R18 Closed Loop Diagnostics : AHRQ R18 Patient Safety Learning Laboratories

摘要 初级保健中的诊断错误通常是由于未能对诊断测试进行后续（“闭环”）， 转诊和症状。更具体地说，（1）诊断测试和转诊往往没有完成，（2） 诊断测试和转诊的结果通常不传达给患者及其初级保健医生， 和（3）初级保健医生经常没有被告知，当症状演变，可能会改变一个 诊断.为了解决这些差距，我们的临床医生，系统工程师和患者的多学科团队将 使用工程生命周期来设计系统，以减少相关诊断错误的数量， 在大型初级保健实践中防止这些类型的失败中的每一种。我们提议的研究采用 创新的循证系统工程（SE）方法，以开发高度可靠和稳健的流程 在其他行业，但尚未广泛应用于医疗保健。我们的具体目标如下： (Aim 1）设计、开发和完善高度可靠的诊断测试和转诊“闭环”系统， 确保这些在临床和患者重要的时间范围内发生; (Aim 2）设计、开发和完善高度可靠的“闭环”症状监测系统，以确保 临床医生接收有关关注症状演变的信息;以及 (Aim 3）确保目标1和目标2的结果具有更广泛的普遍性，确保这些新的进程 在服务不足的社区的社区卫生中心，大型远程医疗系统，以及 模拟的其他卫生系统设置和人群的代表范围。 我们的研究假设是，一个有条不紊的系统方法来闭合诊断过程的循环， 可显著提高及时完成订购的测试，转诊和症状报告，从而减少 诊断错误。我们项目的关键创新是使用高可靠性和人为因素方法， 在整个工程过程中纳入来自其他实践的患者和临床医生，并结合使用 统计，定性和计算机建模方法来评估我们的主要网站的改进 更广泛地说。预计的成果包括增加完成高风险诊断检测、转诊， 关于症状，反过来导致减少诊断错误，负面健康结果，以及相关的 成本学习成果包括提高对闭环诊断和监控问题的理解 在初级保健，病人参与解决这些问题，以及系统工程的效用， 重要的医疗保健问题。我们的项目响应了医学研究所8项建议中的4项， 总统科技顾问理事会在他们的《改善医疗诊断》报告中说， 建议将系统工程应用于初级保健问题，以及PSLL征集 强调基于价值的护理、安全性、患者参与和提供者负担。

Abstract Diagnostic errors in primary care often are due to failures to follow up (“close the loop”) on diagnostic tests, referrals, and symptoms. More specifically, (1) diagnostic tests and referrals often are not completed, (2) results of diagnostic tests and referrals often are not conveyed to patients and their primary care physicians, and (3) primary care physicians frequently are not informed when symptoms evolve that could alter a diagnosis. To address these gaps, our multidisciplinary team of clinicians, systems engineers, and patients will use an engineering life cycle to design systems to decrease the number of associated diagnostic errors by preventing each of these types of failures in a large primary care practice. Our proposed research employs innovative evidenced-based system engineering (SE) methods to develop highly reliable and robust processes in other industries, but not yet widely adopted in healthcare. Our specific aims are as follows: (Aim 1) Design, develop, and refine highly reliable “closed loop” systems for diagnostic tests and referrals that ensure these occur within clinically- and patient-important timeframes; (Aim 2) Design, develop, and refine a highly reliable “closed loop” symptom monitoring system to ensure clinicians receive information about evolving symptoms of concern; and (Aim 3) Ensure broader generalizability of results of Aims 1 and 2 by ensuring these new processes are effective in a community health center in an underserved community, a large telemedicine system, and a representative range of simulated other health system settings and populations. Our research hypothesis is that a methodical systems approach to closing loops on diagnostic processes will measurably improve timely completion of ordered tests, referrals, and symptom reports, leading to reductions in diagnostic errors. Key innovations of our project are the use of high reliability and human factors methods, inclusion of patients and clinicians from other practices throughout the engineering process, and combined use of statistical, qualitative, and computer modeling methods to estimate improvements both in our primary site and more broadly. Projected results include increased completion of high-risk diagnostic tests, referrals, and concerning symptoms, in turn resulting in reduced diagnostic errors, negative health outcomes, and associated costs. Learning outcomes include improved understanding of closed loop diagnostic and monitoring problems in primary care, patient engagement in solutions to such problems, and the utility of systems engineering to important healthcare problems. Our project responds to 4 of the 8 Institute of Medicine recommendations from their Improving Diagnosis in Healthcare report, the President's Council of Advisors on Science and Technology recommendation that systems engineering be applied to primary care problems, and the PSLL solicitation emphases on value-based care, safety, patient engagement, and provider burden.