Top 10 AI Prompts and Use Cases and in the Healthcare Industry in Orem

AI is already reshaping care in Orem by turning local expertise into practical tools: Orem-based Videra Health uses multimodal AI to monitor video, voice, and text for earlier mental-health intervention - claiming measurable gains like cutting 31,600+ administrative hours annually - and Techcyte's AI pathology work (now part of a Mayo Clinic collaboration) is digitizing lab diagnostics to speed and standardize results across systems.

The University of Utah's Center for Evaluation of Health AI is helping evaluate and safely implement these tools so hospitals and clinics in Utah can capture efficiency, improve risk detection, and keep clinicians focused on patients instead of paperwork; the result is not abstract futurism but faster diagnoses, fewer missed warning signs, and clearer paths for local providers to pilot AI responsibly.

For Orem providers and tech teams, this local ecosystem makes AI a practical lever for better, faster, and more equitable care.

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“We're humbled at the opportunity to build on our existing relationship with Mayo Clinic by working with Mayo Clinic Platform to build an open, AI-enabled workflow platform for digital pathology.”

Selection prioritized real-world impact, safety, and adoptability for Utah providers: prompts and use cases were chosen from where evidence and implementation science converge - favoring workflows that study authors say yield measurable efficiency (for example, AI-assisted systematic literature reviews that can shave 40–60% off review timelines) and those highlighted in translational frameworks for generative AI. Each candidate was screened against four criteria drawn from the literature: strength of evidence and reproducibility, clear human-in-the-loop governance and regulatory alignment, measurable ROI for clinical and operational teams, and feasibility for local pilots in systems like Orem's hospitals and clinics.

Sources guiding this approach include OPEN Health's deep dive on AI for evidence synthesis and analysis and the implementation-science roadmap for generative AI integration, which together emphasize tool validation, bias mitigation, and cross‑disciplinary teams for safe deployment.

Where possible, prompts were matched to upstream studies or commercial use cases with documented benefits (clinical documentation, triage, analytics) and a plan for human oversight, audit trails, and staged operationalization so local teams can test, measure, and scale with confidence - turning promising tech into practical gains for patients and staff.

“Healthcare AI is going to make some SLR work quicker and easier to do because the machine will do it, but the important thing is to have trained people asking the questions and working out what we want the AI to do and then trained people interpreting the results.”

DAX Copilot (Nuance Dragon Ambient eXperience) turns ambient conversation into specialty‑specific draft notes so Utah clinicians spend less time chained to the EHR and more time with patients: the Microsoft Dragon Copilot platform captures multi‑party visits, supports dictation, orders, multilingual encounters, and EHR workflows (Epic among supported systems) while running on a HIPAA‑compliant Azure backbone (Microsoft Dragon Copilot clinical workflow platform).

Vendor reports and early adopters point to tangible savings - roughly seven minutes back per visit and steep drops in documentation time - while a cohort study led by Intermountain Health researchers in Utah showed positive trends in provider engagement without harms to patient safety or documentation quality (Intermountain Health cohort study on Dragon Ambient eXperience).

For Orem practices thinking pragmatically about pilots, DAX's combination of automatic note creation, EHR integration, and evidence of local-system benefits makes it a concrete lever to reduce burnout, increase throughput, and keep clinical judgment squarely human-led.

Feature	Primary Benefit
Automatic clinical documentation	Faster, more consistent notes for clinician review
EHR & order integration	Streamlined workflows and fewer clicks into systems like Epic
Multilingual capture & after‑visit summaries	Improved patient communication and follow‑up

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

Ada's AI symptom‑assessment and care‑navigation tools act as a practical digital front door for Orem clinics by turning patient questions into structured, EHR‑ready handovers, scalable triage, and clear next‑step guidance; Ada supports Epic and other major EMRs, covers 10,000+ symptoms and 3,600+ conditions, and is available in multiple languages and markets (see Ada platform overview Ada platform overview).

Real‑world deployments show measurable patient and clinician benefits: the CUF implementation found 66% of users were more certain what care to seek, 40% reported reduced anxiety, 80% felt better prepared for visits, and - vividly - 53% of assessments happened outside normal clinic hours, helping patients when local offices are closed (read the CUF case summary on Ada digital triage CUF case summary: Ada digital triage).

For Utah systems aiming to ease primary‑care demand, improve virtual uptake, and give clinicians a cleaner, semi‑automated history before encounter, Ada offers a clinically validated, integratable building block - not a replacement for judgment but a smarter front door that redirects low‑acuity traffic and surfaces higher‑risk patients faster.

