Dr. Siddharth Jain is CMTO at Mindbowser, where he connects clinical medicine, outcomes research, and health technology in ways most product teams cannot.
He brings 18+ years of experience spanning direct patient care, public health policy, and US health outcomes research, including six years as a Scientist at Children’s Hospital of Philadelphia, four years as a Senior Research Fellow at Penn’s Leonard Davis Institute of Health Economics, and nearly two years as a Health Outcomes Researcher at Yale New Haven Health.
He is a physician, a DrPH-trained outcomes researcher, a published scientist, and the only person on Mindbowser’s team who has treated patients, designed clinical trials, and built research models on Medicare and SEER data.
Sid’s career did not begin in technology. It began at the bedside.
He completed his MBBS at Vikram University between 2001 and 2007, then practiced clinical medicine in India, including cardiac care at Max Healthcare, hospital administration and direct patient care at Muskan Hospital, and an RCT on diabetes management at AIIMS.
That time in direct care was not a detour. It built the clinical lens he would eventually bring to every outcomes dataset, every study design, and every health technology decision.
In 2009, he made a deliberate pivot. He moved to the United States and enrolled in the Master of Public Health Program at the University of Minnesota, focusing on public health administration and policy.
That was followed by a DrPH in Outcomes Research at the University of Alabama at Birmingham, completed in 2017.
The trajectory was intentional: from treating individual patients to understanding what happens to populations, and why.
What came next was 15 years of applied outcomes research at some of the most credible institutions in US health research.
At CHOP, he spent 6 years as a Scientist, working on Alzheimer’s and dementia in Medicare claims populations, multimorbidity in hospitalized older adults, and preterm birth cost-effectiveness.
At Penn’s Leonard Davis Institute, he spent 4 years as a Senior Research Fellow, deepening his work in health economics and cost-effectiveness analysis.
At Yale New Haven Health, he spent nearly 2 years as a Health Outcomes Researcher, working at the intersection of clinical systems and research.
That is what Sid brings to Mindbowser. When a healthcare organization needs to understand whether a clinical AI tool is actually improving outcomes, or whether a care program is cost-effective, Sid brings a lens built from primary research, not vendor white papers.
Physician. Scientist. Outcomes Researcher. 18 years at the intersection of clinical medicine, population health, and evidence.
Sid serves as CMTO, Chief Medical Technology Officer, at Mindbowser, where he brings clinical and research authority to healthcare technology strategy, clinical AI validation, and outcomes-grounded product decisions. He concurrently serves as VP at nVista-Inc.
Sid’s career spans clinical care, public health, doctoral-level outcomes research, and applied health economics, including:
• MBBS, Vikram University, 2001 to 2007
• Clinical practice across cardiac care at Max Healthcare, hospital administration and direct patient care at Muskan Hospital, and diabetes RCT work at AIIMS
• MPH, Public Health Administration and Policy, University of Minnesota, 2009 to 2011
• DrPH, Outcomes Research, University of Alabama at Birmingham, 2011 to 2017
• Scientist II, Children’s Hospital of Philadelphia, for 6 years
• Senior Research Fellow, University of Pennsylvania / Leonard Davis Institute of Health Economics, for 4 years
• Health Outcomes Researcher III, Yale New Haven Health, for 1 year and 10 months
• CMTO, Mindbowser, from August 2025 to present
• VP, nVista-Inc, concurrent role
Sid started where most outcomes researchers never go: inside the clinic.
His 6 years as a practicing physician across cardiac care, hospital administration, and a clinical trial at AIIMS gave him the foundational insight that shapes everything he does with data: the patient is always at the end of the model.
When you have seen what happens when a clinical protocol fails, you build research differently. You ask sharper questions, design for failure modes, and never confuse statistical significance with clinical relevance.
That clinical grounding is what makes his outcomes research distinctive.
Most researchers learn health economics from a textbook. Sid learned it from watching patients move through systems that were not designed for them.
