Evidence map›Paper›PMID 36618758›Full record

ReviewUniversal access in the information society2022

Systematic review of co-design in digital health for COVID-19 research.

Muneer Nusir, Molka Rekik

Abstract readReview
In one paragraph

Review in Universal access in the information society, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 2 pooled it
–field-weighted citation impact
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

8 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Article
  5. Article
  6. Article
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  8. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Muneer Nusir *Department of Information Systems, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Alkharj, 16278 Saudi Arabia.
Molka Rekik *Department of Information Systems, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Alkharj, 16278 Saudi Arabia.ORCID 0000-0002-8639-4922

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Improving the quality of digital health care through information and communication technology can mainly contribute to the clinical, social, financial, and economic systems' success, especially during the COVID-19 pandemic period. The co-design approach, which unleashes the end-user power, can contribute actively in improving the healthcare systems. It deals with understanding the user behaviors, requirements, and motivations through observation, inspection, task analysis, and feedback techniques. Consequently, both the co-design and digital technologies might empower the management of patients' health and that of their families. The research strategy is based on a systematic literature review and meta-analysis to summarize how the co-design methodologies handled the existing technology-based health systems for their improvement. Based on the findings, we establish the following hypotheses: (i) A user-centered methodology for service implementation might offer a promising tool to enhance the healthcare services quality before they be launched; (ii) Several limitations can affect the co-design approach in digital health, such as a bias for a patients' group. Efforts have been made to reduce this risk by identifying bias at an early stage, or different groups should be included in the test phase for example; (iii) Use decision-making devices that handle technologies for patient and clinical healthcare solution.

Indexed as

Co-designCOVID-19Digital health careSystematic review

Identifiers

PMID36618758
PMCPMC9805349

What OpenQuestion holds

Textmetadata
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.