Modernizing Public Health Data Systems and Workforce Capacity: The Centers for Disease Control and Prevention's Public Health Informatics Fellowship Program.

Context: The COVID-19 pandemic exposed governmental public health's outdated information technology and insufficient data science and informatics workforce capacity. The Centers for Disease Control and Prevention's Public Health Informatics Fellowship Program (PHIFP) is well positioned to strengthen public health data science and informatics workforce capacity.

Program: Established in 1996, PHIFP is a 2-year, full-time, on-the-job training program.

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.

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 genotype, we validated major dementia subtypes.

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.

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.

Mapping Local Codes to Read Codes.

Unlabelled: 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.

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.

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.

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.

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.