Improving Intensive Care Medication Safety through EHR-basedAlgorithms.

通过基于 EHR 的算法提高重症监护用药安全。

• 批准号: 9333449
• 负责人: Eric Steven Kirkendall
• 金额: $ 33.97万
• 依托单位: CINCINNATI CHILDRENS HOSP MED CTR
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-15 至 2019-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9333449
• 关键词: Academic Medical Centers; Acute; Address; Admission activity; Adult; Algorithms; Beds; Caring; Censuses; Chronic; Clinical; Clinical Data; Critical Care; Dangerousness; Data; Data Sources; Detection; Devices; Electronic Health Record; Environment; Event; Faculty; Guidelines; Hospitals; Information Systems; Information Technology; Infusion Pumps; Inpatients; Institution; Intensive Care; Intensive Care Units; Intercept; Intervention; Knowledge; Length of Stay; Liquid substance; Manuals; Medical; Medical Errors; Medical Informatics; Medical center; Methodology; Methods; Monitor; Newborn Infant; Operative Surgical Procedures; Parenteral Nutrition; Patients; Performance; Pharmaceutical Preparations; Pharmacists; Physician Executives; Physicians; Pump; Reporting; Research; Research Infrastructure; Risk; Safety; Scanning; System; Techniques; Technology; Time; Universities; Work; authority; base; clinical care; clinical practice; computerized; improved; neonate; novel; patient safety; prospective; software systems; tool; usability; user-friendly

 DESCRIPTION (provided by applicant): In the field of patient safety, the paucity of systematic research is a critical barrier to progress. Notably missing are studies that meticulously investigate Electronic Health Records (EHR) and information technology in detecting intensive care-related errors. Our proposed study seeks to address an identified gap in the current knowledge of safety research by evaluating the usefulness of commercial IT systems and EHRs in reducing medical errors. In our study we seek to shift medication safety research from retrospective error identification towards a real-time automated and computerized approach to achieve a more comprehensive patient safety paradigm. The central hypothesis of our work is that by identifying discrepancies between medication order and administration data sources, we can detect and mitigate medication-related errors. In our study, we will 1) Use real time analysis to detect and intercept medication and parenteral nutrition (PN) administration errors identified by our recently developed Electronic Health Record (EHR) content-based algorithms (Aim 1); 2) Confirm performance of the algorithms in an external institution (Aim 2); and 3) Create new algorithms to identify smart pump infusion errors (Aim 3). By systematically detecting and intercepting medication and PN errors, we will shift medication safety from passive reporting of errors to proactive identification and mitigation of unsafe care. In Aim 1 we will reduce the time patients are at risk for harm through prospective identification o ameliorable medication and PN administration errors using CCHMC-developed medication administration error (MAE) detection algorithms. Using our EHR-based algorithms, we will detect administration errors in real-time and notify clinicians to decrease the time patients are a risk for harm. In Aim 2, we will evaluate the generalizability of the CCHMC-developed EHR-based medication administration error (MAE) detection algorithms by applying the algorithms to retrospective NICU and MICU data at an external institution. We will also develop a user-friendly demonstration package to facilitate usability of the algorithms and enhance the ability to observe their benefits. In Aim 3, we will develop novel algorithms to detect errors in smart pump use and evaluate system-level factors that contribute to pump errors. By detecting smart pump errors, the final step in medication and fluid administration, we will further reduce the rates of dangerous administration errors targeted in Aims 1 and 2. Our proposed work has the potential to accomplish a paradigm shift in the methods of patient safety research and clinical practice. The study is a fundamental step towards automating patient safety monitoring on a large scale and improving error identification and patient safety in the intensive care environment for millions of patients every year.

 描述（由申请人提供）：在患者安全领域，缺乏系统研究是进展的关键障碍。值得注意的是，缺少的是仔细调查电子健康记录（EHR）和信息技术在检测重症监护相关错误方面的研究。我们提出的研究旨在通过评估商业IT系统和EHR在减少医疗差错方面的有用性来解决当前安全研究知识中的一个已确定的差距。 在我们的研究中，我们试图将药物安全性研究从回顾性错误识别转向实时自动化和计算机化的方法，以实现更全面的患者安全范式。我们工作的中心假设是，通过识别药物订单和管理数据源之间的差异，我们可以检测和减轻药物相关的错误。在我们的研究中，我们将1）使用真实的时间分析来检测和拦截我们最近开发的电子健康记录（EHR）基于内容的算法识别的药物和肠外营养（PN）给药错误（目标1）; 2）确认算法在外部机构中的性能（目标2）;和3）创建新算法来识别智能泵输注错误（目标3）。通过系统地检测和拦截药物和PN错误，我们将把药物安全从被动报告错误转变为主动识别和缓解不安全护理。 在目标1中，我们将使用CCHMC开发的给药错误（MAE）检测算法，通过前瞻性识别可改善的药物和PN给药错误，缩短患者处于伤害风险中的时间。使用我们基于EHR的算法，我们将实时检测给药错误，并通知临床医生，以减少患者受到伤害的时间。在目标2中，我们将通过将CCHMC开发的基于EHR的给药错误（MAE）检测算法应用于外部机构的回顾性NICU和MICU数据，评估这些算法的普遍性。我们亦会开发一套方便使用的示范软件，以方便使用算法，并加强观察算法效益的能力。在目标3中，我们将开发新的算法来检测智能泵使用中的错误，并评估导致泵错误的系统级因素。通过检测智能泵错误，药物和液体给药的最后一步，我们将进一步降低目标1和2中的危险给药错误率。 我们提出的工作有可能实现患者安全研究和临床实践方法的范式转变。该研究是大规模自动化患者安全监测和改善错误识别和患者安全的基本步骤， 每年都有数百万的病人在重症监护环境中。