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出院后在家中借助技术实现康复的患者衍生设计原则:混合方法研究

Patient-Derived Design Principles for Technology-Enabled Healing at Home Following Hospital Discharge: Mixed Methods Study.

作者信息

Philpot Lindsey M, Singla Abhinav, Dugani Sagar B, Canning Rachel E, Smith Christina M, DeZutter Meredith A, Ramar Priya, Hovell Jennifer M P, Ebbert Jon O

机构信息

Department of Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, United States, 1 5075381882.

出版信息

JMIR Hum Factors. 2025 Aug 20;12:e72913. doi: 10.2196/72913.

DOI:10.2196/72913
PMID:40835402
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12367353/
Abstract

BACKGROUND

As more patients transition from hospital to home for postacute care, a growing interest exists in leveraging technology to support recovery, yet a limited understanding exists on how to design these tools to align with patient and caregiver needs and preferences.

OBJECTIVE

This study aimed to explore the perceptions, attitudes, and beliefs of recently discharged patients to develop user-centered design principles for digital tools that support safe and effective transitions from hospital to home.

METHODS

A vignette-based, mixed methods survey was conducted, grounded in the technology acceptance model, to explore patient perceptions of digital tools supporting postdischarge care. A random sample of 1000 recently discharged adult patients received a survey featuring validated vignettes and technology acceptance model-informed questions, with both quantitative and qualitative items. Open-ended responses were analyzed using grounded theory to derive design principles that inform the development and implementation of patient-centered digital health tools. Quantitative items were descriptive and are summarized as counts (n) and frequencies (%).

RESULTS

Of the 967 eligible patients contacted, 116 completed the survey (response rate of 2%), with respondents having a median age of 71 (IQR 61-78) years, high rates of chronic illness, and access to smartphones (98/116, 84.5%) and in-home internet (111/116, 95.7%). Qualitative analysis revealed 6 key themes-connection to care, technical ease of use, solution usability, human connection, cost, and privacy-informing 3 patient-centered design principles focused on user experience, affordability, and transparent communication to guide future technology-supported hospital discharge interventions. Respondents reported the following factors as highly important: reassurance that a care team member would reach out if something seemed wrong (107/116, 92.2%), responsiveness to patient needs (95/116, 81.9%), ability to see their data (95/116, 81.9%), and out-of-pocket cost (94/116, 81.0%). Less important factors included duration of device use (22/116, 19.0%) and battery life (21/116, 18.0%).

CONCLUSIONS

Grounded in patient perspectives, this study identified the 3 core design principles of user experience and accessibility, cost and privacy, and communication and transparency that should guide the development and implementation of digital tools to support safe, effective, and human-centered transitions from hospital to home.

摘要

背景

随着越来越多的患者从医院过渡到家中接受急性后期护理,利用技术支持康复的兴趣日益浓厚,但对于如何设计这些工具以符合患者和护理人员的需求及偏好,人们的了解还很有限。

目的

本研究旨在探讨近期出院患者的认知、态度和信念,以制定以用户为中心的数字工具设计原则,支持从医院到家的安全有效过渡。

方法

基于技术接受模型进行了一项基于 vignette 的混合方法调查,以探索患者对支持出院后护理的数字工具的认知。1000 名近期出院的成年患者的随机样本收到了一份包含经过验证的 vignette 和技术接受模型相关问题的调查问卷,包括定量和定性项目。使用扎根理论分析开放式回答,以得出为以患者为中心的数字健康工具的开发和实施提供信息的设计原则。定量项目为描述性,总结为计数(n)和频率(%)。

结果

在联系的 967 名符合条件的患者中,116 名完成了调查(回复率为 2%),受访者的中位年龄为 71 岁(四分位间距 61 - 78 岁),慢性病发生率高,且能够使用智能手机(98/116,84.5%)和家中互联网(111/116,95.7%)。定性分析揭示了 6 个关键主题——与护理的联系、技术易用性、解决方案可用性、人际联系、成本和隐私——形成了 3 条以患者为中心的设计原则,重点关注用户体验、可承受性和透明沟通,以指导未来技术支持的医院出院干预措施。受访者报告以下因素非常重要:如果出现问题护理团队成员会联系的保证(107/116,92.2%)、对患者需求的响应能力(95/116,81.9%)、查看自己数据的能力(95/116,81.9%)以及自付费用(94/116,81.0%)。不太重要的因素包括设备使用时长(22/116,19.0%)和电池续航时间(21/116,18.0%)。

结论

基于患者的观点,本研究确定了用户体验与可及性、成本与隐私以及沟通与透明度这 3 条核心设计原则,应指导数字工具的开发和实施,以支持从医院到家的安全、有效且以人为本的过渡。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/49ce/12367353/e413745f14d8/humanfactors-v12-e72913-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/49ce/12367353/ef34cc310994/humanfactors-v12-e72913-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/49ce/12367353/a716ea5074e9/humanfactors-v12-e72913-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/49ce/12367353/328c58cdb3a7/humanfactors-v12-e72913-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/49ce/12367353/e413745f14d8/humanfactors-v12-e72913-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/49ce/12367353/ef34cc310994/humanfactors-v12-e72913-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/49ce/12367353/a716ea5074e9/humanfactors-v12-e72913-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/49ce/12367353/328c58cdb3a7/humanfactors-v12-e72913-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/49ce/12367353/e413745f14d8/humanfactors-v12-e72913-g004.jpg

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