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Proceedings of the European Modelling and Simulation Symposium (EMSS’09)",{"doi":354},false,{"id":430,"createTime":431,"updateTime":432,"relativeEntities":433,"slug":434,"properties":435,"entityType":189,"verifyStatus":190,"verifyTime":444,"verifyNote":192,"languages":24,"translateLanguages":24,"viewCount":92,"primaryUrl":445,"fullTextUrl":24,"authors":446,"publicationType":285,"publisherRelationship":520,"citationCount":572,"citationInfo":573,"publishDate":577,"publishYear":574,"citationAnalyzeStatus":578,"lastCitationAnalyze":432,"indexDatabases":579,"openAccess":24,"references":580,"isForceReanalyzing":428},"71fcf79a-2cde-4479-8004-0c49dc838ee7","2024-01-10T19:39:20.407+00:00","2026-07-18T03:58:04.435+00:00",[],"Capacity-planning-and-appointment-scheduling-for-new-patient-oncology-consults",{"abstract":436,"title":438,"gsPaper":440,"doi":442},{"EN":437},"To ensure that patients receive timely access to care, it has become increasingly important to use existing care provider capacity as efficiently as possible and to make informed capacity planning decisions. To support this decision-making process at a regional cancer center in British Columbia (Canada), we undertook a simulation and optimization based study that investigated the simultaneous impact of the available number of new patient consultation slots, appointment scheduling policies and oncologist specialization configurations on the timeliness of patient access to care and physician workload. The key contribution of this paper is the methodological framework it provides to decision makers who manage specialty clinics to ensure that they are using their resources efficiently and making informed strategic short- and mid-term capacity planning decisions for new patient demand.",{"EN":439},"Capacity planning and appointment scheduling for new patient oncology consults",{"VOID":441},"[\"15975322834470498732\"]",{"VOID":443},"10.1007\u002Fs10729-015-9331-5","2024-04-30T00:36:02.628+00:00","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10729-015-9331-5",[447,462,479,494,507],{"id":448,"sortIndex":92,"researcher":24,"roles":449,"affiliations":450,"properties":459,"displayName":461,"givenName":24,"familyName":24},"c3d3c5a7-8640-490b-a882-935f4589e78d",[198],[451],{"id":452,"sortIndex":92,"affiliation":453,"properties":24},"c91c5c4e-4058-49ee-824e-8ffde37fe0db",{"id":452,"createTime":24,"updateTime":24,"relativeEntities":454,"slug":24,"properties":455,"entityType":24,"verifyStatus":24,"verifyTime":24,"verifyNote":24,"languages":24,"translateLanguages":24,"viewCount":24,"url":24,"parentIds":458,"statistic":24},[],{"title":456},{"VI":457},"British Columbia Cancer Agency, Vancouver, Canada",[],{"title":460},{"VI":461},"Xiang Ma",{"id":463,"sortIndex":147,"researcher":24,"roles":464,"affiliations":465,"properties":474,"displayName":476,"givenName":24,"familyName":24},"55444cce-5b5d-4090-b9fa-fe6e27675da0",[198],[466],{"id":467,"sortIndex":92,"affiliation":468,"properties":24},"712d6b28-e376-4702-88c3-6b2d90951ec8",{"id":467,"createTime":24,"updateTime":24,"relativeEntities":469,"slug":24,"properties":470,"entityType":24,"verifyStatus":24,"verifyTime":24,"verifyNote":24,"languages":24,"translateLanguages":24,"viewCount":24,"url":24,"parentIds":473,"statistic":24},[],{"title":471},{"VI":472},"Sauder School of Business, University of British Columbia, Vancouver, Canada",[],{"title":475,"gsAuthor":477},{"VI":476},"Antoine Sauré",{"VOID":478},"[\"q3KKrMcAAAAJ\"]",{"id":480,"sortIndex":25,"researcher":24,"roles":481,"affiliations":482,"properties":489,"displayName":491,"givenName":24,"familyName":24},"bf2c03d1-20e2-45b8-94e7-b053bde7f011",[198],[483],{"id":467,"sortIndex":92,"affiliation":484,"properties":24},{"id":467,"createTime":24,"updateTime":24,"relativeEntities":485,"slug":24,"properties":486,"entityType":24,"verifyStatus":24,"verifyTime":24,"verifyNote":24,"languages":24,"translateLanguages":24,"viewCount":24,"url":24,"parentIds":488,"statistic":24},[],{"title":487},{"VI":472},[],{"title":490,"gsAuthor":492},{"VI":491},"Martin L. Puterman",{"VOID":493},"[\"uCCMYzIAAAAJ\"]",{"id":495,"sortIndex":243,"researcher":24,"roles":496,"affiliations":497,"properties":504,"displayName":506,"givenName":24,"familyName":24},"38f1dad0-5b47-44a6-83d3-4a8111bcf574",[198],[498],{"id":452,"sortIndex":92,"affiliation":499,"properties":24},{"id":452,"createTime":24,"updateTime":24,"relativeEntities":500,"slug":24,"properties":501,"entityType":24,"verifyStatus":24,"verifyTime":24,"verifyNote":24,"languages":24,"translateLanguages":24,"viewCount":24,"url":24,"parentIds":503,"statistic":24},[],{"title":502},{"VI":457},[],{"title":505},{"VI":506},"Marianne Taylor",{"id":508,"sortIndex":257,"researcher":24,"roles":509,"affiliations":510,"properties":517,"displayName":519,"givenName":24,"familyName":24},"17df7c78-558e-4c53-9ca1-5c67dfbda2b7",[198],[511],{"id":452,"sortIndex":92,"affiliation":512,"properties":24},{"id":452,"createTime":24,"updateTime":24,"relativeEntities":513,"slug":24,"properties":514,"entityType":24,"verifyStatus":24,"verifyTime":24,"verifyNote":24,"languages":24,"translateLanguages":24,"viewCount":24,"url":24,"parentIds":516,"statistic":24},[],{"title":515},{"VI":457},[],{"title":518},{"VI":519},"Scott Tyldesley",{"url":445,"publisher":521,"properties":568},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":522,"slug":10,"properties":523,"entityType":22,"verifyStatus":23,"verifyTime":24,"verifyNote":24,"languages":24,"translateLanguages":24,"viewCount":25,"subjectFields":528,"manageAffiliations":537,"indexDatabases":548,"url":90,"thumbnailPath":24,"statistic":563,"gsStatistic":24,"type":24,"analyzePriority":24},[],{"country":524,"eissn":525,"issn":526,"title":527},{"VOID":13},{"VOID":15},{"VOID":17},{"EN":19},[529,533],{"id":28,"createTime":24,"updateTime":24,"relativeEntities":530,"label":531,"description":532,"parentId":24,"standard":24,"scholarHubFieldId":24},[],{"EN":31},{},{"id":34,"createTime":24,"updateTime":24,"relativeEntities":534,"label":535,"description":536,"parentId":24,"standard":24,"scholarHubFieldId":24},[],{"EN":37},{},[538,543],{"id":41,"createTime":24,"updateTime":24,"relativeEntities":539,"slug":24,"properties":540,"entityType":24,"verifyStatus":24,"verifyTime":24,"verifyNote":24,"languages":24,"translateLanguages":24,"viewCount":24,"url":24,"parentIds":542,"statistic":24},[],{"title":541},{"EN":45},[],{"id":48,"createTime":24,"updateTime":24,"relativeEntities":544,"slug":24,"properties":545,"entityType":24,"verifyStatus":24,"verifyTime":24,"verifyNote":24,"languages":24,"translateLanguages":24,"viewCount":24,"url":24,"parentIds":547,"statistic":24},[],{"title":546},{"EN":52},[],[549,556],{"id":56,"indexDatabase":550,"url":69,"indexYears":24,"academicFieldIds":555,"indexDatabaseRanking":24},{"id":58,"createTime":24,"updateTime":24,"relativeEntities":551,"label":552,"description":553,"key":65,"publicationTags":554,"standard":24},[],{"EN":61,"VI":61},{"EN":63,"VI":64},[67,68],[71],{"id":73,"indexDatabase":557,"url":84,"indexYears":85,"academicFieldIds":562,"indexDatabaseRanking":89},{"id":75,"createTime":24,"updateTime":24,"relativeEntities":558,"label":559,"description":560,"key":81,"publicationTags":561,"standard":24},[],{"EN":78,"VI":