Clinical ML reporting checklist that survives reviewer pressure
A practical checklist for clinical prediction and imaging ML so results are interpretable, calibrated, and defensible.
Healthcare Research & Technology
From clinical data to funded proposals, published evidence, and working technology
Services
Three integrated service areas designed to move work from question to publication — with capacity to expand.
Medical writing & grant strategy
Manuscripts, NIH grants (R01, SBIR/STTR), systematic reviews, and publication packaging. Federal and foundation experience across NIH, NSF, DOE, and others.
Clinical data analytics
Biostatistics, survival models, AI/ML for clinical data, and reproducible pipelines. Analysis designed around clinical endpoints and reviewer expectations.
Healthcare technology
Custom dashboards, clinical data tools, patient-facing applications, and healthcare API integrations — built with medical context. Portfolio in development.
Use cases
How the practice operates across grants, data, analysis, and publication.
Grant strategy:
From idea to funded proposal
What we do:
Shape the research question, align aims to funder priorities, and build a proposal that reviewers can say yes to. Full narrative, analysis plan, and execution path.
Where we work:
Federal and foundation proposals across NIH, NSF, DOE, ARPA-H, and others
Therapeutic areas spanning oncology, neurobiology, health equity, and diagnostics
Clinical data analytics:
From raw data to defensible analysis
What we do:
Build reproducible cohorts, versioned pipelines, and analysis-ready datasets from raw clinical data. Every assumption documented, every output auditable.
Where we work:
Clinical research across oncology, neurobiology, pharmacology, and translational medicine
Biostatistics, survival analysis, and machine learning for clinical applications
Scientific writing:
From results to publication-ready manuscripts
What we do:
Structure, draft, and revise scientific manuscripts with claims discipline, clear figures, and a revision strategy that reduces back-and-forth with reviewers.
Where we work:
Manuscripts and reviews across neurobiology, pharmacology, oncology, health equity, and translational research
First-author publications and peer-reviewed editorial experience
Healthcare technology:
Tools built with clinical context
What we do:
Build clinical dashboards, data tools, and patient-facing applications with medical context from the start — not bolted on after.
Where we work:
Clinical data visualization and reporting
Healthcare API integrations and workflow tools
Portfolio in development
Frequently Asked Questions
What kinds of clients do you work with?
Pharma and biotech companies, academic research labs, healthcare startups, CROs, and clinical research groups needing medical expertise combined with technical execution.
Do you take on end to end projects or only one part?
Both. Engagements can be scoped to a single service area, or run end to end from grant strategy through data analysis and publication.
What therapeutic areas do you cover?
Oncology, neurobiology, pharmacology, health equity, diagnostics, dermatology, addiction science, translational research, and biocompatible materials, among others.
How do you keep work reproducible and auditable?
Clear cohort definitions, explicit assumptions, versioned outputs, and QA checks designed to make results defensible and easy to revisit.
Do you provide writing without data work?
Yes. Writing engagements focus on structure, claims discipline, and reviewer facing clarity, including revision strategy and responses to reviewers.
Contact
Send a short brief. You will receive a scoped response with next steps.
Email
Scope
Medical writing, analytics, grants, technology
Response
Scope, timeline, and terms
Insights
View all posts »Notes on healthcare research and technology: grants, data, methods, modeling, and publication.
Project intake tracks and what to send first
Pick a track, send the minimal inputs, and get a first pass output quickly without scope drift.
Writing revisions that increase clarity without changing meaning
A repeatable editing pattern for scientific writing that reduces reviewer confusion and strengthens causal logic.