2018 Project Archive

Access to Care Projects

Access to Care 1

Patient Engagement & Hospital Readmissions: The Role of Health Literacy


Value-based reimbursement in healthcare has resulted in an increasing focus on patient engagement as a mechanism to improve post-acute care outcomes, particularly in reducing readmissions. Interventions to address patient engagement should account for health literacy and generational differences, since interventions
that may work with a high literacy population may not be as effective among a population with low literacy. Similarly, interventions used with millennials may not be as effective among baby boomers. This project identifies best practices of health system strategies to address barriers related to health literacy and generational
differences to increase patient engagement and ultimately reduce hospital readmissions.

How this is different than related research?
There has been relatively little research examining how health literacy and generational differences can influence patient engagement and readmission rates, as well as health system strategies to address barriers related to health literacy and generational differences. This study addresses research questions regarding health literacy and generational differences and their impact on patient engagement and hospital readmissions. The systematic literature review will also provide health system strategies to address barriers related to health literacy and generational differences.

Value Proposition

-Examine health literacy and generational differences to increase patient engagement
-Identify strategies to address barriers related to health literacy and generational differences
-Utilize identified strategies to improve patient outcomes

Access to Care 2

Impact of Direct-to-Consumer Telemedicine on Downstream Healthcare Utilization and Costs


Direct-to-consumer (DTC) telemedicine refers topatient-initiated, on-demand primary, and urgent care services provided by licensed healthcare providers. It addresses common, non-emergent conditions, such as respiratory infections and urinary tract
infections, using real-time, interactive technologies (e.g. video and phone). DTC telemedicine has great potential to extend access to care and contain costs,
especially with ongoing research indicating rapid growth of DTC telemedicine offerings nationwide. However, there is concern that unnecessary service
duplication and growth in total costs will result from access to an increase in DTC telemedicine. Using a retrospective study design, the average treatment effects for healthcare utilization and the cost by care site for common DTC telemedicine conditions will be estimated. This project provides healthcare decision makers key insights regarding DTC telemedicine’s impact on healthcare utilization and cost using
rigorous research methods that consider multiple stakeholder perspectives.

How this is different than related research?
There is a scarcity of empirical work related to DTC telemedicine in general, but particularly around downstream impacts. The small body of existing research, most notably generated from the RAND Corporation, is limited largely to the utilization and spending for acute respiratory illness and is specific to a geographically-isolated
and commercially-insured patient population. This project addresses important gaps in DTC telemedicine research, such as the differing utilization patterns and associated costs based on medical condition, insurance coverage, and other factors. This study will also look at payer and patient cost perspectives, an examination that has not yet
been conducted.

Value Proposition

-Compile and interpret insights regarding direct-to-consumer telemedicine’s impact
on healthcare utilization and cost to current and prospective adopters
-Recommend rigorous strategies to evaluate return on investment for direct-to-consumer telemedicine
-Identify opportunities for direct-to-consumer telemedicine to support population health and value-based care

Access to Care 3

Telehealth & Remote Patient Monitoring Systems to Improve Access & Promote Active Patient Engagement in Rural Communities


Timely access to quality healthcare service is a real challenge—as outlined in the 2015 IOM report—and misalignment of resources and demands results in long delay for care. Telehealth can offer alternative and timely care to rural area patients who lack
sufficient healthcare options. Telehealth can also help to improve health conditions and to promote active patient engagement, which is particularly important for chronic disease management. This project identifies drivers and barriers of patient
engagement by population groups (i.e. aged, generational differences) and chronic conditions (i.e. diabetes, obesity, COPD) and provides recommendations for implementing appropriate telehealth/telemedicine interventions given governmental policies, reimbursement payments (i.e. FFS, bundle payments), and delivery of care
models (i.e. ACOs).

How this is different than related research?
The adoption of telemedicine and level of patient engagement and services provided across healthcare facilities remains uneven and far from optimal. There has been relatively little research examining various patient populations’ engagement in the
successful use of telehealth/telemedicine options. By exploring successful applications in rural care settings, this study will define the terms telehealth and telemedicine.

