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The federal health care price transparency regulation from 2019 is aimed at bending the health care cost curve by increasing the availability of hospital pricing information for the public.
This study aims to examine the associations between publicly reported diagnosis-related group chargemaster prices on the internet and quality measures, process indicators, and patient-reported experience measures.
In this cross-sectional study, we collected and analyzed a random 5.02% (212/4221) stratified sample of US hospital prices in 2019 using descriptive statistics and multivariate analysis.
We found extreme price variation in
We found that hospital chargemasters display wide variations in prices for medical services and procedures and match variations in quality measures. Further work is required to investigate 100% of US hospital prices posted publicly on the internet and their relationship with quality measures.
Increases in health care expenditures have persisted throughout the years in the United States despite policy efforts to
As a result, historically, consumers have not been as price-sensitive toward making health care decisions when compared with consumer decision-making behaviors commonly observed in other economic sectors. With the continual increase in US health care spending, a widely held view is that greater consumer engagement in health care will help hold prices down. In turn, greater consumer engagement will slow down the sector’s expansion rate if (and when) consumers place a substantial emphasis on making price-sensitive decisions using pricing transparency information [
Nonetheless, understanding newly available US chargemaster information is vital to patients because American patients are sent a medical bill after receiving treatment. A medical bill will contain the patient’s portion owed of hospital standard charges for medical services and procedures that were delivered net of any contractual allowances and third-party payments. Therefore, standard charges are relevant to the consumer, either directly by influencing their purchase decisions before receiving medical care or indirectly when they receive a medical bill after treatment.
This study aims to assess the variability of publicly available DRG chargemaster data and its relation to quality measures, process indicators, and patient experience measures as a source of information for consumer quality assessment and price-sensitive decision-making purposes. The research benefits three audiences. For policy makers, this study provides an early assessment of the pricing transparency regulation’s utility. For researchers, being able to collect and compare hospitals’ pricing data is an important task if they are to inform policy maker efforts on controlling health care spending. In addition, researchers can inform the public at large and assist other stakeholders, such as nongovernmental organizations, in providing an analysis of pricing information found on chargemasters that is understandable. Finally, for health care administrators, this research can shed new light on the importance of presenting standard charges to the public in compliance with the law.
We conducted a cross-sectional study of web-based publicly available hospital chargemasters from 2019. First, we assessed the descriptive statistics and coefficients of variation (CVs) to describe the standard charges grouped by the DRG code. We aimed to describe the full extent of price variability in hospital standard charges.
We then performed 2 median chi-square tests on standard charges and type of service (either medical or surgical). Median chi-square tests were performed because the standard charges were not normally distributed, that is, standard charges were skewed to the right. The first test was for average standard charge (either above the median or below the median) by the type of service (either medical or surgical). Similarly, the second test was for the CV (either above the median or below the median) by the type of service (either medical or surgical).
Next, we performed a log-linear, ordinary least squares regression model to fit the natural log-transformed standard charges on hospital characteristics. We log-transformed the dependent variable (standard charges) owing to the right-skewness and lack of normal distribution. We removed outliers with residuals of IQR 1.5 below the first quartile or IQR 1.5 above the third quartile. β coefficients,
We retrieved chargemasters from hospital websites on the internet between August 25, 2019, and October 3, 2019, if they were formatted using DRG primary codes (eg, chargemasters in Healthcare Common Procedure Coding System or common procedural terminology primary code were excluded). In line with previous research, we obtained common hospital characteristic data from the Hospital Compare, CMS, American Hospital Association (AHA), and Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS).
We constructed a random, stratified sample to assess US hospitals (
Data sampling strategy. aTotal number of hospitals drawn from Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey from 3rd quarter of 2018. bSome hospitals provided data in an unusable format, such as in the All Patient Refined–diagnosis-related group coding format vs Medicare Severity–diagnosis-related group, providing maximum or minimum charges vs standard charges, etc. DRG: diagnosis-related group.
