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

Cleveland's health systems are moving from AI experiments to everyday clinical use because prompts shape how tools surface faster, safer answers from massive local datasets - think ambient scribing, chatbot scheduling, triage that flags strokes in minutes, and radiology tools that reduce repeat imaging.

The Cleveland Clinic guide to AI in healthcare documents AI across diagnosis, research and patient workflows, while the University Hospitals report on real AI deployments in clinical practice shows implementations (Aidoc, ARCH‑AI recognition) that prioritize governance and clinician oversight; that combination makes prompt-writing a practical skill for Cleveland clinicians and administrators.

For teams building prompt-driven tools or adopting AI, the 15-week AI Essentials for Work bootcamp registration and syllabus teaches concrete prompting and workplace integration to turn models into measurable improvements at the bedside.

Program	Length	Early bird Cost	Registration
AI Essentials for Work	15 Weeks	$3,582	Register for the AI Essentials for Work bootcamp (15-week)

“Big data inputs potentially allow us to be more accurate in identifying biomarkers for disease, radiologic and pathologic diagnoses, and personalized treatments,” - Daniel Simon, MD

Methodology blended the HHS “evaluation” lens with Cleveland-specific operational priorities: each candidate use case had to meet (1) evidence and methodological soundness as in the ASPE selection criteria (importance, design quality, fidelity of conclusions) outlined in Performance Improvement 2001, (2) clear performance measures that show measurable change in outcomes or workflow, and (3) demonstrable local relevance - addressing access and coverage gaps flagged by HHS (populations at risk: very old, rural, near‑poor) or operational pain points in Cleveland health systems such as repeat imaging and throughput; practical payoff was weighted heavily (so what: selected prompts must enable measurable improvements like reduced repeat imaging rates in pilot deployments).

Sources that informed this approach include the HHS evaluation framework used to prioritize federal studies and local Cleveland case examples where radiology AI cut repeat scans and improved detection.

The result is a top‑10 list that favors technically sound, measurable, and equity‑minded prompts likely to show impact within health system pilots and payer evaluations.

Criterion	How it was applied
Evidence & methods	Aligned with ASPE/HHS review criteria for importance and methodological soundness (HHS Performance Improvement 2001 report)
Performance measurability	Required defined outcome/process metrics (e.g., reduced repeat imaging)
Local relevance & equity	Priority for use cases that address access/coverage gaps and Cleveland operational wins (radiology triage reducing repeat scans, per local examples)

Dax/Dragon Copilot brings ambient scribing into practical use for Ohio clinics by capturing multiparty patient–clinician conversations, converting them into specialty‑specific notes, and pushing orders and after‑visit summaries into EHR workflows - including direct order capture for systems like Epic - so documentation is done during the visit instead of after hours.

Trained on over 15 million encounters and supporting offline recording, the platform shortens note time, standardizes quality, and surfaces evidence‑backed suggestions (vitals, family history, coding cues) that reduce denial risk and speed chart closure; a Northwestern Medicine outcomes study tied DAX Copilot capabilities to a 112% ROI and a 3.4% service‑level increase, a concrete “so what” for Cleveland practices seeking rapid throughput and revenue protection.

For practical context on ambient clinical intelligence adoption and time‑saved benchmarks, see Microsoft's Dragon Copilot overview and the 2025 ambient clinical intelligence guide from Twofold Health.

Feature	Benefit
Automatic, customizable notes	Specialty templates + clinician style reduce edit time
Multilingual & offline capture	Covers diverse patients and low‑connectivity settings
Order capture to EHR	Faster ordering, fewer clicks, improved billing accuracy
Summaries & evidence curation	Patient‑facing after‑visit summaries and cited clinical evidence

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

Aidoc's aiOS™ augments diagnostic imaging and ED triage by flagging acute CT findings in real time, prioritizing cases for radiologists and activating care teams so follow‑up and treatment happen faster - a practical lever for Cleveland hospitals wrestling with throughput and repeat‑imaging pressure.

The platform integrates with EHR, PACS and scheduling to surface suspected intracranial hemorrhage, pulmonary embolism and other urgent findings, and a clinical study links AI‑augmented worklist triage to decreased hospital length‑of‑stay for ICH and PE; real‑world validations report up to a 36% increase in detection of subtle critical findings and ICH performance at ~92% sensitivity / 93.3% specificity, signaling measurable downstream benefits for Ohio emergency care pathways.

Learn more on Aidoc's radiology solutions and the clinical study, or see local context for Cleveland deployments and efficiency goals in our Cleveland healthcare roundup.

