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Necessary cookies are absolutely essential for the website to function properly. This example uses artificial data since actual data has many legal and policy constraints on disclosure, and it provides a simple but easily understandable approximation of a payer environment.
In our example, the algorithm is built using a simplified data set that has a selection of inpatient and outpatient diagnoses, treatment intervals, information about changing physicians, total claims, comorbidities, and our outcome of interest—fraud.
To begin with, let us briefly discuss our data mining methodology. A Bayesian belief network BBN is a directed, acyclic graph of conditional dependence. A BBN allows us to estimate the likelihood of a given outcome of interest given prior knowledge.
Further, the manner in which this estimate is derived is through a directed structured network of conditional dependence joint probability that actually provides us with a hierarchy of information: if I want to estimate the likelihood of A, the most useful pieces of a priori knowledge are C, D, and L.
This allows us to be efficient and only use those pieces of information that are most useful in solving the problem at hand. The BBN discussed in this example was constructed using machine learning, meaning that a computer algorithm was used to study a data set of prior evidence in order to discover the optimal structure of the BBN.
Machine learning is a highly efficient method to discover rule sets in highly complex, otherwise impenetrable data sets. We can learn several things just from the network structure itself. For example, if we want to detect fraud in our study population, the four most important factors are whether the enrollee has changed clinicians between visits, what the diagnosis is at the third outpatient visit, what the interval between second and third outpatient visits is, and what the interval between the first and second inpatient visits is.
Having observed the information structure of our population, we can dig deeper to begin to understand the evidence underlying the model and derive the rule sets that predict fraud. We can observe, for instance, that about 10 percent of enrollees have been involved in some type of fraud, while only about 11 percent of enrollees have changed physicians. These two features are conditionally dependent—but how do they impact one another?
In this instance, we now know percent probability that the enrollee in question has not used the same physician for all visits. The posterior probability of fraud is now about 79 percent compared to about 10 percent in the overall population. In addition, if we reverse the evidence and ask how many enrollees committing fraud change physicians, the answer is an estimated 90 percent.
From this we can draw an inference: 79 percent of enrollees who changed physicians commit fraud, and 90 percent of enrollees who commit fraud change physicians. This provides a direct examination of characteristics of those who commit fraud. Here, we find that on the third visit Furthermore, the visit was for pain However, the BBN is a nonlinear model, meaning it can represent complex relationships that may have multiple solutions. In this case, the posterior probability of fraud drops to about 39 percent, still significantly more than the general population but significantly lower than the 79 percent estimate we receive with only the one piece of evidence.
The models allow us to codify large rule sets. For example, if we take only the four nodes most closely associated with fraud in this model and run an inference table a table representing all possible combinations , the total number of potential rules is , even from this relatively simply network.
In these tables, we select only two rules, where we examine an enrollee with short encounter intervals for injury—which typically has a low likelihood of fraud and abuse. However, if the enrollee changes physicians during the course of treatment, the probability of fraud increases to 53 percent.
If we increase the outpatient interval to days, however, the likelihood of fraud decreases to about 21 percent, perhaps reflecting that if you return to the same hospital six months later you are likely to be assigned a different attending physician. The examination of these rules brings us to the policy question. At what predicted probability of fraud do we take action?
This is a significant question because the probability threshold we select will impact whether the system is optimized toward sensitivity detection or specificity accuracy. Does the value of detecting the incremental 10 percent of fraud pay for the cost of reviewing large numbers of false positives?
Finally, once our network is developed, validated, and optimized, we can deploy our rule sets, either by using the classifier in real time through batch inference or by selecting specific rule sets for implantation in systems or workflow. A major concern physicians have in the use of data modeling and mining techniques is that they will be unfairly accused of fraud. A primary advantage of the data mining approach is that the resulting algorithms can be tested, validated, and optimized to an optimal level of sensitivity and specificity that will exclude patterns of normal use.
