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

AI prompts are the bridge between Cincinnati's clinical needs and high-impact models: Cincinnati Children's AI Imaging Research Center is already using AI - trained on more than 40,000 pediatric hand radiographs - to improve bone-age assessment and automate organ segmentation, showing how targeted prompts can turn imaging data into faster, more accurate diagnoses (Cincinnati Children's AI Imaging Research Center).

In parallel, an NLP-based eligibility screener cut clinical trial screening time by 34%, proving that carefully crafted prompts accelerate recruitment and discovery (AI for clinical trial recruitment at Cincinnati Children's).

For Cincinnati clinicians and administrators ready to act, practical training in prompt design - like Nucamp's 15-week AI Essentials for Work bootcamp (early-bird $3,582) - teaches the exact prompts and workflows that translate local data into safer, faster care (Nucamp AI Essentials for Work syllabus and registration).

Metric	Value
Research Experts	10+
Peer-Reviewed Publications (FY23)	25
Grant Funding (FY23)	$12M+

“By leveraging natural language processing and machine learning technologies, ACTES was able to quickly analyze different types of data and automatically determine patients' suitability for clinical trials.” - Yizhao Ni, PhD

Selection prioritized prompts and use cases that are already practical for Ohio clinicians and learners: projects with local pilots or clear plans for deployment (for example, UC's CAR‑E AI coaching tool for medical students and residents), alignment with national education standards and funding pathways (the AMA ChangeMedEd precision education framework and its $12M portfolio for scalable projects), and measurable automation or evaluation methods that can integrate with EHRs (the TRACERs approach to resident‑attributable, automatable measures).

Criteria applied to each candidate prompt included local deployability (initial users or clinical rotations), evidence of faculty evaluation or learner feedback, scalability across health systems, and support from grant programs that enable dissemination; prompts that met multiple criteria were ranked higher because they offer Cincinnati a faster, lower‑risk path from prototype to routine use.

The result: a top‑10 list favoring coachable, EHR‑compatible prompts that can be evaluated in real clinical workflows and scaled with existing AMA and university resources (UC CAR‑E AI coaching tool for medical students and residents, AMA ChangeMedEd precision education framework and TRACERs automatable measures).

Selection Criterion	Source Evidence
Local pilot / learner focus	UC CAR‑E initial users: third‑year students on clinical rotations
Scalability & EHR automation	TRACERs: automatable, scalable resident measures
Grant support & dissemination	AMA precision education portfolio ($12M) for funded projects

“We're really excited about the potential. This is the type of challenge that our medical students need. We are hoping students are able to explore and really internalize the experiences that they are having during their medical education journey, and reflective practice is a big part of that journey.” - Laurah Turner, PhD

DAX Copilot automates clinical documentation by converting multiparty, ambient conversations into specialty‑specific draft notes, orders, referral letters, and patient summaries - reducing after‑hours charting and freeing clinicians to focus on patients; in published deployments clinicians reported spending about 24% less time on notes and seeing an average of 11.3 additional patients per month in early adopter systems (DAX Copilot outcomes and Epic integration (Microsoft blog)).

The capability is part of Microsoft's Dragon Copilot suite - trained on millions of encounters, customizable by specialty, and built to integrate with EHRs like Epic - now commercially available in the United States (May 1, 2025), which makes pilots feasible for Cincinnati health systems using Epic (Dragon Copilot healthcare product page (Microsoft)).

Real‑world evaluation shows adoption trends with no detected safety or documentation harm, supporting careful, audited local pilots and prompt templates focused on Ohio workflows (Nuance DAX cohort study (PMC article)); the practical payoff for Cincinnati: measurable clinician time reclaimed and more appointment capacity without new staffing.

Metric	Value
Time on notes	≈24% less (reported)
Additional patients	≈11.3 per physician/month (reported)
US availability	May 1, 2025

“Since we have implemented DAX Copilot, I have not left clinic with an open note... In one word, DAX Copilot is transformative.”

