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

Madison matters for AI in healthcare because it sits at the intersection of powerful local tech and active clinical scrutiny: Epic's Verona campus shapes EHR tools used nationwide even as reporting highlights risks to staffing and patient safety, while UW–Madison researchers and clinicians are building interpretable models and piloting AI that augments care - not replaces it; see reporting on Epic's impact and critique (Tone Madison report on Epic's impact in Wisconsin healthcare) and a faculty Q&A on interpretable clinical AI from UW–Madison (UW–Madison faculty Q&A on interpretable clinical AI).

Local pilots - like UW Health's expansion of AI notetaking - show tangible time savings for providers, and upskilling programs such as Nucamp's 15-week AI Essentials for Work (Nucamp AI Essentials for Work syllabus (15-week bootcamp)) offer a practical path for Madison teams to write safe prompts, govern tools, and keep clinicians central to patient care.

Bootcamp	Length	Cost (early bird)	Registration
AI Essentials for Work	15 Weeks	$3,582	Register for Nucamp AI Essentials for Work (15-week bootcamp)

“We're one of the four systems that are piloting this.”

The top 10 prompts and use cases were selected through a practical, source-driven scoring process that weighted clinical fit, measurable operational impact, vendor governance, and ethical risk: each candidate prompt needed published guidance on safe deployment and monitoring (see Stanford guidance on implementing AI safely and ethically), evidence it could integrate into clinician workflows and save time - especially for routine tasks like ICD‑10 coding or documentation - and clear vendor accountability for data and maintenance (drawn from AHIMA's “15 Smart Questions to Ask Healthcare AI Vendors”); finally, local alignment with UW‑Health and Epic guidelines was required to ensure Madison teams could pilot without conflicting with institutional policies.

Prompts that passed all filters emphasized human oversight, bias mitigation, and auditable outcomes so the next step after selection is always a small, measurable pilot rather than broad rollout - this ensured the list favors prompts that free clinician time while preserving safety and compliance.

Method step	Rationale / source
Clinical fit & deployment	Stanford - focus on workflow, monitoring, real-world evaluation
Governance & vendor accountability	AHIMA - questions on BAAs, privacy, and vendor responsibilities
Local policy alignment	UW‑Health/Epic guidelines - ensure institutional compliance in Madison

“The greatest benefits are related to the work that's required for a lot of administrative repetitive tasks. There could be streamlined processes in place where AI can alleviate some of the workload and pressure regarding completing those tasks,”

Nuance DAX Copilot (Dragon Ambient eXperience) turns multiparty patient–clinician conversations into specialty‑specific notes at the point of care, using a mobile app and AI models trained on millions of encounters; it integrates with EHRs - including direct order and note workflows for Epic - runs on the Microsoft Cloud/Azure platform, and is designed to reduce administrative burden so clinicians can spend more time with patients.

Vendor reports and demos cite roughly 7 minutes saved per encounter and up to a 50% reduction in documentation time, with improvements in note quality, referral letters, and after‑visit summaries; a peer‑reviewed cohort study of Nuance DAX also reported positive trends in provider engagement without compromising patient safety.

For Madison health teams anchored by Epic's Verona campus and UW pilots, DAX Copilot offers a measurable path to cut clerical time, improve throughput, and deliver consistent, auditable clinical notes while preserving enterprise security and governance - see Microsoft's Dragon Copilot overview for clinical documentation, the Nuance DAX peer-reviewed cohort study, and the vendor time‑savings implementation data from Nuance for implementation details.

Feature	What it delivers
Automatic clinical documentation	Multi‑party ambient capture → specialty notes for review
EHR integration (Epic support)	Direct note delivery, orders, and referral letter workflows
Time savings & quality	~7 minutes saved per encounter; up to 50% less documentation time

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

Ada Health's symptom checker can act as a 24/7 first‑line triage layer for Madison clinics by asking adaptive, patient‑specific questions (age, gender, meds, allergies), explaining medical concepts in plain language, and tracking symptom progression so follow‑ups are easier to monitor; see the Ada Health symptom checker SmythOS writeup (Ada Health symptom checker SmythOS overview) and research on conversational symptom‑checker design (Design considerations for AI‑enabled symptom checkers (PMC study)).

