Paul Schmiedmayer
Postdoctoral Scholar, Bioengineering
Bio
Dr. Schmiedmayer is the Lead Artificial Intelligence and Assistant Director of Digital Health at the Stanford Mussallem Center for Biodesign and a postdoctoral researcher at Stanford University. As a researcher at the intersection of AI, medicine, and software engineering, his work explores novel approaches to developing scalable, patient-centered platforms that harness AI and connected devices to deliver real-time, personalized health insights. His research addresses a critical gap by creating and investigating methodologies in healthcare software engineering, focusing on scalable platforms that enhance patient access to healthcare.
He earned his doctoral degree at the Technical University of Munich, where he studied software engineering, mobile-based systems including smart devices, the applications and integration of machine learning techniques, and the evolution of web service-based distributed systems. He holds a master's and bachelor's degree in computer science from the Technical University of Munich.
Contact
https://orcid.org/0000-0002-8607-9148Current Research and Scholarly Interests
A critical challenge in modern medicine is ensuring equitable access to comprehensive healthcare, particularly for underserved populations. Current healthcare systems often lack scalable, cost-effective, and personalized solutions, struggling to translate research innovations into practical applications.
Dr. Schmiedmayer's research addresses these challenges by developing scalable, intelligent, data-driven systems that leverage patient data and connected devices to provide real-time, personalized healthcare. He aims to validate these solutions by deploying AI-based models on resource-constrained, patient-facing devices, such as smartphones and smart devices, ensuring that personalized medicine is both cost-effective and privacy-preserving. The research's long-term goal is to create closed-loop systems that seamlessly integrate cutting-edge AI research with clinical practice, leveraging software and hardware integrations to identify novel digital biomarkers and making healthcare personalized, scalable, equitable, and accessible to all.
A cornerstone of Dr. Schmiedmayer's research is the Stanford Spezi open-source software ecosystem. Spezi emerged from identifying key challenges in healthcare software development across numerous digital health projects. The open-source software ecosystem features a novel, modular, interoperable architecture that integrates healthcare standards, accelerates digital health innovation, and meets the growing demands of AI integrations. Without dedicated funding, Spezi has thrived on project-based use and open-source contributions, attracting over 500 contributions from more than 20 developers in just two years. Spezi now supports over 20 digital health and AI projects at Stanford and beyond, with 1,800 GitHub stars and contributions from over 90 collaborators, highlighting its significant impact.
You can learn more about Dr. Schmiedmayer's research projects at https://bdh.stanford.edu and visit https://spezi.stanford.edu to learn more about the Stanford Spezi ecosystem.
Lab Affiliations
All Publications
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Comprehensive real time remote monitoring for Parkinson's disease using Quantitative DigitoGraphy.
NPJ Parkinson's disease
2024; 10 (1): 137
Abstract
People with Parkinson's disease (PWP) face critical challenges, including lack of access to neurological care, inadequate measurement and communication of motor symptoms, and suboptimal medication management and compliance. We have developed QDG-Care: a comprehensive connected care platform for Parkinson's disease (PD) that delivers validated, quantitative metrics of all motor signs in PD in real time, monitors the effects of adjusting therapy and medication adherence and is accessible in the electronic health record. In this article, we describe the design and engineering of all components of QDG-Care, including the development and utility of the QDG Mobility and Tremor Severity Scores. We present the preliminary results and insights from an at-home trial using QDG-Care. QDG technology has enormous potential to improve access to, equity of, and quality of care for PWP, and improve compliance with complex time-critical medication regimens. It will enable rapid "Go-NoGo" decisions for new therapeutics by providing high-resolution data that require fewer participants at lower cost and allow more diverse recruitment.
View details for DOI 10.1038/s41531-024-00751-w
View details for PubMedID 39068150
View details for PubMedCentralID PMC11283542
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Quantitative DigitoGraphy: a Comprehensive Real-Time Remote Monitoring System for Parkinson's Disease.
