Advanced analytics is an essential tool for improving population health management. It allows physicians and policymakers to examine and analyze critical data regarding treatments and procedures. These insights inform care decisions that impact population health management and drive precision medicine.
Population health management is critical to realizing precision medicine. Using data analytics of population health management segments and populations of specific demographics can enable precision medicine to identify the best treatment options for each patient. On a broader scale, it can help raise the standards and create more consistencies in quality healthcare.
Why Use Advanced Analytics to Drive Population Health Management in Cancer Care?
Machine learning challenges previously accepted norms by accelerating findings and improving workflow efficiencies across all industries. Healthcare is no different, and more critical to benefit from such technological advancements. From early identification of disease risk factors to monitoring quality health measures, providers who leverage advanced analytics to improve population health management can use precision medicine to guide treatment options and enhance their value-based care initiatives.
Earlier Identification of Risk-factors
In Oncology, one component of advanced analytics that supports oncologists in the diagnostic process is artificial intelligence (AI) image recognition for the early identification of risk factors and malignancies. AI can be a great asset for improving diagnostic accuracy beyond human capability. For example, a recent study published in the European Journal of Cancer shows that trained AI image recognition algorithms outperformed 136 of 157 board-certified dermatologists in identifying melanoma.
Diagnosing cancer early on increases physicians' chances of administering effective treatment and improving survival rates. Performing regular screenings with AI tools can improve the number of patients who receive an early diagnosis of early-stage cancers.
Leveraging Predictive Analytics for Improved Decision-making
Oncologists should consider big data in health care to serve patients better and improve outcomes. According to McKinsey, leveraging advanced analytics is a "must" for oncologists. Aggregating data in your population health management strategy can raise the standard of care as your healthcare organization embraces analytics to reshape cancer care.
Treatment Optimization
According to the ACCC (Association of Community Cancer Centers), despite mounting evidence indicating the importance of biomarkers in lung cancer treatment decision-making, more than 70% of patients treated for lung cancer do not receive biomarker testing, as recommended by the clinical guidelines outlined by the National Comprehensive Cancer Network (NCCN).
Furthermore, over 50% of patients are not offered appropriate precision medicine therapy corresponding to their test results. When looking at underserved patient populations, this gap is even more significant.
Leveraging advanced analytics for patient health management can close treatment gaps by providing accurate health information and value-based care reporting.
Leveraging Advanced Analytics to Improve Population Health Management for Cancer Care
Staying consistent and compliant with quality measures ensures proper follow-up care and treatment throughout a patient's journey.
The better providers can link advanced analytics with clinically trained oncologists, the more they will be able to drive results for patients and contribute to a more robust population health management process.
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References:
https://www.accc-cancer.org/projects/eliminating-disparities/overview
https://www.cancertreatmentreviews.com/article/S0305-7372(21)00071-2/fulltext
https://www.mckinsey.com/industries/life-sciences/our-insights/fulfilling-the-promise-of-advanced-analytics-in-oncology
https://www.ejcancer.com/article/S0959-8049(19)30221-7/fulltext
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8946688/
