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

Boulder is an ideal testing ground for healthcare AI because local hospitals, startups, and public health systems can combine strong regional talent with pressing clinical needs - from faster radiology reads to rural remote monitoring - at a moment when industry momentum and executive demand are high.

National trends (see the CDA‑AMC 2025 Watch List: AI in Health Care from NCBI) highlight practical, near‑term tools (notetaking, diagnostic imaging, remote monitoring) while the 2025 AI Index Report - Stanford HAI documents U.S. leadership, rapid investment, and falling model costs that lower entry barriers for Colorado innovators; local case studies show imaging AI already speeding diagnoses in Boulder radiology departments (Imaging AI speeding diagnostics in Boulder radiology).

For Colorado professionals entering this space, Nucamp's AI Essentials for Work bootcamp - 15-week practical AI prompt writing and workplace adoption course - is a relevant option.

Technology	Why it matters
AI notetaking	reduces clinician admin load
Disease detection	faster, earlier diagnosis
Treatment optimization	personalized care plans
Remote monitoring	better rural access
Training tools	scale workforce skills

We used a pragmatic, evidence‑forward methodology tailored to Colorado's clinical landscape to choose the ten prompts and use cases: first, we prioritized high clinical impact and operational feasibility in Boulder-area settings (radiology triage, EHR-integrated visit summarization, telehealth triage and rural monitoring) and screened candidates against value‑sensitive design and ethics criteria from the health AI literature to reduce bias and protect patient rights (NCBI article: Value Sensitive Design in Applied Health AI); second, we mapped each use case to information‑systems research priorities (responsible AI, healthcare IS, data analytics) to ensure technical maturity and interoperability with local EHRs and cloud services (ICIS 2025 track descriptions: Healthcare and AI); third, we required demonstrable real‑world evidence or clear HEOR value pathways (reduced readmissions, faster diagnoses, staffing efficiencies) before inclusion, following best practices showcased at major HEOR forums (ISPOR 2023 program: AI, RWE, and HEOR sessions).

Final prompts were refined with local clinician feedback, privacy checks (HIPAA), and simple metrics for pilot evaluation in Boulder clinics.

Google's prompt to “Analyze retinal images for diabetic retinopathy and flag urgent cases” maps directly to work showing deep learning can screen retinal photos at specialist‑level accuracy and scale - a model trained on roughly 128,000 images that the Google team reported performs

performs on par with ophthalmologists.

Making it promising for primary‑care screening and urgent referral workflows in Colorado communities.

Local Boulder clinics can combine those models with cloud deployment patterns (FHIR → BigQuery) and HIPAA‑aware pipelines to auto‑triage patients who need urgent ophthalmology follow‑up, reduce waits in safety‑net settings, and route high‑risk cases for same‑day care.

Practical, peer‑reviewed evaluations have also built cloud‑based DR screening platforms for large‑scale primary care use, showing feasibility outside specialist centers.

For Colorado implementers, start with modest pilots integrating model outputs into EHR queues and clinician review, monitor sensitivity/specificity, and use Google's cloud tutorials to standardize FHIR streaming and logging for audits.

Metric	Value
Training images	≈128,000 (Google study)
Reported performance	Comparable to ophthalmologists
Cloud integration	FHIR → BigQuery pipelines

Learn more from the original Google diabetic retinopathy research on detecting diabetic eye disease, the Google Cloud Healthcare API tutorials for FHIR to BigQuery integration, and a published Automated diabetic retinopathy cloud screening study (PMC) to design a Boulder‑friendly pilot.

NVIDIA's GPU‑accelerated AI stack (DGX, CUDA, TensorRT, Triton) is a practical foundation for the prompt “Accelerate MRI reconstruction and prioritize scans with suspected tumors,” enabling faster, lower‑noise reconstructions and real‑time 4D visualization that make rapid tumor triage feasible for Colorado health systems; Boulder hospitals and imaging startups can deploy MONAI‑trained models on cloud or edge GPUs to shorten scan‑to‑read times and feed prioritized cases into EHR triage queues.

Evidence from production deployments shows dramatic throughput and reproducibility gains: federated training and containerized workloads let institutions iterate quickly while protecting local data.

Key local steps include piloting MONAI inference on on‑prem or cloud GPUs, integrating model outputs into PACS/EHR workflows for radiologist review, and using federated learning to pool regional data without moving PHI. Practical results from large academic deployments support this approach:

“Using the MONAI imaging framework integrated into Flywheel, with training done on NVIDIA DGX BasePOD, we can apply our state‑of‑the‑art research tools to every single abdominal CT we've ever performed at UW–Madison since 2004. Ten thousand cases alone used to take six to eight months just to get through, and we can now process them in a day.” - John Garrett, PhD

Metric	Value
GPU platform / frameworks	NVIDIA DGX, MONAI, Triton
Throughput	>1,000,000 images/day (reported)
Case processing improvement	10,000 cases: months → 1 day

For technical background on NVIDIA's medical imaging solutions see the NVIDIA AI‑powered medical imaging platform, the UW–Madison radiology case study for deployment outcomes, and a survey of GPU‑based MRI reconstruction techniques to guide implementation in Boulder.