Metric	Result
Patients more certain of next step	66%
Patients reporting reduced anxiety	40%
Assessments completed outside clinic hours	53%
Clinicians reporting time savings	64%
Regulatory status	Class IIa medical device

“Ada helps patients to access the highest-quality care according to their clinical needs. It smooths the whole journey to care by guiding the patients to take the right steps.” - Dr Micaela Seemann Monteiro, CUF Chief Medical Officer for Digital Transformation

For Orem clinicians looking to bring faster, more precise imaging to the bedside, Butterfly's handheld POCUS platform - now in its iQ3 generation - delivers a compact, AI‑enabled answer: sharp, whole‑body imaging, doubled processing speed, and tools that turn a six‑second clip into actionable data (Auto B‑line Counter, Auto Bladder, NeedleViz and even a cardiac‑output calculator), all in a probe small enough to carry between exam rooms; for procedures it can visualize a needle tip within 1 mm, a vivid detail that can make vascular access and bedside procedures measurably safer and faster.

The iQ3's iQ Slice™ and iQ Fan™ modes create CT‑like coverage at the point of care, while published comparisons rate Butterfly highly among handhelds - useful evidence when health systems plan pilots or fleet deployments in mixed urban‑rural regions like Utah.

Explore the Butterfly iQ3 point‑of‑care ultrasound for features and demos and see the cross‑section comparison of handheld ultrasound devices for independent assessment of image quality and clinical utility.

Feature	Primary Benefit
Auto B‑line Counter / Auto Bladder	Faster, quantitative lung and bladder assessments
NeedleViz™ & Vascular Access presets	Improved procedural success and safety
iQ Slice™ & iQ Fan™	Wide‑angle, multi‑section capture for bedside review
Biplane & Cardiac Output tools	Rapid cardiac assessment for triage and resuscitation

“Butterfly iQ+ is the first where I'm able to have it in my pocket for the entire shift.” - Dr. Haney Mallemat

ClosedLoop - Predictive analytics for patient risk and care management means turning streams of bedside data into timely, interpretable warnings that help clinicians act before crises; recent work underlines how that looks in practice - an FDA‑authorized tool (the Sepsis ImmunoScore) demonstrates regulatory acceptance for AI‑driven risk flags (NEJM AI: Sepsis ImmunoScore), while a real‑time web platform study shows a practical model using a 3‑hour sliding window of eight noninvasive indicators (HR, RR, SpO2, MAP, SBP/DBP, temperature, glucose) with AUROC ≈0.76 and clear Shapley‑based explanations to link predictions to physiology (Real-time sepsis prediction platform study).

For Utah systems and Orem hospitals, the takeaway is concrete: combine explainable models, sensible alert thresholds, and cross‑site data strategies to avoid alarm overload and enable earlier interventions - an approach reinforced by federated‑learning research that flags data heterogeneity as a key barrier to multi‑hospital deployment (PLOS: Federated learning and EHR heterogeneity).

A memorable detail - these models can synthesize brief, 3‑hour physiologic fluctuations into a single risk score with visual explanations - making the “why” behind an alert visible to the bedside team.

Metric / Setting	Result
High‑frequency (with glucose) accuracy	0.70 (95% CI 0.68–0.71)
High‑frequency AUROC	0.76 (95% CI 0.74–0.77)
Routine vital signs (no glucose) accuracy	0.67 (95% CI 0.66–0.69)
Routine vital signs AUROC	0.75 (95% CI 0.74–0.77)

For Orem hospitals and clinics wrestling with prior authorizations and mounting utilization workloads, Xsolis's Dragonfly Utilize turns sprawling charts into actionable, shared decisions by scoring each patient with a Care Level Score (CLS) that compresses vitals, labs, notes, meds, and more into a 0–157 scale - so teams can spot who truly needs inpatient care and who can be managed as observation without endless chart hunts; in national studies Dragonfly delivered up to 83% time savings versus fax reviews, up to 76% versus traditional EMR workflows, and first‑touch determinations roughly 66% of the time, shifting clinicians off routine cases and back toward complex, revenue‑sensitive decisions (see the Dragonfly Utilize overview and the AI‑driven UM summary).

Built to bridge payers and providers, Dragonfly's real‑time dashboards and predictive analytics (over 2.7 billion predictions and a reported 94% CLS accuracy) reduce back‑and‑forth denials, lower observation rates, and make payer‑provider conversations less adversarial - practical outcomes for Utah systems seeking to cut administrative drag while keeping clinical judgment front and center.

Metric	Result
Care Level Score (CLS) range	0–157
Max time savings vs fax	Up to 83%
Max time savings vs EMR	Up to 76%
First‑touch determinations	66%
CLS accuracy / predictions	94% accuracy; 2.7+ billion predictions

“Real‑time data insights are described by XSOLIS customers as akin to ‘having their own personal assistant.'”

