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.

Social entrepreneurship and public health nursing knowledge: Opportunities for innovating nursing education in population health.

Public health nursing (PHN) competencies are fundamental for addressing population health inequities. Few pathways exist for employing these competencies in the United States (US). Social entrepreneurship in nursing education might provide opportunities for innovating engagement in population health. Partnerships between business and nursing schools have the potential to fulfill this opportunity. PURPOSE: Explore opportunities for re-invigorating public health nursing through social entrepreneurship education in nursing-business partnerships in U.S. universities. METHODS: Reviewed programs in nursing/business school partnerships from Carnegie-classified R1 Universities. Identified appropriate coursework. RESULTS: Of 96 identified nursing schools, eight had business school partnerships, providing 12 programs. Most programs (n = 11) targeted graduate students and addressed core competencies for entrepreneurship. Five business schools had entrepreneurship expertise. Five nursing schools had PHN expertise. Three programs included population health competencies. DISCUSSION: Despite missed opportunities for advancing social entrepreneurship education among undergraduate and graduate nursing students, existing curricular offerings in the partnerships provide promise. Business/nursing partnerships and PHN knowledge can stimulate the preparation and agency of nurses in addressing population health inequities.

Harnessing digital footprint data for population health: a discussion on collaboration, challenges and opportunities in the UK.

Digital footprint data are inspiring a new era in population health and well-being research. Linking these novel data with other datasets is critical for future research wishing to use these data for the public good. In order to succeed, successful collaboration among industry, academics and policy-makers is vital. Therefore, we discuss the benefits and obstacles for these stakeholder groups in using digital footprint data for research in the UK. We advocate for policy-makers' inclusion in research efforts, stress the exceptional potential of digital footprint research to impact policy-making and explore the role of industry as data providers, with a focus on shared value, commercial sensitivity, resource requirements and streamlined processes. We underscore the importance of multidisciplinary approaches, consumer trust and ethical considerations in navigating methodological challenges and further call for increased public engagement to enhance societal acceptability. Finally, we discuss how to overcome methodological challenges, such as reproducibility and sharing of learnings, in future collaborations. By adopting a multiperspective approach to outlining the challenges of working with digital footprint data, our contribution helps to ensure that future research can navigate these challenges effectively while remaining reproducible, ethical and impactful.

Policy approaches to decarbonising the transport sector in Aotearoa New Zealand: modelling equity, population health, and health-system effects.

BACKGROUND: Health co-benefits are a key potential advantage of transport decarbonisation policy. However, health effects will occur in the context of existing transport-health inequities and decarbonisation policies will themselves affect inequities. This research examines the effects of national decarbonisation pathways for transport on population health, health inequity, and health-system costs in Aotearoa New Zealand. METHODS: We modelled the health, health-system, and environmental impacts of two pathways to net zero for transport developed by the New Zealand Climate Change Commission using a proportional multistate lifetable model. The behaviour pathway emphasises a mixed approach, including reduced driving, increased cycling and use of public transport, and light vehicle electrification, and the technology pathway focuses on vehicle electrification. We used data from transport, environmental, population health, and health-care sources to populate the model. We simulated changes in health effects through the pathways of physical activity, air pollution (PM2·5 and NO2), and injury for the Aotearoa New Zealand population from 2018 to 2050. We modelled impacts for Maori (the Indigenous People of Aotearoa) and non-Maori. For each pathway to net zero, we calculated changes in overall health-adjusted life-years (HALYs), age-standardised HALYs, and rate ratios for Maori and non-Maori. We also calculated changes in health-system costs and transport greenhouse gas emissions. 95% uncertainty intervals (95% UIs) were derived for all model outputs by use of a Monte Carlo simulation. FINDINGS: Both pathways show improvements in population health, reductions in health-system costs, and reduced lifecycle greenhouse gas emissions compared with baseline, although health gains were substantially larger in the behaviour pathway. For example, an extra 2100 HALYs (95% UI 1500-3100) were gained in the behaviour scenario compared with baseline. Health gains were 20-30% larger for Maori than non-Maori in both pathways, although more HALYs were gained by Maori in the behaviour pathway. For the cohort aged 0-4 years in 2018, healthy life expectancy differences between Maori and non-Maori reduced by 0·5% in the behaviour pathway over their lifetime. HALYs gained by Maori and non-Maori were altered substantially depending on assumptions about the equity of the implemented pathway. INTERPRETATION: Decarbonising transport might improve overall population health, save the health system money, and reduce health inequities between Maori and non-Maori. Pathways that increase physical activity have a larger effect on population health than those that rely on low-emission vehicles. The effects on inequity between Maori and non-Maori are larger in the behaviour pathway than in the technology pathway but dependent on how equitably policies supporting decarbonisation are implemented. FUNDING: Health Research Council of New Zealand and University of Otago.

