Visualizing Hierarchical Condition Codes

Visualizing HCC

The HCC Graph

The Affordable Care Act attempts the ambitious feat of inducing private insurers to sell individual health insurance when medical underwriting is prohibited.  Ordinarily, such a prohibition might give rise to a paralyzing fear of adverse selection and an effort to evade the prohibition. The insurer might, for example, engage in selective advertising or networks that lacked oncologists in an effort to bring healthier individuals to one’s own insurance company and leave the sickly to one’s competitors. The Affordable Care Act attempts to reduce the return on any such subterfuge by a system of transfer payments known as “Risk Adjustment” (42 U.S.C. § 18063). Under Risk Adjustment the net premium the insurer receives on an individual is based on part on the medical risk that individual poses. Adverse selection is not reduced in the typical way of medical classification conducted by the insurer and contract terms such as price being based on the results of the classification. Instead,Risk Adjustment requires that the insurer offer the same contract to all comers but that government in effect conduct the classification and transfer funds to those insurers that happen to take on high risk insureds while transferring funds away from those insurers that happen to take on low risk insureds.

Implementing such a system is an enormously complex matter, as the hundreds of pages of regulations and explanations contained in recent Federal Register entries can attest. See, e.g. 78 Fed. Reg. 15410-15541 (March 11, 2013). From the universe of potential medical conditions, one must create a mapping of projected claims costs. The Department of Health and Human Services (DHHS) has now attempted this mapping through something it calls Hierarchical Condition Codes. The idea is to look at the “ICD9” diagnostic codes typically given patients and map that to a coarser set of Condition Codes that supposedly have roughly similar treatment costs.  But how does one handle the patient with multiple related ICD9 diagnoses?  One could develop a multivariable model that attempts to show a cost factor for every combination of Condition Codes. Such a model would likely be mathematically intractable, however. Instead, the idea is to say that there is a cost hierarchy of Condition Codes and to map certain subsets of Condition Codes into the Condition Code that generates the highest medical costs.  One then attaches some intensity coefficient to each potentially “upcoded” Condition Code. This system, which has apparently been used before in the Medicare program, is known as Hierarchical Condition Codes.

Mathematically, the Department of Health and Human Services has created a graph. The nodes of the graph are the union of the set of ICD9 codes and the set of HHS Condition Codes.  The edges of the graph are the mapping between ICD9 code and HHS Condition Code and the mapping between subsumed HHS Condition Codes and its “basin of attraction”: the Condition Code to which these subsumed codes are remapped.

It turns out that not only has DHHS created a conceptual mathematical object, it has provided the data from which such an object can be visualized. It is contained in an Excel spreadsheet available at http://cciio.cms.gov/resources/files/ra_tables_proposed_1_2013.xlsx. I now show how one can use this data to produce a visualizing of the HCC Graph.  To see this, you will need the free CDF player available here. If all you see here is a picture, you don’t have the Player. Also, a warning. This is a large CDF file.  It may take a minute or so for it to load into WordPress, with the precise time depending on your computer and your connection speed. Be patient.

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To be honest, I am not quite sure what this all proves. It confirms, I believe, the enormous complexity of the enterprise of the Affordable Care Act.  To make health insurance purchases less sensitive to the fortunes of health but to preserve in at least name the idea that this is not a government takeover of health insurance, government permits private insurers to sell policies but prohibits the normally necessary step of medical underwriting.  But to prevent insurers from simply abandoning a system in which adverse selection might ordinarily cause a death spiral, government then undertakes its own surrogate classification system and pays insurers whose pool is drawn from the more expensive patients. But doing this requires huge amounts of data collection, a system of recoding, and then a system that computes a transfer payment based on this and much more information.  It remains to be seen whether this system succeeds in keeping private insurers involved in healthcare finance.  In the mean time, however, it does produce what I regard as some attractive pictures.

 

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