Work with thought leaders and academic experts in health information management

Companies can benefit from collaborating with academic researchers in Health Information Management in several ways. These experts can help enhance data management systems, improve healthcare outcomes, provide valuable insights for decision-making, develop innovative solutions, and contribute to research and development efforts. By partnering with these researchers, companies can stay ahead of industry trends, ensure compliance with regulations, optimize processes, and leverage cutting-edge technology. Additionally, collaboration with academic researchers can lead to knowledge transfer, networking opportunities, and access to funding and grants.

Researchers on NotedSource with backgrounds in health information management include Christos Makridis, Dr. Aalok Thakkar, Jim Samuel, Carissa Clark, Atefeh Abdolmanafi, Ph.D., Gwendolyn Thomas, Ph.D., Bernd Stahl, Sheraz Ch, Xander van Wijk, PhD, DABCC, FADLM, Syed Ishtiaque Ahmed, Asif Khan, and Kayvan Najarian.

Christos Makridis

Nashville, TN
Web3 and Labor Economist in Academia, Entrepreneurship, and Policy
Most Relevant Research Expertise
Health Information Management
Other Research Expertise (15)
Finance
Economics and Econometrics
Accounting
Pharmacology (medical)
Law
And 10 more
About
Christos A. Makridis holds academic appointments at Columbia Business School, Stanford University, Baylor University, University of Nicosia, and Arizona State University. He is also an adjunct scholar at the Manhattan Institute, senior adviser at Gallup, and senior adviser at the National AI Institute in the Department of Veterans Affairs. Christos is the CEO/co-founder of [Dainamic](https://www.dainamic.ai/), a technology startup working to democratize the use and application of data science and AI techniques for small and mid sized organizations, and CTO/co-founder of [Living Opera](https://www.livingopera.org/), a web3 startup working to bridge classical music and blockchain technologies. Christos previously served on the White House Council of Economic Advisers managing the cybersecurity, technology, and space activities, as a Non-resident Fellow at the Cyber Security Project in the Harvard Kennedy School of Government, as a Digital Fellow at the Initiative at the Digital Economy in the MIT Sloan School of Management, a a Non-resident Research Scientist at Datacamp, and as a Visiting Fellow at the Foundation for Defense of Democracies. Christos’ primary academic research focuses on labor economics, the digital economy, and personal finance and well-being. He has published over 70 peer-reviewed research papers in academic journals and over 170 news articles in the press. Christos earned a Bachelor’s in Economics and Minor in Mathematics at Arizona State University, as well a dual Masters and PhDs in Economics and Management Science & Engineering at Stanford University.
Most Relevant Publications (2+)

25 total publications

Ethical Applications of Artificial Intelligence: Evidence From Health Research on Veterans

JMIR Medical Informatics / Jun 02, 2021

Makridis, C., Hurley, S., Klote, M., & Alterovitz, G. (2021). Ethical Applications of Artificial Intelligence: Evidence From Health Research on Veterans. JMIR Medical Informatics, 9(6), e28921. https://doi.org/10.2196/28921

Designing COVID-19 mortality predictions to advance clinical outcomes: Evidence from the Department of Veterans Affairs

BMJ Health & Care Informatics / Jun 01, 2021

Makridis, C. A., Strebel, T., Marconi, V., & Alterovitz, G. (2021). Designing COVID-19 mortality predictions to advance clinical outcomes: Evidence from the Department of Veterans Affairs. BMJ Health & Care Informatics, 28(1), e100312. https://doi.org/10.1136/bmjhci-2020-100312

