The Iowa Health Data Resource (IHDR): an innovative framework for transforming the clinical health data ecosystem.

IMPORTANCE: This manuscript will be of interest to most Clinical and Translational Science Awards (CTSA) as they retool for the increasing emphasis on translational science from translational research. This effort is an extension of the EDW4R work that most CTSAs have done to deploy infrastructure and tools for researchers to access clinical data. OBJECTIVES: The Iowa Health Data Resource (IHDR) is a strategic investment made by the University of Iowa to improve access to real-world health data. The goals of IHDR are to improve the speed of translational health research, to boost interdisciplinary collaboration, and to improve literacy about health data. The first objective toward this larger goal was to address gaps in data access, data literacy, lack of computational environments for processing Personal Health Information (PHI) and the lack of processes and expertise for creating transformative datasets. METHODS: A three-pronged approach was taken to address the objective. The approach involves integration of an intercollegiate team of non-informatics faculty and staff, a data enclave for secure patient data analyses, and novel comprehensive datasets. RESULTS: To date, all five of the health science colleges (dentistry, medicine, nursing, pharmacy, and public health) have had at least one staff and one faculty member complete the two-month experiential learning curriculum. Over the first two years of this project, nine cohorts totaling 36 data liaisons have been trained, including 18 faculty and 18 staff. IHDR data enclave eliminated the need to duplicate computational infrastructure inside the hospital firewall which reduced infrastructure, hardware and human resource costs while leveraging the existing expertise embedded in the university research computing team. The creation of a process to develop and implement transformative datasets has resulted in the creation of seven domain specific datasets to date. CONCLUSION: The combination of people, process, and technology facilitates collaboration and interdisciplinary research in a secure environment using curated data sets. While other organizations have implemented individual components to address EDW4R operational demands, the IHDR combines multiple resources into a novel, comprehensive ecosystem IHDR enables scientists to use analysis tools with electronic patient data to accelerate time to science.

Surgery start time and early implant failure: A case-control study.

OBJECTIVE: To evaluate the influence of surgery start time (SST) and other patient- and therapy-related variables on the risk for early implant failure (EIF) in an academic setting. MATERIAL AND METHODS: Data were extracted from the electronic health records of 61 patients who had at least one EIF and 140 age- and gender-matched, randomly selected, non-EIF controls. Bivariate and multivariable analyses were performed to identify relevant associations between EIF and different variables, such as SST. RESULTS: Incidence of EIF was not significantly associated with SST (HR: 1.9 for afternoon implant placement, 95% CI: 0.9-3.9; p = .105). Other factors that were associated with a significantly increased risk for EIF in a multivariable model were pre-placement ridge augmentation (HR: 7.5, 95% CI: 2.2-25.1; p = .001), intra-operative complications (HR: 5.9, 95% CI: 2.2-16.3; p < .001), simultaneous soft tissue grafting (HR: 5.03, 95% CI: 1.3-19.5; p = .020), simultaneous bone grafting (HR: 3.7, 95% CI: 1.6-8.8; p = .002), and placement with sedation (HR: 3.4, 95% CI: 1.5-7.5; p = .002). CONCLUSIONS: While SST was not associated with the occurrence of EIF in our cohort, other variables, such as ridge augmentation prior to implant placement, simultaneous bone or soft tissue grafting, intra-operative complications, implant placement with sedation, and number of implants in the oral cavity, were associated with an increased risk for this adverse event.