CHOT Scholar Projects 2022-2023
- Virtual Reality For Health Systems Simulation
Virtual Reality (VR) is becoming an essential part of modern education and training. In healthcare, VR can be used to improve decision making and help patients to better connect with reality, cope with pain, and overcome anxiety and depression. This project will integrate sensing technology (i.e., eye and motion tracking) and VR simulation of healthcare environments to improve clinical judgment and treat individuals with mental disorders by improving their metacognitive skills. VR simulations will be compared to traditional patient simulations and mental health screening tools. Researchers will develop analytical models to improve clinical judgment and predict the risk of mental illness.
- Privacy-Preserving Data Analytics for Smart and Interconnected Health Systems
Internet of Things (IoT) transforms traditional health systems into new data-rich environments. This provides an unprecedented opportunity to develop new analytical methods and tools to realize a new paradigm of smart and interconnected health systems. However, data breach and malicious attacks are increasingly seen in healthcare, which brings unexpected disruptions to routine operations and cause the loss of billions of dollars. As healthcare systems are critical to improving the wellbeing of our society, there is an urgent need to protect privacy information of patients, and minimize the risk of model inversion attacks. - Data-enabled Predictive Modeling and Intervention Optimization of Breast Cancer
Breast cancer is a prevailing problem that decreases the quality of patients’ lives, creates high burdens on health systems, and impacts the well-being of society. Advanced sensing provides an unprecedented opportunity to revolutionize cancer care delivery and decrease associated costs. However, disparity, uncertainty, and incompleteness of the sensing data, as well as the lack of tailored decision-making models, are the major challenges that prevent practitioners from gaining substantial information to improve their efficiency. We continue our healthcare innovation by developing advanced analytical models that address the challenges of data and provide in-depth knowledge for decision making from heterogeneous cancer recordings systems. We will investigate on survival model to decipher important factors that play a role in the recurrence of cancer. Also, we will introduce novel analytical models to estimate risk reduction strategies and develop an evidence-based decision support system in breast cancer. The proposed models can also be generally applicable to a variety of medical domains that entail data imputation, analytical solutions, and decision making. - Prediction and Intervention Models for Combating the Opioid Epidemic
Each day, more than 115 Americans die as a result of overdosing on opioids. The objective of this project is to develop a prediction, prevention, and intervention model that seeks to combat the opioid epidemic. This project seeks to connect the multiple facets of the opioid epidemic into a unifying decision support system that not only predicts the risks of opioid addiction, but also proposes reliable, safe, and effective prevention and mitigation strategies. Aim 1: Develop statistical models to predict the effectiveness of treatment services and key factors pertinent to opioid mortality (social-economic data, demographic information, prescription, and insurance information, etc.). Aim 2: Develop stochastic differential equation (SDE) models to simulate the opioid trafficking flows and key factors that control the supply chain (e.g., law enforcement, tax tariffs, prescription, insurance policy). Aim 3: Optimizing the decision policy to combat the opioid crisis. - Optimal Physician-Patient Matching: A Novel Methodology for Health Care Market Economics
The internet has promoted a sharing economy, which has ushered in the need to develop a new generation of health care service platforms. One critical problem is to improve the matching of customers with services. While physicians have limited capacities and patients arrive randomly, optimal recourse allocation in such a dynamic environment is challenging. Very little has been done to investigate online matching algorithms for the optimization of recourse allocation in physician-patient matching, and the aim is to establish a new sharing-economy framework for smart health care service systems.
CHOT Scholar Projects 2020-2021
- The Effectiveness of Substance Abuse Treatment Services in Combating Opioid Crisis
Each day, more than 115 Americans die due to overdosing on opioid. Addiction to opioid (including heroin and fentanyl) becomes a serious national crisis that devastates public health. To combat the opioid crisis, the Substance Abuse Treatment Services (SATS) facilities across the country provide opioid addicts professional counseling and treatments. The objective of this project is to evaluate the effectiveness of the different opioid addiction treatment programs provided by these SATS facilities, by using sophisticated econometric models to analyze the national survey data on SATS facilities, the epidemic data on opioid abusers, and other related data. - Data-driven analytics and machine learning for improving healthcare outcome
Data-driven healthcare has the potential to revolutionize care delivery and trim costs. A major challenge is that providers must sift through and analyze mountains of disparate data to materialize the substantial gain. We continue our healthcare innovation through systems and data analytics. Utilizing EMR and various procedural and personal health data, along with social and behavioral information, we will address all aims – with specific regard to radiologic exam variability. This also had implications in utilizing predictive models to use at the point-of-care when treating infectious disease. - Embedding Routine Informal, Family Caregiver Assessment of Delirium Superimposed on Dementia into Acute Care
The purpose of this pilot study is to assess initial accuracy and feasibility of communication of observed symptoms of delirium in older adults with complex multiple chronic conditions dementia by family caregivers utilizing app-based delivery of the Family Confusion Assessment Method (FAM-CAM) in the acute care setting. A modification of CAM, FAM-CAM, allows family caregivers to report their observations of symptoms of delirium in a standardized method. The FAM-CAM shows potential to improve recognition, and therefore, management of delirium in the acute care setting. - Telemedicine in Primary Care & in the Management of Chronic Conditions: Exploring Patient & Provider Perspectives
Timely access to quality healthcare service is a real challenge—as outlined in the 2015 IOM report—and misalignment of resources and demands result in long delays 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 and chronic conditions and provides recommendations for implementing appropriate telehealth/telemedicine interventions through multiple care settings given governmental policies, reimbursement payments, and delivery of care. - Survival analysis of breast cancer recurrences for treatment planning and optimization
Breast cancer is a common type of cancer and is the second leading cause of death among women in the US. It is estimated that approximately 268,600 women are diagnosed with breast cancer and 42,260 deaths occur in 2019. Advanced sensing provides an unprecedented opportunity to increase information visibility and characterize patterns of event occurrences. However, few, if any, of previous works have investigated survival analysis of breast cancer recurrences based on large amount of data readily available in the health system. This project is aimed at developing survival models for time-to-event analysis of breast cancer recurrences in the surveillance, epidemiology, and end results (SEER) data from year 1973 to 2015. This project will enable and assist healthcare practitioners to delineate important factors that influence the probability of breast cancer recurrences, as well as optimize the prognosis, treatment, and decision-making of breast cancer.