Research square
2024
Abstract
People with Parkinson's disease (PWP) face critical challenges, including lack of access to neurological care, inadequate measurement and communication of motor symptoms, and suboptimal medication management and compliance. We have developed QDG-Care: a comprehensive connected care platform for Parkinson's disease (PD) that delivers validated, quantitative metrics of all motor signs in PD in real time, monitors the effects of adjusting therapy and medication adherence and is accessible in the electronic health record. In this article, we describe the design and engineering of all components of QDG-Care, including the development and utility of the QDG Mobility and Tremor Severity Scores. We present the preliminary results and insights from the first at-home trial using QDG-Care. QDG technology has enormous potential to improve access to, equity of, and quality of care for PWP, and improve compliance with complex time-critical medication regimens. It will enable rapid "Go-NoGo" decisions for new therapeutics by providing high-resolution data that require fewer participants at lower cost and allow more diverse recruitment.
View details for DOI 10.21203/rs.3.rs-3783294/v1
View details for PubMedID 38343821
View details for PubMedCentralID PMC10854288
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Utility of smart watches for identifying arrhythmias in children.
Communications medicine
2023; 3 (1): 167
Abstract
Arrhythmia symptoms are frequent complaints in children and often require a pediatric cardiology evaluation. Data regarding the clinical utility of wearable technologies are limited in children. We hypothesize that an Apple Watch can capture arrhythmias in children.We present an analysis of patients ≤18 years-of-age who had signs of an arrhythmia documented by an Apple Watch. We include patients evaluated at our center over a 4-year-period and highlight those receiving a formal arrhythmia diagnosis. We evaluate the role of the Apple Watch in arrhythmia diagnosis, the results of other ambulatory cardiac monitoring studies, and findings of any EP studies.We identify 145 electronic-medical-record identifications of Apple Watch, and find arrhythmias confirmed in 41 patients (28%) [mean age 13.8 ± 3.2 years]. The arrythmias include: 36 SVT (88%), 3 VT (7%), 1 heart block (2.5%) and wide 1 complex tachycardia (2.5%). We show that invasive EP study confirmed diagnosis in 34 of the 36 patients (94%) with SVT (2 non-inducible). We find that the Apple Watch helped prompt a workup resulting in a new arrhythmia diagnosis for 29 patients (71%). We note traditional ambulatory cardiac monitors were worn by 35 patients (85%), which did not detect arrhythmias in 10 patients (29%). In 73 patients who used an Apple Watch for recreational or self-directed heart rate monitoring, 18 (25%) sought care due to device findings without any arrhythmias identified.We demonstrate that the Apple Watch can record arrhythmia events in children, including events not identified on traditionally used ambulatory monitors.
View details for DOI 10.1038/s43856-023-00392-9
View details for PubMedID 38092993
View details for PubMedCentralID 4937287
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CardinalKit: open-source standards-based, interoperable mobile development platform to help translate the promise of digital health.
JAMIA open
2023; 6 (3): ooad044
Abstract
Smartphone devices capable of monitoring users' health, physiology, activity, and environment revolutionize care delivery, medical research, and remote patient monitoring. Such devices, laden with clinical-grade sensors and cloud connectivity, allow clinicians, researchers, and patients to monitor health longitudinally, passively, and persistently, shifting the paradigm of care and research from low-resolution, intermittent, and discrete to one of persistent, continuous, and high resolution. The collection, transmission, and storage of sensitive health data using mobile devices presents unique challenges that serve as significant barriers to entry for care providers and researchers alike. Compliance with standards like HIPAA and GDPR requires unique skills and practices. These requirements make off-the-shelf technologies insufficient for use in the digital health space. As a result, budget, timeline, talent, and resource constraints are the largest barriers to new digital technologies. The CardinalKit platform is an open-source project addressing these challenges by focusing on reducing these barriers and accelerating the innovation, adoption, and use of digital health technologies. CardinalKit provides a mobile template application and web dashboard to enable an interoperable foundation for developing digital health applications. We demonstrate the applicability of CardinalKit to a wide variety of digital health applications across 18 innovative digital health prototypes.
View details for DOI 10.1093/jamiaopen/ooad044
View details for PubMedID 37485467
View details for PubMedCentralID PMC10356573
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Reducing the Impact of Breaking Changes to Web Service Clients During Web API Evolution
IEEE COMPUTER SOC. 2023: 1-11
View details for DOI 10.1109/MOBILSoft59058.2023.00008
View details for Web of Science ID 001031673900001
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Global Software Engineering in a Global Classroom
IEEE COMPUTER SOC. 2022: 113-121
View details for DOI 10.1109/ICSE-SEET55299.2022.9794211
View details for Web of Science ID 000850196300012