Top 10 AI Prompts and Use Cases and in the Healthcare Industry in Fort Worth

Fort Worth matters for AI in healthcare because its schools and hospitals are moving from pilot projects to real-world training and clinical use: the Anne Burnett Marion School of Medicine at TCU now pairs a 96,000‑square‑foot Arnold Hall with mixed‑reality HoloAnatomy, AI‑driven manikins and a FAB curriculum that teaches genomics, wearables and AI in weeklong intensives (TCU Burnett School of Medicine FAB & HoloAnatomy news), while local reporting shows UNT Health Science Center winning major NIH support for AI research and hospitals piloting AI in neuro critical care and security systems (Fort Worth Report: AI reshaping health care and medical education).

For Fort Worth clinicians and technologists ready to turn those institutional investments into workplace skills, the AI Essentials for Work bootcamp outlines practical prompting and applied AI training to accelerate adoption (AI Essentials for Work bootcamp registration).

Bootcamp	AI Essentials for Work - key facts
Length	15 Weeks
Cost (early bird / after)	$3,582 / $3,942 - 18 monthly payments
Includes	AI at Work: Foundations; Writing AI Prompts; Job Based Practical AI Skills

“The best part about this is getting the three-dimensional relationship with the human body.”

The top 10 prompts and use cases were chosen by mapping national AI categories to what Fort Worth already deploys and teaches: local reporting and institutional announcements were reviewed to prioritize scenarios that can be adopted quickly in classrooms, simulation centers and hospital pilots - for example, curriculum‑integrated mixed‑reality and AI manikin training at TCU and simulation at UNT, NIH‑level research funding that underwrites scale, and live hospital pilots in neuro critical care and campus safety (Fort Worth Report analysis of AI in Fort Worth medical education).

Selection criteria weighted (1) demonstrable local adoption, (2) funding or grant support such as UNT Health Fort Worth's recent AACOM grant for an AI elective (UNT Health Fort Worth AACOM grant for an AI medical education elective), (3) readiness for hands‑on training and measurable ROI for providers, and (4) ethical and governance signals from clinicians and educators; supplemental guidance on funding and pilot design came from local Nucamp resources to align prompts with practical partnership opportunities (Nucamp financing options and partnership guidance for healthcare AI pilots).

The result: prompts focused on simulation, documentation automation, diagnostic augmentation and operational orchestration that local schools and hospitals can teach and test within existing courses and pilot budgets - a methodology that emphasizes immediate impact and scalability rooted in Fort Worth's real projects and grants (not hypothetical use cases).

“As a society, we're all moving toward utilization of AI. It's going to be a tool that frees us up and allows time to actually talk with our patients and help students,”

The Anne Burnett Marion School of Medicine at TCU has turned simulation into a central teaching modality: the Superfloor pairs Microsoft HoloLens and Case Western's HoloAnatomy with AI‑enabled, high‑fidelity manikins and task trainers so students can rehearse urgent scenarios and procedures without risk to patients, while the Alcon‑funded OcuSim virtual reality ophthalmology simulator uses an Oculus headset for deliberate, mastery‑level practice that faculty say improves long‑term recall and clinical confidence (OcuSim virtual reality ophthalmology simulator at TCU Burnett School of Medicine).

Local reporting notes that TCU's curriculum and simulation investments are intentionally designed to teach AI‑augmented clinical workflows, giving Fort Worth students hands‑on experience with the same mixed‑reality and feedback systems hospitals are piloting in care settings (Fort Worth Report: AI reshaping medical education at TCU); the result is measurable: immersive anatomy and simulated crises produce faster skill acquisition and safer clinical transitions for new clinicians.

Simulation tool	Example / use
HoloLens + HoloAnatomy	3‑D anatomy labs on the Superfloor
OcuSim (VR)	Ophthalmology immersive practice with Oculus headset
High‑fidelity manikins & task trainers	Procedural practice and simulated clinical scenarios (e.g., hypercalcemia)
Web-based learning	ScholarRx, Osmosis, Aquifer, MedHub for supplementing simulation

“OcuSim is the wave of the future,” said Adam Jennings, D.O., Executive Director of Simulation, Innovation and Research at the Burnett School of Medicine at TCU.

Fort Worth clinics planning EHR‑embedded pilots should prioritize tightly integrated ambient documentation tools like Nuance's DAX Copilot for Epic because they turn conversational, multiparty encounters into standardized clinical summaries that land directly in the patient's Epic note - reducing after‑visit charting, capturing orders during the visit, and producing patient‑friendly after‑visit summaries.