Quality predictors included benchmark measures for the hospital’s overall rating (hospital rating categories: 1 star, 2 stars, 3 stars, 4 stars, 5 stars, and missing), mortality rate, safety score, readmission rate, effectiveness of care score, efficient use of medical imaging score, and patient experience score. These measures and their categorical values (either below the national average, same as the national average, above the national average, or missing) were obtained using the Hospital General Information data set from the CMS. In addition, we included one additional patient experience measure: the likelihood of patients to recommend the hospital using the quartile categories described in the
Controls included hospital ownership type (government—hospital district or authority, government—local, physician, proprietary, voluntary nonprofit—church, voluntary nonprofit—other, and voluntary nonprofit—private). Next, using data from the AHA Annual Survey, the hospital bed size (6-24 beds, 25-49 beds, 50-99 beds, 100-199 beds, 200-299 beds, 300-399 beds, 400-499 beds, and 500 or more beds) was controlled. Previous work has used the number of competitors in the market as a measure of competition (rather than the Herfindahl-Hirschman index) [
CMS specifically defined 5 services using DRG primary codes to be
Standard charges for Centers for Medicare and Medicaid Services–specified shoppable servicesa.
Medicine and surgery servicesb | DRGc primary code | Hospitals observed (n=212), n (%) | Price variability (US $) | Coefficient of variation (SD divided by mean) | |
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Mean (SD) | Minimum-maximum, range |
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Major joint replacement or reattachment of lower extremity without MCCd | 470 | 75 (35.4) | 68,329 (41,724) | 26,401-224,052 | 0.611 |
Spinal fusion except cervical without MCC | 460 | 50 (23.6) | 123,744 (71,755) | 30,995-427,374 | 0.580 |
Cervical spinal fusion without CCe or MCC | 473 | 44 (20.8) | 89,302 (50,122) | 30,924-249,283 | 0.561 |
Cardiac valve and other major cardiothoracic procedures with cardiac catherization with MCC | 216 | 28 (13.2) | 430,274 (195,719) | 139,460-912,194 | 0.455 |
Uterine and adnexa procedures for nonmalignancy without CC or MCC | 743 | 62 (29.2) | 41,338 (18,662) | 11,863-87,981 | 0.451 |
aThe table is sorted from most to least variable using the coefficient of variation.
bThese are the only 5 selected services using the diagnosis-related group primary code (as opposed to the common procedural terminology or Healthcare Common Procedure Coding System) that the Centers for Medicare and Medicaid Services determined to include in the forthcoming regulation (effective date January 1, 2021), which mandates public disclosure of payer-specific negotiated charges, deidentified minimum and maximum negotiated charges, and discounted cash prices for at least 300 shoppable services, including 70 Centers for Medicare and Medicaid Services–specified shoppable services and 230 hospital-selected shoppable services.
cDRG: diagnosis-related group.
dMCC: major comorbid conditions or complications.
eCC: comorbid conditions.
Next, out of the set of 761 DRG primary codes, the data for the most and least variable services by type of service (either medical or surgical) with at least 30 observations are presented in
Top 10 most and least variable services for diagnosis-related group primary codesa.
