The Pharmacy Benefits Management Strategic Healthcare Group (PBM SHG) is a VA entity responsible for managing the national VA drug formulary process. It carries out a broad set of activities related to pharmacy purchasing, clinical guidelines, and outcomes research. To facilitate its work, PBM SHG has developed software systems and databases to organize and analyze drug data. Three prescription-level extracts may be used by researchers: the Prescription Extract, the Unit Dose Extract and the IV Extract. The three are often referred to collectively as the "PBM V3.0 database." They contain extensive detail on the medications prescribed and characteristics of the prescriptions (e.g., days supplied). Data covering every VA pharmacy are available starting with October, 1998.
The PBM database is not available at the Austin Information Technology Center (AITC). Extracts are made by the PBM SHG staff. To request an extract, a researcher must submit a summary of the study protocol, proof of Institional Review Board (IRB) approval, a Research Data Request form, and a Data Use Agreement form. For additional information, contact Ms. Cheryl Benson (email@example.com).
The Managerial Cost Accounting System (MCA), formerly Decision Support System (DSS), is a management information system that tracks health care utilization (workload) and assigns an approximate cost to each encounter and service. Each VA facility has a separate implementation of MCA. Extracts from each facility are combined into national-level datasets for use by VA managers and researchers. The National Data Extracts (NDEs) are organized at the level of outpatient clinic encounters, inpatient stays (or inpatient bedsection-months), laboratory tests, and pharmacy prescriptions. Both inpatient and outpatient records are available. A patient who filled an outpatient prescription would be coded as having visited the "pharmacy clinic". All visits to the pharmacy clinic on the same day would be rolled into a single record. Individual prescriptions for inpatients cannot be distinguished from other aspects of stays in the inpatient NDE, although there are aggregate pharmacy cost variables for the entire stay. The National Pharmacy Extract (or MCA NDE Pharmacy SAS Dataset) contains a single record for each pharmacy item. It features a significant amount of detail on medication and dispensing details. HERC and VIREC have each documented MCA files. All MCA data are available through a timeshare account at the Austin IT Center.
PHA (Pharmacy). This pharmacy NDE provides detailed information on the drugs used by patients. It includes inpatient and outpatient drugs. Cost details are included; these cost data are included when creating the inpatient (DISCH) and outpatient (OPAT) datasets.
The MCA production data feature "intermediate products," the individual supplies, procedures, and labor effort that together compose a single service or encounter. (For example, intermediate product #3850 is "general surgical service general anesthesia time," representing one aspect of a surgery encounter that required general anesthesia.) The production system is a more direct source of pharmacy data than the NDEs. Data must be extracted separately at each VA station, and the program must be approved and executed by Institutional Resources Management Service (IRMS) staff. For these reasons, individual facility extracts will be most appropriate in two instances: studies requiring data from only one or two facilities, and studies for which the administrative burden of MCA data extraction can be worked into budget and time plans.
In recent years MCA has created a research dataset that aggregates products to the level of departments (Intermediate Product Department Data, or IPDD). Researchers may access it directly using standard statistical software. Due to its vast size, however, the IPDD is not archived in perpetuity, and thus it will not be appropriate for long-term retrospective studies.
VISTA is the primary repository of clinical and administrative data in VA. It consists of computer systems at each VA medical center and the national network that links them. Within each VISTA implementation is a large number of separate 'modules' or 'packages' designed to store data on a particular subject and to produce management reports. VISTA contains all clinical data generated in VA facilities, including inpatient and outpatient stays, laboratory tests, prescriptions, and dentistry. It also features certain types of administrative data, such as drug prices. There is no prescription or pharmacy database per se within VISTA. Prescription-level data from a single facility can be obtained by creating an extract from the "pharmacy package" and other modules of the local system. Both inpatient and outpatient pharmacy data are available. Only direct costs can be extracted directly; indirect costs would have to be estimated.
Pharmacy payments are part of the Non-VA Medical Care program. VA will pay prospectively for medications related to the disability for which the veteran gained program eligibility. Veterans may be reimbursed for medications only if the pharmacist deems them necessary and the situation is urgent or emergent. In either case, the VA payment will be a function of the medication's Average Wholesale Price plus the state-specific Medicaid dispensing fee. In the Pharmacy Payments file there is a record for each outpatient prescription filled, but no clinical information such as drug name, drug class, or quantity dispensed. The Non-VA Medical Care pharmacy file can be linked to other Non-VA Medical Care files and to standard VA encounter files.
