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Funded by Kaiser Permanente Research Foundation Institute & NIH

Funding Years: 2013-2018

 

Patients with type 2 diabetes are increasingly living with multiple concurrent conditions and complicated medical regimens. For these patients, diabetes management decisions and treatment goals must be addressed within the larger context of other competing health concerns. In parallel, clinical advances have led to a substantial increase in the number of tasks that primary care providers must perform during each visit. These twin trends of patient and visit complexity present a formidable challenge to effective diabetes primary care. Although there are a variety of non-visit strategies that can and are being instituted to address these issues, the primary care visit remains a vital opportunity fo catalyzing changes in diabetes care. To achieve this change, efficient and scalable tools to support more productive primary care encounters are needed. We hypothesize that among patients with type 2 diabetes and elevated HbA1c, a systematic approach that enables patients to explicitly prioritize their top diabetes and non-diabetes related health concerns before the primary care visit will result in more effective diabetes care over time. To test this hypothesis, our proposal's aims are to:1) Design and implement a web-based tool linked to the electronic medical record that will enable complex patients to indicate, and PCPs to review, patients'top health priorities for their upcoming visit;2) Conduct a randomized clinical trial among patients with type 2 diabetes and elevated HbA1c testing the impact on intermediate care outcomes (medication adherence, medication intensification) compared to usual care and HbA1c (primary clinical outcome), blood pressure, and lipid control (secondary clinical outcomes) compared to usual care;3) Among a sub-set of intervention participants (patients and PCPs), use mixed qualitative and quantitative methods to examine the impact of pre-visit prioritization on the content of subsequent visit discussions and examine the influence of patient and PCP factors on these discussions. The key conceptual innovation of this study is to test a replicable, low-cost approach to improving diabetes primary care that explicitly integrates non-diabetes problems into the process of diabetes management. We will implement an easy-to-use web-based tool linked to our EPIC(R)-based electronic medical record. This patient-centered care model has the potential to significantly improve the design of primary care systems responsible for providing patient-centered care and offers an innovative approach to improving the care of increasingly complex patients with type 2 diabetes. This study addresses the three NIH priorities of translating evidence into practice, improving medication adherence, and understanding health care disparities. If successful, we will work to actively disseminate the tool throughout our system and to other U.S. care organizations.
Public Health Relevance

Type 2 diabetes is increasing in prevalence, cost, and impact on our health system. A growing number of patients with type 2 diabetes live with multiple concurrent conditions that require complicated medical regimens and daily self-management behaviors. We propose to implement and evaluate a novel, patient-centered intervention designed to facilitate communication between patients and their primary care providers with the goal of enabling more effective primary care for complex patients with poorly controlled diabetes.

UM PI: Michele Heisler

Funded by National Institutes of Health.

Funding Years: 2014-2019.

Randomized controlled trial (RCT) results diffuse into clinical practice slowly - the average time from trial completion to widespread adoption of a new treatment is nearly 20 years. These delays result in suboptimal treatment for patients with neurological diseases. In light of these delays and the enormous societal value of NINDS clinical trials findings, NINDS has recognized the need to accelerate implementation by promoting research to translate trial findings into routine care (T2 translational research). This application seeks to optimize translation of NINDS trials by personalizing clinical trial results ad addressing barriers to translation for clinicians and policy-makers. Using translational research methods, we can move from one-size-fits-all evidence-based medicine towards personalized medicine by estimating treatment benefit for individual patients. Other translational methods can evaluate and address stakeholder concerns that hinder translation. Because clinicians are often skeptical of trial results, changing practice requires convincing them not only that a treatment works in an RCT or that it works in academic medical centers, but that it will work for their patients. Similarly, if policy-makers and payers can be convinced that a new treatment is a good value (e.g., a favorable cost-benefit ratio), they can use their considerable influence on the healthcare delivery system to facilitate translation. Specifically, we will use translational research methods to address three important issues essential to improving trial translation: 1. estimating individual-level outcomes using multivariable outcome prediction 2. Estimating the impact of real world circumstances on outcomes using simulation analyses and 3. Cost effectiveness analysis. Results from these analyses can influence clinicians and policy-makers directly or through the use of tools, such as websites and mobile applications. This proposal has two key objectives. First, we will adapt translational research methods to clinical trials by addressing essential translation-relevant questions for the Carotid Revascularization Endarterectomy versus Stenting (CREST) trial. Second, we will develop a model to concurrently perform similar translational analyses in the Neurology Emergency Treatment Trials (NETT) network. These objectives will be addressed through 3 specific aims: 1. to estimate the expected net benefit of carotid endarterectomy (CEA) vs. carotid artery stenting (CAS) for individual patients in the CREST trial using refined multivariable outcome prediction methods. 2. To estimate the impact of personalized decision-making and real world circumstances (e.g., differing complication rates) on the net benefit of CAS vs. CEA for real world patients using simulation analyses. 3. To assess the feasibility of performing concurrent translational and cost analyses in NETT trials by evaluating a process implementation model in newly initiated and recently completed NETT trials. Dr. Burke has a unique background as a vascular neurologist with training in Translational research methodology through the highly regarded Robert Wood Johnson Clinical Scholars Program. In this proposal, Dr. Burke will develop the additional expertise in clinical trials, multivariable outcome prediction, simulation analyses and cost analyses to become a leader and independently-funded investigator in neurological translational research working to develop a new generation of NETT trials better designed to effectively inform real world clinical practice and improve patient outcomes. This proposal capitalizes on unique environmental strengths at the University of Michigan. Most importantly, Dr. Burke will be supported by an outstanding multi- disciplinary mentorship team including Dr. William Barsan the NETT Clinical Coordinating Center (CCC) principal investigator and a research leader in the emergency treatment of neurological diseases, Dr. Rodney Hayward a Professor of Internal Medicine and a pioneer in translational research and Dr. Lewis Morgenstern, a leader in neurological translational research. All three mentors have excellent track records in mentoring junior faculty and transitioning junior faculty to independence. In addition, Dr. Burke will have te opportunity to participate in a unique hands-on clinical trials immersion through the NETT to gain experience in clinical trial design, management and implementation. Finally, the University of Michigan has recently built the largest academic Translational Research center in the United States (the Institute for Health Policy and Innovation) which will support the advanced statistical methods required for this proposal.

PI(s): James Burke

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