Ada Health's symptom‑checker is positioned as a practical patient self‑assessment and triage “digital front door”: a randomized, head‑to‑head ED study found Ada delivered higher diagnostic accuracy than a competing tool (JMIR randomized head‑to‑head ED study on Ada diagnostic accuracy), and Ada's research library summarizes multiple trials showing real‑world triage safety (94.7%) and the potential to safely steer 43.4% of low‑acuity walk‑in patients toward lower‑intensity care - reducing unnecessary emergency visits and triage workload for overstretched departments.

When combined with ER physician assessment, Ada raised diagnostic accuracy to 87.3% versus 80.9% for the physician alone, a concrete efficiency gain that Cincinnati health systems can test via focused prompt templates and pilot workflows (Ada Health research and publications).

Metric	Value
Triage safety (ED real‑world study)	94.7%
Low‑acuity patients safely redirected	43.4%
Ada + ER physician diagnostic accuracy	87.3% vs 80.9% (physician alone)

For Cincinnati clinicians juggling heavy outpatient panels and after‑hours paperwork, Doximity GPT offers a HIPAA‑compliant, clinician‑focused copilot that drafts histories, assessment & plans, insurance appeal letters, patient education handouts and secure patient messages - features shown to “save over 10 hours a week” for users and to generate useful time‑savings like a 15‑minute reduction on a single referral letter in clinician reports; its free, unlimited access and PHI safeguards make it a low‑cost option to pilot local prompt templates for prior‑authorization workflows, discharge summaries, and bilingual patient instructions in Ohio clinics (Doximity GPT HIPAA-compliant clinical assistant and productivity features).

Real‑world cautions from reviewers stress mandatory human review and enterprise controls - BAAs, secure hosting, and no‑training‑data retention - to protect patient privacy and meet institutional policies (MedCram overview: HIPAA‑compliant AI implications for clinical workflows).

Note the practical tradeoff for Cincinnati systems: productivity gains are immediate for messaging and letters, but broader EHR embedding remains a barrier for seamless charting integration (Healthcare Huddle analysis of Doximity GPT physician workflow integration), so teams should pilot specialty prompt libraries and governance checks before scaling.

Feature	Evidence
Reported time savings	Save over 10 hours/week (vendor testimonials)
Common uses	Notes, appeal letters, patient education, translations
Access & compliance	Free, unlimited access; HIPAA compliant

"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

AIDDISON™ from Merck (MilliporeSigma) packages generative AI and advanced CADD into a cloud‑native, ISO‑27001 secured SaaS that lets medicinal chemists explore huge chemical space - searching tens of billions of virtual and known molecules in minutes - and then move from hit identification to lead optimization with built‑in de novo design (REINVENT 4.0), predictive AI/ML ADMET scoring, and automated retrosynthesis planning, a workflow that can materially shrink the number of early wet‑lab experiments and accelerate candidate selection for Cincinnati's university labs and small biotechs (AIDDISON AI-powered drug discovery product page, Merck AIDDISON overview and future of scientific work).

The so‑what: teams in Ohio can run ultra‑large virtual screens and generate chemically and synthetically plausible libraries in minutes, cutting weeks from lead triage and lowering reagent and assay costs during early discovery.

Feature	Evidence / Detail
Ultra‑large search	60+ billion virtual & known molecules (SA‑Space)
De novo design	REINVENT 4.0-driven library generation
Security & infra	Cloud‑native SaaS, ISO 27001
Predictive models	AI/ML ADMET and docking integration

“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.” - SVP, Drug Discovery, Emerging Biotech

BioMorph–style predictive analytics combine chemical fingerprints with cell‑morphology readouts using similarity‑weighted merger models to raise bioactivity prediction accuracy; a Journal of Cheminformatics study showed that merging structure and morphology models via similarity to training data produces more accurate predictions across diverse endpoints, a capability Cincinnati university labs and small biotechs can adopt to prioritize compounds before costly assays and focus limited wet‑lab capacity on the most promising candidates (Merging bioactivity predictions from cell morphology and chemical fingerprint models - Journal of Cheminformatics (2023)).

