Priya M.
Priya Meghrajani

Priya Meghrajani

Technical Product Manager · Talosix

Keeping patients at the heart of healthcare.

I'm Priya: healthcare problem-solver, product manager, mom, and happiest on a trail. I've cared for patients, studied how hospitals run, and now build clinical trial software at Talosix. Wherever I am, I start with the same question: who is this for, and how does it make their life better?

Now

Updated September 2026,
from Snohomish, WA

  • Leading product for the next generation of the Talosix EDC — study setup, visit schedules, subject management, ePRO, safety, database lock and reporting.
  • Designing AI-assisted study setup: upload a protocol, get a suggested study configuration a data manager can review and approve.
  • Fresh back from SCDM 2026 in Raleigh, thinking about where AI truly reduces data-cleaning burden versus just moving the work upstream.

Journey

I started in healthcare as a clinician, not a coder.

  1. 2006

    Clinical care

    KEM Hospital, Mumbai

  2. 2016

    Hospital administration

    TISS, Mumbai

  3. 2018

    Product Owner

    Optum (UnitedHealth Group)

  4. 2019

    Product Owner → Technical Product Manager

    Talosix, Seattle

Full journey →

Work

What I've helped build

Seven years at Talosix, an AI-enabled clinical research platform used by sponsors, CROs and providers. Before that, CMS Risk Adjustment products at Optum.

Live at Talosix

  • Electronic Data Capture

    Faster data entry for sites: from 50 minutes to under 10 per instrument.

  • Patient Screening

    AI-assisted identification of eligible trial participants.

  • Patient Engagement

    ePRO and surveys that patients complete from home.

  • Study management & reporting

    Analytics dashboards and reporting for sponsors, CROs and sites.

Building now

  • Next-generation Talosix EDC

    Rebuilding study setup through database lock around AI assistance, with a human in the lead.

  • AI-assisted study startup

    Getting from protocol to a live study faster, with reviewable AI suggestions instead of blank forms.

Exploring, after SCDM 2026

  • Cutting data-cleaning burden, not moving it

    Where AI actually removes work for data managers versus shifting it upstream.

  • Risk-proportionate design

    Not every data point deserves equal scrutiny. Designing checks around what matters.

  • Fit-for-purpose data collection

    Collect what the protocol needs, not everything that's possible.

  • Continuous evidence & monitoring

    Governance designed in from the start, not assembled after the fact.

Fluent in 21 CFR Part 11, ICH GCP, HIPAA, CDISC, OMOP and FHIR. New to clinical trials? Start with the plain-English glossary →

What guides me

Clinical instincts, product decisions

01

Identify the right problem first

Everything downstream depends on framing the problem correctly. I spend disproportionate time here.

02

Fit for purpose, not comprehensive

Less is more. The best clinical software asks for exactly what the protocol needs.

03

Risk proportionality

Not every risk deserves equal weight, and treating them as if they do is its own kind of failure.

All principles →

Life · Mindful Miles

Colchuck Lake from Aasgard Pass

Wander, wonder, and evolve.

Backpacking the Enchantments

Four days, one lottery permit, and a pass that cannot be named.

Beyond the roadmap

  • Lead Women in Global Health professional meetups
  • Organize events for Women in Business
  • Certified Scrum Product Owner · AWS Cloud Essentials
  • Speak English, Hindi, Marathi and Sindhi
  • Hiker, backpacker, cyclist and snowboarder; mom to Adamya and three cats

Let's connect

Building in clinical research, healthcare AI, or product? I'd love to trade notes.

hello@priyameghrajani.com