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

New Orleans is emerging as a regional testbed for practical, trust-centered AI in healthcare: the 2025 State Healthcare IT Connect Summit in New Orleans spotlights federal standards, interoperability, and state pilots that aim to turn AI from experiment to measurable outcomes, while local initiatives like the AI 4 Health Outcomes Initiative focus on trust and workforce readiness for deploying models safely; these efforts matter because pilots - from ambient note‑taking to Ochsner's sepsis risk models that flag early deterioration and shorten response times - directly reduce clinician administrative burden and speed critical care decisions.

For Louisiana health leaders, targeted prompts and use cases unlock ROI, improve care coordination across HHS agencies, and create concrete training pathways (consider the practical skills taught in the AI Essentials for Work bootcamp) so hospitals can scale vetted AI without sidelining clinicians.

Bootcamp	Length	Early Bird Cost	Registration
AI Essentials for Work	15 Weeks	$3,582	Register for the AI Essentials for Work bootcamp (15 Weeks)

"This was not just an academic exercise - it was about real-world problem-solving to build the future of medicine."

Selection favored prompts and use cases with local clinical traction, measurable outcomes, and reproducible prompt design: priority went to initiatives highlighted at New Orleans convenings (see the New Orleans panel on AI in healthcare by NOEW) and to projects developed via hands‑on Biodesign methods like the Tulane AI and Healthcare Design Lab inaugural program, which used an adapted Stanford Biodesign approach to move concepts toward pitch‑ready pilots with mentorship on regulatory basics; selection also required concrete prompt‑engineering reproducibility as demonstrated in published work on generative prompts for mHealth content in the JMIR AI journal article on generative prompts for mHealth.

The screening rubric therefore emphasized local pilotability in Louisiana hospitals, clinician and patient safety checks, clear success metrics (e.g., response‑time or workflow gains), and human‑in‑the‑loop review - so leaders in New Orleans receive a top‑10 that is actionable, ethically defensible, and ready for workforce training pathways rather than speculative use cases.

See the New Orleans panel on AI in healthcare by NOEW: Prominent New Orleans company helps define AI's role in the evolution of healthcare (NOEW panel).

See the Tulane AI and Healthcare Design Lab inaugural program: Tulane AI & Healthcare Design Lab inaugural success story.

See the JMIR AI article on generative prompts for mHealth: JMIR AI study on generative prompts for mobile health (mHealth).

Item	Detail
Event dates	November 14–17, 2024
Location	New Orleans BioInnovation Center & Jung Hotel
Participants	29 cross-disciplinary applicants (ages 20–60)
Methodology	Stanford Biodesign adapted to AI + mentorship on regulatory and IP basics

“We're just beginning to see the real impact of AI across healthcare.”

For New Orleans health systems exploring federated learning workflows, synthetic EHRs offer a practical privacy buffer: state‑of‑the‑art diffusion models and GAN pipelines can produce time‑series and snapshot records that preserve clinical signal while breaking direct patient links, letting hospitals exchange model updates and synthetic cohorts instead of raw PHI. Research shows diffusion‑based generators can produce diverse, realistic time‑series data suitable for downstream tasks (JAMIA study on diffusion models for synthetic EHR generation), and production‑focused frameworks like EHR‑Safe demonstrate that models trained on high‑fidelity synthetic EHRs suffer only small performance drops (e.g., ~2.6% AUC difference on MIMIC‑III tasks) while substantially reducing re‑identification risk (Google Research EHR‑Safe synthetic EHR framework).

In practical Louisiana terms, this means New Orleans hospitals can validate sepsis early‑warning code and share reproducible test sets without moving PHI offsite - an important step toward multi‑institution pilots with external partners (Nucamp AI Essentials for Work syllabus).

