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

Exploiting the potential of large databases of electronic health records for research using rapid search algorithms and an intuitive query interface.

Objective: UK primary care databases, which contain diagnostic, demographic and prescribing information for millions of patients geographically representative of the UK, represent a significant resource for health services and clinical research. They can be used to identify patients with a specified disease or condition (phenotyping) and to investigate patterns of diagnosis and symptoms. Currently, extracting such information manually is time-consuming and requires considerable expertise. In order to exploit more fully the potential of these large and complex databases, our interdisciplinary team developed generic methods allowing access to different types of user. Materials and methods: Using the Clinical Practice Research Datalink database, we have developed an online user-focused system (TrialViz), which enables users interactively to select suitable medical general practices based on two criteria: suitability of the patient base for the intended study (phenotyping) and measures of data quality. Results: An end-to-end system, underpinned by an innovative search algorithm, allows the user to extract information in near real-time via an intuitive query interface and to explore this information using interactive visualization tools. A usability evaluation of this system produced positive results. Discussion: We present the challenges and results in the development of TrialViz and our plans for its extension for wider applications of clinical research. Conclusions: Our fast search algorithms and simple query algorithms represent a significant advance for users of clinical research databases

Activity-Centric Computing Systems

• Activity-Centric Computing (ACC) addresses deep-rooted information management problems in traditional application centric computing by providing a unifying computational model for human goal-oriented ‘activity,’ cutting across system boundaries. • We provide a historical review of the motivation for and development of ACC systems, and highlight the need for broadening up this research topic to also include low-level system research and development. • ACC concepts and technology relate to many facets of computing; they are relevant for researchers working on new computing models and operating systems, as well as for application designers seeking to incorporate these technologies in domain-specific applications

Horizons and Perspectives eHealth

EHealth platform represents the combined use of IT technologies and electronic communications in the health field, using data (electronically transmitted, stored and accessed) with a clinical, educational and administrative purpose, both locally and distantly. eHealth has the significant capability to increase the movement in the direction of services centered towards citizens, improving the quality of the medical act, integrating the application of Medical Informatics (Medical IT), Telemedicine, Health Telematics, Telehealth, Biomedical engineering and Bioinformatics. Supporting the creation, development and recognition of a specific eHealth zone, the European Union policies develop through its programs FP6 and FP7, European-scale projects in the medical information technologies (the electronic health cards, online medical care, medical web portals, trans-European nets for medical information, biotechnology, generic instruments and medical technologies for health, ICT mobile systems for remote monitoring). The medical applications like electronic health cards ePrescription, eServices, medical eLearning, eSupervision, eAdministration are integral part of what is the new medical branch-eHealth, being in a continuous expansion due to the support from the global political, financial and medical organizations; the degree of implementation of the eHealth platform varying according to the development level of the communication infrastructure, allocated funds, intensive political priorities and governmental organizations opened to the new IT challenges.eHealth, telemedicine, telehealth, bioinformatics, telematics

User-centered visual analysis using a hybrid reasoning architecture for intensive care units

One problem pertaining to Intensive Care Unit information systems is that, in some cases, a very dense display of data can result. To ensure the overview and readability of the increasing volumes of data, some special features are required (e.g., data prioritization, clustering, and selection mechanisms) with the application of analytical methods (e.g., temporal data abstraction, principal component analysis, and detection of events). This paper addresses the problem of improving the integration of the visual and analytical methods applied to medical monitoring systems. We present a knowledge- and machine learning-based approach to support the knowledge discovery process with appropriate analytical and visual methods. Its potential benefit to the development of user interfaces for intelligent monitors that can assist with the detection and explanation of new, potentially threatening medical events. The proposed hybrid reasoning architecture provides an interactive graphical user interface to adjust the parameters of the analytical methods based on the users' task at hand. The action sequences performed on the graphical user interface by the user are consolidated in a dynamic knowledge base with specific hybrid reasoning that integrates symbolic and connectionist approaches. These sequences of expert knowledge acquisition can be very efficient for making easier knowledge emergence during a similar experience and positively impact the monitoring of critical situations. The provided graphical user interface incorporating a user-centered visual analysis is exploited to facilitate the natural and effective representation of clinical information for patient care