Value Proposition

-Utilize the goals of cost efficiency to improve quality of care
-Identify drivers and barriers of patient engagement to reduce hospital admissions
-Summarize most effective telehealth/telemedicine interventions to improve patient outcomes

Access to Care 4

Development of a Middleware Framework for Medical Device Integration for Telemedicine


Studies have found that the quality of patient-care declines as patient-to doctor/nurse ratios increase. The ability of healthcare providers to effectively and efficiently monitor the current health status of patients can save lives and dramatically improve mortality rates. An integration of mobile, wireless, and sensor technologies has the potential to greatly advance the ability to enable automated data collection for monitoring patient health status in real time and provide a rapid response to a critical healthcare need. The goal of this project is to develop a middle-ware layer with a standardized communication framework for patient monitoring
devices by expanding the capability of the IEEE 11073 protocol, such that a wide range of health monitoring devices could quickly and easily be interfaced and
integrated. The goal is to include the capability of remotely collecting and transmitting data using the standard healthcare protocol (Health Language 7), and then storing the data at a remote location for further data collection and visualization.

How this is different than related research?
The networking capability of currently available health status monitoring devices is limited in functionality and primarily relies on proprietary communication protocols offered by a multitude of different vendors, and current systems are missing critical elements of a truly robust system. The development of a middle-ware layer framework in this project will be able to use the recorded data to continuously mine it in real-time to detect data inconsistencies due to network issues. Then, the
intelligent system engine (knowledge base) could automatically detect potential health-related issues in patients and alert the caregivers.

Value Proposition

-Design a working prototype based on IEEE 11073 protocol for various device integration
-Develop a hardware/software co-designed system used for interfacing biosensors for system prototyping
-Evaluate and expand the existing capabilities of the IEEE 11073 protocol to enable remote patient monitoring

Analytics & Innovative Technology Projects

Analytics & Innovative Tech.            1

Data-Driven Prediction Analytics to Improve Diagnosis, Treatment, Care Coordination, & Resource Utilization


Healthcare providers must be empowered with effective analytical methods and tools that enable and assist them in handling rich datasets, extracting useful and meaningful information at different granularities and across heterogeneous healthcare systems. Insights gained with these effective analytical methods and tools can be used in delivering personalized and effective healthcare services. This project initially focuses on patients with diabetes, cancer, and cardiovascular diseases. The goal is to ensure optimal dosages, cost-effectiveness, and minimal adverse effects, and advance innovation in disease tracking with lab diagnostics.

How this is different than related research?
This project analyzes heterogeneous types of data including imaging, personal collected data (e.g. daily glucose results for diabetics), and utilization data across multiple clinic sites and platforms using large-scale data and predictive analytics. The study involves personal patient-specific data to advance innovative disease tracking with lab diagnostics, design evidence-based personalized treatment for individual patients, and optimize utilization for most efficient delivery. It will contribute to the development of state-of-the-art system data analytics and realtime decision technologies with broad applicability.

Value Proposition

-Establish care pathways for novel disease tracking with lab diagnostics
-Outline the clinical workflow on MRI system usage, utilization patterns, and variability
-Develop mathematical models for personalized treatment optimization
-Utilize machine learning tools for resource prediction

Analytics & Innovative Tech.            2

Machine Learning for Evidence-Based Practice, Resource Allocation, and Risk Prediction


Fueled by rapid digital media advances, healthcare systems are investing more in advanced sensors and robotics, communication technologies, and sophisticated data centers. This facilitates information and knowledge visibility and delivery
standardization and performance efficiency through big-data analytics. Diabetes, hypertension, cardiovascular disease, stroke, and cancer are the initial focus of this study. Ten years of clinical data on 2.7 million patients to perform machine learning
and data mining are used to identify evidence and characteristics of best practice, uncover risk factors of different patient groups, develop effective clinical practice guidelines and disease management strategies, and optimize the service delivery to meet the demand.

How this is different than related research?
The data captures a diverse population across the United States with varying demographics, clinical practices, and outcome measures. This project is the first study of this kind that includes a massive amount of data across heterogeneous hospital and provider sites.

Value Proposition

-Identify effective treatment plans and best practice characteristics for multiple chronic conditions
-Build detailed plans and methods for best-practice transfer across hospital sites
-Identify and stratify risk patterns of poly-chronic patients

Care Coordination Projects

Care Coordination             1

Using Care Coordination to Address Cost, Quality, and Access to Care Across Systems and Populations


Consumers access healthcare in a multitude of settings, ranging from acute care to home-and community-based services (HCBS). With a variety of access points, care coordination programs are essential to facilitate care from one system to another, which emphasizes the isolated nature of the US healthcare system. In an effort to bring about broader systemic changes, this project aims to develop a care coordination program that focuses on the utilization of a bio-psycho-social model and
leveraging community resources to facilitate care coordination outcomes. The project also develops cost, quality, and access metrics, as well as a tool used to assess care coordination best practices. The end goal is to disseminate a toolkit for providers and industry members to assess their practices and identify gaps in care coordination.

