Abdullah Nik Nailah, Tang Jia, Fetrati Hemad, Kaukiah Nor Fadhilah Binti, Saharudin Sahrin Bin, Yong Vee Sim, Yen Chia How
School of Information Technology, Monash University Malaysia, Petaling Jaya, Malaysia.
Faculty of Information Technology, Monash University, Melbourne, Australia.
JMIR Cardio. 2025 Apr 10;9:e55846. doi: 10.2196/55846.
Nonadherence to medication is a key factor contributing to high heart failure (HF) rehospitalization rates. A conversational agent (CA) or chatbot is a technology that can enhance medication adherence by helping patients self-manage their medication routines at home.
This study outlines the conception of a design method for developing a CA to support patients in medication adherence, utilizing design thinking as the primary process for gathering requirements, prototyping, and testing. We apply this design method to the ongoing development of Medical Assistance and Rehabilitation Intelligent Agent (MARIA), a rule-based CA.
Following the design thinking process, at the ideation stage, we engaged a multidisciplinary group of stakeholders (patients and pharmacists) to elicit requirements for the early conception of MARIA. In collaboration with pharmacists, we structured MARIA's dialogue into a workflow based on Adlerian therapy, a psychoeducational theory. At the testing stage, we conducted an observational study using the Wizard of Oz (WoZ) research method to simulate the MARIA prototype with 20 patient participants. This approach validated and refined our application of Adlerian therapy in the CA's dialogue. We incorporated human-likeness and trust scoring into user satisfaction assessments after each WoZ session to evaluate MARIA's feasibility and acceptance of medication adherence. Dialogue data collected through WoZ simulations were analyzed using a coding analysis technique.
Our design method for the CA revealed gaps in MARIA's conception, including (1) handling negative responses, (2) appropriate use of emoticons to enhance human-likeness, (3) system feedback mechanisms during turn-taking delays, and (4) defining the extent to which a CA can communicate on behalf of a health care provider regarding medication adherence.
The design thinking process provided interactive steps to involve users early in the development of a CA. Notably, the use of WoZ in an observational clinical protocol highlighted the following: (1) coding analysis offered guidelines for modeling CA dialogue with patient safety in mind; (2) incorporating human-likeness and trust in user satisfaction assessments provided insights into attributes that foster patient trust in a CA; and (3) the application of Adlerian therapy demonstrated its effectiveness in motivating patients with HF to adhere to medication within a CA framework. In conclusion, our method is valuable for modeling and validating CA interactions with patients, assessing system reliability, user expectations, and constraints. It can guide designers in leveraging existing CA technologies, such as ChatGPT or AWS Lex, for adaptation in health care settings.
不遵医嘱服药是导致心力衰竭（HF）再住院率居高不下的关键因素。对话代理（CA）或聊天机器人是一种技术，可通过帮助患者在家中自我管理服药习惯来提高服药依从性。
本研究概述了一种设计方法的概念，该方法用于开发支持患者服药依从性的CA，将设计思维作为收集需求、制作原型和进行测试的主要过程。我们将此设计方法应用于基于规则的CA——医疗援助与康复智能代理（MARIA）的持续开发中。
遵循设计思维过程，在构思阶段，我们让多学科利益相关者（患者和药剂师）群体参与，以获取MARIA早期概念的需求。我们与药剂师合作，根据心理教育理论阿德勒疗法，将MARIA的对话构建成一个工作流程。在测试阶段，我们使用奥兹巫师（WoZ）研究方法进行了一项观察性研究，以模拟有20名患者参与者的MARIA原型。这种方法验证并完善了我们在CA对话中对阿德勒疗法的应用。在每次WoZ会话后，我们将拟人化和信任评分纳入用户满意度评估中，以评估MARIA在服药依从性方面的可行性和可接受性。使用编码分析技术对通过WoZ模拟收集的对话数据进行分析。
我们针对CA的设计方法揭示了MARIA概念中的差距，包括（1）处理负面回应，（2）适当使用表情符号以增强拟人化，（3）轮流延迟期间的系统反馈机制，以及（4）定义CA在服药依从性方面代表医疗保健提供者进行沟通的程度。
设计思维过程提供了交互式步骤，以便在CA开发早期让用户参与。值得注意的是，在观察性临床方案中使用WoZ突出了以下几点：（1）编码分析为在考虑患者安全的情况下对CA对话进行建模提供了指导方针；（2）在用户满意度评估中纳入拟人化和信任，提供了有助于患者信任CA的属性的见解；（3）阿德勒疗法的应用证明了其在CA框架内激励HF患者坚持服药方面的有效性。总之，我们的方法对于建模和验证CA与患者的互动、评估系统可靠性、用户期望和限制很有价值。它可以指导设计师利用现有的CA技术，如ChatGPT或AWS Lex，以适应医疗保健环境。