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Jim Samuel

Associate Professor at Rutgers University
Most Relevant Research Expertise
Health Information Management
Other Research Expertise (21)
Analytics
Artificial Intelligence
Informatics
Machine Learning
NLP NLU NLG Behavioral Finance
And 16 more
About
Jim Samuel is an Associate Professor of Practice and Executive Director of the Informatics Program at the Bloustein School. He is an information and artificial intelligence (AI) scientist, with significant industry experience in finance, technology, entrepreneurship and data analytics. Dr. Samuel’s primary research covers human intelligence and artificial intelligences interaction and information philosophy.  Dr. Samuel’s applied research focuses on the optimal use of big data and smart data driven AI applications, textual analytics, natural language processing and artificially intelligent public opinion informatics. His expertise extends to socioeconomic implications of AI, applied machine learning, social media analytics, AI education and AI bias. Dr. Samuel completed his Ph.D. from the Zicklin School of Business, Baruch College – City University of New York, and he also has M.Arch and M.B.A (International Finance) degrees.  Dr. Samuel has worked with large multinational financial services corporations, and advises businesses and organizations on data analytics and AI driven value creation strategies. He is passionate about research driven thought leadership in AI, information philosophy, analytics and informatics. 
Most Relevant Publications (1+)

44 total publications

Public Perceptions of COVID-19 Vaccines: Policy Implications from US Spatiotemporal Sentiment Analytics

Healthcare / Aug 27, 2021

Ali, G. G. Md. N., Rahman, Md. M., Hossain, Md. A., Rahman, Md. S., Paul, K. C., Thill, J.-C., & Samuel, J. (2021). Public Perceptions of COVID-19 Vaccines: Policy Implications from US Spatiotemporal Sentiment Analytics. Healthcare, 9(9), 1110. https://doi.org/10.3390/healthcare9091110

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Carissa Clark

Associate Professor at Saint Louis University
Most Relevant Research Expertise
Health Information Management
Other Research Expertise (33)
poverty
mental health
substance abuse
Applied Psychology
Psychiatry and Mental health
And 28 more
About
Carissa van den Berk Clark, Ph.D., LMSW, is an associate professor of family and community medicine. Prior to joining the department, she received her Ph.D. in social welfare policy from the Luskin School of Public Affairs at UCLA where she also completed a pre-doctoral training at the West Los Angeles Veterans Affairs. She was also a NIDA postdoctoral fellow at the Washington University School of Medicine, Department of Psychiatry. Currently, she is a RWJ IRL fellow, as well as outreach and communications director for the SLU ARCHNet practice based research network and the deputy director of the SLU Area Health Education Center. Her research focuses heavily on substance abuse, mental health, care coordination and uses community participatory methodology.
Most Relevant Publications (2+)

69 total publications

Three Types of Intimate Relationships among Individuals with Chronic Pain and a History of Trauma Exposure

Healthcare / Sep 29, 2017

van den Berk-Clark, C., Weaver, T., & Schneider, F. (2017). Three Types of Intimate Relationships among Individuals with Chronic Pain and a History of Trauma Exposure. Healthcare, 5(4), 68. https://doi.org/10.3390/healthcare5040068

Differences in the Association between Depression and Opioid Misuse in Chronic Low Back Pain versus Chronic Pain at Other Locations

Healthcare / Jun 16, 2016

Jaiswal, A., Scherrer, J., Salas, J., van den Berk-Clark, C., Fernando, S., & Herndon, C. (2016). Differences in the Association between Depression and Opioid Misuse in Chronic Low Back Pain versus Chronic Pain at Other Locations. Healthcare, 4(2), 34. https://doi.org/10.3390/healthcare4020034

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Atefeh Abdolmanafi, Ph.D.