Medicine and surgery services (rank) | DRGb primary code | Type | Hospitals (n=212), n (%) | Price variability (US $) | Coefficient of variation (SD divided by mean) | Magnitude of range (US $) | |||||
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Mean (SD) | Minimum-maximum, range |
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Normal newborn (1) | 795 | Medical | 57 (26.9) | 27,052 (167,415) | 1005 | 1,268,646 | 6.189 | 1,267,641 | ||
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Reticuloendothelial and immunity disorders with MCCc (2) | 814 | Medical | 30 (14.2) | 129,016 (376,201) | 13,470 | 2,077,708 | 2.916 | 2,064,238 | ||
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Skin ulcers with CCd (3) | 593 | Medical | 43 (20.3) | 44,730 (80,382) | 6668 | 493,015 | 1.797 | 486,346 | ||
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Other infectious and parasitic diseases diagnoses with MCC (4) | 867 | Medical | 32 (15.1) | 96,332 (170,037) | 8608 | 962,984 | 1.765 | 954,377 | ||
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Other respiratory system operating room procedures without CC or MCC (5) | 168 | Surgical | 38 (17.9) | 75,296 (109,621) | 16,330 | 695,556 | 1.456 | 679,226 | ||
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Neonates, died or transferred to another acute care facility (6) | 789 | Medical | 50 (23.6) | 91,278 (129,536) | 1839 | 687,641 | 1.419 | 685,802 | ||
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Other endocrine, nutritional, and metabolic operating room procedures with MCC (7) | 628 | Surgical | 32 (15.1) | 159,266 (209,529) | 41,388 | 1,188,069 | 1.316 | 1,146,681 | ||
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Other factors influencing health status (8) | 951 | Medical | 52 (24.5) | 19,934 (22,603) | 32 | 109,262 | 1.134 | 109,230 | ||
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Minor skin disorders without MCC (8) | 607 | Medical | 57 (26.9) | 28,153 (30,979) | 5013 | 226,300 | 1.100 | 221,287 | ||
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Depressive neuroses (10) | 881 | Medical | 44 (20.8) | 21,152 (21,902) | 3144 | 140,972 | 1.035 | 137,829 | ||
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Percutaneous intracardiac procedures without MCC (1) | 274 | Surgical | 36 (17) | 112,404 (43,667) | 46,738 | 255,453 | 0.388 | 208,715 | ||
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Kidney and ureter procedures for neoplasm with CC (2) | 657 | Surgical | 38 (17.9) | 78,905 (31,783) | 23,587 | 156,374 | 0.403 | 132,787 | ||
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Other heart assist system implant (3) | 215 | Surgical | 30 (14.2) | 335,647 (139,272) | 153,355 | 702,998 | 0.415 | 549,643 | ||
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Ischemic stroke, precerebral occlusion, or transient ischemia with thrombolytic agent with CC (4) | 62 | Medical | 39 (18.4) | 84,117 (34,993) | 27,457 | 187,137 | 0.416 | 159,680 | ||
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Cardiac pacemaker revision except device replacement with CC (5) | 261 | Surgical | 31 (14.6) | 68,581 (30,005) | 23,154 | 130,492 | 0.438 | 107,338 | ||
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Vaginal delivery with sterilization and/or dilation and curettage (6) | 767e | Surgical | 50 (23.6) | 24,912 (10,938) | 8210 | 54,446 | 0.439 | 46,236 | ||
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Cesarean section without CC or MCC (7) | 766e | Surgical | 56 (26.4) | 24,106 (10,687) | 9737 | 58,931 | 0.443 | 49,194 | ||
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Disorders of the biliary tract without CC or MCC (8) | 446 | Medical | 59 (27.8) | 28,628 (12,805) | 7936 | 72,899 | 0.447 | 64,963 | ||
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Uterine and adnexa procedures for nonmalignancy without CC or MCC (9) | 743 | Surgical | 62 (29.2) | 41,338 (18,662) | 11,863 | 87,981 | 0.451 | 76,118 | ||
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Aortic and heart assist procedures except pulsation balloon with MCC (10) | 268 | Surgical | 31 (14.6) | 251,216 (113,721) | 92,804 | 623,820 | 0.453 | 531,016 |
aOnly services in the diagnosis-related group primary code with at least 30 observations are included. Most and least variable services are measured by the highest and lowest coefficients of variation, respectively.
bDRG: diagnosis-related group.
cMCC major comorbid conditions or complications.
dCC: comorbid conditions.
eDiagnosis-related group (DRG) codes 766 and 767 have been removed from Medicare Severity–DRG version 36.
The relationship between standard charge and type of service (medical or surgical) was assessed for significant differences using 2 different median chi-square tests. The tables are presented in the top and bottom panels of
Contingency table for average standard charge versus the type of service and coefficient of variation for standard charge versus the type of servicea.
