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Table 1.  Patient, Hospital, and Region Characteristics, by Hospital Type
Patient, Hospital, and Region Characteristics, by Hospital Type
Table 2.  Differences in Adjusted Insurer Spending by Hospital Typea
Differences in Adjusted Insurer Spending by Hospital Typea
Table 3.  Differences in Adjusted Utilization by Hospital Typea
Differences in Adjusted Utilization by Hospital Typea
1.
Mariotto  AB, Yabroff  KR, Shao  Y, Feuer  EJ, Brown  ML.  Projections of the cost of cancer care in the United States: 2010-2020.   J Natl Cancer Inst. 2011;103(2):117-128. doi:10.1093/jnci/djq495PubMedGoogle ScholarCrossref
2.
Laviana  AA, Luckenbaugh  AN, Resnick  MJ.  Trends in the cost of cancer care: beyond drugs.   J Clin Oncol. 2020;38(4):316-322. doi:10.1200/JCO.19.01963PubMedGoogle ScholarCrossref
3.
Mariotto  AB, Enewold  L, Zhao  J, Zeruto  CA, Yabroff  KR.  Medical care costs associated with cancer survivorship in the United States.   Cancer Epidemiol Biomarkers Prev. 2020;29(7):1304-1312. doi:10.1158/1055-9965.EPI-19-1534PubMedGoogle ScholarCrossref
4.
Li  M, Lakdawalla  DN, Goldman  DP.  Association between spending and outcomes for patients with cancer.   J Clin Oncol. 2020;38(4):323-331. doi:10.1200/JCO.19.01451PubMedGoogle ScholarCrossref
5.
National Cancer Institute. NCI–designated cancer centers. Accessed October 27, 2020. https://www.cancer.gov/research/infrastructure/cancer-centers
6.
Birkmeyer  NJ, Goodney  PP, Stukel  TA, Hillner  BE, Birkmeyer  JD.  Do cancer centers designated by the National Cancer Institute have better surgical outcomes?   Cancer. 2005;103(3):435-441. doi:10.1002/cncr.20785PubMedGoogle ScholarCrossref
7.
Onega  T, Duell  EJ, Shi  X, Demidenko  E, Gottlieb  D, Goodman  DC.  Influence of NCI cancer center attendance on mortality in lung, breast, colorectal, and prostate cancer patients.   Med Care Res Rev. 2009;66(5):542-560. doi:10.1177/1077558709335536PubMedGoogle ScholarCrossref
8.
Pfister  DG, Rubin  DM, Elkin  EB,  et al.  Risk adjusting survival outcomes in hospitals that treat patients with cancer without information on cancer stage.   JAMA Oncol. 2015;1(9):1303-1310. doi:10.1001/jamaoncol.2015.3151PubMedGoogle ScholarCrossref
9.
Wolfson  JA, Sun  CL, Wyatt  LP, Hurria  A, Bhatia  S.  Impact of care at comprehensive cancer centers on outcome: results from a population-based study.   Cancer. 2015;121(21):3885-3893. doi:10.1002/cncr.29576PubMedGoogle ScholarCrossref
10.
Shulman  LN, Palis  BE, McCabe  R,  et al.  Survival as a quality metric of cancer care: use of the National Cancer Data Base to assess hospital performance.   J Oncol Pract. 2018;14(1):e59-e72. doi:10.1200/JOP.2016.020446PubMedGoogle ScholarCrossref
11.
Koller  CF, Khullar  D.  The commercial differential for hospital prices: responses from states and employers.   JAMA. 2019;322(8):723-724. doi:10.1001/jama.2019.9275PubMedGoogle ScholarCrossref
12.
von Elm  E, Altman  DG, Egger  M, Pocock  SJ, Gøtzsche  PC, Vandenbroucke  JP; STROBE Initiative.  The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies.   Lancet. 2007;370(9596):1453-1457. doi:10.1016/S0140-6736(07)61602-XPubMedGoogle ScholarCrossref
13.
Health Care Cost Institute. Accessed October 27, 2020. https://healthcostinstitute.org/
14.
American Hospital Association. AHA Annual Survey Database. Accessed October 27, 2020. https://www.ahadata.com/aha-annual-survey-database
15.
Dartmouth Atlas Project. The Dartmouth Atlas of Health Care. Accessed October 27, 2020. https://www.dartmouthatlas.org/
16.
US Census Bureau. American Community Survey. Accessed October 27, 2020. https://www.census.gov/programs-surveys/acs
17.
Nattinger  AB, Laud  PW, Bajorunaite  R, Sparapani  RA, Freeman  JL.  An algorithm for the use of Medicare claims data to identify women with incident breast cancer.   Health Serv Res. 2004;39(6 Pt 1):1733-1749. doi:10.1111/j.1475-6773.2004.00315.xPubMedGoogle Scholar
18.
Lavery  JA, Lipitz-Snyderman  A, Li  DG, Bach  PB, Panageas  KS.  Identifying cancer-directed surgeries in Medicare claims: a validation study using SEER-Medicare data.   JCO Clin Cancer Inform. 2019;3:1-24. doi:10.1200/CCI.18.00093PubMedGoogle Scholar
19.
Centers for Medicare & Medicaid Services. Global Surgery Booklet. Accessed June 30, 2021. https://www.cms.gov/outreach-and-education/medicare-learning-network-mln/mlnproducts/downloads/globallsurgery-icn907166.pdf
20.
Fry  DE, Pine  M, Pine  G.  Medicare post-discharge deaths and readmissions following elective surgery.   Am J Surg. 2014;207(3):326-330. doi:10.1016/j.amjsurg.2013.09.007PubMedGoogle ScholarCrossref
21.
Kim  Y, Gani  F, Lucas  DJ,  et al.  Early versus late readmission after surgery among patients with employer-provided health insurance.   Ann Surg. 2015;262(3):502-511. doi:10.1097/SLA.0000000000001429PubMedGoogle ScholarCrossref
22.
McMillan  RR, Berger  A, Sima  CS,  et al.  Thirty-day mortality underestimates the risk of early death after major resections for thoracic malignancies.   Ann Thorac Surg. 2014;98(5):1769-1774. doi:10.1016/j.athoracsur.2014.06.024PubMedGoogle ScholarCrossref
23.
Orcutt  ST, Li  LT, Balentine  CJ,  et al.  Ninety-day readmission after colorectal cancer surgery in a Veterans Affairs cohort.   J Surg Res. 2016;201(2):370-377. doi:10.1016/j.jss.2015.11.026PubMedGoogle ScholarCrossref
24.
American Association of Medical Colleges. Council of Teaching Hospitals and Health Systems (COTH). Accessed October 27, 2020. https://www.aamc.org/members/coth/
25.
Elixhauser  A, Steiner  C, Harris  DR, Coffey  RM.  Comorbidity measures for use with administrative data.   Med Care. 1998;36(1):8-27. doi:10.1097/00005650-199801000-00004PubMedGoogle ScholarCrossref
26.
Quan  H, Sundararajan  V, Halfon  P,  et al.  Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data.   Med Care. 2005;43(11):1130-1139. doi:10.1097/01.mlr.0000182534.19832.83PubMedGoogle ScholarCrossref
27.
Keehan  SP, Cuckler  GA, Poisal  JA,  et al.  National health expenditure projections, 2019-28: expected rebound in prices drives rising spending growth.   Health Aff (Millwood). 2020;39(4):704-714. doi:10.1377/hlthaff.2020.00094PubMedGoogle ScholarCrossref
28.
