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HIP Investigator, Dr. Elizabeth Cox has been named the Associate Director of the Primary Care Research Fellowship. The fellowship is administered in the UW Department of Family medicine and Community Health and funded by HRSA. A principle objective is to train primary care physicians and PhD scientists interested in careers in medical research related to the organization, delivery, or effectiveness of primary health care and preventive medicine. The Primary Care Research Fellowship is among the oldest, most successful primary care research fellowships in the nation. Since 1993, it has trained more than 60 post-doctoral fellows for careers in primary care medical research at academic medical institutions.

The NIH Clinical and Translational Science Award (CTSA) Program exists to help turn research from the lab, clinic, and community into interventions that improve the health of individuals and the public at large. The field of dissemination and implementation (D&I) has essentially the same goal. Building the D&I capacity of CTSA grantees can substantially further a CTSA’s mission.

The purpose of this toolkit is to describe some key D&I resources and activities developed by the UW-Madison CTSA to support dissemination and implementation research and activities. The authors of Developing Dissemination and Implementation Capacity within a CTSA: a Toolkit hope the practical information offered might help other CTSA programs think about and develop the D&I resources that advance their goals.

Diabetic eye disease is the leading cause of blindness among working-age U.S. adults. Early detection and treatment can reduce the risk of blindness by over 90%, but fewer than half of adults with diabetes obtain yearly recommended eye screening. Teleophthalmology makes it easier for patients to obtain diabetic eye screening by providing convenient access to high-quality, vision saving eye care at low cost. HIP Investigator, Dr.

The HIP Model and Tools for Research with Learning Health Systems is now available on HIPxChange. This model and tools defines specific steps for projects to create sustainable change based on research conducted within the health system. The HIP Model builds a bridge between science and clinical care to ensure that high-priority questions are identified and pursued and that results are shared with the health system to support system-wide change.

Colorectal cancer is the second leading cause of cancer deaths in Wisconsin. It is also the most preventable, yet least prevented cancer. Screening is important for early detection, but disparities in screening rates exist between Wisconsin clinics. HIP Investigator, Dr. Jen Weiss and a team of investigators received a research award through the Wisconsin Partnership Program Collaborative Health Sciences Program to identify strategies from high-performing clinics to improve colorectal cancer screening rates at low-performing clinics in rural and urban communities in Wisconsin. The long-term goal of the research is to decrease statewide colorectal cancer incidence and mortality.

Report Overview

The Wisconsin Collaborative for Healthcare Quality (WCHQ), in collaboration with Health Innovation Program, developed the Wisconsin Health Disparities Report to identify where disparities in health outcomes and care exist in Wisconsin and to help inform and accelerate programs that are working to eliminate disparities.

Recently, healthcare has seen a sharp rise in the implementation of machine learning derived algorithms for predicting risk across a broad range of clinical scenarios. The Number Needed to Treat Thresholding Toolkit created by HIP Investigator, Dr. Brian Patterson of the BerbeeWalsh Department of Emergency Medicine, allows users to generate similar graphs, either from raw data of an algorithm’s performance in a given population, or applying an algorithm with known test characteristics at various thresholds to a theoretical population.

Predictive analytics has the potential to transform the health care system by using existing data to predict and prevent poor clinical outcomes, provide targeted care, and lower costs. A challenge for health systems is selecting and implementing predictive models within clinical and operational workflows.

To guide health systems through the process of selecting and implementing a predictive model within their system, the UW Health Applied Data Science team and the Health Innovation Program developed a toolkit to support planning for and implementation of a predictive model. This toolkit was tested through the implementation of a sepsis prediction model in the inpatient setting at UW Health, a large Midwestern academic health system with four hospitals.