SCIO Health Analytics' Chief Evangelist argues for the use of analytics to prevent complications and provide true value-based care.
Patients with chronic illnesses account for more than half of the healthcare dollar. So, it’s clear that any comprehensive strategy to bend the health cost curve must include robust strategies to improve outcomes and appropriate care utilization for patients with chronic conditions. Unfortunately, most current approaches, such as disease management programs targeting patients with expensive conditions such as diabetes and heart disease, are capturing patients who are already sick, and already siphoning off a disproportionate share of healthcare spending.
It’s clear that to make a significant impact on both outcomes and the bottom line in value-based payment arrangements, we need to get ahead of serious chronic diseases and head them off before they erode quality of life and absorb vast resources. The key is to employ emerging predictive analysis platforms that offer healthcare executives and clinicians a window into the future health of their patients. A crystal ball may seem more science fiction than reality, but today’s tools can help target the next level of patients who are bubbling under the surface — who aren’t suffering yet but will be if they continue down the road they’re on.
It’s the first step towards developing actionable preventive care management programs to engage patients who are pre-diabetic or at-risk for heart disease before they become super-utilizers. Indeed, many industry leaders are advocating for healthcare organizations to move in this direction.
“Our traditional model, I think our meme for the way we practice healthcare, is actually not healthcare, it has been sick care,” said Daniel Kraft, MD, during his video presentation entitled Medicine 2064 with Dr. Daniel Kraft. “We wait until often we get sick, we have the heart attack, the stroke, the cancer that pops up. Where things are sort of heading, I believe, is from waiting for disease to happen to wellness and prevention. In fifty years from now, we will have the equivalent of almost an “On-Star” for the body that’s going to be taking information from our environment, from our diet, from our social networks, from our genomics. Health will become something that is truly infused and integrated into our life.”
With visionaries such as Dr. Kraft and others imagining the future, predictive analytics is emerging as a tool that can help shift from focusing on treating patients who have already been diagnosed with certain conditions — such as diabetes, chronic obstructive pulmonary disease (COPD), heart disease and asthma – to preventing people from developing these conditions in the first place.
Let’s take diabetes: predictive analytics could help providers prevent patients from developing type 2 diabetes by analyzing data — in past health assessments, previous medical claims, lab results and existing electronic medical records – to anticipate who is at risk for the disease. Providers could then work with these patients on lifestyle modifications such as healthy eating and exercise to help avert or delay the onset of type 2 diabetes. Without such intervention, prediabetes is likely to progress to diabetes within 10 years for many patients, according to the Mayo Clinic.
Predictive analytics could also help to identify undiagnosed diabetic patients — which could have a significant impact on population health. According to the American Diabetes Association, 29 million Americans have diabetes – and of those more than 8 million are undiagnosed. With predictive analytics, healthcare organizations can segment patients by risk and compliance and identify those who are undiagnosed. The use of zip code data could then help to identify geographic pockets of undiagnosed patients while patient persona data could help move toward a better understanding of why these patients are at risk – and what interventions or therapies are most likely to improve their health.
Once predictive analysis uncovers these previously hidden groups of at-risk patients, other qualitative analytics tools can be used to understand and overcome any challenges to treating these populations. For example, one predictive analytics study involving a Florida-based healthcare organization identified pockets of high-risk patients who were not filling or refilling medications. Qualitative analytics were deployed to help the organization uncover why this gap existed. The analysis showed that patients had to take two left turns to get to the pharmacy — but elderly patients in Florida do not like to take left turns. This knowledge helped providers develop workarounds to remove a simple, logistical obstacle to a serious medication adherence problem.
To move to value-based health care, hospitals and health systems know they must do a better job of addressing population health. But while many population health initiatives currently intercept patients who are already sick, with new predictive analytics tools we have a better chance of heading off chronic illness before it begins. With an ounce of prevention instead of a pound of cure, we can offer our patients better quality of life while reining in unsustainable health costs.
David Hom is Chief Evangelist at SCIO Health Analytics®, an organization dedicated to using healthcare analytics to improve clinical outcomes, operational performance and business results. Dave has been a visionary at SCIO®, building SCIO’s leading products on behavioral economics applications for many health plans and a technology-enabled solution to engage members based on the gap value and impact on avoidable hospital events. He can be reached at [email protected].