How this is different than related research?
Related research focuses on a piecemeal approach to improving care coordination, often focusing on single visits and procedures, rather than the whole continuum of care. This project takes a more holistic approach by defining the continuum of care and developing models of collaborative best practices in care coordination that take into account the full system of care

Value Proposition

-Identify best practices for improving care coordination across systems and populations
-Develop a toolkit for providers to evaluate practice and identify gaps in care coordination

Care Coordination             2

Effects of Care Coordination on the Improvement of Quality Care


Transitions in patient care include home/community to acute care to post-acute care back to home/ community. During these transitions, gaps in care may occur, which can negatively impact quality as well as increase healthcare costs. Two examples are 1. hospital acquired infections (HAI) for admitted patients and medication adherence
and 2. reconciliation for discharged and/or transferred patients. The quality and cost issues affect how future transitions of patient care will be coordinated. Although Centers for Medicare & Medicaid Services (CMS) has initiated penalty programs to reduce care complications, such as HAIs and readmissions, both remain high nationwide, meaning providers have an opportunity to improve the quality of care they offer during care transitions. This project summarizes best practices for HAIs and identify peer-reviewed evaluations of programs for medication management interventions and best practices for medication adherence to reduce readmissions after a transition in care.

How this is different than related research?
The literature contains various and numerous case studies of HAIs and medication nonadherence, but is limited in nature. Hospital acquired infections have been studied, but there is limited literature on the hospital acquired clostridium difficile infection. The literature on programs designed to improve medication adherence and reconciliation for discharged patients has yet to be considered a cohesive body. A systematic review of published evaluations of readmission reduction programs
will allow researchers to identify best practices common to the most effective programs and will identify contextual elements important to the programs’ success along with intervention characteristics that tend to be less effective.

Value Proposition

-Demonstrate practices to reduce certain hospital-acquired conditions and prevent readmissions related to medication adherence
-Identify best practices to increase quality of care while reducing avoidable costs

Care Coordination             3

Effects of Care Coordination on Care Transitions



The United States of America faces a growing population of older adults and people with disabilities who require a coordinated service environment to meet the growing demand for home- and community-based services. Care coordination programs and care transition programs often center on the intersection of acute care and chronic care (such as the transition from nursing home to hospital), but in reality people access healthcare in a multitude of settings. This project aims to develop a care coordination program that focuses on measuring care coordination program impact, utilizing the bio-psycho-social and spiritual model, and creating a model that continually improves provider collaboration specifically for older adults and individuals with disabilities. This approach can play a critical role especially when dealing with under-served and underrepresented populations.

How this is different than related research?

Traditional research in care coordination is related to coordination between acute and/or chronic clinical care providers. But delivery of care to older adults and people with disabilities extends beyond traditional institutional-based clinical care to include home-based care, as well as services which are social, financial, legal, and spiritual in nature. This project significantly broadens the perspective of care coordination by developing a system that also includes non-traditional participants, such as social service and public health agencies, religious organizations, and end-of-life services, in addition to traditional participants such as hospitals, skilled nursing homes, and rehabilitation providers.

Value Proposition

-Develop metrics to measure care coordination program impact on hospital and community-based outcomes Utilize the bio-psycho-social and spiritual model to understand how individual, familial, organizational, and contextual characteristics affect care coordination outcomes

-Build a community-based care coordination model for older adults and people with disabilities that continually improves collaboration among providers

Care Coordination             4

Using Care Coordination to Address Cost, Quality, and Access to Care Across Systems and Populations



Currently, the healthcare industry is going through rapid transformations including readmission penalties, payment bundling, and wellness and patient compliance related medical coding, to refocus efforts on keeping patients healthy and driving the revenue stream from patients’ wellness. Such changes have given rise to different models of healthcare, such as Accountable Care Organizations (ACO) and Managed Care Organizations (MCO). These organizations are directly incentivized to reduce the cost of healthcare; as well as improve quality in order to stay profitable. This project aims to develop a mobile-based care coordination system for critical care patients. The created system will provide a secured, asynchronous messaging system, which will ensure an instant communication with the entire care team for a patient.

How this is different than related research?

