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Cited by 408Electronic Health Records SystemsArtificial Intelligence in Healthcare and EducationBiomedical Text Mining and Ontologies

Clinical Decision Support Systems for Improving Diagnostic Accuracy and Achieving Precision Medicine

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Castaneda, Christian, Nalley, Kip, Mannion, Ciaran, Bhattacharyya, Pritish, Blake, Patrick, Pecora, Andrew, Goy, Andre, Suh, K Stephen·2015·Journal of Clinical Bioinformatics
DOI 10.1186/s13336-015-0019-3PMC PMC4381462
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Abstract

As research laboratories and clinics collaborate to achieve precision medicine, both communities are required to understand mandated electronic health/medical record (EHR/EMR) initiatives that will be fully implemented in all clinics in the United States by 2015. Stakeholders will need to evaluate current record keeping practices and optimize and standardize methodologies to capture nearly all information in digital format. Collaborative efforts from academic and industry sectors are crucial to achieving higher efficacy in patient care while minimizing costs. Currently existing digitized data and information are present in multiple formats and are largely unstructured. In the absence of a universally accepted management system, departments and institutions continue to generate silos of information. As a result, invaluable and newly discovered knowledge is difficult to access. To accelerate biomedical research and reduce healthcare costs, clinical and bioinformatics systems must employ common data elements to create structured annotation forms enabling laboratories and clinics to capture sharable data in real time. Conversion of these datasets to knowable information should be a routine institutionalized process. New scientific knowledge and clinical discoveries can be shared via integrated knowledge environments defined by flexible data models and extensive use of standards, ontologies, vocabularies, and thesauri. In the clinical setting, aggregated knowledge must be displayed in user-friendly formats so that physicians, non-technical laboratory personnel, nurses, data/research coordinators, and end-users can enter data, access information, and understand the output. The effort to connect astronomical numbers of data points, including '-omics'-based molecular data, individual genome sequences, experimental data, patient clinical phenotypes, and follow-up data is a monumental task. Roadblocks to this vision of integration and interoperability include ethical, legal, and logistical concerns. Ensuring data security and protection of patient rights while simultaneously facilitating standardization is paramount to maintaining public support. The capabilities of supercomputing need to be applied strategically. A standardized, methodological implementation must be applied to developed artificial intelligence systems with the ability to integrate data and information into clinically relevant knowledge. Ultimately, the integration of bioinformatics and clinical data in a clinical decision support system promises precision medicine and cost effective and personalized patient care.

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  • EHRs provide clinicians with timely access to critical clinical data structured in ways that enhance diagnostic workflows and support accurate decision making.

    “They provide clinicians with timely access to critical clinical data structured in ways that enhance diagnostic workflows and support accurate decision making [164].”

    From Toward Safer Diagnoses: A SEIPS-Based Narrative Review of Diagnostic Errors (2026) · cites this paper as [164]

    The section “EHRs have laid the foundation for clinical decision support systems” says EHRs provide physicians with a wealth of information and can support diagnostic suggestions when integrated with clinical decision support systems. But the Abstract says existing data are largely unstructured, and “The need for CDSS for precision medicine” says current EHRs have not by themselves improved clinical understanding. The source therefore does not support presenting structured data and enhanced diagnostic decision-making as established benefits of EHRs alone. Page numbers were not available in our copy. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

  • Although specialized and domain-specific CDSS have been successfully implemented in several fields, the overall effectiveness of CDSS still needs further validation and improvement.

    “Although specialized and domain-specific CDSS have been successfully implemented in several fields, the overall effectiveness of CDSS still needs further validation and improvement [164].”

    From Toward Safer Diagnoses: A SEIPS-Based Narrative Review of Diagnostic Errors (2026) · cites this paper as [164]

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Authors
Castaneda, C., Nalley, K., Mannion, C., Bhattacharyya, P., Blake, P., Pecora, A., Goy, A., & Suh, K. S.
Affiliations
Hackensack University Medical Center (US)
Corresponding author
Suh, K Stephen
ORCID
Venue
Journal of Clinical Bioinformatics
Publisher
ClinTransMed, AB
Published
2015-03-26
Volume
5
Issue
1
Pages
4-4
ISSN
2043-9113 (ISSN-L)
DOI
10.1186/s13336-015-0019-3
PMC
PMC4381462
OpenAlex
W2158842434
Cited by
408
References
77
Citations by year
2026: 23 · 2025: 47 · 2024: 43 · 2023: 30 · 2022: 51 · 2021: 41 · 2020: 43 · 2019: 39 · 2018: 37 · 2017: 24 · 2016: 19 · 2015: 10 · 2013: 1
FWCI
58.47
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Journal of Clinical Bioinformatics (publishedVersion)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Funders
Leon Lowenstein Foundation
Topics
  • Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
Keywords
clinical decision support systems, precision medicine, electronic health records, data interoperability, common data elements, clinical laboratory data, data standardization, data integration, artificial intelligence, diagnostic accuracy, patient data privacy, high-performance computing, omics data

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