HMO measures physician performance
A non-profit HMO with 800,000 members in New York is launching a customized business intelligence solution that melds software from three sources.
HIP Health Plans wanted to develop a system to assess the performance of healthcare providers so it could help improve efficiency and quality of care.
“HIP realizes that quality of care is vital in distinguishing ourselves from competitors,” says Dr. Randall Spoeri, HIP’s VP for Medical and Quality Informatics.
Among those backing the project were HIP’s Special Investigations Unit, as well as its Medical and Quality Informatics staff. The solution chosen fuses IBM’s Fraud and Abuse Management System (FAMS) with software from Symmetry Health Data Systems and ProfSoft.
The system incorporates information on all care provided during the course of treatment and accounts for differences in the severity of illness case by case. It supports medical management as well as fraud detection, HIP reports, so that it can combat fraudulent claims while measuring and improving the quality of care physicians give their patients.
“The effort to utilize FAMS software for more than standard fraud detection and prevention techniques was a major collaborative effort that will save HIP hundreds of thousands of dollars,” says Thomas Cantwell, HIP’s Special Investigations Unit’s managing director. “HIP will be the role model for other insurers.”
“This alliance provides a wide range of information that healthcare organizations can use to make decisions and take action,” says Dr. Ed Bassin, ProfSoft president. “The product and services offer providers a better understanding of their actual use of resources, in order to control the cost and improve overall quality of care for their patients.”
IBM is adapting the underlying technology of FAMS--a business intelligence solution--to work with Symmetry’s software, which groups raw health claim data into “Episode Treatment Groups.” ProfSoftEpisode software builds data models from the Symmetry software, adjusting for age, sex and severity of illness.