The design and implementation of an open-source, data-driven cohort recruitment system: the Duke Integrated Subject Cohort and Enrollment Research Network (DISCERN).
(2/18)
OBJECTIVE: Failure to reach research subject recruitment goals is a significant impediment to the success of many clinical trials. Implementation of health-information technology has allowed retrospective analysis of data for cohort identification and recruitment, but few institutions have also leveraged real-time streams to support such activities. DESIGN: Duke Medicine has deployed a hybrid solution, The Duke Integrated Subject Cohort and Enrollment Research Network (DISCERN), that combines both retrospective warehouse data and clinical events contained in prospective Health Level 7 (HL7) messages to immediately alert study personnel of potential recruits as they become eligible. RESULTS: DISCERN analyzes more than 500000 messages daily in service of 12 projects. Users may receive results via email, text pages, or on-demand reports. Preliminary results suggest DISCERN's unique ability to reason over both retrospective and real-time data increases study enrollment rates while reducing the time required to complete recruitment-related tasks. The authors have introduced a preconfigured DISCERN function as a self-service feature for users. LIMITATIONS: The DISCERN framework is adoptable primarily by organizations using both HL7 message streams and a data warehouse. More efficient recruitment may exacerbate competition for research subjects, and investigators uncomfortable with new technology may find themselves at a competitive disadvantage in recruitment. CONCLUSION: DISCERN's hybrid framework for identifying real-time clinical events housed in HL7 messages complements the traditional approach of using retrospective warehoused data. DISCERN is helpful in instances when the required clinical data may not be loaded into the warehouse and thus must be captured contemporaneously during patient care. Use of an open-source tool supports generalizability to other institutions at minimal cost. (+info)
Leveraging standards to support patient-centric interdisciplinary plans of care.
(3/18)
As health care systems and providers move towards meaningful use of electronic health records, the once distant vision of collaborative patient-centric, interdisciplinary plans of care, generated and updated across organizations and levels of care, may soon become a reality. Effective care planning is included in the proposed Stages 2-3 Meaningful Use quality measures. To facilitate interoperability, standardization of plan of care messaging, content, information and terminology models are needed. This degree of standardization requires local and national coordination. The purpose of this paper is to review some existing standards that may be leveraged to support development of interdisciplinary patient-centric plans of care. Standards are then applied to a use case to demonstrate one method for achieving patient-centric and interoperable interdisciplinary plan of care documentation. Our pilot work suggests that existing standards provide a foundation for adoption and implementation of patient-centric plans of care that are consistent with federal requirements. (+info)
Practical challenges in the secondary use of real-world data: the notifiable condition detector.
(4/18)
The interoperability specifications for electronic laboratory reporting specify the use of HL7, LOINC, SNOMED CT and UCUM. We explored the degree to which health care transactions comply with these standards by evaluating laboratory data captured in a health information exchange to support automated detection of public health notifiable diseases. We studied the NCD's ability to detect and report Lead, Influenza and MRSA. We found that due to incomplete LOINC mapping, alternate approaches such as keyword searches within local test names and codes could identify additional potentially reportable messages. We also found that non-adherence to HL7 messaging standards and inconsistently recorded laboratory results require the use of complex systems with complementary NLP techniques to accurately report notifiable conditions. We conclude that the incomplete adoption of and adherence to specified standards poses challenges to deploying processes that utilize real-world data for secondary purposes. (+info)
ADEpedia: a scalable and standardized knowledge base of Adverse Drug Events using semantic web technology.
(5/18)
A source of semantically coded Adverse Drug Event (ADE) data can be useful for identifying common phenotypes related to ADEs. We proposed a comprehensive framework for building a standardized ADE knowledge base (called ADEpedia) through combining ontology-based approach with semantic web technology. The framework comprises four primary modules: 1) an XML2RDF transformation module; 2) a data normalization module based on NCBO Open Biomedical Annotator; 3) a RDF store based persistence module; and 4) a front-end module based on a Semantic Wiki for the review and curation. A prototype is successfully implemented to demonstrate the capability of the system to integrate multiple drug data and ontology resources and open web services for the ADE data standardization. A preliminary evaluation is performed to demonstrate the usefulness of the system, including the performance of the NCBO annotator. In conclusion, the semantic web technology provides a highly scalable framework for ADE data source integration and standard query service. (+info)
Evaluation of HL7 v2.5.1 electronic case reports transmitted from a healthcare enterprise to public health.
(6/18)
Public health surveillance is necessary to prevent and control communicable and non-communicable diseases. An electronic reporting system using HL7 v2.5.1 was implemented between Intermountain Healthcare and the Utah Department of Health. We conducted prospective and retrospective studies to evaluate the timeliness, completeness of content information, and completeness of the electronic reporting process, and compared these metrics against other reporting entities. The electronic reporting system was more timely than other clinical reporting facilities and included more complete information in initial case reports. During a four month period, the electronic reporting system captured 8% of the cases not reported by the paper-based reporting system but missed 5% of the cases reported by the paper-based reporting system. We believe it would be more efficient for Infection Preventionists at hospitals to use their resources to detect cases not captured by the electronic reporting system instead of manually re-reporting cases already transmitted to public health electronically. (+info)
Implementations of the HL7 Context-Aware Knowledge Retrieval ("Infobutton") Standard: challenges, strengths, limitations, and uptake.
(7/18)