CHOT Scholar Projects 2018-2019
- The Effectiveness of Substance Abuse Treatment Services in Combating Opioid Crisis
- Data-driven analytics and machine learning for improving healthcare outcome
- Embedding Routine Informal, Family Caregiver Assessment of Delirium Superimposed on Dementia into Acute Care
- Telemedicine in Primary Care & in the Management of Chronic Conditions: Exploring Patient & Provider Perspectives
CHOT Scholar Projects 2017-2018
- Integration of Population Health Data and Digital Assistants to Reduce Readmission Risks
- Gamification and its Impact on the Population Health Management of Chronic Conditions
- Data-Driven Predictive Analytics to Improve Diagnosis, Treatment, Care Coordination, and Resource Utilization
- Improving Employee and Patient Health through Population Data Mining
- Telehealth and Remote Patient Monitoring Systems to Improve Access & Promote Active Patient Engagement in Rural Communities
- Machine Learning for Evidence-Based Practice, Resource Allocation, and Risk Prediction
CHOT Scholar Projects 2016-2017
- Integration of Genomic Data for Precision Health Decision Support
Chris DeFlitch, PhD, Harriet B. Nembhard, PhD, Yifeng Yu, PhD Candidate - Demand management for Community Paramedicine
Linlin Ma, PhD Candidate, Harriet B. Nembhard, PhD, Mehmet Kilinc, PhD Candidate - A Person-Centered Approach for Individuals with Multiple Chronic Conditions
Lisa Korman, MS, Juxihong Julaiti, PhD Candidate, Andrea Yevchak-Sillner, PhD, RN, Harriet B. Nembhard, PhD - Sensing Systems for Personalized Telehealth Wellness Management
Conrad Tucker, PhD, Sunghoon Lim, PhD Candidate, Harriet B. Nembhard, PhD - Value Based Care: Challenges of a Changing Care Paradigm
Harriet B. Nembhard, PhD, Eric Swenson, PhD Candidate, Kash (TAM), Benneyan (NEU), Lee (GIT)
CHOT Scholar Projects 2015-2016
- Improving Health Promotion: Leveraging Statistical Learning and Electronic Medical Records for Healthcare Market Segmentation
Nathaniel D. Bastian, MS, MEng, PhD Candidate; Eric R. Swenson, MS, PhD Student; Harriet B. Nembhard, PhD - Assessment of Telehealth in Promoting Heart Failure Patient Engagement and Self Care in Rural Areas
Linlin Ma, MS, PhD Student; Harriet Nembhard, PhD; Harleah Buck, PhD, RN - Technology Trends and Smart Interventions to Mitigate Patient Risk at Critical Transitions in Total Joint Arthroplasty (TJA)
Eric R. Swenson, MS, PSU PhD Student; Kayla M. Cline, CPA, MS, TAMU PhD Student; Harriet B. Nembhard, PhD, PSU; Bita A. Kash, PhD, MBA, TAMU - Reducing Readmission after Hip Surgery using Statistical Process Control and Smart Home Care
Yifeng Yu, MS, PhD student; Harriet Nembhard, PhD - Investigating the Impacts of a Patient’s Social Network in Achieving Gamification Solutions in Personalized Wellness Management
Abhinav Singh, Graduate Student; Conrad Tucker, PhD, Harriet Nembhard, PhD - Examining How Lean Six Sigma Processes Reduce Hospital Acquired Conditions
Graduate Student TBA, Deirdre McCaughey, PhD, MBA; Maria Hamilton, MBA, BSIE, CSSBB
CHOT Scholar Projects 2014-2015
- A Data Mining Methodology for Patient Adherence to Home-Based Therapies
- Identifying Emergency Department Efficiency Frontiers and the Factors Associated with their Efficiency Performance
- Gamification for Self-Monitoring of Patients for Enhanced Wellness Outcomes
- Designing Health Information Technologies to Help Patient Care Teams Identify and Manage Information Problems
- Quantifying the Impact of Pay-for-Performance Financial Incentives to Reduce Healthcare-Associated Infections
- Quality Improvement Methods in Pediatric Care Research Translation
- A System Dynamic Approach to Predict the Overall Impact of Interventions to Improve Chronic Kidney Disease Care
- A Combined Human-Factors and Quality Improvement Approach to Assess Health Information Technology Usability
- Understanding the Dual Effect of Hospital Safety Culture on Patients & Care Providers; Optimizing Hospital Safety Culture & Reducing Safety Events
- Using Lean Six Sigma to Reduce Hospital Acquired Conditions (HACs)
CHOT Scholar Projects 2013-2014
- Proliferating ED Throughput: Model Case, Benchmarks, and Future Practice Framework (part of collaborative project)
- Data Visualization (DV) in Obesity Counseling: Getting to Scale
- Data-Driven Laws to Using Financial Incentives to Drive Down Healthcare-Associated Infections (part of collaborative project)
- Pilot Test of Mobile Interventions for Disease Self-Management in Adolescents and Young Adults with Special Healthcare Needs
- Collaborative mHealth Tools for Diabetes Management
- Predicting Parkinson’s Disease From The Comfort Of Your Home: A Connected Healthcare Network Approach