The Use and Significance of a Research Networking System

Kahlon, Maninder; Yuan, Leslie; Daigre, John; Meeks, Eric; Nelson, Katie; Piontkowski, Cynthia; Reuter, Katja; Sak, Rachael; Turner, Brian; Weber, Griffin M; Chatterjee, Anirvan

doi:10.2196/jmir.3137

Cited by 10 publications

(7 citation statements)

References 15 publications

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“…The coauthor network data used in this analysis come from Profiles Research Networking Software that is available for free under an open source license, and has been implemented at over 40 institutions, including Boston University and the University of California at San Francisco. 29,30 Our findings can and should be examined in other settings.…”

Section: Discussionmentioning

confidence: 79%

Faculty Promotion and Attrition: The Importance of Coauthor Network Reach at an Academic Medical Center

et al. 2015

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BACKGROUND: Business literature has demonstrated the importance of networking and connections in career advancement. This is a little-studied area in academic medicine. OBJECTIVE: To examine predictors of intra-organizational connections, as measured by network reach (the number of first-and second-degree coauthors), and their association with probability of promotion and attrition. DESIGN: Prospective cohort study between 2008 and 2012. SETTING: Academic medical center. PARTICIPANTS: A total of 5787 Harvard Medical School (HMS) faculty with a rank of assistant professor or fulltime instructor as of January 1, 2008. MAIN MEASURES: Using negative binomial models, multivariable-adjusted predictors of continuous network reach were assessed according to rank. Poisson regression was used to compute relative risk (RR) and 95 % confidence intervals (CI) for the association between network reach (in four categories) and two outcomes: promotion or attrition. Models were adjusted for demographic, professional and productivity metrics. KEY RESULTS: Network reach was positively associated with number of first-, last-and middle-author publications and h-index. Among assistant professors, men and whites had greater network reach than women and underrepresented minorities (p<0.001). Compared to those in the lowest category of network reach in 2008, instructors in the highest category were three times as likely to have been promoted to assistant professor by 2012 (RR: 3.16, 95 % CI: 2.60, 3.86; p-trend <0.001) after adjustment for covariates. Network reach was positively associated with promotion from assistant to associate professor (RR: 1.82, 95 % CI: 1.32, 2.50; p-trend <0.001). Those in the highest category of network reach in 2008 were 17 % less likely to have left HMS by 2012 (RR: 0.83, 95 % CI 0.70, 0.98) compared to those in the lowest category.CONCLUSIONS: These results demonstrate that coauthor network metrics can provide useful information for understanding faculty advancement and retention in academic medicine. They can and should be investigated at other institutions.

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Section: Discussionmentioning

confidence: 79%

Faculty Promotion and Attrition: The Importance of Coauthor Network Reach at an Academic Medical Center

et al. 2015

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“…Reliable feedback systems (such as those seen in Amazon and eBay) will be central to creating sufficient trust. To date, most sites have focused on fostering researcher-researcher collaboration [ 78 , 79 ], or patient-patient collaboration. The next step would be to foster bi- or tri-directional links (also to clinicians), with the added challenge of increased knowledge asymmetries.…”

Section: Online Communitiesmentioning

confidence: 99%

Systems Medicine 2.0: Potential Benefits of Combining Electronic Health Care Records With Systems Science Models

Tillmann¹,

Gibson²,

Scott³

et al. 2015

J Med Internet Res

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BackgroundThe global burden of disease is increasingly dominated by non-communicable diseases.These diseases are less amenable to curative and preventative interventions than communicable disease. This presents a challenge to medical practice and medical research, both of which are experiencing diminishing returns from increasing investment.ObjectiveOur aim was to (1) review how medical knowledge is generated, and its limitations, (2) assess the potential for emerging technologies and ideas to improve medical research, and (3) suggest solutions and recommendations to increase medical research efficiency on non-communicable diseases.MethodsWe undertook an unsystematic review of peer-reviewed literature and technology websites.ResultsOur review generated the following conclusions and recommendations. (1) Medical knowledge continues to be generated in a reductionist paradigm. This oversimplifies our models of disease, rendering them ineffective to sufficiently understand the complex nature of non-communicable diseases. (2) Some of these failings may be overcome by adopting a “Systems Medicine” paradigm, where the human body is modeled as a complex adaptive system. That is, a system with multiple components and levels interacting in complex ways, wherein disease emerges from slow changes to the system set-up. Pursuing systems medicine research will require larger datasets. (3) Increased data sharing between researchers, patients, and clinicians could provide this unmet need for data. The recent emergence of electronic health care records (EHR) could potentially facilitate this in real-time and at a global level. (4) Efforts should continue to aggregate anonymous EHR data into large interoperable data silos and release this to researchers. However, international collaboration, data linkage, and obtaining additional information from patients will remain challenging. (5) Efforts should also continue towards “Medicine 2.0”. Patients should be given access to their personal EHR data. Subsequently, online communities can give researchers the opportunity to ask patients for direct access to the patient’s EHR data and request additional study-specific information. However, selection bias towards patients who use Web 2.0 technology may be difficult to overcome.ConclusionsSystems medicine, when combined with large-scale data sharing, has the potential to raise our understanding of non-communicable diseases, foster personalized medicine, and make substantial progress towards halting, curing, and preventing non-communicable diseases. Large-scale data amalgamation remains a core challenge and needs to be supported. A synthesis of “Medicine 2.0” and “Systems Science” concepts into “Systems Medicine 2.0” could take decades to materialize but holds much promise.

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“…24,25 Faculty can log in to Profiles to correct any mistakes made by the automated publication matching process or to add publications not indexed by PubMed. For this study, we only used the publications in Profiles that are indexed in PubMed.…”

Section: Data Sourcesmentioning

confidence: 99%