University of Minnesota Information Technology in Healthcare Course: MILI/PUBH 6562 Fall Semester B, 2005 Economic Valuation of Heath-care Fraud Under Different Electronic Health Record Scenarios Could Interoperability Drive Fraud? Stephen T. Parente, Ph.D., M.P.H., M.S. Director, Medical Industry Leadership Institute Karen Mandelbaum, MHA, JD Tilton & Dunn, P.L.L.P, Attorneys at Law December, 2009 Presentation Overview Priors established from Brailler meeting. Valuing non-credence goods Look for detection in digital & physical worlds Compare NHIN scenarios to status quo Economic model approach Preliminary economic model results Implications & caveats Next steps Economic Valuation of Anti-Fraud Priors Literature on anti-fraud valuation is largely driven by return on investment analyses by the anti- fraud industry and government reports. Most analyses are before/after, with few controls. Too diffuse a problem for economically efficient randomized controlled trials. No seminal study to update, and even if so, the system-wide impact of interoperability is not yet known. From Previous Economic Analyses on Fraud We Decided to Focus Primarily on Non-Credence Goods Darby & Karni (1973), Free Competition and the Optimal Amount of Fraud, Journal of Law & Economics 16(1): Distinguish three qualities of a good sold in the market: Search value (e.g., I found this new doctor on the Internet.) Experience value (e.g., The doctor saw me and Im better now.) Credence value (e.g., I like this doctor because she only prescribes brand name drugs). Credence value, as a contributor to fraud (in the form of possible unnecessary care), is costly to value. The regulation of such credence goods may be as expensive if not higher in marginal cost than the actual expenditure for the good. Likely Non-credence Fraud Economic Issues for Health Interoperability Potential Costs Faster, more automated system means less chances of intervention. If identity theft is the key to a robust fraud scheme, a national ID can open new gateways for this data. Potential Benefits More data to detect fraud patterns than before. Real time patient data must come from same area. IDs only from card swipes could be processed. Health benefit cards become tokens. Marriage of mature anti-fraud activities from credit cards ties to health benefits (e.g., United Health Groups Exante Bank/HSAs/MasterCard partnership). Patient verification of diagnosis codes & charges could follow credit card consumer protection model. Only realized with tailored decision support services. Non-credence Fraud Economic Effects in Health Care as Related to IT Insurance related Faked claims: affects profitability/surplus Stolen identities: faked claim jet fuel affects profitability Medical Care related Faked goods (e.g., DME): affects productivity and consumer welfare Faked services: affects productivity and consumer welfare Regulator related Inaccurate intervention: regulatory tax reduces consumer welfare, particularly if a dynamic game by tipping ones hand. Imprecise intervention: regulatory tax reduces consumer welfare found iceberg, didnt probe beneath surface. We also decided to focus on fraud detectable in both the digital and physical worlds with a recognition that the digital world may be cracked depending upon the success of intelligent and analysis tools. FRAUD Some Its there but you cant get it. Its there, but not enough to care Digital surveillance gives you some, but not all of the picture. info of value When you think about valuing anti-fraud activities, think of the Internet in the diagrams as automated surveillance, the physical market as the non-digital health care market and the pentagon as fraud activities. Where We Started: Identify Fraud Pathways & Assign a Value & Probability to Them Prior health care fraud valuations focus on the status quo. These values represent behavior information pathways of consumer, providers, insurers, and organized fraud enterprises. Fraud in an interoperability needs to be valued as treatment case to the status quo control. Physicians Congress Main StreetBiotechnology Courts Federal Government