Evidence map›Paper›PMID 42527466›Full record

Articlenpj health systems2025

Learning from COVID-19: clinical trials, health information technology, and patient mortality.

Christos Nicolaides, Avinash Collis, Seth Benzell, Indranil Bardhan

Abstract read
In one paragraph

Article in npj health systems, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Christos NicolaidesSchool of Economics and Management, University of Cyprus, Nicosia, 2109, Aglantzia, Cyprus.
Avinash CollisHeinz College, Carnegie Mellon University, Pittsburgh, PA, USA.
Seth BenzellArgyros College of Business and Economics, Chapman University, Orange, CA, USA.
Indranil BardhanMcCombs School of Business, The University of Texas at Austin, Austin, TX, USA. Indranil.bardhan@mccombs.utexas.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

During the COVID-19 pandemic, deaths per case in the United States decreased from 7.46% in April 2020 to 1.76% in April 2021. One mechanism that could explain this decline is a learning effect associated with testing of new treatments by hospitals. Hospitals that participated in clinical trials developed better organizational capabilities to diagnose and treat COVID-19 patients. Simultaneously, hospitals used health information technologies (IT) that integrated health information across healthcare providers to facilitate greater learning and sharing of best practices. Using US county-level data on clinical trial participation, use of health IT, and COVID-19 cases and deaths, we show that hospitals in counties that participated in clinical trials, and those with greater IT capabilities, exhibited a lower rate of COVID-19 deaths. Consistent with the learning effect hypothesis, counties with greater hospital IT capabilities performed relatively better at treating COVID-19 patients several months into the pandemic. Counties with hospitals that participated in COVID-19 clinical trials also learned faster, with the learning effect of clinical trials being moderated by hospital health IT capability. We posit that clinical trials and use of health IT systems can help hospitals to achieve lower mortality rates in the long run by enhancing learning effects.

Identifiers

PMID42527466
PMCPMC13354194

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.