Using Electronic Data to Improve Care of Patients with Known or Suspected Cancer

描述（由申请人提供）：患有已知或疑似癌症的患者通过几种不同的门诊护理过渡，以便得到及时的诊断和治疗。早期诊断和治疗所带来的生存益处取决于协调良好的护理。例如，我们的初步工作表明，当患者在门诊护理系统中导航时，护理过程中的故障可能导致某些癌症的诊断和/或治疗的严重延误。本提案的目标是测试使用卫生信息技术（IT）来识别这些延误的患者，并促进他们通过卫生保健系统的移动。医学研究所已将护理的及时性和协调性确定为提高保健质量的目标。然而，很少或没有其他已发表的文献论述了一种全面的方法，以防止与高风险门诊护理转换相关的癌症诊断和/或治疗中的故障和延迟。在这项研究中，我们将使用健康信息技术来解决五种癌症（结肠癌、肺癌、乳腺癌、前列腺癌和宫颈癌）的这些问题，这些癌症的早期检测可能会提高生存率。我们将在以前和正在进行的创新数据挖掘技术的基础上，识别可能延迟癌症诊断或治疗的患者，我们将利用电子通信和监控促进他们的护理过程。这项前瞻性随机对照试验的假设是，与常规护理相比，基于信息技术的干预可以减少延误和错过的机会。我们的具体目标是：目标1：使用基于“触发”的电子健康记录存储库数据挖掘来识别与癌症相关的诊断和治疗延迟的患者。目标2：与常规护理相比，确定基于it的干预（包括使用在目标1中测试的触发器的数据挖掘，然后是有针对性的电子通信和监测技术）加快癌症诊断和治疗启动的有效性。在目标1中，我们将测试使用新的触发器来识别延迟患者，并进行图表审查以确定其效用并改进它们。在目标2中，我们将在自愿的基础上招募提供者，并将他们随机分配到干预组或对照组。干预组的提供者将接受电子通信和监控，如果他们的病人的潜在延误是由触发因素确定的。通过图表审查获得的结果测量将包括最佳护理途径中几个关键步骤之间的时间间隔以及诊断或怀疑患有癌症的患者的诊断和开始治疗的及时性。研究环境包括一个城市退伍军人事务机构和一个大型初级保健网络，代表了内科和家庭医学从业人员、学术和非学术实践、城市和农村患者，以及显著的种族、性别、民族、年龄和社会经济患者多样性。未来，我们的发现可以与癌症导航程序相结合，提高它们的效率。它们还可以为类似干预措施的设计提供信息，以改善对其他严重慢性疾病的管理。

DESCRIPTION (provided by the applicant): Patients with known or suspected cancers transition through several different settings of ambulatory care in order to receive a timely diagnosis and treatment. The survival benefit conferred by early diagnosis and treatment hinges on well-coordinated care. Our preliminary work, for instance, shows that breakdowns in care processes that occur while patients are navigating the ambulatory care system can lead to substantial delays in diagnosis and/or treatment of some cancers. The goal of this proposal is to test the use of health information technology (IT) to identify patients with these delays and facilitate their movement through the health care system. The Institute of Medicine has identified both timeliness and coordination of care as targets for improving health care quality. However, little or no other published literature addresses a comprehensive approach to prevent breakdowns and delays in cancer diagnosis and/or treatment related to high risk ambulatory care transitions. In this study, we will use health IT to address these issues in five cancers for which early detection may improve survival (colon, lung, breast, prostate, and cervical cancer). We will build upon our previous and ongoing work in innovative data mining techniques to identify patients likely to have delays in cancer diagnosis or treatment, and we will facilitate their care processes using electronic communication and surveillance. The hypothesis of this prospective randomized controlled trial is that IT-based interventions can reduce delays and missed opportunities as compared to usual care. Our specific aims are: Aim 1: To identify patients with cancer-related diagnostic and treatment delays using "trigger" based data mining of an electronic health records repository. Aim 2: To determine the effectiveness of an IT-based intervention (consisting of data mining using triggers tested in Aim 1 followed by targeted electronic communication and surveillance techniques) to expedite cancer diagnosis and treatment initiation as compared with usual care. In Aim 1, we will test the use of new triggers to identify patients with delays, and conduct chart reviews to determine their utility and improve them. In Aim 2, we will recruit providers on a voluntary basis and randomize them to intervention or controls. Providers in the intervention group will receive electronic communications and surveillance if potential delays on their patients are identified by triggers. Outcome measures, obtained through chart review, will consist of time intervals between several key steps in the optimal pathway of care and timeliness of diagnosis and treatment initiation among patients either diagnosed with or suspected of cancer. The research settings include an urban Veterans Affairs facility and a large primary care network representing internal and family medicine practitioners, academic and nonacademic practices, urban and rural patients, and significant racial, gender, ethnic, age, and socioeconomic patient diversity. In future, our findings could be integrated with cancer navigation programs and increase their efficiency. They could also inform the design of similar interventions to improve the management of other serious chronic conditions.