Target	Reported Impact
Intracranial hemorrhage (ICH)	~92% sensitivity, 93.3% specificity (real‑world validation)
Pulmonary embolism (PE)	44% increase in treatment opportunities via incidental detection; decreased LOS reported in clinical study

“Our radiologists are well-versed in interpreting AI-assisted findings critically. They consider AI suggestions as part of the overall diagnostic process, relying on their expertise to make the final decision. The combination of AI and human intelligence ensures accurate and comprehensive diagnoses.” - Dr. Ryan Kaliney

Aidoc radiology AI imaging solutions and product overview Clinical study: decreased hospital length-of-stay for ICH and PE using AI-augmented radiological worklist triage How AI is helping healthcare companies in Cleveland to cut costs and improve efficiency

For Cleveland health systems building clinician-facing assistants, the Amazon Kendra GenAI Index offers a managed RAG retriever that turns fragmented local content - EHR notes, policies, imaging reports, and intranet pages - into high‑precision, semantically ranked context for LLMs, so answers are anchored to hospital data instead of generic web knowledge.

Key features include a hybrid vector+keyword index with pre‑optimized parameters and metadata‑based permission filtering, connectors to dozens of enterprise sources (43 listed connectors), and explicit integration so a single GenAI index can be reused across Amazon Bedrock Knowledge Bases and Amazon Q Business applications; the practical payoff is straightforward: index once, then power multiple chatbots and knowledge assistants without rebuilding pipelines, cutting engineering lift and speeding clinician access to vetted, source‑linked guidance.

For technical guidance and healthcare examples that combine Kendra retrieval with Bedrock generation, see the AWS blog post "Introducing Amazon Kendra GenAI Index" and Intuitive Cloud's healthcare pattern "GenAI-powered healthcare queries with AWS Kendra + Bedrock", both useful when designing Cleveland pilots that must balance accuracy, access control, and clinician workflow speed.

AWS blog: Introducing Amazon Kendra GenAI Index and Intuitive Cloud: GenAI-powered healthcare queries with AWS Kendra + Bedrock

Capability	Why it matters for Cleveland health systems
Managed retriever (high semantic accuracy)	Reduces hallucinations by returning contextually relevant passages for RAG
Single GenAI index reuse	Index once and serve multiple assistants (Bedrock Knowledge Bases, Amazon Q Business), lowering build time and cost
Connectors + metadata filtering (43 sources)	Pulls from EHRs, SharePoint, S3 with user‑level access controls to protect PHI and surface the right documents

GE HealthCare's Command Center platform - part of a 302‑hospital, 55,000‑bed ecosystem - applies real‑time agents and predictive control (digital twins, synchronized patient‑flow dashboards) to cut friction in bed assignments, staffing and ED triage, offering a model Ohio systems can replicate to reduce delays and clinician workload; the ecosystem's case studies report measurable wins (a $20M first‑year saving at The Queen's Health Systems) and examples elsewhere (Johns Hopkins) show faster admissions and shorter ED bed‑assignment times, so Cleveland teams can expect operational gains when prompts drive the right alerts to the right teams.

GE also worked with University Hospitals Cleveland Medical Center to pilot an AI‑enhanced mobile x‑ray that flagged 7–15 collapsed lungs per day during evaluation - an operationally tangible “so what” that translated into faster readings and treatment.

Learn more on the GE Command Center ecosystem, the UH pilot, and broader command‑center outcomes across health systems.

Metric	Reported Value / Example
Command Center network	302 hospitals / 55,000 beds
Notable financial outcome	$20M saved (Queen's Health Systems case study)
UH pilot result	7–15 collapsed lungs flagged per day (mobile x‑ray + AI)

“Employing this equipment means better care for our patients.” - Dr. Amit Gupta, modality director, diagnostic radiography and principal investigator

GE HealthCare Command Center ecosystem details and outcomes

News: Cleveland Medical Center adopts GE HealthCare AI solution for operational improvements

Analysis: How America's top hospitals use machine learning (Emerj)

Early‑warning predictive models offer Ohio health systems a tangible advantage: a JMIR Medical Informatics study - authored by MITRE researchers with clinical co‑authors from Cincinnati Children's and Boston Children's - found that feature‑rich machine‑learning models using many predictor variables can predict unplanned transfers to the ICU accurately, in some cases up to 16 hours before deterioration, giving Cleveland hospitals lead time to prioritize monitoring, expedite diagnostics, or prepare ICU capacity; practical Cleveland pilots should therefore prioritize interoperable inputs (vitals, labs, nursing notes) and embed targeted prompts into EHR workflows to avoid alert fatigue while enabling timely escalation.

For teams planning pilots, the JMIR study's methods and local Cleveland AI adoption examples offer a roadmap for building prompts that translate predictive signals into coordinated clinical action.

Learn more in the JMIR study on predicting unplanned ICU transfers (DOI: 10.2196/medinform.8680) and our roundup of Cleveland AI applications.

Study	Journal / Date	DOI
Predicting Unplanned Transfers to the Intensive Care Unit	JMIR Medical Informatics, 2017‑11‑22	10.2196/medinform.8680

“Conclusions: Feature-rich models with many predictor variables allow for patient deterioration to be predicted accurately, even up to 16 hours ...” - PMID: 29167089

Azra AI's end‑to‑end oncology platform, developed with HCA and already deployed in 200+ U.S. hospitals, turns unstructured pathology and radiology text into real‑time case finding so Cleveland health systems can find and treat cancer faster; its Cancer Patient Identification module ingests every pathology report, flags positives into a prioritized “Care Queue,” and enables navigator outreach within 24 hours, cutting diagnosis‑to‑treatment time by roughly a week - a concrete “so what” for Ohio hospitals facing tight oncology capacity and registry backlogs.