78},{"EN":78,"VI":80},[83],[87,88],{"impactFactor":92,"impactFactorByYear":564,"i10Index":105,"i10IndexLast5Year":106,"totalPublication":107,"totalPublicationByYear":565,"totalCitation":126,"totalCitationByYear":566,"totalCitationPerPublication":148,"totalCitationPerPublicationByYear":567,"hindexLast5Year":124,"hindex":124},{"2012":94,"2013":95,"2014":96,"2015":97,"2016":98,"2017":99,"2018":94,"2019":100,"2020":101,"2021":102,"2022":103,"2023":104},{"1998":109,"1999":110,"2000":110,"2001":111,"2002":112,"2003":110,"2004":113,"2005":114,"2006":115,"2007":115,"2008":116,"2009":117,"2010":118,"2011":119,"2012":120,"2013":112,"2014":121,"2015":117,"2016":117,"2017":120,"2018":111,"2019":121,"2020":122,"2021":123,"2022":117,"2023":124,"2024":125},{"2004":128,"2005":111,"2006":129,"2007":130,"2008":131,"2009":132,"2010":133,"2011":134,"2012":135,"2013":136,"2014":137,"2015":138,"2016":139,"2017":140,"2018":141,"2019":142,"2020":143,"2021":144,"2022":145,"2023":146,"2024":147},{"2004":150,"2005":151,"2006":152,"2007":153,"2008":154,"2009":155,"2010":156,"2011":157,"2012":158,"2013":159,"2014":160,"2015":161,"2016":162,"2017":146,"2018":163,"2019":164,"2020":165,"2021":166,"2022":167,"2023":168,"2024":169},{"pages":569,"volume":571},{"VOID":570},"347-361",{"VOID":338},44,{"total":572,"publishYear":574,"statisticByYear":575},2015,{"2017":243,"2018":25,"2019":125,"2020":125,"2022":576,"2023":146,"2024":257,"2025":273,"2026":25},8,"2015-07-09","DONE_ANALYZE_CITATION",[67,89],[581,587,590,593,596,599,602,605,608,611,614,617,623,626,629,632,635,638,641,644,647],{"id":582,"text":583,"url":584,"identifiers":585},"11c49dc0-f01f-499d-a01a-4f723a1c26ae","Adenso-Díaz B, González-Torre P, García V (2002) A capacity management model in service industries. Int J Serv Ind Manag 13(3):286–302","https:\u002F\u002Fwww.emerald.com\u002Finsight\u002Fcontent\u002Fdoi\u002F10.1108\u002F09564230210431983\u002Ffull\u002Fhtml",{"doi":586},"10.1108\u002F09564230210431983",{"id":350,"text":588,"url":352,"identifiers":589},"Begen M, Queyranne M (2011) Appointment scheduling with discrete random durations. Math Oper Res 36(2):240–257",{"doi":354},{"id":350,"text":591,"url":352,"identifiers":592},"Biagi JJ, Raphael MJ, Mackillop WJ, Kong W, King WD, Booth CM (2011) Association between time to initiation of adjuvant chemotherapy and survival in colorectal cancer: a systematic review and meta-analysis. J Am Med Assoc 305(22):2335–2342",{"doi":354},{"id":350,"text":594,"url":352,"identifiers":595},"Cheung WY, Neville BA, Earle CC (2009) Etiology of delays in the initiation of adjuvant chemotherapy and their impact on outcomes for stage II and III rectal cancer. Dis Colon Rectum 52(6):1054–1064",{"doi":354},{"id":350,"text":597,"url":352,"identifiers":598},"Chien CF, Tseng FP, Chen CH (2008) An evolutionary approach to rehabilitation patient scheduling: A case study. Eur J Oper Res 189(3):1234–1253",{"doi":354},{"id":350,"text":600,"url":352,"identifiers":601},"Conforti D, Guerriero F, Guido R (2010) Non-block scheduling with priority for radiotherapy treatments. Eur J Oper Res 201(1):289–296",{"doi":354},{"id":350,"text":603,"url":352,"identifiers":604},"Erdelyi A, Topaloglu H (2009) Computing protection level policies for dynamic capacity allocation problems by using stochastic approximation methods. IIE Trans 41:498–510",{"doi":354},{"id":350,"text":606,"url":352,"identifiers":607},"Erikson C, Salsberg E, Forte G, Bruinooge S, Goldstein M (2007) Future supply and demand for oncologists: challenges to assuring access to oncology services. J Oncol Pract 3(2):79–86",{"doi":354},{"id":350,"text":609,"url":352,"identifiers":610},"Green LV (2004) Capacity planning and management in hospitals. In: Operations research and health care. Springer, pp 15–41",{"doi":354},{"id":350,"text":612,"url":352,"identifiers":613},"Gupta D (2007) Surgical suites’ operations management. Prod Oper Manag 16(6):689–700",{"doi":354},{"id":350,"text":615,"url":352,"identifiers":616},"Gupta D, Denton B (2008) Appointment scheduling in health care: Challenges and opportunities. IIE Trans 40(9):800–819",{"doi":354},{"id":618,"text":619,"url":620,"identifiers":621},"8111633c-1e3a-4886-a2a2-ff9fb7897d9a","Hershman DL, Wang X, McBride R, Jacobson JS, Grann VR, Neugut AI (2006) Delay of adjuvant chemotherapy initiation following breast cancer surgery among elderly women. Breast Cancer Res Tr 99(3):313–321","http:\u002F\u002Flink.springer.com\u002F10.1007\u002Fs10549-006-9206-z",{"doi":622},"10.1007\u002Fs10549-006-9206-z",{"id":350,"text":624,"url":352,"identifiers":625},"Huang J, Barbera L, Brouwers M, Browman G, Mackillop WJ (2003) Does delay in starting treatment affect the outcomes of radiotherapy? A systematic review. J Clin Oncol 21(3):555– 563",{"doi":354},{"id":350,"text":627,"url":352,"identifiers":628},"Jack EP, Powers TL (2009) A review and synthesis of demand management, capacity management and performance in health-care services. Int J Manag Rev 11(2):149–174",{"doi":354},{"id":350,"text":630,"url":352,"identifiers":631},"Patrick J, Puterman M, Queyranne M (2008) Dynamic multipriority patient scheduling for a diagnostic resource. Oper Res 56(6):1507–1525",{"doi":354},{"id":350,"text":633,"url":352,"identifiers":634},"Phadia EG (2013) Prior processes and their applications: nonparametric Bayesian estimation. Springer",{"doi":354},{"id":350,"text":636,"url":352,"identifiers":637},"Santibáñez P, Aristizabal R, Puterman ML, Chow VS, Huang W, Kollmannsberger C, Nordin T, Runzer N, Tyldesley S (2012) Operations research methods improve chemotherapy patient appointment scheduling. Joint Comm J Qual Patient Saf 38(12):541–541",{"doi":354},{"id":350,"text":639,"url":352,"identifiers":640},"Sauré A, Patrick J, Tyldesley S, Puterman M (2012) Dynamic multi-appointment patient scheduling for radiation therapy. Eur J Oper Res 223(2):573–584",{"doi":354},{"id":350,"text":642,"url":352,"identifiers":643},"Stuckless T, Milosevic M, de Metz C, Parliament M, Tompkins B, Brundage M (2012) Managing a national radiation oncologist workforce: a workforce planning model. Radiother Oncol 103(1):123–129",{"doi":354},{"id":350,"text":645,"url":352,"identifiers":646},"Wallace RB, Whitt W (2005) A staffing algorithm for call centers with skill-based routing. Manuf Serv Oper Manag 7(4):276–294",{"doi":354},{"id":350,"text":648,"url":352,"identifiers":649},"Whitt W (1989) Planning queueing simulations. Manag Sci 35(11):1341–1366",{"doi":354},{"id":651,"createTime":652,"updateTime":653,"relativeEntities":654,"slug":655,"properties":656,"entityType":189,"verifyStatus":190,"verifyTime":667,"verifyNote":192,"languages":24,"translateLanguages":24,"viewCount":92,"primaryUrl":668,"fullTextUrl":24,"authors":669,"publicationType":285,"publisherRelationship":818,"citationCount":572,"citationInfo":871,"publishDate":874,"publishYear":872,"citationAnalyzeStatus":23,"lastCitationAnalyze":653,"indexDatabases":875,"openAccess":24,"references":24,"isForceReanalyzing":428},"6f81037b-dddb-4e5e-811d-9bc02650e7a6","2024-01-30T06:32:01.271+00:00","2026-07-16T10:12:05.411+00:00",[],"Predictors-of-Medicare-costs-in-elderly-beneficiaries-with-breast-colorectal-lung-or-prostate-cancer",{"abstract":657,"title":659,"gsPaper":661,"references":663,"doi":665},{"EN":658},"Background: Determining the apportionment of costs of cancer care and identifying factors that predict costs are important for planning ethical resource allocation for cancer care, especially in markets where managed care has grown. Design: This study linked tumor registry data with Medicare administrative claims to determine the costs of care for breast, colorectal, lung and prostate cancers during the initial year subsequent to diagnosis, and to develop models to identify factors predicting costs. Subjects: Patients with a diagnosis of breast (n=1,952), colorectal (n=2,563), lung (n=3,331) or prostate cancer (n=3,179) diagnosed from 1985 through 1988. Results: The average costs during the initial treatment period were $12,141 (s.d.