For operational alignment with local priorities - reduced experimental cost and faster go/no‑go decisions - this fits Cincinnati's broader AI cost‑reduction strategies for healthcare and life‑science innovation (How AI is helping healthcare companies in Cincinnati cut costs and improve efficiency - AI cost reduction strategies for healthcare in Cincinnati); the so‑what is clear: better in silico triage turns scarce bench time into higher‑value experiments, shortening discovery cycles without new capital outlays.

Metric	Value
Journal	Journal of Cheminformatics (2023)
Article	Volume 15, Article 56 (02 June 2023)
Accesses	4,481
Citations	29

Storyline AI brings telehealth closer to Cincinnati patients by letting care teams design the exact voice and demeanor used in virtual visits and follow‑up messaging - its Storyline Communication Style & Tone Prompt documentation lets clinicians specify language, reading level, and empathy (examples include gentle, breast‑cancer support and supportive diabetes medication reminders) while operating inside safety and regulatory guardrails like HIPAA/GDPR. Paired with Storyline's telehealth analytics, these prompts tie patient history and platform inputs to tailored care plans and remote‑monitoring alerts that surface actionable risks to clinicians (Storyline AI telehealth use cases and applications).

The practical payoff for Cincinnati clinics and health systems is concrete: standardized, audit‑ready prompts produce consistent, evidence‑aligned messaging that supports personalized care pathways and improves patient compliance - exactly the kind of generative‑AI personalization that speeds monitoring, reduces errors, and scales follow‑up across diverse populations (Generative AI in healthcare for personalized care).

Moxi, Diligent Robotics' socially intelligent clinical “cobot,” automates routine logistics - delivering lab samples, fetching medications and PPE, and moving supplies - so nurses and techs spend less time on transit and more at the bedside; Diligent's materials report clinical staff devote roughly 30% of a shift to non‑care tasks and sometimes up to 45 minutes for a single drug delivery, and care teams using Moxi saved 284,000 hours in 2024, illustrating a concrete productivity win that Cincinnati health systems can pilot to reduce interruptions and preserve scarce nursing time (Diligent Robotics: Moxi clinical robot product page, Diligent Robotics blog: Meet Moxi - Why We Invested in Diligent Robotics).

Built for crowded, semi‑structured hospitals, Moxi combines mobile manipulation and social behaviors to navigate elevators, open doors, and hand off items reliably, turning small, frequent errands into measurable operational capacity without major infrastructure changes.

Metric	Value / Source
Hours returned to care teams (2024)	284,000 hours
Share of shift on non‑care tasks	≈30%
Healthcare systems deployed	22 systems (early adopters)

“What started out as an innovative project for us, and something we had hoped would save a little time, has turned into a necessity. Moxi has been an incredible benefit to our nursing staff and has allowed them more time to focus on caring for their patients – the reason they went into the profession in the first place.” - Becky Fuentes, Chief Nursing Officer, Shannon Medical Center

Merative brings predictive clinical analytics and continuous monitoring to bear for hospitals and researchers that need faster, actionable insights: its Truven Health Insights platform makes analytics accessible to non‑data scientists and runs on Microsoft Azure with HIPAA‑aligned security, while MarketScan's linked claims+EHR repositories supply longitudinal real‑world data for cohort discovery and outcomes research - features that let Cincinnati teams shorten analytic latency and prioritize high‑risk patients without building a new data warehouse (Merative Truven Health Insights platform for healthcare analytics, MarketScan real‑world data and analytics for outcomes research).

The practical payoff for Ohio: MarketScan advertises up to 10× faster access to research‑ready data and about 60% cloud cost savings on Snowflake, meaning academic groups and system analytics teams can run validated predictive models and generate reports for care managers in hours instead of weeks.