What is the relationship between changes in the size of economies and mortality derived population health measures in high income countries: A causal systematic review.

CONTEXT: The economy has been long recognised as an important determinant of population health and a healthy population is considered important for economic prosperity. AIM: To systematically review the evidence for a causal bidirectional relationship between aggregate economic activity (AEA) at national level for High Income Countries, and 1) population health (using mortality and life expectancy rates as indicators) and 2) inequalities in population health. METHODS: We undertook a systematic review of quantitative studies considering the relationship between AEA (GDP, GNI, GNP or recession) and population health (mortality or life expectancy) and inequalities for High Income Countries. We searched eight databases and grey literature. Study quality was assessed using an adapted version of the Effective Public Health Practice Project's Quality Assessment tool. We used Gordis' adaptation of the Bradford-Hill framework to assess causality. The studies were synthesised using Cochrane recommended alternative methods to meta-analysis and reported following the Synthesis without Meta-analysis (SWiM) guidelines. We assessed the certainty of the evidence base in line with GRADE principles. FINDINGS: Of 21,099 records screened, 51 articles were included in our analysis. There was no evidence for a consistent causal relationship (either beneficial or harmful) of changes in AEA leading to changes in population health (as indicated by mortality or life expectancy). There was evidence suggesting that better population health is causally related to greater AEA, but with low certainty. There was insufficient evidence to consider the causal impact of AEA on health inequalities or vice versa. CONCLUSIONS: Changes in AEA in High Income Countries did not have a consistently beneficial or harmful causal relationship with health, suggesting that impacts observed may be contextually contingent. We tentatively suggest that improving population health might be important for economic prosperity. Whether or not AEA and health inequalities are causally linked is yet to be established.

Modeling the population health impact of accurate and inaccurate perceptions of harm from nicotine.

BACKGROUND: Scientific evidence clearly demonstrates that inhaling the smoke from the combustion of cigarettes is responsible for most of the harm caused by smoking, and not the nicotine. However, a majority of U.S. adults who smoke inaccurately believe that nicotine causes cancer which may be a significant barrier, preventing switching to potentially reduced risk, non-combustible products like electronic nicotine delivery systems (ENDS) and smokeless tobacco (ST). We assessed the population health impact associated with nicotine perceptions. METHODS: Using a previously validated agent-based model to the U.S. population, we analyzed nationally representative data from the Population Assessment of Tobacco and Health (PATH) study to estimate base case rates of sustained (maintained over four waves) cessation and switching to non-combustible product use, by sex. Nicotine perception scenarios were determined from PATH data. The overall switch rate from smoking in Wave 4 to non-combustible product use in Wave 5 (3.94%) was stratified based on responses to the nicotine perception question "Do you believe nicotine is the chemical that causes most of the cancer caused by smoking cigarettes?", (four-item scale from "Definitely not" to "Definitely yes"). The relative percent change between the overall and stratified rates, corresponding to each item, was used to adjust the base case rates of switching, to determine the impact, if all adults who smoke exhibited switching behaviors based on responses to the nicotine perceptions question. The public health impact of nicotine perceptions was estimated as the difference in all-cause mortality between the base case and the four nicotine perception scenarios. RESULTS: Switch rates associated with those who responded, "Definitely not" (8.39%) resulted in a net benefit of preventing nearly 800,000 premature deaths over an 85-year period. Conversely switch rates reflective of those who responded, "Definitely yes" (2.59%) resulted in a net harm of nearly 300,000 additional premature deaths over the same period. CONCLUSIONS: Accurate knowledge regarding the role of nicotine is associated with higher switch rates and prevention of premature deaths. Our findings suggest that promoting public education to correct perceptions of harm from nicotine has the potential to benefit public health.

