CHOT Scholar Projects focus on improving healthcare delivery and quality of care using research and tools that exist across various disciplines. These projects involve students working under the guidance of faculty and/or industry advisors. The projects can often form a key component of their masters thesis or doctoral dissertation.
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CHOT Scholar Projects 2017-2018
- Integration of Population Health Data and Digital Assistants to Reduce Readmission Risks
Frequently, the factors that influence medical readmissions exist outside the confines 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. For patients with a high risk of readmission, digital assistants (e.g., IBM Watson) will provide interactive feedback in an attempt to mitigate the risks of readmission. Researchers will test the hypothesis that intervention by digital assistants reduces medical readmission risks.
- Gamification and its Impact on the Population Health Management of Chronic Conditions
Despite advances is medical technologies and public awareness programs, the rates of chronic conditions such as diabetes and asthma continue to rise. 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 diseases such as diabetes and asthma. Specifically, this project will evaluate the clinical effectiveness and economic impact of different approaches to managing diabetes and asthma. The project will explore secondary data analysis of program operations data and biometric data on participants. Researchers will investigate the impact that gamification methods have had in changing the behavior of patients, towards better healthcare outcomes.
- Data-Driven Predictive Analytics to Improve Diagnosis, Treatment, Care Coordination, and Resource Utilization
Meaningful information and knowledge extraction from diverse and rich healthcare datasets is an emergent critical area of research and development. Healthcare providers must be empowered with effective analytical methods and tools that enable and assist them in (i) handling rich datasets generated from genetic screening to specimen tests to patient monitoring to large-scale hospital operations, (ii) extracting useful and meaningful information at different granularities and across heterogeneous healthcare systems, and (iii) exploiting pertinent knowledge for optimizing processes and performance across healthcare systems and provisioning personalized and effective healthcare services.
- Improving Employee and Patient Health through Population Data Mining
The objective of this project is to explore methods to effectively manage the health of a population, who typically spend a majority of their time outside the confines of a healthcare facility. For example, the majority of patients’ time is spent away from the healthcare facility, where there exists little to no ability of healthcare decision makers to monitor patients’ health improvements or outcomes. A recent study by the Center for Disease Control (CDC) reported that of the 33+ million injuries that occurred between 2004-2007, 54% (for women) and 42% (for men) of them occurred inside/outside the home. The emergence of ubiquitous sensing systems such as mobile phones and wearable sensors has enabled the rapid acquisition of health-related data at population scale. This project will explore the ability to mine ambulatory data in order to improve employee and patient health outcomes.
- Telehealth and Remote Patient Monitoring Systems to Improve Access & Promote Active Patient Engagement in Rural Communities
As discussed in the 2015 IOM report, timely access to (quality) healthcare service has been a real challenge. Misalignment of resources and demands results in long delay, Furthermore, rural area lacks sufficient healthcare options. Telehealth can offer alternative and timely care to these patients. It can also help improving health conditions and promoting active patient engagement, which is particularly important for chronic disease management. However, there has been limited research regarding patients’ acceptance and continued utilization of telehealth/telehealth options. There is also reimbursement challenges. Exploring the current literature, this project will first identify drivers and barriers of patient engagement by population groups (i.e., aged, generational differences) and chronic conditions (i.e., diabetes, obesity, COPD). Second, the terms telehealth and telemedicine will be defined by exploring their successful applications in rural care settings by various patient groups. Third, recommendations will be made for implementing appropriate telehealth/telemedicine interventions given governmental policies, reimbursement payments (i.e., FFS, bundle payments), and delivery of care models (i.e., ACOs).
- Machine Learning for Evidence-Based Practice, Resource Allocation, and Risk Prediction
Fueled by rapid digital media advances, healthcare systems in the 21st century 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. This project focuses on machine learning and data mining of large set of clinical data 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.
To view past CHOT Scholar Projects, visit here.