Keating  NL, Landrum  MB, Huskamp  HA,  et al.  Dartmouth Atlas area-level estimates of end-of-life expenditures: how well do they reflect expenditures for prospectively identified advanced lung cancer patients?   Health Serv Res. 2016;51(4):1584-1594. doi:10.1111/1475-6773.12440PubMedGoogle ScholarCrossref
29.
Jean  RA, Bongiovanni  T, Soulos  PR,  et al.  Hospital variation in spending for lung cancer resection in Medicare beneficiaries.   Ann Thorac Surg. 2019;108(6):1710-1716. doi:10.1016/j.athoracsur.2019.06.048PubMedGoogle ScholarCrossref
30.
Nathan  H, Atoria  CL, Bach  PB, Elkin  EB.  Hospital volume, complications, and cost of cancer surgery in the elderly.   J Clin Oncol. 2015;33(1):107-114. doi:10.1200/JCO.2014.57.7155PubMedGoogle ScholarCrossref
31.
Keating  NL, Landrum  MB, Lamont  EB, Bozeman  SR, McNeil  BJ.  Area-level variations in cancer care and outcomes.   Med Care. 2012;50(5):366-373. doi:10.1097/MLR.0b013e31824d74c0PubMedGoogle ScholarCrossref
32.
Brooks  GA, Li  L, Sharma  DB,  et al.  Regional variation in spending and survival for older adults with advanced cancer.   J Natl Cancer Inst. 2013;105(9):634-642. doi:10.1093/jnci/djt025PubMedGoogle ScholarCrossref
33.
Ellimoottil  C, Li  J, Ye  Z,  et al.  Episode-based payment variation for urologic cancer surgery.   Urology. 2018;111:78-85. doi:10.1016/j.urology.2017.08.053PubMedGoogle ScholarCrossref
34.
Abdelsattar  ZM, Birkmeyer  JD, Wong  SL.  Variation in Medicare payments for colorectal cancer surgery.   J Oncol Pract. 2015;11(5):391-395. doi:10.1200/JOP.2015.004036PubMedGoogle ScholarCrossref
35.
Brooks  GA, Li  L, Uno  H, Hassett  MJ, Landon  BE, Schrag  D.  Acute hospital care is the chief driver of regional spending variation in Medicare patients with advanced cancer.   Health Aff (Millwood). 2014;33(10):1793-1800. doi:10.1377/hlthaff.2014.0280PubMedGoogle ScholarCrossref
36.
Cooper  Z, Craig  SV, Gaynor  M, Van Reenen  J.  The price ain’t right? hospital prices and health spending on the privately insured.   Q J Econ. 2019;134(1):51-107. doi:10.1093/qje/qjy020PubMedGoogle ScholarCrossref
37.
Fulton  BD.  Health care market concentration trends in the United States: evidence and policy responses.   Health Aff (Millwood). 2017;36(9):1530-1538. doi:10.1377/hlthaff.2017.0556PubMedGoogle ScholarCrossref
38.
Porter  ME.  What is value in health care?   N Engl J Med. 2010;363(26):2477-2481. doi:10.1056/NEJMp1011024PubMedGoogle ScholarCrossref
39.
Tandstad  T, Kollmannsberger  CK, Roth  BJ,  et al.  Practice makes perfect: the rest of the story in testicular cancer as a model curable neoplasm.   J Clin Oncol. 2017;35(31):3525-3528. doi:10.1200/JCO.2017.73.4723PubMedGoogle ScholarCrossref
40.
Halm  EA, Anderson  LD  Jr, Gerber  DE.  Understanding the relationship between care volume and clinical outcomes in multiple myeloma.   J Clin Oncol. 2017;35(6):580-582. doi:10.1200/JCO.2016.70.4726PubMedGoogle ScholarCrossref
41.
Epstein  AM.  Volume and outcome—it is time to move ahead.   N Engl J Med. 2002;346(15):1161-1164. doi:10.1056/NEJM200204113461512PubMedGoogle ScholarCrossref
42.
Fong  Y, Patti  MG.  Volume standards for high-risk cancer surgery.   JAMA Surg. 2019;154(11):1012-1013. doi:10.1001/jamasurg.2019.3018PubMedGoogle ScholarCrossref
43.
Shahian  DM, Nordberg  P, Meyer  GS,  et al.  Contemporary performance of U.S. teaching and nonteaching hospitals.   Acad Med. 2012;87(6):701-708. doi:10.1097/ACM.0b013e318253676aPubMedGoogle ScholarCrossref
44.
Mueller  SK, Lipsitz  S, Hicks  LS.  Impact of hospital teaching intensity on quality of care and patient outcomes.   Med Care. 2013;51(7):567-574. doi:10.1097/MLR.0b013e3182902151PubMedGoogle ScholarCrossref
45.
Shahian  DM, Liu  X, Meyer  GS, Torchiana  DF, Normand  SL.  Hospital teaching intensity and mortality for acute myocardial infarction, heart failure, and pneumonia.   Med Care. 2014;52(1):38-46. doi:10.1097/MLR.0000000000000005PubMedGoogle ScholarCrossref
46.
Burke  LG, Frakt  AB, Khullar  D, Orav  EJ, Jha  AK.  Association between teaching status and mortality in US hospitals.   JAMA. 2017;317(20):2105-2113. doi:10.1001/jama.2017.5702PubMedGoogle ScholarCrossref
47.
Burke  L, Khullar  D, Orav  EJ, Zheng  J, Frakt  A, Jha  AK.  Do academic medical centers disproportionately benefit the sickest patients?   Health Aff (Millwood). 2018;37(6):864-872. doi:10.1377/hlthaff.2017.1250PubMedGoogle ScholarCrossref
48.
Merkow  RP, Yang  AD, Pavey  E,  et al.  Comparison of hospitals affiliated with PPS-exempt cancer centers, other hospitals affiliated with NCI-designated cancer centers, and other hospitals that provide cancer care.   JAMA Intern Med. 2019;179(8):1043-1051. doi:10.1001/jamainternmed.2019.0914PubMedGoogle ScholarCrossref
49.
Yasaitis  L, Bekelman  JE, Polsky  D.  Relation between narrow networks and providers of cancer care.   J Clin Oncol. 2017;35(27):3131-3135. doi:10.1200/JCO.2017.73.2040PubMedGoogle ScholarCrossref
50.
Furlow  B.  Skepticism about new US government hospital pricing transparency rule.   Lancet Oncol. 2019;20(2):188. doi:10.1016/S1470-2045(19)30002-6PubMedGoogle ScholarCrossref
51.
Batty  M, Ippolito  B.  Mystery of the chargemaster: examining the role of hospital list prices in what patients actually pay.   Health Aff (Millwood). 2017;36(4):689-696. doi:10.1377/hlthaff.2016.0986PubMedGoogle ScholarCrossref
52.
Agarwal  A, Dayal  A, Kircher  SM, Chen  RC, Royce  TJ.  Analysis of price transparency via National Cancer Institute–designated cancer centers’ chargemasters for prostate cancer radiation therapy.   JAMA Oncol. 2020;6(3):409-412. doi:10.1001/jamaoncol.2019.5690PubMedGoogle ScholarCrossref
53.
Medicare and Medicaid Programs: CY 2020 Hospital Outpatient PPS Policy Changes and Payment Rates and Ambulatory Surgical Center Payment System Policy Changes and Payment Rates. Price Transparency Requirements for Hospitals To Make Standard Charges Public. Accessed October 27, 2020. https://www.federalregister.gov/documents/2019/11/27/2019-24931/medicare-and-medicaid-programs-cy-2020-hospital-outpatient-pps-policy-changes-and-payment-rates-and
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    Original Investigation
    Health Policy
    August 3, 2021

