Evidence map›Paper›PMID 40327851›Full record

SynthesisJMIR human factors2025

Optimizing Clinical Decision Support System Functionality by Leveraging Specific Human-Computer Interaction Elements: Insights From a Systematic Review.

Ali Azadi, Francisco José García-Peñalvo

Abstract readSystematic Review
In one paragraph

Synthesis in JMIR human factors, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 1 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

12 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  6. Review
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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.

Ali AzadiGRIAL Research Group, Computer Science Department, Universidad de Salamanca, Salamanca, Spain.ORCID 0000-0002-7166-1565
Francisco José García-PeñalvoGRIAL Research Group, Computer Science Department, Universidad de Salamanca, Salamanca, Spain.ORCID 0000-0001-9987-5584

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundClinical decision support systems (CDSSs) play a pivotal role in health care by enhancing clinical decision-making processes. These systems represent a significant advancement in medical information systems. However, optimizing their effectiveness requires accounting for various human-computer interaction (HCI) elements that influence their functionality and user acceptance.

objectiveThis study aimed to identify and categorize key HCI elements that impact CDSS performance to enhance system usability, adaptability, and decision-making accuracy.

methodsWe conducted a systematic literature review, identifying 923 studies from the databases PubMed, Scopus, and Web of Science. Papers were screened and selected based on predefined inclusion criteria. A rigorous quality assessment process was applied to ensure the relevance and reliability of the included studies. Ultimately, of the 923 papers identified, 43 (4.7%) that specifically addressed HCI elements applicable to CDSS environments were included in the final analysis. Data extraction and synthesis were performed to answer the research questions regarding HCI elements.

resultsA total of 12 distinct HCI elements were identified, each with the potential to influence CDSS functionality. These elements align with the International Organization for Standardization (ISO) 9241-11 framework, which defines usability in terms of effectiveness, efficiency, and satisfaction. "User satisfaction," "flexibility," and "individuality" enhance satisfaction by improving system adaptability and user acceptance. "Visibility," "explainability," and "user control" strengthen effectiveness by supporting decision-making and error prevention. "Ease of use" improves efficiency by streamlining interactions and reducing cognitive load. Some elements influence effectiveness and efficiency, such as "data entry," which ensures structured inputs for decision accuracy while optimizing workflows. Likewise, "alerts" provide timely information for effective decision-making and, simultaneously, are designed to avoid overwhelming users and maintain system efficiency. "Simplification" and "mental effort" also optimize workflows and reduce complexity. Furthermore, "interface" impacts effectiveness and efficiency by supporting accurate decision-making and streamlining user interaction. This categorization, aligned with ISO 9241-11, underscores the context and task dependency of usability, highlighting that HCI elements must be adapted to different user needs and environments for effective clinical decision-making.

conclusionsThis study addresses a critical gap in CDSS research by offering a comprehensive framework of HCI elements tailored to the CDSS environment. Incorporating these elements into system design can improve user satisfaction, reduce data errors, and enhance the accuracy of medical decisions. The findings lay the groundwork for future research, offering practical guidelines for developing more reliable and efficient CDSS systems in medical informatics fields.

Indexed as

Decision Support Systems, ClinicalUser-Computer InterfaceHumansartificial intelligenceclinical decision support systemhuman-computer interactionusabilityuser-centered design

Identifiers

PMID40327851
PMCPMC12093079

What OpenQuestion holds

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LicenceCC BY
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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.