|
From the May 2004 Issue Breathing New Life Into Home Care: Case History |
Tech and Tenderness TPA uses predictive modeling based on artificial intelligence, wrapped with personalized nurse intervention, to lead high-risk patients to medical care. By Robin Blair, Editor
On the surface, predictive modeling might not light up the sky the way wireless devices or voice recognition systems do. But make no mistake: Its impact is so far- reaching that soon it will influence and possibly direct the healthcare services many of us receive. Disease management (DM), with its predictive modeling corollary, have of late come under increased scrutiny. The DM concept, of course, made perfect sense in the beginning: Manage those patients with chronic, high-cost conditions to avert admissions or emergency episodes and, hence, reduce costs. But how does a health plan with 500,000 members actively influence 10 percent of its membership with diabetes, congestive heart failure and asthma in such a way that the intervention moderates behavior that, in turn, moderates costs? That’s 50,000 people and a lot of intervention. How much smarter might it be to predict who could be in the high-risk population years before they get there, and modify their behavior now? The advent of more sophisticated predictive modeling technology has turned attention to high-risk health plan members who aren’t yet high-cost patients, but soon will be without effective intervention and lifestyle changes. These may be patients in the early throes of utilization, as well as consumers who aren’t yet active utilizers. Experts postulate that 2 percent of a health plan’s membership may be at very high risk and may, eventually, drive 70, 80 or even 90 percent of the health plan’s expenses. Those numbers alone make it a problem worthy of the IT microscope.
Artificial Intelligence
As part of a suite of medical management services that includes disease management and case management, HSB also offers High Impact, a predictive modeling-based program that proactively identifies tomorrow’s high-risk, high-cost patients. Today, about 40 percent of HSB’s total membership is eligible for the High Impact program. The technology foundation of High Impact is Risk Navigator Clinical, a forecasting tool from Orlando, Fla.-based MEDai (Medical Artificial Intelligence). The technology foundation of Risk Navigator Clinical is MITCH (Multiple Intelligent Tasking Computer Heuristics), a prediction engine that allows health plans and TPAs to not only predict which members are tomorrow’s high-cost patients, even if they are not now heavy utilizers, but also to predict which individuals in the high-risk population are likely to respond to intervention. MEDai CEO and co-founder Steve Epstein says the technology is third-generation. It started with rules-based systems, he says, then progressed to groupers, and now systems are heuristic. They have become “smart.” “It’s a tremendous advantage,” says Epstein, “for a health plan to take subsets of a populations and say, ‘Five years down the road, if you continue your current lifestyle, you run an 80 percent chance of developing heart disease or lung disease. Here is what you can do now to prevent that.’ It’s an even greater advantage for the health plan to be able to predict which members are likely to respond to its intervention.”
Juggling Data
MITCH’s mission is to spot high risk from miles away. Being able to deal with linear and nonlinear data, “MITCH can compare a consumer sitting at home, with no prior hospital admissions or ER episodes, to his neighbors with similar backgrounds, ages, zip codes and insurance coverage, and can generate an accurate prediction,” says Epstein. Equally important, he says, is the system’s ability to identify the drivers. “It’s not enough to identify the risks,” he continues. “Care management programs must know the drivers of risk. Is it a drug, a lab value, or something in the patient’s history at the foundation of his risk score?” Modeling with artificial intelligence (AI) also makes crystal clear the difference in value of healthcare dollars spent. In their article, “Predictive Modeling in Health Plans,” authors Randy Axelrod and David Vogel cite as an example two health plan members that each generate $3,000 of service utilization in a plan year. One member generates those costs in a single episode of hospital treatment; the other, through months of prescription utilization. While the dollar amounts might render similar financial profiles, AI-based predictive modeling would manifest dramatically different risk forecasts for those patients. MITCH relies upon more than 68 types of mathematical components or configurations to clean, analyze and mine data. For example, says Epstein, “If you have a data set with age as a component, but you know data are missing or some have been coded incorrectly, MITCH can determine where age is likely to be in error and can modify the results so age isn’t too heavily weighted an influence on the final analysis.”
Personal Touch
Cindy Furgerson is the director of High Impact. She says that after the program’s first year of operation, HealthSCOPE Benefits’ clients experienced these collective results:
During initial contact, the High Impact life coach uses a survey instrument to measure the member’s perception of his own health. “The member’s perception can alter our approach to facilitating care,” she says. “A member who understands his medical needs and is already using services or is on the threshold is very different from an at-risk member who perceives himself to be in good health.” The initial intervention also allows the life coach to identify barriers to compliance and care—for example, claims not yet paid or co-pays so high that the members fails to get medications. “We look for issues that would lead to noncompliance. We strive to establish credibility in the relationships so we can help guide as the patient accesses healthcare service.” Once the patient is onboard, the life coach develops a customized care plan that includes continual needs assessment, education, a monitoring schedule to document medication and treatment compliance and, where needed, advocacy and facilitation. “Our life coaches work as clinical navigators to help patients make their way through a complicated, fragmented and sometimes broken healthcare system. If we find it that way—and we’re clinicians—imagine how it appears to patients. When we can help them make the right connection, remove obstacles, and bring them together with resources and services they need, their health will improve.”
Little Things Mean a Lot
Another time, a congestive heart failure patient tried to make an appointment with her cardiologist when she noticed swelling in her feet—a symptom she learned from her life coach—but was told she had a one-month wait. When the life coach followed up with the cardiologist’s office, she learned that the patient hadn’t described her symptoms in requesting the appointment—and that one-month wait vanished. The intervention isn’t difficult, says Furgerson, but “sometimes the hardest part of the program now is contacting members. There are many privacy-related barriers that can impair the initial contact. Once the life coach is able to speak with a potential participant, enrollment rates are very good. Our life coaches are skilled as clinicians, personal motivators and salespeople.” Most employers prepare their employees for High Impact with a series of informative newsletters and brochures, so that first contact isn’t a surprise. In the past, the term “high-risk” usually equated with “high-cost,” but that’s no longer true. Today, “high-risk” might equate with a disease and its related medical costs, both under control.
For more information about Risk Navigator Clinical and predictive
modeling solutions from MEDai, |
||
“A
member who understands his medical needs and is already using
services or is on the threshold is very different from an at-risk
member who perceives himself to be in good health.”
“It’s
not enough to identify the risks. Care management programs must know
the drivers of risk.”