Welcome back everyone. In this lesson, I provide more information about open source groupers that help group diagnosis and procedure codes. Some of these algorithms are extremely valuable given that they are free and that they can help group complex codes into many actionable groups. Health care analysts that need to group codes or claims should consider using freely available tools. This is not simply because the tools are free. Government agencies have often invested a great deal of resources into open source groupers that can help medical researchers more effectively create comparable reports. Thus, just because a group or system is free, do not always assume that it is not powerful or sophisticated to help you develop a set of algorithms for grouping codes or records. At the end of this lesson, you will be able to access open source groupers online and prepare an analytical plan to map codes to more general and usable diagnosis and procedure categories. First, let's review tools created by the Healthcare Cost and Utilization Project or HCUP. HCUP is a federally-funded project to help researchers group codes into clinically meaningful categories. The effort creates a standard that facilitates aggregate statistical reporting. Some important tools offered by HCUP include; the Clinical Classification Software, which is abbreviated CCS. In addition, there's the Chronic Condition Indicator of the CCI. Of course, there's also some Comorbidity software for procedure classes. First, let's consider the Clinical Classification Software in this table. The following description was taken from the Healthcare Cost and Utilization Project website about the Clinical Classification Software or CCS. The CCS for ICD-10 or ICD-9 is a diagnosis and procedure categorization scheme that can be used for many types of projects when analyzing data concerning diagnoses and procedures. Based on ICD codes, the CCS is a uniform standardized coding system. There are over 14,000 diagnosis codes and nearly 4,000 procedure codes for the ICD-9 version. These get collapsed into a smaller number of clinically meaningful categories that are sometimes more useful in presenting descriptive statistics that are individual ICD codes. There are multiple and single level groups. The single level has about 280 groups. The multiple level of CCS can be helpful for analytical projects because it puts codes into mutually exclusive hierarchies. For example, the general group of infectious and parasitic diseases is broken out into bacterial infection, mycoses, viral infection, and other infection and immunizations for screening an infectious diseases. Clearly, it's much easier and much more practical to analyze these groups of codes rather than hundreds of specific ICD codes. In addition to software to group ICD-9 codes, there is of course a Beta version of the software available to group ICD-10 codes. Next, this image provides a summary of Clinical Classification Software for CPT or Current Procedure Terminology codes. This algorithm is also created by HCUP. Its purpose is to group procedure codes into clinically meaningful procedure categories. The algorithm can work with any data type, either administrative or clinical, that have CPT codes. It groups codes into categories that are either in multiple or single levels. Overall, more than 9,000 CPT codes and 6,000 HCPCS codes or H-C-P-C-S are grouped into about 244 categories. To help with development of reports about specific procedures, the algorithm can be used to identify procedure specific populations. The Chronic Condition Indicator is another Healthcare Costs and Utilization Project or HCUP algorithm. The Chronic Condition Indicator or the CCI, was developed to categorize ICD diagnosis codes into one of two categories, chronic or not chronic. These are very specific definitions about how researchers defined chronic and non-chronic, and all of this is shown on the HCUP website. Examples of chronic conditions include malignancies, diabetes, most forms of mental illness, hypertension, many forms of heart disease, and congenital anomalies. Description of the indicator includes a review about the clinical review process that was used to create the categories. In addition to the ICD-9 version, a Beta version is available for the ICD-10 codes. Next, the Comorbidity software, also developed by HCUP, is a useful grouper for some analyses. The purpose of the algorithm is to identify comorbidities and hospital discharge records using the diagnosis coding of ICD-9, or ICD-10 of course. The software creates variables to identify the specific comorbidities. Some examples of comorbid condition include blood loss anemia, deficiency anemias, alcohol abuse, drug abuse, psychosis, depression, and dozens of others. The input dataset must have the following two variables, diagnosis-related groups, either the DRGs or the MS-DRGs, and diagnosis codes as defined by ICD. Now let's move along to a different type of open source grouper. This next grouper is the Berenson-Eggers Type of Service system or BETOS. This developer is CMS or the Centers for Medicare and Medicaid Services. The purpose of the grouper is to analyze the growth of Medicare expenditures. The grouper system does the following; it covers all of the HCPCS or Healthcare Common Procedure Codes. It only has one BETOS category for each HCPCS code, maps to clinically meaningful categories, they have objective code assignments that are stable through time. Finally, it was created to limit the impact for minor changes in technology or practice patterns. This image illustrates the BETOS grouper as a simple flat text file that has three fields. This simple file summary shows the record layout. The first column shows the field number. The second is the position in digits. The last two columns document the content with name and then a short description. The first field is captured in the first five positions in the file. This capture the CPT or HCPCS codes. Next, there is a three-digit BETOS code. Finally, there is a date field to show the last date in which the procedure code may be used by Medicare providers. This table illustrates the major categories of the BETOS grouper. If you look at the top left, you'll see evaluation and management with M1A next to it in the center column. Then you look to the right, and still in the top row, you'll see office visits, new. In the next row, there is a category for procedures. The BETOS code is P1A that refers to major procedure, breast. In the third row is imaging that refers to standard imaging of the chest. Next, there are tests that categorize lab tests for glucose. The last three codes are as follows, durable medical equipment, specifically wheelchairs, other with the code O1A that mainly categorizes codes related to ambulance transport. Finally, there are exceptions with the codes Z1, and these are used to categorize local or nonstandard codes. That's a good place to stop for this lesson, but I have not finished the open source groupers. I will continue on with several other open source groupers in your next lesson.
