Wide State-Level Variation In Commercial Health Care Prices Suggests Uneven Impact Of Price Regulation

Do Insurers With Greater Market Power Negotiate Consistently Lower Prices for Hospital Care? Evidence From Hospital Price Transparency Data

This study examined if greater insurer market power was associated with consistently lower negotiated prices within each hospital for 44 shoppable and emergency procedures, using price transparency data disclosed by 1,506 hospitals in metropolitan areas. We used multi-level fixed effects models to estimate the within-hospital variation in plan-level insurer-negotiated prices (from the largest insurer, the second largest insurer, other major insurers, and nonmajor insurers) and cash-pay prices as a function of insurer market power. For shoppable services, relative to nonmajor insurers, the largest, second largest, and other major insurers negotiated 23%, 16%, and 3% lower prices, respectively, while cash prices were 17% higher. For emergency room visits, while the largest insurers paid 5% less than nonmajor insurers, the second largest and other major insurers did not pay lower prices. Stratified analyses by type of shoppable services found varying magnitudes and patterns of price discounts associated with insurer market power.

Role of prices in driving the variation in spending across medical groups

OBJECTIVE

To understand the relative role of prices versus utilization in the variation in total spending per patient across medical groups.

DATA SOURCES

We conducted a cross-sectional analysis of medical claims for commercially insured adults from a large national insurer in 2018.

STUDY DESIGN

After assigning patients to a medical group based on primary care visits in 2018, we calculated total medical spending for each patient in that year. Total spending included care provided by clinicians within the medical group and care provided by other providers, including hospitals. It did not include drug spending. We estimated the case mix adjusted spending per patient for each medical group. Within each market, we categorized medical groups into quartiles based on the group's spending per patient. To decompose spending variation into price versus utilization, we compared spending differences between highest and lowest quartile medical groups under two scenarios: (1) using actual prices (2) using a standardized price (same price used for a given service across the nation).

PRINCIPAL FINDINGS

In total, 3,921,736 patients were assigned to 7284 medical groups. Per-patient spending in the highest quartile of spending medical groups was $1813 higher than per-patient spending in the lowest spending quartile of medical groups (50% higher relative spending). This overall difference was primarily driven by differences in inpatient care, imaging, and specialty care. In the scenario where we used standardized prices, the difference in spending between medical groups in the top and bottom quartiles decreased to $1425, implying that 79% of the $1813 difference in spending between the top and bottom quartile groups is explained by utilization and the remaining 21% by prices. The likely explanation for the modest impact of prices is that patients cared for by a given medical group receive care across a wide range of providers.

CONCLUSIONS

Prices explained a modest fraction of the differences in spending between medical groups.

Comparing ambulatory commercial spending in Rhode Island and Massachusetts, 2016-2019

OBJECTIVE

To evaluate trends and drivers of commercial ambulatory spending and price variation.

DATA SOURCES AND STUDY SETTING

Commercial claims data from the Massachusetts and Rhode Island All-Payer Claims Databases from 2016 to 2019.

STUDY DESIGN

Observational study of spending in major ambulatory care settings. We calculated per member per year spending, average price, and utilization rates to consider drivers of spending, and constructed site-specific price indices to evaluate price variation.

DATA COLLECTION/EXTRACTION METHODS

We analyzed commercial claims data from All-Payer Claims Databases in the two states.

PRINCIPAL FINDINGS

Ambulatory spending levels in Massachusetts were 38.0% higher than those in Rhode Island in 2019. Overall utilization rates were similar, but Massachusetts had a 6.2 percentage point higher share of visits occurring in hospital outpatient departments (HOPD). Average prices were 31.5% higher in Massachusetts in 2016 and 36.4% higher in 2019. We observed extensive price variation in both states across both office and HOPD settings.

CONCLUSIONS

States seeking to address increases in health care spending, including those with cost growth benchmarks and rate review policies, should consider additional interventions that mitigate market failures in the establishment of commercial health care prices.

The Price-Quality Mismatch: Are Negotiated Prices for Total Joint Arthroplasty Associated With Hospital Quality in a Large California Health System?

BACKGROUND

Price variations in healthcare can be caused by quality or factors other than quality such as market share, negotiating power with insurers, or hospital ownership model. Efforts to improve care value (defined as the ratio between health outcomes and price) by making healthcare prices readily accessible to patients are driven by the assumption this can help patients more easily identify high-quality, low-price clinicians and health systems, thus reducing price variations. However, if price variations are driven by factors other than quality, then strategies that involve payments for higher-quality care are unlikely to reduce price variation and improve value. It is unknown whether prices for total joint arthroplasty (TJA) are correlated with the quality of care or whether factors other than quality are responsible for price variation.