Impact of twelve immunization-preventable infectious diseases on population health using disability-adjusted life years (DALYs) in Spain.

BACKGROUND: The objective of this study is to estimate the burden of selected immunization-preventable infectious diseases in Spain using the Burden of Communicable Diseases in Europe (BCoDE) methodology, as well as focusing on the national immunization programme and potential new inclusions. METHODS: The BCoDE methodology relies on an incidence and pathogen-based approach to calculate disease burden via disability-adjusted life year (DALY) estimates. It considers short and long-term sequelae associated to an infection via outcome trees. The BCoDE toolkit was used to populate those trees with Spanish-specific incidence estimates, and de novo outcome trees were developed for four infections (herpes zoster, rotavirus, respiratory syncytial virus [RSV], and varicella) not covered by the toolkit. Age/sex specific incidences were estimated based on data from the Spanish Network of Epidemiological Surveillance; hospitalisation and mortality rates were collected from the Minimum Basic Data Set. A literature review was performed to design the de novo models and obtain the rest of the parameters. The methodology, assumptions, data inputs and results were validated by a group of experts in epidemiology and disease modelling, immunization and public health policy. RESULTS: The total burden of disease amounted to 163.54 annual DALYs/100,000 population. Among the selected twelve diseases, respiratory infections represented around 90% of the total burden. Influenza exhibited the highest burden, with 110.00 DALYs/100,000 population, followed by invasive pneumococcal disease and RSV, with 25.20 and 10.57 DALYs/100,000 population, respectively. Herpes zoster, invasive meningococcal disease, invasive Haemophilus influenza infection and hepatitis B virus infection ranked lower with fewer than 10 DALYs/100,000 population each, while the rest of the infections had a limited burden (< 1 DALY/100,000 population). A higher burden of disease was observed in the elderly (≥ 60 years) and children < 5 years, with influenza being the main cause. In infants < 1 year, RSV represented the greatest burden. CONCLUSIONS: Aligned with the BCoDE study, the results of this analysis show a persisting high burden of immunization-preventable respiratory infections in Spain and, for the first time, highlight a high number of DALYs due to RSV. These estimates provide a basis to guide prevention strategies and make public health decisions to prioritise interventions and allocate healthcare resources in Spain.

Investigate-Design-Practice-Reflect: An Iterative Community-Engaged Action Process to Improve Population Health.

BACKGROUND: Community-based coalitions are a common strategy for community engagement efforts targeting the improvement of a variety of population health outcomes. The typical processes that coalitions follow to organize efforts include steps that are sequential, slow, and time intensive. These processes also limit local decision-making to the selection of evidence-based policies or programs. METHODS: We present a process control theory-based Community Action Process, Investigate-Design-Practice-Reflect (IDPR), where community hubs (i.e., coalitions) organize agile efforts in a non-sequential, rapid, and efficient manner to harness local assets and data to make decisions regarding the provision and production of population health services. Using qualitative methods, we illustrate and analyze the use of IDPR in a one community case study as part of Wellscapes, a Type 3-hybrid implementation-effectiveness community randomized controlled trial to improve children's population health physical activity. RESULTS: We found community members followed the IDPR Community Action Process to rapidly design, organize, deliver, and receive feedback on a community-based, children's population physical activity prototype, an afterschool Play-in-the-Park opportunity for all children. DISCUSSION: Following IDPR afforded the community coalition timely learning through feedback within a process that coordinated decisions regarding what community services met community needs (provision decisions) and how to organize the production of the population health services (production decisions).