Epic's announcement on DAX Express highlights the copilot role for Dragon Medical users within Epic workflows (Epic announcement: Nuance and Epic DAX Express ambient documentation integration), while Microsoft's DAX Copilot guidance details quick‑start workflows, specialty templates, multilingual capture (including Spanish), and reviewer/edit best practices that clinics must include in training (Microsoft support: Learn DAX Copilot for Epic guidance and best practices).

The practical payoff is concrete: Dragon/DAX deployments have produced measurable operational returns (Northwestern Medicine reported a 112% ROI), so a small Fort Worth clinic pilot - focused on staff training, BAA contracts, and template tuning - can convert clinician time saved into more visits and fewer denials without leaving Epic.

Feature	Why it matters
Ambient recording → standardized summaries	Less after‑hours charting, consistent notes
Order capture from conversation	Faster care delivery, fewer transcription errors
Specialty templates & multilingual support	Better accuracy and broader access to care

“Dragon Copilot helps doctors tailor notes to their preferences, addressing length and detail variations.”

Diagnostics augmentation in Fort Worth can follow proven imaging-AI playbooks that both speed interpretation and surface high-risk findings for timely follow-up: Merative's clinical white paper shows its MUV advanced lung‑nodule workflow cut radiologist chest‑CT interpretation time by 23% (Merative study on advanced lung‑nodule workflow reducing chest CT interpretation time), while worklist‑reprioritization tools - studied by UT Southwestern researchers - moved positive CTPA exams to the top of reading lists and shortened report turnaround from 59.9 to 47.6 minutes (wait time 33.4 → 21.4 minutes), a concrete operational gain that Texas health systems can translate into faster anticoagulation and ED throughput (UT Southwestern study on AI worklist reprioritization reducing CT turnaround times).

Those workflow interventions scale beyond tertiary centers; a large system implementation that flagged incidental pulmonary nodules helped detect over 70% of lung cancers at stage 1–2, illustrating the “so what”: faster reads + automated follow‑up tracking materially improve early detection and curative treatment opportunities (Sutter Health report on early lung cancer detection using AI flagging incidental pulmonary nodules).

“It takes my breath away to think about how we inverted that curve - from mostly late-stage diagnoses to now catching over 70% of cases early, when survival is vastly improved and many patients can be treated with curative intent,” said Jason Wiesner, MD.

UNT Health Science Center (HSC) positions Fort Worth as a practical hub for predictive analytics that aim to detect patient deterioration earlier and more equitably by combining large, diverse data sources with clinician training: HSC won NIH funding to lead the AIM‑AHEAD coordinating center - bringing a multi‑institution effort and federal support to Fort Worth to build AI/ML approaches using EHRs, genomics, imaging and social‑determinants data to reduce bias and improve model fairness (UNT Health Science Center AIM‑AHEAD NIH $50M award for AI health equity) - and TCOM's AACOM‑funded elective on health informatics and AI will train third‑ and fourth‑year students to interpret and act on AI signals in clinical rotations (TCOM AACOM grant for an AI medical education elective at UNT Health Fort Worth).

The “so what” for Texas providers: local infrastructure plus curriculum means predictive alerts for deterioration can be developed with diverse data and clinicians already learning how to evaluate those alerts, shortening the path from model to safer bedside decisions.

Item	Detail
Award (headline)	$50 million NIH award to HSC (AIM‑AHEAD coordinating center)
Data focus	EHRs, genomics, imaging, social determinants of health
Local training	TCOM elective: Health Informatics, AI, AuI in Medical Education (2025–26)
Initial phase dates	Sept 22, 2021 – Sept 16, 2023 (AIM‑AHEAD start)

“This consortium will bring together research institutions, minority-serving institutions, private sector and community organizations in mutually beneficial, coordinated, and trusted partnerships to enhance the participation and representation of researchers and communities currently underrepresented in the development of artificial intelligence and machine learning,”

JPS Health's experience underscores how an adaptable healthcare LMS can be the backbone for operational optimization and workflow orchestration: a LearnSoft case study documents how configurable learning and process modules solved “unique workflow needs” at JPS, showing that centralizing protocols and training content lets scheduling tools enforce consistent rules across departments and reduce variability when staff rotate between clinics (LearnSoft case study: JPS Health adaptable LMS solutions).

