Data FAQs

The Managerial Cost Accounting Office (MCAO) report “Cost and Workload for Selected DSS Clinic Stop/Credit Stop” includes average costs by stop code at the national, station, and facility level by fiscal year. The also report includes additional useful information such as total encounters by stop code, total costs by stop code, and cost components including variable and fixed costs. The report generator is available to VA data users through the MCA report generator page: https://mcareports.va.gov/ReportIndex.aspx (VA intranet only).

Information about requesting access to MCA reports for research and operations is available on the VHA Data Portal: https://vaww.vhadataportal.med.va.gov/Data-Sources/MCA-formerly-DSS-Web-Reports (VA intranet only). 

If you end up publishing or releasing this information, make sure to only release values in aggregate (such as average costs); you should not release unaggregated costs that could be used to disadvantage VA in negotiations.

Looking for the average daily cost of inpatient stays? Estimates by categories of care are available on the HERC Inpatient Average Cost Data page. 

All VA cost data (MCA, HERC Average Costs, and OMOP) are nominal values, i.e., the costs are in the year the data represents. Therefore, studies combining data across fiscal years should inflation adjust. 

Researchers should typically use a price index to adjust costs, although the methods and index depends on your objectives. The Agency for Healthcare Research and Quality (AHRQ) Medical Expenditure Panel Survey (MEPS) has created a useful guide to help researchers determine the appropriate price index for health care cost data analyses. This guide includes the recommended index based on the analysis objectives as well as actual price indices for use in analyses.

The guide "Using Appropriate Price Indices for Analyses of Health Care Expenditures or Income Across Multiple Years" is available on the AHRQ website.

VA provides several types of telehealth, including clinic-based real-time video telehealth (where a patient is in a VA clinic/hospital and has a telehealth communication with a VA provider in another clinic or facility); home video telehealth (where a patient is at home and the provider is at a VA clinical site); store-and-forward telehealth (where a patient at a VA clinic/hospital and a video/photos that are stored in the VA health record for a provider to review and diagnose at a later date – generally for radiology, pathology, dermatology, ophthalmology, and wound care); and remote patient monitoring (where there is ongoing assessment, monitoring, and case management of a patient using a disease management protocol in the patient’s residential environment). Below, we focus on clinic-based real-time video telehealth and home video telehealth, but details on how to identify all the above-mentioned types of telehealth are outlined in HERC’s “Overview of Telehealth” presentation from the VA Data Bootcamp (intranet only): https://vaww.herc.research.va.gov/include/page.asp?id=va-data-bootcamp-2022.

For clinic-based real-time video telehealth and home video telehealth, you need to identify a primary and secondary stop code pair. A stop code is a three-digit code that characterizes a VA outpatient encounter to define clinical work units for resource allocation. The primary stop code is the clinic of interest. For example, if you wish to identify primary care telehealth visits, you would identify primary clinic stops 301 and 323. Secondary stop codes identify the specific type of telehealth provided (see details below). All stop codes (primary and secondary) can be identified by using the list of active stop codes from the VA Managerial Cost Accounting Office (MCAO) http://vaww.dss.med.va.gov/programdocs/pd_oident.asp (VA intranet only).

Clinic-based real time video telehealth. The secondary stop codes for scheduled, general clinic-based real time video telehealth visits are 690 (Real Time Clinical Video Telehealth – Patient Site), 692 (Real Time Clinical Video Telehealth – Provider Site (Same Station)), 693 (Real Time Clinical Video Telehealth – Provider Site (Not Same Station)). There are typically 2 records for clinic based real time telehealth appointments: 1 record for the patient site; 1 record for the provider site. Both records will have the same primary stop code but different secondary stop codes, which differentiate the patient-site record from the provider-site record.

