Evidence map›Paper›PMID 33094221›Full record

ArticleFuture healthcare journal2020

Improving the digital health of the workforce in the COVID-19 context: an opportunity to future-proof medical training.

Yang Chen, Amitava Banerjee

Abstract read
In one paragraph

Article in Future healthcare journal, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
–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

10 citing papers in PubMed.

  1. Article
  2. Digital literacy in undergraduate pharmacy education: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2024
    Article
  3. Review
  4. Article
  5. Review
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
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.

Yang ChenSt Bartholomew's Hospital, London, UK.
Amitava BanerjeeSt Bartholomew's Hospital, London, UK, University College London, London, UK and University College London Hospitals NHS Foundation Trust, London, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As well as its profound effects on healthcare and wider society, the COVID-19 pandemic will have far-reaching implications for the future training and professional development of healthcare workers and, in particular, doctors. While initial educational priorities focused on creating a more agile workforce with better cross-specialty skill-mix, attention must now shift to how our system can prepare a proportionate response that not only addresses the needs of the pandemic but also the underlying challenges of healthcare: multimorbidity, bridging the evidence-practice gap and delivering integrated, personalised medicine for all. It is our contention that meeting such challenges will require a rapid upskilling of the digital capabilities of the healthcare workforce. In short, optimising the health of the nation will depend, in part, on improving the digital health of the workforce. In this review, we examine how digital technology played its part in the COVID-19 response, and how fundamental changes to medical training are urgently needed in the context of a 'healthcare reset'. Familiarity with health informatics, data science and digital technology have to move to centre stage in order to future-proof our profession in the years to come. The people that deliver care are our systems' greatest asset, and at a time when change is accelerating, we cannot knowingly allow current and future colleagues to be ill-equipped to survive and thrive in the practice of medicine.

Indexed as

Data sciencedigitalhealth informaticstraining

Identifiers

PMID33094221
PMCPMC7571768

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.