    Differences in Cancer Care Expenditures and Utilization for Surgery by Hospital Type Among Patients With Private Insurance

    Author Affiliations
    • 1Division of Hematology and Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia
    • 2Penn Center for Cancer Care Innovation, Abramson Cancer Center, University of Pennsylvania, Philadelphia
    • 3Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia
    • 4Division of General Internal Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia
    • 5Division of Urology, Perelman School of Medicine, University of Pennsylvania, Philadelphia
    • 6Center for Health Equity Research and Promotion, Corporal Michael J. Crescenz VA Medical Center, Philadelphia, Pennsylvania
    • 7Departments of Radiation Oncology and Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia
    JAMA Netw Open. 2021;4(8):e2119764. doi:10.1001/jamanetworkopen.2021.19764
    Key Points

    Question  Are there differences in insurer spending and care utilization for patients with private insurance undergoing cancer surgery at National Cancer Institute (NCI) centers vs community hospitals?

    Findings  In this cross-sectional study of 66 878 patients with breast, colon, or lung cancer, surgery at NCI centers, compared with community hospitals, was associated with higher insurer prices paid and higher 90-day postdischarge payments, without differences in length of stay, emergency department use, or hospital readmission.

    Meaning  In this study of patients with private insurance undergoing cancer surgery, insurer spending for a surgical episode was higher at NCI centers than community hospitals, without differences in care utilization.

    Abstract

    Importance  With rising expenditures on cancer care outpacing other sectors of the US health system, national attention has focused on insurer spending, particularly for patients with private insurance, for whom price transparency has historically been lacking. The type of hospital at which cancer care is delivered may be an important factor associated with insurer spending for patients with private insurance.

    Objective  To examine differences in spending and utilization for patients with private insurance undergoing common cancer surgery at National Cancer Institute (NCI) centers vs community hospitals.

    Design, Setting, and Participants  This retrospective cross-sectional study included adult patients with an incident diagnosis of breast, colon, or lung cancer who underwent cancer-directed surgery from 2011 to 2014. Mean risk-adjusted spending and utilization outcomes were examined for each hospital type using multilevel generalized linear mixed-effects models, adjusting for patient, hospital, and region characteristics. Data were collected from the Health Care Cost Institute’s national multipayer commercial claims data set, which encompasses claims paid by 3 of the 5 largest commercial health insurers in the United States (ie, Aetna, Humana, and UnitedHealthcare). Data analyses were conducted from February 2018 to February 2019.

    Exposures  Hospital type at which cancer surgery was performed: NCI, non-NCI academic, or community.

    Main Outcomes and Measures  Spending outcomes were surgery-specific insurer prices paid and 90-day postdischarge payments. Utilization outcomes were length of stay (LOS), emergency department (ED) use, and hospital readmission within 90 days of discharge.

    Results  The study included 66 878 patients (51 569 [77.1%] women; 31 585 [47.2%] aged ≥65 years) with incident breast (35 788 [53.5%]), colon (21 378 [32.0%]), or lung (9712 [14.5%]) cancer undergoing cancer surgery at 2995 hospitals (5522 [8.3%] at NCI centers; 10 917 [16.3%] at non-NCI academic hospitals; 50 439 [75.4%] at community hospitals). Treatment at NCI centers was associated with higher surgery-specific insurer prices paid compared with community hospitals ($18 526 [95% CI, $16 650-$20 403] vs $14 772 [95% CI, $14 339-$15 204]; difference, $3755 [95% CI, $1661-$5849]; P < .001) and 90-day postdischarge payments ($47 035 [95% CI, $43 289-$50 781] vs $41 291 [95% CI, $40 350-$42 231]; difference, $5744 [95% CI, $1659-9829]; P = .006). There were no significant differences in LOS, ED use, or hospital readmission within 90 days of discharge.

    Conclusions and Relevance  In this cross-sectional study, surgery at NCI centers vs community hospitals was associated with higher insurer spending for a surgical episode without differences in care utilization among patients with private insurance undergoing cancer surgery. A better understanding of the factors associated with prices and spending at NCI cancer centers is needed.

    Introduction

    With rising expenditures on cancer care outpacing other sectors of the US health system,1-3 national attention has focused on identifying and promoting high-value cancer hospitals, ie, those that consistently deliver excellent outcomes at relatively low cost.4 National Cancer Institute (NCI)–designated cancer centers (hereafter NCI centers) are academic hospitals recognized for their scientific and research leadership, training and education programs, and clinical expertise in cancer care.5 Treatment at NCI centers may be associated with improved outcomes, particularly for patients with more severe illness and/or more advanced cancers.6-10 However, little is known about the degree to which treatment at NCI centers may be associated with higher prices paid or spending for a care episode, limiting an assessment of value in cancer care. Because of their comprehensive service offerings, market share, and prestige, NCI centers may exercise greater leverage in negotiations with commercial insurers, resulting in higher reimbursement rates for cancer care services. This is particularly true for patients with private insurance, for whom health care prices are negotiated between insurers and clinicians and price transparency is lacking.11

    Therefore, we examined the association between hospital type (NCI center vs community hospital) and insurer spending and care utilization during a surgical episode for patients with private insurance who underwent common cancer surgery. We hypothesized that treatment at NCI centers, compared with community hospitals, would be associated with higher surgery-specific prices paid and episode spending and decreased acute care utilization.