Standard Charge | Type of service | Total (n=758), n (%) | ||||||||
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Medical | Surgical |
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Observed (n=372), n (%) | Expected (n=372), n (%) | Chi-square contribution | Observed (n=386), n (%) | Expected (n=386), n (%) | Chi-square contribution |
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Less than median | 302 (79.7) | 186 (50) | 72.3 | 77 (20.3) | 193 (50) | 69.7 | 379 (50) | ||
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Greater than median | 70 (18.5) | 186 (50) | 72.3 | 309 (81.5) | 193 (50) | 69.7 | 379 (50) | ||
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Less than median | 167 (44.1) | 186 (50) | 1.9 | 212 (55.9) | 193 (50) | 1.9 | 379 (50) | ||
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Greater than median | 205 (54.1) | 186 (50) | 1.9 | 174 (45.9) | 193 (50) | 1.9 | 379 (50) |
aCounts are individual diagnosis-related group primary codes, for example, 70 medical-type diagnosis-related group codes have averages greater than the median standard charge.
We examined standard charges across hospital characteristics: hospital ownership, hospital rating, mortality, safety, readmission, effectiveness of care, patient experience, competition, efficient use of medical imaging, patient recommendation, region, bed size, US state, and DRG primary code (
Hospital characteristics of included standard charges (N=29,167)a.
Variables | Hospital, n (%) | |||
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Hospital district or authority | 1534 (5.26) | |
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Local | 1197 (4.1) | |
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Physician | 150 (0.51) | ||
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Proprietary | 9021 (30.93) | ||
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Church | 1390 (4.77) | |
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Other | 4102 (14.06) | |
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Private | 11,773 (40.36) | |
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1 star (worst) | 2336 (8.01) | ||
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2 stars | 9363 (32.1) | ||
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3 stars | 6003 (20.58) | ||
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4 stars | 9459 (32.43) | ||
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5 stars (best) | 1903 (6.52) | ||
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Missing | 103 (0.35) | ||
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Below the national average | 4474 (15.34) | ||
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Same as the national average | 17,696 (60.67) | ||
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Above the national average | 5926 (20.32) | ||
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Missing | 1071 (3.67) | ||
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Below the national average | 8619 (29.55) | ||
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Same as the national average | 5695 (19.53) | ||
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Above the national average | 13,633 (46.74) | ||
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Missing | 1220 (4.18) | ||
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Below the national average | 15,237 (52.24) | ||
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Same as the national average | 2975 (10.20) | ||
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Above the national average | 10,388 (35.62) | ||
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Missing | 567 (1.94) | ||
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Below the national average | 4383 (15.03) | ||
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Same as the national average | 23,394 (80.21) | ||
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Above the national average | 1287 (4.41) | ||
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Missing | 103 (0.35) | ||
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Below the national average | 11,932 (40.91) | ||
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Same as the national average | 8296 (28.44) | ||
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Above the national average | 8537 (29.27) | ||
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Missing | 402 (1.38) | ||
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1 (least) | 22,625 (77.57) | ||
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2 | 4360 (14.95) | ||
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3 (most) | 2182 (7.48) | ||
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Below the national average | 4936 (16.92) | ||
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Same as the national average | 16,343 (56.03) | ||
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Above the national average | 6028 (20.67) | ||
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Missing | 1860 (6.38) | ||
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1 (lowest quartile) | 5670 (19.44) | ||
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2 | 8672 (29.73) | ||
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3 | 9807 (33.62) | ||
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4 (highest quartile) | 5018 (17.2) | ||
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New England | 1384 (4.75) | ||
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Mid Atlantic | 2845 (9.75) | ||
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South Atlantic | 4616 (15.83) | ||
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East North Central | 4359 (14.94) | ||
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East South Central | 2713 (9.3) | ||
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West North Central | 1083 (3.71) | ||
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West South Central | 4797 (16.45) | ||
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Mountain | 2766 (9.48) | ||
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Pacific | 4604 (15.78) | ||
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6-24 | 329 (1.13) | ||
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25-49 | 1017 (3.49) | ||
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50-99 | 3049 (10.45) | ||
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100-199 | 6017 (20.63) | ||
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200-299 | 4650 (15.94) | ||
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300-399 | 5249 (17.99) | ||
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400-499 | 4057 (13.91) | ||
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500 or more | 4799 (16.45) |
aThe table does not show values for each category for US state and diagnosis-related group primary code.
