 ACGs in the Literature: Focus on Complementary and Alternative Medicine |
The use of complementary and alternative medicine, or CAM, has experienced considerable growth in the United States during the past decade. CAM modalities, such as chiropractic, acupuncture, and massage therapy have been used by patients with diseases such as cancer, arthritis, and diabetes, as well as by healthy patients as a way of maintaining well-being. CAM is increasingly covered by insurance plans, and several recent papers have explored utilization patterns in general and for specific diseases .
In a recent paper published in Arthritis & Rheumatism, Bonnie K. Lind from the University of Washington and colleagues from Boise State University investigated the utilization and expenditures of insured patients with fibromyalgia syndrome (FMS) who did and did not use CAM. The researchers used the ACG System to determine the expected resource utilization for each patient. They found that patients with FMS who did use CAM had more visits than FMS patients who did not use CAM, and patients with a more severe disease burden were also more likely to pursue CAM therapies. However, because the expenditure per CAM visit is lower than for a visit to a conventional physician, their overall expenditures were no higher. The authors believe that the use of CAM may be an economic alternative for patients who seek relief from the symptoms of FMS. The paper, “Use of Complementary and Alternative Medicine Providers by Fibromyalgia Patients Under Insurance Coverage” can be found in the February 15, 2007 issue (Vol. 57, No. 1, pp71-6).
In another paper “The Effect of Complementary and Alternative Medicine Claims on Risk Adjustment,” Lind, et al sought to assess how the inclusion of claims from CAM providers affects measures of morbidity burden and projected healthcare utilization in an insured population. The researchers looked at claims data from two large insurers in Washington State for 2002 and used the ACG System group individuals into five morbidity groups based on expected resource use (from very low risk to very high risk). They developed two models, one in which morbidity groups were based on all provider visits and one based on groups that excluded CAM provider diagnoses. One particularly interesting finding is that in the high risk group, patients who used CAM services were either less expensive or had equal expenses to non-CAM users, depending on the model used. They also found that inclusion of services from CAM providers under third-party payment increases risk scores, but costs were lower than expected. The article appears in the December 2006 issue of Medical Care (Vol. 44, No. 12, pp. 1078-84).
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ACG International: Global Update
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Recognizing that no two health care systems are the same, the ACG Development Team has worked with numerous governments and academicians to develop country specific models. The recent release of Version 7.1 is fully compliant with WHO standard ICD-10 claims codes. In addition, several research projects in the United Kingdom have demonstrated the validity of the ACG System using Read codes, the coding system most commonly used in primary care practices. An Rx-PM model which enables the capture of pharmaceutical information based on ATC codes will soon be released. As a reflection of the ACG System’s commitment to the global health care community, we are pleased to announce that Dr. Karen Kinder-Siemens has been named the Director of International Operations. Dr. Kinder-Siemens will continue to work out of her home base in Germany and can be reached at kkinder@web.de.
Save the date!
To further the mission of ensuring equitable health care delivery and financing around the world, we are planning The Johns Hopkins University's 2007 European ACG Conference in Karlskrona, Sweden, on September 18-19, 2007. Its objective is to demonstrate to decision-makers, politicians, chief physicians, health economists and planners, and health professionals what is being done from an international perspective .The program will include top level experts in the field of risk adjustment and predictive modeling. Presentations will highlight real-world examples of morbidity based resource allocation, performance profiling, and patient identification from across Europe, North America, and Asia. As they are finalized, details will be posted on the ACG website at www.acg.jhsph.edu.
Technical Corner: Understanding the Predictive Model Score
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The nomenclature is the same for all three of the ACG Predictive Models: the Dx-PM, based on diagnosis codes; the Rx-PM based on pharmacy codes; or DxRx-PM, the combined model that uses both diagnosis and pharmacy codes. Below is a handy guide to understanding the output of these models:
| Rxpm_ |
lt65_ |
rxcost_ |
rx_ |
prob |
1 |
2 |
3 |
4 |
5 |
1. Indicates the type of ACG Predictive Model. Possible values include:
– Dxpm (for diagnosis based predictive modeling),
– Rxpm (for pharmacy based predictive modeling) or
– Dxrxpm (for diagnosis plus pharmacy based predictive modeling).
2. Indicates the population to which the model has been calibrated. Possible values include:
– lt65 for less than 65 years old and
– ge65 for populations 65 years or older.
3. Indicates whether or not and the type of prior cost information included in the calibration of the predictive model. Possible values include:
– nocost (for no cost information was incorporated),
– ttcost (for total cost) or
– rxcost (for pharmacy cost).
4. Indicates what is being predicted. Possible values include:
– tt (for total cost including pharmacy cost) or
– rx (for pharmacy costs only).
5. Indicates the type of score being output. Possible values include:
– prob (for probability score)
– pri (for predicted resource index, reference population)
– prir (for predicted resource index, rescaled so mean is 1.0).
For more information on how to interpret predictive scores, please see the Technical User Guide.
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ACG Team Member Profile: Klaus Lemke, PhD, MSc
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 Since joining the ACG System team in September 1998, Dr. Lemke’s statistical expertise has been invaluable to the development and analysis of the growing ACG System. Dr. Lemke is a SAS Certified Professional and health services researcher with extensive experience in the analysis of large administrative databases. He has worked closely with other members of the team to highlight the differences between the ACG System’s person-based methodology and an episode-based approach to health risk assessment and adjustment. Dr. Lemke’s other focus is on predictive modeling algorithms and their utility in improving actuarial underwriting and care management.
Dr. Lemke received his Ph.D. in Statistics and Industrial Engineering with a concentration in Operations Research from Iowa State University in 1992. He also holds an M.Sc. degree in Statistics from the University of Georgia. His prior experience includes work as a health services researcher for the Maryland Health Care Commission.
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