Realistic use in Madison - where nurse lines and urgent‑care capacity are finite - means Ada can filter low‑acuity questions, direct patients to self‑care or telehealth, and free clinicians for higher‑risk visits, but teams should heed documented limits (rigid inputs, incomplete history capture) and embed clear escalation paths and human oversight as recommended in broader chatbot triage guidance (Chatbot triage and routing guidance for healthcare).

Strengths	Limitations
Tailored questioning, plain‑language explanations, symptom tracking	Rigid symptom input, variable history capture, not a substitute for clinician judgment

“Healthcare chatbots are like having a knowledgeable, tireless medical assistant in your pocket, ready to help at a moment's notice.”

For Madison health systems facing stretched urgent‑care capacity and a large rural catchment, telehealth augmentation that combines personalized, AI‑driven triage, continuous remote monitoring, and conversational patient engagement can expand access while keeping clinicians in control; research shows AI can tailor care plans from EHR and wearable data, predict deterioration for early intervention, and make virtual assistants useful for scheduling and follow‑ups (AI for remote patient care: predictive monitoring and AI-driven triage, which outlines remote monitoring and predictive analytics).

Platforms that prioritize personalization and usable workflows - matching the approaches described in industry guides on AI‑driven telehealth personalization - improve adherence and patient satisfaction by delivering recommendations matched to history and preferences (AI-driven personalization in telehealth services and patient matching).

Given telehealth adoption has remained far above pre‑pandemic norms, Madison teams piloting narrative‑focused engagement tools (e.g., storyline‑style visit summaries plus clear escalation paths) can measurably reduce no‑shows and unnecessary in‑person visits - but must bake in bias checks, consent, and interoperability from day one (Telemedicine integration with AI for predictive healthcare and implementation best practices); the practical payoff is clearer access for rural Wisconsin patients and earlier interventions that lower avoidable admissions.

Aiddison from Merck and predictive platforms like BioMorph give Madison's academic labs and biotech startups a practical way to shorten discovery cycles: AIDDISON's cloud‑native SaaS blends ligand‑ and structure‑based workflows, de‑novo generative design, molecular docking, ADMET prediction and ultra‑large searches (the platform can search more than 60 billion virtual and known molecules) to produce prioritized candidates for synthesis, while BioMorph's cell‑response predictive analytics helps flag compounds likely to move the needle on cell health far faster than manual review - together they can reduce reagent use and wet‑lab rounds so local teams can iterate faster and cost‑effectively; see the Sigma‑Aldrich AIDDISON product overview for platform features (AIDDISON AI drug discovery platform product overview by Sigma‑Aldrich), the peer‑reviewed article describing the SaaS AI/ML and CADD methodology (AIDDISON: Empowering Drug Discovery - peer‑reviewed J Chem Inf Model article), and a TechTarget feature highlighting BioMorph and AIDDISON's impact on molecule selection speed (Top AI tools in healthcare: coverage of BioMorph and AIDDISON on TechTarget).

“AIDDISON™ is an integrated and easy-to-use tool for lead identification that brings together a suite of tools for modeling, docking and scoring molecules.”

Clinical and operational analytics turn EHR data into actionable risk scores that Madison teams can embed directly into Epic-based workflows to prioritize patients, reduce avoidable admissions, and tighten capacity management; predictive tools that push near-real-time readmission probabilities into the point-of-care huddle have delivered measurable gains - Children's Hospital of Orange County lowered seven-day readmissions from ~3.8% to ~3.3% after operationalizing a near-real-time score and moving development to the cloud (HIMSS Davies case study on readmission risk predictors).

Clinical leaders should expect modest but meaningful improvements rather than miracles: population-level readmission risk still hovers near national rates reported by CMS, and model discriminations in recent research range from AUROC ~0.62 for early, nursing-inclusive models to ~0.64 when full-stay data are available - signals that analytics enable earlier, targeted interventions (medication reconciliation, home supports, timely follow-up) rather than perfect prediction (JMIR study on nursing-data readmission prediction models).

For Madison health systems and UW-affiliated clinics, the practical payoff is operational: integrate validated risk scores into discharge workflows, assign case managers to high-risk flags, and track small percentage drops in readmissions as real cost and capacity wins while continually auditing for data quality and bias (MGH IHP: From Data to Decisions - leveraging predictive analytics to transform hospital readmissions).