Regression results for standard charges in a sample of US hospitals (N=27,530)a.
Variables | β (robust SE) | |||||
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Hospital district or authority | .856 (0.104) | <.001 | ||
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Local | .499 (0.159) | .002 | ||
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Physician | −1.879 (0.257) | <.001 | |||
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Proprietary | .828 (0.177) | <.001 | |||
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Church | 1.008 (0.089) | <.001 | ||
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Other | Reference | N/Ab | ||
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Private | .172 (0.111) | .13 | ||
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1 star (worst) | .499 (0.212) | .02 | |||
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2 star | .422 (0.076) | <.001 | |||
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3 star | Reference | N/A | |||
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4 star | .133 (0.185) | .47 | |||
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5 star (best) | .109 (0.207) | .60 | |||
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Missing | −.042 (0.756) | .96 | |||
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Below the national average | .514 (0.051) | <.001 | |||
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Same as the national average | Reference | N/A | |||
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Above the national average | .244 (0.125) | .05 | |||
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Missing | .961 (0.188) | <.001 | |||
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Below the national average | −.085 (0.046) | .06 | |||
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Same as the national average | Reference | N/A | |||
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Above the national average | .102 (0.063) | .11 | |||
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Missing | −.007 (0.187) | .97 | |||
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Below the national average | .929 (0.158) | <.001 | |||
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Same as the national average | Reference | N/A | |||
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Above the national average | .578 (0.147) | <.001 | |||
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Missing | −1.023 (0.325) | .002 | |||
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Below the national average | −.046 (0.055) | .41 | |||
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Same as the national average | Reference | N/A | |||
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Above the national average | .294 (0.075) | <.001 | |||
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Missing | 3.707 (0.346) | <.001 | |||
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Below the national average | .081 (0.102) | .43 | |||
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Same as the national average | Reference | N/A | |||
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Above the national average | −.919 (0.119) | <.001 | |||
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Below the national average | −.277 (0.055) | <.001 | |||
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Same as the national average | Reference | N/A | |||
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Above the national average | −.458 (0.128) | .001 | |||
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Missing | .321 (0.084) | <.001 | |||
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1 (lowest quartile) | Reference | N/A | |||
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2 | −.236 (0.066) | .001 | |||
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3 | −.169 (0.074) | .03 | |||
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4 (highest quartile) | −.414 (0.072) | <.001 | |||
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1 (least) | Reference | N/A | |||
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2 | .546 (0.065) | <.001 | |||
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3 (most) | .552 (0.119) | <.001 | |||
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6-24 | Reference | N/A | |||
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25-49 | 2.063 (0.287) | <.001 | |||
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50-99 | 1.518 (0.261) | <.001 | |||
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100-199 | 1.832 (0.32) | <.001 | |||
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200-299 | 2.63 (0.341) | <.001 | |||
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300-399 | 2.082 (0.286) | <.001 | |||
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400-499 | 2.013 (0.294) | <.001 | |||
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500 or more | 2.075 (0.262) | <.001 | |||
Constant | 10.004 (0.418) | <.001 |
aThe table shows the results for a log-linear regression using the natural log function to transform the dependent variable, that is, standard charge. Other covariates for individual state code and diagnosis-related group Code Dummy Variables are not shown. Outliers with residuals IQR 1.5 below the first quartile or IQR 1.5 above the third quartile are omitted. Robust SEs are clustered on hospital to correct for related observations. Standard charges are in dollars. R2=0.8955 and number of observations=27,530.
bN/A: not applicable.
