Research

The Health Analytics Consortium (HAC) is an open incubator for collaboration and digital scholarship that emphasizes team-based transdisciplinary data science and advanced health analytics. A core mission of HAC is to foster integration of innovative research, development, education and training, and outreach in data and health sciences.

 The University of Michigan Health System provides leadership and support of “Collaborative Quality Initiatives” (CQIs) which seek to address some of the most common, complex, and costly areas of surgical and medical care. CQI Coordinating Centers, led by UMHS faculty, work collaboratively with health care providers throughout Michigan to collect data to a centralized registry; analyze and share data to identify processes that lead to improved delivery of care and outcomes, and guide quality improvement interventions. 

 This is an open-access archive of a diverse collection of datasets that can be used for demonstrations, algorithm development, instrument testing, exploratory analytics and hands-on experiential practice of data-driven inference. The data are classified by type. Meta-data is provided to frame the information into the scope of specific driving motivational challenges. Observed and simulated data are included in the SOCR Data archive. The data can be redistributed (CC-BY).

 ABL consults and collaborates with investigative teams in nursing, contributing their expertise to the designing of experiments, statistical analysis planning (including sample size justification), statistical analysis in support of grants, and preparing/submitting grant proposals.

The Center for Complexity and Self-management of Chronic Disease (CSCD) advances the science of self-management (SM) by addressing complexity, including the study of complex multi-component interventions and SM for people with complex comorbid conditions. In addition, the Center provides the infrastructure to facilitate interdisciplinary approaches and expand the pool of investigative teams who are equipped to successfully develop and implement externally funded programs of research in self-management.

 Synthetic Patient Data Sets (HAPI FHIR server in AWS)

EMS' SIMULATIONiQ™ Enterprise solution provides a single integrated platform with a full spectrum of options for mid- to large-size standardized patient (SP) and mannequin-based simulation centers. From audio-visual hardware and software to management, evaluation, and mobile device access, SIMULATIONiQ Enterprise enables evaluators to leverage their full simulation efforts to drive tangible results.

State-of-the-art clinical learning facility that focus on active learning that fosters greater understanding and more advanced clinical reasoning. The Clinical Learning Center has simulation rooms housed with high-fidelity mannequins for replicating realistic health care situations, to skills labs for honing basic and advanced skills, to staff rich with knowledge, experience and expertise, this environment enables students to apply their knowledge of nursing theory in an interactive and challenging yet safe and supportive environment.

 (Longitudinal Online Research and Imaging System) is a web-based data and project management software for neuroimaging research studies. It is an OPEN SOURCE framework for storing and processing behavioural, clinical, neuroimaging and genetic data. LORIS also makes it easy to manage large datasets acquired over time in a longitudinal study, or at different locations in a large multi-site study.

 The Data Dashboard webapp provides a mechanism to integrate dispersed multi-source data and service the mashed information via human and machine interfaces in a secure, scalable manner. The Dashboard enables the exploration of subtle associations between variables, population strata, or clusters of data elements, which may be opaque to standard independent inspection of the individual sources. This a new platform includes a device agnostic tool for graphical querying, navigating and exploring the multivariate associations in complex heterogeneous datasets.