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

Rochester's hospitals and clinics are at a tipping point: 2025 is the year many health systems move from AI curiosity to targeted, ROI-driven deployments - from ambient listening that eases documentation to wearables and RPM that keep patients healthier at home - and leaders are asking vendors for clear value before adopting new tools (see HealthTech 2025 AI trends in healthcare overview and the AMA's roadmap for documentation, wearables, and telehealth).

With roughly 80% of hospitals already using AI to speed workflows and a data deluge in ICUs (from about 7 to ~1,300 data points per patient), Rochester health organizations can capture efficiency and quality gains - provided local teams gain practical skills in prompt design and tool use; Nucamp's Nucamp AI Essentials for Work bootcamp registration offers a 15-week, no-technical-background path to apply AI across clinical and administrative roles.

Program	Length	Early Bird Cost	Registration
AI Essentials for Work	15 Weeks	$3,582	Register for Nucamp AI Essentials for Work bootcamp

“AI is not going anywhere; expect more tools in 2025.”

Selection of the top 10 AI prompts and use cases combined a structured evidence review with local relevance: the framework leaned on a recent systematic review of stakeholder perspectives and the seven “RETREAT” criteria to prioritize approaches that are implementable, acceptable, and measurable (JMIR systematic review of stakeholder perspectives on clinical AI), while local Nucamp research flagged near-term wins for Rochester such as diagnostic image‑analysis speedups and practical pilot tools; these signals helped filter use cases that deliver clear ROI and match available workforce reskilling pathways (AI Essentials for Work syllabus - diagnostic image analysis and practical AI use cases, AI Essentials for Work registration - pilot tools and case studies for healthcare teams).

The methodology blended evidence synthesis, stakeholder input, and local deployment feasibility so each prompt maps to measurable outcomes - faster reads, fewer administrative delays, or safer perioperative decisions - making the “so what?” easy for Rochester leaders to act on.

Source	Key Methodological Element
JMIR systematic review	RETREAT criteria and stakeholder perspectives to prioritize implementable AI
Nucamp: How AI Is Helping (Rochester)	Local signals: diagnostic image‑analysis speedups and efficiency wins
Nucamp: Complete Guide (Rochester)	Practical tools and case studies (Clario) for trialing prompts locally

University of Rochester Medical Center's system-wide push to make point-of-care ultrasound (POCUS) an everyday tool in Rochester care settings has already remapped workflows and access: a phased partnership with Butterfly Network and Compass software put hundreds of handheld scanners into pockets across primary care, emergency, ICU, and medical education, turning imaging into a near‑instant extension of the physical exam and even gifting new medical students personal probes as part of a revamped curriculum; the results are concrete - a 116% jump in POCUS charge capture, a roughly fivefold increase in device availability, and nearly 50,000 scanning sessions since 2022 - showing how portable, cloud-integrated ultrasound can speed diagnosis (from detecting cholecystitis to identifying pediatric fractures), improve documentation into the EHR, and create measurable ROI for Rochester health systems.

Read the Butterfly case study on URMC's deployment and explore the URMC Health Lab projects that pair POCUS with AI for new imaging biomarkers and workflow tools.

Metric	Value
Butterfly devices deployed	862
POCUS charge capture increase	116%
Scanning sessions (since 2022)	49,492
Images generated	175,197
Finalized reports	15,524
Departments/programs live	64

“Our phased deployment of Butterfly devices and Compass software has yielded impressive clinical and administrative results at URMC to date.”

For Rochester hospitals wrestling with denials and the two‑midnight rule, utilization‑management scoring platforms - think Xsolis' Dragonfly Utilize - translate clinical notes into evidence‑mapped decisions that align with MCG/InterQual and CMS criteria, helping teams document why an admission is medically necessary and which patients truly need inpatient care; New York law already defines “Medically Necessary Service” around preventing, diagnosing, managing, or treating conditions that endanger life or function, so tooling that surfaces the required history, expected length of stay, and risk of deterioration can cut downstream denials and reclaim revenue (see AdmissionCare's approach to admission documentation and the MCG primer on utilization review).