Home video telehealth/telehealth to a non-VA site.* The secondary stop codes for home video telehealth are 179 (Real Time Clinical Video Telehealth to Home – Provider Site), 648 (Real Time Clinical Video Telehealth with Non-VAMC Location- Provider Site) and 679 (National Center Real Time Clinical Video Telehealth to Home- Provider Site).** In 2019, the vast majority of home video telehealth visits use stop code 179, although some visits use codes 648 and 679. There is only 1 record for home telehealth visits.***

*Telehealth to non-VA sites includes programs that enable homeless Veterans to access video visits in a library setting, or a community center.

**Two additional stop codes for home telehealth are 684 (Home Telehealth Non-Video Intervention) and 685 (Care of Home Telehealth Program Patients). These stop codes are not for video encounters.

*** There is some evidence that the home telehealth stop codes were not always used consistently in previous years. Studies that compared stop codes with VA-issued tablet utilization data found that approximately 25% of possible video encounters were not recorded with a VA stop code in 2016.

For VA-approved research and operations projects, the Pharmacy Managerial Cost Accounting National Data Extract contains information on the cost of a medication itself (variable: act_cost) as well as the dispensing fee (variable: dispcost). Sum the actual total cost of the medication and the dispensing fee to calculate the total drug cost. For more information, see the guidebook “VIReC Research User Guide: Pharmacy Managerial Cost Accounting National Data Extract (PHA MCA NDE)” Available on the internal VA intranet only (https://vaww.virec.research.va.gov/RUGs/MCA-NDEs/RUG-MCA-PHA-NDE.pdf).

For those looking for cost information outside of a VA-approved project, or for cost information more generally, the VA National Acquisition Center provides a searchable Pharmaceutical Catalogue which includes the Federal Supply Schedule (FSS) price and Big 4 agency price (VA, Department of Defense, Public Health Service, and the U.S. Coast Guard) of specific drugs.

The actual amount VA Pharmacy Benefits Management (PBM) spends on a medical product may be lower than the FSS price or Big 4 price after rebates and discounts. The components of the Actual Total Cost (e.g., contracted price paid for the drug product) is confidential and may not be disclosed. If researchers need to report the pharmaceutical unit price, HERC recommends using the FSS price listed on the VA National Acquisition Center Pharmaceutical Catalog.

Additional information on the cost of pharmaceuticals for cost-effectiveness analysis is available in the HERC presentation, “Pharmaceutical Costs for Cost-Effectiveness Analysis,” and the webpage “Determining the Cost of Pharmaceuticals for a Cost-Effectiveness Analysis.”

First, determine how you will define the condition of interest (e.g., diagnosis codes), and create a comprehensive list of codes. When setting the cohort parameters, consider whether patients can enter and/or leave the cohort (e.g., if they have a condition that is curable) or if there is a time frame for the diagnosis.

Next, choose which cost data is the best fit for the study (Managerial Cost Accounting (MCA) data or Health Economics Resource Center (HERC) Average Cost data). For more information on the two data sources, review the question Which VA cost data source should I use: HERC or MCA?.

After choosing your cost data source, determine how you want to calculate annual cost, e.g., calendar year, fiscal year, a year from randomization date. Determine how you want to treat stays that continue beyond the study year. Define the boundaries of the data you want to include. Do you want to include inpatient, outpatient and pharmacy data? Do you want to include VA data only; VA data plus data for care paid for by VA but received in the community (i.e. community care data); or VA data, community care data, and non-VA data (e.g. Medicare)?

After DART approval, researchers will work with a VINCI data manager to pull data for the defined cohort. Merge the cohort file with the cost data. For more information on how to merge these data, see VINCI documentation on linking patient data in CDW, available on VINCI Central https://vincicentral.vinci.med.va.gov/SitePages/Home.aspx (VA intranet only). Consider whether you want to include all costs or only costs related to the diagnosis. If you want to limit to costs for a specific condition, consider partnering with a clinical expert and/or limiting to a condition with clearly defined associated costs.

First, determine how you will define the condition of interest (e.g., diagnosis codes), and create a comprehensive list of codes.

