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

Fort Collins health systems are pushing AI beyond diagnostics into community care and workflow automation because the local scale makes "keeping people healthy" measurable: UCHealth Poudre Valley Hospital logged 77,333 ER visits and 584,225 outpatient visits as of June 30, 2023, and is investing about $150 million to expand behavioral health - so tools that speed triage, flag sepsis, reduce readmissions, or generate patient-friendly summaries can cut cost and improve access across northern Colorado; see Becker's Hospital Review article on shifting from “healthcare to health” (Becker's Hospital Review: moving from healthcare to health), UCHealth's overview of AI applications like sepsis detection and radiology triage (UCHealth: AI in health care overview), and local program and capacity details for UCHealth Poudre Valley Hospital (UCHealth Poudre Valley Hospital location and services), making Fort Collins a pragmatic testbed for prompts and use cases that prioritize equity and outpatient impact.

Bootcamp	AI Essentials for Work
Length	15 Weeks
Focus	Use AI tools, write effective prompts, apply AI across business functions
Cost (early bird)	$3,582 (Registration: AI Essentials for Work registration)
Syllabus	AI Essentials for Work syllabus

“Our mission is to move from healthcare to health.” - Kevin Unger, President and CEO, UCHealth Poudre Valley Hospital

Selection prioritized prompts and use cases that match Fort Collins' operational needs (reducing unnecessary utilization, speeding triage, supporting outpatient care) and the AI development pipeline best practices: each candidate was evaluated for clinical impact, data availability, validation pathway, and implementation risk following the framework in the Royal College review on “key considerations for the use of artificial intelligence in healthcare” (Royal College AI development pipeline review (AI in healthcare)); local relevance was then scored by potential to lower avoidable visits or readmissions and by alignment with regional workforce support and training (see resources on Fort Collins predictive analytics for patient risk and CSU and Front Range Community College local AI training pathways); priority went to solutions that show clear, measurable downstream savings for northern Colorado clinics and a realistic path from prototype to bedside.

Author	Role
Christopher A Lovejoy	Physician
Anmol Arora	Medical student
Varun Buch	Director of AI development
Ittai Dayan	Lecturer

In Fort Collins clinics and Medicare Advantage practices, the Inferscience HCC Assistant applies NLP to structured and unstructured EHR data to surface missed diagnoses and display HCC code suggestions at the point of care via a simple three‑step workflow - analyze the chart, review suggested HCCs, and submit codes back into the record; learn more about the eClinicalWorks integration on Inferscience's HCC Assistant page (Inferscience HCC Assistant for eClinicalWorks - real-time HCC coding integration) and why this matters for regional risk-based care in northern Colorado (Fort Collins predictive analytics for patient risk and regional care optimization).

The practical payoff is measurable: Inferscience reports average RAF uplifts (near 35%) and chart-level examples where accurate capture of HCCs yields thousands of additional dollars in annual Medicare Advantage funding - reducing coder/provider time while improving reimbursement integrity.

Description	RAF	RAF score	Expected annual expenditure and funding
Male 75 to 79 years old	1.062	$9,611
HCC 86, Acute myocardial infarction	0.282	$2,552
HCC 111, Chronic obstructive pulmonary disease	0.355	$3,213
HCC 137, Renal failure stage IV	0.230	$2,082
Total:	1.929		$17,457

“Inferscience really speeds up the HCC process. It's a great tool for providers as well as coding personnel in catching opportunities throughout the entire chart.” - athenaOne User, Administrative Staff, Multispecialty

A ResNet50V2-based diagnostic imaging workflow adapted for chest X‑rays can be implemented in Fort Collins emergency and outpatient radiology to help radiologists prioritize likely infectious cases: mirror the TVFx study's neural‑network feature‑thresholding approach for COVID‑19 chest X‑ray classification (open access; TVFx - COVID-19 Chest X‑Ray Classification (BMC Medical Imaging, 2023)) and build a prompt that returns a probability score, a localization heatmap, and recommended next steps for the ordering clinician (e.g., expedited PCR, isolation, or radiologist review); see the TVFx article for the underlying feature‑thresholding method (TVFx COVID-19 Chest X‑Ray Classification (BMC Medical Imaging, 2023)).