QUESTIONS/PURPOSES

(1) How do prices insurers negotiate for TJA paid to a single, large health system vary across payer types? (2) Are the mean prices insurers negotiate for TJA associated with hospital quality?

METHODS

We analyzed publicly available data from 22 hospitals in a single, large regional health system, four of which were excluded owing to incomplete quality information. We chose to use data from this single health system to minimize the confounding effects of between-hospital reputation or branding and geographic differences in the cost of providing care. This health system consists of large and small hospitals serving urban and rural populations, providing care for more than 3 million individuals. For each hospital, negotiated prices for TJA were classified into five payer types: commercial in-network, commercial out-of-network, Medicare Advantage (plans to which private insurers contract to provide Medicare benefits), Medicaid, and discounted cash pay. Traditional Medicare plans were not included because the prices are set statutorily, not negotiated. We obtained hospital quality measures from the Centers for Medicare and Medicaid Services. Centers for Medicare and Medicaid Services quality measures included TJA-specific complication and readmission rates in addition to hospital-wide patient survey star rating (measure of patient care experience) and total performance scores (aggregate measure of clinical outcomes, safety, patient experience, process of care, and efficiency). We evaluated the association between the mean negotiated hospital prices and Centers for Medicare and Medicaid Services quality measures using Pearson correlation coefficients and Spearman rho across all payer types. Statistical significance was defined as p < 0.0025.

RESULTS

The mean ± SD overall negotiated price for TJA was USD 54,500 ± 23,200. In the descriptive analysis, the lowest negotiated prices were associated with Medicare Advantage (USD 20,400 ± 1800) and Medicaid (USD 20,300 ± 8600) insurance plans, and the highest prices were associated with out-of-network care covered by commercial insurance plans (USD 78,800 ± 9200). There was no correlation between the mean negotiated price and TJA complication rate (discounted cash price: r = 0.27, p = 0.29; commercial out-of-network: r = 0.28, p = 0.26; commercial in-network: r = -0.07, p = 0.79; Medicare Advantage: r = 0.11, p = 0.65; Medicaid: r = 0.03, p = 0.92), readmission rate (discounted cash price: r = 0.19, p = 0.46; commercial out-of-network: r = 0.24, p = 0.33; commercial in-network: r = -0.13, p = 0.61; Medicare Advantage: r = -0.06, p = 0.81; Medicaid: r = 0.09, p = 0.74), patient survey star rating (discounted cash price: r = -0.55, p = 0.02; commercial out-of-network: r = -0.53, p = 0.02; commercial in-network: r = -0.37, p = 0.13; Medicare Advantage: r = -0.08, p = 0.75; Medicaid: r = -0.02, p = 0.95), or total hospital performance score (discounted cash price: r = -0.35, p = 0.15; commercial out-of-network: r = -0.55, p = 0.02; commercial in-network: r = -0.53, p = 0.02; Medicare Advantage: r = -0.28, p = 0.25; Medicaid: r = 0.11, p = 0.69) for any of the payer types evaluated.

CONCLUSION

There is substantial price variation for TJA that is not accounted for by the quality of care, suggesting that a mismatch between price and quality exists. Efforts to improve care value in TJA are needed to directly link prices with the quality of care delivered, such as through matched quality and price reporting mechanisms. Future studies might investigate whether making price and quality data accessible to patients, such as through value dashboards that report easy-to-interpret quality data alongside price information, moves patients toward higher-value care decisions.

CLINICAL RELEVANCE

Efforts to better match the quality of care with negotiated prices such as matched quality and price reporting mechanisms, which have been shown to increase the likelihood of choosing higher-value care in TJA, could improve the value of care.

The Relationships Among Cash Prices, Negotiated Rates, And Chargemaster Prices For Shoppable Hospital Services

Hospitals must disclose their cash prices, commercial negotiated rates, and chargemaster prices for seventy common, shoppable services under the hospital price transparency rule. Examining prices reported by 2,379 hospitals as of September 9, 2022, we found that a given hospital's cash prices and commercial negotiated rates both tended to reflect a predetermined and consistent percentage discount from its chargemaster prices. On average, cash prices and commercial negotiated rates were 64 percent and 58 percent of the corresponding chargemaster prices for the same procedures at the same hospital and in the same service setting, respectively. Cash prices were lower than the median commercial negotiated rates in 47 percent of instances, and most likely so at hospitals with government or nonprofit ownership, located outside of metropolitan areas, or located in counties with relatively high uninsurance rates or low median household incomes. Hospitals with stronger market power were most likely to offer cash prices below their median negotiated rates, whereas hospitals in areas where insurers had stronger market power were less likely to do so.