Next, determine whether you want to use Managerial Cost Accounting (MCA) data or Health Economics Resource Center (HERC) Average Cost data. For more information on the two data sources, review the question Which VA cost data source should I use: HERC or MCA?

After DART approval, researchers will work with a VINCI data manager to pull data for the defined cohort. Merge the cohort file with the cost data and pull costs. For more information on how to merge these data, see VINCI documentation on linking patient data in CDW, available on the VINCI intranet site https://vaww.vinci.med.va.gov/ (VA intranet only). Link your cohort file to any other data sources your team has decided to include (e.g., Community Care or Medicare).

As you design your analysis plan, consider which data you want to include. Do you want to include inpatient, outpatient, and pharmacy costs? Do you want to include VA data only; VA data plus data for care paid for by VA but received in the community (i.e., community care data); or VA data, community care data, and non-VA data (e.g., Medicare)? Recognize the unknown costs that are omitted from your analyses, e.g. Medicare Advantage, Medicaid, and private insurance. Consider whether cleaned datasets already exist for your cohort, e.g., Health and Retirement Survey (HRS) data or Medical Expenditure Panel Survey (MEPS) data.

for additional advice including sample SAS files, contact HERC at herc@va.gov.

Health care costs are a combination of labor costs and capital costs; therefore, health care costs are more expensive in geographic areas that have higher wages (e.g., Boston, San Francisco). There are two common ways to control for geographic variation in cost analyses. The first involves including a dummy variable (fixed effect) for each medical center. The second method is including the Medicare Wage Index in the analysis. Medicare creates the Wage Index to keep track of labor costs in different geographic markets; updated annually, it’s used to calculate prospective payments to hospitals. This method is typically a more accurate way to control for geographic variation, especially if the data has a time dimension because the wage index changes over time within a facility. HERC created a Medicare Wage Index for VA Facilities by linking the Medicare Wage Index to VA hospitals using facility location data from the VHA Support Service Center (VSSC). The wage index for VA facilities is available through 2022 in an excel file on the HERC website. See the guidebook “Medicare Wage Index for VA Facilities” for more information about using the wage index.

The HERC outpatient cost estimates are created using the MedSAS outpatient dataset. (Through FY17, the MedSAS Outpatient file was created using National Patient Care Database (NPCD) data; beginning FY18, it is sourced from CDW data). The MedSAS outpatient data do not include all the records that can be found in the CDW Outpatient schema; MedSAS files only include a subset of visits that are considered “workload,” i.e., an encounter that is billable in the private sector. A visit is considered an encounter when it involves professional contact with a patient for diagnosis, evaluation, or treatment. (Therefore, administrative or duplicate records would not count as workload.) Data users can look at WorkloadLogicFlag within the CDW Outpatient schema to see whether a visit is considered workload. More information about the Workload logic flag and can be found in Appendix D of VIReC’s CDW Outpatient Factbook (available on the VIReC intranet site). Note that while the Workload logic flag is the primary filter, some additional logic is also applied to create the final MedSAS Outpatient dataset.

To determine the cost of procedure X at VA, first identify the procedure codes of interest. CPT codes are used for outpatient data, while ICD-10 procedure codes are used for inpatient care. Note that some inpatient procedures are captured with CPT, but that is the discretion of the physician. VA employs professional coders to enter ICD-10 codes for inpatient care, so best to use these.

You can either use the MCA National Data Extracts (NDEs) or HERC Average Cost data to identify health care costs. Because neither data source contains sufficient clinical information such as procedures, you will first need to use the procedure codes to extract encounters of interest from the encounter files (i.e., MedSAS, CDW, OMOP). For example, if you were interested in cataract, you could extract all encounters where one of the CPT codes is 66984 (basic cataract) and 66982 (complex cataract). This would yield all cataracts for a given period of time and/or location. You would then merge these encounters to the HERC or MCA data. See HERC's Average Cost guidebooks or the MCA NDE Guidebook for details on merging datasets.

