Are Volume Standards Accurate Measures of Quality?

描述（由申请人提供）：为响应IOM患者安全议程，AHRQ赞助的一份报告已将“将特定手术和程序定位于高容量中心”指定为患者安全研究的高优先级领域。在没有国家质量报告卡的情况下，主张有选择地将病人转诊到高手术量医院进行高风险手术，理由是手术量是质量的代表。随着Leapfrog集团安全倡议的启动，这些政策建议目前正在私营部门实施。然而，关于支持量-结果关系的证据的强度，提出了重要的问题。拟议项目的主要目标是调查使用“数量”标准而不是“质量”标准选择性地将病人转介到医院的影响。为了检验这一假设，我们将首先使用增强的行政数据来开发风险调整后的质量指标。然后，我们将在模拟中使用这些质量指标来检验主要假设，即通过将患者从低质量中心转移到高风险手术区域，将导致比从低容量中心转移患者更好的人群结局。该项目的第二个产品将解决另一个核心问题，即使用管理数据对绩效进行基准测试，即第二次诊断通常无法区分并发症和原有疾病。无法将两者分开，可能导致有偏见的风险调整和不准确的质量衡量。本研究将使用加州州住院患者数据库（SID）进行。该数据集中的所有ICD-9-CM代码都有日期戳，以区分入院时存在的疾病和入院后出现的疾病。只有加州和纽约两个州在所有二次诊断上都有日期戳。没有日期戳偏差质量测量的发现将支持在其他州采用日期戳。从该项目中获得的知识将推动质量测量领域的发展，并将提供信息，支持在全国范围内制定关于高风险手术区域化的合理政策。

DESCRIPTION (provided by applicant): In response to the IOM patient safety agenda, an AHRQ-sponsored report has designated "localizing specific surgeries and procedures to high volume centers" as a high priority area for patient safety research. In the absence of a national quality report card, selective referral of patients to high-volume hospitals has been advocated for high-risk surgery on the grounds that procedure volume is a quality proxy. With the launching of the Leapfrog Group safety initiative, these policy recommendations are now being implemented in the private sector. Significant questions, however, have been raised regarding the strength of the evidence supporting the volume-outcome relationship. The primary objective of the proposed project is to investigate the implications of selectively referring patients to hospitals using "volume" standards as opposed to "quality" criteria. To test this hypothesis, we will first use enhanced administrative data to develop risk-adjusted measures of quality. We will then use these quality measures in a simulation to test the primary hypothesis that Regionalizing high-risk surgery by diverting patients from low-quality centers will lead to better population outcomes than diverting patients from low-volume centers. A secondary product of this project will address another central issue pertaining to the use of administrative data for bench-marking performance - namely, that secondary diagnoses do not typically distinguish between complications and preexisting conditions. The inability to separate the two is likely to lead to biased risk adjustment and inaccurate quality measures. This study will be conducted using the California State Inpatient Databases (SID). All ICD-9-CM codes within this data set are date stamped, distinguishing conditions present at the time of admission from conditions that developed following hospital admission. Only two states, California and New York, include a date stamp on all secondary diagnoses. The finding that the absence of date stamping biases quality measurement would lend support to the adoption of date stamping in other states. The knowledge gained from this project will advance the field of quality measurement and will provide information in support of rational policy making regarding the regionalization of high-risk surgery on a national scale.