Cancer information and population health resource: a resource for catchment area data and cancer outcomes research.

BACKGROUND: The University of North Carolina at Chapel Hill Lineberger Comprehensive Cancer Center has developed a novel data resource, the Cancer Information and Population Health Resource (CIPHR), for conducting catchment area evaluation and cancer population health research that links the North Carolina Central Cancer Registry (NCCCR) to medical and pharmacy claims data from Medicare, Medicaid, and private plans operating within North Carolina. This study's aim was to describe the CIPHR data and provide examples of potential cohorts available in those data. METHODS: We present the underlying populations included in the NCCCR and claims data before linkage and demonstrate estimated sample sizes when these data are linked and commonly used insurance enrollment criteria are applied. RESULTS: Data for the years 2003-2020 are present in CIPHR and include 947 977 cancer cases from the NCCCR and 21.6 million enrollees in public and private health insurance (cancer and noncancer cases). When limited to first or only cancers (n = 672 377), 86% could be linked to insurance enrollment for at least 1 month during 2003-2020 (n = 582 638), with 62% of individuals linking to enrollment during the month of cancer diagnosis. Among all registry cancer cases, 47% (n = 317 898) had continuous insurance enrollment for at least 12 months before and after cancer diagnosis. CONCLUSION: CIPHR illustrates the utility of establishing and maintaining a statewide, comprehensive cancer population health database. This resource serves to characterize the cancer center catchment area and aids in tracking cancer outcomes and trends in care delivery as well as identifying disparities that require intervention and policy focus.

Population stratification based on healthcare trajectories: A method for encouraging adaptive learning at meso level.

This paper proposes a method to support population management by evaluating population needs using population stratification based on healthcare trajectories. Reimbursed healthcare consumption data for the first semester of 2017 contained within the inter-mutualist database were analysed to create healthcare trajectories for a subset of the population aged between 60 and 79 (N = 22,832) to identify (1) the nature of health events, (2) key transitions between lines of care, (3) the relative duration of different events, and (4) the hierarchy of events. These factors were classified using a K-mers approach followed by multinomial mixture modelling. Five population groups were identified using this healthcare trajectory approach: "low users", "high intensity of nursing care", "transitional care & nursing care", "transitional care", and "long time in hospital". This method could be used by loco-regional governing bodies to learn reflectively from the place where care is provided, taking a systems perspective rather than a disease perspective, and avoiding the one-size-fits-all definition. It invites decision makers to make better use of routinely collected data to guide continuous learning and adaptive management of population health needs.

Catchment area and cancer population health research through a novel population-based statewide database: a scoping review.

BACKGROUND: Population-based linked datasets are vital to generate catchment area and population health research. The novel Cancer Information and Population Health Resource (CIPHR) links statewide cancer registry data, public and private insurance claims, and provider- and area-level data, representing more than 80% of North Carolina's large, diverse population of individuals diagnosed with cancer. This scoping review of articles that used CIPHR data characterizes the breadth of research generated and identifies further opportunities for population-based health research. METHODS: Articles published between January 2012 and August 2023 were categorized by cancer site and outcomes examined across the care continuum. Statistically significant associations between patient-, provider-, system-, and policy-level factors and outcomes were summarized. RESULTS: Among 51 articles, 42 reported results across 23 unique cancer sites and 13 aggregated across multiple sites. The most common outcomes examined were treatment initiation and/or adherence (n = 14), mortality or survival (n = 9), and health-care resource utilization (n = 9). Few articles focused on cancer recurrence (n = 1) or distance to care (n = 1) as outcomes. Many articles discussed racial, ethnic, geographic, and socioeconomic inequities in care. CONCLUSIONS: These findings demonstrate the value of robust, longitudinal, linked, population-based databases to facilitate catchment area and population health research aimed at elucidating cancer risk factors, outcomes, care delivery trends, and inequities that warrant intervention and policy attention. Lessons learned from years of analytics using CIPHR highlight opportunities to explore less frequently studied cancers and outcomes, motivate equity-focused interventions, and inform development of similar resources.

