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Optimizing Medication Use through Health Information Technology
Published in Salvatore Volpe, Health Informatics, 2022
Troy Trygstad, Mary Ann Kliethermes, Anne L. Burns, Mary Roth McClurg, Marie Smith, John Easter
The Unified Medical Language System (UMLS) is sponsored by the National Library of Medicine (NLM) and has focused its efforts of late on enabling interoperability between often disparate proprietary and non-proprietary classification systems embedded within electronic medical and other health records systems. In addition to classifying diseases and procedures, relationships are defined between terms to create ontological structures. The Systematized Nomenclature of Medicine-Clinical Terms (SNOMED-CT), maintained by the International Health Terminology Standards Development Organization (IHTSDO), and RxNorm, produced by the (NLM) itself, are probably the most well-known and widely used ontologies in the US healthcare system, with the latter being used to classify pharmaceuticals to aid interoperable functions such as electronic prescribing and computerized physician order entry systems.
US Health and Healthcare Current State: Nurse Executives
Published in Connie White Delaney, Charlotte A. Weaver, Joyce Sensmeier, Lisiane Pruinelli, Patrick Weber, Deborah Trautman, Kedar Mate, Howard Catton, Nursing and Informatics for the 21st Century – Embracing a Digital World, 3rd Edition, Book 1, 2022
Robyn Begley, Laura Reed, Julibeth Lauren
Through the application and codification using such systems as ICD-10, RxNorm, NIC/NOC/NANDA, and other coding and tagging systems in the digital plan of care and diagnosis codes, the EHR can suggest relevant patient education and information for the nurses to select and provide patients with information sheets, videos or audio clips. Through these systems, documentation of patient education, improving the health literacy of patients, engaging them in shared decision-making as part of the healthcare team, can improve patient care outcomes and safety.
Introduction to Artificial Intelligence and Deep Learning with a Case Study in Analyzing Electronic Health Records for Drug Development
Published in Harry Yang, Binbing Yu, Real-World Evidence in Drug Development and Evaluation, 2021
Rich content, tremendous opportunities, and unseen challenges are presented with this data set. Along with all the difficulties brought by the pure size of the data, how to utilize the raw and unstructured data seems an even harder question. Diagnosis information might not be recorded due to the missingness of the ICD-9/ICD-10 code. Medications are not perfectly categorized due to rxnorm missing or they are simply hard to classify. There are large percentages of missing entries in longitudinal data, especially for medical history. Typos and wrong units also impose great threats by increasing the number of outliers. In addition, there are unclassified observations and physician notes that are impossible for manual coding considering the size of the data.
Treatment characteristics among patients with binge-eating disorder: an electronic health records analysis
Published in Postgraduate Medicine, 2023
William M. Spalding, Monica L. Bertoia, Cynthia M. Bulik, John D. Seeger
An International Classification of Diseases (ICD) code for BED was introduced in 2016 in ICD-10. Without a specific code for BED in ICD-9 (previously under the general ICD-9 code 307.50 [Eating Disorder, Not Otherwise Specified]) during the timeframe that this study was conducted, patients with BED were identified using an algorithm comprised of NLP terms. The NLP system was used to extract and organize concepts, attributes, sentiments, and modifiers related to BED from free text clinical notes. The system was developed using vocabulary from the Unified Medical Language System, which includes multiple medical dictionaries (eg, the Logical Observation Identifiers Names and Codes, the Systematized Nomenclature of Medicine–Clinical Terms, and RxNorm [a listing of generic and branded drugs]). The NLP system is updated regularly and supplemented with new terms and information as refinements are identified. The architecture of the NLP system is based on the OASIS Unstructured Information Management Architecture [27] and is similar to other previously described systems [28]. The types of NLP concepts included medications; clinical measurements; diagnostic and therapeutic procedures; and signs, diseases, and symptoms. Modifiers of the NLP concepts included sentiments (eg, negations, affirmations), descriptive attributes (eg, stage, grade, severity, duration), and the notes section (eg, medical history, history of current illness, assessments, plans).
Characteristics of patients with major depressive disorder switching SSRI/SNRI therapy compared with those augmenting with an atypical antipsychotic in a real-world setting
Published in Current Medical Research and Opinion, 2021
David M. Kern, M. Soledad Cepeda, Ruby C. Castilla-Puentes, Adam Savitz, Mila Etropolski
Patient demographics (age and gender) were captured on the index date. Comorbid conditions, psychiatric symptoms and the Charlson Comorbid Index15 were captured during the one-year pre-index period, which included the index date. One diagnosis code for the comorbidity of interest was required during this time frame. Comorbidities were defined according to the Systematized Nomenclature of Medicine – Clinical Terms (SNOMED CT) classification system, which maps various diagnostic languages, including ICD-9-CM and ICD-10-CM, to a single standardized set of concepts. Medication use in the one-year pre-index period, not including the index date, was captured according to the RxNorm ingredient and for specific treatment classes of interest (anxiolytics, hypnotics/sedatives, anticonvulsants, stimulants, lithium). Additionally, the type of SSRI/SNRI filled during the 90 days prior to the index treatment change was captured and compared between cohorts.
Differential Risk of Cancer Associated with Glucagon-like Peptide-1 Receptor Agonists: Analysis of Real-world Databases
Published in Endocrine Research, 2022
In Explorys, patients with T2DM were identified with both Systematized Nomenclature of Medicine (SNOMED) Clinical Term “Diabetes mellitus type 2” and Logical Observation Identifiers Names and Codes (LOINC) finding “Hemoglobin A1c|Bld-Ser-Plas” value equal or above 6.5%. SNOMED pharmacological class “Incretin mimetic agent” and “Dipeptidyl peptidase IV inhibitor” was used to identify GLP1Ra and DPP4 inhibitor, respectively. RxNorm term “metformin” was used to identify the medication metformin. The “temporal attributes” function of Explorys was used to define lag time and follow-up time, with the first ever use of antidiabetic agents as the index event. The Explorys search interface is shown in Supplemental Figure 1.