=$10,434) for breast cancer, $24,910 (s.d.=$14,870) for colorectal cancer, $21,351 (s.d.=$14,813) for lung cancer, and $14,361 (s.d.=$11,216) for prostate cancer. Using least squares regression analysis, factors significantly associated with cost included comorbidity, hospital length of stay, type of therapy, and ZIP level income for all four cancer sites. Access to health care resources was variably associated with costs of care. Total R\n                        2 ranged from 38% (prostate) to 49% (breast). The prediction error for the regression models ranged from \u003C1% to 4%, by cancer site. Conclusions: Linking administrative claims with state tumor registry data can accurately predict costs of cancer care during the first year subsequent to diagnosis for cancer patients. Regression models using both data sources may be useful to health plans and providers and in determining appropriate prospective reimbursement for cancer, particularly with increasing HMO penetration and decreased ability to capture complete and accurate utilization and cost data on this population.",{"EN":660},"Predictors of Medicare costs in elderly beneficiaries with breast, colorectal, lung, or prostate cancer",{"VOID":662},"[\"15877840388696289549\"]",{"VOID":664},"D.P. Rice, T.A. Hodgson and F. Capell, The economic burden of cancer, 1985: United States and California, in: Cancer Care and Cost: DRGs and Beyond, eds. R.M. Scheffler and N.C. Andrews (Health Administration Press, Ann Arbor, MI, 1989), pp. 39–59.\nR. Rundle, Sellick pioneers selling cancer care in HMOs, Wall Street Journal 12 (August 1996) 81–82.\nJ. Fowles, P. Weiner, D. Knutson, E. Fowler, A. Tucker and M. Ireland, Taking health status into account when setting capitation rates; a comparison of risk-adjustment methods, Journal of the American Medical Association 276(16) (1996) 1316–1321.\nG.F. Riley, A.L. Potosky, J.D. Lubitz and L.G. Kessler, Medicare payments from diagnosis to death for elderly cancer patients by stage at diagnosis, Medical Care 33 (1995) 828–841.\nG.L. Lu-Yao, D. McLerran, M. Wasson and J.E. Wennberg, An assessment of radical prostatectomy time trends, geographic variation and outcomes, Journal of the American Medical Association 269(20) (1993) 2633–2636.\nC.E. Desch, L. Penberthy, C.J. Newschaffer, B.E. Hillner, M. Whittemore, D. McClish, T.J. Smith and S.M. Retchin, Factors that determine the treatment for local and regional prostate cancer, Medical Care 34 (1996) 152–162.\nA.B. Nattinger, M.S. Gottlieb, J. Veum, D. Yahnke and J.S. Goodwin, Geographic variation in the use of breast-conserving treatment for breast cancer, New England Journal of Medicine 326 (1992) 1102–1107.\nT.J. Smith, L. Penberthy, C.E. Desch, M. Whittemore, C. Newschaffer, B.E. Hillner, D. McClish and S.M. Retchin, Differences in initial treatment patterns and outcomes of lung cancer in the elderly, Lung Cancer 13 (1995) 235–252.\nC.J. Newschaffer, L. Penberthy, C.E. Desch, S.M. Retchin and M. Whittemore, The effect of age and comorbidity of non-metastatic breast cancer in elderly women, Archives of Internal Medicine 156 (1996) 85–90.\nAnonymous, Revised standardized per capita rate of payment for 1995 (Health Care Financing Administration, 1995).\nD.K. McClish, L. Penberthy, M. Whittemore, C. Newschaffer, C. Woolard, C.E. Desch and S.M. Retchin, Ability of Medicare claims data and cancer registries to identify cancer cases and treatment, American Journal of Epidemiology 145 (1997) 227–233.\nA.L. Potosky, G.F. Riley, J.D. Lubitz, R.M. Mentnech and L.G. Kessler, Potential for cancer related health services research using a linked Medicare-tumor registry database, Medical Care 31 (1993) 732–748.\nM. Charlson and A. Feinstein, An analytic critique of existing systems of staging for breast cancer, Surgery 73(4) (1973) 579–598.\nD.E. Henson, L. Ries and E.M. Shambaugh, Survival results depend on the staging system, Semin. Surgical Oncology 8 (1992) 57–61.\nM.E. Charlson, P. Pompei, K.L. Ales and C.R. MacKenzie, A new method of classifying prognostic comorbidity in longitudinal studies: development and validation, Journal of Chronic Diseases 40 (1987) 373–380.\nP.S. Romano, L.L. Roos and J.G. Jollis, Adapting a clinical comorbidity index for use with ICD-9-CM administrative data: differing perspectives, Journal of Clinical Epidemiology 46 (1993) 1075–1079.\nP.B. Ginsburg and J.R. Gabel, Tracking health care costs: what's new in 1998?, Health Affairs 17(3) (1998) 141–146.\nK. Godfrey, Medical Uses of Statistics, 2nd ed., Chapter 12 (New England Journal of Medicine Books, Boston, MA, 1992).\nKleinbaum, Kupper and Muller, Applied Regression Analysis and Other Multivariable Methods, Chapter 12: Regression Diagnostics, 2nd ed. (Kent Publishing Company, Boston, MA, 1988), pp. 197–209.\nJ. Cohen and P. Cohen, Applied Multiple Regression\u002FCorrelation Analysis for the Behavioral Sciences, 2nd ed. (Lawrence Erlbaum, 1983).\nS.H. Taplin, W. Barlow, N. Urban, M.T. Mandelson, D.J. Timlin, L. Ichikawa et al., Stage, age, comorbidity, and direct costs of colon, prostate, and breast cancer care, Journal of the National Cancer Institute 87 (1995) 417–426.\nM. Von Korff, E. Wagner and K. Saunders, A chronic disease score from automated pharmacy data, Journal of Clinical Epidemiology 45 (1992) 197–203.\nR.M. Scheffler and K.A. Phillips, DRGs and the financing of cancer care in the United States: new estimates and new issues, in: Cancer Care and Cost: DRGs and Beyond, eds. R.M. Scheffler and N.C. Andrews (Health Administration Press Perspectives, Ann Arbor, MI, 1989), pp. 3–23.\nJ.Z. Ayanian and E. Guadagnoli, Variations in breast cancer treatment by patient and provider characteristics, Breast Cancer Res. Treat. 40 (1996) 65–74.\nL.I. Iezzoni, J.Z. Ayanian, D.W. Bates and H.R. Burstin, Paying more fairly for Medicare capitated care, N. Engl. J. Med. 339 (1998) 1933–1938.\nJ.C. Robinson, Consolidation of medical groups into physician practice management organizations, JAMA 279 (1998) 144–149.\nD.C. Hsai, W.M. Krushat, A.B. Fagan, J.A. Tebbutt and R.P. Kusserow, Accuracy of diagnostic coding for Medicare patients under the prospective-payment system, N. Engl. J. Med. 318 (1988) 352–355.\nS. Long, J. Gibbs, J. Crozier et al., Medical expenditures of terminal cancer patients during the last year of life, Inquiry 21 (1984) 315–327.\nG. Riley, J. Lubitz, R. Prihoda and E. Rabey, Use and costs of Medicare services by cause of death, Inquiry 24 (1987) 233–244.