Health Improvement by Design Through Partnership and Empathy.

The application of technology in precision medicine (i.e., using data to tailor care for specific groups) and machine learning (modeling data to develop new ways to meet particular needs) plays a vital role in population health. And yet, technology alone cannot resolve the challenges presented by the underlying social determinants of health (SDOH) and the structure of healthcare institutions. Progress requires a partnership of stakeholders to build holistic solutions. Typically, a rigid institutional reliance on a sequential process of hypothesis, study, experimentation, and evaluation drives the development of health programs and policies. While this process works in many cases, the results are not always long-lasting. The roots of many health problems persist in their communities, despite expanded funding and advanced resources. In light of this reality, should healthcare leaders do more of what has been done in the past, or should they explore another way-a new way of thinking-to design collaborative, people-driven programs and policies that can improve and sustain the health of their communities? The response presented here and detailed in our book, Public Health and Health Systems Management: A Population Health Perspective (scheduled for publication in 2025 by Health Administration Press), considers healthcare's complex structure and SDOH and promotes the value of design thinking to address those factors.

Towards Regional Population Health Management: A Prospective Analysis Using the Adjusted Clinical Groups Classification.

This research seeks to assess the potential of regionally integrated health management for specific sub-populations, including the incorporation of self-management initiatives. It will achieve this by conducting a thorough stratification analysis of hospital data, utilizing the Adjusted Clinical Groups (ACG) classification system. The approach involves a retrospective review of healthcare data spanning five years, which includes patient demographics, health outcomes, and healthcare utilization metrics. We intend to use the ACG method to classify the patient population into pertinent groups that mirror their health requirements and resource use. The insights obtained from this analysis will be used to create a localized adaptation of the Kaiser Permanente Pyramid Model of Care. This adaptation aims to identify the distribution of costs among patients treated in the Rivierenland Hospital. We anticipate that stratifying data with the ACG method will identify distinct multimorbid subgroups. These subgroups will have unique healthcare requirements. Early interventions and customized health management strategies, based on these insights, could enhance health outcomes and resource efficiency for high-risk patients. This analysis will serve as a foundation for constructive discussions with hospital management and clinical staff, fostering a deeper comprehension of the patients' burden of disease. It might also foster multidisciplinary collaboration opportunities between medical specialties as with regional healthcare partners such as general practitioners (GPs), mental health and other long-term care organizations. Moreover, we anticipate that self-care initiatives, supported by customized health information, will encourage increased patient engagement and strategies for enhancing lifestyle improvements. This strategy is expected to enable the personalization of advanced care planning based on individual needs profiles, thereby improving the management of complex and chronic conditions, and encouraging self-care practices. Our anticipated findings highlight the potential benefits of a data-informed approach to advancing healthcare outcomes and present opportunities for future investigations to refine and implement such integrated care models across the region.

Predicting the Population Health Economic Impact of Current and New Cancer Treatments for Colorectal Cancer: A Data-Driven Whole Disease Simulation Model for Predicting the Number of Patients with Colorectal Cancer by Stage and Treatment Line in Australia.

