Development of a Comprehensive Decision Support Tool for Chemotherapy-Cycle Prescribing: Initial Usability Study.

IF 2 Q3 HEALTH CARE SCIENCES & SERVICES JMIR Formative Research Pub Date : 2025-03-31 DOI:10.2196/62749
Sanna Iivanainen, Reetta Arokoski, Santeri Mentu, Laura Lang, Jussi Ekström, Henri Virtanen, Vesa Kataja, Jussi Pekka Koivunen
{"title":"Development of a Comprehensive Decision Support Tool for Chemotherapy-Cycle Prescribing: Initial Usability Study.","authors":"Sanna Iivanainen, Reetta Arokoski, Santeri Mentu, Laura Lang, Jussi Ekström, Henri Virtanen, Vesa Kataja, Jussi Pekka Koivunen","doi":"10.2196/62749","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Chemotherapy cycle prescription is generally carried out through a multistep manual process that is prone to human error. Clinical decision support tools can provide patient-specific assessments that support clinical decisions, improve prescribing practices, and reduce medication errors.</p><p><strong>Objective: </strong>We hypothesized that a knowledge-based, patient-derived, evidence-directed decision support tool consisting of multiple modules focusing on the core duties preceding chemotherapy-cycle prescription could result in a more cost-effective and error-free approach and streamline the workflow.</p><p><strong>Methods: </strong>A 1-arm, multicenter, prospective clinical trial (\"Follow-up of Cancer Patients Receiving Chemotherapy or Targeted Therapy by Electronic Patient Reported Outcomes-tool\" [ECHO] 7/2019-1/2021; NCT04081558) was initiated to investigate the tool. The most important inclusion criteria were the presence of colorectal cancer (CRC) treated with oxaliplatin-based chemotherapy, age ≥18 years, Eastern Cooperative Oncology Group [ECOG] performance score of 0 to 2, and internet access. A decision support tool that included digital symptom monitoring, a laboratory value interface, and treatment schedule integration for semiautomated chemotherapy cycle prescribing was integrated into the care pathway. Performance was assessed by the percentage of chemotherapy cycles with sent and completed symptom questionnaires, while perceptions of health care professionals (HCPs) on the feasibility of the approach were collected through a 1-time semistructured interview.</p><p><strong>Results: </strong>The ECHO trial included 43 patients with CRC treated with doublet or triplet chemotherapy in an adjuvant or metastatic setting. Altogether, 843 electronic patient-reported outcome (ePRO) symptom questionnaires were completed. Of the 15 recorded symptoms, fatigue (n=446, 52.9%) and peripheral neuropathy (n=429, 50.9%) were reported most often, while 137 grade 3 to 4 symptoms were recorded, of which diarrhea (n=5, 4%) and peripheral neuropathy (n=4, 3%) were the most common. During the study, 339 chemotherapy cycles were prescribed, and for the 77% (n=262) of new chemotherapy cycles, ePRO questionnaire data were available within preset limits (completed within 3 days prior to chemotherapy scheduling) while 65% of the cycles (n=221) had symptom questionnaire grading at ≤1%, and 67% of the cycles (n=228) had laboratory values in a preset range. The recommendations by the tool for a new chemotherapy cycle were tier 1 (green; meaning \"go\") in 145 (42.8%) of the cycles, tier 2 (yellow; \"evaluate\") in 83 (25%), and tier 3 (red; \"hold\") in 111 (32.7%). HCPs (n=3) were interviewed with a questionnaire (comprising 8 questions), revealing that they most valued the improved workflow, faster patient evaluation, and direct messaging option.</p><p><strong>Conclusions: </strong>In this study, we investigated the feasibility of a decision support system for chemotherapy-cycle pre-evaluation and prescription that was developed for the prospective ECHO trial. The study showed that the functionalities of the investigated tool were feasible and that an automated approach to chemotherapy-cycle prescription was possible for nearly half of the cycles.</p>","PeriodicalId":14841,"journal":{"name":"JMIR Formative Research","volume":"9 ","pages":"e62749"},"PeriodicalIF":2.0000,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11975257/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"JMIR Formative Research","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2196/62749","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"HEALTH CARE SCIENCES & SERVICES","Score":null,"Total":0}
引用次数: 0

Abstract

Background: Chemotherapy cycle prescription is generally carried out through a multistep manual process that is prone to human error. Clinical decision support tools can provide patient-specific assessments that support clinical decisions, improve prescribing practices, and reduce medication errors.

Objective: We hypothesized that a knowledge-based, patient-derived, evidence-directed decision support tool consisting of multiple modules focusing on the core duties preceding chemotherapy-cycle prescription could result in a more cost-effective and error-free approach and streamline the workflow.

Methods: A 1-arm, multicenter, prospective clinical trial ("Follow-up of Cancer Patients Receiving Chemotherapy or Targeted Therapy by Electronic Patient Reported Outcomes-tool" [ECHO] 7/2019-1/2021; NCT04081558) was initiated to investigate the tool. The most important inclusion criteria were the presence of colorectal cancer (CRC) treated with oxaliplatin-based chemotherapy, age ≥18 years, Eastern Cooperative Oncology Group [ECOG] performance score of 0 to 2, and internet access. A decision support tool that included digital symptom monitoring, a laboratory value interface, and treatment schedule integration for semiautomated chemotherapy cycle prescribing was integrated into the care pathway. Performance was assessed by the percentage of chemotherapy cycles with sent and completed symptom questionnaires, while perceptions of health care professionals (HCPs) on the feasibility of the approach were collected through a 1-time semistructured interview.

