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Background: Reusable, machine-processable clinical decision support system (CDSS) rules have not been widely achieved in the medical informatics field. This study introduces the process, results, challenges faced, and lessons learned while converting the United States of America Centers for Disease Control and Prevention (CDC)-recommended immunization schedules (2022) to machine-processable CDSS rules.
Methods: We converted the vaccination schedules into tabular, charts, MS Excel, and clinical quality language (CQL) formats. The CQL format can be automatically converted to a machine-processable format using existing tools. Therefore, it was regarded as a machine-processable format. The results were reviewed, verified, and tested.
Results: We have developed 465 rules for 19 vaccines in 13 categories, and we have shared the rules via GitHub to make them publicly available. We used cross-review and cross-checking to validate the CDSS rules in tabular and chart formats. The CQL files were tested for syntax and logic with hypothetical patient HL7 FHIR resources. Our rules can be reused and shared by the health IT industry, CDSS developers, medical informatics educators, or clinical care institutions. The unique contributions of our work are twofold: (1) we created ontology-based, machine-processable, and reusable immunization recommendation rules, and (2) we created and shared multiple formats of immunization recommendation rules publicly which can be a valuable resource for medical and medical informatics communities.
Conclusions: These CDSS rules can be important contributions to informatics communities, reducing redundant efforts, which is particularly significant in resource-limited settings. Despite the maturity and concise presentation of the CDC recommendations, careful attention and multiple layers of verification and review are necessary to ensure accurate conversion. The publicly shared CDSS rules can also be used for health and biomedical informatics education and training purposes.
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http://dx.doi.org/10.3390/vaccines13050437 | DOI Listing |
BJU Int
July 2025
Guy's and St Thomas' NHS Foundation Trust, London, UK.
Objectives: To evaluate the effectiveness of a rules-based artificial intelligence (AI) clinical decision support system (CDSS) called the PROState AI Cancer-Decision Support (PROSAIC-DS) in streamlining the prostate cancer multidisciplinary team (MDT) pathway by identifying patients meeting standard of care (SoC) guidelines for reduced discussion in MDT meetings.
Subjects/patients And Methods: This study consisted of two phases. Phase one involved a retrospective concordance analysis of 287 patients referred to the prostate MDT at King's College Hospital over a 2-year period.
Int J Med Inform
November 2025
Department of Medical Informatics, Hôpital Européen Georges Pompidou, Hôpital Necker Enfants Malades, APHP, Paris, France; Université Paris Cité, INSERM UMR1163, Imagine Institute, Clinical Bioinformatics Laboratory, Paris F-75006, France.
Background: Telephone triage could limit admissions to emergency departments. However, telephone triage is challenging in pediatrics due to nonspecific symptoms, reliance on parental description, and emotional distress. Clinical decision support systems (CDSSs) could improve the accuracy and quality of telephone triage.
View Article and Find Full Text PDFAMIA Jt Summits Transl Sci Proc
June 2025
Clemson University, Clemson, SC, USA.
Clinical decision support systems (CDSS) are routinely employed in clinical settings to improve quality of care, ensure patient safety, and deliver consistent medical care. However, rule-based CDSS, currently available, do not feature reusable rules. In this study, we present CDSS with reusable rules.
View Article and Find Full Text PDFVaccines (Basel)
April 2025
Department of Public Health, College of Behavioral, Social and Health Sciences, Clemson University, Clemson, SC 29634, USA.
Background: Reusable, machine-processable clinical decision support system (CDSS) rules have not been widely achieved in the medical informatics field. This study introduces the process, results, challenges faced, and lessons learned while converting the United States of America Centers for Disease Control and Prevention (CDC)-recommended immunization schedules (2022) to machine-processable CDSS rules.
Methods: We converted the vaccination schedules into tabular, charts, MS Excel, and clinical quality language (CQL) formats.
Comput Biol Med
July 2025
Department of Pediatric Cardiology and Pediatric Intensive Care Medicine, Hannover Medical School, Hannover, Germany.
Background: Acute kidney injury (AKI) is common in children with congenital heart disease following open-heart surgery with cardiopulmonary bypass (CPB). Early AKI detection in critically ill children requires clinician expertise to compile various data from different sources within a stressful and time-sensitive environment. However, as electronic health records provide data in a machine-readable format, this process could be supported by computerized systems.
View Article and Find Full Text PDF