Top 10 AI Prompts and Use Cases and in the Healthcare Industry in New York City

New York City healthcare leaders face a clear opportunity: generative AI is already being pursued or implemented by over 70% of U.S. healthcare organizations and promises the highest value in clinician productivity and patient engagement, from faster image reads to automated note-taking that many physicians say can save 20%+ of their time; see the McKinsey report on generative AI adoption for national trends and expected ROI Generative AI in Healthcare: Adoption Trends and Expected ROI (McKinsey).

At the same time New York's Attorney General flagged privacy, bias, and the need for human-in-the-loop governance after a statewide symposium on AI risks and clinical uses New York Attorney General Symposium Report on the Next Decade of AI.

For NYC health teams looking to move from pilots to safe, scalable deployment, practical upskilling is essential - consider a focused training path like Nucamp's AI Essentials for Work bootcamp to teach prompt-writing, tool use, and governance-ready skills AI Essentials for Work bootcamp registration - Nucamp.

Program	Details
AI Essentials for Work	Length: 15 weeks; Early-bird cost: $3,582; AI Essentials for Work bootcamp syllabus - Nucamp
Registration	Register for AI Essentials for Work bootcamp - Nucamp Enrollment

Selection prioritized real-world impact for New York City health systems by filtering prompts against three evidence-based lenses: clinical urgency and multimodal data potential (favoring time-sensitive workflows like trauma and imaging highlighted in NYU's AI for Medicine and Healthcare research), governance and human-in-the-loop safeguards called for in the New York Attorney General generative AI symposium report for AI governance in healthcare (New York Attorney General generative AI symposium report), and local vendor maturity so solutions can be operationalized quickly in NYC hospitals and clinics (informed by a survey of NYC multimodal AI companies driving healthcare innovation (NYC multimodal AI companies driving healthcare innovation)).

Prompts and use cases were scored for: 1) measurable clinician time savings or diagnostic speed, 2) alignment with NY policy priorities (transparency, auditing, privacy), and 3) feasibility using existing NYC-focused vendors and multimodal models; this approach ensures each recommended prompt maps to a concrete hospital workflow - such as prehospital triage or imaging prioritization - while meeting the Attorney General's call for oversight and patient protections.

Selection Criterion	Representative Source
Clinical urgency & multimodal data	NYU AI for Medicine and Healthcare research center (clinical AI and multimodal imaging)
Risk, governance & human-in-loop	New York Attorney General symposium report on the next decade of AI (governance guidance)
Local vendor feasibility	Survey of NYC AI companies driving multimodal healthcare innovation

“When using ChatGPT for health services, obtaining the patient's consent is crucial,” says John Loike, Ph.D., emphasizing ethical safeguards in clinical AI deployment.

Nuance's DAX Copilot (now part of Microsoft's Dragon Copilot) embeds ambient voice capture into Epic workflows so NYC hospitals using Epic can convert multiparty conversations into specialty-specific draft notes in Hyperspace and Haiku, reduce documentation burden, and capture orders directly into the EHR; see the Epic DAX Express integration announcement and the Microsoft Dragon Copilot overview for feature and deployment details (Epic DAX Express integration announcement, Microsoft Dragon Copilot overview and features).

Independent reporting and vendor data show ambient voice can cut documentation time roughly 50% (about 6–7 minutes per encounter), translate to measurable throughput gains, and - in published outcomes - deliver a 112% ROI and a 3.4% service-level increase in early adopters; for New York systems balancing clinician burnout and patient access, that means reclaiming “pajama time,” closing notes before leaving clinic, and scaling ambulatory capacity without expanding headcount (Healthcare IT Today deep dive on Epic–Nuance DAX Copilot integration).

Metric	Reported Result / Source
Documentation time reduction	~50% (~6–7 minutes per encounter) - Healthcare IT Today
Northwestern Medicine outcomes	112% ROI; 3.4% service-level increase; 24% less time on notes; +11.3 patients/month - Microsoft study
Epic integration	Embedded in Haiku & Hyperspace; populates smart data elements - Epic / Healthcare IT Today

DAX Copilot was the first ambient solution to be integrated into the Epic electronic health record (EHR) workflow and allows clinicians to seamlessly document patient visits directly within the EHR.

AI-driven symptom checkers can triage large volumes of anxious or low-acuity patients before they arrive at overwhelmed NYC emergency departments, and Ada's clinical validation shows why: in a BMJ Open-style vignette study Ada covered 99% of cases, advised at a clinically safe urgency level 97% of the time, and placed the correct diagnosis among its top three suggestions in 71% of vignettes - compared with human GPs' 100% coverage and 82% top‑3 accuracy - indicating symptom-checker triage can safely extend access without replacing clinicians (Clinical validation of Ada's symptom checker).

