The Great Lakes Practice Transformation Network (GLPTN) is a collaborative project led by the Center for Health Innovation and Implementation Science (CHIIS) at Indiana University. Funded by the Centers for Medicare and Medicaid Services (CMS), its goal is to improve healthcare quality and prepare doctors for value-based care models by making care more efficient and more affordable.
Value-based care, for those unfamiliar, differs from traditional fee-for-service care models in that it determines payment to providers based on patients’ health outcomes as part of a CMS program that rewards providers with incentive payments for care given to people with medicare. The GLPTN supports over 15,000 healthcare providers in Indiana, Illinois, Kentucky, Ohio, and Michigan in improving care for individuals, improving health for populations, and lowering costs.
The GLPTN has created a network of support in which Quality Improvement Advisors (QIAs) work with healthcare providers to streamline and coordinate care by helping them assess areas that could benefit from change, training and implementation support, and customized reports detailing the practice’s progress and milestones. To do so, GLPTN provides a dashboard that allows QIAs access to data submitted by the practices they assist in the digestible form of interactive visualizations for recent quarters and months, and over the lifetime of the program. The QIAs use the dashboard as a coaching tool. They show the dashboard results to the health care practice manager to highlight the clinical quality metrics that are undergoing excellent changes in a positive direction as well as the metrics that show need of improvement. Members of the network also have access to faculty partners from member-state universities who can help the providers rethink the best ways to coordinate care.
To support the GLPTN in its mission, Spencer Lourens, Ph.D. and his colleagues at CHIIS partnered with Indiana University UITS Research Technologies (RT) division to design a tailored software solution that would give QIAs and state leaders within the network access to the data they need to evaluate providers’ progress and identify areas for future improvement. Ed Sieferd, a member of the CHIIS team, designed and employed a user-centered design process throughout the ongoing development phase. This lowered total development cost and created a better experience for their users. RT helped CHIIS set up a RStudio Connect (RSC) server, and offered essential technical expertise. RSC hosts the dashboard used by QIAs, and provides a web-based solution for hosting/deploying applications, automating reports and dissemination of reports. According to Lourens, “RT offers us access to experts who we can work with, and a reliable service to house our software so that our users can access it any time they need it.”
For Lourens, the collaboration between RT and GLPTN offers the chance to move closer to the team’s quadruple aim: “better care, improved outcomes, at lower costs, and with enhanced experiences for patient and provider alike.” With the rapid development of the Biostatistics and Data Science fields, users want more services, better access to services, and on-demand access to the insights Biostatisticians and Data Scientists can offer. The movement toward value-based care has created a landscape within the healthcare field that requires data-based feedback to manage the care of patient populations. This shift has intensified the need for interactive, insight-generating tools. The software Lourens and his colleagues have developed provides a glimpse into the future of applied Biostatistics and Data Science.
The GLPTN would like to acknowledge the following individuals for their contributions to the project:
Spencer Lourens, PhD, Director of Data Science
Patrick Monahan, PhD, Chief Analytics Officer
Anthony Perkins, MS, Senior Analyst
Ed Sieferd, MFA, UX Designer
Tierra Pinkins, MPH, CHES, GLPTN Program Director
Andrea Burkhardt, MBA, Business Improvement Advisor
NaKeita Boyd, BSM, Executive Director
Rebecca Gorman, BS, Data Science Intern
Daniel Hauersperger, BS, Data Science Intern