Outpatient procedures using MCA data: MCA OUT NDE contains outpatient costs per clinic stop per day. Once you’ve merged your encounter and cost datasets, you can use the Total Cost variable (ActTotCost or ocst_tot) in the MCA Outpatient (OUT) NDE to determine the cost of the encounter associated with your procedure of interest for all outpatient encounters.

Outpatient procedures using HERC Average Cost data: HERC outpatient average cost data contain a VA cost estimate that is based on hypothetical Medicare reimbursement per CPT code. HERC costs are reconciled so that the estimates tally to actual national VA expenditures for that type of care. HERC Outpatient average cost estimates include the HERC Value (PAYMHERC), the National VA average cost (COSTN), and the local VA average cost (COSTL) for each encounter.

Inpatient procedures using MCA Data: Both the Discharge (DISCH) and Treating Specialty (TRT) NDEs contain information about the cost of inpatient stays. One advantage to using the TRT NDE is that a new record is created for each change in treating specialty during an encounter. This can be used to limit costs to those related to the procedure of interest and remove unrelated costs (e.g. the cost of long-term care). Use the Placeholder Total Cost variable (TotCost or tcst_tot) to determine costs for the inpatient stay.

Inpatient procedures using HERC Average Cost Data: HERC Inpatient Average Cost data contain the national average cost of a hospital stay given its Diagnosis Related Group (DRG), overall length of stay, and days in intensive care. Files include local cost (costl) and national cost (costn) estimates. The average cost method assigns the same cost to all inpatient stays with the same demographic and discharge information. Therefore, stays with identical characteristics will have the same cost. if you are interested in comparing the cost of two procedures, these data may not be appropriate.

The costs described here are the costs associated with the encounter for the procedure of interest. Depending on your study, you may want to consider whether to include additional costs (e.g. follow up visits, travel time, medication costs) or break down costs to the department level, using the Intermediate Product Department (IPD) Inpatient and Outpatient NDEs.

For more information on MCA NDEs, visit HERC’s MCA NDEs webpage. Information on HERC Inpatient and Outpatient Average Cost data is available in the Average Cost guidebooks.

First, determine which VA cost data source better fits your study’s needs: Managerial Cost Accounting (MCA) data or Health Economics Resource Center (HERC) Average Cost data. Both data sources include clinic stop. For more information on the two data sources, review the question Which VA cost data source should I use: HERC or MCA?

Are you interested in costs for a specific cohort? If so, merge the cohort file to the cost data (HERC or MCA). For more information on how to merge these data, see VINCI documentation on linking patient data in CDW, available on the VINCI intranet site https://vaww.vinci.med.va.gov/ (VA intranet only).

Consider whether you are looking at the cost for a given time frame or for select VA Medical Centers. If so, limit the data accordingly. Consider also whether to include all costs (fixed and/or variable). You can then limit to the specific clinic stop to calculate the average cost of treating a patient.

Negative costs in the MCA pharmacy data are often due to a medication being returned, and data users should treat these negative costs as valid and keep these costs in the dataset. See the guidebook Research Guide to the Managerial Cost Accounting National Cost Extracts for additional details about the reasons for the presence of negative costs. We recommend applying cost cleaning procedures (see the question How do I correct outliers in pharmacy data?) to the absolute value of the cost and retaining the original negative sign.

VA uses the Managerial Cost Accounting Office (MCA) for fiscal management and to determine the cost of patient care. National Data Extracts (NDEs) have been created to facilitate access to workload and cost information. These extracts report costs of inpatient and outpatient encounters, reflecting actual resource use within VA. These data contain rare outlier observations with cost inconsistent to the care provided.

HERC has developed SAS code to flag and adjust outlier observations in the Managerial Cost Accounting Pharmacy National Data Extract (the MCA PHA NDE). The PHA file contains prescription-level records which include detailed cost information on each prescription filled, both inpatient and outpatient.