#➤# Clinical decision support and AI validation in clinical workflows
#➤# Health outcomes research, including population-based and patient-based methods
#➤# Cost-effectiveness analysis and health economic modeling using TreeAge, SEER-Medicare, and CMS data
#➤# Value-based care economics and program evaluation
#➤# Medicare and CMS data analytics for claims-based research
#➤# Health Information Exchange evaluation
#➤# Biostatistical methods and clinical study design using SAS, STATA, and JMP
#➤# Oncology outcomes, cancer disparities, and IMRT-related research
#➤# Chronic disease management, including Alzheimer’s, diabetes, multimorbidity, and Parkinson’s
#➤# Health policy and public health administration
• MBBS, Vikram University, 2001 to 2007, providing his medical and clinical foundation
• MPH, Public Health Administration and Policy, University of Minnesota, 2009 to 2011
• DrPH, Outcomes Research, University of Alabama at Birmingham, 2011 to 2017, with doctoral training in health economics and study design
• AcademyHealth Full Member, connected to the primary professional society for health services research in the US
• Sigma Xi member, reflecting scientific research community involvement
• CMTO at Mindbowser, bringing physician-level and doctoral-level outcomes research expertise to healthcare technology strategy
• AcademyHealth Full Member and Sigma Xi member, credentialed by the research community as well as the technology community
• Published as a scientist across multiple disease areas, with Medicare and SEER-level data expertise
• 15+ years in US health research institutions, including CHOP, Penn, and Yale New Haven Health
• Alzheimer’s and Dementia, 2020 —Using Medicare claims in identifying Alzheimer’s disease and related dementias
• Practical Radiation Oncology, 2019 —Radiation Oncologist Characteristics and their Association with Outcomes in Patients with Head and Neck Cancer
• 6 years as Scientist II at Children’s Hospital of Philadelphia, with published research on Alzheimer’s and dementia, head and neck cancer disparities, preterm birth cost-effectiveness, and multimorbidity in hospitalized older adults
• 4 years as Senior Research Fellow at Penn / Leonard Davis Institute of Health Economics, one of the leading health economics research centers in the US
• Nearly 2 years as Health Outcomes Researcher III at Yale New Haven Health
• Published in peer-reviewed journals including Alzheimer’s and Dementia, Practical Radiation Oncology, and American Journal of Obstetrics and Gynecology
• Clinical RCT experience at AIIMS on diabetes population management, combining trial design and direct patient care
Sid’s approach is grounded in the discipline of outcomes research: asking the right question before running the analysis.
• Starts with the clinical question, not the data: what does this model actually need to tell a clinician to be useful?
• Validates AI and CDSS outputs against clinical trial design standards, not just benchmark metrics
• Reads CMS and SEER data at the claims level, where many health technology assumptions fall apart
• Applies cost-effectiveness frameworks to value-based care decisions because “it works” is never enough if “it works at what cost?” remains unanswered
• Brings study design rigor to product development: what is the comparison, what is the endpoint, and what would count as a failed result?
• Keeps the patient population visible in every analytical decision because population averages can hide the subgroups where clinical AI most often fails
Evidence-based medicine does not stop at the clinic. It should not stop at the product specification either.
Explore the pros and cons of clinical decision support systems (CDSS) and the challenges healthcare organizations face in...
Explore the pros and cons of clinical decision support systems (CDSS) and the challenges healthcare organizations face in implementing them effectively.
Read More →
AI-powered CDS tools reduce alert fatigue and improve care decisions in real time.
AI-powered CDS tools reduce alert fatigue and improve care decisions in real time.
Read More →
COPD remote patient monitoring helps reduce readmissions with connected devices, CMS billing, and home-based respiratory monitoring.
Read More →Partner with us to design, build, and scale digital solutions that drive better outcomes.
Let’s discuss your goals, workflows, and next steps in a focused consultation call.
Schedule a Call 
BLOGS 
NEWSROOM 
CASE STUDIES 
WEBINARS 
PODCASTS 
ASSET HUB 
EVENT CALENDAR 
