A Decision Support Tool for Identifying Abuse of Controlled Substances by ForwardHealth Medicaid Members

Abstract

Objective: Our objective was to use Wisconsin's Medicaid Evaluation and Decision Support (MEDS) data warehouse to develop and validate a decision support tool (DST) that (1) identifies Wisconsin Medicaid fee-for-service recipients who are abusing controlled substances, (2) effectively replicates clinical pharmacist recommendations for interventions intended to curb abuse of physician and pharmacy services, and (3) automates data extraction, profile generation and tracking of recommendations and interventions.

Methods: From pharmacist manual reviews of medication profiles, seven measures of overutilization of controlled substances were developed, including (1–2) 6-month and 2-month “shopping” scores, (3–4) 6-month and 2-month forgery scores, (5) duplicate/same day prescriptions, (6) count of controlled substance claims, and the (7) shopping 6-month score for the individual therapeutic class with the highest score. The pattern analysis logic for the measures was encoded into SQL and applied to the medication profiles of 190 recipients who had already undergone manual review. The scores for each measure and numbers of providers were analyzed by exhaustive chi-squared automatic interaction detection (CHAID) to determine significant thresholds and combinations of predictors of pharmacist recommendations, resulting in a decision tree to classify recipients by pharmacist recommendations.

Results: The overall correct classification rate of the decision tree was 95.3%, with a 2.4% false positive rate and 4.0% false negative rate for lock-in versus prescriber-alert letter recommendations. Measures used by the decision tree include the 2-month and 6-month shopping scores, and the number of pharmacies and prescribers. The number of pharmacies was the best predictor of abuse of controlled substances. When a Medicaid recipient receives prescriptions for controlled substances at 8 or more pharmacies, the likelihood of a lock-in recommendation is 90%.

Conclusion: The availability of the Wisconsin MEDS data warehouse has enabled development and application of a decision tree for detecting recipient fraud and abuse of controlled substance medications. Using standard pharmacy claims data, the decision tree effectively replicates pharmacist manual review recommendations. The DST has automated extraction and evaluation of pharmacy claims data for creating recommendations for guiding pharmacists in the selection of profiles for manual review. The DST is now the primary method used by the Wisconsin Medicaid program to detect fraud and abuse of physician and pharmacy services committed by recipients.

KEYWORDS:

• decision tree
• fraud and abuse
• Medicaid
• controlled substances
• drug abuse

This research was sponsored by the Division of Health Care Financing, State of Wisconsin Department of Health and Family Services, One West Wilson Street, PO Box 309, Madison, WI 53701.

Notes

∗Excerpted from Reference 6–42 CFR Subpart K 456.705 and 456.709.

∗The Misclassification Matrix is produced by the AnswerTree Module of SPSS. The Predicted Recommendation is the DST's preliminary recommendation produced by the decision tree.

^aRisk Estimate = 9 misclassified recommendations/190 total recommendations

^ωCost saving analysis compared expenditures 6 months pre-intervention to 6 months post-intervention with a 1-month washout period. The analysis for lock-in and alert letters to prescribers included recipients who received the intervention between January 1997 and July 1998. The analysis for warning letters included recipients who received the intervention between January 2001 and March 2002. Recipients included in the analysis were continuously eligible and had no other interventions during the review period.

^τClinical Service Categories are mutually exclusive and represent all Wisconsin Medicaid fee-for-service paid claim expenditures for the time frame.

^αThe category “All Other” represents claims from providers such as chiropractors, optometrists, rural health clinics, and ambulatory surgery centers.

^δDurable medical supplies/disposable medical supplies.