The economy of a country is attached with deviations in population health. In other words, when a natural calamity occurs the economy takes a toll too. As we have all witnessed the effects of a pandemic, the numbers are self-explanatory. The latest statistics display that the pandemic has affected more than 45 crore people worldwide, of which 60 lakh have died. COVID has impacted the economy by $3.4 trillion per year.
Subsequently, the healthcare sector shifted to data analysis for solutions. In no time, the analysts observed lower readmission rates, reduce errors, and identify at-risk populations. One way to optimize the population health data analysis is through risk stratification.
What is Health Risk Stratification?
When we talk about the risk, we mainly refer to the downside of the risk: something that will go wrong or the likelihood of an event happening or consequence of the event.
Health Risk Stratification is commonly used when we are dealing with the health of a large group or population. The core concept of risk stratification involves dividing people or things into groups with varying degrees of risk. It also improves the ability to understand your population, identify opportunities and improve care delivery.
Moreover, health providers could prioritize patients’ individual and distinct health signs as per the risk status. As a result, make better and quality health decisions. In the era of value-based care, risk stratification becomes necessary: to sort patients into high, moderate, and low health risk tiers and deliver appropriate care to address their health needs.
Population Health Data Analytics
Healthcare at the macro level involves a variety of determinants like a community-level drastic change, systemic problems, and challenging societal norms and influences. Policymakers weave the data from these determinants into a healthcare policy. In effect of a good policy, the quality of personal health services and biomedical research could drastically improve. The key to achieving this is through extracting the determinant data, stratifying the population as per their risk status, and pulling out valuable insights through data analysis.
Although data is just a means to an end, it has a variety of applications to make risk stratification less cumbersome.
Population Health Data Optimization
Health diagnosis must go beyond clinical and claim data. Population health rests on four pillars: chronic care management, quality and safety, public health, and health policy. Integrating these pillars lets health providers understand what works best for patients.
Performing Analytics
Data Analysis can assist population health management in improving patient outcomes, enhancing care management, and addressing social determinants of health.
Moreover, risk analysis in healthcare management encompasses the evolution of past experiences and current exposure to eliminate or limit risk in cash flow, community image, and employee & medical staff morale substantially.
Monitoring Growth and Outcomes
Data from past successes and failures could help health teams redesign care plans and ensure complete patient-centric care.
Incorporating Data into Risk Scores
Risk scores are a mixture of behavioral, demographic, and clinical data. It gives physicians a holistic view of patients to improve outcomes.
The data-driven approach in healthcare yields results. Take, for instance, a case study of a value-focused organization. It saw incremental growth in real-time hospital admissions, discharge, and transfer data after analyzing their data. Moreover, the organization predicted that it could provide more than 1800 Primary Care Practices, insights into patient risk status, and population health trends. Interestingly, within the last year, their efforts have been rewarded with a 5 percent drop in total data analysis costs along with a 26 percent reduction in inpatient admissions and half the number of preventable readmissions.