    Methods
    Study Design

    We conducted a retrospective cross-sectional study to evaluate the association between hospital type and insurer spending and care utilization during a surgical episode. The study adhered to Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines12 and was exempted by the University of Pennsylvania institutional review board given the use of deidentified data only.

    Data Sources

    We used data from the Health Care Cost Institute (HCCI) national multipayer commercial claims data set, which includes claims paid by 3 of the 5 largest commercial health insurers in the United States (ie, Aetna, Humana, and UnitedHealthcare).13 The HCCI offers a comprehensive picture of private health insurer spending, encompassing data on more than 40 million covered individuals annually, spanning all 50 states and the District of Columbia. The data set includes reporting of actual prices paid for claims submitted, thus illuminating negotiated transaction prices between clinicians, health systems, and payers.

    Hospital-level data were obtained from the 2014 American Hospital Association Annual Survey.14 Regional data were captured at the level of the Hospital Referral Region (HRR), encompassing health care markets for tertiary medical care, using publicly available data from the Dartmouth Atlas Project15 and 2014 American Community Survey.16

    Participants

    The study sample included adult patients (age >18 years) with incident breast, colon, or lung cancer who underwent cancer-directed surgery from 2011 to 2014. We selected these cancers because they are common and routinely treated with extirpative surgery in the nonmetastatic setting across both community and academic sites. We used validated, claims-based algorithms, modified for use in private rather than Medicare claims, to identify incident cancer cases and cancer-directed surgical procedures meeting our specifications. To identify patients with incident breast cancer, we applied the multistep validated algorithm described by Nattinger et al.17 To identify patients with incident colon or lung cancer, we applied the multistep validated algorithms described by Lavery et al.18 Then, we identified cancer-directed surgical procedures by the presence of an International Classification of Diseases, Ninth Revision (ICD-9) or Healthcare Common Procedure Coding System (HCPCS) procedure code and corresponding primary diagnosis code on an inpatient or outpatient claim (eFigure 1 in the Supplement). If multiple cancer-directed surgical procedures were performed during the study period for a given patient, the first was considered the index surgery. We excluded patients with length of stay in the top 1% (n = 258), as these patients were judged to be outliers who likely had surgical or other complications.

    Patient-level data were merged with hospital-level data via an encrypted hospital National Provider Identifier (NPI) present on the claim associated with the index surgery. Thus, patients were attributed to the facility at which their index surgery was performed. Patients with breast cancer undergoing outpatient surgical procedures were attributed to the hospital affiliated with the outpatient place of service; all surgical procedures for colon and lung cancer were performed in the inpatient setting. Patients with missing or nonmerging NPIs were excluded (n = 16 669). HRR-level data were merged based on the zip code of the hospital in which the index surgery was performed. Finally, we limited our analysis to those patients with continuous enrollment for 6 months before and 1 month after the index surgery for baseline covariate and outcome ascertainment, respectively (eFigure 2 in the Supplement).

    Outcomes and Measures

    The primary spending outcomes were surgery-specific insurer price paid and 90-day postdischarge payments. For inpatient procedures, the price paid was defined as the sum of all prices paid on surgery-specific facility and physician claims from admission through discharge. For outpatient procedures, the price paid was defined as the sum of all surgery-specific facility and physician claims on the day of the index procedure. Postdischarge spending was calculated by aggregating payments on inpatient, outpatient, physician, and pharmacy claims during the 90-day period after discharge (or date of index surgery for those patients undergoing outpatient procedures). All dollar amounts were adjusted for differences in economic conditions across regions using the Centers for Medicare & Medicaid Services (CMS) Wage Index and were inflated to 2014 dollars (study termination) using the Consumer Price Index for Medical Care.

    The primary utilization outcomes were hospital length of stay (LOS), emergency department (ED) use, and hospital readmission within 90 days of discharge. We chose 90-day event rates for our main analysis because CMS considers this interval policy relevant,19 and prior studies have advocated for its use.19-23

    The independent variable was the hospital type at which cancer surgery was performed, separated into 3 mutually exclusive categories: NCI center, non-NCI academic hospital, or community hospital. We used publicly available data to identify hospitals affiliated with an NCI center.5 Academic hospitals were identified as those with membership in the Council of Teaching Hospitals and Health Systems of the Association of American Medical Colleges.24 Hospitals were attributed to the group corresponding to their highest designation only, and all designations were current as of the year 2014.

    Patient-level covariates included age, sex, severity of illness using Elixhauser25,26 comorbidities, median household income of the zip code of residence, tumor type, surgical procedure, year of procedure, and enrollment in a Medicare Advantage plan (vs not). We additionally generated a variable of historical monthly spending by aggregating insurer claims paid in the 6 months prior to index surgery and calculating a mean monthly spend over this period.

    Hospital-level variables included number of hospital beds, number of intensive care unit beds, annual surgical volume, hospital ownership (ie, for profit, nonprofit, or government), and oncology and chemotherapy services arrangement (ie, hospital owned or joint venture). HRR-level variables included density of physicians, surgeons, specialists, and acute care hospitals. To adjust for regional health care market competition, we calculated the Herfindahl-Hirschman Index by HRR, defined by the share of hospital beds squared, summed over all hospitals in the market area, with higher values indicating less competition.

    Statistical Analysis

    We estimated mean adjusted spending and utilization outcomes by hospital type using multilevel mixed-effects generalized linear models, adjusting for patient, hospital, and region characteristics and accounting for clustering of patients within hospitals. Patient-level spending outcomes were modeled with a generalized linear model with log link function and γ distribution, given their positive right-skewed distribution. Patient-level LOS was similarly modeled. Patient-level ED use and hospital readmission were modeled as binary variables using multivariable mixed-effects logistic regression models. After modeling patient-level outcomes, we then estimated mean adjusted spending and utilization outcomes for each hospital type using postestimation predictive margins. The marginal effect of each hospital type was calculated to determine whether the differences in estimated outcomes for NCI centers (and, secondarily, non-NCI academic hospitals) compared with community hospitals were statistically different than zero.

    We conducted a sensitivity analysis to test the robustness of our findings to an alternate outcome specification by modeling 30-day event rates for spending and utilization outcomes. All statistical analyses used 2-tailed testing with a significance threshold of P < .01 (Bonferroni correction for testing 5 hypotheses across spending and utilization outcomes). Analyses were conducted using SAS version 9.4 (SAS Institute) and Stata version 15.0 (StataCorp).

