Purpose: The use of patient reported outcome (PRO) measures in clinical practice is increasing. Following the creation of a 'User's Guide to Implementing PRO Assessment in Clinical Practice' by the International Society for Quality of Life Research (ISOQOL), volunteers from ISOQOL sought to create a Companion Guide to assist health care providers with the scientific and practical considerations involved in implementing and using PRO measures in clinical care by using information from real-world case studies. This paper summarizes the key issues presented in the Companion Guide. Methods: Ten respondents who were members of the ISOQOL's CP-SIG and worked in various clinical areas, participated in a survey or telephone interview. Participants were from Canada (n=2), Denmark (n=1), England (n=2), Holland (n=1) and the United States (n=4). Results: Based on the information provided by respondents, a Companion Guide was produced, organized according to the nine questions presented in the User's Guide. An additional section for key take-home messages was also provided. This guide provides examples of issues and considerations related to the implementation of PRO measures in clinical practice. Conclusions: Respondents provided insight into their experiences and emphasized that PRO initiatives were likely to be more successful if there is purposeful, designed integration into clinical practice, meaningful substantive engagement with all stakeholders and access to necessary organizational resources. The ability to leverage existing technology as well as realistic and stakeholder consensus-driven expectations for planning and timing were also key to the successful implementation of PRO measures.
PRO dashboards are a promising approach for integrating patient-generated data into prostate cancer care. Informed by human-centered design principles, this work establishes guidance on dashboard content, tailoring, and clinical use that patients and providers find meaningful.
Introduction: Many patients use mobile devices to track health conditions by recording patient-generated health data. However, patients and clinicians may disagree how to use these data. Objective: To systematically review the literature to identify how patient-generated health data and patient-reported outcomes collected outside of clinical settings can affect patient–clinician relationships within surgery and primary care. Methods: Six research databases were queried for publications documenting the effect of patient-generated health data or patient-reported outcomes on patient–clinician relationships. We conducted thematic synthesis of the results of the included publications. Results: Thirteen of the 3204 identified publications were included for synthesis. Three main themes were identified: patient-generated health data supported patient–clinician communication and health awareness, patients desired for their clinicians to be involved with their patient-generated health data, which clinicians had difficulty accommodating, and patient-generated health data platform features may support or hinder patient–clinician collaboration. Conclusion: Patient-generated health data and patient-reported outcomes may improve patient health awareness and communication with clinicians but may negatively affect patient–clinician relationships.
Objective Chatbots have potential to deliver interactive self-management interventions but have rarely been studied in the context of hypertension or medication adherence. The objective of this study was to better understand patient information needs and perceptions of chatbots to support hypertension medication self-management. Materials and Methods Mixed methods were used to assess self-management needs and preferences for using chatbots. We purposively sampled adults with hypertension who were prescribed at least one medication. Participants completed questionnaires on sociodemographics, health literacy, self-efficacy, and technology use. Semi-structured interviews were conducted, audio-recorded, and transcribed verbatim. Quantitative data were analyzed using descriptive statistics, and qualitative data were analyzed using applied thematic analysis. Results Thematic saturation was met after interviewing 15 participants. Analysis revealed curiosity toward chatbots, and most perceived them as humanlike. The majority were interested in using a chatbot to help manage medications, refills, communicate with care teams, and for accountability toward self-care tasks. Despite general enthusiasm, there were concerns with chatbots providing too much information, making demands for lifestyle changes, invading privacy, and usability issues with deployment on smartphones. Those with overall positive perceptions toward chatbots were younger and taking fewer medications. Discussion Chatbot-related informational needs were consistent with existing self-management research, and many felt chatbots would be valuable if customizable and compatible with patient portals, pharmacies, or health apps. Conclusion Although most were not familiar with chatbots, patients were interested in interacting with them, but this varied. This research informs future design and functionalities of conversational interfaces to support hypertension self-management.
Deductive analysis considering model constructs provides a useful approach to designing collaborative HIT systems, allowing designers to consider both empirical user data and existing knowledge from the literature. This method has the potential to improve designs for collaborative HIT systems.
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