Data from the Non-VA Medical Care system input in SAS files at the VA Austin Information Technology Center (AITC). The files that contain Non-VA Medical Care are named in the form:
MDPPRD.MDP.SAS.FEN.FYyy where yy is the last two digits of the year in which the payment was processed.
Payments to pharmacies:
There is an alternative method for accessing summaries of Non-VA Medical Care expenditure data, one that does not require an AITC time-share account. A "Non-VA Care Cube" has been created as part of the national Financial and Clinical Data Mart. The data cube shows combined payments from the four central Non-VA Medical Care files: Inpatient, Outpatient, Inpatient Ancillary, and Pharmacy. One can view and download a series of standard reports or create a unique "view". The data cube does not present individual encounter records. Rather, it shows payment totals summed within category (Purpose of Visit) and time period (month or fiscal year). The data cube is accessed through the VISN Support Services Center (VSSC) web site, also known as the KLFMenu. The site is accessible only through the VA intranet.
|Proc Contents for Non-VA Medical Care Pharmacy Files (FY00-FY12)|
|Non-VA Medical Care Data||FY00||FY01||FY02||FY03||FY04||FY05||FY06||FY07||FY08||FY09||FY10||FY11||FY12|
Questions to consider when choosing a pharmacy data source include the following: What data elements do I need? How much time and effort can I spend on obtaining the data? What other types of data need to be linked to the pharmacy data? Researchers needing details of the medication and prescription will need to use the MCA Pharmacy Extract, the PBM database, or VISTA. Obtaining VISTA data is significantly more difficult and thus is only advisable when it is the only source of needed information. There is also a trade-off between time and money. VISTA data are free but require special permissions and programs. PBM data are extracted by the PBM SHG staff and so require relatively little effort, but funded studies will be charged for their assistance. MCA is free but has relatively little overlap with PBM on clinical or cost variables.
In general only VA employees may directly access these pharmacy data sources. The PBM SHG will create an extract from the PBM V3.0 Database for non-VA researchers only if those researchers collaborate with a VA employee or belong to an official oversight body. The timeshare accounts needed to access MCA data are restricted to VA employees, as is direct access to VISTA systems. In sum, non-VA researchers will need to find a VA collaborator in order to use VA pharmacy data.
Pharmacy data will inevitably contain some missing or erroneous values. Many of these can be located through simple searches based on variable values. Here are some examples:
- NDC begins with '00000' or '99999'
- Days Supplied and Quantity Supplied are not integers greater than 0
- Unit or total cost = $0
Records with these values should not be discarded without investigation; it may be possible to rectify an obvious error using other data on the record.
We recommend using PROC UNIVARIATE in SAS, or similar commands in other applications, as a baseline for checking data quality. When it is possible to focus on a small number of medications, another useful check is to calculate the implied dose per day:
Daily Dose = (# Units Dispensed * Strength per Unit) / (Days Supplied)
For example, if a 15-day supply consists of 30 pills at 200mg per pill, then the daily dose would be 400 mg/day:
400 = (30*200)/15
Package inserts may be consulted to determine standard dose ranges. Doses that are too small to be clinically meaningful or so high as to be toxic should be flagged as potentially erroneous. Investigation of individual prescription records would then be indicated, and potentially utilization records as well if available.
A potential hazard in this method is variation across facilities in the assignment of dispensed units. For example, a 50ml injection may be recorded at one facility as 50 units and at another as 1 unit. In theory the two may be reconciled based on other information on the record, such as the NDC and the dosage instructions. Variations in dispensed units are quite common. One approach to locating them is to tabulate the range of dispensed units for selected NDCs. Variation by a factor of more than 3 or 4 between greatest and least is probably a sign that further investigation is warranted.
Checking the validity of cost data is likewise important but potentially complicated. Depending on when local pharmacies update their drug cost files, in theory one could observe two prices for a single NDC on the same day. Likewise, Blanket Purchase Agreements may cause disparities between two facilities on the same day for the same product. Moreover, not every local pharmacy updates its own cost file every day. What may be done to find or avoid errors then? Again, univariate statistics can alert researchers to outlying values. And cost changes for particular NDCs should not be too great. Cost variation across facilities and across time within a single facility should be moderate; extreme changes are likely signs of error.