Aiddison‑style AI for drug discovery - the class of tools that pair deep learning with virtual libraries and automated labs - can shrink the front end of discovery from months to a matter of weeks by triaging ultra‑large chemical spaces and flagging high‑quality hits for local wet‑lab follow‑up; a prospective validation showed approaches like SpectraView and HydraScreen accelerate hit identification for targets such as IRAK1 (prospective validation in Journal of Cheminformatics), while industry reviews document how ML can screen “tens of billions” of virtual molecules, predict binding and ADME properties, and even propose synthetic routes to de‑risk candidates before a chemist ever touches a flask (how AI can accelerate early drug discovery).

For Orem's biotech and academic labs that juggle limited bench time and regulatory sensitivity, the practical payoff is clear: fewer dead‑end syntheses, faster hit‑to‑lead cycles, and a higher signal‑to‑noise pipeline so local teams can prioritize the experiments that matter most.

“AI will not replace drug discovery scientists, but drug discovery scientists who use AI will replace those who don't”

For Utah hospitals and clinics looking to reclaim clinical time, Moxi hospital delivery robot by Diligent Robotics automates routine, non‑patient‑facing work - running patient supplies, delivering lab specimens and medications, fetching from central supply - without a heavy infrastructure buildout (Moxi hospital delivery robot by Diligent Robotics).

Built for busy, semi‑structured units, Moxi uses social intelligence to open doors and elevators, mobile manipulation to handle drawers and small items, and human‑guided learning so it adapts to local workflows; pilots at U.S. systems show concrete gains (Cedars‑Sinai Moxi pilot results reported faster turnaround with robots answering requests in minutes and nearly 300 miles of walking saved, while Children's Hospital L.A. logged 2,500+ deliveries and roughly 1,620 work hours reclaimed in months) - vivid proof that technologies like this can return a chunk of the 30% of a nurse's day spent “hunting and gathering” back to bedside care and burnout reduction (Cedars‑Sinai Moxi pilot results).

Metric / Setting	Result
Common tasks automated	Deliveries (meds, labs), supplies, PPE, pickups
Reported nurse time reclaimed	Up to ~30% of time spent on fetching tasks
CHLA early performance	2,500+ deliveries; ~132 miles traveled; ~1,620 work hours saved
Cedars‑Sinai pilot	Requests replied within minutes; nearly 300 miles of walking saved
ThedaCare six‑week results	~1,200 deliveries; ~630 active hours; 20‑minute average delivery

“What we have seen is that nurses can spend an astonishing 30% of their time fetching and gathering… We designed Moxi to be a good teammate for nurses and health care workers.”

Operationalizing clinical models in Orem means treating ML like software: a centralized model registry, automated CI/CD pipelines, robust data pipelines, and real‑time monitoring so predictions stay reliable and auditable at scale.

For teams like ClosedLoop or payer partners such as Healthfirst, healthcare MLOps case studies show concrete wins - centralized repos and automated deployments can cut manual ML work by roughly 75% and support a ~70% increase in model workload capacity (Veritis MLOps case study on healthcare frameworks and results), while productionized forecasting pipelines with strict retraining gates have achieved low errors (MAPE ≈5.35%) by promoting only better models into service (SciForce scalable forecasting MLOps case study).

Architectures that pair feature stores, containerized serving (Kubernetes) and hybrid cloud reduce risky, year‑long rollouts to repeatable weeks or months (AIMultiple MLOps deployment benchmarks and vendor examples), making it realistic for Orem hospitals to run governed pilots that automatically swap a model at 3 a.m.

after passing health checks - so clinicians see explainable, audited scores when they need them, not stale research artifacts.

Metric / Outcome	Value / Example	Source
Reduced manual ML intervention	~75% reduction	Veritis MLOps case study on healthcare frameworks
Support for increased ML workloads	~70% increase	Veritis MLOps case study on healthcare frameworks
Forecasting accuracy (regional pipeline)	MAPE ≈ 5.35%	SciForce scalable forecasting MLOps case study
Deployment time reduction (example)	12 months → 30–90 days	AIMultiple MLOps deployment benchmarks and vendor examples

For Orem hospitals and perioperative teams that need crisp situational awareness rather than noisy dashboards, Sickbay offers a practical path: a vendor‑neutral clinical platform that centralizes device waveforms, supports virtual ICU workflows and telemetry at scale, and feeds near real‑time analytics back into care teams so clinicians can spot deterioration earlier and streamline documentation and staffing (Sickbay's platform is built to integrate with existing EHRs and monitor thousands of beds from any PC or mobile device Sickbay platform for centralized device monitoring and virtual ICU).

