Research Efforts
Dr. Nirmish Shah's research is aimed at improving the care for people living with sickle cell disease, cancer, undergoing bone marrow transplant, and experiencing pain. Shah is particularly interested in the gathering of symptom data and a variety of wearable device data to build predictive algorithms to better prepare patients and their providers for their condition.
mHealth
Sickle Cell Disease Therapies
Pain
mHealth
Patient Centered eHealth interventions for Children, Adolescents, and Adults with Sickle Cell Disease: Systematic Review - Journal of Medical Internet Research
In this article, Shah and his team review various different technological tools that are being used for people living with sickle cell disease. Throughout the review, Shah and his team further explain what self-management activities the technologies were used for, and assess the efficacy of these technology tools.
Use of Mobile Health Apps and Wearable Technology to Assess Changes and Predict Pain During Treatment of Acute Pain in Sickle Cell Disease: Feasibility Study - JMIR mHealth and uHealth
Utilizing the Microsoft Band 2, Shah and team utilize machine learning to combine vital signs collected via the Microsoft Band 2 and submitted pain scores via the connected mobile application to predict pain scores in people living with sickle cell disease. In this article, Shah and team found that the Microsoft Band 2 could predict pain scores with more than 70% accuracy.
Usability and Feasibility of an mHealth Intervention for Monitoring and Managing Pain Symptoms in Sickle Cell Disease: The Sickle Cell DIsease Mobile Application to Record Symptoms via Technology (SMART)
In one of the earliest symptom reporting and disease management mobile applications utilized in people living with sickle cell disease. SMART app was developed in collaboration with Dr. Shah, and was found to be a feasible method at further understanding and communicating the disease experience of people living with sickle cell disease.
Patients Welcome the Sickle Cell Disease Mobile Application to Record Symptoms via Technology (SMART)
Shah and team further research and understand the effectiveness and perceived benefit of the SMART mobile application for people living with sickle cell disease. The purpose of the app was to report pain scores to improve self-management of the disease and healthcare delivery. People living with sickle cell found that the SMART mobile application was useful at communicating their disease experience.
Sickle Cell Disease Therapies
Development of a Severity Classification System for Sickle Cell Disease
In this article, Shah and his team develop a severity classification system for people living with Sickle Cell Disease. Utilizing various case studies, the a 3-level system was developed to better characterize the condition of people living with sickle cell.
Real-world effectiveness of voxelotor for treating sickle cell disease in the US: a large claims data analysis
In one of the earliest symptom reporting and disease management mobile applications utilized in people living with sickle cell disease. SMART app was developed in collaboration with Dr. Shah, and was found to be a feasible method at further understanding and communicating the disease experience of people living with sickle cell disease.
Pain
Pain Intensity Assessment in Sickle Cell Disease Patients Using Vital Signs During Hospital Visits
In this article, Shah and his team develop a severity classification system for people living with Sickle Cell Disease. Utilizing various case studies, the a 3-level system was developed to better characterize the condition of people living with sickle cell.
Improving Pain Management in Patients with Sickle Cell Disease from Physiological Measures Using Machine Learning Techniques
In this article, Shah and his team work to create methodologies to predict the pain scores of people living with sickle cell utilizing machine learning. Through this paper, Shah and team first identify what variables can be collected and are useful for pain score prediction, as well as explore different ways to predict pain ranging from trends to specific scores. Machine learning proved to be an effective method at utilizing EHR vital sign data to predict pain in people living with SCD.
Research Collaborators
Tanvi Banerjee, Ph.D.
Associate Professor
Wright State Department of Computer Science and Engineering
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Lisa Klesges, Ph.D., MS
Professor of Surgery
Washington University School of Medicine in St. Louis
Biree Andemariam, MD
Hematology-Oncology Specialist
University of Connecticut School of Medicine
Daniel Abrams, Ph.D.
Professor of Engineering Sciences and Applied Mathematics
Northwestern University
Ahmar Zaidi, MD
Medical Director
Agios Pharmaceuticals
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Sherif Badawy, MD, MBBCh, MS
Assistant Professor of Pediatrics (Hematology, Oncology, and Stem Cell Transplantation)
Northwestern University School of Medicine
Jane Hankins, MD, MS
Pediatric Hematology-Oncology Specialist
St. Jude Children's Research Hospital
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Abdullah Kutlar, MD
Professor of Medicine and Director of the Sickle Cell Center
Medical College of Georgia at Augusta University
Charles Jonassaint, Ph.D., MS
Assistant Professor
Medicine, Social Work and Clinical and Translational Science at UPMC
Ify Osunkwo, MD, MPH
Chief Patient Officer, SVP
Forma Therapeutics