Department of Medicine, Stanford University School of Medicine, Palo Alto, California; Department of Health Research and Policy, Stanford University School of Medicine, Palo Alto, California.
Cancer. 2014 Jan 1;120(1):103-11. doi: 10.1002/cncr.28395. Epub 2013 Sep 24.
Understanding of cancer outcomes is limited by data fragmentation. In the current study, the authors analyzed the information yielded by integrating breast cancer data from 3 sources: electronic medical records (EMRs) from 2 health care systems and the state registry.
Diagnostic test and treatment data were extracted from the EMRs of all patients with breast cancer treated between 2000 and 2010 in 2 independent California institutions: a community-based practice (Palo Alto Medical Foundation; "Community") and an academic medical center (Stanford University; "University"). The authors incorporated records from the population-based California Cancer Registry and then linked EMR-California Cancer Registry data sets of Community and University patients.
The authors initially identified 8210 University patients and 5770 Community patients; linked data sets revealed a 16% patient overlap, yielding 12,109 unique patients. The percentage of all Community patients, but not University patients, treated at both institutions increased with worsening cancer prognostic factors. Before linking the data sets, Community patients appeared to receive less intervention than University patients (mastectomy: 37.6% vs 43.2%; chemotherapy: 35% vs 41.7%; magnetic resonance imaging: 10% vs 29.3%; and genetic testing: 2.5% vs 9.2%). Linked Community and University data sets revealed that patients treated at both institutions received substantially more interventions (mastectomy: 55.8%; chemotherapy: 47.2%; magnetic resonance imaging: 38.9%; and genetic testing: 10.9% [P < .001 for each 3-way institutional comparison]).
Data linkage identified 16% of patients who were treated in 2 health care systems and who, despite comparable prognostic factors, received far more intensive treatment than others. By integrating complementary data from EMRs and population-based registries, a more comprehensive understanding of breast cancer care and factors that drive treatment use was obtained.
由于数据碎片化,人们对癌症结果的理解受到限制。在目前的研究中,作者分析了整合来自三个来源的乳腺癌数据所产生的信息:来自两个医疗保健系统的电子病历 (EMR) 和州注册处。
从 2000 年至 2010 年在加利福尼亚州的两个独立机构接受治疗的所有乳腺癌患者的 EMR 中提取诊断测试和治疗数据:一个是基于社区的实践机构(斯坦福大学医学中心;“社区”),另一个是学术医疗中心(斯坦福大学;“大学”)。作者纳入了基于人群的加利福尼亚癌症登记处的记录,然后将社区和大学患者的 EMR-加利福尼亚癌症登记处数据集进行链接。
作者最初确定了 8210 名大学患者和 5770 名社区患者;链接数据集显示患者重叠率为 16%,共生成 12109 名独特患者。所有社区患者而非大学患者在这两个机构接受治疗的比例随着癌症预后因素的恶化而增加。在链接数据集之前,社区患者似乎比大学患者接受的干预措施更少(乳房切除术:37.6% 比 43.2%;化疗:35% 比 41.7%;磁共振成像:10% 比 29.3%;基因检测:2.5% 比 9.2%)。链接的社区和大学数据集显示,在这两个机构接受治疗的患者接受了大量干预措施(乳房切除术:55.8%;化疗:47.2%;磁共振成像:38.9%;基因检测:10.9%[每 3 种机构比较,P <.001])。
数据链接确定了 16%在两个医疗保健系统中接受治疗的患者,尽管预后因素相当,但他们接受的治疗强度远高于其他患者。通过整合来自 EMR 和基于人群的登记处的互补数据,对乳腺癌护理和驱动治疗使用的因素有了更全面的了解。