Recent emergency‑department research also highlights that on-demand symptom tools can improve patient flow when integrated into workflows, making them a practical supplemental route for NYC health systems to reduce unnecessary visits and speed care for higher‑acuity patients (Emergency department comparison and usability study of symptom checkers).

Metric	Human GPs	Ada	Other apps (range)
Coverage	100%	99%	~50% (many apps)
Safety (appropriate urgency)	97%	97%	80%–95%
Accuracy (correct in top 3)	~82%	71%	~23%–43%

Personalized outreach - whether modeled on UnityPoint-style automated campaigns or K Health–style symptom messaging - scales chronic care engagement for NYC practices by combining conversational AI with Medicare-backed Chronic Care Management (CCM) workflows: automated messages identify high‑risk patients, prompt enrollment, and deliver monthly, tailored touchpoints that meet the CCM requirement of at least 20 minutes of non‑face‑to‑face care per patient, preserving clinician time while improving adherence and outcomes; vendors and guides show conversational AI can boost medication adherence (patients who interact more with clinicians are 2.57× more likely to take meds as prescribed) and, when paired with full‑service CCM, translate into concrete system gains - examples include a reported 20% drop in hospital admissions and a 13% drop in ED visits for enrolled populations - so New York ambulatory networks and community clinics can reduce avoidable high‑cost use while meeting value‑based targets by automating enrollment, consent, and personalized check‑ins via secure messaging and voice channels.

For implementation guidance, see the ProviderTech article on conversational AI for chronic disease outreach and the ChartSpan CCM outcomes and implementation guide.

Metric	Reported Result
Monthly non‑face‑to‑face care	At least 20 minutes per patient (CCM requirement)
Medication adherence	Patients who interact more with clinicians: 2.57× more likely to adhere
Hospital admissions	−20% for CCM‑enrolled patients (reported ACO example)
Emergency department visits	−13% for CCM‑enrolled patients (reported ACO example)
Outsourced enrollment impact	Enrollment rates reported: ~60% vs ~10% (outsourced vs in‑house)

“Trust is important in every doctor–patient relationship.” - Dr. Mikulecky, Carelon Health

Conversational AI for chronic disease outreach - ProviderTech article • Chronic care management outcomes and implementation guide - ChartSpan

Doximity GPT supplies free, HIPAA‑compliant clinical decision support and chart‑summarization tools that are immediately practical for busy New York teams: it can summarize patient charts, analyze labs and reports, draft prior‑authorization and appeal letters, generate instant notes, and translate discharge instructions into patients' native languages - functionality detailed on Doximity's product page (Doximity GPT clinical reference and administrative support).

Clinicians who tested targeted prompts report meaningful time savings (examples range from ~1 hour/day on test‑organization tasks to vendor claims of >10 hours/week), so NYC practices can clear administrative backlog and redirect hours to bedside care; see physician prompt examples and real workflows in Doximity's prompt guide (Doximity prompts to simplify administrative workload).

For diverse, multilingual NYC populations, the tool's bedside translation and patient‑education capabilities - reported to support translations in 95+ languages - help reduce discharge confusion and improve follow‑up adherence (Doximity patient translation and education with AI).

Capability	Notes
Chart summarization & instant notes	Generate draft progress notes, H&Ps, consults; integrates with Dialer workflows
Administrative drafting	Priors, appeal letters, patient correspondence - clinicians report hours saved weekly
Language & patient education	Translates complex medical info into simpler terms and 95+ languages
Access & compliance	Free for verified clinicians, HIPAA‑compliant, desktop and mobile

"Doximity GPT is a powerful AI tool that excels in clinical support. It understands clinical queries, provides contextual responses, and summarizes relevant literature, streamlining decision-making at the bedside and saving me time and effort." - Dr. Felix Reyes, Pulmonology

AIDDISON™ from Merck/MilliporeSigma packages generative AI, predictive ADMET models, and CADD workflows into a cloud-native SaaS that lets NYC‑based medicinal chemists and small biotech teams move from idea to synthesizable lead faster: the platform can virtually search and prioritize candidates across a chemical space of more than 60 billion compounds, generate de novo libraries optimized for QED and ADMET, and link designs to practical synthesis planning via integrated retrosynthesis tools - making it practical for academic labs and startups in New York to compress early discovery cycles and reduce costly wet‑lab screening.

Built‑in ML scoring and explainability features help prioritize compounds with higher developability while ISO‑27001 security and scalable cloud compute lower the IT barrier for hospital‑affiliated research groups; see the AIDDISON product overview for platform capabilities and the Explorer feature set for practical deployment details (AIDDISON product overview - MilliporeSigma, AIDDISON Explorer features and deployment - Digital Chemistry).