Results: The ECHO trial included 43 patients with CRC treated with doublet or triplet chemotherapy in an adjuvant or metastatic setting. Altogether, 843 electronic patient-reported outcome (ePRO) symptom questionnaires were completed. Of the 15 recorded symptoms, fatigue (n=446, 52.9%) and peripheral neuropathy (n=429, 50.9%) were reported most often, while 137 grade 3 to 4 symptoms were recorded, of which diarrhea (n=5, 4%) and peripheral neuropathy (n=4, 3%) were the most common. During the study, 339 chemotherapy cycles were prescribed, and for the 77% (n=262) of new chemotherapy cycles, ePRO questionnaire data were available within preset limits (completed within 3 days prior to chemotherapy scheduling) while 65% of the cycles (n=221) had symptom questionnaire grading at ≤1%, and 67% of the cycles (n=228) had laboratory values in a preset range. The recommendations by the tool for a new chemotherapy cycle were tier 1 (green; meaning "go") in 145 (42.8%) of the cycles, tier 2 (yellow; "evaluate") in 83 (25%), and tier 3 (red; "hold") in 111 (32.7%). HCPs (n=3) were interviewed with a questionnaire (comprising 8 questions), revealing that they most valued the improved workflow, faster patient evaluation, and direct messaging option.

Conclusions: In this study, we investigated the feasibility of a decision support system for chemotherapy-cycle pre-evaluation and prescription that was developed for the prospective ECHO trial. The study showed that the functionalities of the investigated tool were feasible and that an automated approach to chemotherapy-cycle prescription was possible for nearly half of the cycles.

Abstract Image

Abstract Image

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
化疗周期处方综合决策支持工具的开发:初步可用性研究。
背景:化疗周期处方通常是通过一个多步骤的人工过程进行的,容易出现人为错误。临床决策支持工具可以提供针对患者的评估,从而支持临床决策、改进处方实践并减少用药错误。目的:我们假设一个以知识为基础,以患者为基础,以证据为导向的决策支持工具,由多个模块组成,重点关注化疗周期处方前的核心职责,可以产生更具成本效益和无错误的方法,并简化工作流程。方法:一项单组、多中心、前瞻性临床试验(“通过电子患者报告结果工具随访接受化疗或靶向治疗的癌症患者”[ECHO] 7/2019-1/2021;NCT04081558)被启动以调查该工具。最重要的纳入标准是存在以奥沙利铂为基础的化疗治疗的结直肠癌(CRC),年龄≥18岁,东部肿瘤合作组[ECOG]表现评分为0至2分,以及互联网接入。决策支持工具,包括数字症状监测、实验室值接口和半自动化化疗周期处方的治疗计划整合,被整合到护理途径中。通过发送和完成症状问卷的化疗周期百分比来评估效果,同时通过1次半结构化访谈收集卫生保健专业人员(HCPs)对该方法可行性的看法。结果:ECHO试验纳入了43例在辅助或转移性环境下接受双重或三重化疗的结直肠癌患者。共完成843份电子患者报告结果(ePRO)症状问卷。15种症状中,以疲劳(n=446, 52.9%)和周围神经病变(n=429, 50.9%)最为常见,3 ~ 4级症状共137例,其中以腹泻(n=5, 4%)和周围神经病变(n=4, 3%)最为常见。研究期间共开了339个化疗周期,其中77% (n=262)的新化疗周期的ePRO问卷数据在预设范围内(化疗计划前3天内完成),65% (n=221)的症状问卷评分≤1%,67% (n=228)的实验室值在预设范围内。该工具对新化疗周期的建议为1级(绿色;在145个(42.8%)循环中表示“前进”,第2层(黄色;“评估”),83级(25%)和3级(红色;“持有”),占32.7%。HCPs (n=3)接受了一份包括8个问题的问卷调查,结果显示他们最看重的是改进的工作流程、更快的患者评估和直接消息传递选项。结论:在本研究中,我们研究了为前瞻性ECHO试验开发的化疗周期预评估和处方决策支持系统的可行性。该研究表明,所研究的工具的功能是可行的,并且在近一半的周期中,可以实现化疗周期处方的自动化方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
JMIR Formative Research
JMIR Formative Research Medicine-Medicine (miscellaneous)
CiteScore
2.70
自引率
9.10%
发文量
579
审稿时长
12 weeks
期刊最新文献
Performance of DeepSeek V3, DeepSeek R1, ChatGPT 4o, and ChatGPT o1 on the National Health Professional and Technical Qualification Examination (Intermediate Level) in China: Comparative Analysis. Development and Evaluation of a German Suicide Prevention Website for Men: Exploratory Study. Evaluation of GPT-5 in Periodontitis Staging and Grading: Retrospective Observational Study. Validity of Smartphone-Based Measurement for Assessing Lower Limb Power for Sarcopenia and Frailty Discrimination: Cross-Sectional Study. Screening the Digital Skills of Patients in Geriatric Rehabilitation: Multicenter Cross-Sectional Study.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1