Metric	Value
Healthcare providers served	4,500+ (includes 9 of top 10 US hospitals)
Research data speed / cost	≈10× faster access; ≈60% cloud cost savings (MarketScan on Snowflake)
Security & compliance	Built on Microsoft Azure; HIPAA-aligned

“We know that MarketScan data is trusted and of top quality. The real-world data helps us answer questions earlier, that is priceless because we can help our customers quicker and more efficiently.” - Paul Petraro, Global Head of Real World Evidence, Boehringer Ingelheim

The University of Cincinnati's CAR‑E (Coaching with AI‑Reinforced Education) is a web‑based, on‑demand AI coach designed to prompt reflective practice for third‑year medical students and residents - asking learners to revisit clinical encounters, expose knowledge gaps, and practice decision‑making so faculty time stretches farther across Ohio's training pipeline; funded by an American Medical Association grant ($30,000) and led by clinicians and educators including Matthew Kelleher, MD and Laurah Turner, PhD, CAR‑E aims to create “memories” of prior conversations, simulate clinical experiences, and deliver precision medical education that remembers a learner's history and flags when human coaches are needed, making it a practical, low‑risk pilot for Cincinnati hospitals and residency programs seeking scalable coaching aligned with national reform efforts like the UC CAR‑E AI coaching tool and pilot and the broader AMA Reimagining Residency and ChangeMedEd Initiative, which emphasizes competency‑based and transition‑focused coaching across medical schools.

Item	Detail
Initial users	Third‑year medical students during clinical rotations
Grant	American Medical Association, $30,000
Key goals	Personalized coaching, simulated clinical experiences, precision education

“We're really excited about the potential. This is the type of challenge that our medical students need. We are hoping students are able to explore and really internalize the experiences that they are having during their medical education journey, and reflective practice is a big part of that journey.” - Laurah Turner, PhD

BioNLP 2025 foregrounds the exact challenges Cincinnati teams must address when moving research prompts into EHR‑connected care: the workshop (co‑located with ACL; program includes Cincinnati Children's researcher Brian Connolly) emphasizes evaluation, transparency, and factuality for biomedical LLMs, while the ArchEHR‑QA shared task defines practical guardrails - answers limited to 75 words, must cite provided clinical note excerpts, and require at least one run with no external knowledge - so prompts can be stress‑tested for hallucination risk before local deployment (BioNLP 2025 workshop and shared tasks for biomedical NLP).

ArchEHR‑QA's scoring (strict Citation F1 for factuality plus relevance metrics) and PhysioNet datasets give Cincinnati researchers a reproducible pipeline to validate prompt templates and measure real evidence‑grounding against clinician‑curated questions (ArchEHR‑QA grounded EHR question answering benchmark), a tangible step that reduces the chance of unsafe summaries and accelerates IRB‑ready pilot testing in Ohio health systems.

Item	Key detail
BioNLP workshop date	August 1, 2025
ArchEHR constraints	75‑word answers; cite provided note excerpts; one run without external knowledge
Factuality metric	Strict Citation F1 (plus relevance: BLEU/ROUGE/BERTScore)

Practical next steps for Cincinnati beginners: start with governance and focused pilots - stand up a small multidisciplinary review (learn from CAIPA's “go slow to go fast” approach) and choose one high‑value workflow to test in a 6–12 week pilot (EHR‑embedded note drafting or a patient triage pilot), because real pilots show concrete wins - DAX Copilot pilots reclaimed roughly 24% of clinicians' note time and Ada redirected >40% of low‑acuity ED visits in studies.

Protect data from day one by using HIPAA‑aware anonymization tools and workflows when sharing charts for model training or research (HIPAA-compliant AI-powered healthcare data anonymization guide), prioritize EHR‑first features that Epic documents for clinicians to reduce integration risk (Epic AI for Clinicians documentation), and invest in practical staff skill building - consider Nucamp's 15‑week AI Essentials for Work to teach prompt design and workflow integration to care teams (Nucamp AI Essentials for Work syllabus (15-week)).

The so‑what: a small, governed pilot plus minimal staff training can turn one prompt into measurable time saved, safer handoffs, and a clear case to scale across Ohio health systems.

Next Step	Quick Resource
Form multidisciplinary AI review team	Epic AI for Clinicians documentation and governance guidance
Run a short pilot (6–12 weeks)	Epic AI for Clinicians documentation; DAX Copilot pilot evidence
Ensure safe data sharing	HIPAA-compliant AI-powered healthcare data anonymization guide
Train staff in prompts & workflows	Nucamp AI Essentials for Work syllabus (15-week)

“You are responsible for anything an LLM puts out as though you wrote it yourself.”