Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
J Am Med Inform Assoc. 2023 Apr 19;30(5):899-906. doi: 10.1093/jamia/ocad020.
To improve problem list documentation and care quality.
We developed algorithms to infer clinical problems a patient has that are not recorded on the coded problem list using structured data in the electronic health record (EHR) for 12 clinically significant heart, lung, and blood diseases. We also developed a clinical decision support (CDS) intervention which suggests adding missing problems to the problem list. We evaluated the intervention at 4 diverse healthcare systems using 3 different EHRs in a randomized trial using 3 predetermined outcome measures: alert acceptance, problem addition, and National Committee for Quality Assurance Healthcare Effectiveness Data and Information Set (NCQA HEDIS) clinical quality measures.
There were 288 832 opportunities to add a problem in the intervention arm and the problem was added 63 777 times (acceptance rate 22.1%). The intervention arm had 4.6 times as many problems added as the control arm. There were no significant differences in any of the clinical quality measures.
The CDS intervention was highly effective at improving problem list completeness. However, the improvement in problem list utilization was not associated with improvement in the quality measures. The lack of effect on quality measures suggests that problem list documentation is not directly associated with improvements in quality measured by National Committee for Quality Assurance Healthcare Effectiveness Data and Information Set (NCQA HEDIS) quality measures. However, improved problem list accuracy has other benefits, including clinical care, patient comprehension of health conditions, accurate CDS and population health, and for research.
An EHR-embedded CDS intervention was effective at improving problem list completeness but was not associated with improvement in quality measures.
提高问题清单的记录质量和护理质量。
我们开发了算法，以利用电子健康记录（EHR）中的结构化数据推断患者未在编码问题清单中记录的临床问题，这些问题涉及 12 种具有临床意义的心脏、肺和血液疾病。我们还开发了一种临床决策支持（CDS）干预措施，用于建议将缺失的问题添加到问题清单中。我们在 4 个不同的医疗保健系统中使用 3 种不同的 EHR 进行了一项随机试验，使用 3 个预定的结果衡量标准评估了该干预措施：警报接受率、问题添加率和国家质量保证医疗效果数据和信息集（NCQA HEDIS）临床质量衡量标准。
干预组有 288832 次添加问题的机会，添加了 63777 次问题（接受率为 22.1%）。干预组添加的问题数量是对照组的 4.6 倍。任何临床质量衡量标准均无显著差异。
CDS 干预措施在提高问题清单的完整性方面非常有效。然而，问题清单利用率的提高与质量衡量标准的提高无关。对质量衡量标准没有影响表明，问题清单的记录与国家质量保证医疗效果数据和信息集（NCQA HEDIS）质量衡量标准所衡量的质量改进并无直接关联。但是，提高问题清单的准确性还有其他好处，包括临床护理、患者对健康状况的理解、准确的 CDS 和人群健康，以及用于研究。
EHR 嵌入式 CDS 干预措施在提高问题清单完整性方面非常有效，但与质量衡量标准的提高无关。
