The application of artificial intelligence in health policy: a scoping review

BACKGROUND

Policymakers require precise and in-time information to make informed decisions in complex environments such as health systems. Artificial intelligence (AI) is a novel approach that makes collecting and analyzing data in complex systems more accessible. This study highlights recent research on AI's application and capabilities in health policymaking.

METHODS

We searched PubMed, Scopus, and the Web of Science databases to find relevant studies from 2000 to 2023, using the keywords "artificial intelligence" and "policymaking." We used Walt and Gilson's policy triangle framework for charting the data.

RESULTS

The results revealed that using AI in health policy paved the way for novel analyses and innovative solutions for intelligent decision-making and data collection, potentially enhancing policymaking capacities, particularly in the evaluation phase. It can also be employed to create innovative agendas with fewer political constraints and greater rationality, resulting in evidence-based policies. By creating new platforms and toolkits, AI also offers the chance to make judgments based on solid facts. The majority of the proposed AI solutions for health policy aim to improve decision-making rather than replace experts.

CONCLUSION

Numerous approaches exist for AI to influence the health policymaking process. Health systems can benefit from AI's potential to foster the meaningful use of evidence-based policymaking.

Using Collaborative Simulation Modeling to Develop a Web-Based Tool to Support Policy-Level Decision Making About Breast Cancer Screening Initiation Age

BACKGROUND

There are no publicly available tools designed specifically to assist policy makers to make informed decisions about the optimal ages of breast cancer screening initiation for different populations of US women.

OBJECTIVE

To use three established simulation models to develop a web-based tool called Mammo OUTPuT.

METHODS

The simulation models use the 1970 US birth cohort and common parameters for incidence, digital screening performance, and treatment effects. Outcomes include breast cancers diagnosed, breast cancer deaths averted, breast cancer mortality reduction, false-positive mammograms, benign biopsies, and overdiagnosis. The Mammo OUTPuT tool displays these outcomes for combinations of age at screening initiation (every year from 40 to 49), annual versus biennial interval, lifetime versus 10-year horizon, and breast density, compared to waiting to start biennial screening at age 50 and continuing to 74. The tool was piloted by decision makers (n = 16) who completed surveys.

RESULTS

The tool demonstrates that benefits in the 40s increase linearly with earlier initiation age, without a specific threshold age. Likewise, the harms of screening increase monotonically with earlier ages of initiation in the 40s. The tool also shows users how the balance of benefits and harms varies with breast density. Surveys revealed that 100% of users (16/16) liked the appearance of the site; 94% (15/16) found the tool helpful; and 94% (15/16) would recommend the tool to a colleague.

CONCLUSIONS

This tool synthesizes a representative subset of the most current CISNET (Cancer Intervention and Surveillance Modeling Network) simulation model outcomes to provide policy makers with quantitative data on the benefits and harms of screening women in the 40s. Ultimate decisions will depend on program goals, the population served, and informed judgments about the weight of benefits and harms.

Cardiovascular screening to reduce the burden from cardiovascular disease: microsimulation study to quantify policy options

OBJECTIVES

 To estimate the potential impact of universal screening for primary prevention of cardiovascular disease (National Health Service Health Checks) on disease burden and socioeconomic inequalities in health in England, and to compare universal screening with alternative feasible strategies.

DESIGN

 Microsimulation study of a close-to-reality synthetic population. Five scenarios were considered: baseline scenario, assuming that current trends in risk factors will continue in the future; universal screening; screening concentrated only in the most deprived areas; structural population-wide intervention; and combination of population-wide intervention and concentrated screening.

SETTING

 Synthetic population with similar characteristics to the community dwelling population of England.

PARTICIPANTS

 Synthetic people with traits informed by the health survey for England.

MAIN OUTCOME MEASURE

 Cardiovascular disease cases and deaths prevented or postponed by 2030, stratified by fifths of socioeconomic status using the index of multiple deprivation.

RESULTS

 Compared with the baseline scenario, universal screening may prevent or postpone approximately 19 000 cases (interquartile range 11 000-28 000) and 3000 deaths (-1000-6000); concentrated screening 17 000 cases (9000-26 000) and 2000 deaths (-1000-5000); population-wide intervention 67 000 cases (57 000-77 000) and 8000 deaths (4000-11 000); and the combination of the population-wide intervention and concentrated screening 82 000 cases (73 000-93 000) and 9000 deaths (6000-13 000). The most equitable strategy would be the combination of the population-wide intervention and concentrated screening, followed by concentrated screening alone and the population-wide intervention. Universal screening had the least apparent impact on socioeconomic inequalities in health.

CONCLUSIONS

 When primary prevention strategies for reducing cardiovascular disease burden and inequalities are compared, universal screening seems less effective than alternative strategies, which incorporate population-wide approaches. Further research is needed to identify the best mix of population-wide and risk targeted CVD strategies to maximise cost effectiveness and minimise inequalities.

The Health Impact Fund: how might it work for novel anticoagulants in atrial fibrillation?

Cardiovascular diseases represent the greatest burden of global disease. Spending on cardiovascular diseases is higher than for other diseases, with the majority being spent on drugs. Therefore, these drugs and these diseases are hugely important to health systems, society, and pharmaceutical companies. The Health Impact Fund represents a new mechanism by which pharmaceutical innovators would be rewarded on the basis of the health impact of their new drugs. This review illustrates the concept of the Health Impact Fund using the example of novel anticoagulants for prevention of stroke and thromboembolism in atrial fibrillation. By considering existing data and the current situation for novel anticoagulants, we suggest that epidemiologic data and modeling techniques can be used to predict future trends in disease and the health impact of new drugs. The Health Impact Fund may offer potential benefits to pharmaceutical companies, patients, and governments and warrants proper investigation.

Projections of preventable risks for cardiovascular disease in Canada to 2021: a microsimulation modelling approach

BACKGROUND

Reductions in preventable risks associated with cardiovascular disease have contributed to a steady decrease in its incidence over the past 50 years in most developed countries. However, it is unclear whether this trend will continue. Our objective was to examine future risk by projecting trends in preventable risk factors in Canada to 2021.

METHODS

We created a population-based microsimulation model using national data on births, deaths and migration; socioeconomic data; cardiovascular disease risk factors; and algorithms for changes in these risk factors (based on sociodemographic characteristics and previous cardiovascular disease risk). An initial population of 22.5 million people, representing the Canadian adult population in 2001, had 13 characteristics including the risk factors used in clinical risk prediction. There were 6.1 million potential exposure profiles for each person each year. Outcome measures included annual prevalence of risk factors (smoking, obesity, diabetes, hypertension and lipid levels) and of co-occurring risks.

RESULTS

From 2003 to 2009, the projected risks of cardiovascular disease based on the microsimulation model closely approximated those based on national surveys. Except for obesity and diabetes, all risk factors were projected to decrease through to 2021. The largest projected decreases were for the prevalence of smoking (from 25.7% in 2001 to 17.7% in 2021) and uncontrolled hypertension (from 16.1% to 10.8%). Between 2015 and 2017, obesity was projected to surpass smoking as the most prevalent risk factor.

INTERPRETATION

Risks of cardiovascular disease are projected to decrease modestly in Canada, leading to a likely continuing decline in its incidence.