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BACKGROUND Clinical decision support systems (CDSS) play a critical role in the quality and safety of healthcare delivery. Although CDSS rules direct CDSS behavior, CDSS rules have not been routinely shared and reused. OBJECTIVE Ontology has the potential to promote the reusing of CDSS rules. Therefore, we systematically screened literature to elaborate on the current status of ontology applied in CDSS rules, e.g., rule management (uses the captured CDSS rule usage data and users’ feedback data to customize CDSS services to be more accurate) and maintenance (updates CDSS rules). We aim to delineate the frontlines of ontologies applied in CDSS rules via this systematic literature review. METHODS The literature search was focused on ontology, clinical decision support, and rules in PubMed, the Association for Computing Machinery (ACM) Digital Library, and the Nursing & Allied Health Database. Grounded theory and PRISMA 2020 guidelines were followed. One author started the screening and literature analysis, and two validated the processes and results. Inclusion and exclusion criteria were developed and refined iteratively. RESULTS Among the 81 included publications, CDSS were mainly applied to managing chronic conditions, alerts for medication prescriptions, reminders for immunizations and preventive services, diagnoses, and treatment recommendations. The CDSS rules were presented in Semantic Web Rule Language, Jess, or Jena formats. Despite ontologies being used to supply medical knowledge, CDSS rules, and terminologies, ontologies have not been used in CDSS rule management or facilitate the reusing of CDSS rules. CONCLUSIONS Ontologies have been used to organize and represent medical knowledge and controlled vocabularies that contribute to CDSS and the content of CDSS rules. So far, there is a lack of reusing CDSS rules. More efforts are needed to improve the reusability and interoperability of CDSS rules. This paper delineated the ontologies, enabling Semantic Web technology, despite inadequate, and their applications in CDSS rules.

BACKGROUND Disease status (eg, cancer stage) has been used in routine clinical practice to determine more accurate treatment plans. Health-related indicators, such as mortality, morbidity, and population group life expectancy, have also been used. However, few studies have specifically focused on the comprehensive and objective measures of individual health status. OBJECTIVE The aim of this study was to analyze the perspectives of the public toward 29 health indicators obtained from a literature review to provide evidence for further prioritization of the indicators. The difference between health status and disease status should be considered. METHODS This study used a cross-sectional design. Online surveys were administered through Ohio University, ResearchMatch, and Clemson University, resulting in three samples. Participants aged 18 years or older rated the importance of the 29 health indicators. The rating results were aggregated and analyzed as follows (in each case, the dependent variables were the individual survey responses): (1) to determine the agreement among the three samples regarding the importance of each indicator, where the independent variables (IVs) were the three samples; (2) to examine the mean differences between the retained indicators with agreement across the three samples, where the IVs were the identified indicators; and (3) to rank the groups of indicators into various levels after grouping the indicators with no mean differences, where the IVs were the groups of indicators. RESULTS In total, 1153 valid responses were analyzed. Descriptive statistics revealed that the top five–rated indicators were drug or substance abuse, smoking or tobacco use, alcohol abuse, major depression, and diet and nutrition. Among the 29 health indicators, the three samples agreed upon the importance of 13 indicators. Inferential statistical analysis indicated that some of the 13 indicators held equal importance. Therefore, the 13 indicators were categorized by rank into seven levels: level 1 included blood sugar level and immunization and vaccination; level 2 included LDL cholesterol; level 3 included HDL cholesterol, blood triglycerides, cancer screening detection, and total cholesterol; level 4 included health literacy rate; level 5 included personal care needs and air quality index greater than 100; level 6 included self-rated health status and HIV testing; and level 7 included the supply of dentists. Levels 1 to 3 were rated significantly higher than levels 4 to 7. CONCLUSIONS This study provides a baseline for prioritizing 29 health indicators, which can be used by electronic health record or personal health record system designers or developers to determine what can be included in the systems to capture an individual’s health status. Currently, self-rated health status is the predominantly used health indicator. Additionally, this study provides a foundation for tracking and measuring preventive health care services more accurately and for developing an individual health status index.

Background Disease status (eg, cancer stage) has been used in routine clinical practice to determine more accurate treatment plans. Health-related indicators, such as mortality, morbidity, and population group life expectancy, have also been used. However, few studies have specifically focused on the comprehensive and objective measures of individual health status. Objective The aim of this study was to analyze the perspectives of the public toward 29 health indicators obtained from a literature review to provide evidence for further prioritization of the indicators. The difference between health status and disease status should be considered. Methods This study used a cross-sectional design. Online surveys were administered through Ohio University, ResearchMatch, and Clemson University, resulting in three samples. Participants aged 18 years or older rated the importance of the 29 health indicators. The rating results were aggregated and analyzed as follows (in each case, the dependent variables were the individual survey responses): (1) to determine the agreement among the three samples regarding the importance of each indicator, where the independent variables (IVs) were the three samples; (2) to examine the mean differences between the retained indicators with agreement across the three samples, where the IVs were the identified indicators; and (3) to rank the groups of indicators into various levels after grouping the indicators with no mean differences, where the IVs were the groups of indicators. Results In total, 1153 valid responses were analyzed. Descriptive statistics revealed that the top five–rated indicators were drug or substance abuse, smoking or tobacco use, alcohol abuse, major depression, and diet and nutrition. Among the 29 health indicators, the three samples agreed upon the importance of 13 indicators. Inferential statistical analysis indicated that some of the 13 indicators held equal importance. Therefore, the 13 indicators were categorized by rank into seven levels: level 1 included blood sugar level and immunization and vaccination; level 2 included LDL cholesterol; level 3 included HDL cholesterol, blood triglycerides, cancer screening detection, and total cholesterol; level 4 included health literacy rate; level 5 included personal care needs and air quality index greater than 100; level 6 included self-rated health status and HIV testing; and level 7 included the supply of dentists. Levels 1 to 3 were rated significantly higher than levels 4 to 7. Conclusions This study provides a baseline for prioritizing 29 health indicators, which can be used by electronic health record or personal health record system designers or developers to determine what can be included in the systems to capture an individual’s health status. Currently, self-rated health status is the predominantly used health indicator. Additionally, this study provides a foundation for tracking and measuring preventive health care services more accurately and for developing an individual health status index.