In order to identify costs that are likely to be truly erroneous, rather than legitimately expensive medications, strict criteria are applied to define outliers. The product cost of the medication and the dispensing cost are considered separately. In consultation with MCA and after review of cost distributions by VA Class, product cost outliers are defined as costs greater than 120% of the maximum price of all drugs in the VA drug class, as shown in the drug price list from Pharmacy Benefits Management Services. This criterion is designed to select only gross outliers. Since dispensing costs are typically low and do not vary much across VA drug class, the outlier cut-off for dispensing costs is set at a fixed $100. (Note that median dispensing costs by VA drug class range from $3.56 to $9.95 in FY15.) Absolute values are used in order to identify both positive and negative outliers. Out of 243 million records in the FY15 PHA file, 33,410 product costs (0.014%) and 158,635 dispensing costs (0.065%) are classified as outliers by these criteria.

Both product and dispensing outlier costs are adjusted by replacing the outlier value with the median cost for the VA drug class (and retaining the sign of the original cost). Although this code is designed to adjust the prescription-level records in the MCA PHA dataset, with some additional coding to restrict to outpatient fills and summarize by dispensing date, the prescription-level adjustments can also be used to adjust the daily pharmacy cost records in the MCA Outpatient NDEs.

This work was conducted as part of a project funded by the Geriatric & Extended Care Data & Analysis Center (GECDAC). Researchers interested in obtaining this code should contact HERC at herc@va.gov.

HERC researchers recommend using the Primary Care Management Module (PCMM) or Reengineered Primary Care Management Module (RPCMM) in the Corporate Data Warehouse (CDW) to identify a patient’s primary care provider. RPCMM is the current system used at most, but not all, sites. PCMM is the legacy system that is still in use. In general, if you are interested in years prior to fiscal year 2016 (FY16), use the PCMM file; if you are interested in FY16 or later, use both the PCMM and RPCMM.

PCMM/RPCMM list an assignment between the primary care provider (PCP) and the patient. Each record in PCMM/RPCMM lists the date the assignment started (RelationshipStartDate) and the date the assignment ended (RelationshipEndDate), if applicable. Relationship end dates may be missing for two reasons: the relationship has not ended, or the ending date was never posted to the dataset. In general, patients can have only 1 assigned primary care provider at a time, unless they have multiple residences.

Researchers working PCMM/RPCMM should note that both domains include records for care assignments other than primary care (such as case managers). It is important to distinguish these, and not regard them as primary care assignments, when filling in missing values for the end date for the primary care relationship. To identify a list of PCPs in the PCMM table PatientProviders, try the following logic: (ProviderRole="PC ASSIGNMENT" OR "PC ATTENDING") AND (TeamPurpose="Primary CARE" OR "PRIMARY CARE _ HBPC"). The resulting list allows you to identify a provider using the variable PrimaryProviderSID.

To identify current PCP assignments in the CDW PCMM/RPCMM domain table, only use records where: [(CurrentProviderFlag=YES) AND (RelationshipEndDate=(.))].

When you have identified a list of providers in PCMM/RPCMM, you can use the variables PrimaryProviderSID and sta3n to identify these providers in other files. The combination of PrimaryProviderSID+sta3n from the CDW PCMM.PatientProviders table can be crosswalked to the CDW Staff table as StaffSID+sta3n. (Essentially, PrimaryProviderSID is the same as StaffSID for a given sta3n).

Resources on PCMM/RPCMM can be found on the VIReC CDW Documentation intranet site (Intranet-only: https://vaww.virec.research.va.gov/CDW/Documentation.htm) and the CDW MetaData Reports Page on the CDW SharePoint site (Intranet-only: https://vaww.cdw.va.gov/Pages/CDWHome.aspx).