\nL.C. Hanson, M. Danis, J.M. Garrett and E. Mutran, Who decides? Physicians' willingness to use life-sustaining treatment, Arch. Intern. Med. 156 (1996) 785–789.\nS.M. Retchin and D.J. Ballard, Establishing standards for the utility of administrative claims data, Health Services Research 32 (1998) 861–866.\nS.M. Retchin and R.E. Hurley, The revision of governmentsponsored health care, American Journal of Medicine 98 (1995) 529–530.\nW.P. Welch and H.G. Welch, Fee-for-data: a strategy to open the HMO black box, Health Affairs 14(4) (1995) 104–116.",{"VOID":666},"10.1023\u002FA:1019096030306","2024-06-23T07:51:58.578+00:00","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1023\u002FA:1019096030306",[670,703,716,729,742,762,777,790,803],{"id":671,"sortIndex":92,"researcher":24,"roles":672,"affiliations":673,"properties":700,"displayName":702,"givenName":24,"familyName":24},"7314bfbf-ca8c-4fe5-9b62-97a2b7515798",[198],[674,682,691],{"id":675,"sortIndex":92,"affiliation":676,"properties":24},"a68545ec-2399-49c0-9511-ff77cb86baf6",{"id":675,"createTime":24,"updateTime":24,"relativeEntities":677,"slug":24,"properties":678,"entityType":24,"verifyStatus":24,"verifyTime":24,"verifyNote":24,"languages":24,"translateLanguages":24,"viewCount":24,"url":24,"parentIds":681,"statistic":24},[],{"title":679},{"VI":680},"Massey Cancer Center, Medical College of Virginia, Virginia Commonwealth University, Richmond, USA",[],{"id":683,"sortIndex":147,"affiliation":684,"properties":690},"e35e8513-23ef-4c7d-8d11-9d1c4738b22b",{"id":683,"createTime":24,"updateTime":24,"relativeEntities":685,"slug":24,"properties":686,"entityType":24,"verifyStatus":24,"verifyTime":24,"verifyNote":24,"languages":24,"translateLanguages":24,"viewCount":24,"url":24,"parentIds":689,"statistic":24},[],{"title":687},{"VI":688},"Department of Internal Medicine, Medical College of Virginia, Virginia Commonwealth University, Richmond, USA",[],{},{"id":692,"sortIndex":25,"affiliation":693,"properties":699},"f582609e-ae1e-4197-ad95-0c79ea57b1bc",{"id":692,"createTime":24,"updateTime":24,"relativeEntities":694,"slug":24,"properties":695,"entityType":24,"verifyStatus":24,"verifyTime":24,"verifyNote":24,"languages":24,"translateLanguages":24,"viewCount":24,"url":24,"parentIds":698,"statistic":24},[],{"title":696},{"VI":697},"Department of Biostatistics, Medical College of Virginia, Virginia Commonwealth University, Richmond, USA",[],{},{"title":701},{"VI":702},"Lynne Penberthy",{"id":704,"sortIndex":147,"researcher":24,"roles":705,"affiliations":706,"properties":713,"displayName":715,"givenName":24,"familyName":24},"08441495-1914-4d5d-a703-d184ac395ffb",[198],[707],{"id":683,"sortIndex":92,"affiliation":708,"properties":24},{"id":683,"createTime":24,"updateTime":24,"relativeEntities":709,"slug":24,"properties":710,"entityType":24,"verifyStatus":24,"verifyTime":24,"verifyNote":24,"languages":24,"translateLanguages":24,"viewCount":24,"url":24,"parentIds":712,"statistic":24},[],{"title":711},{"VI":688},[],{"title":714},{"VI":715},"Sheldon M. 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Tone, Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References and DEA Software (Kluwer Academic Publishers, Boston, 2000).\nD.A. Draper, I. Solti and Y.A. Ozcan, Characteristics of health maintenance organizations and their influence on efficiency, Health Services Management Research 13 (2000) 40–56.\nM.J. Farrell, The measurement of productive efficiency, Journal of the Royal Statistical Society A 120 (1957) 253–290.\nT.L. Higgins, Severity of illness indices and outcome prediction: Development and evaluation, in: Textbook of Critical Care Medicine, 4th edn., eds. A. Grenvik, S. Ayers, P. Holbrook and W. Shoemaker (W.B. Saunders Company, Philadelphia, 2000).\nS.M. Hollenberg and J.E. Parillo, Contemporary issues in critical care medicine, Journal of the American Medical Association 25 (1996) 1799–1781.\nC. Hukkelhoven, M. Eijkemans and E.W. Steyerberg, Correspondence to predicting survival using simple clinical variables: A case study in traumatic brain injury, Journal of Neurology, Neurosurgery and Psychiatry 68 (2000) 396–397.\nR. Jacobs, Alternative methods to examine hospital efficiency: Data envelopment analysis and stochastic frontier analysis, Health Care Management Science 4 (2001) 103–115.\nS. Joshi, Augmenting critical perfusion pressure. When is enough too much? Journal of Neurosurgical Anesthesiology 8 (1996) 249–252.\nN. Juul, G.F. Morris, S.B. Marshall et al., Intracranial hypertension and cerebral perfusion pressure: Influence on neurological deterioration and outcome in severe head injury, Journal of Neurosurgery 92 (2000) 1–6.\nW.A. Knaus, D.P. Wagners, E.A. Draper et al., The APACHE III prognostic systems: Risk prediction of hospital mortality for critically ill hospitalized adults, Chest 100 (1991) 1619–1636.\nJ.R. Le Gall and S. Lemeshow, A new simplified acute physiology score (SAPS II) based on a European\u002FNorth American multi center study, Journal of the American Medical Association 270 (1993) 2957– 2963.\nS. Lemeshow, D. Teres et al., Mortality probability models (MPM II) based on an international cohort of intensive care patients, Journal of the American Medical Association 270 (1993) 2478–2486.\nI.C.F. Lewin, The Cost of Disorders of the Brain (The National Foundation for the Brain, Washington, 1992).\nD.W. Marion and T.P. Spiegel, Changes in the management of severe traumatic brain injury, 1991–1997, Critical Care Medicine 28 (2000) 16–18.\nNIH Consensus Development Panel on Rehabilitation of Persons with Traumatic Brain Injury, Rehabilitation of persons with traumatic brain injury, Journal of the American Medical Association 282 (1999) 974– 983.\nJ.J. O'Connor, E.F. Robertson and P. De Maupertuis, http:\u002F\u002Fwww. groups.dcs.st-and.ac.uk\u002F~history\u002Fmathematicians.html (December 2, 2000).\nP.A. Patel and B.J. Grant, Application of mortality prediction systems to individual intensive care units, Intensive Care Medicine 25 (1999) 977–982.\nPhysics time line to 1799, http:\u002F\u002Fwww.weburbia.com\u002Fpg\u002Fhist1.htm (December 2, 2000).\nT. Price, L. Miller and M. de Scossa, The Glasgow coma scale in intensive care: A study, Nursing in Critical Care 5 (2000) 170–173.\nL.M. Seiford and R.M. Thrall, Recent developments in DEA: The mathematical approach to frontier analysis, Journal of Econometrics 46 (1990) 7–38.\nD.F. Signnorini, P.J. Andrews, P.A. Jones et al., Predicting survival using simple clinical variables: A case study in traumatic brain injury, Journal of Neurology, Neurosurgery and Psychiatry 66 (1999) 20–25.\nG. Teasdale and B. Jennet, Assessment of coma and impaired consciousness: A practical scale, Lancet 2 (1974) 81–84.\nE. Thanassoulis, A comparison of regression analysis and data envelopment analysis as alternative methods for performance assessments, Journal of the Operational Research Society 44 (1993) 1129–1144.\nD.J. Thurman, C. Alverson, K.A. Dunn, J. Guerrero and J.E. Sniezek, Traumatic brain injury in the United States: A public health perspective, Journal of Head Trauma Rehabilitation 14 (1999) 602–615.\nM.J. Vassar, F.R. Lewis Jr., J.A. Chambers et al., Prediction of outcome in intensive care unit trauma patients: A multicenter study of acute physiology and chronic health evaluation (APACHE), trauma and injury severity score (TRISS), and a 24-hour intensive care unit (ICU) point system, Journal of Trauma-Injury Infection and Critical Care 47 (1999) 324–329.