Evidence map›Paper›PMID 33444156›Full record

ArticleJMIR medical informatics2021

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.

Qian He, Fei Du, Lianne W L Simonse

Erratum issuedOpen access · goldAbstract read
In one paragraph

Article in JMIR medical informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
3.7field-weighted citation impact, top 7% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed, 27 citations in OpenAlex.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors at 1 institution in 1 country.

Qian He *Department of Design Organisation & Strategy, Faculty of Industrial Design Engineering, Delft University of Technology, Delft, Netherlands.ORCID https://orcid.org/0000-0001-8390-5769
Fei Du *Department of Design Organisation & Strategy, Faculty of Industrial Design Engineering, Delft University of Technology, Delft, Netherlands.ORCID https://orcid.org/0000-0001-6216-0200
Lianne W L Simonse *Department of Design Organisation & Strategy, Faculty of Industrial Design Engineering, Delft University of Technology, Delft, Netherlands.ORCID https://orcid.org/0000-0001-8860-6522
Delft University of Technology · NL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIn 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.

objectiveThe 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.

methodsA 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.

resultsThe 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.

conclusionsThe 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.

Indexed as

AIartificial intelligenceCOVID-19designdigital service solutions in healtheHealthhome isolationpatient journey mapservice designtouchpointuser-centered design

Identifiers

PMID33444156
PMCPMC8043148
OpenAlexW3119393345

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.