While communication or lack of it is the main reason for missed diagnosis, hospital admission, readmission and duplication of care, it has not yet been successfully addressed in any electronic health record (EHR) system. Though several new mobile healthcare messaging applications have been implemented, they are basically HIPAA-compliant text messaging among doctors (i.e. HIPAA compliant WhatsApp) and effectively create more silos. This project proposes to build a mobile EHR agnostic application connecting the patient with their outpatient and inpatient doctors, staff, and others related to care for intelligent communication, which has potential to improve healthcare and provide opportunities.

Value Proposition

-Design a HIPAA-compliant messaging platform to ensure a timely delivery of messages to a care team with a critical patient information attached with each message

-Facilitate tight communication, collaboration, and coordination among care team members

Population Health Projects

Pop. Health 1

Integration of Population Health Data and Digital Assistants to Reduce Readmission Risks



Frequently, the factors that influence medical readmissions exist outside the borders of a healthcare setting and include patient-level decisions and societal interactions. The objective of this project is to leverage the size and availability of population health data to model and predict readmission risk factors. Data will be acquired on a large scale by mining publicly-available websites. The collected data will then be used to segment, model, and identify patients at risk of medical readmissions. For patient segments at a high risk of readmission, digital assistants (e.g. IBM Watson) will provide interactive feedback in an attempt to mitigate the risks.

How this is different than related research?

Typically, medical readmission research focuses on investigating clinical-level factors (such as age and medical condition) that have the potential of increasing medical readmission. Yet, when patients leave the hospital, a wide range of factors may influence their risk profiles, such as their support system and social norms. This project includes population health data that provides a more holistic understanding of what happens to patients once they are discharged from the hospital and utilizes a digital assistant that can provide real time decision support to patients who have been predicted to be at a higher risk of readmission.

Value Proposition

-Evaluate the value of publicly-available social media data in modeling patient-specific outcomes

-Measure the impact of digital assistants in serving as ubiquitous decision support systems

Pop. Health 2

Gamification and its Impact on the Population Health Management of Chronic Conditions



The rate of chronic conditions, including diabetes and asthma, continue to rise despite of the advances in medical technologies and public awareness programs. For example, in the United States, more than 29 million individuals have been diagnosed with diabetes, with a new diagnosis occurring every 23 seconds. The objective of this project is to evaluate the efficacy of chronic condition treatment programs for chronic diseases, such as diabetes and asthma. Specifically, this project evaluates the clinical effectiveness and economic impact of different approaches to managing diabetes and asthma by exploring secondary data analysis of program operations data and biometric data on participants. Researchers also explore the impact that gamification methods have in chronic disease management and in changing the behavior of patients toward better healthcare outcomes.

How this is different than related research?

Existing research has focused on predicting factors that influence chronic diseases, but a knowledge gap exists between research on chronic disease management and translating the recommended practices for disease management. This project aims to identify specific practices that best translate into practice, as well as explore the influence of gamification in chronic disease management to determine whether successful implementations in other settings (e.g. education and rehabilitation) can be adapted for management of chronic disease.

Value Proposition

-Summarize sets of basic research that may contribute to better management of chronic conditions

-Explore the role of gamification in changing behavior toward positive health outcomes

Pop. Health 3

Improving Employee and Patient Health through Population Data Mining



Most patients spend a majority of their time away from healthcare facilities, where there is little to no ability to monitor health improvements or outcomes. A recent study by the Center for Disease Control (CDC) reported that of the 33+ million injuries that occurred between 2004 and 2007, 54% of women and 42% of men were injured inside/outside the home. With the emergence of ubiquitous sensing systems, such as mobile phones and wearable sensors, acquisition of population health-related data can occur quickly. This project explores methods used to effectively manage the health of those who typically spend a majority of their time outside the walls of a healthcare facility to improve employee and patient health outcomes. The goal is to design and develop a mobile app that has the ability to capture patient-specific data that can then be aggregated to answer population-level questions.

How this is different than related research?

Existing research related to population health is limited by data acquisition tools (e.g. mobile app) currently available. Rather than utilize existing data acquisition tools, this project will design and create a data acquisition tool that is based on patient and employee feedback. Such feedback will guide tool development to ensure it is highly customizable, user friendly, patient access friendly, and valued by the healthcare decision makers and patients.

Value Proposition

-Create a healthcare app that can be deployed to capture patient-specific data

-Develop a data mining tool that can extract valuable information from patient-centered data and inform delivery of patient-centered healthcare