    Results
    Patient and Hospital Characteristics

    We identified 66 878 patients (51 569 [77.1%] women; 31 585 [47.2%] aged ≥65 years) with incident breast (35 788 [53.5%]), colon (21 378 [32.0%]), or lung (9712 [14.5%]) cancer undergoing index cancer surgery at 2995 hospitals (5522 [8.3%] at NCI centers, 10 917 [16.3%] at non-NCI academic hospitals, and 50 439 [75.4%] at community hospitals). Patients treated at NCI centers vs those treated at community hospitals were younger (aged 18-54 years: 2175 [39.4%] vs 12 879 [25.5%]; standardized difference, 0.37) and more likely to be women (4455 [80.7%] vs 38 527 [76.4%]; standardized difference, 0.11). Patients treated at NCI centers had higher historical mean (SD) monthly spending than those treated at community hospitals ($4052 [$6705] vs $2124 [$4292]; standardized difference, 0.34) but similar Elixhauser comorbidity scores (≥3: 1931 [35.0%] vs 17 157 [34.0%]; standardized difference, 0.05). Medicare Advantage coverage was less frequent among those undergoing surgery at NCI centers compared with community hospitals (964 [17.5%] vs 16 270 [32.3%]; standardized difference, 0.35).

    NCI centers, compared with community hospitals, were larger (≥200 beds: 59 [96.8%] vs 1031 [37.9%]; standardized difference, 2.60), with higher median (interquartile range) annual surgical volume (30 765 [19 701-47 675] cases vs 5235 [2909-8968] cases; standardized difference, 1.61) and were located in more populated and medically resourced referral regions (median [interquartile range] physicians per 100 000 residents, 223.4 [201.3-259.6] vs 194.8 [180.4-217.3]; standardized difference, 0.90). Lumpectomy was infrequently performed across hospital types (348 of 3248 breast operations [10.7%] at NCI centers vs 2401 of 26 366 [9.1%] at community hospitals). NCI centers, compared with community hospitals, had higher rates of laparoscopic partial colectomy (510 of 1046 colon operations [48.8%] vs 7585 of 17 522 [43.3%]) and pneumonectomy (96 of 1228 lung operations [7.8%] vs 7585 of 17 522 [43.3%]). Table 1 reports other key differences in patient, hospital, and region characteristics by hospital type. The eTable in the Supplement reports characteristics of the study sample as a whole and of those excluded for missing or nonmerging NPI. These groups were similar with standardized differences across measured variables less than 0.1.

    Spending Outcomes

    Treatment at NCI centers was associated with higher surgery-specific insurer prices paid compared with community hospitals ($18 526 [95% CI, $16 650 to $20 403] vs $14 772 [95% CI, $14 339 to $15 204]; difference, $3755 [95% CI, $1661 to $5849]; P < .001), driven predominantly by differences in facility payments ($17 704 [95% CI, $15 845 to $19 563] vs $14 120 [95% CI, $13 691 to $14 549]; difference, $3584 [95% CI, $1525 to $5643]; P < .001). We also found that 90-day postdischarge payments were higher at NCI centers compared with community hospitals ($47 035 [95% CI, $43 289 to $50 781] vs $41 291 [95% CI, $40 350 to $42 231]; difference, $5744 [95% CI, $1659 to $9829]; P = .006). Table 2 additionally shows spending outcomes at non-NCI academic hospitals, which, compared with community hospitals, had numerically higher surgery-specific insurer prices paid ($16 131 [95% CI, $15 201 to $17 060] vs $14 772 [95% CI, $14 339 to $15 204]; difference, $1359 [95% CI, $280 to $2438]; P = .01) and 90-day postdischarge payments ($42 775 [95% CI, $40 824 to $44 726] vs $41 291 [95% CI, $40 350 to $42 231]; difference, $1484 [95% CI, −$775 to $3743]; P = .20), but the differences were not statistically significant.

    Utilization Outcomes

    There were no significant differences by hospital type in LOS, ED utilization, or hospital readmission within 90 days. Mean LOS was comparable at NCI centers and community hospitals (5.1 [95% CI, 4.8-5.4] days vs 5.1 [95% CI, 5.1-5.2] days, P = .73). The probability of ED utilization (13.1% [95% CI, 11.9%-14.3%] vs 13.2% [95% CI, 12.8%-13.5%]; P = .93) or hospital readmission (10.4% [95% CI, 9.2%-11.5%] vs 10.8% [95% CI, 10.5%-11.1%]; P = .48) within 90 days was also similar between NCI centers and community hospitals. Table 3 additionally highlights utilization outcomes at non-NCI academic hospitals compared with community hospitals which were not significantly different.

    Sensitivity Analyses

    The results of sensitivity analyses modeling 30-day event rates across spending and utilization outcomes are included in Table 2 and Table 3. These results were consistent with those of our main analyses, with patients treated at NCI centers incurring higher 30-day post-discharge payments than those treated at community hospitals ($32 692 [95% CI, $29 785-$35 599] vs $28 390 [95% CI, $27 662-$29 119]; difference, $4301 [95% CI, $1102-$7500]; P = .008), without significant differences in rates of ED utilization (8.1% [95% CI, 7.2%-9.1%] vs 7.6% [95% CI, 7.3%-7.9%]; P = .33) or hospital readmission (6.4% [95% CI, 5.5%-7.3%] vs 6.7% [95% CI, 6.5%-7.0%]; P = .49).

    Discussion

    Using a national multipayer commercial claims data set, we found that surgery-specific and 90-day postdischarge spending were higher at NCI centers than community hospitals and in an intermediate range at non-NCI academic hospitals without differences in acute care utilization for patients with private insurance undergoing surgery for breast, colon, or lung cancer. Facility rather than physician payments accounted for most of the differences in spending outcomes, consistent with national trends showing that hospital payments occupy a disproportionate and growing share of overall health care spending.27 These results support our hypothesis that insurer spending would be higher at NCI centers than community hospitals, possibly due to their size, market share, and prestige, affording leverage in negotiations with private payers. However, contrary to our hypothesis, there were comparable rates of postdischarge acute care utilization across hospital types, suggesting that negotiated transaction prices rather than utilization may be driving site-level differences in spending.

    To our knowledge, this is the first study to report on variations in insurer prices paid and episode spending by hospital type for privately insured patients undergoing common cancer surgery. Prior research has explored spending variation for older patients with cancer covered by Medicare.28-34 However, because prices are administered rather than negotiated under Medicare, these data shed little insight into insurer spending patterns for patients with private insurance. Whereas utilization is the primary driver of spending for Medicare beneficiaries, price is an additional driver of spending for those with private insurance.35,36 Consistent with this, we found meaningful differences by hospital type in both surgery-specific insurer prices paid and 90-day postdischarge payments without concomitant differences in postdischarge acute care utilization.

