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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

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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

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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)

Image by Vojtech Bruzek

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)

Image by Vojtech Bruzek

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

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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

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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

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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

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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.jfif

Tanvi Banerjee, Ph.D.

Associate Professor

Wright State Department of Computer Science and Engineering

lisa klesges.jfif

Lisa Klesges, Ph.D., MS

Professor of Surgery

Washington University School of Medicine in St. Louis

biree andemariam.jfif

Biree Andemariam, MD

Hematology-Oncology Specialist

University of Connecticut School of Medicine

daniel abrams.jfif

Daniel Abrams, Ph.D.

Professor of Engineering Sciences and Applied Mathematics

Northwestern University

ahmar zaidi.jfif

Ahmar Zaidi, MD

Medical Director

Agios Pharmaceuticals

sherif badawy.jfif

Sherif Badawy, MD, MBBCh, MS

Assistant Professor of Pediatrics (Hematology, Oncology, and Stem Cell Transplantation)

Northwestern University School of Medicine

jane hankins.jfif

Jane Hankins, MD, MS

Pediatric Hematology-Oncology Specialist

St. Jude Children's Research Hospital

abdullah kutlar.jfif

Abdullah Kutlar, MD

Professor of Medicine and Director of the Sickle Cell Center

Medical College of Georgia at Augusta University

charles jonassaint.jfif

Charles Jonassaint, Ph.D., MS

Assistant Professor

Medicine, Social Work and Clinical and Translational Science at UPMC

ify osunkwo.jfif

Ify Osunkwo, MD, MPH

Chief Patient Officer, SVP

Forma Therapeutics

Interested in other publications by Dr. Nirmish Shah?
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