Health Fidelity is trying to help carriers, and providers, do a better job of finding and organizing the health record information they need to analyze how sick patients really are.
The company has developed a process for getting records out of proprietary record systems, emails, individual physicians’ computers, and even their paper files. The company then puts the records through computer systems that can make sense out of natural language.
Once insurers locate, structure and analyze all of that patient health information, “they’re surprised by how much additional risk they find,” Steve Whitehurst, Health Fidelity’s chief executive officer, said last week in an interview.
Identifying health risk buried in scattered health records may be a great way to come up with ideas for improving patients’ health, such as identifying the patients who ought to be invited back for thyroid test follow-up tests.
Program architects at the U.S. Department of Health and Human Services (HHS) have raised the stakes even further by tying provider and carrier compensation to patient risk analysis programs. HHS program designers see use of health record-driven risk-adjustment programs as a way to compensate providers and carriers for the cost of working with high-risk patients, and to give providers and carriers a financial incentive to focus on improving the high-risk patients’ health.
In the Medicare Advantage market, the Medicare Advantage risk-adjustment program helps determine how much the Medicare program pays a carrier.
In the PPACA risk-adjustment program, carriers with what appear to be relatively low-risk enrollees are supposed to send cash to the carriers with what appears to be higher risk enrollees.
When carriers develop risk analyses for the PPACA risk-adjustment program, “they’re looking for protection,” Whitehurst said.
Managers of some of the failed Consumer Operated and Oriented Plans (CO-OP) have complained that unfair PPACA risk-adjustment program rules contributed to their demise. One of their arguments is that older, better-established carriers were in a better position to identify high-risk enrollees.
Even for big, experienced carriers, a major obstacle to doing quick, automated risk analyses of U.S. patient records is the fact that most of the records are a jumble of unstructured mush. The Health Fidelity approach can increase the percentage of a patient’s record that can be used in an automated analysis to about 70 percent to 80 percent, from a typical starting range of about 15 percent to 20 percent, Whitehurst said.
The new emphasis on risk identification could affect insurers’ medical underwriting programs.
In the past, physicians may have glanced at blood test results and other screening results without always bothering to associate the results with every possible condition. Now, physicians and plans may have a much stronger incentive to name condition names.
Agents and brokers may have to work hard to help underwriters distinguish between applicants with obvious, serious conditions and applicants who’ve been packaged to maximize providers’ or insurers’ risk-adjustment program performance.
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