Gilbertson-White Stephanie, Albashayreh Alaa, Ji Yuwen, Bandyopadhyay Anindita, Zeinali Nahid, Cherwin Catherine
College of Nursing, University of Iowa, Iowa City, Iowa, United States.
Department of Business Analytics, University of Iowa, Iowa City, Iowa, United States.
Appl Clin Inform. 2024 Oct;15(5):1130-1139. doi: 10.1055/s-0044-1791820. Epub 2024 Dec 25.
The integration of patient-reported outcomes (PROs) into clinical care, particularly in the context of cancer and multimorbidity, is crucial. While PROs have the potential to enhance patient-centered care and improve health outcomes through improved symptom assessment, they are not always adequately documented by the health care team.
This study aimed to explore the concordance between patient-reported symptom occurrence and symptoms documented in electronic health records (EHRs) in people undergoing treatment for cancer in the context of multimorbidity.
We analyzed concordance between patient-reported symptom occurrence of 13 symptoms from the Memorial Symptom Assessment Scale and provider-documented symptoms extracted using NimbleMiner, a machine learning tool, from EHRs for 99 patients with various cancer diagnoses. Logistic regression guided with the Akaike Information Criterion was used to identify significant predictors of symptom concordance.
Our findings revealed discrepancies in patient and provider reports, with itching showing the highest concordance (66%) and swelling showing the lowest concordance (40%). There was no statistically significant association between multimorbidity and high concordance, while lower concordance was observed for women, patients with advanced cancer stages, individuals with lower education levels, those who had partners, and patients undergoing highly emetogenic chemotherapy.
These results highlight the challenges in achieving accurate and complete symptom documentation in EHRs and the necessity for targeted interventions to improve the precision of clinical documentation. By addressing these gaps, health care providers can better understand and manage patient symptoms, ultimately contributing to more personalized and effective cancer care.
将患者报告的结果（PROs）纳入临床护理，尤其是在癌症和多重疾病的背景下，至关重要。虽然PROs有可能通过改善症状评估来加强以患者为中心的护理并改善健康结果，但医疗团队并不总是能充分记录这些结果。
本研究旨在探讨在多重疾病背景下接受癌症治疗的患者报告的症状发生情况与电子健康记录（EHRs）中记录的症状之间的一致性。
我们分析了99例患有各种癌症诊断的患者中，患者报告的来自纪念症状评估量表的13种症状的发生情况与使用机器学习工具NimbleMiner从EHRs中提取的医疗服务提供者记录的症状之间的一致性。使用以赤池信息准则为指导的逻辑回归来确定症状一致性的显著预测因素。
我们的研究结果揭示了患者和医疗服务提供者报告之间的差异，瘙痒的一致性最高（66%），肿胀的一致性最低（40%）。多重疾病与高一致性之间没有统计学上的显著关联，而女性、癌症晚期患者、教育水平较低的个体、有伴侣的人以及接受高度致吐性化疗的患者的一致性较低。
这些结果凸显了在EHRs中实现准确和完整症状记录的挑战，以及进行有针对性干预以提高临床记录准确性的必要性。通过弥补这些差距，医疗服务提供者可以更好地理解和管理患者症状，最终有助于提供更个性化和有效的癌症护理。