Capability	Why it matters for NYC teams
De novo generative design	Rapidly expand candidate space without large medicinal chemistry teams
Virtual screening & similarity search (60+ billion)	Prioritize hits before costly synthesis or assays
ADMET & ML‑based scoring	Reduce downstream attrition by flagging liabilities early
Retrosynthesis integration (SYNTHIA™)	Translate designs into actionable, cost‑aware synthesis routes
Cloud SaaS + ISO‑27001	Secure, scalable access for academic and startup labs

“Our platform enables any laboratory to count on generative AI to identify the most suitable drug‑like candidates in a vast chemical space. This helps ensure the optimal chemical synthesis route for development of a target molecule in the most sustainable way possible.” - Karen Madden, CTO, Merck Life Science

IQVIA combines healthcare‑grade AI, declarative LLM prompt optimization, and real‑world data to accelerate clinical trial optimization and patient matching in complex, diverse markets like New York City: prompt‑engineering work improves precision and recall in automated matching (reducing manual review) (IQVIA blog post: Prompt and Proper on declarative LLMs), while Direct‑to‑Patient Recruitment and Decentralized Trial toolsets reduce time, cost, and uncertainty around enrollment (IQVIA patient recruitment solutions).

The IQVIA Patient Finder leverages structured and unstructured EMR data (over 70% of EMR content is text), machine learning, and self‑service cohort tools to identify eligible participants up to ~40× faster in routine workflows, with client case studies reporting identification gains as high as 95× - a practical advantage for NYC teams running rare‑disease, oncology, or community‑based trials that must recruit across boroughs and reduce site burden (IQVIA Patient Finder solution details).

The net result: faster enrollments, more diverse cohorts, and fewer costly protocol delays for New York research sites.

Capability	Impact for NYC trial teams
Prompt‑optimized matching	Improves match precision/recall; reduces manual chart review
IQVIA Patient Finder	Uses EMR text + ML; speeds eligible‑patient ID up to ~40× (case studies up to 95×)
Direct‑to‑Patient Recruitment	Finds site‑based and external participants; lowers time, cost, uncertainty
Site identification (RWD + ML)	Picks high‑performing sites to accelerate milestones and reduce amendments

AI and RPA are proving practical levers for New York City revenue-cycle teams: by automating eligibility checks, claim‑scrubbing, and appeal generation, systems reduce denials and free staff for higher‑value work - national scans report about 46% of hospitals already use AI in RCM and 74% have some revenue‑cycle automation in place, with generative AI lifting call‑center productivity 15–30% in early studies (American Hospital Association report: 3 Ways AI Can Improve Revenue Cycle Management).

Concrete examples matter for NYC safety‑net and municipal systems: an Auburn, New York community hospital using RPA/NLP/ML cut discharged‑not‑final‑billed cases ~50%, boosted coder productivity >40%, and raised case‑mix index 4.6% - showing how near‑term automation can convert administrative drag into measurable revenue and capacity (Xsolis blog: AI Changing Revenue Integrity).

For NYC leaders, the takeaway is simple: targeted RPA pilots on front‑end eligibility and claim scrubbing deliver fast wins while preserving human review for complex appeals and equity checks.

Metric	Reported Result / Source
Hospitals using AI in RCM	≈46% - AHA market scan
Hospitals with some RCM automation	≈74% - AHA market scan
Auburn (NY) outcomes	−50% discharged‑not‑final‑billed; +40% coder productivity; +4.6% case‑mix index - AHA case study

The Cleveland Clinic's Palantir‑backed Virtual Command Center stitches together real‑time census, bed and OR availability, and staffing forecasts into one actionable dashboard so nurse managers stop hunting for siloed spreadsheets and phone calls; built on Palantir Foundry, the system's Hospital 360, Staffing Matrix, and OR Stewardship modules use machine learning to predict demand, suggest staffing adjustments, and identify OR scheduling opportunities - turning previously time‑intensive coordination into proactive recommendations that reduce last‑minute add‑ons and the “fire drills” that disrupt throughput.

New York City hospitals facing chronic bed pressure and staffing churn can adopt the same pattern - centralized AI modules feeding scheduling and communication tools - to cut manual work, improve shift forecasting, and speed patient flow across multiple sites (see the Cleveland Clinic overview and TechTarget deployment summary for module and partnership details).

The practical payoff is simple: a single, sharable operational view that makes staffing decisions visible days in advance instead of reactive in the moment.

Module	Primary function
Hospital 360	Real‑time census and capacity forecasting
Staffing Matrix	Align staffing to predicted volumes with live updates
OR Stewardship	Predict caseloads, optimize OR schedules and PACU capacity

“Knowing our staffing availability days ahead of time leads to fewer last-minute changes, earlier scheduling, and less manual and operational management burden,” adds Iuppa.