\nA. Young and S. Willats, Controversies in management of acute brain trauma, Lancet 353 (1998) 164–166.\nJ.E. Zimmerman, D.P. Wagner, E.A. Draper et al., Evaluation of acute physiology and chronic health evaluation III prediction of hospitality in an independent database, Critical Care Medicine 28 (1998) 1317–1326.",{"VOID":1036},"10.1023\u002FA:1021912320922","2024-05-10T23:04:28.195+00:00","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1023\u002FA:1021912320922",[1040,1055,1070,1083,1107],{"id":1041,"sortIndex":92,"researcher":24,"roles":1042,"affiliations":1043,"properties":1052,"displayName":1054,"givenName":24,"familyName":24},"1d0b53f2-6453-4b4c-b1de-db05e327afc3",[198],[1044],{"id":1045,"sortIndex":92,"affiliation":1046,"properties":24},"d186987e-42f7-4d72-9024-a6c2e8509656",{"id":1045,"createTime":24,"updateTime":24,"relativeEntities":1047,"slug":24,"properties":1048,"entityType":24,"verifyStatus":24,"verifyTime":24,"verifyNote":24,"languages":24,"translateLanguages":24,"viewCount":24,"url":24,"parentIds":1051,"statistic":24},[],{"title":1049},{"VI":1050},"Department of Mechanical and Industrial Engineering, University of Massachusetts, Amherst, USA",[],{"title":1053},{"VI":1054},"Brian Harris Nathanson",{"id":1056,"sortIndex":147,"researcher":24,"roles":1057,"affiliations":1058,"properties":1067,"displayName":1069,"givenName":24,"familyName":24},"7e7edaf1-49e9-4dd8-9c0f-a4868c04c87b",[198],[1059],{"id":1060,"sortIndex":92,"affiliation":1061,"properties":24},"7dd65c60-0da0-4fdb-8584-932602dcd9b7",{"id":1060,"createTime":24,"updateTime":24,"relativeEntities":1062,"slug":24,"properties":1063,"entityType":24,"verifyStatus":24,"verifyTime":24,"verifyNote":24,"languages":24,"translateLanguages":24,"viewCount":24,"url":24,"parentIds":1066,"statistic":24},[],{"title":1064},{"VI":1065},"Adult Critical Care Service, Baystate Medical Center, Springfield, USA",[],{"title":1068},{"VI":1069},"Thomas L. 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Moreover, efficiency estimates represent a relative measure meaning that the implications from a hospital efficiency analysis based on a single-country dataset are limited by the availability of suitable benchmarks. Our first objective is to demonstrate the application of advanced nonparametric methods that overcome the limitations of the traditional nonparametric frontier techniques. Our second objective is to provide guidance on how an international comparison of hospital efficiency can be conducted using the example of two countries: Italy and Germany. We rely on a partial frontier of order-m to obtain efficiency estimates robust to outliers and extreme values. We use the conditional approach to incorporate hospital and regional characteristics into the estimation of efficiency. The obtained conditional efficiency estimates may deviate from the traditional unconditional efficiency estimates, which do not account for the potential influence of operational environment on the production possibilities. We nonparametrically regress the ratios of conditional to unconditional efficiency estimates to examine the relation of hospital and regional characteristics with the efficiency performance. We show that the two countries can be compared against a common frontier when the challenges of international data compatibility are successfully overcome. The results indicate that there are significant differences in the production possibilities of Italian and German hospitals. 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Health Policy 85(3):263–276",{"doi":354},{"id":350,"text":1328,"url":352,"identifiers":1329},"Hollingsworth B (2008) The measurement of efficiency and productivity of health care delivery. Health Economics 17(10):1107–1128",{"doi":354},{"id":24,"text":1331,"url":24,"identifiers":1332},"Ozcan YA (2014) Health Care benchmarking and performance evaluation An assessment using data envelopment analysis (DEA), 2nd edn. Springer, Newton, MA",{},{"id":350,"text":1334,"url":352,"identifiers":1335},"Daraio C, Simar L (2007) Advanced robust and nonparametric methods in efficiency analysis [electronic resource]: Methodology and applications, vol 4. Springer, New York",{"doi":354},{"id":1337,"text":1338,"url":1339,"identifiers":1340},"1077d288-be1f-4e44-88b2-dc0003680a42","Daraio C, Bonaccorsi A, Simar L (2015) Efficiency and economies of scale and specialization in european universities: a directional distance approach. J Informetrics 9(3):430–448. doi:10.1016\u002Fj.joi.2015.03.002","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS1751157715000437",{"doi":1341},"10.1016\u002Fj.joi.2015.03.002",{"id":1343,"text":1344,"url":1345,"identifiers":1346},"305f532a-1242-40c3-801e-6de52755240d","De Witte K, Marques RC (2010) Designing performance incentives, an international benchmark study in the water sector. Central European Journal of Operations Research 18(2):189–220","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10100-009-0108-0",{"doi":1347},"10.1007\u002Fs10100-009-0108-0",{"id":350,"text":1349,"url":352,"identifiers":1350},"Medin E, Häkkinen U, Linna M, Anthun KS, Kittelsen SA, Rehnberg C (2013) international hospital productivity comparison: experiences from the nordic countries. Health Policy 112(1):80–87",{"doi":354},{"id":350,"text":1352,"url":352,"identifiers":1353},"Dervaux B, Ferrier GD, Leleu H, Valdmanis V (2004) Comparing French and US hospital technologies: a directional input distance function approach. Applied Economics 36(10):1065–1081",{"doi":354},{"id":350,"text":1355,"url":352,"identifiers":1356},"Mobley LR IV, Magnussen J (1998) An international comparison of hospital efficiency: does institutional environment matter? Applied Economics 30(8):1089–1100",{"doi":354},{"id":1358,"text":1359,"url":1360,"identifiers":1361},"aa202469-e0c7-4b01-a6b2-6c72d63f028b","Steinmann L, Dittrich G, Karmann A, Zweifel P (2004) Measuring and comparing the (in) efficiency of german and swiss hospitals. The European Journal of Health Economics 5(3):216–226","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10198-004-0227-4",{"doi":1362},"10.1007\u002Fs10198-004-0227-4",{"id":350,"text":1364,"url":352,"identifiers":1365},"Mateus C, Joaquim I, Nunes C (2015) Measuring hospital efficiency—comparing four european countries. European Journal of Public Health 25(suppl 1):52–58",{"doi":354},{"id":1367,"text":1368,"url":1369,"identifiers":1370},"1ba09507-b12c-46a7-8ed5-e617492cfe7e","Cazals C, Florens J-P, Simar L (2002) Nonparametric frontier estimation: a robust approach. J Econometrics 106(1):1–25","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS030440760100080X",{"doi":1371},"10.1016\u002Fs0304-4076(01)00080-x",{"id":350,"text":1373,"url":352,"identifiers":1374},"Daraio C, Simar L (2005) Introducing environmental variables in nonparametric frontier models: a probabilistic approach. Journal of Productivity Analysis 24(1):93–121",{"doi":354},{"id":1376,"text":1377,"url":1378,"identifiers":1379},"534b6fd8-c4a0-4cff-9667-9b5d43ace4fb","Bădin L, Daraio C, Simar L (2014) Explaining inefficiency in nonparametric production models: the state of the art. Annals of