    With national trends of health care consolidation,37 health systems increasingly encompass both community and academic sites and are faced with strategic decisions about how to rationalize care services across facilities. Many have advocated for a hub and spoke model, with regionalization of complex care at academic referral centers, such as NCI centers, and more routine care delivered at community sites.38-42 This stems in part from evidence suggesting improved outcomes for patients with cancer treated at NCI centers6-10 and academic hospitals,43-47 particularly for those with more advanced and complex disease. This study focused on common cancer surgical procedures and illuminates important price differences that should factor into such decisions, revealing that there is a premium associated with receipt of surgical cancer care at NCI centers. While acceptable to pay higher prices for care that is expected to be of higher quality, we found no differences in short-term postsurgical outcomes (90-day ED utilization and hospital readmission) by hospital type, which is consistent with results from a recent study comparing postsurgical outcomes for Medicare patients across varying types of cancer hospitals.48 Further research examining hospital-level differences in long-term postsurgical outcomes, such as mortality, paired with spending outcomes, is necessary to judge whether and under what circumstances the premium price of NCI centers is justified.

    This study has other important implications. For commercial payers, who are keenly aware that discrepancies in hospital negotiated prices contribute to premium growth, our findings suggest an incentive to steer patients away from high-cost hospitals. Consistent with this, research has shown that insurers are increasingly excluding oncologists affiliated with NCI centers from narrow health care networks.49 For health systems operating in a predominantly fee-for-service environment, our findings suggest an incentive to maximize surgical volume at more lucrative referral centers, despite upward pressure on total health care spending. Value-based or bundled payment reimbursement for surgical episodes, particularly when paired with mandatory reporting on surgical outcomes, could help to rectify this misalignment.

    Finally, for policy makers who have long touted the prospects of price transparency to help consumers comparison shop across institutions and prepare for expected financial burden, our findings underscore the continued importance of such efforts. A historical lack of transparency into negotiated transaction prices for individuals with private insurance has led to limited available data on the prices paid for hospital services. Beginning January 1, 2019, CMS mandated that all hospitals publish their chargemasters, which detail standard prices for all hospital services and procedures. However, these list prices bear little resemblance to what is actually charged or ultimately paid by patients and payers.50,51 Moreover, there is substantial variability in the availability, accessibility, and comprehensiveness of published chargemasters, further degrading the effectiveness of this policy.52 A more recent executive order by former President Trump mandates disclosure of negotiated prices between insurers, hospitals, and physicians. Future research will be necessary to evaluate the effects of this policy, which took effect on January 1, 2021.53

    Limitations

    This study has limitations. First, the analysis was limited to patients with private insurance undergoing cancer-directed surgery for breast, colon, or lung cancer, and may not generalize to patients with other malignant neoplasms or those receiving nonsurgical cancer care. However, these are 3 of the 4 most common incident cancers and make up most of cancer surgical volumes nationally, suggesting that our results may at least extend to other oncologic surgical populations. Second, this study did not analyze patient out-of-pocket spending and thus cannot infer the degree to which patients were exposed to the observed price and spending differences. Future research should explore this important dimension of spending. Third, while every attempt was made to adjust for differences in case mix, our claims-based analysis did not allow for complete adjustment of clinical factors such as stage at diagnosis, surgical complexity, and pathologic status, which limited our ability to judge surgical quality. However, we used validated algorithms to identify incident cancer diagnoses and cancer-directed surgical procedures, which are likely to identify, predominantly, patients with early-stage cancer fit enough for cancer surgery. Also, we paired spending outcomes with utilization outcomes, which provide at least high-level insight into hospital quality of care. Additionally, due to the observational nature of this study, observed differences by hospital type may be attributable to unmeasured factors, including differences in coding intensity or clinical severity, unmeasured in our data sets.

    Conclusions

    In this study of patients with private insurance undergoing surgery for incident breast, colon, or lung cancer, surgery at NCI centers, compared with community hospitals, was associated with higher insurer spending across a surgical care episode without differences in care utilization. A better understanding of the drivers of prices and spending at NCI centers is needed.

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    Article Information

    Accepted for Publication: May 29, 2021.

    Published: August 3, 2021. doi:10.1001/jamanetworkopen.2021.19764

    Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2021 Takvorian SU et al. JAMA Network Open.

    Corresponding Author: Samuel U. Takvorian, MD, MSHP, Division of Hematology and Oncology, Perelman School of Medicine, Perelman Center for Advanced Medicine, 3400 Civic Center Blvd, 10 South, Philadelphia, PA 19104 (samuel.takvorian@pennmedicine.upenn.edu).

    Author Contributions: Dr Takvorian had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

    Concept and design: Takvorian, Yasaitis, Bekelman.

    Acquisition, analysis, or interpretation of data: All authors.

    Drafting of the manuscript: Takvorian, Liu.

    Critical revision of the manuscript for important intellectual content: Takvorian, Yasaitis, Lee, Werner, Bekelman.

    Statistical analysis: Takvorian, Yasaitis, Liu.

    Obtained funding: Bekelman.

    Administrative, technical, or material support: Yasaitis, Lee.

    Supervision: Werner, Bekelman.

    Conflict of Interest Disclosures: Dr Bekelman reported receiving grants from Pfizer, UnitedHealth Group, Embedded Healthcare, and Blue Cross Blue Shield of North Carolina and receiving personal fees from UnitedHealthcare, the Centers for Medicare & Medicaid Services, the National Comprehensive Cancer Network, and Optum outside the submitted work. No other disclosures were reported.

    Funding/Support: This work was funded by a grant from the Commonwealth of Pennsylvania and additionally supported by grant K12 CA076931 from the National Cancer Institute (Dr Takvorian).

    Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

    References
    1.
    Mariotto  AB, Yabroff  KR, Shao  Y, Feuer  EJ, Brown  ML.  Projections of the cost of cancer care in the United States: 2010-2020.   J Natl Cancer Inst. 2011;103(2):117-128. doi:10.1093/jnci/djq495PubMedGoogle ScholarCrossref
    2.
    Laviana  AA, Luckenbaugh  AN, Resnick  MJ.  Trends in the cost of cancer care: beyond drugs.   J Clin Oncol. 2020;38(4):316-322. doi:10.1200/JCO.19.01963PubMedGoogle ScholarCrossref
    3.
    Mariotto  AB, Enewold  L, Zhao  J, Zeruto  CA, Yabroff  KR.  Medical care costs associated with cancer survivorship in the United States.   Cancer Epidemiol Biomarkers Prev. 2020;29(7):1304-1312. doi:10.1158/1055-9965.EPI-19-1534PubMedGoogle ScholarCrossref
    4.
    Li  M, Lakdawalla  DN, Goldman  DP.  Association between spending and outcomes for patients with cancer.   J Clin Oncol. 2020;38(4):323-331. doi:10.1200/JCO.19.01451PubMedGoogle ScholarCrossref
    5.
    National Cancer Institute. NCI–designated cancer centers. Accessed October 27, 2020. https://www.cancer.gov/research/infrastructure/cancer-centers
    6.
    Birkmeyer  NJ, Goodney  PP, Stukel  TA, Hillner  BE, Birkmeyer  JD.  Do cancer centers designated by the National Cancer Institute have better surgical outcomes?   Cancer. 2005;103(3):435-441. doi:10.1002/cncr.20785PubMedGoogle ScholarCrossref
    7.
    Onega  T, Duell  EJ, Shi  X, Demidenko  E, Gottlieb  D, Goodman  DC.  Influence of NCI cancer center attendance on mortality in lung, breast, colorectal, and prostate cancer patients.   Med Care Res Rev. 2009;66(5):542-560. doi:10.1177/1077558709335536PubMedGoogle ScholarCrossref
    8.
    Pfister  DG, Rubin  DM, Elkin  EB,  et al.  Risk adjusting survival outcomes in hospitals that treat patients with cancer without information on cancer stage.   JAMA Oncol. 2015;1(9):1303-1310. doi:10.1001/jamaoncol.2015.3151PubMedGoogle ScholarCrossref
    9.
    Wolfson  JA, Sun  CL, Wyatt  LP, Hurria  A, Bhatia  S.  Impact of care at comprehensive cancer centers on outcome: results from a population-based study.   Cancer. 2015;121(21):3885-3893. doi:10.1002/cncr.29576PubMedGoogle ScholarCrossref
    10.
    Shulman  LN, Palis  BE, McCabe  R,  et al.  Survival as a quality metric of cancer care: use of the National Cancer Data Base to assess hospital performance.   J Oncol Pract. 2018;14(1):e59-e72. doi:10.1200/JOP.2016.020446PubMedGoogle ScholarCrossref
    11.
    Koller  CF, Khullar  D.  The commercial differential for hospital prices: responses from states and employers.   JAMA. 2019;322(8):723-724. doi:10.1001/jama.2019.9275PubMedGoogle ScholarCrossref
    12.
    von Elm  E, Altman  DG, Egger  M, Pocock  SJ, Gøtzsche  PC, Vandenbroucke  JP; STROBE Initiative.  The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies.   Lancet. 2007;370(9596):1453-1457. doi:10.1016/S0140-6736(07)61602-XPubMedGoogle ScholarCrossref
    13.
    Health Care Cost Institute. Accessed October 27, 2020. https://healthcostinstitute.org/
    14.
    American Hospital Association. AHA Annual Survey Database. Accessed October 27, 2020. https://www.ahadata.com/aha-annual-survey-database
    15.
    Dartmouth Atlas Project. The Dartmouth Atlas of Health Care. Accessed October 27, 2020. https://www.dartmouthatlas.org/
    16.
    US Census Bureau. American Community Survey. Accessed October 27, 2020. https://www.census.gov/programs-surveys/acs
    17.
    Nattinger  AB, Laud  PW, Bajorunaite  R, Sparapani  RA, Freeman  JL.  An algorithm for the use of Medicare claims data to identify women with incident breast cancer.   Health Serv Res. 2004;39(6 Pt 1):1733-1749. doi:10.1111/j.1475-6773.2004.00315.xPubMedGoogle Scholar
    18.
    Lavery  JA, Lipitz-Snyderman  A, Li  DG, Bach  PB, Panageas  KS.  Identifying cancer-directed surgeries in Medicare claims: a validation study using SEER-Medicare data.   JCO Clin Cancer Inform. 2019;3:1-24. doi:10.1200/CCI.18.00093PubMedGoogle Scholar
    19.
    Centers for Medicare & Medicaid Services. Global Surgery Booklet. Accessed June 30, 2021. https://www.cms.gov/outreach-and-education/medicare-learning-network-mln/mlnproducts/downloads/globallsurgery-icn907166.pdf
    20.
    Fry  DE, Pine  M, Pine  G.  Medicare post-discharge deaths and readmissions following elective surgery.   Am J Surg. 2014;207(3):326-330. doi:10.1016/j.amjsurg.2013.09.007PubMedGoogle ScholarCrossref
    21.
    Kim  Y, Gani  F, Lucas  DJ,  et al.  Early versus late readmission after surgery among patients with employer-provided health insurance.   Ann Surg. 2015;262(3):502-511. doi:10.1097/SLA.0000000000001429PubMedGoogle ScholarCrossref
    22.
    McMillan  RR, Berger  A, Sima  CS,  et al.  Thirty-day mortality underestimates the risk of early death after major resections for thoracic malignancies.   Ann Thorac Surg. 2014;98(5):1769-1774. doi:10.1016/j.athoracsur.2014.06.024PubMedGoogle ScholarCrossref
    23.
    Orcutt  ST, Li  LT, Balentine  CJ,  et al.  Ninety-day readmission after colorectal cancer surgery in a Veterans Affairs cohort.   J Surg Res. 2016;201(2):370-377. doi:10.1016/j.jss.2015.11.026PubMedGoogle ScholarCrossref
    24.
    American Association of Medical Colleges. Council of Teaching Hospitals and Health Systems (COTH). Accessed October 27, 2020. https://www.aamc.org/members/coth/
    25.
    Elixhauser  A, Steiner  C, Harris  DR, Coffey  RM.  Comorbidity measures for use with administrative data.   Med Care. 1998;36(1):8-27. doi:10.1097/00005650-199801000-00004PubMedGoogle ScholarCrossref
    26.
    Quan  H, Sundararajan  V, Halfon  P,  et al.  Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data.   Med Care. 2005;43(11):1130-1139. doi:10.1097/01.mlr.0000182534.19832.83PubMedGoogle ScholarCrossref
    27.
    Keehan  SP, Cuckler  GA, Poisal  JA,  et al.  National health expenditure projections, 2019-28: expected rebound in prices drives rising spending growth.   Health Aff (Millwood). 2020;39(4):704-714. doi:10.1377/hlthaff.2020.00094PubMedGoogle ScholarCrossref
    28.
