Human factors methods in the design of digital decision support systems for population health: a scoping review.

BACKGROUND: While Human Factors (HF) methods have been applied to the design of decision support systems (DSS) to aid clinical decision-making, the role of HF to improve decision-support for population health outcomes is less understood. We sought to comprehensively understand how HF methods have been used in designing digital population health DSS. MATERIALS AND METHODS: We searched English documents published in health sciences and engineering databases (Medline, Embase, PsychINFO, Scopus, Comendex, Inspec, IEEE Xplore) between January 1990 and September 2023 describing the development, validation or application of HF principles to decision support tools in population health. RESULTS: We identified 21,581 unique records and included 153 studies for data extraction and synthesis. We included research articles that had a target end-user in population health and that used HF. HF methods were applied throughout the design lifecycle. Users were engaged early in the design lifecycle in the needs assessment and requirements gathering phase and design and prototyping phase with qualitative methods such as interviews. In later stages in the lifecycle, during user testing and evaluation, and post deployment evaluation, quantitative methods were more frequently used. However, only three studies used an experimental framework or conducted A/B testing. CONCLUSIONS: While HF have been applied in a variety of contexts in the design of data-driven DSSs for population health, few have used Human Factors to its full potential. We offer recommendations for how HF can be leveraged throughout the design lifecycle. Most crucially, system designers should engage with users early on and throughout the design process. Our findings can support stakeholders to further empower public health systems.

Human factors applications in the design of decision support systems for population health: a scoping review.

INTRODUCTION: Public health professionals engage in complex cognitive tasks, often using evidence-based decision support tools to bolster their decision-making. Human factors methods take a user-centred approach to improve the design of systems, processes, and interfaces to better support planning and decision-making. While human factors methods have been applied to the design of clinical health tools, these methods are limited in the design of tools for population health. The objective of this scoping review is to develop a comprehensive understanding of how human factors techniques have been applied in the design of population health decision support tools. METHODS AND ANALYSIS: The scoping review will follow the methodology and framework proposed by Arksey and O'Malley. We include English-language documents between January 1990 and August 2021 describing the development, validation or application of human factors principles to decision support tools in population health. The search will include Ovid MEDLINE: Epub Ahead of Print, In-Process and Other Non-Indexed Citations, Ovid MEDLINE Daily and Ovid MEDLINE 1946-present; EMBASE, Scopus, PsycINFO, Compendex, IEEE Xplore and Inspec. The results will be integrated into Covidence. First, the abstract of all identified articles will be screened independently by two reviewers with disagreements being resolved by a third reviewer. Next, the full text for articles identified as include or inconclusive will be reviewed by two independent reviewers, leading to a final decision regarding inclusion. Reference lists of included articles will be manually screened to identify additional studies. Data will be extracted by one reviewer, verified by a second, and presented according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews. ETHICS AND DISSEMINATION: Ethics approval is not required for this work as human participants are not involved. The completed review will be published in a peer-reviewed, interdisciplinary journal.

The Use of Cremation Data for Timely Mortality Surveillance During the COVID-19 Pandemic in Ontario, Canada: Validation Study.

BACKGROUND: Early estimates of excess mortality are crucial for understanding the impact of COVID-19. However, there is a lag of several months in the reporting of vital statistics mortality data for many jurisdictions, including across Canada. In Ontario, a Canadian province, certification by a coroner is required before cremation can occur, creating real-time mortality data that encompasses the majority of deaths within the province. OBJECTIVE: This study aimed to validate the use of cremation data as a timely surveillance tool for all-cause mortality during a public health emergency in a jurisdiction with delays in vital statistics data. Specifically, this study aimed to validate this surveillance tool by determining the stability, timeliness, and robustness of its real-time estimation of all-cause mortality. METHODS: Cremation records from January 2020 until April 2021 were compared to the historical records from 2017 to 2019, grouped according to week, age, sex, and whether COVID-19 was the cause of death. Cremation data were compared to Ontario's provisional vital statistics mortality data released by Statistics Canada. The 2020 and 2021 records were then compared to previous years (2017-2019) to determine whether there was excess mortality within various age groups and whether deaths attributed to COVID-19 accounted for the entirety of the excess mortality. RESULTS: Between 2017 and 2019, cremations were performed for 67.4% (95% CI 67.3%-67.5%) of deaths. The proportion of cremated deaths remained stable throughout 2020, even within age and sex categories. Cremation records are 99% complete within 3 weeks of the date of death, which precedes the compilation of vital statistics data by several months. Consequently, during the first wave (from April to June 2020), cremation records detected a 16.9% increase (95% CI 14.6%-19.3%) in all-cause mortality, a finding that was confirmed several months later with cremation data. CONCLUSIONS: The percentage of Ontarians cremated and the completion of cremation data several months before vital statistics did not change meaningfully during the COVID-19 pandemic period, establishing that the pandemic did not significantly alter cremation practices. Cremation data can be used to accurately estimate all-cause mortality in near real-time, particularly when real-time mortality estimates are needed to inform policy decisions for public health measures. The accuracy of this excess mortality estimation was confirmed by comparing it with official vital statistics data. These findings demonstrate the utility of cremation data as a complementary data source for timely mortality information during public health emergencies.