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Background: MetaMap is a valuable tool for processing biomedical texts to identify concepts. Although MetaMap is highly configurative, configuration decisions are not straightforward.Objective: To develop a systematic, data-driven methodology for configuring MetaMap for optimal performance.Methods: MetaMap, the word2vec model, and the phrase model were used to build a pipeline. For unsupervised training, the phrase and word2vec models used abstracts related to clinical decision support as input. During testing, MetaMap was configured with the default option, one behavior option, and two behavior options. For each configuration, cosine and soft cosine similarity scores between identified entities and gold-standard terms were computed for 40 annotated abstracts (422 sentences). The similarity scores were used to calculate and compare the overall percentages of exact matches, similar matches, and missing gold-standard terms among the abstracts for each configuration. The results were manually spot-checked. The precision, recall, and F-measure (β =1) were calculated.Results: The percentages of exact matches and missing gold-standard terms were 0.6–0.79 and 0.09–0.3 for one behavior option, and 0.56–0.8 and 0.09–0.3 for two behavior options, respectively. The percentages of exact matches and missing terms for soft cosine similarity scores exceeded those for cosine similarity scores. The average precision, recall, and F-measure were 0.59, 0.82, and 0.68 for exact matches, and 1.00, 0.53, and 0.69 for missing terms, respectively.Conclusion: We demonstrated a systematic approach that provides objective and accurate evidence guiding MetaMap configurations for optimizing performance. Combining objective evidence and the current practice of using principles, experience, and intuitions outperforms a single strategy in MetaMap configurations. Our methodology, reference codes, measurements, results, and workflow are valuable references for optimizing and configuring MetaMap.

Interoperable clinical decision support system (CDSS) rules are a pathway to achieving interoperability which is a well-recognized challenge in health information technology. Building an ontology facilitates the creation of interoperable CDSS rules, which can be achieved by identifying the keyphrases (KP) from the existing literature. However, KP identification for labeling the data requires human expertise, consensus, and contextual understanding. This paper aims to present a semi-supervised framework for the CDSS using minimal labeled data based on hierarchical attention over the documents fused with domain adaptation approaches. Then, evaluate the effectiveness of KP identification with this framework. In the view of semi-supervised learning, our methodology toward building this framework outperforms the prior neural architectures by learning with document-level context, no explicit hand-crafted features, knowledge transfer from pre-trained models (on unlabeled corpus), and post-fine-tuning with smaller gold standard-labeled data. To the best of our knowledge, this is the first functional framework for the CDSS sub-domain to identify the KP, which is trained on limited labeled data. It contributes to the general natural language processing (NLP) architectures in areas such as clinical NLP, where manual data labeling is challenging.

Clinical decision support systems (CDSSs) are important for the quality and safety of healthcare delivery. Although CDSS rules guide CDSS behavior, they are not routinely shared and reused.Ontology has the potential to promote the reuse of CDSS rules. Therefore, we systematically screened literature to elaborate on the current status of ontology applied in CDSS rules, such as rule management (which uses captured CDSS rule usage data and user feedback data to tailor CDSS services to be more accurate) and maintenance (which updates CDSS rules). Through this systematic literature review, we aim to identify the frontiers of ontologies used in CDSS rules.The literature search was focused on the intersection of ontology, clinical decision support, and rules in PubMed, the Association for Computing Machinery (ACM) Digital Library, and the NursingAllied Health Database. Grounded theory and PRISMA 2020 guidelines were followed. One author initiated the screening and literature review, while the other two validated the processes and results independently. Inclusion and exclusion criteria were developed and refined iteratively.CDSSs were primarily used to manage chronic conditions, alerts for medication prescriptions, reminders for immunizations and preventive services, diagnoses, and treatment recommendations among the 81 included publications. The CDSS rules were presented in Semantic Web Rule Language, Jess, or Jena formats. Despite the fact that ontologies have been used to provide medical knowledge, CDSS rules, and terminologies, they have not been used in CDSS rule management or to facilitate the reuse of CDSS rules.Ontologies have been used to organize and represent medical knowledge, controlled vocabularies, and the content of CDSS rules. So far, there has been little reuse of CDSS rules. More work is needed to improve the CDSS rule reusability and interoperability. This paper identified and described the ontologies that, despite their limitations, enable Semantic Web technology and their applications in CDSS rules.

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.

ObjectiveClinical decision support systems (CDSS) have a critical role in improving the quality and safety of health care delivery. CDSS rules direct the behavior of CDSS. However, the CDSS rules have not been routinely shared and reused, and ontology can promote the reusing of CDSS rules. We systematically screened literature to elaborate on the current status of ontology applied in CDSS rule management.MethodsWe searched PubMed, the Association for Computing Machinery (ACM) Digital Library, and the Nursing & Allied Health Database for publications focusing on ontology, clinical decision support, and rules. Grounded theory and PRISMA 2020 guidelines were followed. One author started the screening and literature analysis, and two authors validated the processes and results. Inclusion and exclusion criteria were developed and refined iteratively.ResultsAmong 81 included publications, the identified 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 ontology was used to supply medical knowledge, CDSS rules, and terminologies to CDSS, ontology has not been used in CDSS rule management.ConclusionsAlthough ontology can facilitate the reuse, management, and maintenance of CDSS rules, CDSS ontology remains unavailable indicating that more efforts are needed to improve the reusability and interoperability of CDSS rules.

Interoperability is essential to address limitations caused by the ad hoc implementation of clinical information systems and the distributed nature of modern medical care. The HL7 V2 and V3 standards have played a significant role in ensuring interoperability for healthcare. FHIR is a next generation standard created to address fundamental limitations in HL7 V2 and V3. FHIR is particularly relevant to OpenMRS, an Open Source Medical Record System widely used across emerging economies. FHIR has the potential to allow OpenMRS to move away from a bespoke, application specific API to a standards based API. We describe efforts to design and implement a FHIR based API for the OpenMRS platform. Lessons learned from this effort were used to define long term plans to transition from the legacy OpenMRS API to a FHIR based API that greatly reduces the learning curve for developers and helps enhance adhernce to standards.

Efficient use of health care resources in low-income countries by providers and local and national managers requires timely access to patient data.To implement electronic health records (EHRs) in HIV clinics in Kenya, Tanzania, and Uganda.We initially developed and implemented an EHR in Kenya through a mature academic partnership. The EHR was then implemented in six HIV clinics in Tanzania and Uganda in collaboration with their National AIDS Control Programmes. All implementations were successful, but the system's use and sustainability varied depending on who controlled clinic funding.Successful EHR use and sustainability were enhanced by local control of funds, academic partnerships (mainly by leveraging research funds), and in-country technology support.