    Keating  NL, Landrum  MB, Huskamp  HA,  et al.  Dartmouth Atlas area-level estimates of end-of-life expenditures: how well do they reflect expenditures for prospectively identified advanced lung cancer patients?   Health Serv Res. 2016;51(4):1584-1594. doi:10.1111/1475-6773.12440PubMedGoogle ScholarCrossref
    29.
    Jean  RA, Bongiovanni  T, Soulos  PR,  et al.  Hospital variation in spending for lung cancer resection in Medicare beneficiaries.   Ann Thorac Surg. 2019;108(6):1710-1716. doi:10.1016/j.athoracsur.2019.06.048PubMedGoogle ScholarCrossref
    30.
    Nathan  H, Atoria  CL, Bach  PB, Elkin  EB.  Hospital volume, complications, and cost of cancer surgery in the elderly.   J Clin Oncol. 2015;33(1):107-114. doi:10.1200/JCO.2014.57.7155PubMedGoogle ScholarCrossref
    31.
    Keating  NL, Landrum  MB, Lamont  EB, Bozeman  SR, McNeil  BJ.  Area-level variations in cancer care and outcomes.   Med Care. 2012;50(5):366-373. doi:10.1097/MLR.0b013e31824d74c0PubMedGoogle ScholarCrossref
    32.
    Brooks  GA, Li  L, Sharma  DB,  et al.  Regional variation in spending and survival for older adults with advanced cancer.   J Natl Cancer Inst. 2013;105(9):634-642. doi:10.1093/jnci/djt025PubMedGoogle ScholarCrossref
    33.
    Ellimoottil  C, Li  J, Ye  Z,  et al.  Episode-based payment variation for urologic cancer surgery.   Urology. 2018;111:78-85. doi:10.1016/j.urology.2017.08.053PubMedGoogle ScholarCrossref
    34.
    Abdelsattar  ZM, Birkmeyer  JD, Wong  SL.  Variation in Medicare payments for colorectal cancer surgery.   J Oncol Pract. 2015;11(5):391-395. doi:10.1200/JOP.2015.004036PubMedGoogle ScholarCrossref
    35.
    Brooks  GA, Li  L, Uno  H, Hassett  MJ, Landon  BE, Schrag  D.  Acute hospital care is the chief driver of regional spending variation in Medicare patients with advanced cancer.   Health Aff (Millwood). 2014;33(10):1793-1800. doi:10.1377/hlthaff.2014.0280PubMedGoogle ScholarCrossref
    36.
    Cooper  Z, Craig  SV, Gaynor  M, Van Reenen  J.  The price ain’t right? hospital prices and health spending on the privately insured.   Q J Econ. 2019;134(1):51-107. doi:10.1093/qje/qjy020PubMedGoogle ScholarCrossref
    37.
    Fulton  BD.  Health care market concentration trends in the United States: evidence and policy responses.   Health Aff (Millwood). 2017;36(9):1530-1538. doi:10.1377/hlthaff.2017.0556PubMedGoogle ScholarCrossref
    38.
    Porter  ME.  What is value in health care?   N Engl J Med. 2010;363(26):2477-2481. doi:10.1056/NEJMp1011024PubMedGoogle ScholarCrossref
    39.
    Tandstad  T, Kollmannsberger  CK, Roth  BJ,  et al.  Practice makes perfect: the rest of the story in testicular cancer as a model curable neoplasm.   J Clin Oncol. 2017;35(31):3525-3528. doi:10.1200/JCO.2017.73.4723PubMedGoogle ScholarCrossref
    40.
    Halm  EA, Anderson  LD  Jr, Gerber  DE.  Understanding the relationship between care volume and clinical outcomes in multiple myeloma.   J Clin Oncol. 2017;35(6):580-582. doi:10.1200/JCO.2016.70.4726PubMedGoogle ScholarCrossref
    41.
    Epstein  AM.  Volume and outcome—it is time to move ahead.   N Engl J Med. 2002;346(15):1161-1164. doi:10.1056/NEJM200204113461512PubMedGoogle ScholarCrossref
    42.
    Fong  Y, Patti  MG.  Volume standards for high-risk cancer surgery.   JAMA Surg. 2019;154(11):1012-1013. doi:10.1001/jamasurg.2019.3018PubMedGoogle ScholarCrossref
    43.
    Shahian  DM, Nordberg  P, Meyer  GS,  et al.  Contemporary performance of U.S. teaching and nonteaching hospitals.   Acad Med. 2012;87(6):701-708. doi:10.1097/ACM.0b013e318253676aPubMedGoogle ScholarCrossref
    44.
    Mueller  SK, Lipsitz  S, Hicks  LS.  Impact of hospital teaching intensity on quality of care and patient outcomes.   Med Care. 2013;51(7):567-574. doi:10.1097/MLR.0b013e3182902151PubMedGoogle ScholarCrossref
    45.
    Shahian  DM, Liu  X, Meyer  GS, Torchiana  DF, Normand  SL.  Hospital teaching intensity and mortality for acute myocardial infarction, heart failure, and pneumonia.   Med Care. 2014;52(1):38-46. doi:10.1097/MLR.0000000000000005PubMedGoogle ScholarCrossref
    46.
    Burke  LG, Frakt  AB, Khullar  D, Orav  EJ, Jha  AK.  Association between teaching status and mortality in US hospitals.   JAMA. 2017;317(20):2105-2113. doi:10.1001/jama.2017.5702PubMedGoogle ScholarCrossref
    47.
    Burke  L, Khullar  D, Orav  EJ, Zheng  J, Frakt  A, Jha  AK.  Do academic medical centers disproportionately benefit the sickest patients?   Health Aff (Millwood). 2018;37(6):864-872. doi:10.1377/hlthaff.2017.1250PubMedGoogle ScholarCrossref
    48.
    Merkow  RP, Yang  AD, Pavey  E,  et al.  Comparison of hospitals affiliated with PPS-exempt cancer centers, other hospitals affiliated with NCI-designated cancer centers, and other hospitals that provide cancer care.   JAMA Intern Med. 2019;179(8):1043-1051. doi:10.1001/jamainternmed.2019.0914PubMedGoogle ScholarCrossref
    49.
    Yasaitis  L, Bekelman  JE, Polsky  D.  Relation between narrow networks and providers of cancer care.   J Clin Oncol. 2017;35(27):3131-3135. doi:10.1200/JCO.2017.73.2040PubMedGoogle ScholarCrossref
    50.
    Furlow  B.  Skepticism about new US government hospital pricing transparency rule.   Lancet Oncol. 2019;20(2):188. doi:10.1016/S1470-2045(19)30002-6PubMedGoogle ScholarCrossref
    51.
    Batty  M, Ippolito  B.  Mystery of the chargemaster: examining the role of hospital list prices in what patients actually pay.   Health Aff (Millwood). 2017;36(4):689-696. doi:10.1377/hlthaff.2016.0986PubMedGoogle ScholarCrossref
    52.
    Agarwal  A, Dayal  A, Kircher  SM, Chen  RC, Royce  TJ.  Analysis of price transparency via National Cancer Institute–designated cancer centers’ chargemasters for prostate cancer radiation therapy.   JAMA Oncol. 2020;6(3):409-412. doi:10.1001/jamaoncol.2019.5690PubMedGoogle ScholarCrossref
    53.
    Medicare and Medicaid Programs: CY 2020 Hospital Outpatient PPS Policy Changes and Payment Rates and Ambulatory Surgical Center Payment System Policy Changes and Payment Rates. Price Transparency Requirements for Hospitals To Make Standard Charges Public. Accessed October 27, 2020. https://www.federalregister.gov/documents/2019/11/27/2019-24931/medicare-and-medicaid-programs-cy-2020-hospital-outpatient-pps-policy-changes-and-payment-rates-and
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