The Morningside Initiative: Collaborative Development of a Knowledge Repository to Accelerate Adoption of Clinical Decision Support

The Morningside Initiative is a public-private activity that has evolved from an August, 2007, meeting at the Morningside Inn, in Frederick, MD, sponsored by the Telemedicine and Advanced Technology Research Center (TATRC) of the US Army Medical Research Materiel Command. Participants were subject matter experts in clinical decision support (CDS) and included representatives from the Department of Defense, Veterans Health Administration, Kaiser Permanente, Partners Healthcare System, Henry Ford Health System, Arizona State University, and the American Medical Informatics Association (AMIA). The Morningside Initiative was convened in response to the AMIA Roadmap for National Action on Clinical Decision Support and on the basis of other considerations and experiences of the participants. Its formation was the unanimous recommendation of participants at the 2007 meeting which called for creating a shared repository of executable knowledge for diverse health care organizations and practices, as well as health care system vendors. The rationale is based on the recognition that sharing of clinical knowledge needed for CDS across organizations is currently virtually non-existent, and that, given the considerable investment needed for creating, maintaining and updating authoritative knowledge, which only larger organizations have been able to undertake, this is an impediment to widespread adoption and use of CDS. The Morningside Initiative intends to develop and refine (1) an organizational framework, (2) a technical approach, and (3) CDS content acquisition and management processes for sharing CDS knowledge content, tools, and experience that will scale with growing numbers of participants and can be expanded in scope of content and capabilities. Intermountain Healthcare joined the initial set of participants shortly after its formation. The efforts of the Morningside Initiative are intended to serve as the basis for a series of next steps in a national agenda for CDS. It is based on the belief that sharing of knowledge can be highly effective as is the case in other competitive domains such as genomics. Participants in the Morningside Initiative believe that a coordinated effort between the private and public sectors is needed to accomplish this goal and that a small number of highly visible and respected health care organizations in the public and private sector can lead by example. Ultimately, a future collaborative knowledge sharing organization must have a sustainable long-term business model for financial support

Sharing Detailed Research Data Is Associated with Increased Citation Rate

BACKGROUND: Sharing research data provides benefit to the general scientific community, but the benefit is less obvious for the investigator who makes his or her data available. PRINCIPAL FINDINGS: We examined the citation history of 85 cancer microarray clinical trial publications with respect to the availability of their data. The 48% of trials with publicly available microarray data received 85% of the aggregate citations. Publicly available data was significantly (p = 0.006) associated with a 69% increase in citations, independently of journal impact factor, date of publication, and author country of origin using linear regression. SIGNIFICANCE: This correlation between publicly available data and increased literature impact may further motivate investigators to share their detailed research data

Computer Decision Support as a Source of Interpretation Error: The Case of Electrocardiograms

Objective: The aim of this study was to determine the effect that the computer interpretation (CI) of electrocardiograms (EKGs) has on the accuracy of resident (noncardiologist) physicians reading EKGs. Design: A randomized, controlled trial was conducted in a laboratory setting from February through June 2001, using a two-period crossover design with matched pairs of subjects randomly assigned to sequencing groups. Measurements: Subjects' interpretive accuracy of discrete, cardiologist-determined EKG findings were measured as judged by a board-certified internist. Results: Without the CI, subjects interpreted 48.9% (95% confidence interval, 45.0% to 52.8%) of the findings correctly. With the CI, subjects interpreted 55.4% (51.9% to 58.9%) correctly (p < 0.0001). When the CIs that agreed with the gold standard (Correct CIs) were not included, 53.1% (47.7% to 58.5%) of the findings were interpreted correctly. When the correct CI was included, accuracy increased to 68.1% (63.2% to 72.7%; p < 0.0001). When computer advice that did not agree with the gold standard (Incorrect CI) was not provided to the subjects, 56.7% (48.5% to 64.5%) of findings were interpreted correctly. Accuracy dropped to 48.3% (40.4% to 56.4%) when the incorrect computer advice was provided (p = 0.131). Subjects erroneously agreed with the incorrect CI more often when it was presented with the EKG 67.7% (57.2% to 76.7%) than when it was not 34.6% (23.8% to 47.3%; p < 0.0001). Conclusions: Computer decision support systems can generally improve the interpretive accuracy of internal medicine residents in reading EKGs. However, subjects were influenced significantly by incorrect advice, which tempers the overall usefulness of computer-generated advice in this and perhaps other areas

Raw Citation Counts and Covariates

Raw bibliometric data used in the analysis, combining data extracted from Thomson ISI Web of Science, PubMed, the Ntzani and Ioannidis 2003 Lancet paper, and the author's own investigations

The development and validation of a simulation tool for health policy decision making

AbstractComputer simulations have been used to model infectious diseases to examine the outcomes of alternative strategies for managing their spread. Methicillin resistant Staphylococcus aureus (MRSA) skin and soft tissue infections have become prominent in many communities and efforts are underway to reduce the spread of this organism both in hospitals and communities. Currently, there are few tools for policy makers to use to examine the outcome of various choices when making decisions about MRSA. Using the example of MRSA, we describe, in this paper, a rigorous approach for development and validation of a tool that simulates the spread of MRSA infections. We used sensitivity analyses in a novel way and validated the simulation results against local data over time. Our approach for simulation development and validation is generalizeable to simulations of other diseases