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

ROMOP: a light-weight R package for interfacing with OMOP-formatted electronic health record data.

Objectives:Electronic health record (EHR) data are increasingly used for biomedical discoveries. The nature of the data, however, requires expertise in both data science and EHR structure. The Observational Medical Out-comes Partnership (OMOP) common data model (CDM) standardizes the language and structure of EHR data to promote interoperability of EHR data for research. While the OMOP CDM is valuable and more attuned to research purposes, it still requires extensive domain knowledge to utilize effectively, potentially limiting more widespread adoption of EHR data for research and quality improvement. Materials and methods:We have created ROMOP: an R package for direct interfacing with EHR data in the OMOP CDM format. Results:ROMOP streamlines typical EHR-related data processes. Its functions include exploration of data types, extraction and summarization of patient clinical and demographic data, and patient searches using any CDM vocabulary concept. Conclusion:ROMOP is freely available under the Massachusetts Institute of Technology (MIT) license and can be obtained from GitHub (http://github.com/BenGlicksberg/ROMOP). We detail instructions for setup and use in the Supplementary Materials. Additionally, we provide a public sandbox server containing synthesized clinical data for users to explore OMOP data and ROMOP (http://romop.ucsf.edu)

Real-time predictive analytics for sepsis level and therapeutic plans in intensive care medicine

"Accepted for publication"This work aims to support doctor’s decision-making on predicting sepsis level and the best treatment for patients with microbiological problems. A set of Data Mining (DM) models was developed using forecasting techniques and classification models which will enable doctors’ decisions about the appropriate therapy to apply, as well as the most successful one. The data used in DM models were collected at the Intensive Care Unit (ICU) of the Centro Hospitalar do Porto, in Oporto, Portugal. Classification models where considered to predict sepsis level and therapeutic plan for patients with sepsis in a supervised learning approach. Models were induced making use of the following algorithms: Decision Trees, Support Vector Machines and Naïve Bayes classifier. Confusion Matrix, including associated metrics, and Cross-validation were used for the evaluation. Analysis of the total error rate, sensitivity, specificity and accuracy were the associated metrics used to identify the most relevant measures to predict sepsis level and treatment plan under study. In conclusion, it was possible to predict with great accuracy the sepsis level (2nd and 3rd), but not the therapeutic plan. Although the good results attained for sepsis (accuracy: 100%), therapeutic plan does not present the same level of accuracy (best: 62.8%).FCT -Fundação para a Ciência e a Tecnologia(PEst-OE/EEI/UI0319/2014

Web application of physiological data based on FHIR

This paper works toward implementing a prototype demonstrating some of the capabilities of the FHIR specification. The specification requires a clear understanding of its different components in order to be successfully implemented, therefore the primary concern of this work is to understand and analyse FHIR’s concepts. The research conducted in this work revealed that FHIR is a well-designed specification, based on a powerful data model and technologies. Therefore, it sould help solving the interoperability issues of the healthcare eco-system. It has also been pointed that since FHIR is a recent standard, many of its uses and benefits are still to be discovered. Moreover, FHIR integrates well in the current health information technology context since it can be used in addition to existing standards

Explanation-Based Auditing

To comply with emerging privacy laws and regulations, it has become common for applications like electronic health records systems (EHRs) to collect access logs, which record each time a user (e.g., a hospital employee) accesses a piece of sensitive data (e.g., a patient record). Using the access log, it is easy to answer simple queries (e.g., Who accessed Alice's medical record?), but this often does not provide enough information. In addition to learning who accessed their medical records, patients will likely want to understand why each access occurred. In this paper, we introduce the problem of generating explanations for individual records in an access log. The problem is motivated by user-centric auditing applications, and it also provides a novel approach to misuse detection. We develop a framework for modeling explanations which is based on a fundamental observation: For certain classes of databases, including EHRs, the reason for most data accesses can be inferred from data stored elsewhere in the database. For example, if Alice has an appointment with Dr. Dave, this information is stored in the database, and it explains why Dr. Dave looked at Alice's record. Large numbers of data accesses can be explained using general forms called explanation templates. Rather than requiring an administrator to manually specify explanation templates, we propose a set of algorithms for automatically discovering frequent templates from the database (i.e., those that explain a large number of accesses). We also propose techniques for inferring collaborative user groups, which can be used to enhance the quality of the discovered explanations. Finally, we have evaluated our proposed techniques using an access log and data from the University of Michigan Health System. Our results demonstrate that in practice we can provide explanations for over 94% of data accesses in the log.Comment: VLDB201

Interoperability in health care

With the advancement of technology, patient information has been being computerized in order to facilitate the work of healthcare professionals and improve the quality of healthcare delivery. However, there are many heterogeneous information systems that need to communicate, sharing information and making it available when and where it is needed. To respond to this requirement the Agency for Integration, Diffusion, and Archiving of medical information (AIDA) was created, a multi-agent and service-based platform that ensures interoperability among healthcare information systems. In order to improve the performance of the platform, beyond the SWOT analysis performed, a system to prevent failures that may occur in the platform database and also in machines where the agents are executed was created. The system has been implemented in the Centro Hospitalar do Porto (one of the major Portuguese hospitals), and it is now possible to define critical workload periods of AIDA, improving high availability and load balancing. This is explored in this chapter.(undefined