All quality indicators were associated with standard charges at the statistically significant α=.05 level, except for the patient safety indicator. The 2 quality indicators associated with the largest significant
Finally, for
Wide differences exist between hospital billed charges and the amount of money that hospitals expect to receive for services [
Reviewing
Thereafter, we sought to test the differences in variability in the type of service (either medical or surgical). The estimated CVs for surgical-type DRGs were significantly smaller than those for medical DRGs using standard charge data for Maryland between 1979 and 1981 [
Afterward, we sought to understand whether the wide variances observed were systematically related to hospital characteristics for quality performance indicators. A number of hospital characteristics were shown to be significantly associated with standard charges, including physical characteristics such as bed size or ownership structure, geographical characteristics, controls for the service or procedure code, competition, and quality indicators (such as patient recommendation scores or readmission rates).
Overall, our results were largely consistent with those of a previous study that found that standard charges in hospital chargemasters were well predicted using hospital characteristics [
On the other hand, there is not a singular positive or negative relationship between price and quality, and at times, price and quality can either have a positive or negative relationship [
Complexities exist in modern health care, which causes gaps in the ability of health care systems to deliver consistent, effective, and efficient care [
At this juncture, it is important to digress from the first phase of health price transparency regulation and discuss the implications of the second phase briefly to shed some light on other implications of this study in the context of present health care systems and policies. Although hospitals provide chargemaster data, the
Gross charge: the charge for an individual item or service that is reflected on a hospital’s chargemaster, absent any discounts
Discounted cash price: the charge that applies to an individual who pays cash or cash equivalent for a hospital item or service
Payer-specific negotiated charge: the charge that a hospital has negotiated with a third-party payer for an item or service
Deidentified minimum negotiated charges: the lowest charge that a hospital has negotiated with all third-party payers for an item or service
Deidentified maximum negotiated charges: the highest charge that a hospital has negotiated with all third-party payers for an item or service.
Patients may use this additional information in 2021 to more accurately price-shop, insurers may use this information to bargain for better reimbursement rates, and other facilities may use this information to alter their pricing strategies and compete more effectively in the more transparent health care market. Therefore, this information is closely guarded by health plans [
While conducting this study on health care price transparency, there are 2 important limitations that need to be discussed. First, we did not analyze pricing information from other coding systems, such as common procedural terminology, Healthcare Common Procedure Coding System, or other proprietary formats. Some hospitals published chargemasters using other codes that were not mandated. Thus, the study results can only be generalized to the extent that DRG codes bundle services together correctly and correspond accurately to services rendered for patients. Some of these other coding systems rely on billing specialists to itemize services rendered, and they may or may not result in more accurate pricing, which could be higher or lower on average when compared with DRG-coded charges we analyzed in this study. However, the DRG coding system is one of the most widely used systems for preparing patient bills, and the results of this study are directly applicable to this most commonly used hospital pricing system in the United States.
Second, we did not follow up, investigate, or verify individual observations of standard charges. It is possible (and quite likely) that hospital chargemasters unintentionally contain outdated, erroneous, or inaccurate standard charges. These mistakes may have been published on the web for the public unbeknownst to hospital administrators. We mitigated these effects as much as possible by using statistical techniques where appropriate, such as analyzing median values and removing outliers.
Patients are not solely influenced by costs when making health care decisions; they base their decisions on several factors, including the opinions and information supplied by their health care providers and insurers. Moreover, previous literature has shown that patients just do not want to be a cog in the health care system, but in reality, they want to share in the decision-making processes regarding where to seek treatment with their health care providers [
In summary, the results of this cross-sectional study, which analyzed the pricing behavior at hospitals in the first phase of the price transparency regulations, draw attention to the fact that policy makers, researchers, and health care administrators as well as, ultimately, consumers all need to be vigilant about health care price transparency and its relation to quality measures. There was extreme variation in
It is crucial for researchers, policy makers, and health care administrators to work together to design a holistic registry or database system to document these chargemasters. This study has demonstrated the potential value of such information using publicly available chargemaster data on the internet from a cross-sectional random, stratified sample of 5.02% of the US hospitals. This process can be scaled up to collect, clean, and document chargemasters for all US hospitals multiple times per year, such as quarterly or semiannually.
American Hospital Association
Centers for Medicare and Medicaid Services
coefficient of variation
diagnosis-related group
Hospital Consumer Assessment of Healthcare Providers and Systems
None declared.