Whole body health and personalized medicine is one of the secret sauces for optimizing the cost of healthcare without compromising quality. Progress cannot be accomplished in personalized precision care without adopting highly accurate predictive analytics on individual members across all chronic diseases.
Extracting information from databases which includes claims, electronic health records, and medical images could be a good start to understand disease risk patterns. Traditional machine learning models are taught to assimilate a wide variety of data and provide outputs based on how outcomes are defined. Unlike financial and weather forecasting, there are several variations on how diseases manifest even within the same chronic conditions, and it gets more complex on how social determinants and behavioral health influences disease progression. To add to the complexity, there is also a significant impact of one chronic condition on another, which further adds more variations on emerging risk.
Hence, there is a need for a clinically driven risk identification grid which sets certain rules on high-yield factors based upon evidence based medicine which empowers the machine learning process on recognizing meaningful variables across a wide data set while predicting rising risk and disease progression.
Clinical practice variations amongst care providers could also contribute to the complexity in delivering scalable Precision Medicine. If there is standardization across risk identification and mitigation, along with real-time risk data from patients could improve touch point engagement, both at the patient and provider level.
“SaaS solutions like OptMyCare have a unique advantage of a clinically pre-mapped machine learning process that extracts data from the payers, providers and patients and meaningfully identifies rising risk across all disease states and its cost implications empowering risk bears to adequately allocate disease mitigation resources” says Bill Lucia, former CEO of HMS Holdings (Health Management Solutions) . Adding genomics in addition to other variables could further increase the power of risk analytic tools in the recognition of emerging risk and the implementation of preventive strategies.
With increasing shortage of nursing staff and physicians, the scalability of Precision Medicine across a large population becomes a distant reality. Though all citizens could benefit from personalized risk prevention and mitigation even while they are at a relatively healthy state, early attention would be required for those who are at a higher risk of immediate health events. Stratifying risk across large populations and implementing mitigation strategies using digital touch points would be the most realistic option in scaling personalized medicine. Member engagement with digital health has a wide range between 30% -70% across different regions in the country based on age, sex, ethnicity, social determinants, financial status, and other factors. The increased utilization of mobile devices and generative AI could be a way forward for engaging more of the general population and reserving nursing staff for home visits for subgroups who are particularly at higher risk for worsening disease.
“Utilizing a simple and easy to understand engagement platform powered by accurate predictive risk stratification would be the most practical approach in implementing Precision Medicine Care” says Dr. Bala, CEO of OptMyCare Inc.
Reinsurance and Stop-loss insurance provide coverage in the event of multiple complex conditions such as cancer, rare disease, and organ failure. that frequently necessitate a multitude of high and ultra-high-cost claims. People who suffer from these conditions will greatly benefit from analytic software that stratifies risk and cost a priori. AI solutions could facilitate keeping patients out of costly inpatient settings such as emergency departments and critical care units, guiding patients to outpatient care settings where they can be more effectively and efficiently managed. Such revolutionary software will actively empower insurance underwriters, medical reviewers, and claims agents throughout our industry.
While patients avoid unnecessary hospitalizations and their attendant complications such as nosocomial infections and blood clots; their physicians are promptly alerted to dynamic risk via risk assessment updates—updates which are based on the most current data available. In this way, AI tools can update the payor from the traditional threat of being blindsided by catastrophic claims.
AI platforms like OptMyCare utilizes advanced analytics for easy-to-use risk management pathways to the online dialogue. With our platform, we firmly believe we will empower patients to take charge of their healthcare trajectories: past, present, and future, says Mike Kemp, former President of Swiss Re Corporate Solutions North America . Simply put, we want to better equip patients to successfully navigate the healthcare arena in the same way that online financial software helps individuals maximize their finances. By minimizing uncertainty and unanticipated losses for insurance carriers, OptMyCare analytics maximizes the patients’ health, resulting in an optimized outcome for all.