Educating physicians to understand that data modeling and mining will help alleviate suspicion of fraud and abuse should go a long way to addressing their concerns. In order to adequately address issues of fraud and abuse, responsibility, ownership, and consequences for actions must cross the continuum at the individual physician, healthcare provider, organizational, and federal levels.
Providers as well as consumers must be committed to providing appropriate documentation to address abuse issues and take a moral and ethical stand against fraud in the healthcare environment.
This may mean taking advantage of the FCA whistleblower laws to identify fraudulent claims to the appropriate federal authorities. Healthcare providers and organizations must invest in offering education and training programs, creating coding and fraud and abuse committees, and utilizing data mining and modeling software.
National Center for Biotechnology Information , U. Perspect Health Inf Manag. Find articles by William J Rudman. Find articles by John S Eberhardt. Find articles by William Pierce. Find articles by Susan Hart-Hester.
Author information Copyright and License information Disclaimer. This article has been cited by other articles in PMC. Abstract In Texas, a supplier of durable medical equipment was found guilty of five counts of healthcare fraud due to submission of false claims to Medicare.
Key words: fraud and abuse, computer assisted coding, data mining. Introduction The above are some examples of fraud presented by the HHS and Department of Justice fraud and abuse report for What Is Healthcare Fraud?
Solutions to Fraud and Abuse Under the above definitions, it is impossible to delineate between fraud and abuse on the basis of evaluating a single case or record. From our review of the literature, the following four solutions to identifying and reducing fraud and abuse are suggested: Training and education Implementation of computer-assisted coding CAC Increased federal enforcement of fraud and abuse monitoring Use of data modeling and data mining. Training and Education Educational training programs focused on deterring fraud and abuse must first and foremost stress the importance of appropriate documentation and coding in accurately identifying the patient's condition in order to provide timely and effective care.
Increased Federal Enforcement of Fraud and Abuse Monitoring One of the most effective ways of controlling fraud and abuse is through reinforcement of federal penalties. Use of Data Modeling and Data Mining As noted above, fraud and abuse often involves multiple actors committing subtle acts over a long period of time.
Open in a separate window. Figure 1. Figure 2. Figure 3. Figure 4. Figure 5. Figure 6. Figure 7. Overcoming Physician Resistance to Use of Data Mining A major concern physicians have in the use of data modeling and mining techniques is that they will be unfairly accused of fraud. Conclusion In order to adequately address issues of fraud and abuse, responsibility, ownership, and consequences for actions must cross the continuum at the individual physician, healthcare provider, organizational, and federal levels.
Notes 1. Chamber of Commerce Report. Mercy Health Plans, Fraud Hotline, Harman, Laurinda. Rudman W. Rudman, W. Garvin J. H, Watzlaf V, Moeini S. References Fraud Geis et al. Morrison, Morrison James, Morrison Theodore. Klein et al. Klein, Roger D. McCall et al. McCall, Nelda, Harriet L.
Komisar, Andrew Petersons, and Stanley Moore. Iglehart and John, Iglehart, John K. Levit et al. Kalb, Kalb Paul E. Wynia et al. Wynia, Matthew K. Cummins, Jonathan B. VanGeest, et al. Murkofsky et al. Murkofsky, Rachel L. Phillips, Ellen P. McCarthy, Roger B. Davis, and Mary Beth Hamel. Grogan et al. Grogan, Colleen M. Feldman, John A. Nyman, and Janet Shapiro. The Experience of Medicare Carriers.
Jacobson et al. Jacobson, Peter D. KPMG et al. Fraud Survey Pontell et al. Clearly, participants in any federal healthcare program must understand the federal government is committed to aggressively combatting healthcare fraud and abuse, and take affirmative steps to ensure their business practices operate in strict compliance with the law.
Candidate at St. Keenly interested in health law subjects, Magallanes is the founding member of the St. Magallanes can be reached at patrick.
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