Local relevance is clear: Fort Collins systems focused on faster triage and outpatient throughput can pair these models with existing predictive analytics and staff training resources to move from prototype to pilot (Fort Collins predictive analytics for patient risk and throughput, Local AI adoption guide for Fort Collins healthcare (2025)); so what: a model tuned for rapid chest X‑ray flagging can shorten the time to clinician action in high‑volume sites like UCHealth Poudre Valley Hospital, improving isolation and downstream care coordination without replacing radiologist oversight.

Field	Value
Title	TVFx – CoVID-19 X-Ray images classification approach using neural networks based feature thresholding technique
Journal / Date	BMC Medical Imaging - 02 October 2023
Authors	Syed Thouheed Ahmed et al.
Article	Volume 23, Article 146 (2023)
Access	Open access - 1482 accesses, 3 citations

ChatGPT can serve as a virtual health assistant in Fort Collins primary care and telemedicine by producing plain‑language visit summaries, drafting short care‑plan checklists, and generating symptom‑guided triage questions that clinicians vet before sending to patients; local implementation pairs clinician prompts and workflows with existing predictive analytics used by area providers - Fort Collins predictive analytics for patient risk (Fort Collins predictive analytics for patient risk).

Operational steps include training staff on prompt design and limits; see CSU TILT Events for AI workshops and trainings (CSU TILT AI workshops and trainings), and applying safety checks informed by recent red‑teaming research coauthored by clinicians experienced in virtual health - the study found roughly one in five prompts produced inappropriate outputs in early models, so deployments must include clinician review and clear escalation paths (see Malathi Srinivasan's virtual health and AI research publications for details) (Malathi Srinivasan - virtual health and AI research publications); the practical payoff for Fort Collins is faster, more patient‑centered communication without replacing clinician judgment.

Study / Source	Prompts Tested	% Inappropriate (GPT‑3.5)	% Became Inappropriate in Updates
Red‑teaming ChatGPT in medicine (NPJ Digital Medicine)	376	20.1%	21.5%

Medtronic's GI Genius™ intelligent endoscopy module brings real‑time computer vision to Fort Collins endoscopy suites, highlighting suspicious polyps so less‑experienced operators can match expert optical diagnosis: a prospective American Journal of Gastroenterology study of GI Genius–assisted colonoscopy found trainees using CADx achieved a negative predictive value (NPV) of 90.2% for diminutive rectosigmoid polyps (experts without AI 90.3%, CADx alone 93.2%), and the vendor reports a 99.7% sensitivity with under 1% false positives for polyp detection - so what: these numbers support PIVI‑2 “diagnose‑and‑leave” workflows that can safely reduce unnecessary resections and speed throughput in high‑volume northern Colorado clinics; see the Medtronic GI Genius product page for device details and the prospective AJG study for clinical performance and trial methods.

Metric	Value
GI Genius sensitivity	99.7% (Medtronic)
False positives	<1% (Medtronic)
NPV, trainees + CADx (diminutive rectosigmoid)	90.2% (AJG study)
NPV, experts without CADx	90.3% (AJG study)
NPV, CADx alone	93.2% (AJG study)

“Go and screen. It's the smartest thing anyone could do - eliminate a problem before it occurs.” - Patrick, colon cancer survivor

A predictive readmission prompt for Fort Collins hospitals should combine traditional scores with machine‑learned features to flag patients for targeted transitional care -

Evaluate 30‑day readmission risk using EHR diagnoses, recent ED visits, length of stay, med list changes, and social‑determinant flags; return a risk score, top 3 contributing factors, and recommended actions (med reconciliation, 7‑day follow‑up, home‑health referral).

Evidence shows machine‑learned models can outperform LACE (AUC ~0.66) with test AUCs near 0.83, improving discrimination between high‑ and low‑risk discharges and helping clinics prioritize scarce post‑discharge visits - see the BMC study on machine‑learned readmission models (BMC study on machine‑learned readmission models).

