DIGI Grant Winners
The DIGI Grant provides awards to UMass Memorial Medical Group providers to support novel approaches to the use of digital health technologies that have a positive impact on quality, efficiency, patient and/or provider engagement, health equity, or financial outcomes.
The goal of this grant award is to support our health care providers and foster innovation in digital health across UMass Memorial Health and is intended to cover the time spent by faculty/staff to implement the proposed project with a maximum award of $40,000 over 12 months.
Each of the 2023 DIGI Grant award recipients listed below received $35,000. Recipients include:
Utility and Feasibility of Continuous Glucose Monitoring for Insulin Dosing and Improving Glucose Control in Hospitalized Patients
|Approximately one third of hospitalized patients have diabetes as either a primary or secondary diagnosis. Dysglycemia (due to multiple factors, including timing of insulin delivery relative to glucose checks) is associated with increased length of stay, associated morbidities and mortality. Continuous glucose monitoring (CGM) is well established in outpatients with improved outcomes but has not been officially approved for inpatient use. Our aim is to establish use of CGM in our hospitalized patients with diabetes, evaluate accuracy relative to glucometer and plasma glucose measurements, and assess the contribution of inappropriate timing of insulin delivery to dysglycemia in non-ICU wards.
Use of GLP1-RAs in Patients with Diabetes and Established ASCVD: Underutilization and Proposal to Improve Adherence to Standards of Care
|GLP1-RA drugs (glucagon-like peptide-1 receptor agonist) drugs such as dulaglutide and semaglutide have been associated in large, randomized controlled trials with improved cardiovascular outcomes in high-risk patients with type 2 diabetes. Despite the evidence and the additional potential benefit of significant weight loss, these agents are underused. This project aims to identify barriers to implementing the use of these evidence-backed agents in target patients, and increase awareness of their benefits among providers, while utilizing electronic tools and resources to optimize dissemination.
AI-Based Population Screening for Pulmonary Emphysema Using CT
This project aims to identify previously unknown pulmonary emphysema in non-symptomatic patients using a combination of imaging and artificial intelligence. The goal of the project is to enable earlier treatment with improved prognosis and outcome.
Remote Patient Monitoring Navigator
Remote Patient Monitoring (RPM) is an innovative strategy designed to give patients and caregivers better access to clinical data about patients between visits to the clinic or hospital. This program seeks to optimize access to this technology for our UMass Memorial patients and streamline the process of onboarding and managing the flow of data. By integrating a multidisciplinary care team, including Mobile Integrated Health (MIH) Emergency Physicians and Primary Care Physicians, the hope is to make this resource more available and easier to access.
Optimization of Night and Weekend Resources to Improve Emergency Department Output
Limited resources on nights and weekends can significantly alter patient flow and length of stay for our Emergency Department patients and hospital inpatients. The objective of this project is to use sophisticated optimization algorithms to allocate limited resources on nights and weekends to minimize length of stay. This project is being conducted in coordination with our research colleagues within MIT's Operations Research Center.
Optimizing Prescription of Proven Therapies for Osteoporosis
Osteoporosis and its clinical outcome of fragility fracture is an ever-increasing public health problem due to our aging population. Despite a number of FDA-approved therapies for the treatment of osteoporosis, a significant number of individuals who would qualify for these treatments are not yet being prescribed them. The aim of this project is to use existing data within the electronic health record to identify individuals with osteoporosis who do not appear to have been treated and link them into treatment for fracture-risk reduction.
Continuous Glucose Monitoring in the ICU: Validation and Implementation
John Mordes, MD
Eric Cucchi, MS, PA-C, CAQ-HM, DFAAAP
Despite continuous glucose monitoring (CGM) achieving a high level of accuracy and durability for glucose measurement in the outpatient setting, its use within the Intensive Care Unit (ICU), where dysglycemia is associated with increased morbidity and mortality, is not well validated. The current point-of-care glucose and plasma glucose testing have been the gold standard in the hospital setting, but these methods have significant limitations in that they capture only intermittent single-point-in-time data. CGM technology offers significant advantages in capturing blood glucose trends over time that could allow for earlier and timelier therapeutic interventions, especially with regard to hypoglycemia. This project aims to study the validity and efficacy of Continuous Glucose Monitoring in the ICU.
