How Predictive Analytics Will Change the Managed Care Landscape
The conversation surrounding the future of healthcare can be complex and confusing, but one thing is clear: healthcare costs need to be brought under control. Predictive analytics offers invaluable insights in many disciplines, but in the healthcare sphere, these insights can directly impact patient care costs. Not only is there opportunity to reduce costs, but the utilization of predictive analytics offers providers the ability to improve patient care as well as to reduce waste within the system. Predictive analytics is all about applying what doctors have been doing already but on a much larger scale. What’s changed is our ability to better measure, aggregate, and make sense of previously hard-to-obtain or non-existent behavioral, psychosocial, and biometric data. Applying these additional streams of data within a clinical context can help interdisciplinary care management teams better understand their patient population so they can better plan for strategic interventions that can impact patient care outcomes over the long-term.
An article published by Definitive Healthcare outlines three key areas where predictive analytics could be applied to both improve patient care and reduce healthcare costs. Read on to see Innovista’s take on these three opportunities.
1. Predictive analytics could help medical professionals to administer better preventative care
A large percentage of the United States population is affected by chronic illness such as heart disease, lung and kidney disease, and diabetes, and “a tremendous portion of what we spend on healthcare is for chronic disease” (Waldron, 2019, ¶3). Many of these conditions are preventable through simple lifestyle changes as they can be tied to “excessive alcohol and tobacco use, poor nutrition, and lack of physical activity” (Waldron, 2019, ¶4). Value-based care models look to facilitate preventative care measures to keep patients out of the hospital, where they might need to undergo expensive treatments to address the side effects of unmanaged chronic conditions otherwise. Using predictive analytics, providers can examine trends in patient conditions and treatments, thereby helping some patients to avoid the reality of a full-fledged chronic disease and administering more timely treatments to those who have already been afflicted by a chronic condition.
2. Healthcare over-treatment could be reduced by incorporating predictive analytics into treatment assessments
‘Unnecessary services’, treatments and procedures that are unnecessary to improve or stabilize the health of a patient, “are the largest contributor to waste in US health care, accounting for approximately $210 billion of the estimated $750 billion in excess spending each year” (Waldron, 2019, ¶6). Many instances of overtreatment stem from concerns regarding malpractice, patient pressure, and issues accessing EMRs. The current, widespread fee-for-service (FFS) model also encourages some practitioners to “perform unnecessary procedures when they profit from them. Therefore, de-emphasizing fee-for-service physician compensation in favor of value-based care might reduce healthcare utilization and costs” (Waldron, 2019, ¶7). Access to enhanced patient data through predictive analytics tools promoted by value-based care programs can assist physicians in prescribing and administering more appropriate medication, treatments, and tests based on a patient’s individual history or trends seen in a patient population with similar symptoms.
3. Including predictive analytics in care coordination can improve plan and treatment effectiveness
The use of predictive analytics can take a mountain of raw patient data and turn it into actionable insights. More and more consumers are utilizing wearable technologies such as Apple Watches and Fitbits, and, with the patient’s consent, providers can access this data, known as remote patient monitoring. The data collected by these devices, such as heart rate or time spent exercising, may not always be helpful as individual data points, but with predictive analytics, it can offer providers an intimate look at patients’ long-term, holistic health. AI technology has also been implemented to detect cancer earlier than was previously possible, identifying genetic disorders in children simply by reviewing photos of them. Early detection oftentimes can help a patient circumvent a more serious illness or condition as well as reduce the overall cost to treat a condition. Predictive analytics also allow providers to identify and monitor patients at the highest risk of hospitalization, reducing cost by providing measured, consistent care as opposed to emergency treatment for a complication associated with an unmanaged condition.
AI is invaluable in ensuring that appropriate, timely care is provided to the patients who need it most before their health is in crisis. Following great success with a similar initiative in our Illinois market, Innovista has recently begun collecting social determinants data from patient HRAs in our North Texas market to build out a stratification model. This model utilizes a rules-based scoring tool that groups patients into three categories: low, medium, and high. Patients grouped into the “High” category in the Illinois market were enrolled in a care management program, designed to ensure steps were taken to provide those patients with care interventions that were likely to improve their overall health, ultimately lowering costs, as well.
Innovista’s population health platform brings this AI functionality, combined with the John Hopkins ACG model, designed to predict population trends and utilization activity based on clinical coding and historical trends. The ability to not only stratify a population-based on their risk but also provide a care team with a second metric based on preventative care opportunities is well beyond methods used previously in value-based care models. In addition to having this analytic capability, Innovista offers a second tool that integrates with cloud-based EMR systems and pushes the information directly to the provider in the primary care setting. With this new technology and a care management team, Innovista ACOs saw huge successes in managing value-based and shared savings arrangements, particularly in the Medicare Shared Savings Program. To read more about this success, visit this link.
Innovista Health Solutions is a Management Service Organization that enables independent physicians to engage, support and manage populations in new value-based savings and shared-risk models. With their extensive industry experience, population health-focused technology and innovative approach, Innovista delivers a truly unique, state-of-the-art solution for healthcare providers, partnering organizations and facilities.
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