For answers to more commonly asked questions about identifying providers in VA administrative data, visit the webpage Identifying Providers in VA Administrative Data

There are two main sources of encounter-level cost data in VA administrative data: Managerial Cost Accounting (MCA) data and Health Economics Resource Center (HERC) Average Cost data. VA researchers and operations users who need to evaluate costs may find themselves asking, “Which source of VA cost data is best for my project?” These datasets are created for different purposes and are built using different methodology, so understanding the characteristics of both datasets is important before deciding which to use.

We provide a brief summary to help researchers and operations users determine which VA cost data source is best for their project needs.

Purpose of the data

MCA data is created using a local, activity-based method and is the official managerial cost accounting system for the entire VA. Each VAMC determines the relative value units of products, the components of encounters, and then uses staff labor mapping along with RVUs to assign costs to encounters. MCA data contain the cost of each VA hospital stay, outpatient visit, and dispensed outpatient prescription. Since MCA data are designed for management, users will find specific cost information for encounters. MCA is the most frequently used source of data for VA economic research.

HERC Average Cost data represents the estimated cost of each VA encounter. These estimates are created by distributing VA aggregate costs to patient encounter-level utilization using the hypothetical Medicare payment for each service. HERC Average Cost data include national estimates which eliminates the geographic variations in cost for similar encounters. HERC Average Cost data are designed for research; the purpose of these data is for users to quickly understand the average cost of all VA health care services and compare costs to systems outside VA (e.g. Medicare).

Advantages of MCA data

• MCA cost estimates reflect facility differences in the production of health care services
• MCA includes pharmacy cost data
• MCA includes state nursing home stays
• MCA data are considered more precise than HERC data

Advantages of HERC Average Cost data

• HERC data provides costs that are consistent across similar encounters and across VAMCs
• HERC Average Cost data allow for comparison to non-VA healthcare costs

MCA data is best when

• You need local cost estimates
• You want to compare costs between VA facilities, providers, or procedures (Make sure to control for geographic wage differences, if applicable)
• When conducting implementation work where you want to focus on variable costs rather than total costs

HERC data is best when

• You want to generalize VA costs to non-VA health systems
• You want data that is already cleaned and summarized
• The precise costs of specific services are not important

Recommendation

Consider the specific needs and goals of your project. If possible, use both cost data sources; one as the primary data source and one as a sensitivity analysis.

Learn More

More information is available in the seminar VA Costs: HERC versus MCA.

Visit the Managerial Cost Accounting (MCA) webpage and the HERC Average Cost Inpatient and Outpatient pages for more information about each of these data sources.

Although they share the same name, Relative Value Units (RVUs) created for the Centers for Medicare and Medicaid Services (CMS) are not the same RVUs used in the VA Managerial Cost Accounting (MCA) system.

MCA RVUs

MCA is an activity-based costing system. Activity-based costing combines activity reports, financial data, workload, and intermediate products used in encounters and hospitals stays (Azoulay 2007). These cost estimates are regarded as a more accurate measure of resource use than cost-adjusted charges (Ross 2004; Udpa 2006). RVUs are essential part of MCA and other activity-based costing systems.

MCA RVUs are used to find the cost of intermediate products, specific health care services such as a cholesterol test, a chest x-ray, or a 20-minute clinic visit. MCA tracks the cost of each department and the intermediate products it produces. Each intermediate product has an RVU. The total cost of the department is divided by number of RVUs it produces, giving the cost per RVU, so that the cost of each intermediate product can be found. The cost of a hospital stay or outpatient visits is found by adding together the cost of all the intermediate products used in that stay (or visit).

MCA tracks each type of cost separately (e.g., labor, supplies, management overhead, contract services, and as many as 13 different cost types). Each type of cost has its own set of RVUs. MCA has a standard set of RVUs, but these may be tailored to reflect the specifics of the cost of production at each medical center or department within the medical center. The result is an estimate of costs based on the cost of the department and the intermediate products it produces. MCA cost estimates thus reflect the variations in cost that result from differences in staff and supply costs, efficiency, and the mix of intermediate products used in providing care. The resulting cost estimate reflects resources used in providing care, providing researchers with the opportunity to study variations in cost. For example, HERC researchers have used MCA to find the difference cost that result from different techniques for Coronary Artery Bypass Graft (CABG) surgery.

