Implementation of Smart Triage combined with a quality improvement program for children presenting to facilities in Kenya and Uganda: An interrupted time series analysis

“…Based on the feasibility study (22), the increase in the proportion of children receiving a bundle of care within one hour in this study was 21.4%, and the proportion of children receiving IVA was 8.2%. Based on these observations, we estimated that using an alpha of .05, at a power of 80%, we would require 10 600 triages (1290 treatments) in the pre-intervention and post-intervention phases combined, to confirm this effect observed in the previous study.…”

“…Qualitative interviews were used to optimize the platform (21). A cost-effectiveness study was completed to determine utilization costs and implications for scale-up (22).…”

“…We have developed, validated, implemented, and evaluated a digital triage platform called Smart Triage (18)(19)(20)(21)(22). The Smart Triage platform comprises data-driven risk assessment (triage), patient and treatment tracking, a real-time dashboard, and automated reports to improve emergency case recognition and time-to-treatment.…”

Smart triage: Development of a rapid pediatric triage algorithm for use in low-and-middle income countries

“…These include limited availability of smartphones, computer infrastructure, and a continuous supply of electricity, in addition to staffing attitude and training challenges. In response, to complement the digital triage interventional study, we are conducting a cost-effectiveness analysis to maximize the benefit of implementing such technology in low resource hospitals ( 35 ). The WHO views digital health as an accessible and affordable solution for global health delivery in LMICs and an important tool in achieving sustainable development goals ( 36 ).…”

Cost-effectiveness analysis of Smart Triage, a data-driven pediatric sepsis triage platform in Eastern Uganda

“…Key assumptions of our model include the sustainability of Smart Triage beyond study end date, the generalizability of secondary linkage data used in the model to the Smart Triage population, and the adequacy of our seven-day duration of patient follow-up. The rationale behind these assumptions, and further details on our model, are previously published [ 30 ]. Our analysis was designed and conducted according to the 2013 version of the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) statement [ 31 ], the most recent version at the time of analysis design, and is also in compliance with the revised 2022 version [ 32 ].…”