Regional differences in total hospital costs for radical cystectomy in the United States

OBJECTIVES

To test for regional differences in total hospital costs (THC) across the United States in bladder cancer patients treated with open radical cystectomy (ORC) or robotic-assisted radical cystectomy (RARC).

MATERIALS

We relied on the National Inpatient Sample (NIS) database (2016-2019) and stratified RC patients according to census region (Midwest, Northeast, South, West). Primary statistical analyses consisted of THC-trend analyses and multivariable log-link linear regression models, after adjustment for hospital clustering (Generalized Estimating Equation function) and discharge disposition weighting. Finally, sensitivity analysis, relying on most favorable patient cohort, was performed.

RESULTS

Of 5280 eligible patients, 1441 (27%), 1031 (20%), 1854 (35%) and 954 (18%) underwent RC in the Midwest, Northeast, South and West, respectively. Median THC was 28,915$ and differed significantly between regions (Midwest: 28,105$; Northeast: 28,886$; South: 26,096$; West: 38,809$; p < 0.001). After stratification between ORC and RARC, highest THC was invariably recorded in the West: ORC 36,137$ vs 23,941-28,850$ and RARC 43,119$ vs 28,425-29,952$ (both p < 0.05). In multivariable log-link linear regression models, surgery in the West was independently associated with higher THC: ORC (Exponent beta [Exp[β]]: 1.39; 95%-CI: 1.32-1.47; p < 0.001) and RARC (Exp[β]: 1.46; 95%-CI: 1.38-1.55; p < 0.001). Results remained unchanged when analyses were refitted in most favorable patient subgroup.

CONCLUSIONS

Important regional differences in ORC and RARC THC distinguish the West from other United States regions. The THC discrepancy clearly requires closer examination to identify underlying processes that contribute to inflated costs in the West.

Why the US spends more treating high-need high-cost patients: a comparative study of pricing and utilization of care in six high-income countries

One of the most pressing challenges facing most health care systems is rising costs. As the population ages and the demand for health care services grows, there is a growing need to understand the drivers of these costs across systems. This paper attempts to address this gap by examining utilization and spending of the course of a year for two specific high-need high-cost patient types: a frail older person with a hip fracture and an older person with congestive heart failure and diabetes. Data on utilization and expenditure is collected across five health care settings (hospital, post-acute rehabilitation, primary care, outpatient specialty and drugs), in six countries (Canada (Ontario), France, Germany, Spain (Aragon), Sweden and the United States (fee for service Medicare) and used to construct treatment episode Purchasing Power Parities (PPPs) that compare prices using baskets of goods from the different care settings. The treatment episode PPPs suggest other countries have more similar volumes of care to the US as compared to other standardization approaches, suggesting that US prices account for more of the differential in US health care expenditures. The US also differs with regards to the share of expenditures across care settings, with post-acute rehab and outpatient speciality expenditures accounting for a larger share of the total relative to comparators.

Understanding Differences in Health Care Spending: A Comparative Study of Prices and Volumes Across OECD Countries

Variations across OECD countries in the prices of health care and hospital services can be vast. These price differences mean that comparisons of such services should be adjusted to reflect the ‘real’ volumes consumed. Purchasing power parities (PPPs) can be used to make such comparisons more accurately, going beyond simple GDP-based comparisons, by aggregating the prices of actual individual consumption of health items. These health and hospital PPPs demonstrate that GDP PPPs are a weak substitute, as price structures vary widely. Moreover, there is tentative evidence that higher relative prices for health care tend to bloat health expenditure and are associated with lower life expectancy.

Hospital resource allocation decisions when market prices exceed Medicare prices

OBJECTIVE

To examine nonprofit hospitals' financial and spending allocations when the private sector payment rate is higher than the Medicare's payment rate.

DATA SOURCES

Hospital financial data for 2014-2018 from Center for Medicare and Medicaid Services Hospital Cost Reports, hospital characteristics from the American Hospital Association (AHA) Annual Survey.

STUDY DESIGN

Hospital and year level fixed effects regressions modeling each hospital's (1) operating net income per discharge equivalent (DE); (2) administrative cost per DE; (3) patient care cost per DE; (4) registered nurse per bed; charity care cost per DE; and (5) provision of unprofitable services as a function of the private sector to Medicare payment ratio (PMR).

DATA COLLECTION/EXTRACTION METHODS

Hospital/year-level data from hospital cost reports merged with AHA data. Samples included general short-term hospitals with nonprofit ownership, excluding critical access hospitals.