Risk Metric	Synthetic (typical)	Real Data
Membership inference (F1)	≈ 0.29–0.31	0.91
Attribute inference (F1)	≈ 0.13–0.14	0.97

Insilico Medicine's generative‑AI stack - Pharma.AI with Chemistry42 and the new nach0 LLM - illustrates how AI can collapse months or years from target discovery to a testable lead, a practical capability Louisiana centers can leverage for faster translational projects: the company reported first “hit” discovery in 30 days using AlphaFold‑predicted structures and advanced a generative‑AI‑designed idiopathic pulmonary fibrosis candidate into Phase II after reaching Phase I in roughly 2.5 years, while designing and synthesizing under 80 molecules for a preclinical campaign - reducing typical costs to about one‑tenth and timelines to roughly one‑third of legacy workflows (see the Insilico Medicine platform overview and milestones at Insilico Medicine platform overview and milestones and the NVIDIA case study on AI‑accelerated drug discovery at NVIDIA case study on AI‑accelerated drug discovery).

Local research hospitals and startups in New Orleans could use similar pipelines to iterate on rare‑disease or population‑specific targets without the multi‑year lead times that typically block regional translational work, and domain models like nach0 promise promptable, chemistry‑aware outputs that speed medicinal‑chemistry triage (see the nach0 domain model technical summary at nach0 domain model technical summary).

Metric	Value
Time to first hit (AlphaFold case)	30 days
Molecules designed/synthesized (IPF campaign)	<80
Time to Phase I (example)	≈2.5 years
Reported cost/time reduction	≈1/10 cost, ≈1/3 time
Programs in pipeline	>30; 18 preclinical nominees since 2021, 6 advanced to clinical

“This first drug candidate that's going to Phase 2 is a true highlight of our end-to-end approach to bridge biology and chemistry with deep learning.” - Alex Zhavoronkov

Radiology enhancement platforms like CerebraAI tap into two complementary advances shown in recent peer‑reviewed work: deep‑learning denoising can recover CT perfusion quality when iodinated contrast is reduced - making scans safer for patients with renal risk - while iodine‑boosting, deep‑learning contrast augmentation substantially raises vessel‑occlusion sensitivity in poorly contrasted CTA (a single‑centre study of 102 patients found sensitivity improved from 81.6% to 94% with a Deep Learning‑enhanced CTA) for more reliable stroke triage; parallel systematic reviews show AI models are maturing for automated MRI stroke detection, strengthening the evidence base for integration into acute workflows in Louisiana hospitals (AJNR feasibility study, Oct 2024, HealthAI Register DLe‑CTA sensitivity analysis, Dec 2024, Insights into Imaging systematic review on MRI stroke detection, 2024).

For New Orleans systems this means fewer nondiagnostic scans, higher stroke detection rates, and stronger justification for targeted pilots that link AI outputs to rapid clinical pathways and radiologist oversight.

Study	Key result	Year / Source
Deep learning denoising (CTP)	Improves image quality with lower iodinated contrast dosing	AJNR, Oct 2024 (AJNR DOI link)
Deep Learning‑enhanced CTA (DLe‑CTA)	Sensitivity for vessel occlusion: 81.6% → 94% (102 patients)	HealthAI Register, Dec 2024 (HealthAI Register DLe‑CTA study summary)
AI for MRI stroke detection	Systematic review/meta‑analysis supporting AI performance in automated stroke detection	Insights into Imaging, 2024 (Insights into Imaging systematic review (open access))

Nuance DAX Copilot (branded in Microsoft materials as Dragon Copilot) uses ambient listening, speech recognition, and generative AI to turn multi‑party clinical encounters into EHR‑ready notes, patient‑friendly after‑visit summaries, referral letters, and searchable transcripts - functionality that directly addresses Louisiana clinics' chronic documentation backlog by producing specialty‑specific, editable notes at point of care; the platform is built on Microsoft Azure with privacy and safety safeguards, trained on 15+ million encounters, and - per a published outcomes study - has reported measurable operational lift (112% ROI and a 3.4% service‑level increase), making pilot investments recoverable in months for systems that see high outpatient volumes in New Orleans hospitals (Microsoft Dragon Copilot clinical workflow overview).