For Fort Worth systems exploring schedulers and orchestration platforms, the practical payoff is clear - codifying shift rules and orientation in a single, auditable system makes process changes repeatable and measurable, which helps justify pilots and tie saved clinician hours to tangible returns (Measurable ROI targets for Fort Worth healthcare scheduling improvements), so scheduling improvements become a durable lever for throughput and staff resilience rather than a one‑off workaround.

Virtual health assistants can turn routine follow‑up into a scalable, patient‑centered workflow for Texas clinics: Doximity GPT, a free, HIPAA‑compliant clinician copilot, can draft discharge instructions, patient education handouts and insurance appeal letters, translate complex instructions into more than 95 languages (one clinician described translating a discharge summary into Tagalog at the bedside), and - by reducing paperwork - reportedly save clinicians “over 10 hours a week,” making same‑day follow‑up and medication reconciliation more realistic for busy practices (Doximity GPT HIPAA-compliant clinical assistant details).

Practical pilot guidance from virtual‑assistant case studies shows measurable gains in patient satisfaction, fewer no‑shows and faster scheduling when assistants integrate with telehealth or EHR workflows, so a small Fort Worth clinic that automates triage messages and post‑visit reminders can convert saved charting time into clearer follow‑ups and better adherence (AI virtual assistant healthcare case study and outcomes; Doximity GPT clinician workflow impact analysis).

The so‑what: multilingual, draftable follow‑up that saves clinician hours becomes a concrete lever for fewer readmissions and higher patient engagement across Texas practices.

“This tool has been a game-changer for my charting process, whether it's creating a plan for congestive heart failure or an HPI for atrial fibrillation.”

Remote monitoring in Fort Worth can move from novelty to clinical impact by pairing continuous sensor streams with context-aware models - evidenced by a UNTHSC smartphone pilot that used almost 5,000 in‑the‑moment surveys from 78 participants to predict 82% of drinking episodes and deliver tailored, real‑time interventions that lowered drinking over four weeks (UNTHSC $3M smartphone study on reducing alcohol use among the unhoused).

Hospitals and home‑monitoring pilots in Tarrant County, including systems such as Methodist Mansfield considering remote‑care expansion, should combine device telemetry, patient‑reported context, and clinician‑tuned thresholds so alerts surface imminent risk instead of noise; local academic support and funding ecosystems (see UNT Health Fort Worth's research and training infrastructure) make Fort Worth a practical place to scale those workflows (UNT Health Fort Worth research and training infrastructure).

The concrete takeaway: an 82% episode‑prediction rate in a real pilot shows wearables and phones can turn brief signals into timely outreach that averts deterioration and reduces downstream utilization.

Metric	Value
Participants	78
In‑the‑moment surveys	~5,000
Prediction accuracy	82% of drinking episodes
Primary outcome	Reduced drinking over 4 weeks with tailored messages

“Machine learning allowed us to use things like people's moods and urges, and alcohol availability, to predict whether or not they were likely to start drinking. The app then gave tailored suggestions that could curb people's intentions to drink.”

Merck's AIDDISON™ is a software‑as‑a‑service drug‑discovery platform that uses generative AI, machine learning and computer‑aided drug design to search a chemical universe of more than 60 billion candidates and then recommend practical synthesis routes - bridging the gap between virtual molecule design and real‑world manufacturability - making it a tool Texas research groups and biotechs should watch when planning discovery pilots or partnerships (Merck AIDDISON SaaS drug discovery platform announcement, Merck AIDDISON research overview).

Trained on more than two decades of experimentally validated R&D data and integrated with Synthia™ retrosynthesis via API, AIDDISON flags candidates with properties like non‑toxicity, solubility and stability and suggests reagents and building blocks for safer, higher‑yield manufacturing - claims that vendors project could cut discovery time and costs substantially (industry coverage of the launch and implications summarized here: Biopharm International analysis of Merck AIDDISON launch).

The concrete payoff: faster candidate triage plus synthesis guidance can shorten the path from molecule to manufacturable lead, turning expensive, multi‑year screens into focused experimental programs.