Microsoft's Dragon Copilot enables the prompt “Summarize clinic visit audio and generate an EHR‑friendly problem list,” a practical tool Boulder clinics can pilot to cut clinician documentation time and populate structured problem lists for Epic or other local EHRs.

Dragon captures ambient or dictated encounter audio, generates transcripts, and responds to natural‑language prompts such as “Summarize note” or “Draft after visit summary”; admins can curate reusable prompts centrally to enforce consistent wording across departments.

Practical deployment notes from Microsoft stress that output quality depends on transcript fidelity and clinician verbalization practices, and the product is positioned with healthcare safeguards and EHR integrations to support orders and after‑visit summaries.

For Colorado implementers, start small: create an admin‑managed prompt that extracts chief complaints and diagnostic impressions, map outputs to your problem‑list fields, measure time saved and accuracy, and iterate.

“The DAX technology has allowed them to listen to the patient. And the more that the clinician listens to the patient, the more likely that the care plan will meet the needs of the patient.”

Feature	Value
Summarize notes	Patient-friendly after‑visit summaries
Prompt library	Org-wide consistency and reuse
US availability	May 1, 2025 (initial rollout)

Learn more from the Microsoft Dragon Copilot clinical workflow overview and features (Microsoft Dragon Copilot clinical workflow overview), the sample Dragon Copilot clinical prompts guide for deployment and prompt examples (Dragon Copilot sample clinical prompts guide), and HIMSS coverage of the Dragon Copilot clinical productivity launch for additional deployment considerations and quotes (HIMSS25 coverage of Dragon Copilot clinical productivity launch).

For Boulder teams implementing the prompt “Run genomics variant calling and recommend candidate targets for oncology,” AWS provides a pragmatic, HIPAA‑aware path from raw reads to actionable target suggestions - use AWS HealthOmics to run PacBio HiFi WGS pipelines at scale, Amazon Omics/Step Functions to automate ingestion and variant stores for downstream queries, and Illumina DRAGEN on AWS for low‑latency, high‑accuracy secondary analysis depending on license and speed needs.

Benchmarks show a production configuration that balances price and time (GPU‑accelerated DeepVariant on omics.g5 with NVIDIA A10G completed the HiFi WGS pipeline in ~8.67 hours with run_analyzer suggesting an optimal per‑sample compute cost near $19.15), and AWS patterns include KMS, IAM, CloudWatch logging and BAA‑compatible controls suitable for Colorado clinical pilots.

Local recommendations for Boulder: start with a 5–10 sample clinical pilot integrated into your EHR queue, use Amazon Omics workflows and HealthOmics run_analyzer to tune instance types, enforce VPC/private S3 and KMS keys for data residency, and partner with a local academic lab or Tempus/clinical genomics provider for validation and IRB oversight.

Key references and implementation guides:

Instance / GPU	Runtime	Reported/Optimal Cost (USD)
omics.g5.2xlarge (NVIDIA A10G)	8.67 hours	$21.26 reported / $19.15 optimal

AWS HealthOmics PacBio HiFi WGS benchmark guide for variant calling on AWS, Automated Amazon Omics end-to-end genomics data storage and analysis guide, and Illumina DRAGEN on AWS guide for fast variant calling.

Philips offers a practical stack for the prompt “Use imaging AI to support radiation therapy planning for breast cancer” that Boulder clinics can pilot to shorten time‑to‑treatment and improve planning accuracy: MR‑based tools (MRCAT) generate continuous Hounsfield Units from a single MR for contouring and dose calculation, SmartSpeed and SmartExam speed and standardize MR acquisition to reduce motion and improve lesion conspicuity, and IntelliSpace Radiation Oncology harmonizes protocol‑driven workflows so multidisciplinary teams can move from referral to treatment more predictably.

All Philips features emphasize clinician oversight and clinical validation; local teams in Boulder should pilot MR‑RT workflows with small cohorts, integrate outputs into PACS/EHR queues for radiologist and physicist review, and use vendor practice‑management support for training and QA. For technical background and product details see the Philips AI-enabled imaging solutions overview, the Philips IntelliSpace Radiation Oncology workflow platform, and the Toolbox Consortium review on AI in breast cancer radiotherapy to align pilots with emerging clinical consensus.

