MORE DATA MINING FOR MEDICAL MISREPRESENTATION?
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1 MORE DATA MINING FOR MEDICAL MISREPRESENTATION? Admissibility of Statistical Proof Derived from Predictive Methods of Detecting Medical Reimbursement Fraud Neil Issar With thanks to Prof. Edward K. Cheng
2 Outline Medical Coding ICD-9 vs. ICD-10 Complications in Transition Health Care Fraud Upcoding Fraud Detection Data Mining Problems of Statistical Evidence Scientific Reliability Reference Class Problem Blue Bus Paradox The new, complex ICD-10 code set poses many complications that may result in increased coding errors and opportunities for coding fraud, which supports greater involvement of data mining methods of fraud detection.
3 Medical Coding Communication to payer for reimbursement Diagnosis codes + Procedure codes = Code set International Classification of Diseases (ICD) th Version (ICD-6) th Version (ICD-7) th Version (ICD-8) th Version (ICD-9) th Version (ICD-10)
4 ICD-9 vs. ICD-10 ICD-9 Body system ran out of codes Numeric, 5 digits ICD-10 Greater number of codes Alphanumeric, 7 letters + digits Primary generalized osteoarthritis M15.0XXX 14,567 diagnosis codes 3,878 procedure codes 69,832 diagnosis codes 71,920 procedure codes
5 ICD-10 Too Much Detail? Whether you run into a turkey or the turkey runs into you, you re pecked by a turkey or you re bitten by a turkey, there s a difference between being pecked and being bitten. So they have nine codes for turkeys. Do they need to go through this much detail to get information to the federal government? I don t think they do. -- Texas Congressman Ted Poe Interview on Fox Business Network April 25, 2013
6 ICD-10 Detail W56 Contact with nonvenomous marine animal W57 Bitten or stung by nonvenomous insect and other nonvenomous arthropods W58 Contact with crocodile or alligator W59 Contact with other nonvenomous reptiles W60 Contact with nonvenomous plant thorns and spines and sharp leaves W61 Contact with birds (domestic) (wild) W62 Contact with nonvenomous amphibians
7 ICD-10 Detail W56 Contact with nonvenomous marine animal W57 Bitten or stung by nonvenomous insect and other nonvenomous arthropods W58 Contact with crocodile or alligator W59 Contact with other nonvenomous reptiles W60 Contact with nonvenomous plant thorns and spines and sharp leaves W61 Contact with birds (domestic) (wild) W62 Contact with nonvenomous amphibians
8 ICD-10 Detail W61 Contact with birds (domestic) (wild) W61.0 Contact with parrot W61.1 Contact with macaw W61.2 Contact with other psittacines W61.3 Contact with chicken W61.4 Contact with turkey W61.5 Contact with goose W61.6 Contact with duck W61.9 Contact with other birds
9 ICD-10 Detail W61 Contact with birds (domestic) (wild) W61.0 Contact with parrot W61.1 Contact with macaw W61.2 Contact with other psittacines W61.3 Contact with chicken W61.4 Contact with turkey W61.5 Contact with goose W61.6 Contact with duck W61.9 Contact with other birds
10 ICD-10 Detail W61 Contact with birds (domestic) (wild) W61.4 Contact with turkey W61.42 Struck by turkey W61.43 Pecked by turkey W61.49 Other contact with turkey
11 ICD-10 Detail W61 Contact with birds (domestic) (wild) W61.4 Contact with turkey W61.42 Struck by turkey W61.43 Pecked by turkey W61.49 Other contact with turkey
12 ICD-10 Detail W61 Contact with birds (domestic) (wild) W61.4 Contact with turkey W61.42 Struck by turkey W61.42XA Initial Encounter W61.42XD Subsequent Encounter W61.42XS Sequela W61.43 Pecked by turkey W61.43XA Initial Encounter W61.43XD Subsequent Encounter W61.43XS Sequela W61.49 Other contact with turkey W61.49XA Initial Encounter W61.49XD Subsequent Encounter W61.49XS Sequela 9 codes for turkeyrelated medical encounters
13
14 ICD-10 Detail S Laceration of unspecified blood vessel at shoulder and upper arm level, right arm S Puncture wound without foreign body of left great toe with damage to nail R46.0 Very low level of personal hygiene T Asphyxiation due to being trapped in a (discarded) refrigerator, assault Y92.65 Oil rig as the place of occurrence of the external cause Y92.72 Chicken coop as the place of occurrence of the external cause T Poisoning by caffeine, intentional self-harm V04.09 Pedestrian on snow skis injured in collision with heavy transport vehicle or bus in non-traffic accident Source: Ronald V. Bucci, ICD-10 and Its Impact on Radiology, 12 RADIOLOGY TODAY 10, 10 (2011)
15 Complications in Transition Insufficient coding training Lack of unanimity in coding Health care providers Professional coders Convoluted codes Transition mappings Initial lack of productivity post-implementation Lack of preparedness
16
17 Health Care Fraud During the past decade, we have literally seen a feeding frenzy on the Medicaid program, a period of unprecedented white-collar theft in which wave after wave of multimillion dollar fraud has swept through nursing homes and hospitals, to clinics and pharmacies Thomas F. Staffa Chief of the Criminal Division in the Office of the New York State Special Prosecutor for Medicaid Fraud Control Aug. 2, 1993
18 Health Care Fraud Growing problem Upcoding Insurers getting in on the act United States v. Janke (S.D. Fla. Feb. 10, 2009) Data sweeps to find additional diagnoses for coding Hiring unlicensed physicians Reporting false data to the government Threatening employees to not cooperate with investigations Depositing $12M into personal bank account $22.6M settlement
19 Fraud Detection Complicated by size and complexity Data mining can extract specific information from very large quantities of data Supervised Unsupervised Hybrid / Semi-supervised Problem with using data that identifies unusual patterns of potentially fraudulent activity as evidence?
20 Scientific Admissibility Standards People v. Collins (1968): use of product rule rejected. Lacked adequate foundation Distracted the jury from its proper function of weighing the evidence Data mining in health care different? U.S. ex rel. Martin v. Life Care Ctrs. Of Am., Inc. (E.D. Tenn. Sep. 29, 2014) Court allowed extrapolation from random sample People v. Collins concerns
21 Reference Class Problem Statistical inferences United States v. Shonubi (E.D.N.Y. 1995) Problem with use of one reference class Data mining Feature or attribute selection algorithms Model selection and feature selection as solutions? Can algorithms define relevance?
22 Blue Bus Problem Blue Bus Company Naked statistical proof Applicability to DNA? Data mining Prof. Jay Koehler s Exemplar Cueing Theory Coincidental match exemplars False negatives vs. false positives
23 Conclusion The new, complex ICD-10 code set poses many complications that may result in coding errors and opportunities for coding fraud. The sheer scope and scale of health care fraud may necessitate data mining methods of fraud detection. The concerns typically associated with statistical evidence are adequately addressed with results of data mining algorithms.
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