Metric	Example / Value
Seven-day readmission change (CHOC)	~3.8% → ~3.3% after near-real-time scoring
Early prediction AUROC (nursing data)	~0.62 (early) - ~0.64 (entire stay)

Generative models such as ChatGPT and Anthropic's Claude are already practical tools for clinical writing and workflow automation in Madison: when fed EHR notes, visit audio, or targeted research, they can synthesize concise patient histories, draft discharge summaries, and generate referral letters in seconds - freeing clinicians from repetitive typing while producing auditable drafts that clinicians review and sign.

Best practice combines retrieval‑augmented generation and local fine‑tuning so outputs are grounded in verified records and institutional protocols; benchmarking work also shows distinct strengths by model and task, for example GPT variants for history summarization and Claude for complex board‑style reasoning, but all require human oversight to catch hallucinations and to meet privacy requirements.

Madison teams should pair pilots with clear governance, on‑prem or VPC deployment where possible, and upskilling so prompts and guardrails align with UW–Health and Epic workflows; practical guides on LLM healthcare roles and deployment strategies can help shape safe, time‑saving pilots (Applications and benefits of LLMs in healthcare, Comparison and benchmarks of large language models in healthcare, Nucamp AI Essentials for Work syllabus and practical guide).

Model	Typical generative workflow / use case
GPT (ChatGPT/GPT‑4)	Summarizing patient histories, discharge summaries, RAG‑backed clinical note drafts
Claude 3	Complex clinical reasoning tasks and oncology board–style planning with RAG and prompt engineering

Madison health teams choosing an LLM front end should pick solutions that pair a healthcare‑ready model with enforceable guardrails: Hathr AI's Claude‑powered workspace isolates each account in an AWS GovCloud environment, refuses to reuse PHI for model training, and supports large clinical contexts (Claude 3.5 Sonnet's ~200k‑token window) to handle long EHR notes, while operational checklists from TechMagic emphasize mandatory BAAs, end‑to‑end encryption, RBAC, and prompt/output logging so PHI never leaks to public models; combining a vendor like Hathr with HIPAA‑aware observability (audit logs retained and monitored as Datadog recommends) turns generative speed into a controlled productivity lift rather than a compliance risk.

The practical payoff for Madison: measurable documentation and billing time saved without raising the multi‑million dollar breach exposure TechMagic documents, provided teams enforce de‑identification, human‑in‑the‑loop review, and continuous log auditing before scaling beyond pilot projects.

Solution	Compliance highlight	Price / note
Hathr AI healthcare LLM compliance details	Isolated AWS GovCloud storage, no data reuse, RBAC	$45/mo (vendor listing)
TechMagic HIPAA-compliant LLM deployment and checklist	BAA guidance, encryption, logging, deployment options (self‑host/cloud/vendor)	Operational checklist for safe LLM use
Datadog HIPAA-compliant log management and observability	HIPAA‑enabled observability and long‑term audit logs	Recommended for monitoring and incident response

“GPT or platforms powered by GPT leak your proprietary information and reuse your data to make their platform better for free.” - James Vincent, The Verge

Moxi, Diligent Robotics' mobile manipulator, automates routine but time‑consuming non‑patient‑facing tasks - running patient supplies, delivering lab specimens and medications, distributing PPE - and is designed to work side‑by‑side with nursing teams using social intelligence and a compliant arm so staff feel supported rather than displaced; see the Moxi healthcare robot overview - Diligent Robotics (Moxi healthcare robot overview - Diligent Robotics) for capabilities and deployment notes.

Because Moxi installs without special infrastructure (uses existing Wi‑Fi) and can move from pilot to frontline in weeks, Madison hospitals and UW–affiliated clinics facing nursing shortages can pilot it to reclaim bedside time: Diligent reports its fleet has completed over one million deliveries and saved clinical staff hundreds of thousands of hours - concrete, audited time savings that translate directly into more hands‑on care and fewer corridor trips for nurses (Diligent Robotics one million deliveries fleet milestone and impact).

Metric	Value
Fleet deliveries	1,000,000+ total hospital deliveries
Clinical time saved	~575,000 hours saved across fleet
Average task time	20–26 minutes per delivery

“Moxi stands out for being a socially intelligent robot that can aid nurses without making humans feel uncomfortable.” - ZDNET

UW–Madison amplifies clinical training with high‑fidelity simulation that translates directly to safer patient care: the Wichman Clinical Teaching and Assessment Center (CTAC) provides 24 clinical exam rooms equipped with ceiling‑mounted cameras and microphones so learners get recorded, reviewable encounters with standardized patients, while the UW Health Clinical Simulation Program (CSP) and specialty tracks - like Emergency Medicine's crisis‑management labs and the Urology Simulation Education Program's bootcamps - use high‑fidelity mannequins, procedural trainers, and interdisciplinary scenarios to accelerate skills and team coordination.

