Dataset Information

A Patient Journey Map to Improve the Home Isolation Experience of Persons With Mild COVID-19: Design Research for Service Touchpoints of Artificial Intelligence in eHealth.

ABSTRACT:

Background

In the context of the COVID-19 outbreak, 80% of the persons who are infected have mild symptoms and are required to self-recover at home. They have a strong demand for remote health care that, despite the great potential of artificial intelligence (AI), is not met by the current services of eHealth. Understanding the real needs of these persons is lacking.

Objective

The aim of this paper is to contribute a fine-grained understanding of the home isolation experience of persons with mild COVID-19 symptoms to enhance AI in eHealth services.

Methods

A design research method with a qualitative approach was used to map the patient journey. Data on the home isolation experiences of persons with mild COVID-19 symptoms was collected from the top-viewed personal video stories on YouTube and their comment threads. For the analysis, this data was transcribed, coded, and mapped into the patient journey map.

Results

The key findings on the home isolation experience of persons with mild COVID-19 symptoms concerned (1) an awareness period before testing positive, (2) less typical and more personal symptoms, (3) a negative mood experience curve, (5) inadequate home health care service support for patients, and (6) benefits and drawbacks of social media support.

Conclusions

The design of the patient journey map and underlying insights on the home isolation experience of persons with mild COVID-19 symptoms serves health and information technology professionals in more effectively applying AI technology into eHealth services, for which three main service concepts are proposed: (1) trustworthy public health information to relieve stress, (2) personal COVID-19 health monitoring, and (3) community support.

SUBMITTER: He Q

PROVIDER: S-EPMC8043148 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature

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Publications

A Patient Journey Map to Improve the Home Isolation Experience of Persons With Mild COVID-19: Design Research for Service Touchpoints of Artificial Intelligence in eHealth.

He Qian Q Du Fei F Simonse Lianne W L LWL

JMIR medical informatics 20210412 4

<h4>Background</h4>In the context of the COVID-19 outbreak, 80% of the persons who are infected have mild symptoms and are required to self-recover at home. They have a strong demand for remote health care that, despite the great potential of artificial intelligence (AI), is not met by the current services of eHealth. Understanding the real needs of these persons is lacking.<h4>Objective</h4>The aim of this paper is to contribute a fine-grained understanding of the home isolation experience of p ...[more]

PMID: 33444156

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Project description:PurposeThe successful design and innovation of eHealth solutions directly involve end users in the process to seek a better understanding of their needs. This article presents user-innovated eHealth solutions targeting older persons with hearing impairment. Our research question was: What are the key users' needs, expectations, and visions within future hearing rehabilitation service delivery?MethodWe applied a participatory design approach to facilitate the design of future eHealth solutions via focus groups. We involved older persons with hearing impairment (n = 36), significant others (n = 10), and audiologists (n = 8) following 2 methods: (a) human-centered design for interactive systems and (b) user innovation management. Through 3 rounds of focus groups, we facilitated a process progressing from insights and visions for requirements (Phase 1), to app such as paper version wireframes (Phase 2), and to digital prototypes envisioning future eHealth solutions (Phase 3). Each focus group was video-recorded and photographed, resulting in a rich data set that was analyzed through inductive thematic analysis.ResultsThe results are presented via (a) a storyboard envisioning future client journeys, (b) 3 key themes for future eHealth solutions, (c) 4 levels of interest and willingness to invest time and effort in digital solutions, and (d) 2 technical savviness types and their different preferences for rehabilitation strategies.ConclusionsFuture eHealth solutions must offer personalized rehabilitation strategies that are appropriate for every person with hearing impairment and their level of technical savviness. Thus, a central requirement is anchoring of digital support in the clients' everyday life situations by facilitating easy access to personalized information, communication, and learning milieus. Moreover, the participants' visions for eHealth solutions call for providing both traditional analogue and digital services.Supplemental materialhttps://doi.org/10.23641/asha.7310729.

Project description:BackgroundNeovascular age-related macular degeneration (nAMD) is one of the largest single-disease contributors to hospital outpatient appointments. Challenges in finding the clinical capacity to meet this demand can lead to sight-threatening delays in the macular services that provide treatment. Clinical artificial intelligence (AI) technologies pose one opportunity to rebalance demand and capacity in macular services. However, there is a lack of evidence to guide early-adopters seeking to use AI as a solution to demand-capacity imbalance. This study aims to provide guidance for these early adopters on how AI-enabled macular services may best be implemented by exploring what will influence the outcome of AI implementation and why.MethodsThirty-six semi-structured interviews were conducted with participants. Data were analysed with the Nonadoption, Abandonment, Scale-up, Spread and Sustainability (NASSS) framework to identify factors likely to influence implementation outcomes. These factors and the primary data then underwent a secondary analysis using the Fit between Individuals, Technology and Task (FITT) framework to propose an actionable intervention.ResultsnAMD treatment should be initiated at face-to-face appointments with clinicians who recommend year-long periods of AI-enabled scheduling of treatments. This aims to maintain or enhance the quality of patient communication, whilst reducing consultation frequency. Appropriately trained photographers should take on the additional roles of inputting retinal imaging into the AI device and overseeing its communication to clinical colleagues, while ophthalmologists assume clinical oversight and consultation roles. Interoperability to facilitate this intervention would best be served by imaging equipment that can send images to the cloud securely for analysis by AI tools. Picture Archiving and Communication Software (PACS) should have the capability to output directly into electronic medical records (EMR) familiar to clinical and administrative staff.ConclusionThere are many enablers to implementation and few of the remaining barriers relate directly to the AI technology itself. The proposed intervention requires local tailoring and prospective evaluation but can support early adopters in optimising the chances of success from initial efforts to implement AI-enabled macular services.Protocol registrationHogg HDJ, Brittain K, Teare D, Talks J, Balaskas K, Keane P, Maniatopoulos G. Safety and efficacy of an artificial intelligence-enabled decision tool for treatment decisions in neovascular age-related macular degeneration and an exploration of clinical pathway integration and implementation: protocol for a multi-methods validation study. BMJ Open. 2023 Feb 1;13(2):e069443. https://doi.org/10.1136/bmjopen-2022-069443 . PMID: 36725098; PMCID: PMC9896175.

Dataset Information

A Patient Journey Map to Improve the Home Isolation Experience of Persons With Mild COVID-19: Design Research for Service Touchpoints of Artificial Intelligence in eHealth.

Background

Objective

Methods

Results

Conclusions

Publications

A Patient Journey Map to Improve the Home Isolation Experience of Persons With Mild COVID-19: Design Research for Service Touchpoints of Artificial Intelligence in eHealth.

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