A Query Integrator and Manager for the Query Web

We introduce two concepts: the Query Web as a layer of interconnected queries over the document web and the semantic web, and a Query Web Integrator and Manager (QI) that enables the Query Web to evolve. QI permits users to write, save and reuse queries over any web accessible source, including other queries saved in other installations of QI. The saved queries may be in any language (e.g. SPARQL, XQuery); the only condition for interconnection is that the queries return their results in some form of XML. This condition allows queries to chain off each other, and to be written in whatever language is appropriate for the task. We illustrate the potential use of QI for several biomedical use cases, including ontology view generation using a combination of graph-based and logical approaches, value set generation for clinical data management, image annotation using terminology obtained from an ontology web service, ontology-driven brain imaging data integration, small-scale clinical data integration, and wider-scale clinical data integration. Such use cases illustrate the current range of applications of QI and lead us to speculate about the potential evolution from smaller groups of interconnected queries into a larger query network that layers over the document and semantic web. The resulting Query Web could greatly aid researchers and others who now have to manually navigate through multiple information sources in order to answer specific questions

Optimizing Electronic Medication Prior Authorization: Reducing Prescription Delays

Background: Within the United States, chronic disease in children has doubled over the last 20 years. Many diseases defined as chronic (attention deficit, epilepsy, and diabetes) require daily medication regimens for optimal management. To be covered by insurance, many of these medications require prior authorization (PA) from the patients’ pharmacy benefits policy. Delays in processing and receiving PA orders can lead to worsening disease and inadequate disease management. In 2014, a pediatric academic medical center in the Midwest found that processing medications from prescription order to PA approval took nurses an average of over 90 hours. In August 2020, the organization implemented an electronic prior authorization (ePA) system that interfaced with the organization’s electronic health record (EHR). The primary goals of this implementation were to reduce medication PA turnaround times and to increase employee engagement with the ePA system. The goals of this quality improvement (QI) project are to optimize the existing ePA system with the medication PA process to reduce average medication PA turnaround times and to increase the approval rates for medication PAs by five percent. Project Design: Three interventions support the outputs of this QI project. Increase the availability of the ePA system by changing the patient and pharmacy benefits insurance matching interface logic. Reduce the number of medications falsely requiring PA by removing them from the ePA system. Increase PA processing efficiency by improving the workflow for attaching documents required for PA approval. To accomplish and measure these interventions, data reports and surveys were developed to establish baselines and to measure ePA turnaround times, PA approval rates, and user satisfaction both pre- and post-intervention. User satisfaction was measured utilizing a secure online survey emailed to ambulatory division nurses. Results: The median medication ePA turnaround pre- and post-interventions was unchanged at 36 hours. The ePA approval percentage dropped from 55.7% in June 2021 to 46.9% in August 2021. The primary QI project outcomes of reducing turnaround time and increasing the approval rate by 5% were not met. A user involvement survey was sent to 194 nurses with a response rate of 29% pre intervention and 8% post intervention. Overall user satisfaction was measured using a net promotor score which registered scores of –70 pre- and –82 post-intervention, revealing overall dissatisfaction with the ePA system. The use of an alternative ePA system outside the organization’s EHR was discovered after the QI project data was reviewed and showed that roughly 45% of ePAs were completed using this alternative system during the QI project timeframe. Recommendations: User involvement surveys measure user engagement with electronic systems and measuring user satisfaction is beneficial to providing direction for interventions as well as predicting future utilization of healthcare informatics projects. Conclusion: Though most of the goals for this QI project outcome were not met, use of the alternative ePA system confirmed the Technology Acceptance Model that users prefer the electronic system that they perceive as being the most useful. Nurses using ePA will use the system that best addresses their own user experiences regarding content, accuracy, format, timeliness, ease of use, and overall satisfaction

Learning from AI : new trends in database technology

Recently some researchers in the areas of database data modelling and knowledge representations in artificial intelligence have recognized that they share many common goals. In this survey paper we show the relationship between database and artificial intelligence research. We show that there has been a tendency for data models to incorporate more modelling techniques developed for knowledge representations in artificial intelligence as the desire to incorporate more application oriented semantics, user friendliness, and flexibility has increased. Increasing the semantics of the representation is the key to capturing the "reality" of the database environment, increasing user friendliness, and facilitating the support of multiple, possibly conflicting, user views of the information contained in a database

The Development of Citizen Oriented Informatics

We define the concept of citizen-oriented computer application. Quality characteristics are set for computer applications developed in the conditions of citizen-oriented computing and outline the development cycle for these applications. It defines the conditions of existence for citizen-oriented applications. Average and long-term strategies are elaborated.Distributed Applications, Metrics, Citizen-Orientation, Strategies