Current Research Projects
Project Name: Unique Medical Biometric Recognition Enforcement of Legitimate and Large-scale Authentication (UMBRELLA)
Problem to Solve: Challenges of ensuring accurate and scalable patient identification within a secure and interoperable model to exchange health data
An the United States healthcare system, challenges surrounding patient identification, interoperable exchange of health records, and complexities in providing a robust security system capable of protecting patient identifiable information (PII) have been widely documented. Consequences include incomplete and inaccurate electronic patient health records, redundant expensive medical tests caused by the lack of interoperability between healthcare institutions, and a significant amount of cases of medical fraud because of inadequate identification mechanisms. To date, no proven solution exists to offer consistent and accurate identification for users within the United States Healthcare system. Past literature describes techniques of developing algorithms used to match duplicate patient records and merge into a single record, but results have not been met with high accuracy. Biometric identification research has been conducted extensively, however there have been no successful attempts to link a scalable National Patient Identifier (NPI) to a user’s biometric output.
We propose a new technique of user identification in healthcare by developing algorithms capable of establishing a Unique Health Identifier (UHID) based on the user’s fingerprint biometric, with the utilization of facial-recognition as a secondary validation step before health records can be accessed. Biometric captures may be completed using standard smartphones and Web cameras in a touchless method. Patients and healthcare professionals no longer need to remember complex passwords or worry about their login credentials being stolen as they authenticate using biometrics. Interoperability and security mechanisms are developed and configured to provide an end-to-end accurate national identification and health data exchange.
The proposed solution is called UMBRELLA or Unique Medical Biometric Enforcement of Legitimate and Large-scale Authentication. We present a series of experiments to demonstrate the formation of an accurate and consistent UHID and reveal the scalability and security of the identification solution through a large-scale distributed network, with a developed use case to illustrate the functionality and accuracy of our research. Our goal is to further develop the UMBRELLA solution in hopes of dramatically reducing the complexities associated with user misidentification in healthcare and enhance the interoperability and security mechanisms affiliated with health data exchange, resulting in lowering healthcare costs, enhancing population health monitoring and improving patient-safety.
Project Name: Optimization of a Markerless Gait Analysis Application Aimed at Orthopedic Care for Developing Countries
Problem to Solve: Optimization of a Markerless Gait Analysis Application Aimed at Orthopedic Care for Developing Countries
In this work, a low-budget markerless gait analysis application that is aimed at orthopedic care is built and optimized for use in developing countries and small practices. The application utilizes Microsoft Kinect to detect and track body joints and then calculates nine gait parameters that are important for performing a gait assessment. The measurements of the hip flexion/extension, hip abduction/adduction and knee flexion/extension followed the graphs of standard gait pattern. Also they were consistent and homogeneous among all ten participants. The Microsoft Kinect has gained wide popularity in health-related applications due to its low-cost, portability and ability to detect and track multiple body joints. Nevertheless, studies showed that the Kinect manifested inaccuracy when tracking the joints of the lower body, especially in non-ideal tracking conditions. In this work, a new algorithm was proposed for rectifying this tracking problem. As a proof of concept, the proposed algorithm was implemented only for the ankle joint because its readings contained the noisiest return values when compared to other joints. The proposed algorithm was used for inferring the joint's position in the 3D space and successfully infer the ankle joint's position using the subject's gait pattern as expressed by the lower joints' angles and geometric relationships between these joints.
Project Name: Design of a Holistic mHealth Community Library Model to Empower Rural America
Problem to Solve: To develop a robust and secure model to overcome the challenges of rural health and improve self-management, literacy, and empowerment of a community’s health
Healthcare delivery in rural America poses additional challenges than its urban counterpart. Rural locations more commonly face shortage of physicians, a lack of high-paying jobs with adequate insurance benefits, transportation, health literacy, a stigma with health conditions due to lack of anonymity and difficulties accessing specialty care. Rural communities see higher rates of suicide, heart diseases, respiratory disease, stroke, social isolation, and public health crisis such as the opioid epidemic. More than 46 million Americans, or 15% of the population, live in rural areas within the United States. Communities play an important role in the health of their residents, as social and economic factors, physical environment, and healthy behaviors make up 80% of an individual’s overall health, while clinical care accounts for only 20%. Chronic disease doesn’t occur in isolation. Conditions such as diabetes, asthma, heart disease, and obesity are all tied very closely to the environments, culture, and behaviors that surround individuals. Therefore, a significant amount of human health is determined beyond clinical care. For many individuals who are at an elevated risk of developing chronic disease, episodic care that begins and ends inside a hospital or clinic is not adequate to accurately treat the patient. We propose a holistic mHealth community model for residents to overcome significant barriers of care in rural America by providing an application capable of integrating multiple health and safety data sources through a mobile digital personal health library application. Users are able to securely share their health data with others (e.g. primary care physician, caregiver). Artificial Intelligence (AI) algorithms can strategically connect residents to community resources and provide customized health education aimed at increasing the health literacy, empowerment, and self-management of the user. Communities can use de-identified population health data from this model to improve decision-making and allocation of community resources.
Project Name: Mixed Reality Emergency Medical Training Analysis for Rural EMS Personnel
Problem to Solve: Challenges with states to effectively offer timely new emergency medical training to rural EMS personnel and improve patient safety
Increasing patient safety within Emergency Medical Services (EMS) to Emergency Room Department handoffs for rural communities using mixed reality development training exercises with Microsoft HoloLens. This study, focuses on integration of mixed reality with traditional training to enhance EMS to ED handover communications and procedures. The main goal is this work is to develop an intuitive and accurate mixed reality simulation application using the Microsoft HoloLens to provide education and training designed to improve patient safety in pediatric patients during emergency incidents.
- Develop and test a mixed reality application, designed to enhance students’ overall NREMT performance scores on pediatric content (15%) of the exam, leading to improved patient safety
- Qualitatively analyze collected and recorded data from 1 to further identify root cause(s) of medical errors affiliated with pediatric patients in prehospital transport by EMS
- Plan and implement interventions through key enhancements of the mixed reality application to reduce medical errors, improve clinical outcomes and patient safety based on information derived from root cause analysis
- Assess the success of implemented interventions by qualitative assessments by licensed paramedics, EMT project participants, and overall NREMT performance scores on pediatric portion of the exam and determine the usability and efficacy of mixed reality simulation in medical emergency education and training.