While the MCA estimates can be highly accurate, the method has the potential for occasional errors. Incorrect mapping of costs and products can distribute the cost of a department to the wrong products or to too few products. MCA has worked over the years to develop auditing and cleaning systems, but researchers should verify MCA cost data. MCA National Data Extracts are available at CDW. Record layouts, metadata, and the Technical Guide are available on the MCA Office website (intranet only).

CMS RVUs

The RVUs developed for the Centers for Medicare and Medicaid Services (CMS) are used to calculate payments to providers. These RVUs do not represent cost, but were designed to compensate providers based on time, mental effort, technical skill, judgment, and stress. Services are characterized by Current Procedure and Terminology (CPT) code, and separate RVUs are created for physician work, practice expense, and malpractice expense. If the researcher knows the CPT coding of a service, CMS

RVUs can be used to estimate the Medicare reimbursement. Medicare reimbursement is sometimes used as a surrogate for costs; it is used that way in the HERC outpatient average cost datasets.

There are limitations with the CMS RVU approach. Notably, the source of CMS RVUs, the Resource Based Relative Value System (RBRVS), was not designed to reflect the actual costs of care, but to set reimbursement as part of a public process that includes comment and input from medical specialty organizations. Critics of the RBRVS reimbursement system say that it is fundamentally flawed because of its reliance on CPT codes to represent provider services. This criticism holds that the CPT coding system can distinguish greater complexity for procedures but not for evaluation and management services (Kumetz and Goodson 2013). MCA does not use RBRVS RVUs, but instead bases its relative values on measures of resource use, such are minutes of time in the operating room or length of the clinic visit.

Given that MCA and CMS RVUs are different entities, the correlation between CMS and MCA is an empirical question. HERC creates the HERC average cost data using RVUs from CMS and other sources which researchers could use to test the correlation. Studies by Chapko et al (2008) and Phibbs and Schmit (2007) indicate that the datasets are highly correlated.

References

• Azoulay A, Doris NM, Filion KB, Caron J, Pilote L, Eisenberg MJ. The use of the transition cost accounting system in health services research. Cost Eff Resour Alloc. 2007;5:11.
• Chapko M, Liu C, Perkins M, et al. Equivalence of two healthcare costing methods: bottom-up and top-down. Health Econ (2008); 18(10):1188-201.
• Kumetz E, Goodson JD. The undervaluation of evaluation and management professional services: the lasting impact of current procedural terminology code deficiencies on physician payment. Chest (2013); 144(3):740-745.
• Phibbs CS, Schmitt SK. A Comparison of Outpatient Costs from the FY 2001 HERC and DSS National Data Extract Datasets. Technical Report 14. Menlo Park CA. VA Palo Alto, Health Economics Resource Center; 2007.
• Ross TK. Analyzing health care operations using ABC. J Health Care Finance. Spring 2004;30(3):1-20.
• Udpa S. Activity-based costing for hospitals. Health Care Manage Rev. Summer 1996;21(3):83-96.

Diagnosis-Related Groups (DRGs) were created by the Health Care Financing Administration (now CMS) in the early 1980s for inpatient payment. Although VA has included the DRG variable, it does not have the same meaning within VA. VA has a different definition of inpatient stay than CMS. Imagine a VA patient transferred from medical/surgical, to nursing home, back to medical/surgical, then to rehab. If these bedsections were contiguous, then VA considers this one stay and assigns it one DRG. However, CMS would treat this as more than one stay (possibly four) and assign a DRG to each. Additionally, there is a lot that affects costs and resource use beyond DRG. Therefore, we suggest using the MCA or HERC data rather than DRG to understand cost or resource use.

HERC has created guidebooks for researchers interesting in learning more about MCA and HERC data. The inpatient data webpage contains more information on DRG and how that goes into estimating payment.