Towards Unified Data Exchange Formats for Reporting Molecular Drug Susceptibility Testing.

BACKGROUND: With the rapid development of new advanced molecular detection methods, identification of new genetic mutations conferring pathogen resistance to an ever-growing variety of antimicrobial substances will generate massive genomic datasets for public health and clinical laboratories. Keeping up with specialized standard coding for these immense datasets will be extremely challenging. This challenge prompted our effort to create a common molecular resistance Logical Observation Identifiers Names and Codes (LOINC) panel that can be used to report any identified antimicrobial resistance pattern. OBJECTIVE: To develop and utilize a common molecular resistance LOINC panel for molecular drug susceptibility testing (DST) data exchange in the U.S. National Tuberculosis Surveillance System using California Department of Public Health (CDPH) and New York State Department of Health as pilot sites. METHODS: We developed an interface and mapped incoming molecular DST data to the common molecular resistance LOINC panel using Health Level Seven (HL7) v2.5.1 Electronic Laboratory Reporting (ELR) message specifications through the Orion Health™ Rhapsody Integration Engine v6.3.1. RESULTS: Both pilot sites were able to process and upload/import the standardized HL7 v2.5.1 ELR messages into their respective systems; albeit CDPH identified areas for system improvements and has focused efforts to streamline the message importation process. Specifically, CDPH is enhancing their system to better capture parent-child elements and ensure that the data collected can be accessed seamlessly by the U.S. Centers for Disease Control and Prevention. DISCUSSION: The common molecular resistance LOINC panel is designed to be generalizable across other resistance genes and ideally also applicable to other disease domains. CONCLUSION: The study demonstrates that it is possible to exchange molecular DST data across the continuum of disparate healthcare information systems in integrated public health environments using the common molecular resistance LOINC panel.

Investigating the Relationship Between Type 2 Diabetes and Dementia Using Electronic Medical Records in the GoDARTS Bioresource.

OBJECTIVE: To investigate the impact of type 2 diabetes on incidence of major dementia subtypes, Alzheimer and vascular dementia, using electronic medical records (EMR) in the GoDARTS bioresource. RESEARCH DESIGN AND METHODS: GoDARTS (Genetics of Diabetes Audit and Research in Tayside Scotland) comprises a large case-control study of type 2 diabetes with longitudinal follow-up in EMR. Dementia case subjects after recruitment were passively identified in the EMR, and using a combination of case note review, an Alzheimer-specific weighted genetic risk score (wGRS), and APOE4 genotype, we validated major dementia subtypes. We undertook a retrospective matched cohort study to determine the risk of type 2 diabetes status for incident dementia accounting for competing risk of death. RESULTS: Type 2 diabetes status was associated with a significant risk of any dementia (cause-specific hazard ratio [csHR] 1.46, 95% CI 1.31-1.64), which was attenuated, but still significant, when competing risk of death was accounted for (subdistribution [sd]HR 1.26, 95% CI 1.13-1.41). The accuracy of EMR-defined cases of Alzheimer or vascular dementia was high-positive predictive value (PPV) 86.4% and PPV 72.8%, respectively-and wGRS significantly predicted Alzheimer dementia (HR 1.23, 95% CI 1.12-1.34) but not vascular dementia (HR 1.02, 95% CI 0.91-1.15). Conversely, type 2 diabetes was strongly associated with vascular dementia (csHR 2.47, 95% C 1.92-3.18) but not Alzheimer dementia, particularly after competing risk of death was accounted for (sdHR 1.02, 95% CI 0.87-1.18). CONCLUSIONS: Our study indicates that type 2 diabetes is associated with an increased risk of vascular dementia but not with an increased risk of Alzheimer dementia and highlights the potential value of bioresources linked to EMR to study dementia.

Design and Implementation of Data Exchange Formats for Molecular Detection of Drug-Resistant Tuberculosis.

Drug-resistant tuberculosis (TB) remains a public health threat to the United States and worldwide control of TB. Rapid and reliable drug susceptibility testing (DST) is essential for aiding clinicians in selecting an optimal treatment regimen for TB patients and to prevent ongoing transmission. Growth-based DST results for culture-confirmed cases are routinely reported to the U.S. Centers for Disease Control and Prevention through the National TB Surveillance System (NTSS). However, the NTSS currently lacks the capacity and functionality to accept laboratory results from advanced molecular methods that detect mutations associated with drug resistance. The objective of this study is to design and implement novel comprehensive data exchange formats that utilize the Health Level Seven (HL7) version 2.5.1 messaging hierarchy to capture, store, and monitor molecular DST data, thereby, improving the quality of data, specifications and exchange formats within the NTSS as well as ensuring full reporting of drug-resistant TB.

The research data management platform (RDMP): A novel, process driven, open-source tool for the management of longitudinal cohorts of clinical data.

