HERC: Veterans Choice Program - Program Integrity Tool (PIT)
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Veterans Choice Program - Program Integrity Tool (PIT)

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Reconciliation and Auditing: Program Integrity Tool (PIT)

It is our understanding that the Program Integrity Tool (PIT) is an IBM product purchased by VA that aggregates many sources of data and allows for checking for fraud, waste, and abuse. This data source has an elevated privilege and operations users can request access via ePAS. It appears that starting in FY2016, Choice data began bypassing the Fee Basis Claims System (FBCS) and residing in the PIT. By June 2017, no Choice stays are found in FBCS.

PIT went online in 2013 and FBCS claims in PIT can have a claim create date back to 2013. Providers and Third-Party Administrators (TPAs) can submit older claims so you can see a claim submitted in 2018 with a date of service as far back as the start of PIT. We believe the data flow into PIT begins with non-VA providers rendering services and submitting claims to TPAs. They submit EDI837 files to EMDEON and from there it goes into ERepos in VA, then to FPPS to FBCS and finally to PIT. Within each step of data processing, the potential for data loss is great. In one instance, a TPA was not passing any of the Service Facility NPIs to VA for a few years so for those claims, it looked like the provider was the TPA; which is incorrect. There are known issues with the PIT mappings for Rendering Provider NPI and Service Facility NPI.  Efforts have been made to correct some such errors in the past and more checks are in place to ensure such errors are not allowed moving forward.

VA payments to non-VA providers are limited to Medicare payments, by law. In general, providers can bill VA whatever amount they feel like and it is up to the TPA, VA, or FSC to ensure the billed amount is correct, prior to releasing payments. Choice and PC3 payments should be based on the Medicare rates times the CLIN (which can be 85%, 100%, 150%, 250%, and a few other multipliers, based on the provider contracts) but the price validation was not set up during the initial processing of Choice and PC3 claims, so VA paid the billed amounts on submitted claims. The result is that for a single CPT code, you may find different payments which vary from a couple hundred dollars to tens of thousands of dollars. Since the onset of Choice there have been efforts to go back to reprice and collect money from overpayment to providers but it is a time-consuming effort and is being remedied moving forward.

The PIT Domain was released into the CDWWork environment on September 25, 2018. The domain has 66 views, 27 dimensions, and 39 fact views. The PIT domain contains VA claims that have been processed through the PIT, including but not limited to Choice claims. CDW documentation includes:

  • PIT Release documents (Intranet-only: https://vaww.cdw.va.gov/metadata/Metadata%20Documents/Forms/AllItems.aspx?RootFolder=/metadata/Metadata%20Documents/PIT%201.0&FolderCTID= 0x0120007BD83FE7EC890F42B79E1DA11A744B1E&View=%7bA9968955-5886-4DEC-A6BF-0CB219ADD175%7d)
  • CDW Metadata Report (Intranet-only: https://vaww.cdw.va.gov/metadata/_layouts/15/ReportServer/RSViewerPage.aspx?rv%3aRelativeReportUrl=/metadata/Reports/Metadata%20Report.rdl&Source=http%3a//vaww.cdw.r02.med.va.gov/metadata/ Reports/Forms/AllItems.aspx&DefaultItemOpen=1)
  • How to Get Access (Intranet-only: https://vaww.cdw.va.gov/Pages/CDWHome.aspx; select "Data Access Information")

How to identify Choice utilization in PIT tables

We have summarized 11 different methods for identifying Choice claims in the PIT tables. Below is a file that shows you the T-SQL search strings for each method. Please note that the summary table figures were calculated on 2/13/2019. Since PIT data are active and updated nightly, the numbers may have changed slightly.

How to identify Choice data in PIT

Exploratory Work

HSR&D researchers are conducting exploratory research using PIT data to analyze health outcomes and costs for cataract surgery. We focus our work on cataract surgery since it is a high-volume procedure, handled exclusively by ophthalmologists, and with well know procedure codes. Below we describe the steps we are taking to create the research dataset using PIT data.

Extracting PIT Claims Data

We extracted FY15 professional and institutional PIT claims data for cataract surgeries using the CPT codes 66982 and 66984. To illustrate how we joined the PIT tables together, we have created two Excel files. One is for the professional component, and the other is for the institutional component.

PIT Professional Claims

PIT Institutional Claims

The PIT data, along with the metadata for these tables, can be found on the VHACDWA01.VHA.MED.VA.GOV server in VINCI. Specifically, these Excel files are adapted from the metadata from [CDWWork].[Meta].[DWViewField]. Our Excel files contain information as of October 2018. For the most up-to-date variables and variable descriptions, please check the original metadata.

Description: Each tab in the Excel files represents a particular PIT table. The yellow rows denote the linking keys between each subsequent table. For the professional component, the orange rows are the variables we chose to keep for our analysis. For the institutional component, this is denoted by the blue rows.

Assumptions: In an earlier analysis, we extracted data using three filters:

  • WHERE CurrentFlag = “Y” AND
  • ClaimStatus = “Accepted” AND
  • PayFlag = “Y”

This will return current, accepted, and paid claims. If you are interested in looking at acted upon utilization and not only paid claims, you might want to consider leaving out the third filter, PayFlag = “Y”. However, this may leave you with duplicate records. Keep in mind that PIT only has records on which there has been some action. If there was no action on the claim, then it will not show up in PIT.

Caveats: Please note that the PITProcedureCode variable can be messy. In our analysis that focuses on using cataract surgeries, we only found three variations, “66982”, “66984”, and “66.984” when we intended to search for CPTs 66982 and 66984. We assume that “66.984” is a data entry error for “66984”. Thus, it was relatively easy to re-code “66.984” to lump with the “66984” records. However, users should be aware that the data could be more complex when one expands to other procedure codes that might contain typos and/or other variations.

