1 Pseudo-Bills and Cost-Regressions Ciaran S. Phibbs, PhD Todd Wagner, PhD

2 Cost Theory Outside of health, most items that we purchase daily have a readily observable cost (i.e. market regulates cost). Not true with health care. Arrow K. Uncertainty and the welfare economics of medical care. American Economic Review. 1963;53(5):

3 Opportunity Cost The real cost to society of a resource consumed or freed up as part of a health intervention is the value of that resource in its next best use to society. Gold et al. (1996) pg. 36.

4 Cost Estimation Cost are idiosyncratic to the institution (e.g., RX costs in VA). Most US hospital accounting systems focus on billing and payments. –Charges do not equal costs. –Natural variation in accounting practices can lead to large discrepancies in charges. Some researchers use payments (Medicare) –Payments are limited to covered benefits –Payments may involve incentives –Payments may differ from costs

5 Estimating Costs Micro-costing (activity based or bottom up approach) –Sum the product of quantity*price of all inputs –DSS uses this approach –Researchers use this approach in some circumstances –Gold standard but resource intensive Average costing (gross costing or top down) –Less precise than micro-costing –Can estimate costs for large samples

6 Costing Micro costing and average costing represent ends of a spectrum Micro costing Average Costing

Costing Methods Direct measurement Pseudo-bill Reduced list costing Clinical cost function Estimate Medicare inpatient payment Average cost per visit microaverage

8 What is a Pseudo Bill? It is a method of assigning prices or costs to patient care encounters Typically applied to care provided by health care systems that do not normally bill patients for care Examples include, VA, some HMOs, many foreign health care systems

9 What is a Pseudo Bill? Is is an attempt to duplicate the information normally found on a provider bill for care that does not have a bill. There are two parts to a pseudo bill: –What services were used/provided –The unit costs of each service

10 When Do We Need a Pseudo Bill? When average cost methods not good enough. E.g. evaluating an intervention where there are systematic differences between study groups that will not be captured by the average cost method

11 When Do We Need Pseudo Bill? This method may be necessary for only some of the services a health care system provides E.g. Canada –Physicians, bill fee for service –Hospitals, global budgets, no patient bills

12 When Do We Need Pseudo Bill? For a specific study, this method may be necessary for only some of the services study subjects receive, such as the treatment under study. For most studies, average cost methods can be used for all subsequent care.

13 Creating a Pseudo Bill: Determine Feasibility Determine what data are available on the utilization of services Determine what data are available on unit costs Determine if the utilization data match up with the unit cost data

14 Example of a Pseudo Bill: HERC Outpatient Average Cost Data-1 Utilization data. VA records CPT codes for all outpatient encounters –CPT codes are what the private sector uses for billing for outpatient encounters Is this level of detail sufficient to answer the study question?

15 Current Procedureal Terminology (CPT) Codes 5 Digit code Represents physician services –clinic visits –surgery and other procedures Represents Ancillaries e.g. laboratory, radiology HCPCS is Medicare version, which adds codes for durable medical equipment, etc.

16 Example of a Pseudo Bill: HERC Outpatient Average Cost Data-2 Limits of NPCD outpatient data: –No data for outpatient pharmacy –Most VA CPT code data not used for billing, doesn’t have standard billing screens run –SE file didn’t allow duplicate CPT codes before FY 05 (retrospective SE file will be built for FY 03 and FY 04)

17 Example of a Pseudo Bill: HERC Outpatient Average Cost Data-3 Sources of unit cost data –Medicare RBRVS –Other Medicare fee schedules –Ingenix Gap codes These account for over 80% of CPT codes, and over 90% of procedures provided by VA Various other sources used for the rest

18 Example of a Pseudo Bill: HERC Outpatient Average Cost Data-4 Provider vs. Facility fees The standard provider fee includes payment for the provider’s practice expenses When service is provided in a facility, the facility can also submit a bill –E.g., for use of OR for outpatient surgery

19 Example of a Pseudo Bill: HERC Outpatient Average Cost Data-5 HERC scaled payments so that they equaled actual VA costs, as reported in the CDR This required several assumptions, details available from the documentation at:

20 Pseudo Bills for Other Types of Care VA outpatient care a relatively easy example of a pseudo bill –Utilization data recorded with necessary detail for pseudo bill (CPT codes) –Cost/billing data readily available for CPT codes This is not the case for some other types of care

21 Pseudo Bills for Inpatient Services? Not feasible in VA to collect all of the detailed utilization information needed to create a pseudo bill for acute inpatient care Very difficult to get non-VA price vectors to apply to VA utilization data; need to know exactly which items are billed individually, and which are included in the daily rates.

Summary: pseudo-bill Assumptions –Schedule of charges reflects relative resource use –Cost-adjusted charges reflect VA costs Advantages –Captures effect of intervention on pattern of care within an encounter Disadvantages –Expense of obtaining detailed utilization data

Cost Function Function is used to estimate relative value weights Estimated from external data on cost and characteristics of stays (not from own study data) Obtain characteristics of stay from own study Apply function to estimate cost of stay Advantage: fewer variables than a pseudo-bill Disadvantage: could have large error for individual bills

24 Cost Regressions A statistical technique to use discharge abstracts to impute the average cost of care. “All models are wrong, but some models are useful.” - George P. E. Box., distinguished statistician

Cost Regression Dependent variable is charges or cost-adjusted charge from non-VA data Independent variables: –Clinical information –Diagnosis Related Group –Diagnosis –Procedures –Vital status at discharge –Length of stay –Days of ICU care Anything that helps predict cost and is in both datasets.

Transformation of Dependent Variable Cost data are frequently skewed –Skewed errors violates assumptions of Ordinary Least Squares –Error terms not normally distributed with identical means and variance –Transformation Typical method: log of cost Can make OLS assumptions more tenable

Interpretation of Semi-Log Regression Results Need to correct the coefficients using the smearing estimator –The mean of the anti-log of the residuals is not 0. –Duan, N. (1983) Smearing estimate: a nonparametric retransformation method, Journal of the American Statistical Association, 78, Easy to do in Stata. –Biased when heteroskedasticity is present.

References for Retransformation Manning WG, Mullahy J. Estimating log models: to transform or not to transform? J Health Econ 2001 Jul;20(4): Basu A, Manning WG, Mullahy J. Comparing alternative models: log vs Cox proportional hazard? Health Economics 2004 Aug;13(8): See HERC web site FAQ response:

29 Limitations The cost regression is based on fitting a model to averages. This method reduces the number of outliers. Can create statistical anomalies. The cost estimates may not reflect true production costs; market-level forces are not taken account.

30 Example HERC AC Datasets Med/surg hospitalizations We made a statistical model to estimate cost –Step 1: Build a model with inpatient discharge data (Medicare) –Dependent variable is cost adjusted charges (CAC) CAC i =  1 length of stay i +  2 DRG i +  3 icudays i +  4 age i +…+e i

31 Cost Regression: Med/surg hospitalizations Step 2: From the regression model, save the parameter estimates (  ’s) Step 3: With our new function, plug in VA data to estimate costs CAC i =  1 length of stay i +  2 DRG i +  3 icudays i +  4 age i +…+e i  1 length of stay i +  2 DRG i +  3 icudays i +  4 age i =Est. VA costs Model from previous page

32 Variance Attenuation

33 Implausible costs A handful of stays had costs <$0 Other stays had costs <$20 Statistical artifacts