Ph.D. in Computer Science with publications on Medical AI
Most Relevant Research Expertise
Health Information Management
Other Research Expertise (12)
Pattern recognition
Medical image analysis
Machine learning
Deep learning
Biotechnology
And 7 more
About
Throughout my research journey, I have demonstrated a commitment to advancing the field of medical imaging and artificial intelligence (AI) applications in healthcare. Starting with my master's program in physics, where I specialized in optical phenomena, I built a strong foundation in imaging principles that laid the groundwork for my subsequent research endeavors. My doctoral work focused on coronary artery tissue characterization for pediatric patients with Kawasaki Disease, utilizing innovative approaches such as Convolutional Neural Networks and 3D reconstruction techniques. This work garnered international recognition, culminating in a presentation at the 12th International Symposium on Kawasaki Disease in Japan. During my postdoctoral fellowship, I led the development of a groundbreaking computer-aided diagnostic framework, addressing a critical need in healthcare and presenting at prestigious conferences. Transitioning to industry, I joined Aligo Innovation to bridge the gap between academia and industry applications, successfully contributing to technology transfer and business development. In collaboration with ViTAA Medical Solutions, I played a pivotal role in developing an automated system for analyzing computed tomography images in abdominal aortic aneurysms, resulting in filed patents and impactful publications. More recently, I have taken on a more active role in academia, mentoring students, collaborating on innovative projects, and launching the "MedTech Innovations Journal (MIJ)" to bridge technology and healthcare. Beyond my research pursuits, I am a passionate advocate for the synergy of art and science, as reflected in my book "Being Fully Connected" and recent art exhibitions in Toronto and Montreal. My multifaceted background underscores my dedication to pushing the boundaries of knowledge and creativity in the intersection of technology and healthcare.
Most Relevant Publications (1+)

9 total publications

Intra-Slice Motion Correction of Intravascular OCT Images Using Deep Features

IEEE Journal of Biomedical and Health Informatics / May 01, 2019

Abdolmanafi, A., Duong, L., Dahdah, N., & Cheriet, F. (2019). Intra-Slice Motion Correction of Intravascular OCT Images Using Deep Features. IEEE Journal of Biomedical and Health Informatics, 23(3), 931–941. https://doi.org/10.1109/jbhi.2018.2878914

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Gwendolyn Thomas, Ph.D.

Degreed Exercise Physiologist (PhD) with 15+ years experience in clinical research in oncology, obesity and chronic disease. Strong investigative and analytical skills that fostered relationships with expert faculty and collaborators across department, division and institution, as well as within industry, foundations and government to develop Clinical Trial Protocols, scientific publications, and presentations.
Most Relevant Research Expertise
Health Information Management
Other Research Expertise (27)
resistance exercise
obesity
breast cancer
chronic disease
inflammation
And 22 more
About
Strong training and experience in clinical research regulatory requirements as both Principal Investigator and Internal Review Board member.Strong analytical skills with the ability to interpret clinical trial data and synthesize conclusions. Facilitates interactions between key stakeholders across department, division, institution and industry and government partners using a proactive approach, strategic thinking and leadership to accomplish study goals and timelines. Ability to manage multiple competing priorities with good planning, time management and prioritization skills. Project milestones met through problem solving, prioritization, conflict resolution and critical thinking skills. Advanced communication, scientific writing and presentation skills at both university and national/international stakeholder conferences. As Principal Investigator responsibilities include completing high quality literature reviews of clinical trials, peer-reviewed articles, clinical guidelines and other medical data sources to allow for oversight of project deliverables. Extensive experience in independently designing and developing abstracts for peer-reviewed publications, scientific posters and podium presentations at both national and international conferences.
Most Relevant Publications (1+)

47 total publications

Qualitative analysis addressing physician-perceived barriers to usage of electronic patient questionnaires in a colorectal clinic

Journal of Communication in Healthcare / Sep 25, 2014

Solomon, E. R., Thomas, G., & Gurland, B. (2014). Qualitative analysis addressing physician-perceived barriers to usage of electronic patient questionnaires in a colorectal clinic. Journal of Communication in Healthcare, 7(3), 208–213. https://doi.org/10.1179/1753807614y.0000000059

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Bernd Stahl

Director of the Centre for Computing and Social Responsibility
Most Relevant Research Expertise
Health Information Management
Other Research Expertise (52)
critical theory
information systems
computer ethics
information ethics
responsible innovation
And 47 more
Most Relevant Publications (1+)

145 total publications

Who is Responsible for Responsible Innovation? Lessons From an Investigation into Responsible Innovation in Health Comment on "What Health System Challenges Should Responsible Innovation in Health Address? Insights From an International Scoping Review"

International Journal of Health Policy and Management / May 19, 2019

Stahl, B. C. (2019). Who is Responsible for Responsible Innovation? Lessons From an Investigation into Responsible Innovation in Health Comment on “What Health System Challenges Should Responsible Innovation in Health Address? Insights From an International Scoping Review.” International Journal of Health Policy and Management, 8(7), 447–449. https://doi.org/10.15171/ijhpm.2019.32

