Evidence map›Paper›PMID 40591612›Full record

ArticlePloS one2025

Examining doctors' business analytics capabilities in using the electronic medical record system for decision-making effectiveness in intensive care units: Impact of the COVID-19 pandemic.

Ewilly Jie Ying Liew, Andrei O J Kwok, Sharon G M Koh, Shairil R Ruslan, M Shahnaz Hasan, Yeh Han Poh

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

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1 · What the graph read from it

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2 · The registry

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Ewilly Jie Ying LiewDepartment of Econometrics & Business Statistics, School of Business, Monash University Malaysia, Subang Jaya, Malaysia.ORCID https://orcid.org/0000-0002-6338-1686
Andrei O J KwokDepartment of Management, School of Business, Monash University Malaysia, Subang Jaya, Malaysia.
Sharon G M KohDepartment of Economics, School of Business, Monash University Malaysia, Subang Jaya, Malaysia.
Shairil R RuslanDepartment of Anaesthesiology, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia.ORCID https://orcid.org/0000-0002-8426-223X
M Shahnaz HasanDepartment of Anaesthesiology, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia.
Yeh Han PohDepartment of Anaesthesiology and Intensive Care, Hospital Kuala Lumpur, Kuala Lumpur, Malaysia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAdvancements in electronic medical record (EMR) systems raise the demand for doctors' digital and analytical skills to process large-scale healthcare data for evidence-based decisions. The present challenge arises with the need to understand how doctors can develop business analytics capabilities using the EMR system for decision-making from an end user's perspective.

aimIntegrating the technology acceptance model and the business analytics model for healthcare, this study examines how individual doctors' technology perceptions of using an EMR system influence their ability to develop business analytics capabilities for making effective healthcare decisions in intensive care units (ICUs). The research questions are: How do doctors' perceptions of using an EMR system influence their ability to develop business analytics capabilities? and How do doctors' business analytics capabilities affect the effectiveness of their healthcare decisions? This study focuses on the context of using the EMR system as a business analytics-enabled architecture rather than a general information system.

methodsWe surveyed a final sample of 130 ICU doctors from public tertiary hospitals in Malaysia, a developing country. This study uses PLS-SEM to analyze two phases, comparing doctors' technology perception and business analytics capabilities before and during the pandemic.

resultsWe found significant shifts in ICU doctors' perceptions of using the EMR system (i.e., perceived ease of use and usefulness) influencing the development of their business analytics capabilities (i.e., data aggregation, data analysis, and data interpretation) for decision-making effectiveness. Data analysis was the only capability contributing to decision-making effectiveness during the pandemic. Significant differences in the relationships were observed before and during the COVID-19 pandemic.

conclusionWe demonstrate that COVID-19 has accelerated favorable technology perceptions and the increasing dependency on developing business analytics capabilities to inform healthcare decisions. Our findings contribute to the critical importance, challenges, and opportunities of using the EMR system for more data-driven decision-making, especially in the post-COVID era.

Indexed as

COVID-19Decision MakingElectronic Health RecordsIntensive Care UnitsPhysiciansAdultFemaleHumansMalaysiaMalePandemicsSARS-CoV-2Surveys and Questionnaires

Identifiers

PMID40591612
PMCPMC12212584

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.