Improving Discharge by Noon and Discharge by 2 pm
Given the ongoing high demand for inpatient services, tremendous efforts have been underway to improve patient flow across the care continuum. With the support of the DIGI Grant this project will use digital tools, such as existing EPIC infrastructure and Tableau data, to improve and track discharge processes. This project’s primary goal is to improve discharge orders before noon to >35% across the Hospital Medicine Division and Department of Medicine, more generally; and a secondary goal is to improve overall discharge from the hospital by 2 pm.
Virtual Reality Technology as Complementary Pain Management Modality for Pediatric Patients
Supported by Bryce Pepin
Sickle cell disease (SCD) is an inherited, complex, lifelong blood disorder with varying genotypes and a spectrum of phenotypes. Uncontrolled pain secondary to vaso-occlusion is the most common reason for hospital admission for patients with SCD. However, the complex nature of sickle cell-related pain complicates the care of this vulnerable and underserved patient population. While opioids are standard pharmacologic treatments for vaso-occlusive crises, their use may be limited by significant side effects. Growing awareness of the multidimensional aspects of pain has led to the increasing use of adjunctive non-pharmacologic interventions to address the physical, psychological, social and environmental factors that contribute to the overall experience of pain. This project will examine the usability and utility of immersive virtual reality as an adjunctive modality in managing sickle cell disease-related pain for pediatric patients at UMass Memorial Medical Center.
An Epic Upgrade for the Transitional Care Team: Incorporating the Hospital Medicine Patient Triaging System Into the Current Electronic Medical Record
The Transitional Care Team is responsible for accepting and tracking patients for admission or transfer to the Department of Medicine and Medicine subspecialties. This project will leverage the current capabilities of EPIC to overhaul the patient tracking and triaging system, improving patient safety and reducing the workload of members on the Transitional Care Team.
Optimization of Weekend Service Offerings at UMass Memorial Medical Center
Reduced availability in weekend clinical services (blood bank, endoscopy, cardiac interventions, interpreters, IV/PICC placement, laboratory medicine, medical and surgical consultations, pathology, pharmacy, physical therapy, radiology, speech and swallow, social work, and transportation) results in unnecessary bed days and discharge delays. Deciding how best to expand upon these services in a way that will maximally improve probability of weekend discharge while minimizing additional expense is challenging with traditional methods of analysis. Yet, creation of an optimization model utilizing the robust, clinical data available in our EMR holds the potential to efficiently arrive at such a solution. Such methods have been employed in retail, energy and transportation sectors. Translating such work to UMass Memorial Medical Center holds not only significant academic promise worthy of publication but also hopes to increase equitable access to care for Central Massachusetts.
Optimizing Medical Therapy for Diabetic Kidney Disease
This program seeks to identify patients with cardiometabolic disorders who will benefit from advanced therapeutic interventions using SGLT2 inhibitors. Patients with chronic kidney disease, heart failure and diabetes are enrolled and followed with the goal of reducing morbidity and mortality.
Improving Emergency Department Emergency Severity Index (ESI) Acuity Leveling Using Machine Learning and Clinical Natural Language Processing
Kenneth Shanahan, MSN, RN, CCRN-K
Emergency departments are experiencing a severe demand/capacity mismatch, which leads to increased risk. The post-COVID era has significantly impacted the workforce leading to a significant increase in turnover of nursing staff, decreasing institutional knowledge and experience. The nurse performing triage only has a few minutes with each patient, therefore this project aims to implement a tool to support staff and ensure accurate triaging of our patients. KATE AI uses machine learning and Clinical Natural Language Processing to improve acuity assignments, patient safety, and reduce bias, errors and risk.
Advanced Lipid-Lowering Therapeutics
The effect of low-density lipoprotein (LDL) on atherosclerotic cardiovascular disease (ASCVD) risk is both causal and cumulative over time, thus lowering plasma LDL is critical to reducing ASCVD. Only one-third of patients at high risk for ASCVD have LDL at levels recommended by the American Heart Association and American College of Cardiology. This project aims to reduce ASCVD risk in this vulnerable population by improving access to advanced lipid-lowering therapeutics.