Abstract:Results of medical research studies are often contradictory or cannot be reproduced. One reason is that there may not be enough patient subjects available for observation for a long enough time period. Another reason is that patient populations may vary considerably with respect to geographic and demographic boundaries thus limiting how broadly the results apply. Even when similar patient populations are pooled together from multiple locations, differences in medical treatment and record systems can limit whic… Show more
Background Traditional surveillance systems produce estimates of influenza-like illness (ILI) incidence rates, but with one-to three-week delay. Accurate real-time monitoring systems of influenza outbreaks could be useful for public health decisions. Several works have investigated the possibility to use internet-users' activity data and different statistical models to predict influenza epidemics in near real-time. However, very few studies have investigated hospital big data. Objective Here, we compared internet and electronic health records (EHR) data and different statistical models to identify the best approach (data type and statistical model) for ILI estimates in real time. Methods We used Google Data for internet data and the clinical data warehouse eHOP, that includes all EHR from Rennes University Hospital (France), for hospital data. We compared three statistical models, Random Forest (RF), Elastic Net, and Support Vector Machine (SVM).
Background Traditional surveillance systems produce estimates of influenza-like illness (ILI) incidence rates, but with one-to three-week delay. Accurate real-time monitoring systems of influenza outbreaks could be useful for public health decisions. Several works have investigated the possibility to use internet-users' activity data and different statistical models to predict influenza epidemics in near real-time. However, very few studies have investigated hospital big data. Objective Here, we compared internet and electronic health records (EHR) data and different statistical models to identify the best approach (data type and statistical model) for ILI estimates in real time. Methods We used Google Data for internet data and the clinical data warehouse eHOP, that includes all EHR from Rennes University Hospital (France), for hospital data. We compared three statistical models, Random Forest (RF), Elastic Net, and Support Vector Machine (SVM).
“…Utilizing the availability of patient data from federated EHR systems in many different sites, as well as in international multilingual settings is still challenging [2]. Although promising clinical data re-use for research is being enabled through the building of major emerging research infrastructures such as SHARPn [16], i2b2-SHRINE [6,11], EHR4CR [2], limitations and new issues arise [5,8].…”
SummaryObjective: To select and summarize key contributions to current research in the field of Clinical Research Informatics (CRI). Method: A bibliographic search using a combination of MeSH and free terms search over PubMed was performed followed by a blinded review. Results: The review process resulted in the selection of four papers illustrating various aspects of current research efforts in the area of CRI. The first paper tackles the challenge of extracting accurate phenotypes from Electronic Healthcare Records (EHRs). Privacy protection within shared de-identified, patient-level research databases is the focus of the second selected paper. Two other papers exemplify the growing role of formal representation of clinical data -in metadata repositories -and knowledge -in ontologies -for supporting the process of reusing data for clinical research. Conclusions: The selected articles demonstrate how concrete platforms are currently achieving interoperability across clinical research and care domains and have reached the evaluation phase. When EHRs linked to genetic data have the potential to shift the research focus from research driven patient recruitment to phenotyping in large population, a key issue is to lower patient re-identification risks for biomedical research databases. Current research illustrates the potential of knowledge engineering to support, in the coming years, the scientific lifecycle of clinical research.
“…[5] The inclusion criteria were manually translated at each site to the equivalent local codes. Each site then ran the resulting query and their local i2b2 server generated a "patient set"-a list of de-identified patient numbers.…”
Background: Electronic Health Records (EHRs) are ubiquitous and yet little is known about their use for prospective research purposes, and even less is known about patient perspectives regarding the use of the EHR for research.Objective: The aim of this paper is to report on the initial obesity project from the Greater Plains Collaborative (GPC) that is part of the Patient Centered Outcomes Research Institute (PCORI) National Patient Centered Clinical Research Network (PCORNet). The purpose of the project was to prospectively assess caregivers' willingness for their children to participate in medical research, and to assess their views regarding the use of the electronic health record for recruitment and data collection.Methods: The electronic health records (EHRs) of 10 Midwestern academic medical centers were used to select patients for a survey was designed to assess patient willingness to participate in research, as well as the use of their EHRs for research. Survey questions included questions regarding interest in medical research, as well as basic demographic and health information. A variety of contact methods were used.Results: A cohort of 54,269 patients was created and 3,139 (5.78%) responded. Completers were more likely to be female and Caucasian, although these and other factors differed significantly by site.Respondents were overwhelmingly positive about using EHRs for research.Conclusions: EHRs are an important resource for engaging patients in research, and our respondents concurred. However, this investigation had a very low response rate which varied by method of contact, geographic location, and respondent characteristics. But unlike other health studies, EHRs directed research can know with certainty the clinically observed characteristics of non-respondents and respondents. Thus reliable study estimates can be derived by weighting responses and over sampling difficult to reach subpopulations. These data suggest that EHRs are a promising new and effective tool for patient engaged health research.
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