Utilizing RxNorm to Support Practical Computing Applications: Capturing Medication History in Live Electronic Health Records

RxNorm was utilized as the basis for direct-capture of medication history data in a live EHR system deployed in a large, multi-state outpatient behavioral healthcare provider in the United States serving over 75,000 distinct patients each year across 130 clinical locations. This tool incorporated auto-complete search functionality for medications and proper dosage identification assistance. The overarching goal was to understand if and how standardized terminologies like RxNorm can be used to support practical computing applications in live EHR systems. We describe the stages of implementation, approaches used to adapt RxNorm's data structure for the intended EHR application, and the challenges faced. We evaluate the implementation using a four-factor framework addressing flexibility, speed, data integrity, and medication coverage. RxNorm proved to be functional for the intended application, given appropriate adaptations to address high-speed input/output (I/O) requirements of a live EHR and the flexibility required for data entry in multiple potential clinical scenarios. Future research around search optimization for medication entry, user profiling, and linking RxNorm to drug classification schemes holds great potential for improving the user experience and utility of medication data in EHRs.Comment: Appendix (including SQL/DDL Code) available by author request. Keywords: RxNorm; Electronic Health Record; Medication History; Interoperability; Unified Medical Language System; Search Optimizatio

Ontologies in medicinal chemistry: current status and future challenges

[Abstract] Recent years have seen a dramatic increase in the amount and availability of data in the diverse areas of medicinal chemistry, making it possible to achieve significant advances in fields such as the design, synthesis and biological evaluation of compounds. However, with this data explosion, the storage, management and analysis of available data to extract relevant information has become even a more complex task that offers challenging research issues to Artificial Intelligence (AI) scientists. Ontologies have emerged in AI as a key tool to formally represent and semantically organize aspects of the real world. Beyond glossaries or thesauri, ontologies facilitate communication between experts and allow the application of computational techniques to extract useful information from available data. In medicinal chemistry, multiple ontologies have been developed during the last years which contain knowledge about chemical compounds and processes of synthesis of pharmaceutical products. This article reviews the principal standards and ontologies in medicinal chemistry, analyzes their main applications and suggests future directions.Instituto de Salud Carlos III; FIS-PI10/02180Programa Iberoamericano de Ciencia y Tecnología para el Desarrollo; 209RT0366Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; CN2012/217Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; CN2011/034Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; CN2012/21

Patient Health Record Systems Scope and Functionalities: Literature Review and Future Directions

Background: A new generation of user-centric information systems is emerging in health care as patient health record (PHR) systems. These systems create a platform supporting the new vision of health services that empowers patients and enables patient-provider communication, with the goal of improving health outcomes and reducing costs. This evolution has generated new sets of data and capabilities, providing opportunities and challenges at the user, system, and industry levels. Objective: The objective of our study was to assess PHR data types and functionalities through a review of the literature to inform the health care informatics community, and to provide recommendations for PHR design, research, and practice. Methods: We conducted a review of the literature to assess PHR data types and functionalities. We searched PubMed, Embase, and MEDLINE databases from 1966 to 2015 for studies of PHRs, resulting in 1822 articles, from which we selected a total of 106 articles for a detailed review of PHR data content. Results: We present several key findings related to the scope and functionalities in PHR systems. We also present a functional taxonomy and chronological analysis of PHR data types and functionalities, to improve understanding and provide insights for future directions. Functional taxonomy analysis of the extracted data revealed the presence of new PHR data sources such as tracking devices and data types such as time-series data. Chronological data analysis showed an evolution of PHR system functionalities over time, from simple data access to data modification and, more recently, automated assessment, prediction, and recommendation. Conclusions: Efforts are needed to improve (1) PHR data quality through patient-centered user interface design and standardized patient-generated data guidelines, (2) data integrity through consolidation of various types and sources, (3) PHR functionality through application of new data analytics methods, and (4) metrics to evaluate clinical outcomes associated with automated PHR system use, and costs associated with PHR data storage and analytics

Design of a Domain Information Model for a Medication Profile to support Patient Care and Clinical Research

Use of medicines is the commonest intervention in healthcare. Information about an individual’s medication use over their lifetime, managed as a coherent whole but presented appropriately for context, is central to providing good quality care. Considerable investment continues to be made in specifying the information structures underpinning electronic health systems to provide clinicians with patient information to support care provision, yet medication errors continue to occur at unacceptable rates. At the same time, the quantity of healthcare information - which includes medication information – is increasing, and there is growing interest in “secondary uses” of this, particularly to support clinical research. Unfortunately, for both primary and secondary uses, the requirements for the data elements that are needed for medication information are poorly specified, despite a variety of major national and international initiatives and effort. The process for population of those data elements with high quality, consistent, trustworthy information that can be presented to the use cases efficiently and clearly is even more poorly specified. By gathering requirements from processes within clinical research alongside the requirements from the processes of patient care, an integrated data element view of a patient’s medication use over their lifetime has been described; this is termed the patient’s Medication Profile. Examination of the care processes that provide the data to populate that integrated view elicits the method and rules for the realisation of the Medication Profile. These together are provided in a formally scoped fully specified information model which defines the data elements of the Medication Profile (the static model) and the processes and rules to instantiate it (the dynamic model). The Medication Profile, populated with data based on the rules and processes of the dynamic model, is evaluated against test scenarios to assess its success to support use cases from both clinical care and clinical research. This evaluation indicated that the model provided both sufficiency of information coverage and clarity in the information presented

The Gene Ontology Handbook

bioinformatics; biotechnolog