Laursen Sisse Heiden, Hæsum Lisa Korsbakke Emtekær, Egmose Julie, Kronborg Thomas, Udsen Flemming Witt, Hejlesen Ole Kristian, Hangaard Stine
Department of Health Science and Technology, Aalborg University, Gistrup, Denmark.
Steno Diabetes Center North Denmark, Aalborg University Hospital, Aalborg, Denmark.
Int J Nurs Stud Adv. 2024 Oct 22;7:100257. doi: 10.1016/j.ijnsa.2024.100257. eCollection 2024 Dec.
Prediction algorithms may improve the ability of telehealth solutions to assess the risk of future exacerbations in patients with chronic obstructive pulmonary disease. Learning from patients' and clinicians' evaluations and experiences about the use of such algorithms is essential to evaluate its potential and examine factors that could potentially influence the implementation and sustained use.
To investigate the patients' and clinicians' perceptions and satisfaction with an algorithm for predicting exacerbations in patients with chronic obstructive pulmonary disease.
Multimethod study.
Three community nursing sites in Aalborg Municipality, Denmark.
One hundred and eleven adults with chronic obstructive pulmonary disease and four clinicians (three nurses and one physiotherapist) specialized in telehealth monitoring of the disease.
The study was performed from November 2021 to November 2022 alongside a clinical trial in which a prediction algorithm was integrated into an existing telehealth system. The patients' perspectives were investigated using a self-constructed questionnaire. The clinicians' perspective was explored using semistructured individual interviews.
Most patients (84.0 %-90.8 %) were satisfied with the algorithm and the additional measurements required by the algorithm. Approximately 71.7 %-75.9 % found that the algorithm could be a useful tool for disease assessment. Patients elaborated that they could see an exacerbation prevention potential in the algorithm. Patients trusted the algorithm and found an increased sense of security. The clinicians showed a positive response toward the algorithm and its user-friendliness. However, they were concerned that the additional measurements could be too demanding for some patients and questioned the accuracy of the measurements. Some felt that the algorithm could risk being time-consuming and harm the overall assessment of the individual patient. They expressed a need for continuous information about the algorithm to understand its functions and alarms.
Optimal use of the algorithm would require that patients perform additional pulse and oxygen saturation measurements. Furthermore, it will require in-depth insight among clinicians regarding the algorithm's functions and alarms.
The study was performed alongside a clinical trial, which was first registered September 9, 2021, at clinicaltrials.gov (registration number NCT05218525). Date of first recruitment was September 28, 2021.
预测算法可能会提高远程医疗解决方案评估慢性阻塞性肺疾病患者未来病情加重风险的能力。了解患者和临床医生对使用此类算法的评估及体验对于评估其潜力以及研究可能影响实施和持续使用的因素至关重要。
调查患者和临床医生对慢性阻塞性肺疾病患者病情加重预测算法的看法和满意度。
多方法研究。
丹麦奥尔堡市的三个社区护理点。
111名患有慢性阻塞性肺疾病的成年人以及4名专门从事该疾病远程医疗监测的临床医生（3名护士和1名物理治疗师）。
该研究于2021年11月至2022年11月与一项临床试验同时进行，在该临床试验中，一种预测算法被集成到现有的远程医疗系统中。通过自行编制的问卷调查患者的观点。通过半结构化的个人访谈探索临床医生的观点。
大多数患者（84.0%-90.8%）对算法以及算法要求的额外测量感到满意。约71.7%-75.9%的患者认为该算法可能是疾病评估的有用工具。患者详细说明他们能在算法中看到预防病情加重的潜力。患者信任该算法并感到安全感增强。临床医生对该算法及其易用性表现出积极反应。然而，他们担心额外测量对一些患者要求过高，并对测量的准确性提出质疑。一些人认为该算法可能耗时且会损害对个体患者的整体评估。他们表示需要关于该算法的持续信息以了解其功能和警报。
算法的最佳使用要求患者进行额外的脉搏和血氧饱和度测量。此外，临床医生需要深入了解算法的功能和警报。
该研究与一项临床试验同时进行，该临床试验于2021年9月9日首次在clinicaltrials.gov上注册（注册号NCT05218525）。首次招募日期为2021年9月28日。