That “practice‑before‑patients” approach matters in Madison because it turns rare, high‑stakes events into repeatable learning opportunities and measurable readiness - standardized patients are trained to deliver consistent feedback and are paid ($20/hr) for intermittent, realistic role‑plays that let learners iterate communication and exam technique under faculty debriefing.

Teams wanting to pilot AI‑augmented training (audio capture, automated feedback, RAG‑backed curricula) can plug into existing CTAC and CSP infrastructure to run auditable, video‑backed assessments that improve competence without adding clinical risk; see CTAC's center details and how to become a standardized patient or request resources from CSP for program collaboration.

Resource	Key detail
Wichman Clinical Teaching and Assessment Center (CTAC)	24 clinical rooms with ceiling‑mounted video/audio capture for recorded learner encounters
Standardized Patient Program	Trained community actors provide consistent cases and are paid $20/hour for work and training
UW Health Clinical Simulation Program (CSP)	7,500 sq ft simulation center supporting interdisciplinary procedural and team‑based training

Meeting and workflow assistants built on Microsoft 365 Copilot and Azure AI can turn Madison clinical meetings and crowded inboxes into tangible time savings: Copilot in Teams generates real‑time meeting summaries, action items, speaker attributions and “catch‑up” notes for late arrivals (transcription must be enabled per organizer settings), while Copilot across Outlook, Word and Excel drafts messages, summarizes long threads, and converts spreadsheet queries to charts so operational leaders can spot scheduling bottlenecks quickly; see Microsoft's healthcare scenario guidance for clinician workflows (Microsoft Copilot in Healthcare scenarios for clinician workflows) and the Teams feature notes on summaries and meeting controls (How to use Copilot in Microsoft Teams meetings: summaries and meeting controls).

Real deployments show concrete gains - Teladoc reports individual users save up to five hours per week and thousands of hours annually across the enterprise - so Madison clinics that pair short pilots, transcript governance, and clinician review can measurably reclaim time for patient care and reduce meeting‑prep overhead (Microsoft and partner case examples for clinical workflow improvements).

Feature	Practical impact
Meeting summaries & action items	Faster debriefs, easier follow‑up
Catch‑up notifications	Quickly onboard staff who missed meetings
Time savings (Teladoc)	Up to 5 hours/week per user; thousands of hours enterprise

“Copilot saves us thousands of hours as an enterprise just through eliminating daily processes - it's driving operational efficiency across Teladoc like a personal assistant that never sleeps.”

Madison teams should treat the past two years of UW Health pilots as a playbook: start with small, measurable pilots that embed human review and institutional governance, measure time saved, and scale what demonstrably reduces clinician burden - UW Health's generative messaging work has already produced more than 3,000 nurse‑generated drafts (5,000+ messages systemwide) and ambient‑listening notetaking moved from a 20‑provider pilot toward a 2025 goal of ~400 clinic users, showing that incremental pilots can deliver concrete time back to bedside; see UW Health expands AI to improve patient visit experience (UW Health expands AI to improve patient visit experience) and Becker's report on UW Health AI message drafting (Becker's: UW Health AI pilot generates 3,000 patient messages).

Pair each pilot with enforceable BAAs, audit logging, and clinician upskilling (practical prompt writing, oversight, and workflow design) such as the 15‑week Nucamp AI Essentials for Work syllabus (Nucamp AI Essentials for Work 15-week syllabus) so local teams convert early wins into sustained workflow savings while protecting patient privacy and equity.

Next step	Local example / metric
Pilot generative messaging with human review	UW Health: 75+ nurses, 3,000+ nurse‑generated messages (5,000+ total)
Deploy ambient notetaking in clinics	Pilot began June 2024 with 20 providers; 2025 rollout target ~400 users
Upskill staff on prompts & governance	Nucamp AI Essentials for Work - 15‑week practical bootcamp

“This tool allows our care team members to look away from their computer screen and not split focus between their notes and their patient.” - Dr. Joel Gordon, UW Health