Feature	Benefit / Note
MRCAT (MR‑RT)	MR‑only planning: generates HU for dose calculation
SmartSpeed / SmartExam	Faster, higher‑resolution MR (up to ~3× speed; up to 65% resolution gain)
IntelliSpace Radiation Oncology	Protocol-driven orchestration to reduce referral‑to‑treatment time

Siemens Healthineers' CT platforms bring AI-assisted reconstruction and on‑board automation that Boulder hospitals can leverage to accelerate stroke triage - shortening scan‑to‑decision times for CT angiography and perfusion studies essential to thrombolysis and thrombectomy workflows.

Features like fast rotation times, metal‑artifact reduction, automated contouring and on‑scanner decision support (DirectORGANS/Direct i4D/DirectDensity) reduce manual reconstructions and speed review, enabling ED/PACS queues to receive prioritized alerts for suspected large‑vessel occlusion; local pilots should pair edge or cloud inference with EHR/PACS alerting, clinician verification, and QA metrics (door‑to‑needle and imaging turnaround).

“Working with our new CT, the SOMATOM go.Sim, fascinates me, because it does everything in the background. It is easy to use and offers the right tools for Radiation Therapy.”

Metric	Value
DirectORGANS time saving	≈70% per OAR (autocontouring)
Rotation time (CT)	0.351 / 0.5 / 1.0 s

For Boulder teams planning pilots, see the Siemens SOMATOM go.Sim CT simulator details, SOMATOM go.Open Pro CT simulator features, and broader Siemens AI reconstruction whitepapers to map device capabilities to local stroke pathways and HIPAA‑compliant deployment patterns.

Tempus' stack makes the prompt “Integrate EHR, labs, and genomics to propose a personalized oncology treatment plan” practical for Boulder clinics by delivering bi‑directional ordering, discrete genomic results into Epic's genomics module, and AI‑enabled reporting that combines molecular data with guideline‑linked therapy options; Colorado teams can pilot with a small cohort (5–20 patients) to validate workflows, map outputs into local Epic/OncoEMR problem lists, and use Tempus Hub for clinician access and trial matching.

Start by pairing Tempus' EHR connectors with a validated sequencing panel and a defined clinical pathway (e.g., stage IV NSCLC or metastatic breast cancer), run parallel clinician reviews for safety, and measure change in time‑to‑treatment and trial enrollment.

Tempus resources emphasize structured genomic data and real‑world evidence to power precision decisions - use their integration playbooks to ensure HL7/FHIR mapping, discrete variant coding, and lab‑to‑EHR audit trails.

“The integration of Epic and Tempus is a major advance in caring for patients with cancer. Until now in most institutions across the country, cancer genomic testing is done outside of their EHR platform.”

Tempus capability	Metric / reach
Direct data connections	600+
Connected institutions	3,000+
De‑identified research records	~8,000,000

For vendor details and integration guides see Tempus' EHR integration overview, the Tempus genomic profiling platform, and the Tempus life‑sciences data platform to design a Boulder‑friendly pilot and compliance plan.

For Boulder clinics aiming to “Create a HIPAA‑compliant chatbot for virtual triage and urgent mental‑health referrals,” prioritize patient safety, clear escalation paths, and vendor accountability: use RAG to ground responses in vetted sources, de‑identify PHI or sign BAAs, and require automatic escalation to licensed clinicians and local crisis resources for high‑risk signals.

Teladoc's scale and mental‑health focus make it a practical model for partnership or benchmarking; see the Teladoc virtual care overview for service scope and provider integration: Teladoc virtual care overview and provider integration.

Development best practices - conversation design, transcript fidelity, hybrid human‑in‑the‑loop workflows, and on‑prem or BAA‑backed hosting - are detailed in medical chatbot development and HIPAA guidance: HHS guidance for HIPAA compliance and health IT best practices.

Build a small Boulder pilot that integrates with local EHR queues, measures time‑to‑referral and ED diversion, offers bilingual support, and logs auditable decisions per HIPAA‑aligned engineering standards (encryption, RBAC, MFA); for implementation patterns and compliance controls, consult HIPAA security rule software development guidance: HHS HIPAA Security Rule software development best practices.

Teladoc metric	Value
U.S. members	~100 million
Providers & therapists	40,000+
Hospital partnerships	600+ U.S. hospitals

“[Chatbots] are automated systems which replicate users behavior on one side of the chatting communication.”

Start with a controlled pilot in Boulder (primary care + behavioral health), enforce human escalation rules, and evaluate safety, referral accuracy, and patient acceptance before scaling.

DataRobot's prompt for predicting 30‑day readmission risk is a pragmatic AutoML workflow Boulder hospitals can pilot to prioritize patients and rank interventions (in‑person discharge review, early outpatient follow‑up, or care‑manager outreach) before discharge; the approach ingests EHR/admissions/medication/lab features, outputs per‑patient probabilities with interpretable XEMP explanations, and supports EMR integration for daily batch scoring and clinician triage.