Combine clinical scores (LACE/HOSPITAL/PREADM) with tree‑based or ensemble methods and operational rules (follow‑up within 7 days, med reconciliation) to translate better AUC into fewer avoidable returns; see a practical review of readmission calculations and implementation (Practical review of readmission calculations and implementation) and an ensemble approach achieving AUC ≈0.879 for reference (Joint ensemble model achieving AUC 0.879).

Local caveats: address data imbalance, cross‑institution readmissions, and model interpretability before clinical rollout so Fort Collins systems convert accuracy gains into measurable reductions in return visits.

Model / Tool	Reported AUC
LACE index (baseline)	≈ 0.66 ± 0.0064
Machine‑learned model (test set)	≈ 0.83 ± 0.0045
Joint ensemble approach	≈ 0.879

Wearables such as Apple Watch ECGs and wrist‑based sensors are practical tools for Fort Collins chronic disease management because they deliver continuous, actionable biometric data to clinicians and care teams - accelerating detection of arrhythmias, prompting timely follow‑up, and supporting remote patient monitoring across a geographically dispersed population; see the practical overview of wearable integration into telehealth for how real‑time streams augment clinical workflows (Telehealth integration of wearable devices: practical overview) and local adoption guidance for deploying these devices in northern Colorado care pathways (Fort Collins AI adoption guide for healthcare (2025)).

The Stanford/Apple Heart Study metrics and follow‑up behavior show why this matters in practice: consumer wearables flagged pulses that had a 71% positive predictive value and, importantly, 57% of those alerted sought medical attention - turning a watch notification into a prioritized clinic touchpoint; complementary evaluations of single‑lead transmitters and Apple Watch monitoring explore the devices' role in home heart‑failure care (Evaluation of Apple Watch heart-rate monitoring for heart-failure management).

For Fort Collins practices the immediate payoff is clearer triage and targeted outreach for high‑risk patients without adding routine clinic visits for low‑risk cohorts.

Metric	Value
Apple Watch positive predictive value (Stanford/Apple Heart Study)	71%
Of positive tachograms confirmed as AFib	84%
% who sought medical attention after irregular pulse notification	57%
Fall‑prediction study accuracy (University of Illinois)	73.7% accuracy, 81.1% precision

“The performance and accuracy we observed in this study provides important information as we seek to understand the potential impact of wearable technology on the health system.” - Marco Perez, MD, Stanford School of Medicine

A Kvertus‑style "Time‑to‑Lead" prompt for Fort Collins biotech and translational labs asks multimodal AI to ingest proteomics/mass‑spec signatures, chemical libraries, and clinical annotations, then prioritize candidate chemotypes by predicted target engagement and translational tractability - accelerating the handoff from discovery to first‑in‑class leads while keeping clinicians and regulatory pathways in the loop; practical building blocks and why they matter are laid out in work on integrating proteomics into drug development (Translating proteomics into drug development - Nautilus Bio), AI that interprets mass‑spec and natural product space (Enveda mass spectrometry and AI coverage - SynBioBeta), and the case for multimodal models that combine molecular, structural and clinical data to raise confidence in leads (Multimodal AI in drug discovery - Drug Target Review).

So what for northern Colorado: local teams can pair CSU labs, small biotechs, and AI startups to prioritize fewer, better‑characterized candidates for synthesis and testing - Enveda's platform, for example, has characterized >1 million natural compounds in four years, showing how scaleable data curation speeds decision‑ready hypotheses for downstream validation.

Startup	Notable Focus
Exscientia	AI‑driven molecule design (in‑silico lead optimization)
Recursion	AI + experimental biology for cellular phenotypes
Insilico Medicine	Generative AI for cancer and age‑related disease targets

“Some of the world's greatest pharmaceutical breakthroughs derive from just 0.1% of nature's chemistry.” - Viswa Colluru, Enveda

Use Runway ML or Midjourney to generate clear, culturally tailored patient‑education visuals - stepwise diagrams, large‑type infographics, and iconography for postop care - that are paired with AI‑simplified copy to improve comprehension across Fort Collins' diverse outpatient population; an AI rewrite study of cataract surgery materials showed average reading levels fell from 12th grade to 8th grade after prompting the model to simplify text, bringing content much closer to the US adult average and the AMA's 6th‑grade target (AAO article: AI improves readability of cataract patient education), so visuals that reinforce short, plain‑language prompts can make consent, medication, and follow‑up instructions easier to scan during brief clinic visits.