These systems also reduce the burden on clinicians by nudging EHR entries toward the seven E/M components and payer‑specific rules, because a missing sentence about expected hospital stay or objective risk can be the difference between paid claims and time‑consuming appeals - making a clear, auditable trail that New York providers can use for both Medicaid and commercial payers.

Documentation Component	Why it matters
History of Present Illness / Exam	Supports clinical rationale for admission
Expected LOS & Risk Assessment	Key to CMS two‑midnight guidance and payer review
Evidence‑based Criteria (MCG/InterQual)	Aligns clinical choice with payer policies to reduce denials

“health care services that a physician, exercising prudent clinical judgment, would provide to a patient.”

OSF HealthCare's Fabric-powered virtual assistant, Clare, implemented in 2019, operates as a true 24/7 “virtual front door” that guides symptom checks, schedules appointments (including telehealth and asynchronous visits), handles bill pay, and connects patients to live nurse chats - diverting call center volume while improving access; in fact, 45% of Clare's interactions occur outside business hours, showing how an always-on navigator meets patients where they are.

The Fabric case study documents a $2.4M first‑year ROI - split into $1.2M contact center cost avoidance and $1.2M in new patient net revenue - illustrating a practical playbook Rochester and other New York systems can adapt to reduce administrative burden, route patients to the right level of care, and capture revenue through better digital access (read OSF's overview and the Fabric case study for implementation details).

Metric	Value
Implemented	2019
Availability	24/7
Interactions outside business hours	45%
First‑year ROI	$2.4M
Contact center cost avoidance	$1.2M
New patient annual net revenue	$1.2M

“Clare acts as a single point of contact, allowing patients to navigate to many self-service care options and find information when it is convenient for them.”

Automated ML feature engineering and ModelOps can turn messy EHR fields into reliable signals for predicting costly readmissions - a priority in New York where the Hospital Readmissions Reduction Program and AHRQ‑linked estimates (about $41.3B associated with 30‑day readmissions) make accuracy and auditability essential; studies show that combining data‑driven features with rule‑based scores can boost sensitivity by 2–17% and nudge AUC up about 1% compared with standard models (IEEE study on readmission ensemble models and feature engineering).

For payer and provider teams (think vendors like ClosedLoop and plans such as Healthfirst), automating feature pipelines and wrapping models in repeatable ModelOps workflows helps move from experimental notebooks to monitored, interpretable systems that stakeholders trust - echoed in comparative work on ML algorithms and feature construction for heart‑failure readmission prediction (JMIR study on heart‑failure readmission modeling and feature construction) and in calls for interpretable approaches that match clinical needs (BMC Medical Informatics 2023 paper on interpretable clinical models).

The “so what?” is simple: automated feature engineering plus disciplined ModelOps can detect at‑risk patients earlier, reduce avoidable readmissions, and create an auditable trail that New York payers and hospitals can use to defend performance and guide interventions.

Finding	Value / Impact
Sensitivity improvement (ensemble vs standard)	2–17% (IEEE)
AUC uplift	~1% (IEEE)
Estimated 30‑day readmission cost (AHRQ association)	$41.3 billion (IEEE)

Perioperative continuous-monitoring synthesis for Rochester and New York hospitals starts with the basics: know exactly what is being recorded, how it's sampled, and how artifacts are handled - because aggregate data without clear definitions will mislead clinicians and analysts.

Practical implementations mirror the telePORT model: a VitalNode‑style backend that streams waveforms and vital signs to mobile dashboards while storing both high‑frequency raw data and downsampled trends for research and QI, but they must also face real‑world limits (telePORT showed wireless reliability can erode day‑to‑day use).

Key choices - continuous vs five‑minute sampling, multisource switching for ECG vs pulse oximetry, and artifact rejection upstream - determine whether a dataset can catch a fleeting QRS during arrest or only a bland average.

Design decisions should bake in audit trails, discrete fields for downstream analysis, and legal/backup protections so New York systems can turn perioperative data into defensible, actionable insights (see the APSF discussion on intraoperative physiologic data collection and the telePORT implementation study for technical lessons and pitfalls).