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Syed Ishtiaque Ahmed

University of Toronto
Most Relevant Research Expertise
Health Information Management
Other Research Expertise (26)
HCI
ICTD
Responsible AI
Sustainability
Applied Psychology
And 21 more
Most Relevant Publications (2+)

158 total publications

A data-driven validation of mobile-based care (mCARE) project for children with ASD in LMICs

Smart Health / Dec 01, 2022

Rabbani, M., Haque, M. M., Dipal, D. D., Zarif, M. I. I., Iqbal, A., Schwichtenberg, A., Bansal, N., Soron, T. R., Ahmed, S. I., & Ahamed, S. I. (2022). A data-driven validation of mobile-based care (mCARE) project for children with ASD in LMICs. Smart Health, 26, 100345. https://doi.org/10.1016/j.smhl.2022.100345

Informing Developmental Milestone Achievement for Children With Autism: Machine Learning Approach

JMIR Medical Informatics / Jun 08, 2021

Haque, M. M., Rabbani, M., Dipal, D. D., Zarif, M. I. I., Iqbal, A., Schwichtenberg, A., Bansal, N., Soron, T. R., Ahmed, S. I., & Ahamed, S. I. (2021). Informing Developmental Milestone Achievement for Children With Autism: Machine Learning Approach. JMIR Medical Informatics, 9(6), e29242. https://doi.org/10.2196/29242

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Asif Khan

Researcher
Most Relevant Research Expertise
Health Information Management
Other Research Expertise (31)
Data Science
ML with Python
AI
Robot Vision
Pharmacology
And 26 more
About
### I am a highly accomplished individual with a strong educational background, having completed my Postdoc and PhD from UESTC, along with an MTech and MCA from Aligarh Muslim University (AMU). I have received multiple awards for my academic achievements, including the Excellent Research Award and the Academic Achievement Award. My expertise lies in the fields of Data Science and Robot Vision, and I am also a proficient Abroad Education Guide, Career Counselor, and Protectional Developer Trainer. I have helped numerous individuals achieve their career goals by providing personalized guidance and sharing my knowledge and experience. My passion for research and development has led me to publish several papers in renowned international journals. With my extensive skills and expertise, I am committed to empowering individuals and organizations in achieving their full potential.
Most Relevant Publications (1+)

79 total publications

IIMFCBM: Intelligent Integrated Model for Feature Extraction and Classification of Brain Tumors Using MRI Clinical Imaging Data in IoT-Healthcare

IEEE Journal of Biomedical and Health Informatics / Oct 01, 2022

Haq, A. U., Li, J. P., Agbley, B. L. Y., Khan, A., Khan, I., Uddin, M. I., & Khan, S. (2022). IIMFCBM: Intelligent Integrated Model for Feature Extraction and Classification of Brain Tumors Using MRI Clinical Imaging Data in IoT-Healthcare. IEEE Journal of Biomedical and Health Informatics, 26(10), 5004–5012. https://doi.org/10.1109/jbhi.2022.3171663

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Kayvan Najarian

Professor of Comp Med and Bioinf, Emergency Med, and Electrical and Comp Engineering
Most Relevant Research Expertise
Health Information Management
Other Research Expertise (48)
biomedical inforamtics
bioinformatics
singal processing
image processing
machine learning
And 43 more
Most Relevant Publications (6+)

106 total publications

Accounting for Label Uncertainty in Machine Learning for Detection of Acute Respiratory Distress Syndrome

IEEE Journal of Biomedical and Health Informatics / Jan 01, 2019

Reamaroon, N., Sjoding, M. W., Lin, K., Iwashyna, T. J., & Najarian, K. (2019). Accounting for Label Uncertainty in Machine Learning for Detection of Acute Respiratory Distress Syndrome. IEEE Journal of Biomedical and Health Informatics, 23(1), 407–415. https://doi.org/10.1109/jbhi.2018.2810820