Peer research and cohort analyses of DAX ambient‑listening deployments document reduced clinician documentation burden and improved note completeness, which in practice means more face‑to‑face time with patients and fewer after‑hours charting shifts for busy LSU‑affiliated and private clinics (Peer‑reviewed cohort study on Nuance DAX ambient‑listening impact).

Metric	Value / Note
Training data	15+ million ambient encounters (platform training)
Key outcomes	112% ROI; 3.4% service‑level increase (Outcomes Study)
Core features	Ambient capture, EHR integration, after‑visit summaries, referral letters
US availability	Listed for United States (availability date noted in product materials)

"Dragon Copilot helps doctors tailor notes to their preferences, addressing length and detail variations." - R. Hal Baker, MD

Tempus combines genomic sequencing, multimodal real‑world data, and an AI‑enabled clinical assistant to turn patient signals into actionable, personalized care plans - capabilities that matter in Louisiana because Tempus already integrates discrete genomic results into EHR workflows (including a first‑of‑its‑kind integration with Epic's Aura alongside Ochsner Health) so clinicians see genomic guidance where they make decisions; its platform can query records across the chart via Tempus One and patients can consolidate personal records in the new olivia app, enabling rapid identification of targeted therapies and clinical trials at the point of care.

The practical payoff: Tempus' dataset and connectivity have helped identify 30,000+ patients for potential trial enrollment and power algorithmic tests used by academic centers and community systems, meaning New Orleans providers could close care gaps faster by combining EHR‑integrated genomics, trial matching, and AI‑summarized patient timelines.

Learn more about Tempus' AI‑enabled precision medicine and EHR work below.

Metric	Value
Direct data connections / institutions	600+ connections across 3,000+ institutions
De‑identified research records	~8,000,000
Patients identified for trial enrollment	30,000+
Biopharma partnerships	200+

“Now, as AI becomes increasingly integrated into healthcare, tools like olivia will be essential in helping patients understand and navigate their care.” - Eric Lefkofsky

Ada Health's conversational AI acts as a 24/7 digital triage and patient‑facing medical assistant that can help New Orleans clinics triage symptoms, reduce unnecessary ED visits, and improve clinician handovers by delivering structured histories into the chart; clinical deployments show tangible patient and workflow benefits - 66% of users are more certain what care to seek, 40% report reduced anxiety, 80% feel better prepared for consultations, and 53% of assessments happen outside normal clinic hours - making it a practical tool for safety‑net hours and surge periods in Louisiana hospitals (Ada digital triage platform for symptom checking).

Peer evaluations and ED studies further show Ada can increase diagnostic accuracy when used alongside clinicians and save clinician time, supporting pilots that pair conversational AI with clear clinician oversight rather than replacing it (Ada clinical research and publications, JMIR usability study of Ada in emergency departments).

The upshot for New Orleans leaders: promptable, evidence‑backed symptom checking can reduce after‑hours uncertainty and funnel patients to the right level of care quickly, preserving ED capacity for true emergencies.

Metric	Value / Finding
Patients more certain what care to seek	66%
Patients reporting reduced anxiety	40%
Assessments completed outside conventional hours	53%
Patients feeling more prepared for consultation	80%
More patients seeking primary care after use	77%
Physicians reporting time savings	64%
Physicians feeling more prepared	78%
Instances of underestimated severity	0 reported

“Ada helps patients to access the highest-quality care according to their clinical needs. It smooths the whole journey to care by guiding the patients to take the right steps.” - Dr Micaela Seemann Monteiro

Mayo Clinic's machine‑learning work shows how predictive analytics can move from population insight to actionable triage in Louisiana: their cardiac‑ICU study identified five discrete heart‑failure phenotypes - uncomplicated, iron‑deficient, cardiorenal, inflamed, and hypoperfused - each with distinct lab patterns and risk profiles, and recognizing the hypoperfused group (the highest mortality risk) creates a clear priority for rapid intervention at the bedside (Mayo Clinic heart failure phenotypes study).