A more comprehensive way to avoid cost errors is to use the Pharmacy Benefit Management staff's historical drug cost file to assign a standard price for each NDC covered by federal contracts. (See https://www.pbm.va.gov for contact information.) This reveals an opportunity for sensitivity analyses as well: determining the difference in outcome caused by using alternative medication cost systems. Aside from observed VA costs, choices include the optimal VA cost based on the historical cost file; federal contract prices without the "Big Four" discounts; and publicly available prices, such as a fixed percentage of AWP plus a nominal dispensing fee. (Without information on Blanket Purchase Agreements it will not be possible to recreate the optimal cost for every VA facility. Ignoring BPAs is unlikely to cause significant errors, however.)
If erroneous values are located, there are several options. Each NDC corresponds to a particular package size, thereby providing an alternative measure of units dispensed. Outlier values of dispensing unit (mg, ml, etc.) may be corrected by reference to other prescriptions with the same NDC. Cost values may be corrected by reference to the historical file created by the PBM staff or by taking an average of costs for the same NDC. As much as possible, draw data for cost corrections from the same facility and day or week.
VIREC staff performed a careful validation of the MCA National Data Extract that features prescription-level pharmacy data, comparing it to the PBM prescription-level database. The report revealed the range of errors and inconsistencies that occur in these two VA databases and shows some methods for dealing with them. The report is available on the VIREC web site.
Judgment of proper dosage requires clinical expertise. The presence of comorbid conditions or concomitant medications can lead dosages to deviate from the typical range, and many medications are started at subclinical dosages in order to detect intolerance or adverse events. Only highly unusual values should be considered suspect.
IP Number, the first five digits of the MCA (DSS) Feeder Key, is increasingly important in VA pharmacy research. Like NDC it indicates the medication dispensed. If the IP Number in a MCA pharmacy record consists of zeros or does not match the drug corresponding to the NDC, then the NDC should be used to identify the prescription.
Additional information about pharmacy data quality can be found in the archives of the HSRData-L listserv sponsored by VIREC. To sign up visit the VIREC web site, https://www.virec.research.va.gov.
MCA PHA NDE consists of information from three VistA data sources: Outpatient, IV and Unit Dose packages. Some records in the inpatient and outpatient MCA pharmacy data may contain negative quantity and/or cost values for prescriptions because all three packages allow returns to stock. Here are a few examples of why negative balances exist in the MCA pharmacy data:
- The MCA VistA extracts check all records against the Patient Movement File (#405) as they are created. The Application Program Interface (API) obtains the patient's internal entry number (DFN) and date/time of the occurrence and checks to see if the patient was admitted at the time. If so, the MCA extracts mark that record as an inpatient record, otherwise they mark it as an outpatient record. If either IV or Unit Dose returns from a ward are recorded in their respective VistA Pharmacy packages after the patient has been discharged, MCA will mark the record as outpatient. This also explains why there may be a large number of negative values in the MCA outpatient pharmacy files.
- MCA outpatient pharmacy records with negative balances may also emerge from Pharmacy IV and Unit Dose returns made on a different date than the date the prescriptions were issued. MCA creates a separate encounter for each combination of SSN + Date + Primary Stop Code. Since returns are processed at a date or time after discharge, MCA records them as an outpatient transaction. This is assuming that at large hospitals, there are separate inpatient and outpatient sections of the pharmacy. For instance, if a patient receives multiple prescriptions on a given day and one of them is returned on a different day, MCA will create two separate encounters: one from the multiple issues on the first day and a separate one for the return because the return was made on a different day.
- Medications that are issued in individual-dose amounts to patients for consumption on the same day, such as those from the ward, are pulled a day in advance. Given that a large hospital will have large quantities of prescriptions to fill and will have to allow time for quality control, the pharmacy technician has to prepare a day’s supply of prescriptions prior to the day of patients’ consumption. Many of the ward medications are returned after the patient has been discharged. Since they process at a date/time after discharge, MCA records them as an outpatient transaction.
- Consolidated Mail Outpatient Pharmacy (CMOP) undeliverable medications that are returned and turned back into stock also contribute to negative quantities in the MCA pharmacy data.
Negative values in MCA pharmacy data are real, and should be included in an analysis. Failure to include them will result in an overestimate of the actual costs of care.