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

Columbus matters for healthcare AI because a deep, local ecosystem - anchored by Ohio State Wexner Medical Center and Nationwide Children's Hospital - already combines research, talent, and capital, and leaders predict rapid expansion into a global med‑tech hub; Les Wexner has forecast “probably the largest AI investment in the world” in Columbus alongside plans for “100 more” medical‑tech firms, while major projects like OSU's nearly $2 billion hospital tower and Amgen's $900 million New Albany expansion show concrete scale and jobs to support AI deployment (Columbus AI and medical‑tech hub forecast by Les Wexner).

Homegrown teams - from Ohio State student ventures building access tools to Techstars‑backed startups - mean prompts and clinical use cases can be tested locally (Ohio State student AI healthcare ventures and research), and Columbus professionals can upskill for these roles via practical courses like the AI Essentials for Work bootcamp syllabus and course details, making the city a pragmatic launchpad for hospital AI pilots and revenue‑cycle automation.

The biggest flaw in the health care system is getting people in touch with the care they need. Once we're providing care, we do an excellent job. But we have serious difficulties getting there... AI provides an opportunity to erase these resource problems to get people to the places they need to be to receive care.

Bootcamp	Length	Early bird cost
AI Essentials for Work	15 Weeks	$3,582

Selection prioritized prompts and use cases that Columbus can realistically test, scale, and govern: criteria included closeness to anchor systems (OSU Wexner Medical Center and Nationwide Children's Hospital), compatibility with Ohio State's clinical‑informatics mission and workforce pipelines, technical feasibility given local HPC and data engineering, and measurable operational impact (for example, revenue‑cycle or documentation gains).

Emphasis went to items that map to Ohio State's Division of Artificial Intelligence in Digital Health (AICIIS) priorities and to pipelines already demonstrated at scale - most notably the plan to de‑identify and operationalize over 200M Epic clinical notes - so teams can move from prompt concept to compliance‑checked validation without rebuilding core infrastructure (OSU Division of Artificial Intelligence in Digital Health (AICIIS), OSU de-identified Epic clinical notes pipeline).

Practicality and short feedback loops drove ranking: prompts that reduce clinician documentation time or accelerate claims resolution rose to the top because they can show hospital budgets a clear ROI within pilot timelines.

Criterion	Evidence from Columbus research
Clinical anchors	OSU Wexner Medical Center & Nationwide Children's Hospital as local hubs
Data readiness	De‑identification pipeline for >200M Epic notes (OSU)
Compute & research capacity	Ohio Supercomputer Center GPU clusters and NextGenAI partnerships

“probably the largest AI investment in the world will happen in Columbus”

Nationwide, headquartered in Columbus, has paired H2O.ai's automated ML with internal generative AI pilots to shrink documentation friction and accelerate content creation for product and clinical‑adjacent teams: a centralized “model factory” built with H2O Driverless AI runs at scale across underwriting, fraud and customer‑360 use cases - reporting over 25 billion model scores and “savings in the millions” - while LLM experiments (including internal Bing Chat Enterprise and GPT‑4 access) produced initial drafts for 71 medical or veterinary conditions and reclaimed hundreds of hours from subject‑matter experts, creating cleaner, single‑source content for platforms like Nationwide Pet HealthZone; see the H2O.ai case study on Nationwide's ML factory and the Emerj summary of Nationwide's GenAI initiatives for details on scope and outcomes (H2O.ai case study: Nationwide Insurance ML factory, Emerj analysis: Nationwide GenAI initiatives and LLM use cases).

Metric	Value
Model scores	25 billion
Reported savings	Millions of dollars
Content drafts from LLMs	71 conditions
Expert hours saved	300+ hours

“H2O.ai provides us the power and flexibility we need to solve business problems with machine learning. We are able to do more with less and do it faster. Our results are proof of the power of AI in action. Working with H2O.ai platforms allows us to quickly provide stable, statistically unbiased models that we can trust in our production environment.”

H2O.ai's automated ML stack - most notably H2O Driverless AI - powers a Columbus‑anchored “model factory” at Nationwide that centralizes model development, monitoring, and deployment so hospitals and payers can move from hypothesis to production much faster; the platform delivered stable, statistically unbiased models, cut prototyping time and produced “savings in the millions” while running over 500,000 model instantiations and more than 25 billion model scores in production.