Additionally, we found that only ~ 40-50% of institutional and professional claims from FY15-18 have non-missing SSN and PatientICN in the [CDWWork].[SVeteran].[PITPatient] table. This figure is for all claims and not only cataracts. However, we discovered that the MemberID variable in SVeteran.PITPatient actually contains the patient’s SSN as well. This variable is rarely missing. Thus, MemberID is recommended to be used as a patient identifier.

Identifying Costs in PIT Data

Background:  We wish to estimate the total cost for a CHOICE cataract surgery that was performed in FY18.  Our initial analyses showed that the mean cost was much lower than we expected, especially compared to a cataract surgery performed at a VA hospital.  Thus, we developed three algorithms to clean and process the data. Ultimately, we will compare mean/median costs across the three algorithms.

INCLUSION criteria for all algorithms:

  1. FY18 claims found in the CDW PIT data
  2. Claim must be current, paid, and accepted
  3. CPT code for the cataract is either 66982 or 66984

 

EXCLUSION criteria for all algorithms:

  1. Claims that contain a CPT modifier value of 55 (post-operative care)
  2. Claims that contain a CPT modifier value of 56 (pre-operative care)
  3. Claims that contain a CPT modifier value of 79 (unrelated procedure or service performed by the same physician during the post-operative period)

 

NOTES:

It is important to extract the PITPlaceOfService variable.  This variable can be obtained by linking PITPlaceOfServiceSID to [CDWWork].[NDim].[PITPlaceOfService].  Our analysis concentrates on records with Ambulatory Surgical Center (ASC), Outpatient Hospital, and Office places of service.

 

Algorithm A:

Dataset is at the patient x surgery date x CPT x eye side x PITPlaceOfService level

Surgeries that have no eye side information are excluded

A concordance variable (CONCORDANCE) is created:

  • CONCORDANCE = 1 if the number of records is equal to the expected number of records for the type of claim, based on PITPlaceOfService
  • CONCORDANCE = 2 if the number of records is more than the expected number of records for the type of claim, based on PITPlaceOfService
  • CONCORDANCE = 3 if the number of records is less than the expected number of records for the type of claim, based on PITPlaceOfService
  • CONCORDANCE = 4 if the type of claim is not expected, based on PITPlaceOfService.  For example, this could be an institutional record for a cataract surgery with an ASC place of service.  Typically, over 99% of ASC records are professional claims.
  • Records with CONCORDANCE = 1 are prioritized

Costs were calculated according to these rules:

  • PITPlaceOfService = Office à take the highest professional cost
  • PITPlaceOfService = ASC à take the highest professional cost
  • PITPlaceOfService = Outpatient Hospital à take the highest institutional and the highest professional cost and sum together

 

Algorithm B:

Dataset is at the patient x surgery date x CPT x eye side x PITPlaceOfService level

All records are kept irrespective of eye side modifiers

If eye side is missing, sum all costs for that day

A concordance variable (CONCORDANCE) is created:

  • CONCORDANCE = 1 if the number of records is equal to the expected number of records for the type of claim, based on PITPlaceOfService
  • CONCORDANCE = 2 if the number of records is more than the expected number of records for the type of claim, based on PITPlaceOfService
  • CONCORDANCE = 3 if the number of records is less than the expected number of records for the type of claim, based on PITPlaceOfService
  • CONCORDANCE = 4 if the type of claim is not expected, based on PITPlaceOfService.  For example, this could be an institutional record for a cataract surgery with an ASC place of service.  Typically, over 99% of ASC records are professional claims.
  • Records with CONCORDANCE = 1 are prioritized

Costs were calculated according to these rules:

  • PITPlaceOfService = Office à take the highest professional cost
  • PITPlaceOfService = ASC à take the highest professional cost
  • PITPlaceOfService = Outpatient Hospital à take the highest institutional and the highest professional cost and sum together

 

Algorithm C:

Same as Algorithm B above, except records where cost is less than $200 are dropped from the analysis

Note: We know that some of the costs appear to be too low, therefore we chose $200 as a ballpark figure that is approximately a little higher than the cost for the average pre-operative visit.

 

Conclusions:  The analyses reflect that there were 23,000 – 24,000 cataract surgeries paid by Choice in FY18.  It is encouraging to see that the mean and median costs (as well as the interquartile range) are quite similar between the three algorithms.  Typically, the mean cost for a cataract surgery ranged from $600 - $900 for surgeries performed at an ASC or in an office.  For surgeries performed at an outpatient hospital, the mean cost is ~ $1,800.  Median costs were only barely higher than the mean costs across all algorithms and places of service. 

The payments we found were just for the cataract surgery.  This does not include other procedures that may have been done with the cataract, such as anesthesia.  The PIT claims detail is like an à la carte menu.  Each service has a bundle of possible procedures, so it is critical to understand the bundle—in this case, we just found the cost of the cataract CPT.  We used the PIT authorization ID to find other CPT codes that share the same authorization.  Unfortunately, the PIT authorization ID is sometimes used for services that may not be related to the cataract.  For example, a patient had acupuncture and cataract with the same authorization ID.  We used VA data to see what CPT codes were used simultaneously with a cataract CPT; this allowed us to create a possible list of ancillary CPT codes.  We then used the PIT authorization ID merged with the list of possible ancillary CPT codes to find likely ancillary costs.  This worked reasonably well, but it was time consuming.

Last updated: 08/22/2019