Motion Artifact Suppression in Impedance Pneumography Signal for Portable Monitoring of Respiration: An Adaptive Approach

IEEE Journal of Biomedical and Health Informatics / Mar 01, 2017

Ansari, S., Ward, K. R., & Najarian, K. (2017). Motion Artifact Suppression in Impedance Pneumography Signal for Portable Monitoring of Respiration: An Adaptive Approach. IEEE Journal of Biomedical and Health Informatics, 21(2), 387–398. https://doi.org/10.1109/jbhi.2016.2524646

A hierarchical expert-guided machine learning framework for clinical decision support systems: an application to traumatic brain injury prognostication

npj Digital Medicine / May 07, 2021

Farzaneh, N., Williamson, C. A., Gryak, J., & Najarian, K. (2021). A hierarchical expert-guided machine learning framework for clinical decision support systems: an application to traumatic brain injury prognostication. Npj Digital Medicine, 4(1). https://doi.org/10.1038/s41746-021-00445-0

Adaptive Real-Time Removal of Impulse Noise in Medical Images

Journal of Medical Systems / Oct 02, 2018

HosseinKhani, Z., Hajabdollahi, M., Karimi, N., Soroushmehr, R., Shirani, S., Najarian, K., & Samavi, S. (2018). Adaptive Real-Time Removal of Impulse Noise in Medical Images. Journal of Medical Systems, 42(11). https://doi.org/10.1007/s10916-018-1074-7

A Novel Tropical Geometry-Based Interpretable Machine Learning Method: Pilot Application to Delivery of Advanced Heart Failure Therapies

IEEE Journal of Biomedical and Health Informatics / Jan 01, 2023

Yao, H., Derksen, H., Golbus, J. R., Zhang, J., Aaronson, K. D., Gryak, J., & Najarian, K. (2023). A Novel Tropical Geometry-Based Interpretable Machine Learning Method: Pilot Application to Delivery of Advanced Heart Failure Therapies. IEEE Journal of Biomedical and Health Informatics, 27(1), 239–250. https://doi.org/10.1109/jbhi.2022.3211765

Learning Using Partially Available Privileged Information and Label Uncertainty: Application in Detection of Acute Respiratory Distress Syndrome

IEEE Journal of Biomedical and Health Informatics / Mar 01, 2021

Sabeti, E., Drews, J., Reamaroon, N., Warner, E., Sjoding, M. W., Gryak, J., & Najarian, K. (2021). Learning Using Partially Available Privileged Information and Label Uncertainty: Application in Detection of Acute Respiratory Distress Syndrome. IEEE Journal of Biomedical and Health Informatics, 25(3), 784–796. https://doi.org/10.1109/jbhi.2020.3008601

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Example health information management projects

How can companies collaborate more effectively with researchers, experts, and thought leaders to make progress on health information management?

Data Management System Enhancement

An academic researcher in Health Information Management can collaborate with a company to enhance their data management system. By analyzing existing systems, identifying gaps, and implementing best practices, the researcher can help the company streamline data collection, storage, and analysis processes, leading to improved efficiency and accuracy.

Healthcare Outcome Improvement

Collaborating with an academic researcher in Health Information Management can help a company improve healthcare outcomes. The researcher can analyze healthcare data, identify patterns and trends, and provide insights on how to optimize treatment plans, reduce readmission rates, and enhance patient satisfaction.

Decision-Making Support

Companies can benefit from the expertise of academic researchers in Health Information Management to support decision-making processes. These researchers can analyze data, conduct market research, and provide evidence-based recommendations to help companies make informed decisions regarding product development, marketing strategies, and resource allocation.

Innovative Solution Development

Academic researchers in Health Information Management can collaborate with companies to develop innovative solutions. By leveraging their knowledge of emerging technologies, data analytics, and healthcare systems, these researchers can help companies create new products, services, or processes that address industry challenges and meet customer needs.

Research and Development Contribution

Collaborating with academic researchers in Health Information Management allows companies to contribute to research and development efforts. By partnering with these experts, companies can support studies, trials, and experiments that advance the field and lead to new discoveries, technologies, and treatments.