For Ohio health systems exploring risk stratification, claims‑triage models, or readmission prediction pilots, that proven scale means teams can iterate rapidly with documented pipelines and automatic feature engineering rather than rebuilding ML ops from scratch (see the H2O.ai case study on Nationwide and the H2O.ai press release on results for details).

Metric	Value
Model instantiations	500,000+
Model scores	25 billion+
Reported savings	Millions of dollars
Prototyping time	Decreased

“H2O.ai provides us the power and flexibility we need to solve business problems with machine learning. We are able to do more with less and do it faster. Our results are proof of the power of AI in action. Working with H2O.ai platforms allows us to quickly provide stable, statistically unbiased models that we can trust in our production environment.”

Nuance DAX Copilot embeds generative AI directly into the Epic EHR to automate creation of clinical documentation, turning Dragon Ambient capture and clinician inputs into draft notes inside the chart so providers can review and sign instead of reconstructing encounters (Nuance DAX Copilot general availability in Epic EHR announcement).

Earlier DAX Express work shows the same workflow copilot model applied to Dragon Medical One and Dragon Ambient eXperience users within Epic, which lowers integration friction for health systems that already use those tools (DAX Express integration announcement for Epic EHR).

Real deployments - for example, WellSpan's use of DAX to automatically draft clinical notes - illustrate a practical path for Columbus hospitals using Epic to cut documentation load and accelerate chart completion while keeping work inside clinicians' existing workflows (WellSpan DAX Copilot deployment case study).

Doximity GPT is a clinician‑facing GPT that brings HIPAA‑aligned drafting and clinical reference directly to the Doximity app, enabling Columbus clinicians to generate instant notes, patient handouts, prior‑authorization letters, and evidence‑backed clinical answers so busy hospitalists and primary care teams can review and sign instead of rebuilding encounters; the platform is free on desktop and mobile, claims HIPAA/HITECH safeguards with BAAs and SOC 2 controls, and advertises time savings of “over 10 hours a week” plus a zero‑data‑retention posture for sensitive inputs - practical for Ohio practices that need quick, auditable documentation without exposing PHI to consumer chat services (Doximity GPT clinician-facing AI details, Doximity HIPAA and HITECH compliance details, MedCram article on HIPAA-compliant clinical AI).

The immediate payoff: a measurable reclaim of clinician after‑hours time that can be redirected to patient visits or local quality projects.

Feature	Detail
HIPAA protections	BAA available; SOC 2/HIPAA/HITECH controls
Access	Free, unlimited on desktop & mobile
Impact	Claims: save >10 hours/week; zero‑data retention

"This tool has been a game-changer for my charting process, whether it's creating a plan for congestive heart failure or an HPI for atrial fibrillation. It provides accurate, comprehensive support that saves me time and has also streamlined tasks like writing appeal letters and providing educational information on new prescriptions." - Dr. Munir Janmohamed, Cardiology

UpDoc's voice‑first insulin dosing assistant converts multi‑turn spoken check‑ins into clinician‑configured titration decisions - delivered through smart speakers and optionally wired to CGMs or glucometers - so patients with Type 2 diabetes can receive dose instructions, log adherence, and complete safety check‑ins at home; a 2023 Stanford trial cited in industry summaries reported 81% of patients reached target glucose with the voice assistant versus 25% with standard care, a stark signal for Ohio practices looking to boost at‑home control without adding clinic visits (UpDoc voice‑first insulin dosing assistant summary).

The system's published patent and application describe clinician‑set algorithms, generative voice prompts, multi‑turn dialogue, and lock‑outs for safety - features that map to existing Columbus remote patient monitoring stacks and make pilot governance, logging, and EHR interfaces more straightforward (UpDoc voice‑based diabetes management patent filing).

Patent: US Patent No.