Window	Why it matters
Definition	Shared, precise signal definitions enable valid aggregation and comparisons
Reduce artifacts at source	Cleaner signals avoid post‑hoc guessing and spurious alerts
Collect the right amount	Sampling cadence (1s vs 5min) must match the clinical question
Inspect & protect	Validation, storage, and legal safeguards preserve utility and trust

“If you don't know what you're recording when you aggregate the data, it will come back to bite you.” - Intraoperative Physiologic Data Collection (APSF)

Multimodal generative AI - models that fuse radiologic images with clinical notes and metadata - are emerging as practical partners for Rochester radiology teams that want faster, context‑rich reads without losing clinical traceability; recent reviews describe systems that combine LLMs with 2D x‑rays to 3D CT/MRI and enable tasks from preliminary report drafting to visual question answering (KJR review of multimodal LLMs in medical imaging) and outline fusion techniques like transformers and graph neural nets that improve diagnostic signal when imaging is paired with clinical metadata (narrative review of multimodal AI for imaging and clinical metadata).

“so what?”

is tangible for New York practice: a region‑grounded model that highlights the exact CT region linked to a quoted sentence from the ED note could cut ambiguity at handoffs - but hurdles remain, including scarce curated multimodal datasets, risks of hallucinated findings, taxonomy and bias issues, and high compute needs.

Practical next steps for Rochester systems include local dataset curation, cross‑institution validation, and deploying region‑grounded reasoning to make multimodal outputs auditable and clinically actionable (Rochester diagnostic image‑analysis speedups using AI).

Synthetic data - especially GAN‑generated EHR snapshots - offers a practical bridge for Rochester health systems to share and test models without exposing patient records: tutorials that use MIMIC‑IV demonstrate how EMR‑WGAN can recreate the statistical structure of a roughly 180k‑patient snapshot so teams can train algorithms, run software tests, or simulate clinical trials while keeping PHI out of circulation (see the JMIR GAN-based EHR synthesis tutorial: GAN-based EHR synthesis tutorial from JMIR).

Reviews and editorials also frame synthetic cohorts as a privacy‑preserving accelerator for rare‑disease research and cross‑institutional studies, while cautioning about pitfalls - low‑prevalence concepts are hard to model and utility/privacy tradeoffs must be measured with metrics like dimension‑wise distance and membership‑inference risk (learn more in this privacy-preserving synthetic data review: privacy-preserving synthetic data review at PMC).

For Rochester, that means practical wins - local radiology and ML teams can prototype diagnostic workflows against realistic, non‑identifiable cohorts before scaling pilots across systems (examples of regional diagnostic speedups are summarized by Nucamp's local resources).

The bottom line: synthetic cohorts make large, realistic datasets usable for training and education, but selecting the right run means balancing utility, fairness, and measurable privacy risk.

Metric	Example / Value
Cohort size (MIMIC‑IV example)	~181,294 patients (preprocessing)
Membership inference risk	Real data 0.91 vs synthetic runs ~0.29–0.31
Attribute inference risk	Real data 0.97 vs synthetic runs ~0.13–0.14

RAG-powered clinical evidence summarization can bring perioperative blood‑pressure guidance into the OR team's workflow by surfacing high‑quality recommendations - pulling the PeriOperative Quality Initiative consensus and classic reviews into a concise, auditable prompt that clinicians can query at the bedside; for example, the Cleveland Clinic review frames a practical preoperative target (systolic ≤140 mmHg and diastolic ≤90 mmHg) and flags that patients with systolic ≥180 or diastolic ≥110 should usually have surgery postponed until better control, while also reminding teams that most antihypertensives are continued perioperatively (see the perioperative hypertension review and the POQI international consensus for arterial‑pressure management).

For Rochester teams, a RAG setup that returns the exact numeric thresholds, the most relevant medication guidance, and the source citation can reduce last‑minute cancellations that disrupt patients' lives and hospital schedules - and it creates an evidence trail useful for QI and consent conversations.