Operations Research 214(1):5–30","http:\u002F\u002Flink.springer.com\u002F10.1007\u002Fs10479-012-1173-7",{"doi":1380},"10.1007\u002Fs10479-012-1173-7",{"id":350,"text":1382,"url":352,"identifiers":1383},"Cordero JM, Alonso-Morán E, Nuño-Solinis R, Orueta JF, Arce RS (2015) Efficiency assessment of primary care providers: a conditional nonparametric approach. European Journal of Operational Research 240(1):235–244",{"doi":354},{"id":1385,"text":1386,"url":1387,"identifiers":1388},"7224d3a0-a5aa-4046-ba86-a3cf093eb0e5","Ferreira D, Marques R (2014) Should inpatients be adjusted by their complexity and severity for efficiency assessment? Evidence from Portugal. Health Care Manag Sci:1–15. doi:10.1007\u002Fs10729-014-9286-y","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10729-014-9286-y",{"doi":1389},"10.1007\u002Fs10729-014-9286-y",{"id":350,"text":1391,"url":352,"identifiers":1392},"Barbetta GP, Turati G, Zago AM (2007) Behavioral differences between public and private not-for-profit hospitals in the italian national health service. Health Economics 16(1):75–96",{"doi":354},{"id":1394,"text":1395,"url":1396,"identifiers":1397},"8bb0983f-123c-41d3-bc7c-45e0eafa78fb","Berta P, Callea G, Martini G, Vittadini G (2010) The effects of upcoding, cream skimming and readmissions on the italian hospitals efficiency: a population-based investigation. Economic Modelling 27(4):812–821","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0264999309001916",{"doi":1398},"10.1016\u002Fj.econmod.2009.11.001",{"id":350,"text":1400,"url":352,"identifiers":1401},"Cellini R, Pignataro G, Rizzo I (2000) Competition and efficiency in health care: an analysis of the italian case. Int Tax Public Finan 7(4–5):503–519",{"doi":354},{"id":350,"text":1403,"url":352,"identifiers":1404},"Daidone S, D’Amico F (2009) Technical efficiency, specialization and ownership form: evidences from a pooling of italian hospitals. Journal of Productivity Analysis 32(3):203–216",{"doi":354},{"id":350,"text":1406,"url":352,"identifiers":1407},"Matranga D, Bono F, Casuccio A, Firenze A, Marsala L, Giaimo R, Sapienza F, Vitale F (2013) Evaluating the effect of organization and context on technical efficiency: a second-stage dea analysis of italian hospitals. Epidemiology, Biostatistics and Public Health 11(1):1–11",{"doi":354},{"id":1409,"text":1410,"url":1411,"identifiers":1412},"f1ea6a5f-4dd3-4276-9bf2-d988d458d347","Siciliani L (2006) Estimating Technical Efficiency in the Hospital Sector with Panel Data. Applied Health Economics and Health Policy 5(2):99–116","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.2165\u002F00148365-200605020-00004",{"doi":1413},"10.2165\u002F00148365-200605020-00004",{"id":24,"text":1415,"url":1416,"identifiers":1417},"Atella V, Belotti F, Daidone S, Ilardi G, Marini G (2012) Cost-containment policies and hospital efficiency: evidence from a panel of Italian hospitals. CEIS Working Paper No 228. doi:10.2139\u002Fssrn.2038398","https:\u002F\u002Fdoi.org\u002F10.2139\u002Fssrn.2038398",{"mag":1418,"openalex":1419,"doi":1420},"1591920734","W1591920734","10.2139\u002Fssrn.2038398",{"id":350,"text":1422,"url":352,"identifiers":1423},"Herr A (2008) Cost and technical efficiency of german hospitals: does ownership matter? Health Economics 17(9):1057–1071",{"doi":354},{"id":350,"text":1425,"url":352,"identifiers":1426},"Herr A, Schmitz H, Augurzky B (2011) Profit efficiency and ownership of german hospitals. Health Economics 20(6):660–674",{"doi":354},{"id":350,"text":1428,"url":352,"identifiers":1429},"Herwartz H, Strumann C (2012) On the effect of prospective payment on local hospital competition in Germany. Health Care Manag Sci 15(1):48–62",{"doi":354},{"id":350,"text":1431,"url":352,"identifiers":1432},"Staat M (2006) Efficiency of hospitals in germany: a dea-bootstrap approach. Applied Economics 38(19):2255–2263",{"doi":354},{"id":1434,"text":1435,"url":1436,"identifiers":1437},"b49a7d49-d1b2-494d-8f89-2b384439ec5c","Tiemann O, Schreyögg J (2009) Effects of ownership on hospital efficiency in germany. Business Research 2(2):115–145","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002FBF03342707",{"doi":1438},"10.1007\u002FBF03342707",{"id":1440,"text":1441,"url":1442,"identifiers":1443},"8f4b51e0-2b7a-430b-b054-0f80e3b98d01","Tiemann O, Schreyögg J (2012) Changes in hospital efficiency after privatization. Health Care Management Science 15(4):310–326. doi:10.1007\u002Fs10729-012-9193-z","http:\u002F\u002Flink.springer.com\u002F10.1007\u002Fs10729-012-9193-z",{"doi":1444},"10.1007\u002Fs10729-012-9193-z",{"id":350,"text":1446,"url":352,"identifiers":1447},"Lindlbauer I, Schreyögg J (2014) The relationship between hospital specialization and hospital efficiency: do different measures of specialization lead to different results? Health Care Management Science 17(4):365–378",{"doi":354},{"id":1449,"text":1450,"url":1451,"identifiers":1452},"9c26d589-2aca-42d9-9be8-c1be9e090c92","Büchner VA, Hinz V, Schreyögg J (2014) Health systems: changes in hospital efficiency and profitability. Health Care Manag Sci: 1–14. doi:10.1007\u002Fs10729-014-9303-1","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10729-014-9303-1",{"doi":1453},"10.1007\u002Fs10729-014-9303-1",{"id":350,"text":1455,"url":352,"identifiers":1456},"Ferrer F, de Belvis AG, Valerio L, Longhi S, Lazzari A, Fattore G, Ricciardi W, Maresso A (2014) Italy: health system review. Health Systems in Transition 16(4):1–168",{"doi":354},{"id":24,"text":1458,"url":24,"identifiers":1459},"OECD (2014) Health statistics. Organisation for Economic Co-Operation and Development (OECD). doi:10.1787\u002Fhealth-data-en",{"doi":1460},"10.1787\u002Fhealth-data-en",{"id":24,"text":1462,"url":1463,"identifiers":1464},"Piacenza M, Turati G, Vannoni D (2010) Restructuring hospital industry to control public health care expenditure: the role of input substitutability. Economic Modelling 27(4):881–890. doi:10.1016\u002Fj.econmod.2009.10.006","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.econmod.2009.10.006",{"mag":1465,"openalex":1466,"doi":1467},"2002695261","W2002695261","10.1016\u002Fj.econmod.2009.10.006",{"id":350,"text":1469,"url":352,"identifiers":1470},"Tauchmann H (2012) Partial frontier efficiency analysis. Stata J 12(3):461–478",{"doi":354},{"id":1472,"text":1473,"url":1474,"identifiers":1475},"839d43c9-91fd-4ec7-ac6e-0ad08d6a2934","Bădin L, Daraio C, Simar L (2010) Optimal bandwidth selection for conditional efficiency measures: a data-driven approach. The European Journal of Health Economics 201(2):633–640","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0377221709002148",{"doi":1476},"10.1016\u002Fj.ejor.2009.03.038",{"id":350,"text":1478,"url":352,"identifiers":1479},"Jeong S-O, Park BU, Simar L (2010) Nonparametric conditional efficiency measures: asymptotic properties. Annals of Operations Research 173(1):105–122",{"doi":354},{"id":350,"text":1481,"url":352,"identifiers":1482},"Simar L, Wilson PW (2011) Two-Stage DEA: caveat emptor. Journal of Productivity Analysis 36(2):205–218",{"doi":354},{"id":350,"text":1484,"url":352,"identifiers":1485},"Simar L, Wilson PW (2007) Estimation and inference in two-stage, semi-parametric models of production processes. J Econometrics 