12,251,242 B1 (granted Mar 18, 2025); related application 20250248666
Clinical evidence: 2023 Stanford trial: 81% reached target glucose with voice assistant vs 25% with standard care
Funding / partners: Polaris Partners, Eli Lilly, Mayo Clinic (strategic funding 2024–25)
Key features: Smart‑speaker delivery, clinician‑configured titration algorithms, CGM/glucometer interfacing, medication logging

Ensemble Health Partners is commercializing EHR summarization and revenue‑cycle automation that Ohio health systems can adopt to cut manual claims work and speed payment: its patented Next Best Action predicts, optimizes and automates the next step in a healthcare claim workflow and the company's Jan 2025 patent notice details dynamic data retrieval, autonomous summary generation, decisioning and automated actions across EHRs and payer systems (Ensemble Health Partners Next Best Action patent, GlobeNewswire: Ensemble's Jan 2025 patent notice and generative AI capabilities).

The practical payoff is concrete: Ensemble reports automated intelligent actions on roughly 25% of targeted transactions while managing $37B in annual net patient revenue and driving about 5% average net revenue improvement - figures that signal faster denials appeals, fewer chart pulls, and measurable revenue recovery for Columbus hospitals without rebuilding core EHR integrations.

Metric	Value
Automated intelligent actions	~25% of targeted transactions
Annual net patient revenue managed	$37 billion
Average net revenue improvement	5% per year

“This is another step forward in revenue cycle automation and efficiency. Our latest patented technologies maximize the value of human intelligence by combining it with the latest AI technologies.” - Pieter Schouten, Chief Innovation Officer, Ensemble Health Partners

GE HealthCare's Spine Auto Views uses deep‑learning post‑processing to produce anatomically aligned, “ready‑to‑read” CT spine reformats that automatically label vertebral bodies and disc spaces and can network up to six labeled series to predefined DICOM destinations - features that directly tackle manual reconstruction bottlenecks and inconsistent annotations common in busy radiology suites (GE HealthCare Spine Auto Views product page - advanced visualization).

With reported vertebral‑labeling and axial‑oblique orientation accuracies above 90% and real‑world testimonials showing multi‑minute savings per scan, the tool offers Ohio hospitals a practical path to faster reads, fewer PACS reconstructions, and more clinician time at the bedside (Medical News Observer summary of Spine Auto Views FDA clearance and evaluation).

Feature	Detail
Labeling accuracy	>90% for vertebrae and disc spaces
Auto‑generated series	Up to 6 labeled reformats per protocol
Workflow	Automatic DICOM transfer to prescribed destinations
Readiness	“Ready to read” anatomically aligned reformats

“Before using Spine Auto Views, scanning a CT spine would take about 15 minutes, I would say from start to finish. And now I can say with a patient that has much mobility, we can finish a scan in 5 minutes.” - Elise Capel, Certified Radiology Nurse, UZ Brussel

Mauna Kea's Cellvizio platform brings real‑time, in‑vivo cellular visualization and AI‑assisted Confocal Laser Endomicroscopy (CLE) into workflows Columbus hospitals can pilot for earlier cancer detection, sharper in‑procedure decision‑making, and tighter surgical margins; the system's small OR footprint, DICOM compatibility, and probe range for gastroenterology and pulmonology make integration into endoscopy and robotic‑assisted surgery practical, while Mauna Kea's expanding AI IP (AURA/EVA) and a data‑annotation partnership with V7 enable real‑time image enhancement and physician‑facing decision support (Mauna Kea Cellvizio CLE platform overview, AURA AI patent for real‑time CLE enhancement and details, V7 partnership for annotated endomicroscopy datasets and collaboration).

A concrete local payoff: a transoral robotic surgery case series using Cellvizio reported clean margins in every case - a direct signal that OSU and Columbus surgical teams could reduce positive‑margin reoperations and preserve patient function while shortening diagnostic uncertainty.