Background: The Health Informatics Centre at the University of Dundee provides a service to securely host clinical datasets and extract relevant data for anonymized cohorts to researchers to enable them to answer key research questions. As is common in research using routine healthcare data, the service was historically delivered using ad-hoc processes resulting in the slow provision of data whose provenance was often hidden to the researchers using it. This paper describes the development and evaluation of the Research Data Management Platform (RDMP): an open source tool to load, manage, clean, and curate longitudinal healthcare data for research and provide reproducible and updateable datasets for defined cohorts to researchers. Results: Between 2013 and 2017, RDMP tool implementation tripled the productivity of data analysts producing data releases for researchers from 7.1 to 25.3 per month and reduced the error rate from 12.7% to 3.1%. The effort on data management reduced from a mean of 24.6 to 3.0 hours per data release. The waiting time for researchers to receive data after agreeing a specification reduced from approximately 6 months to less than 1 week. The software is scalable and currently manages 163 datasets. A total 1,321 data extracts for research have been produced, with the largest extract linking data from 70 different datasets. Conclusions: The tools and processes that encompass the RDMP not only fulfil the research data management requirements of researchers but also support the seamless collaboration of data cleaning, data transformation, data summarization and data quality assessment activities by different research groups.

Mapping Local Codes to Read Codes.

Background & Objectives: Legacy laboratory test codes make it difficult to use clinical datasets for meaningful translational research, where populations are followed for disease risk and outcomes over many years. The Health Informatics Centre (HIC) at the University of Dundee hosts continuous biochemistry data from the clinical laboratories in Tayside and Fife dating back as far as 1987. However, the HIC-managed biochemistry dataset is coupled with incoherent sample types and unstandardised legacy local test codes, which increases the complexity of using the dataset for reasonable population health outcomes. The objective of this study was to map the legacy local test codes to the Scottish 5-byte Version 2 Read Codes using biochemistry data extracted from the repository of the Scottish Care Information (SCI) Store. METHODS: Data mapping methodology was used to map legacy local test codes from clinical biochemistry laboratories within Tayside and Fife to the Scottish 5-byte Version 2 Read Codes. RESULTS: The methodology resulted in the mapping of 485 legacy laboratory test codes, spanning 25 years, to 124 Read Codes. CONCLUSION: The data mapping methodology not only facilitated the restructuring of the HIC-managed biochemistry dataset to support easier cohort identification and selection, but it also made it easier for the standardised local laboratory test codes, in the Scottish 5-byte Version 2 Read Codes, to be mapped to other health data standards such as Clinical Terms Version 3 (CTV3); LOINC; and SNOMED CT.

Synoptic operative reports for spinal cord injury patients as a tool for data quality.

The advent of synoptic operative reports has revolutionized how clinical data are captured at the time of care. In this article, an electronic synoptic operative report for spinal cord injury was implemented using interoperable standards, HL7 and Systematized Nomenclature of Medicine-Clinical Terms. Subjects (N = 10) recruited for a pilot study completed recruitment and feedback questionnaires, and produced both an electronic synoptic operative report for spinal cord injury report and a dictated narrative operative report for an actual patient case. Results indicated heterogeneity by subjects in access and use of electronic sources of patient data. Feedback questionnaire results confirmed that subjects were comfortable using both methods for data entry of operative reports, and that some were unable to find the diagnosis terms they needed in electronic synoptic operative report for spinal cord injury. Data quality improved. Electronic synoptic operative report for spinal cord injury reports were more complete (95.26%) than dictated (80%) for all subjects. An accuracy assessment, which considered usability for secondary data use, was conducted and the electronic synoptic operative report for spinal cord injury was demonstrated to improve accuracy.

Design and implementation of synoptic operative report template using interoperable standards.

The increasing use of synoptic operative reports in clinical settings represents a major milestone in the advancement of health information technology. Synoptic operative report templates enable clinicians to capture and display succinct clinical information in a standardized and logical manner. Synoptic operative report templates also provide the optimum goal of enriching personalized health information of a given patient at the point of care so as to support the exchange of clinical information across the continuum of multiple healthcare providers. However, most of the available synoptic operative report templates in many clinical settings do not incorporate interoperable standards in their design and implementation. This paper proposes a novice template (i.e., eSOR-SCI) that uses interoperable standards for its design and implementation.

The use of biometrics in the Personal Health Record (PHR).

The emergence of the Personal Health Record (PHR) has made individual health information more readily accessible to a wide range of users including patients, consumers, practitioners, and healthcare providers. However, increased accessibility of PHR threatens the confidentiality, privacy, and security of personalized health information. Therefore, a need for robust and reliable forms of authentication is of prime concern. The concept of biometric authentication is now highly visible to healthcare providers as a technology to prevent unauthorized access to individual health information. Implementing biometric authentication mechanisms to protect PHR facilitates access control and secure exchange of health information. In this paper, a literature review is used to explore the key benefits, technical barriers, challenges, and ethical implications for using biometric authentication in PHR.

Is it appropriate, or ethical, to use health data collected for the purpose of direct patient care to develop computerized predictive decision support tools?

The increasing use of clinical decision support systems (CDSS) to assist clinicians in decision-making is pushing the limits of information technology. The emergence of Electronic Health Records (EHR) coupled with enriched health information standards such as HL7 CDA, SNOMED, ICD-10 and LOINC have provided a rich environment for massive data collection and analysis by healthcare providers. This immense increase in data collection has also provided a gateway for the application of various data mining techniques on clinical datasets so as to measure health status (i.e. function, comfort and likelihood of dying) of patients. In measuring health status, many clinicians have opted to use CDSS to assist in decision-making and enhance clinical experience. However, even as the use of CDSS in clinicians' office continues to grow, the question that remains in the minds of many patients and the general public is whether it is appropriate, or ethical, for researchers to use health data collected for the purpose of direct patient care to develop computerized predictive decision support tool. In this paper, a systematic review is used to highlight the relevant technical barriers and ethical issues surrounding the secondary use of health data in developing CDSS.