For pragmatic local adoption, pair image prompts and simplified copy with Fort Collins implementation guides and training resources to ensure clinician review and alignment with local workflows (Fort Collins 2025 AI adoption guide for healthcare).

Metric	Value
Average pre‑AI reading level (English)	12th grade
Average post‑AI reading level (English)	8th grade
Spanish post‑AI reading level (approx.)	7th grade
AMA recommended target	~6th grade

Fort Collins clinics can cut billing cycle friction and no‑show churn by embedding Inferscience-style administrative automation - EHR‑integrated HCC and claims assistants that auto‑extract diagnoses, run gap analyses, and suggest billing edits at the point of care - so schedulers and coders spend less time on manual lookups and more on exception review; Inferscience's market literature shows AI in administration can deliver dramatic wins (document processing time down ~91% and routine admin time trimmed ~20%) and supports SOC2/HIPAA workflows that matter for regional safety net systems (AI benefits for healthcare administration - Inferscience, AI in healthcare administration: benefits and challenges - Inferscience); so what: that efficiency converts into faster claims, fewer denials, and measurable capacity for high‑volume sites across northern Colorado to reallocate staff toward patient outreach and care coordination.

Metric / Capability	Reported Result
Document processing time	~91% decrease
Time spent on administrative tasks	~20% reduction
Projected U.S. savings (admin automation)	Up to $150 billion by 2026
Compliance credentials	SOC2 Type II, HIPAA (vendor-reported)

“Cybercrime is the single largest danger to every business on the planet.” - Ginni Rometty

A Claude‑style clinical summarization and research‑support prompt for Fort Collins clinics can ingest an encounter's unstructured notes and return a one‑paragraph clinician summary (problem list, recent med changes, top 3 follow‑ups), a two‑line patient‑facing summary, and a short PICO‑style research query to accelerate evidence lookup - paired with local predictive scores so high‑need patients flagged by Fort Collins predictive analytics for patient risk (healthcare) get prioritized for outreach.

Operationalize with clinician review and staff training using regional programs and CSU and Front Range Community College local training resources for clinicians, and align governance with the Fort Collins AI adoption guide for healthcare (2025) to manage risk and validation pathways.

So what: a vetted summarization prompt turns long visit notes into an actionable plan and a short patient summary clinicians can trust, shortening time‑to‑action for high‑risk patients without adding charting burden.

Getting started with AI in Fort Collins means pairing clear, short-term goals (faster triage, targeted outreach, fewer avoidable returns) with concrete safeguards: design pilots that anticipate scale, secure a clinical champion, require external validation and privacy reviews, and lock in post-pilot “Month 7” adoption steps before you start - practical lessons drawn from local UCHealth deployments and recent critiques of pilot‑first strategies.

Use UCHealth's AI overview to align use cases with existing EHR and data‑warehouse assets (UCHealth AI in health care overview), and invest in staff prompt-writing and governance training so outputs are clinician‑vetted; a ready option is Nucamp's AI Essentials for Work (15 weeks, syllabus and enrollment info at AI Essentials for Work syllabus and enrollment) which teaches prompt design, risk controls, and practical rollout skills.

The payoff for northern Colorado is tangible: validated pilots that cut triage time, reduce readmissions, and free clinical time for outreach instead of administrative catch‑up.

Program	AI Essentials for Work
Length	15 Weeks
Cost (early bird)	$3,582
Registration	Register for AI Essentials for Work (Nucamp)

“Our mission is to move from healthcare to health.” - Kevin Unger, President and CEO, UCHealth Poudre Valley Hospital

Contact: Ludo Fourrage, CEO, Nucamp - info@nucamp.co