Why is cost estimation difficult in Medicare Advantage encounter data?

Medicare Advantage encounter data describe services but generally do not include payment amounts. Encounter files can also include duplicate submissions, chart review records, missing rendering provider information, and variation in completeness across contracts.

How can standardized prices be created for Medicare Advantage encounters?

Standardized prices can be created by using Traditional Medicare FFS claims and published fee schedules to estimate what MA encounter services would have cost under FFS pricing rules, with fallback average FFS payments when no CMS fee schedule is available.

Which service categories can be included?

The approach can include inpatient, outpatient, carrier, DME, SNF, hospice, home health, Part B drugs, laboratory services, anesthesia, ambulance, facility outpatient services, and Part D Prescription Drug Event data.

Medicare Advantage Analytics

Medicare Advantage Encounter Cost Estimation Case Study

FastHSR estimated healthcare costs in Medicare Advantage encounter data by using MA encounters to measure service quantities and Traditional Medicare FFS claims and fee schedules to estimate standardized prices.

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Client question

A client needed to estimate healthcare costs for Medicare Advantage populations. The challenge was that MA encounter data describe utilization but do not provide the same payment fields as Traditional Medicare claims. The client needed a defensible method to quantify resource use and estimate what MA services would have cost under FFS pricing structures.

Data foundation

FastHSR used MA encounter data to determine service quantities and used Traditional Medicare FFS claims, Part D Prescription Drug Event data, and published CMS fee schedules to estimate prices. The data foundation included inpatient, outpatient, carrier, DME, SNF, hospice, home health, and Part D files.

Encounter data preparation

Medicare Advantage encounter data require cleaning before cost measurement. FastHSR built a preparation pipeline that addressed common encounter-data issues before applying standardized prices.

Duplicate submissions: encounter records were grouped using beneficiary, provider, service date, facility type, and service type keys.
Latest version logic: when multiple submissions described the same service, the latest version was retained.
Chart review handling: chart review records were separated from service-based utilization where appropriate.
Missing fields: methods accounted for missing variables that are often available in FFS claims but absent in MA encounters.
Contract completeness: outputs were interpreted with attention to variation in encounter completeness across MA contracts.

Discharge-level inpatient construction

Inpatient MA encounter records can include multiple submissions for a single stay. FastHSR consolidated inpatient records into discharge-level events so utilization and spending measures reflected stays rather than fragmented encounter submissions.

Multiple inpatient records for the same stay were consolidated into one discharge-level record.
Overlapping records were merged by using the earliest admission date and latest discharge date.
Chart reviews within an encounter date range were removed from discharge construction.
Stays with a discharge date and next admission date on the same day were treated as separate stays.

Standardized pricing method

FastHSR developed standardized prices based on FFS payments in Traditional Medicare. The goal was to estimate the spending that would have occurred if MA encounter services had been paid using FFS pricing structures.

For services with published CMS fee schedules, FastHSR calculated adjustment factors so the standardized price schedule reproduced total FFS payments for the relevant service category. For services without a published CMS fee schedule, FastHSR estimated per-unit standardized prices from average FFS payments observed in Medicare claims.

Service categories

The pricing framework was applied across major Medicare service categories, with service-specific logic for units, modifiers, site of care, and available fee schedules.

Inpatient: DRG-based pricing for acute hospital stays.
Physician services: RBRVS fee schedules by site of care, HCPCS, and modifier.
Laboratory: Clinical Laboratory Fee Schedule amounts and service units.
Part B drugs: Average Sales Price-based pricing by HCPCS and units.
DMEPOS: DMEPOS fee schedule amounts by HCPCS and modifier.
Anesthesia: base units, time units, HCPCS, and modifier logic.
Ambulance and outpatient facility services: average FFS payments where claims-based pricing was more appropriate.
Part D: drug dosage unit pricing calculated from Prescription Drug Event data.
Hospice and home health: per-diem or service-specific pricing based on applicable Medicare payment logic.

Findings

The work produced standardized MA resource-use and cost measures that could be compared across service categories, contracts, populations, and time periods. The results helped the client interpret MA utilization in a payment-like framework without relying on unavailable encounter payment amounts.

The final outputs emphasized methodological transparency, including where estimates were based on published fee schedules, where they relied on actual FFS payments, and where encounter completeness could affect interpretation.

Deliverables

Cleaned and de-duplicated MA encounter analytic files.
Discharge-level inpatient file for stay-based measurement.
Standardized price schedules by service category.
Encounter-level and aggregate standardized cost estimates.
Service-category documentation for pricing sources, units, modifiers, and fallback methods.
Quality checks comparing standardized price schedules with FFS payment totals.

Use cases

Medicare Advantage cost and resource-use estimation.
Plan, contract, provider, or market comparison using MA encounter data.
Value-based care performance analytics in MA populations.
Service-line cost decomposition across inpatient, outpatient, physician, pharmacy, and post-acute care.
Research-grade methods for comparing MA utilization with Traditional Medicare benchmarks.

References

Jung J, Carlin C, Feldman R. Measuring resource use in Medicare Advantage using Encounter data. Health Services Research. 2022;57(1):172-181. doi:10.1111/1475-6773.13879
Jung J, Carlin C, Feldman R, Tran L. Implementation of resource use measures in Medicare Advantage. Health Services Research. 2022;57(4):957-962. doi:10.1111/1475-6773.13970

Frequently asked questions

Why not use payment amounts directly from MA encounter data?

MA encounter data generally describe services and utilization, but payment information is not included in the same way it is in Traditional Medicare claims. Estimation requires a separate pricing method.

Why use FFS prices for MA encounters?

FFS prices provide a standardized benchmark for measuring resource use. They estimate what the observed MA services would have cost under Traditional Medicare payment structures.

Why is inpatient processing separate?

Inpatient encounters can have multiple records for one stay. Discharge-level construction creates a stay-based unit of analysis before assigning standardized costs.

For Medicare Advantage encounter cost estimation, standardized pricing, resource-use measurement, or MA/FFS comparison analytics, please email us.

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