Bone Health ECHO Case Report: Fractures and Hypercalcemia in a Patient with Stage 5 Chronic Kidney Disease.

Bone Health ECHO (Extension for Community Healthcare Outcomes) is a virtual community of practice with the aim of enhancing global capacity to deliver best practice skeletal healthcare. The prototype program, established at the University of New Mexico, has been meeting online weekly since 2015, focusing on presentation and discussion of patient cases. These discussions commonly cover issues that are relevant to a broad range of patients, thereby serving as a force multiplier to improve the care of many patients. This is a case report from Bone Health ECHO about a patient with stage 5 chronic kidney disease, hypercalcemia, and low bone density, and the discussion that followed.

Evaluation of a Concept Mapping Task Using Named Entity Recognition and Normalization in Unstructured Clinical Text.

In this pilot study, we explore the feasibility and accuracy of using a query in a commercial natural language processing engine in a named entity recognition and normalization task to extract a wide spectrum of clinical concepts from free text clinical letters. Editorial guidance developed by two independent clinicians was used to annotate sixty anonymized clinic letters to create the gold standard. Concepts were categorized by semantic type, and labels were applied to indicate contextual attributes such as negation. The natural language processing (NLP) engine was Linguamatics I2E version 5.3.1, equipped with an algorithm for contextualizing words and phrases and an ontology of terms from Intelligent Medical Objects to which those tokens were mapped. Performance of the engine was assessed on a training set of the documents using precision, recall, and the F1 score, with subset analysis for semantic type, accurate negation, exact versus partial conceptual matching, and discontinuous text. The engine underwent tuning, and the final performance was determined for a test set. The test set showed an F1 score of 0.81 and 0.84 using strict and relaxed criteria respectively when appropriate negation was not required and 0.75 and 0.77 when it was. F1 scores were higher when concepts were derived from continuous text only. This pilot study showed that a commercially available NLP engine delivered good overall results for identifying a wide spectrum of structured clinical concepts. Such a system holds promise for extracting concepts from free text to populate problem lists or for data mining projects.

Experience with Integrating Diagnostic Decision Support Software with Electronic Health Records: Benefits versus Risks of Information Sharing.

INTRODUCTION: Reducing misdiagnosis has long been a goal of medical informatics. Current thinking has focused on achieving this goal by integrating diagnostic decision support into electronic health records. METHODS: A diagnostic decision support system already in clinical use was integrated into electronic health record systems at two large health systems, after clinician input on desired capabilities. The decision support provided three outputs: editable text for use in a clinical note, a summary including the suggested differential diagnosis with a graphical representation of probability, and a list of pertinent positive and pertinent negative findings (with onsets). RESULTS: Structured interviews showed widespread agreement that the tool was useful and that the integration improved workflow. There was disagreement among various specialties over the risks versus benefits of documenting intermediate diagnostic thinking. Benefits were most valued by specialists involved in diagnostic testing, who were able to use the additional clinical context for richer interpretation of test results. Risks were most cited by physicians making clinical diagnoses, who expressed concern that a process that generated diagnostic possibilities exposed them to legal liability. DISCUSSION AND CONCLUSION: Reconciling the preferences of the various groups could include saving only the finding list as a patient-wide resource, saving intermediate diagnostic thinking only temporarily, or adoption of professional guidelines to clarify the role of decision support in diagnosis.