Cleveland Clinic Virtual Command Center overview - how AI assists with staffing and scheduling • TechTarget article - Cleveland Clinic AI command center deployment details

AI-augmented remote monitoring and telehealth can turn scattered device feeds into actionable workflows for New York City clinics by surfacing “rising‑risk” patients earlier, cutting nurse review time, and enabling targeted outreach before an ER visit is needed: vendors like Mindbowser illustrate RPMCheck AI and DischargeFollow AI that flag concerning trends before fixed thresholds are crossed, reducing manual chart hunting and late alerts (Mindbowser: AI in Remote Patient Monitoring), while platform analyses of top use cases show AI's strength in real‑time anomaly detection and predictive hospitalization risk - exactly the capabilities NYC safety‑net networks need to manage high chronic‑disease burden across boroughs (HealthSnap: Top RPM Use Cases (2025)).

Practical nurse‑workflow data are compelling: Welby Health reports AI dashboards and rising‑risk alerts save roughly an hour of staff time per enrolled patient per month and drove rapid control of chronic metrics (84% BP control and 81% A1c targets within 90 days), a concrete lever to scale panels without hiring more clinicians (Welby Health: MARKUS AI Results).

For NYC teams, the net effect is fewer false alarms, faster interventions, and more capacity to serve complex patients across multiple sites.

Metric / Capability	Reported Result / Source
Staff time saved (per enrolled patient)	≈1 hour/month - Welby Health
Blood pressure control (enrolled patients)	84% within 90 days - Welby Health
A1C target achievement (enrolled patients)	81% within 90 days - Welby Health
AI RPM capabilities	Real‑time anomaly detection, predictive risk, reduced alarm fatigue - Mindbowser / HealthSnap

Moxi by Diligent Robotics offers a pragmatic, safety‑first automation option for New York hospitals: Rochester Regional Health has already mapped and is deploying Moxi robots across Rochester General and Unity Hospital to handle pharmacy, lab and supply runs so clinicians spend more time at the bedside (Rochester Regional Health Moxi deployment in New York hospitals); the robot combines sensors, a mobile base, a manipulator arm and locked drawers to navigate hallways, badge‑access doors, and elevator use without entering patient rooms or handling narcotics.

Real deployments show tangible gains - Children's Hospital Los Angeles reported two Moxis made 2,500+ deliveries, traveled 132 miles, saved ~383,000 staff footsteps and about 1,620 work hours in the first four months, with pharmacy teams reclaiming 20–30 minutes per delivery for higher‑value tasks - while Diligent's fleet recently surpassed one million deliveries, underscoring operational maturity for scale in complex systems like NYC health networks (CHLA Moxi robot performance metrics and delivery results, Diligent Robotics one million hospital deliveries milestone).

For NYC leaders facing staffing shortages, Moxi presents a near‑term, subscription‑friendly way to reduce low‑value walking and free clinicians for direct patient care.

Metric	Value / Source
Rochester, NY deployments	4 Moxi at Rochester General + 4 at Unity Hospital - Rochester Regional Health
CHLA early performance	2,500+ deliveries; 132 miles; ~383,000 footsteps saved; ~1,620 work hours saved (first 4+ months) - CHLA
Diligent fleet scale	Surpassed 1,000,000 deliveries - The Robot Report / Diligent Robotics

“Bringing Moxi to CHLA is a great example of how we are ensuring our team members are able to do their best work at the top of their skill set.” - Omkar Kulkarni

New York City health systems should treat the next 12–18 months as a structured build‑out: inventory AI use cases and the data they touch, classify PHI levels, and pick a compliant deployment path (self‑host, HIPAA‑eligible cloud, or a healthcare vendor) while securing a signed BAA and enforcing encryption, RBAC, and audit logging - practical guardrails TechMagic calls non‑negotiable in its HIPAA‑Compliant LLMs guide and a necessary shield given the average cost of a healthcare breach ($9.77M) TechMagic HIPAA-Compliant LLMs guide.

Prioritize pilots with clear ROI (documentation automation, triage, RPM, RCM) paired with clinician review and monitoring, track New York's evolving privacy rules (see the NYHIPA analysis) and broader state activity, and staff a cross‑functional governance team to vet models and measure safety Analysis of New York Health Information Privacy Act (NYHIPA).

Finally, close the skills gap by upskilling care managers and admins in prompt design and safe tool use - enroll teams in focused programs like Nucamp's AI Essentials for Work to turn pilots into governed, scalable workflows Nucamp AI Essentials for Work bootcamp - registration.

Program	Length	Early‑bird Cost	Registration
AI Essentials for Work	15 weeks	$3,582	Register for Nucamp AI Essentials for Work (15 weeks)

“When using ChatGPT for health services, obtaining the patient's consent is crucial.” - John Loike, Ph.D.