That same analytic mindset underpins modern sepsis early‑warning efforts and the Mayo Clinic Platform's AI work on earlier recognition of sepsis (Mayo Clinic Platform early recognition of sepsis), which New Orleans systems can pair with local deployments - like Ochsner's sepsis risk models that flag early deterioration - to shorten response times, prioritize ICU transfers, and focus scarce resources on patients most likely to benefit (Ochsner sepsis risk AI models for early deterioration).

Phenotype	Mortality Risk (study)
Uncomplicated	Best outcomes
Iron‑deficient	Intermediate
Cardiorenal	Intermediate
Inflamed	Intermediate
Hypoperfused	Highest risk

"Recognizing that a critically ill heart failure patient belongs to one of these groups can help clinicians understand their likely underlying disease process and prognosis, allowing individualized therapy with the goal of improving outcomes."

FundamentalVR's Fundamental Surgery platform brings haptic‑enabled VR and digital‑twin simulation to surgical education in a package that New Orleans residency programs can deploy on off‑the‑shelf headsets to scale hands‑on practice, track performance, and reduce dependence on costly wet labs - Training Industry reports hardware‑agnostic @HomeVR can cost less than one‑tenth of traditional learning setups and supports single‑user remote rehearsal and unified performance data (Fundamental Surgery @HomeVR launch); the platform's multimodal stack (HapticVR, @HomeVR, Teaching Space, Data Insights, MultiuserVR) is explicitly designed for repeatable skills transfer and faculty‑led debriefing (Fundamental Surgery platform overview).

For New Orleans health systems the payoff is practical: affordable, measurable simulation that lets community hospitals and academic centers run standardized procedural curricula with remote faculty supervision and objective dashboards - already validated in specialty deployments including ophthalmology training with Orbis (Orbis partnership on cataract simulation), making rapid scale‑up of residency and outreach training programs achievable without major capital buildouts.

Component	Primary benefit for New Orleans systems
HapticVR	Realistic touch for muscle‑memory and procedural rehearsal
@HomeVR	Stand‑alone headset practice; low‑cost, anytime learning
Teaching Space / MultiuserVR	Remote, multi‑user classroom for faculty‑led sessions
Data Insights	Objective dashboards for competency tracking and debriefs

“Think of FundamentalVR's medical training system as a ‘flight simulator' for both medical students and their instructors.”

Wysa offers New Orleans clinicians and community health programs an always‑on, conversational AI that delivers evidence‑based self‑help - CBT, DBT and mindfulness tools - plus optional human coaching, making it a practical adjunct during long waitlists or between therapy sessions rather than a replacement for clinicians; the vendor frames the app as suitable for users 13+ (11+ by provider agreement), provides free AI chat with premium paid upgrades, and reports >99% service availability to support real‑time access for patients across shift changes and nights (Wysa FAQ: intended use and limits – Wysa, Wysa product overview – Wysa).

User‑research and expert reviews reinforce the upside - trust and ubiquitous access - but also highlight limits in crisis recognition and conversational depth, so New Orleans health leaders should pilot Wysa as a monitored, privacy‑aware adjunct (data‑sharing options and institutional deployments are available) while routing high‑risk users to human care as advised by clinicians and recent mixed‑methods evaluations (mHealth study on user perceptions of Wysa – AME Publishing).