A Denver‑area or Boulder health system could run a 5–10 patient pilot (or a 70k diabetic cohort analogue) to validate thresholds, measure time‑to‑follow‑up, and estimate ROI using the readmission cost formula DataRobot provides; common safeguards include excluding post‑discharge signals to avoid target leakage and auditing error rates across race/age/gender for fairness.

“[DataRobot] easily outperformed the LACE model with a 5% reduction in readmissions in the first quarter of the year.”

Metric	Value
Sample dataset	70,000 diabetic inpatient visits
Raw source scale	74M visits / 18M patients
Top predictive features	Past inpatient visits; discharge disposition; medical specialty
Partial dependence	0→2 visits: 37%→53%; >4 visits ≈59%

For technical details and the downloadable Jupyter notebook see the DataRobot 30‑day readmission notebook (DataRobot 30-day readmission notebook and tutorial), the DataRobot readmissions business accelerator for deployment and ROI guidance (DataRobot readmissions business accelerator for deployment and ROI), and DataRobot prediction explanations (XEMP) for clinician‑facing interpretability (DataRobot XEMP prediction explanations for clinician interpretability).

For Boulder hospitals and clinics, Chooch AI offers practical computer‑vision prompts to

Detect patient falls and PPE compliance in hospital video feeds

by running pre‑trained models on existing cameras at the edge, sending locationed alerts to staff, and integrating with nurse‑call or EHR queues for fast response; these capabilities let smaller Colorado systems pilot low‑cost safety monitoring without new wearable infrastructure.

Chooch combines on‑device Vision AI for PPE detection (Chooch Vision AI PPE detection) with Autonomous AI for patient behavior and fall detection (Chooch Autonomous AI remote patient monitoring), and uses generative visual AI to synthesize rare fall scenarios for safer model training (Chooch generative AI for risk detection).

Recommended Boulder pilot: start with 5–10 rooms, verify HIPAA‑compliant on‑prem processing, measure door‑to‑response and false alert rates, and iterate with clinical staff to tune thresholds for local workflows.

Metric	Value
Detection latency	as little as 0.2 ms (Vision AI claim)
Typical pilot deployment	pre‑trained models: days; clinical pilot <90 days
Reported clinical outcomes	≈50% faster response; 40% fewer incidents; 60% reduction in false alerts

Sopris Health, a Denver‑based startup, targets the prompt "Auto‑generate clinical documentation summaries and billing codes from visit audio" with its Sopris Assistant mobile AI chat - a workflow‑driven scribe that walks clinicians through visit types, asks specialty‑specific follow‑ups, and imports reviewed notes into the EHR in 45 seconds or less, a model informed by “tens of thousands” of prior visits and on‑site clinician feedback that reduced after‑hours note work for early customers; Boulder practices can pilot it to cut documentation time and surface candidate CPT/ICD suggestions when paired with coding models.

Practical deployment in Colorado requires HIPAA‑aligned hosting, a BAA, clinician final review gates, and EHR mapping (Epic/FHIR) for audit trails; vendors like Corti show how real‑time templates, fact extraction, and code endpoints can be combined with scribe audio to produce structured outputs for problem lists and billing.

For technical context on speech‑to‑text accuracy and deployment tradeoffs in clinical settings, see industry analyses on STT performance and workflow impact.

“We served tens of thousands of visits with our customers and have worked with several large institutions … to really get a deep understanding of how they do documentation,”

Metric	Value
EHR import time	≤45 seconds
Operational experience	tens of thousands of visits
Availability	U.S. providers on iOS

Learn more: Sopris Assistant AI clinical documentation launch coverage, Corti API guide to clinical templates and coding for real‑time documentation, and a sector overview on how speech‑to‑text transformed healthcare and medical transcription.

For beginners in Boulder exploring AI prompts in healthcare, start local and practical: attend community events to learn and network (see the Boulder Startup Week 2025 schedule and events and join hands‑on sessions), sign up for student hackathons like the T9Hacks Health‑a‑thon registration and hackathon details to build an “AI in healthcare” prototype, and run very small, measurable pilots (e.g., 5–20 patients for genomics or 5–10 rooms for video monitoring) that prioritize HIPAA controls, EHR mapping, and clinician review.

Focus first on one clear outcome (reduced documentation time, faster triage, or higher trial‑matching rates), instrument simple metrics, and iterate with clinicians and IT for safety and fairness.

A practical training path for non‑technical professionals is Nucamp's AI Essentials for Work to learn prompt writing, deployment basics, and pilot design before partnering with vendors or hospital IT.

performs on par with ophthalmologists.

Below is a quick Nucamp summary to get started:

Program	Length	Early bird cost	Register
AI Essentials for Work	15 weeks	$3,582	Nucamp AI Essentials for Work registration

Use local events to validate ideas, protect data, and scale what improves patient care.