136(1):31–64",{"doi":354},{"id":350,"text":1487,"url":352,"identifiers":1488},"Daraio C, Simar L, Wilson PW (2010) Testing whether two-stage estimation is meaningful in non-parametric models of production. ISBA Discussion Papers,",{"doi":354},{"id":350,"text":1490,"url":352,"identifiers":1491},"Bădin L, Daraio C, Simar L (2012) How to measure the impact of environmental factors in a nonparametric production model. Eur J Health Econ 223(3):818–833",{"doi":354},{"id":24,"text":1493,"url":24,"identifiers":1494},"Simar L (2003) Detecting outliers in frontier models: a simple approach. Journal of Productivity Analysis 20(3):391–424",{},{"id":350,"text":1496,"url":352,"identifiers":1497},"De Witte K, Kortelainen M (2013) What explains the performance of students in a heterogeneous environment? conditional efficiency estimation with continuous and discrete environmental variables. Applied Economics 45(17):2401–2412",{"doi":354},{"id":350,"text":1499,"url":352,"identifiers":1500},"Li Q, Racine JS (2008) Nonparametric estimation of conditional cdf and quantile functions with mixed categorical and continuous data. Journal of Business & Economic Statistics 26(4):423–434",{"doi":354},{"id":350,"text":1502,"url":352,"identifiers":1503},"Hayfield T, Racine JS (2008) Nonparametric econometrics: The np package. J Stat Softw 27(5):1–32",{"doi":354},{"id":24,"text":1505,"url":24,"identifiers":1506},"OECD (2014) Regional Statistics. Organisation for Economic Co-Operation and Development (OECD). doi:10.1787\u002Fregion-data-en",{"doi":1507},"10.1787\u002Fregion-data-en",{"id":350,"text":1509,"url":352,"identifiers":1510},"Worthington AC (2004) Frontier efficiency measurement in health care: a review of empirical techniques and selected applications. Medical Care Research and Review 61(2):135–170",{"doi":354},{"id":1512,"text":1513,"url":1514,"identifiers":1515},"a8c4ef86-86eb-4a20-8c2b-8ad828e56bf2","Linna M, Häkkinen U, Peltola M, Magnussen J, Anthun KS, Kittelsen S, Roed A, Olsen K, Medin E, Rehnberg C (2010) Measuring cost efficiency in the nordic hospitals—a cross-sectional comparison of public hospitals in 2002. Health Care Management Science 13(4):346–357","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10729-010-9134-7",{"doi":1516},"10.1007\u002Fs10729-010-9134-7",{"id":350,"text":1518,"url":352,"identifiers":1519},"Varabyova Y, Schreyögg J (2013) International comparisons of the technical efficiency of the hospital sector: panel data analysis of oecd countries using parametric and non-parametric approaches. Health Policy 112(1):70–79",{"doi":354},{"id":350,"text":1521,"url":352,"identifiers":1522},"Clark JR, Huckman RS (2012) Broadening focus: spillovers, complementarities, and specialization in the hospital industry. Management Science 58(4):708–722",{"doi":354},{"id":24,"text":1524,"url":24,"identifiers":1525},"Kobel C, Theurl E (2013) Hospital specialisation within a DRG-framework: The Austrian case. Working papers in economics and statistics,",{},{"id":24,"text":1527,"url":24,"identifiers":1528},"DeLellis NO, Ozcan YA (2013) Quality outcomes among efficient and inefficient nursing homes: a national study. Health Care Manage R 38(2):156–165",{},{"id":1530,"text":1531,"url":1532,"identifiers":1533},"4d7f3cf3-1a9b-431c-90df-aa579b7c11b2","Narcı HÖ, Ozcan YA, Şahin İ, Tarcan M, Narcı M (2015) An examination of competition and efficiency for hospital industry in Turkey. Health Care Manag Sci 18(4):407–418. doi:10.1007\u002Fs10729-014-9315-x","http:\u002F\u002Flink.springer.com\u002F10.1007\u002Fs10729-014-9315-x",{"doi":1534},"10.1007\u002Fs10729-014-9315-x",{"id":350,"text":1536,"url":352,"identifiers":1537},"Zwanziger J, Melnick GA (1988) The effects of hospital competition and the medicare pps program on hospital cost behavior in california. Journal of Health Economics 7(4):301–320",{"doi":354},{"id":350,"text":1539,"url":352,"identifiers":1540},"Carr WJ, Feldstein PJ (1967) The relationship of cost to hospital size. Inquiry 4(2):45–65",{"doi":354},{"id":1542,"text":1543,"url":1544,"identifiers":1545},"d8cba157-53f8-478d-a741-85b831a66c7e","Tiemann O, Schreyögg J, Busse R (2012) Hospital ownership and efficiency: a review of studies with particular focus on germany. Health Policy 104(2):163–171","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0168851011002508",{"doi":1546},"10.1016\u002Fj.healthpol.2011.11.010",{"id":24,"text":1548,"url":24,"identifiers":1549},"Ettelt S, Thomson S, Nolte E, Mays N (2007) The regulation of competition between publicly-financed hospitals. London School of Hygiene and Tropical Medicine, London, UK",{},{"id":1551,"text":1552,"url":1553,"identifiers":1554},"3371407d-c128-423d-be8d-25c2f4b49ab9","Araújo C, Barros CP, Wanke P (2014) Efficiency determinants and capacity issues in brazilian for-profit hospitals. Health Care Management Science 17(2):126–138","http:\u002F\u002Flink.springer.com\u002F10.1007\u002Fs10729-013-9249-8",{"doi":1555},"10.1007\u002Fs10729-013-9249-8",{"id":1557,"text":1558,"url":1559,"identifiers":1560},"8d27c449-36ba-4d45-8a70-f9bdb62d6ec1","Porter ME (2010) What is value in health care? New Engl J Med 363(26):2477–2481. doi:10.1056\u002FNEJMp1011024","http:\u002F\u002Fwww.nejm.org\u002Fdoi\u002Fabs\u002F10.1056\u002FNEJMp1011024",{"doi":1561},"10.1056\u002Fnejmp1011024",{"id":350,"text":1563,"url":352,"identifiers":1564},"Fattore G, Torbica A (2006) Inpatient reimbursement system in italy: how do tariffs relate to costs? Health Care Management Science 9(3):251–258",{"doi":354},{"id":1566,"createTime":1567,"updateTime":1568,"relativeEntities":1569,"slug":1570,"properties":1571,"entityType":189,"verifyStatus":190,"verifyTime":1580,"verifyNote":192,"languages":24,"translateLanguages":24,"viewCount":92,"primaryUrl":1581,"fullTextUrl":24,"authors":1582,"publicationType":285,"publisherRelationship":1615,"citationCount":92,"citationInfo":1668,"publishDate":1671,"publishYear":1669,"citationAnalyzeStatus":23,"lastCitationAnalyze":1568,"indexDatabases":1672,"openAccess":24,"references":1673,"isForceReanalyzing":428},"807763ff-7a0b-4d04-8071-8cd0b5dda07c","2023-12-29T22:41:04.102+00:00","2025-08-28T14:30:15.006+00:00",[],"Assessing-nursing-home-care-quality-through-Bayesian-networks",{"abstract":1572,"title":1574,"gsPaper":1576,"doi":1578},{"EN":1573},"This article demonstrates how Bayesian networks can be employed as a tool to assess the quality of care in nursing homes. For the data sets analyzed, the proposed model performs comparably to existing quantitative assessment models. In addition, a Bayesian network approach offers several uniques advantages. The structure and parameters of a Bayesian network provide rich insight into the multidimensional aspects of the quality of care. Bayesian networks can be used as a guide in implementing limited resources by identifying information that would be most relevant to an assessment. Finally, Bayesian networks provide a straightforward framework for integrating nursing home care quality research that is conducted in various locations and for various purposes.",{"EN":1575},"Assessing nursing home care quality through Bayesian