Metric / Guideline	Value / Recommendation
Preoperative BP target	Systolic ≤140 mmHg, Diastolic ≤90 mmHg
Postpone threshold	Systolic ≥180 mmHg or Diastolic ≥110 mmHg
US hypertension prevalence (context)	~46% of population

“We consider a reasonable goal to aim for a systolic pressure 140 mmHg or less and diastolic pressure 90 or less preoperatively.” - Perioperative management of hypertension

Chart‑to‑letter chatbots and “prior authorization” agents are moving from clever demos to concrete wins for New York practices: clinicians already spend roughly 12–13 hours per week on authorizations, so generative AI that drafts request letters, pulls supporting notes, and triages appeals can reclaim clinical time while creating an auditable trail for payers and compliance; Advisory Board coverage shows doctors halving time on appeals with tools like Doximity GPT, while vendor pilots and RCM firms (AKASA, qBotica, Availity) highlight three practical automation targets - identify auth needs, submit requests, and track status - and products such as AKASA Auth Status automate portal checks and document outcomes to cut staff load.

For denials and appeals, retrieval‑grounded workflows and a “LLM‑as‑a‑judge” pattern can score and fast‑track high‑value cases, reducing revenue loss (John Snow Labs estimates denials drive billions in U.S. losses).

The bottom line for Rochester and other New York systems: chart‑to‑letter automation turns tedious admin work into measurable time and revenue recovery, even as providers and payers prepare for a possible “battle of the bots.”

Metric	Value / Source
Average clinician time on prior auth	~12–13 hours/week (Advisory Board; Medical Economics)
U.S. denial‑related cost	$31 billion (John Snow Labs)
Auth work queue reduction (vendor case)	22% (AKASA Auth Status)

“AI can significantly reduce the time physicians spend on prior authorizations.”

Real‑world evidence cohorts built from EHRs are becoming the backbone of population‑health and HEOR work on heart‑failure readmission in New York: by extracting signals from both structured fields and free‑text, teams can assemble regional cohorts that support readmission risk stratification, intervention timing, and cost‑effectiveness analysis.

Large‑scale reviews show AI and large language models unlock value in narrative notes for cardiovascular prediction (systematic review: EHR and AI for cardiovascular disease risk prediction - JAHA), while focused work demonstrates practical tools - an NLP model trained across two hospitals to identify congestive heart failure cases (JMIR Medical Informatics study on CHF identification) and data‑mining methods that pull ejection‑fraction values from clinical notes to enable heart‑failure subtyping (BMC Research Notes on ejection fraction extraction).

The payoff is concrete: transformer‑style models already reach very high discrimination for heart failure (BEHRT HF AUROC ≈ 0.909), meaning Rochester systems can curate auditable, region‑calibrated RWE cohorts - turning buried EF numbers and narrative findings into sortable columns that drive HEOR analyses and targeted readmission reduction programs.

Metric / Task	Example / Value
HF model discrimination (BEHRT)	AUROC ≈ 0.909 (Journal of the American Heart Association review)
CHF identification by NLP	EHRs from two hospitals (JMIR Medical Informatics study)
EF extraction for HF subtyping	Data‑mining methodology improves subclassification (BMC Research Notes)

Getting started with AI prompts in Rochester healthcare means pairing short, practical training with low‑risk pilots and clear governance: begin by upskilling clinicians and staff on approachable resources like URMC's Artificial Intelligence Resource Hub and introductory training videos so teams learn responsible use and where AI fits the workflow (URMC Artificial Intelligence Resource Hub), adopt prompt best practices (be specific, “act as if…”, and iterate) from established playbooks (Harvard prompt engineering tips and best practices), and run fast pilots on administrative or documentation tasks where human oversight can catch errors - this sequence delivers visible ROI and builds trust.

For formal reskilling, a focused course that teaches prompt design, retrieval‑grounded workflows, and practical AI use cases helps move projects from experiments to monitored tools; Nucamp's AI Essentials for Work is one such 15‑week path for nontechnical teams (Nucamp AI Essentials for Work registration and syllabus).

Start with a single, well‑scoped prompt that drafts an evidence‑backed letter or summarizes a guideline; that small win makes the “so what?” tangible while governance, monitoring, and human‑in‑the‑loop checks protect patients and institutions as capabilities scale.

Program	Length	Early Bird Cost	Registration
AI Essentials for Work	15 Weeks	$3,582	Register for Nucamp AI Essentials for Work

“We're cranking out tools in days to weeks; tools that typically would have taken our engineering and data science teams six months to a year to build.” - Michael Hasselberg, URMC