The result is measurable operational leverage for Utah systems - virtual ops and configurable risk indicators that cut alarm fatigue, telemetry features that enable higher‑ratio monitoring (Sickbay cites pathways to a 1:50 staffing model), and automation that can eliminate manual ECG strip work that once cost hospitals hundreds of thousands annually - plus the ability to spin up a monitored ICU bed in minutes when capacity surges (Intel & MIC Scale to Serve program for rapid monitored ICU scaling).

A vivid payoff: bedside teams and remote vICU nurses have used Sickbay's Decomp Score to catch an imminent code and start compressions - seconds that changed an outcome - making the platform a concrete tool for safer, faster perioperative and real‑time monitoring in Utah.

Feature	Benefit for Orem Hospitals
Centralized monitoring & Telemetry	Near‑real‑time device data across units; supports virtual ops and higher monitoring ratios (1:50)
Analytics & Historical Data	Build, validate, and deploy clinical algorithms; better handoffs and research-ready datasets
Collaborate & Automate	Annotate events, reduce manual ECG/documentation burden (150‑bed example: >$400,000/yr savings)
Rapid scaling to vICU	Turn acute beds into monitored ICU beds in minutes for surge capacity

“During a change of shift, the bedside nurse was giving report; the vICU nurse saw the Decomp Score and saw that the patient was about to code. vICU nurse called to the bedside staff and bedside staff checked a pulse and started chest compressions. Decomp score and vICU nurse were able to act quickly and effectively and resulted in a 'good catch'.” - Registered Nurse - Sickbay Virtual Ops User

Storyline's intelligent behavioral AI platform recasts telehealth and patient engagement into precision care pathways that feel personal, not transactional - built for clinics that want automated triage, seamless messaging, and scalable follow‑up without losing the human touch; the platform's library of clinically validated assessments, automated triggers, and secure telemedicine tools (HIPAA‑grade) make it a practical fit for Utah providers, especially given Storyline's partnerships with research centers like Huntsman Mental Health Institute that underline local relevance (Storyline's intelligent behavioral AI platform).

For Orem teams facing heavy patient outreach and fragmented follow‑up, Storyline promises measurable operational lift - Storyline cites a 4x productivity boost, higher patient recommendation rates, and program revenue gains - while examples from peers show how a robust digital front door (OSF's Clare) can handle 24/7 patient interactions, with 45% of contacts happening outside business hours and multimillion‑dollar ROI, a vivid reminder that smarter automation can free clinicians for high‑value work and keep patients engaged between visits (OSF HealthCare's Clare virtual assistant case).

Outcome	Reported Value
Team productivity (Storyline)	4×
Patient recommendation (Storyline)	97% would recommend
Program revenue increase (Storyline)	17%
After‑hours interactions (OSF Clare)	45% outside business hours
OSF reported first‑year ROI	$2.4M

“Storyline lets us build robust care pathways that extend beyond the clinic to support clinical interventions and patients.” - Benjamin Lewis, MD, Huntsman Mental Health Institute

Getting started in Orem means pairing a clear, small pilot with measurable goals, intentional governance, and practical staff training: begin with one workflow (triage, documentation, or monitoring), define the outcomes you'll track, and build human‑in‑the‑loop checks so clinicians stay in control.

This mirrors national guidance - Manatt's implementation playbook urges a maturity‑model approach and highlights ambient documentation as an early, high‑impact app for reducing clinician burden (Manatt Navigating AI Strategy and Adoption white paper) - and the nursing literature underlines concrete benefits for nurse mental health and patient care quality when tools are thoughtfully integrated (Artificial Intelligence in Nursing review (PMC)).

For local teams that need prompt‑engineering and nontechnical AI skills to run safe pilots, structured training (for example, Nucamp AI Essentials for Work bootcamp - AI Essentials for Work (15 Weeks)) builds the everyday capabilities - writing effective prompts, setting evaluation metrics, and operationalizing outcomes - so gains aren't theoretical but repeatable across clinics.

Start small, measure what matters, and scale only after validating safety, ROI, and staff buy‑in - turning promising models into practical improvements for Utah patients and clinicians.

Bootcamp	Length	Early‑bird Cost	Register
AI Essentials for Work	15 Weeks	$3,582	Register for Nucamp AI Essentials for Work (15‑Week Bootcamp)

“Ambient documentation appears to be health AI's first gamechanging app - delivering a magical experience for physicians while reducing pajama time and risk of burnout.”