networks",{"VOID":1577},"[\"6395437432834684421\"]",{"VOID":1579},"10.1007\u002Fs10729-008-9063-x","2024-05-03T22:06:23.611+00:00","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10729-008-9063-x",[1583,1600],{"id":1584,"sortIndex":92,"researcher":24,"roles":1585,"affiliations":1586,"properties":1595,"displayName":1597,"givenName":24,"familyName":24},"304f51da-dd6f-4cf8-9a99-e3f0f8abc5ea",[198],[1587],{"id":1588,"sortIndex":92,"affiliation":1589,"properties":24},"d22751f2-30cd-473e-93fc-5a8fa5713937",{"id":1588,"createTime":24,"updateTime":24,"relativeEntities":1590,"slug":24,"properties":1591,"entityType":24,"verifyStatus":24,"verifyTime":24,"verifyNote":24,"languages":24,"translateLanguages":24,"viewCount":24,"url":24,"parentIds":1594,"statistic":24},[],{"title":1592},{"VI":1593},"Department of Management Sciences, Henry B. Tippie College of Business, University of Iowa, Iowa City, USA",[],{"title":1596,"gsAuthor":1598},{"VI":1597},"Justin Goodson",{"VOID":1599},"[\"N024YM8AAAAJ\"]",{"id":1601,"sortIndex":147,"researcher":24,"roles":1602,"affiliations":1603,"properties":1612,"displayName":1614,"givenName":24,"familyName":24},"4af1f6fd-1e8c-4460-9d9e-82d70e2f888f",[198],[1604],{"id":1605,"sortIndex":92,"affiliation":1606,"properties":24},"9ef7f118-b5d9-4a56-8dc7-053d1178cc8f",{"id":1605,"createTime":24,"updateTime":24,"relativeEntities":1607,"slug":24,"properties":1608,"entityType":24,"verifyStatus":24,"verifyTime":24,"verifyNote":24,"languages":24,"translateLanguages":24,"viewCount":24,"url":24,"parentIds":1611,"statistic":24},[],{"title":1609},{"VI":1610},"Department of Industrial and Manufacturing Systems Engineering, University of Missouri—Columbia, Columbia, USA",[],{"title":1613},{"VI":1614},"Wooseung Jang",{"url":1581,"publisher":1616,"properties":1663},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":1617,"slug":10,"properties":1618,"entityType":22,"verifyStatus":23,"verifyTime":24,"verifyNote":24,"languages":24,"translateLanguages":24,"viewCount":25,"subjectFields":1623,"manageAffiliations":1632,"indexDatabases":1643,"url":90,"thumbnailPath":24,"statistic":1658,"gsStatistic":24,"type":24,"analyzePriority":24},[],{"country":1619,"eissn":1620,"issn":1621,"title":1622},{"VOID":13},{"VOID":15},{"VOID":17},{"EN":19},[1624,1628],{"id":28,"createTime":24,"updateTime":24,"relativeEntities":1625,"label":1626,"description":1627,"parentId":24,"standard":24,"scholarHubFieldId":24},[],{"EN":31},{},{"id":34,"createTime":24,"updateTime":24,"relativeEntities":1629,"label":1630,"description":1631,"parentId":24,"standard":24,"scholarHubFieldId":24},[],{"EN":37},{},[1633,1638],{"id":41,"createTime":24,"updateTime":24,"relativeEntities":1634,"slug":24,"properties":1635,"entityType":24,"verifyStatus":24,"verifyTime":24,"verifyNote":24,"languages":24,"translateLanguages":24,"viewCount":24,"url":24,"parentIds":1637,"statistic":24},[],{"title":1636},{"EN":45},[],{"id":48,"createTime":24,"updateTime":24,"relativeEntities":1639,"slug":24,"properties":1640,"entityType":24,"verifyStatus":24,"verifyTime":24,"verifyNote":24,"languages":24,"translateLanguages":24,"viewCount":24,"url":24,"parentIds":1642,"statistic":24},[],{"title":1641},{"EN":52},[],[1644,1651],{"id":56,"indexDatabase":1645,"url":69,"indexYears":24,"academicFieldIds":1650,"indexDatabaseRanking":24},{"id":58,"createTime":24,"updateTime":24,"relativeEntities":1646,"label":1647,"description":1648,"key":65,"publicationTags":1649,"standard":24},[],{"EN":61,"VI":61},{"EN":63,"VI":64},[67,68],[71],{"id":73,"indexDatabase":1652,"url":84,"indexYears":85,"academicFieldIds":1657,"indexDatabaseRanking":89},{"id":75,"createTime":24,"updateTime":24,"relativeEntities":1653,"label":1654,"description":1655,"key":81,"publicationTags":1656,"standard":24},[],{"EN":78,"VI":78},{"EN":78,"VI":80},[83],[87,88],{"impactFactor":92,"impactFactorByYear":1659,"i10Index":105,"i10IndexLast5Year":106,"totalPublication":107,"totalPublicationByYear":1660,"totalCitation":126,"totalCitationByYear":1661,"totalCitationPerPublication":148,"totalCitationPerPublicationByYear":1662,"hindexLast5Year":124,"hindex":124},{"2012":94,"2013":95,"2014":96,"2015":97,"2016":98,"2017":99,"2018":94,"2019":100,"2020":101,"2021":102,"2022":103,"2023":104},{"1998":109,"1999":110,"2000":110,"2001":111,"2002":112,"2003":110,"2004":113,"2005":114,"2006":115,"2007":115,"2008":116,"2009":117,"2010":118,"2011":119,"2012":120,"2013":112,"2014":121,"2015":117,"2016":117,"2017":120,"2018":111,"2019":121,"2020":122,"2021":123,"2022":117,"2023":124,"2024":125},{"2004":128,"2005":111,"2006":129,"2007":130,"2008":131,"2009":132,"2010":133,"2011":134,"2012":135,"2013":136,"2014":137,"2015":138,"2016":139,"2017":140,"2018":141,"2019":142,"2020":143,"2021":144,"2022":145,"2023":146,"2024":147},{"2004":150,"2005":151,"2006":152,"2007":153,"2008":154,"2009":155,"2010":156,"2011":157,"2012":158,"2013":159,"2014":160,"2015":161,"2016":162,"2017":146,"2018":163,"2019":164,"2020":165,"2021":166,"2022":167,"2023":168,"2024":169},{"pages":1664,"volume":1666},{"VOID":1665},"382-392",{"VOID":1667},"11",{"total":92,"publishYear":1669,"statisticByYear":1670},2008,{},"2008-03-15",[67,89],[1674,1677,1680,1683,1686,1690,1696,1699,1702,1705,1708,1711,1714,1717,1723,1726,1729,1735,1738,1741,1744,1747,1750,1753,1756,1759,1762,1765,1768,1771,1774,1777,1783,1786,1789,1792],{"id":350,"text":1675,"url":352,"identifiers":1676},"Atchley S (1991) A time-ordered, systems approach to quality assurance in longterm care. 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Int J Geriatr Psychiatry 18(3):250–257",{"doi":354},{"id":1796,"createTime":1797,"updateTime":1798,"relativeEntities":1799,"slug":1800,"properties":1801,"entityType":189,"verifyStatus":190,"verifyTime":1798,"verifyNote":192,"languages":24,"translateLanguages":24,"viewCount":92,"primaryUrl":1810,"fullTextUrl":24,"authors":1811,"publicationType":285,"publisherRelationship":1827,"citationCount":24,"citationInfo":24,"publishDate":1880,"publishYear":1881,"citationAnalyzeStatus":23,"lastCitationAnalyze":24,"indexDatabases":1882,"openAccess":24,"references":24,"isForceReanalyzing":428},"692e29d0-14b5-412d-b555-922c0e2ce86d","2024-02-11T01:22:13.517+00:00","2025-02-27T00:21:08.913+00:00",[],"Do-caesarean-section-rates-catch-up-Evidence-from-14-European-countries",{"abstract":1802,"title":1804,"references":1806,"doi":1808},{"EN":1803},"This study investigated the catch up effect of Caesarean Section (CS) birth rates across 14 European countries during 1980–2009 for the first time. The panel stationary test incorporating multiple structural breaks and cross-sectional dependence was used to provide reliable evidence for the existence of the catch up effect of CS birth rates. Our results suggested that the CS birth rates in 14 European countries have mostly exhibited signs of convergence through a steady upward trend from 1980 to 2009. Policymakers in low CS birth rate countries should be cautioned concerning the negative impact of the increase of CS births.",{"EN":1805},"Do caesarean section rates ‘catch-up’? Evidence from 14 European countries",{"VOID":1807},"UNICEF, WHO, UNFPA (1997) Guidelines for monitoring the availability and use of obstetric services. http:\u002F\u002Fwww.childinfo.org\u002Ffiles\u002Fmaternal_mortality_finalgui.pdf. Accessed 8 Nov 2012\nWorld Health Organization, UNFPA, UNICEF and AMDD (2009) Monitoring emergency obstetric care: a handbook. 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