Population Health Management Involving a Personalized Health Care Approach: An Example with Cervical Cancer Screening

The latest report on global cervical cancer epidemiology suggests that there were an estimated 604,127 cervical cancer cases and 341,831 deaths in 2020. ¹ To tackle this population health problem, World Health Organization has set a “90-70-90” target for achievement by 2030. ² One component of this target is to achieve an intervention for 70% of women involving screening with a high-performance test by 35 years of age and again by 45 years of age. Such screening, accompanied by the effective treatment of cervical pre-cancers, has been a highly effective policy in reducing the incidence and mortality caused by cervical cancer.

This “one-size-fits-all” strategy was designed during the 1960s to deliver a single-screening technology (cytology test) and a single diagnostic technology (colposcopy) to a large segment of the female population. However, new technologies for screening test (DNA-based human papillomavirus [HPV] testing), the data mining and artificial intelligence-based computational methodologies and accumulating evidence opens an avenue for innovation—to tailor prevention effort based on individual risk of cervical cancer and in offering the personalized scheme of cancer screening. ³

In 2019, the University of Tartu, Rīga Stradiņš University, Lithuanian University of Health Sciences, and the Norwegian Cancer Registry began a project “Towards elimination of cervical cancer: intelligent and personalized solutions for cancer screening” (2020–2023). The project's main objective is to develop improved and personalized cancer screening methods within a sustainable health care system. The methods developed through this initiative will integrate knowledge of biological disease mechanisms and available data from national population-based health registries, health care provision data, surveys, and the Estonian genome bank. These data will be used to develop, validate, and determine the cost-effectiveness of specific artificial intelligence technology for the purpose of preventive medicine in cervical cancer.

The foundation of this project relies on combining population-based multifaceted individual data (HPV-status, health and reproductive behavior information; individual histories of cervical cancer screening; genetic data) with the advantages of high-performance computing and analytics to leverage existing knowledge and experience for transforming cancer screening systems toward higher inclusiveness as well as making them increasingly flexible, scalable, and sustainable. ⁴ It is expected that the results from this project will have a sustainable effect and will facilitate further reduction of cervical cancer burden in the Baltic states. Moreover, this practice could be a transferable example for other countries in Central and Eastern Europe that also are tackling similar population health challenges.