Metric	Value
Patient procedures	>100,000
Clinical papers	>1,200
U.S. AI patents (CLE enhancement)	13th–14th patent series (AURA/EVA)
Regulatory clearances	>20 (FDA/CE mentions)

“This technology represents a paradigm shift in how we might assess surgical margins.” - Dr. Bharat Akhanda Panuganti

AIDDISON™ from Merck (MilliporeSigma in the U.S.) is a software‑as‑a‑service platform that couples generative AI, machine learning and computer‑aided drug design to virtually screen more than 60 billion chemical targets and recommend real‑world synthesis routes via Synthia™ retrosynthesis API - capabilities that let Columbus‑area medicinal chemists and translational teams move faster from in‑silico hits to manufacturable leads by weeding out non‑synthesizable candidates early and suggesting reagents and building blocks for higher‑yield, safer production; the platform is trained on over two decades of experimentally validated R&D data and, according to company materials, could cut time and cost in discovery by up to 70% and contribute to an industry savings estimate of more than US$70 billion by 2028 (see the Merck AIDDISON press release and platform overview for details).

The so‑what: local pilots that tie AIDDISON outputs into Columbus contract labs or OSU process chemistry can shorten the risky handoff between discovery and manufacturing, turning promising leads into actionable development campaigns sooner.

Feature	Detail
Product	AIDDISON™ (SaaS)
Chemical space screened	> 60 billion targets
Integration	Synthia™ retrosynthesis API
Training data	2+ decades of experimentally validated R&D datasets
Claimed impact	Up to 70% time/cost savings; >US$70B potential industry savings by 2028

“With millions of people waiting for the approval of new medicines, bringing a drug to market, still takes on average, more than 10 years and costs over US$2 billion.” - Karen Madden

StaffDNA blends AI-powered job matching with pay transparency to help Columbus-area clinicians and allied professionals close staffing gaps faster: its AI job‑matching engine profiles credentials, location, shift preferences and certifications to surface travel, staff, local, per diem and locum tenens roles from a nationwide pool - more than 35,000 real jobs updated in real time - and the platform's interactive “My Rate” pay calculator lets candidates personalize pay and benefits before they apply, reducing negotiation back‑and‑forth for Ohio hospitals.

In-app compliance checks, timecards and per‑diem shift booking simplify onboarding and reduce administrative churn for busy units such as ED, perioperative suites and imaging departments.

For Columbus health systems trying to cut vacancy‑driven overtime and retain clinicians, StaffDNA's transparent listings and smart matching offer a practical pilot path; learn more on the StaffDNA AI-powered job-matching overview and the StaffDNA Candidates Find, Book, Manage platform for details (StaffDNA AI-powered job-matching overview, StaffDNA Candidates Find, Book, Manage platform).

Metric	Value
Real-time job listings	Over 35,000
Job types	Travel, staff, local, per diem & locum tenens
RN Travel (example)	Up to $4,714/Wk
RN Per Diem (example)	Up to $68.50/Hr

"... StaffDNA® Is The Best Company I've Worked With..." - Ryan M.

Practical next steps for Columbus healthcare beginners: start small, tie pilots to local capacity, and learn skills that let teams act fast - first, connect with Ohio State's Division of AI in Digital Health (AICIIS) and OSU AI resources to find research partners and governance pathways (OSU Division of AI in Digital Health (AICIIS) - OSU AI in Digital Health); second, pick a high‑ROI pilot such as ambient documentation or revenue‑cycle automation (these pilots reclaim clinician time and speed claims) and test clinician‑facing tools already in use locally; and third, upskill operational and clinical staff with a practical, workplace‑focused course like Nucamp's AI Essentials for Work - Practical AI Skills for Business so teams can write effective prompts, run safe pilots, and measure impact.

Use OSU Wexner's documented ambient‑AI rollout as a playbook - its pilot quickly scaled and in early expansion 100 clinicians regained 64 hours in two weeks - then leverage Columbus workforce programs and state commercialization grants to move validated pilots toward production (OSU Wexner ambient AI pilot and public survey on AI in health care).

Immediate next step	Local resource
Find research & governance partner	OSU Division of AI in Digital Health (AICIIS) - Research & Governance
Run a quick documentation or RCM pilot	OSU Wexner DAX ambient documentation pilot playbook
Train staff to write and test prompts	Nucamp AI Essentials for Work - Workplace AI Prompting and Pilot Training

“We found it saved up to four minutes per visit. That's time the physician can use to connect with the patient, do education and make sure they understand the plan going forward,” Ravi Tripathi said.