Casalino Lawrence P, Pesko Michael F, Ryan Andrew M, Nyweide David J, Iwashyna Theodore J, Sun Xuming, Mendelsohn Jayme, Moody James
*Division of Health Policy and Economics, Department of Healthcare Policy and Research †Department of Healthcare Policy and Research, Weill Cornell Medical College, New York, NY ‡Department of Health Management and Policy, School of Public Health, University of Michigan, Ann Arbor, MI §Centers for Medicare & Medicaid Services, Center for Medicare and Medicaid Innovation, Baltimore, MD ∥Department of Pulmonary and Critical Care, University of Michigan, Ann Arbor, MI ¶HIV Epidemiology and Field Services Program, Department of Health and Mental Hygiene, Bureau of HIV/AIDS Prevention and Control, New York, NY #Department of Sociology, Duke University **King Abdulaziz University, Durham, NC.
Med Care. 2015 Jun;53(6):534-41. doi: 10.1097/MLR.0000000000000365.
Research on the quality and cost of care traditionally focuses on individual physicians or medical groups. Social network theory suggests that the care a patient receives also depends on the network of physicians with whom a patient's physician is connected.
The objectives of the study are: (1) identify physician networks; (2) determine whether the rate of ambulatory care-sensitive hospital admissions (ACSAs) varies across networks--even different networks at the same hospital; and (3) determine the relationship between ACSA rates and network characteristics.
We identified networks by applying network detection algorithms to Medicare 2008 claims for 987,000 beneficiaries in 5 states. We estimated a fixed-effects model to determine the relationship between networks and ACSAs and a multivariable model to determine the relationship between network characteristics and ACSAs.
We identified 417 networks. Mean size: 129 physicians; range, 26-963. In the fixed-effects model, ACSA rates varied significantly across networks: there was a 46% difference in rates between networks at the 25th and 75th performance percentiles. At 95% of hospitals with admissions from 2 networks, the networks had significantly different ACSA rates; the mean difference was 36% of the mean ACSA rate. Networks with a higher percentage of primary-care physicians and networks in which patients received care from a larger number of physicians had higher ACSA rates.
Physician networks have a relationship with ACSAs that is independent of the physicians in the network. Physician networks could be an important focus for understanding variations in medical care and for intervening to improve care.
传统上，关于医疗质量和成本的研究主要集中在个体医生或医疗集团。社会网络理论表明，患者接受的治疗还取决于其医生所连接的医生网络。
本研究的目的是：（1）识别医生网络；（2）确定非卧床护理敏感型医院入院率（ACSA）在不同网络间是否存在差异——即使是同一家医院的不同网络；（3）确定ACSA率与网络特征之间的关系。
我们通过将网络检测算法应用于2008年5个州98.7万名受益人的医疗保险索赔来识别网络。我们估计了一个固定效应模型来确定网络与ACSA之间的关系，并估计了一个多变量模型来确定网络特征与ACSA之间的关系。
我们识别出417个网络。平均规模：129名医生；范围为26 - 963名。在固定效应模型中，ACSA率在不同网络间存在显著差异：第25和第75百分位的网络之间的比率相差46%。在95%有来自两个网络入院患者的医院中，这两个网络的ACSA率存在显著差异；平均差异为平均ACSA率的36%。初级保健医生比例较高的网络以及患者接受治疗的医生数量较多的网络，其ACSA率较高。
医生网络与ACSA之间存在独立于网络中医生的关系。医生网络可能是理解医疗差异和进行干预以改善医疗的重要关注点。