Capability	Detail
Platform	AIDDISON™ SaaS (MilliporeSigma / Merck Life Science)
Chemical space	>60 billion virtual compounds
Integration	Synthia™ retrosynthesis API for synthesis planning
Training data	Over two decades of experimentally validated R&D datasets
Outcomes claimed	Identifies drug‑like candidates and proposes synthesis routes; industry projects large time/cost savings

“Our platform enables any laboratory to count on generative AI to identify the most suitable drug-like candidates in a vast chemical space. This helps ensure the optimal chemical synthesis route for development of a target molecule in the most sustainable way possible.” - Karen Madden, CTO, Life Science business sector of Merck KGaA

Fort Worth health systems that move from pilots to production must pair people and policies with tooling that makes compliance auditable and transparent: Securiti's Data Command Center markets unified data intelligence, automated HIPAA controls (vendor risk and assessment automation), and AI governance capabilities that catalog models, trace data lineage and enforce least‑privilege access across structured and unstructured PHI - features that directly address audit demands and the operational friction that kills pilots (Securiti HIPAA compliance guide for healthcare compliance, Securiti Data Command Center data governance product).

The concrete payoff: automated lineage, access maps and tamper‑proof reporting shorten response times for audits and breach inquiries, and help contain exposures that HIPAA guidance shows can reach penalties up to $1,919,173 for serious violations; pairing platform automation with a local compliance partner - such as Dallas‑Fort Worth HIPAA services - makes BAAs, risk assessments and remediation actionable on the ground (Dallas Fort Worth HIPAA compliance services by Brevall).

For Texas providers, building these controls into pilot budgets and documentation is the practical lesson: governance tooling turns regulatory risk into repeatable, auditable processes that accelerate safe scale‑up of AI in care.

Capability	Why it matters for Fort Worth providers
Data discovery & classification	Finds PHI across clouds and files so nothing is missed during audits
Unstructured data governance	Sanitizes notes and images before use with GenAI
Data access governance & least privilege	Reduces over‑privileged accounts and audit scope
AI governance & model registry	Documents model lineage, risks and controls for reviewers
Automated assessments & reporting	Speeds breach notification and regulatory response

GenAI-enabled clinician decision support is moving from helper to hub for personalized treatment pathways in Texas: Doximity GPT - free for U.S. clinicians, HIPAA‑compliant, and advertised to save “over 10 hours a week” on notes, letters and patient instructions - now gains evidence‑first depth after Doximity's July 2025 acquisition of Pathway, which curates guideline, meta‑analysis and trial data and will be available to Doximity members, shortening the time from clinical question to an evidence‑backed plan (Doximity GPT clinical assistant (HIPAA-compliant AI for clinicians); TechTarget: Doximity acquisition of Pathway clinical decision support).

Fort Worth clinicians and trainees who already train with AI‑augmented simulation can use these tools to draft differential diagnoses and treatment steps faster, but every output requires clinician verification and a signed BAA or HIPAA controls - best practices highlighted by HIPAA‑focused reviews that recommend human‑in‑the‑loop review, secure environments and prompt discipline before signing notes or acting on suggestions (MedCram review on HIPAA-compliant AI and clinical workflow cautions).

The so‑what: with Pathway's curated evidence plus Doximity's copilot features, a Fort Worth clinician can turn a bedside question into a cited, patient‑ready plan in minutes - freeing time for more direct care while requiring transparent oversight.

“We're thrilled to welcome the Pathway team to Doximity. They've painstakingly built one of the best datasets in medicine, and it's going to take our clinical reference capabilities to an entirely new level.”

Practical next steps for Fort Worth beginners: start with focused skills, local partners, and bite‑sized pilots - enroll in the AI Essentials for Work bootcamp to learn prompting, workflow integration, and measurable pilot design (AI Essentials for Work bootcamp registration and syllabus); reach out to UNT Health Science Center's AIM‑AHEAD program (named in President Biden's Executive Order and backed by major NIH/Congress funding, including $102M for the coordinating center) to align projects with equity‑focused datasets and potential grant support (AIM‑AHEAD program named in Biden Executive Order and grant support details); and choose one quick ROI pilot - ambient documentation, scheduler automation, or remote monitoring - and scope it to measurable outcomes using local ROI playbooks and funding guides so leadership can see time saved and reduced readmissions within six months (Measurable ROI targets and pilot guidance for Fort Worth healthcare AI projects).

Those three steps - learn, partner, pilot - turn institutional investment into repeatable, auditable improvements at the bedside.

First step	Why it matters
Skill building (AI Essentials)	Prompting + applied workflows to run pilots
Partner (AIM‑AHEAD / UNT HSC)	Access to diverse datasets, equity focus, and grant channels
Small pilot (documentation, scheduling, monitoring)	Fast, measurable ROI and governance-ready scale

“The specific mention of AIM‑AHEAD in the Executive Order is a testament to the important work that AIM‑AHEAD program is doing in engaging our underserved communities and increasing researcher diversity.”