OBJECTIVES: Effective healthcare planning, resource allocation, and budgeting require accurate predictions of the number of patients needing treatment at specific cancer stages and treatment lines. The Predicting the Population Health Economic Impact of Current and New Cancer Treatments (PRIMCAT) for Colorectal Cancer (CRC) simulation model (PRIMCAT-CRC) was developed to meet this requirement for all CRC stages and relevant molecular profiles in Australia. METHODS: Real-world data were used to estimate treatment utilization and time-to-event distributions. This populated a discrete-event simulation, projecting the number of patients receiving treatment across all disease stages and treatment lines for CRC and forecasting the number of patients likely to utilize future treatments. Illustrative analyses were undertaken, estimating treatments across disease stages and treatment lines over a 5-year period (2022-2026). We demonstrated the model's applicability through a case study introducing pembrolizumab as a first-line treatment for mismatch-repair-deficient stage IV. RESULTS: Clinical registry data from 7163 patients informed the model. The model forecasts 15 738 incident and 2821 prevalent cases requiring treatment in 2022, rising to 15 921 and 2871, respectively, by 2026. Projections show that over 2022 to 2026, there will be a total of 116 752 treatments initiated, with 43% intended for stage IV disease. The introduction of pembrolizumab is projected for 706 patients annually, totaling 3530 individuals starting treatment with pembrolizumab over the forecasted period, without significantly altering downstream utilization of subsequent treatments. CONCLUSIONS: PRIMCAT-CRC is a versatile tool that can be used to estimate the eligible patient populations for novel cancer therapies, thereby reducing uncertainty for policymakers in decisions to publicly reimburse new treatments.

Modeling the population health impact of incorporating a multi-cancer early detection (MCED) test to existing cancer screening among immunocompromised individuals.

OBJECTIVE: To assess the screening efficiency of an multi-cancer early detection (MCED) test added to standard of care (SoC) screening, compared to SoC screening alone, among immunocompromised individuals, and to estimate the diagnostic workup costs associated with positive screening results. METHODS: We estimated the potential impact of cancer screening among immunocompromised individuals aged 50-79 years within the University of Utah Health system who underwent a stem cell/solid organ transplant or were diagnosed with a primary or secondary immunodeficiency disorder between January 2000 and February 2018. We derived cancer incidence rates from the Huntsman Cancer Institute Tumor Registry, and screening performance of SoC screening and an MCED test from published literature. Outcomes of screening efficiency included the true-positive to false-positive (TP:FP) ratio, diagnostic yield (DY), and cancer detection rate (CDR) for SoC screening alone and an incremental MCED test. Scenario and probabilistic sensitivity analyses were conducted. RESULTS: Among 4932 immunocompromised individuals aged 50-79 years, we estimated that 2595 tests would be done under SoC screening and assumed that all individuals received an additional MCED test. Adding an MCED test to SoC screening substantially improved screening efficiency (TP:FP = 1:1, DY = 5.15/1000 tests, CDR = 42.0%), compared to SoC screening alone (TP:FP = 1:99, DY = 1.23/1000 tests, CDR = 5.3%), assuming an MCED test with 100% uptake. Our findings were also robust to parameter uncertainty. CONCLUSION: Adding an MCED test to complement existing screening may be a highly efficient strategy to increase the detection of cancers among immunocompromised individuals. These results could help to improve cancer prevention and detection efforts among individuals with multiple cancer risk factors.

Community-engaged Systems for Population Health Improvement: A Novel Approach to Improve Diabetes Outcomes in Rural Communities.

BACKGROUND: Approaches to prevent and manage diabetes at a community population level are hindered because current strategies are not aligned with the structure and function of a community system. We describe a community-driven process based on local data and rapid prototyping as an alternative approach to create diabetes prevention and care management solutions appropriate for each community. We report on the process and provide baseline data for a 3-year case study initiative to improve diabetes outcomes in two rural Nebraska communities. METHODS: We developed an iterative design process based on the assumption that decentralized decision-making using local data feedback and monitoring will lead to the innovation of local sustainable solutions. Coalitions act as community innovation hubs and meet monthly to work through a facilitated design process. Six core diabetes measures will be tracked over the course of the project using the electronic health record from community clinics as a proxy for the entire community. RESULTS: Baseline data indicate two-thirds of the population in both communities are at risk for prediabetes based on age and body mass index. However, only a fraction (35% and 12%) of those at risk have been screened. This information led both coalitions to focus on improving screening rates in their communities. DISCUSSION: In order to move a complex system towards an optimal state (e.g., improved diabetes outcomes), stakeholders must have access to continuous feedback of accurate, pertinent information in order to make informed decisions. Conventional approaches of implementing evidence-based interventions do not facilitate this process.