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Texas APP Consulting Services (Nina Nwoko)

IndustryConsultantFiled October 15, 2026382 words · 1 attachmentFDA-2026-N-7874-0215
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Themes it raises

3 of the 21 themes in the docket, each with the passage we counted, verbatim.
Whether the user can judge the outputFDA Q3, Q4
“The discussion of generalist versus specialist clinicians should explicitly include registered nurses and nurse practitioners, who are often the first and most frequent users of AI outputs in hospitals, yet are rarely represented in validation studies.”
Whether human oversight is real oversightFDA Q3, Q4, Q14, Q20, Q21, Q26
“(3) specific assessment of automation bias under realistic workload, such as a nurse carrying a full assignment at 3 a.m., not a rested clinician in a controlled study.”
Proving the device works in real careFDA Q11, Q12, Q13, Q14, Q15
“If a device is validated only with physician users, its real-world performance with the clinicians who actually use it most is unknown.”
Machine-assisted draft, pending human review. The source text and highlighted passages appear below. Read the filing on regulations.gov ↗

The comment as filed

Comment submitted on regulations.gov. Passages we counted are highlighted.

The discussion of generalist versus specialist clinicians should explicitly include registered nurses and nurse practitioners, who are often the first and most frequent users of AI outputs in hospitals, yet are rarely represented in validation studies.
Nurses act on AI-generated summaries, early-warning scores and documentation tools around the clock, frequently without a physician present. If a device is validated only with physician users, its real-world performance with the clinicians who actually use it most is unknown.
I recommend that premarket evaluation require: (1) testing with the intended user population as it exists in practice, including RNs and NPs where they will use the device; (2) reporting of human-AI team performance, not just autonomous model performance; and (3) specific assessment of automation bias under realistic workload, such as a nurse carrying a full assignment at 3 a.m., not a rested clinician in a controlled study.

Attachment

Attachment, text extracted from the filed document. Passages we counted are highlighted.

SPEAKER · PRODUCT REVIEWER · CLINICAL ADVISOR

Nina Nwoko, MSN, AGACNP-BC
ICU-trained nurse practitioner making AI and health tech work for the
clinicians who use them.

11+ yrs 24 12 6
Critical care State NP licenses Full practice authority AI workflows built
states

Nina is a board-certified acute care nurse practitioner who works in ICU practice, remote hospital
coverage and asynchronous telehealth. As founder of Texas APP Consulting Services, she helps
telehealth and health tech companies build workflows that fit real clinical work, and she builds and
uses AI workflows every day.

TA L K S PRODUCT REVIEWS & FEEDBACK

AI on Your Next Shift: What Nurses and NPs Tested on Shift — every AI or health tech product
Can Use Safely Today is scored 1–5 on:
Practical tools, real limits and the privacy rules every
Workflow fit
clinician needs.
Safety and privacy
Why Health Tech Fails Clinicians, and How Time saved
to Fix It Before You Scale Learning curve
Workflow, escalation and adoption lessons from Value for cost
remote and async care.
REVIEW POLICY
Beyond the Bedside: Building a Multi-State
Remote NP Career Tested before reviewed. Limitations always
Licensure, telehealth and contract models from included. Payment never buys a positive review.
practice in 24 states. Every paid or gifted review is disclosed.

F O R M AT S

Keynote · Panel · Workshop · Webinar · Podcast ·
Advisory session

info@texasappconsulting.com linkedin.com/in/nnwoko Texas APP Consulting Services · Forney, TX