Holding the line on health costs

HMO steers Medicaid patients to higher-quality, lower-cost care

Before Ohio Medicaid officials required HMOs to focus on improving care for the most expensive patients, CareSource was doing just that.

Bob Gladden’s CareSource team was already saving the state money by identifying Medicaid recipients who visited emergency rooms for routine problems and filled multiple, duplicative prescriptions. “Their health care use was not only expensive – it was potentially ineffective and even dangerous,” said Gladden, Vice President of Decision Support and Informatics. The HMO works to redirect the patients to primary physician offices, where they can get better and more cost-effective care.

There is this point where it becomes very difficult to turn the patient’s health around. Using analytics, predictive modeling and forecasting, we’re trying to find that patient before they reach that threshold.
Bob Gladden

Bob Gladden
Vice President of Decision Support and Informatics

It’s not just an issue with Medicaid patients. The Agency for Healthcare Research and Quality reports that 5 percent of the population accounts for almost half the total of health care expenses, with 1 percent accounting for a fifth of health care expenditures.

So when Ohio mandated that Medicaid HMOs identify the top 1 percent of most expensive patients and find a better way to manage their care, Gladden’s team not only met the challenge – it took it a step further. Rather than just pulling a list of recipients with the highest bills in the past 12 months, Gladden identifies individuals with long-term chronic health problems and uses data to predict future high-cost patients. Then CareSource assigns nurses to help them manage their health better, including finding a medical home with a nearby physician. The goal is to save money and keep people out of the hospital for deadly complications related to treatable illnesses like diabetes and high blood pressure. CareSource has nearly 1 million Medicaid recipients enrolled in its program, 40 percent of the state’s total. It processes 2.5 million claims a month.

The efforts are working: High-risk patients’ hospital bills dropped by an average of $1,600 per patient. While that might not seem like much, with close to 1 million members, it adds up quickly. In addition, the emergency department utilization rate dropped from 1.5 emergency department visits a year to 1.1 visits. Inpatient hospitalization utilization dropped from 0.5 visits per year to 0.4 visits per year for this high-risk population.

The HMO is finding that wellness and case management programs can’t be separated. It is rare for high-cost patients to suffer from only one chronic illness; they often have multiple health problems that need to be addressed together. “There is this point where it becomes very difficult to turn the patient’s health around. Using analytics, predictive modeling and forecasting, we’re trying to find that patient before they reach that threshold,” Gladden said.

Clues in the pharmacy records

CareSource didn’t start out trying to predict high-cost patients. Gladden just wanted to be able to ask simple questions. Analytic groups were spread throughout the organization, each creating its own set of reports. “If you asked a question to four different people, you probably got six different answers,” Gladden said. He needed an analytic framework – not a patchwork solution that needed to be linked together.

Gladden said a consolidated view of the data showed CareSource could save both lives and funds. Among the discoveries:

  • A patient with 27 unique doctor visits in one month and 30 different prescriptions written (seven of them just for asthma).
  • Patients who were taking medicine that was contraindicated, prescribed by different doctors.
  • Patients who visited the ER so many times for headaches that they were exposed to dangerous amounts of radiation because of multiple CAT scans.

It’s also much easier to figure out who to assign to care management services. “Case managers used to have to log in to the source system and scroll through hundreds of pages of information looking for test authorizations, notes from primary care physicians and claims for clues to patients needing help,” said Cathy Meade, CareSource Director of Health Care Analytics.

Taking a closer look at the top 1 percent

When the state decided it wanted to pay more attention to patients with the greatest expenses, CareSource made a convincing case to use the analytical approach it had already pioneered. “A patient could be in the top 1 percent because of a one-time incident, such as an otherwise healthy baby in the neonatal unit because of premature birth,” Gladden explained. In addition, the HMO has figured out that some patients are simply more receptive to efforts to manage their health – especially now that CareSource can predict the best time of day for a nurse to call them.

Meanwhile, the HMO worked hard to get useful data into its network doctors’ offices to make diagnosis and treatment decisions easier and more accurate. The information can be used in many ways:

  • An office can generate a list of patients overdue for a blood sugar test and call the patients for reminder appointments.
  • Prescriptions are immediately visible to doctors when a patient visits, allowing them to better manage a member’s health.
  • Physicians receive customized treatment suggestions based on a patient’s individual profile.

“We want to save money, but as a mission-driven, nonprofit company that isn't the primary reason we do this,” Gladden said. “We're really concerned about helping the members stay healthy.”

Challenge

Identify and redirect patients with chronic conditions to higher-quality, lower-cost care options that better manage their conditions.

Solution

SAS®Analytics

Benefits

A consolidated view of the data saves lives and reduces costs:

  • Hospital bills dropped by an average of $1,600 per patient.
  • Emergency room visits dropped from 1.5 a year to 1.1.
  • Inpatient hospitalization dropped from 0.5 inpatient visits per year to 0.4 visits.
The results illustrated in this article are specific to the particular situations, business models, data input, and computing environments described herein. Each SAS customer’s experience is unique based on business and technical variables and all statements must be considered non-typical. Actual savings, results, and performance characteristics will vary depending on individual customer configurations and conditions. SAS does not guarantee or represent that every customer will achieve similar results. The only warranties for SAS products and services are those that are set forth in the express warranty statements in the written agreement for such products and services. Nothing herein should be construed as constituting an additional warranty. Customers have shared their successes with SAS as part of an agreed-upon contractual exchange or project success summarization following a successful implementation of SAS software. Brand and product names are trademarks of their respective companies.

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