Metric	Value (source)
Play Store rating	4.6 (Google Play listing)
Downloads / Reviews	1M+ downloads; ~151K reviews (Google Play)
Intended age	13+ (11+ by provider agreement) (Wysa FAQ)
Availability	>99% service availability (Wysa FAQ)
Clinical framing	Self‑help + adjunct human coaching; not a crisis tool (Wysa FAQ)

FDA's new internal generative‑AI assistant Elsa is already reshaping how regulatory and administrative work gets done - running in a FedRAMP‑High GovCloud and used to synthesize clinical narratives, auto‑compare labeling, summarize adverse events, and rank inspection targets - so New Orleans health systems and medtech startups should treat dossier quality and machine‑readable metadata as strategic assets rather than optional extras; Elsa's pilots cut reviewer “touch time” dramatically and, if sustained, could shorten device and imaging AI clearance timelines (industry modeling suggests gains that can translate to 4–6 weeks earlier clearances for imaging tools), but the rollout also surfaced real reliability and governance risks that demand internal validation, human‑in‑the‑loop checks, and pre‑submission AI‑QC engines to catch hallucinations and false citations before regulators see them (see reporting on FDA pilots and narrative analytics and on Elsa's accuracy and oversight concerns).

For Louisiana, the practical takeaway is simple: invest now in structured authoring, metadata tagging, and AI‑governance roles so local hospitals and startups can both speed approvals and avoid costly regulatory queries.

FDA pilots of Elsa LLM for regulatory narrative analytics and Investigative report on Elsa AI tool accuracy and oversight concerns provide essential grounding for preparation.

Item	Detail
Launch / Environment	Agency‑wide rollout (June 2025); runs in FedRAMP‑High GovCloud
Key functions	Narrative summarization, adverse‑event synthesis, label comparisons, inspection prioritization, code generation
Efficiency signal	Pilot reviewers report tasks dropping from days to minutes; potential 4–6 week gains on some imaging AI clearances
Known issues	Reported hallucinations / false citations - requires human oversight and model validation
Practical step for New Orleans	Adopt structured authoring, metadata tagging, internal AI‑QC, and AI governance roles

“One of the challenges that came out from the initial release of the Elsa model for FDA is that it was prone to hallucination. By that, I mean it was making stuff up. … We can't have our AI do that when it comes to critical analysis of core ingredients and component structures that are required.” - Marcel Botha

New Orleans healthcare leaders should translate the momentum from local convenings into three concrete steps: (1) accelerate workforce readiness by enrolling clinical and IT teams in a practical program like the 15‑week AI Essentials for Work bootcamp (AI Essentials for Work syllabus and registration - 15‑week bootcamp) so staff can write repeatable prompts, evaluate outputs, and reduce clinician burden on day one; (2) validate high‑impact pilots (for example, sepsis early‑warning models and imaging enhancement) at regional summits where federal guardrails and interoperability are foregrounded - plan to send CMIOs/CIOs to the State Healthcare IT Connect Summit (Apr 30–May 2, 2025) and request an invitation to the Healthcare IT Institute (June 8–10, 2025) to align strategy, procurement, and vendor governance (State HIT Connect Summit 2025 details and registration, Healthcare IT Institute 2025 invitation and event details); and (3) harden AI governance - adopt structured authoring, metadata tagging, and human‑in‑the‑loop validation so pilots scale without regulatory or safety setbacks.

These three moves convert conference insights into measurable pilots that protect privacy, shorten validation cycles, and free clinicians to care.

Next Step	Resource / Date / Cost
Workforce upskilling	AI Essentials for Work - 15 Weeks; early bird $3,582; AI Essentials for Work syllabus and registration - 15‑week bootcamp
Policy & interoperability alignment	State HIT Connect Summit - Apr 30–May 2, 2025; State HIT Connect Summit 2025 details and registration
Executive strategy & vendor governance	Healthcare IT Institute - June 8–10, 2025, Four Seasons, New Orleans; Healthcare IT Institute 2025 invitation and event details

“One of the challenges that came out from the initial release of the Elsa model for FDA is that it was prone to hallucination. By that, I mean it was making stuff up. … We can't have our AI do that when it comes to critical analysis of core ingredients and component structures that are required.” - Marcel Botha