PRINCIPAL FINDINGS

The final sample included a total of 8862 hospital-year observations, with a mean PMR of 1.62. Nonprofit hospitals having a 0.1 higher PMR were associated with $257 (95% CI: $181-$334) increase in operating net income per DE; $66 (95% CI: $32-$99) increase in administrative cost per DE; $170 (95% CI: $120-$220) increase in patient care cost per DE; and $18 (95% CI: $10-$25) increase in charity care cost per DE. We found hospitals hired 0.86 (95% CI: -0.08 to 1.81) more registered nurses per 100 beds, but no evidence on providing more beds for unprofitable services, such as obstetric care, burn care, alcohol/drug abuse treatment, or psychiatric care.

CONCLUSIONS

Higher private sector prices led primarily to greater surplus and administrative cost for nonprofit hospitals and smaller increases in spending on services that will directly benefit patients.

Trends In Hospital Prices Paid By Private Health Plans Varied Substantially Across The US

Commercial health plans pay higher prices than public payers for hospital care, which accounts for more than 5 percent of US gross domestic product. Crafting effective policy responses requires monitoring trends and identifying sources of variation. Relying on data from the Healthcare Provider Cost Reporting Information System, we describe how commercial hospital payment rates changed relative to Medicare rates during 2012-19 and how trends differed by hospital referral region (HRR). We found that average commercial-to-Medicare price ratios were relatively stable, but trends varied substantially across HRRs. Among HRRs with high price ratios in 2012, ratios increased by 38 percentage points in regions in the top quartile of growth and decreased by 38 percentage points in regions in the bottom quartile. Our findings suggest that restraining the growth rate of HRR commercial hospital price ratios to the national average during our sample period would have reduced aggregate spending by $39 billion in 2019.

Implementation of a hybrid angiography-CT system: increased short-term revenue at an academic radiology department

PURPOSE

To analyze the financial impact following implementation of a hybrid Angio-CT system at a tertiary care academic medical center.

METHODS

Aggregate case types and volumes were compared 24 months before and 12 months after a hybrid Angio-CT system replaced a traditional interventional C-arm angiography suite at an academic medical center. Procedure revenues from this 36-month study period were derived from five payors mixes (Medicare, Medicaid, commercial insurance, out-of-pocket and managed care program) and Medicare-rate adjusted to each individual payor types.

RESULTS

Average case volume per month increased 12% in the hybrid Angio-CT suite when compared to the previous traditional angiography suite (P < 0.05). The variety of IR procedures in the hybrid Angio-CT suite also expanded to include more complex interventional radiology and interventional oncology procedures; the breadth of cases performed in the hybrid Angio-CT suite were associated with CPT codes of higher rates (average CPT value/case increased from $2,334.61 to $2,567.25). The estimated average annual revenue of the hybrid Angio-CT suite increased 23% as compared to previous traditional angiography suite.

CONCLUSION

A hybrid Angio-CT system is a financially feasible endeavor at a tertiary care academic medical center that facilitated higher complexity procedure codes and increased procedure-related revenue.

Colonoscopy in poorly prepped colons: a cost effectiveness analysis comparing standard of care to a new cleansing technology

Background

The objective of this Markov model lifetime cost-effectiveness analysis was to evaluate a new medical device technology which minimizes redo colonoscopies on the outcomes of cost, quality of life, and aversion of colorectal cancers (CRC).

Methods

A new technology (PureVu® System) which cleans inadequately prepped colons was evaluated using TreeAge 2019 software in patients who presented with inadequate prep in outpatient settings in the US. PureVu was compared to the standard of care (SOC). Peer reviewed literature was used to identify the CRC incidence cancers based on missing polyps. Costs for procedures were derived from 2019 Medicare and from estimated private payer reimbursements. Base case costs, sensitivity analysis and incremental cost effectiveness (ICE) were evaluated. The cost of PureVu was $750.

Results

Assuming a national average compliance rate of 60% for colonoscopy, the use of PureVu saved the healthcare system $833–$992/patient depending upon the insurer when compared to SOC. QALYs were also improved with PureVu mainly due to a lower incidence of CRCs. In sensitivity analysis, SOC becomes less expensive than PureVu when compliance to screening for CRC using colonoscopy is ≤ 28%. Also, in order for SOC to be less expensive than PureVu, the list price of PureVu would need to exceed $1753. In incremental cost effectiveness analysis, PureVu dominated SOC.

Conclusion

Using the PureVu System to improve bowel prep can save the healthcare system $3.1–$3.7 billion per year, while ensuring a similar quality of life and reducing the incidence of CRCs.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12962-021-00277-5.