Trained on over 100 million reports and reporting a 98% precision rate, the platform also surfaces incidental findings (lung nodules, pancreatic, ovarian and other sites), automates NAACCR‑formatted registry abstraction, and integrates with EHRs to reduce manual case‑finding and free navigators for direct patient care.

For Cleveland pilots, review Azra AI's end‑to‑end platform and its Cancer Patient Identification module to map workflows and measurable KPIs before deployment.

Metric	Value / Source
Reduction in time to treatment	~6–7 days (reported)
Precision / false positives	98% precision rate
Training data	Trained on 100+ million pathology & radiology reports
Deployment footprint	Used in 200+ hospitals (including HCA)

“Now that we know the denominator, or the number of diagnosed patients per year, we can size the programs, including nurse navigators, and needs for the system… get patients to where they need to be – the right patient, the right treatment and at the right time.” - Dr. Richard Geer, Physician‑in‑Chief of Surgical Oncology, HCA Healthcare

Azra AI end-to-end oncology platform information and overview Azra AI Cancer Patient Identification module details and solution page

Biology foundation models are now practical tools Ohio researchers can use to compress early R&D cycles: Sanofi describes AI‑driven platforms (CodonBERT, mRNA‑LM and the BioAIM program) that design and optimize biologics and vaccines in silico, reducing wet‑lab churn, while AWS HealthOmics provides a HIPAA‑eligible, fully managed environment that scales workflows to “tens of thousands of tests per day” with predictable cost‑per‑sample and built‑in provenance for compliance; together with frontier protein language models like EvolutionaryScale's ESM3 on AWS, teams can generate novel protein and antibody candidates, run AlphaFold-style structure predictions, and stitch virtual screening into reproducible pipelines - so what: Ohio labs and translational teams can iterate orders of magnitude more candidates without building bespoke HPC, shortening the time from hypothesis to IND‑ready molecules.

For technical and deployment guidance see Sanofi's overview of AI across R&D, AWS HealthOmics product details, and the AWS blog on bringing ESM3 to researchers.

Capability	Why it matters for Ohio researchers
BioFMs for protein & mRNA design	Enables in‑silico generation/optimization of candidates (CodonBERT, ESM3)
AWS HealthOmics orchestration	Managed, HIPAA‑eligible pipelines at scale with Ready2Run workflows
Prebuilt GPU blueprints & workflows	Faster virtual screening and AlphaFold/DiffDock steps via NVIDIA/AWS assets

“We're not just accelerating discovery. We're reimagining how we understand and combat disease at its core.” - Maria Wendt

Ada's AI symptom assessment and care‑navigation platform offers Ohio health systems a scalable digital front door that can triage patients 24/7, deliver easy‑to‑understand education, and hand structured clinical reports into workflows - critical for Cleveland sites balancing high ED demand and after‑hours access gaps.

Enterprise deployments show fast operational effects: CUF reported 53% of assessments occur outside normal clinic hours, 66% of users are more certain what care to seek, and 80% feel better prepared for consultations, while Ada's EHR‑friendly stack (Epic, Cerner, Meditech integrations) and enterprise handover reports support clinician readiness and reduce admin burden.

For Cleveland systems, that translates into fewer low‑acuity visits routed to EDs, higher telehealth uptake (13% same‑day telehealth in CUF data), and more focused in‑person visits when needed - measurable throughput and patient‑experience wins that fit local priorities.

Learn more about Ada's consumer assessments and enterprise care navigation and CUF results in Ada's product overview and case study, and see local AI adoption context in the Cleveland Clinic's AI guide.

Metric / Capability	Value / Source
Assessments outside clinic hours	53% (CUF case study)
Patients more certain what care to seek	66% (CUF case study)
EHR integrations	Epic, Cerner, Meditech (AVIA marketplace)

“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, CUF Chief Medical Officer for Digital Transformation

Ada AI symptom assessment and enterprise solutions - product overview Ada case study: Improving patient pathways with AI (CUF case study) Cleveland Clinic guide: How AI is being used to benefit healthcare

Clinical trial recruitment in Ohio gains practical leverage when cohort discovery moves from manual chart review to automated matching of normalized eligibility criteria against EHR records: the CriteriaMapper project demonstrated an approach to “establishing the automatic identification of clinical trial cohorts from electronic health records by matching normalized eligibility criteria and patient clinical characteristics,” which Cleveland sites can harness to reduce pre‑screen time and widen candidate pools for investigator‑initiated and industry trials (CriteriaMapper study in Scientific Reports (2024)).

Tying that capability to local deployment patterns and workforce training - see our Complete Guide to Using AI in Cleveland (2025) - creates a clear operational path: normalized criteria + EHR indexing = faster pre‑screen alerts to study teams, fewer missed eligible patients, and shorter time to randomization, a concrete “so what” for Ohio sites competing for federal and industry funding.

Field	Value
Article	CriteriaMapper: establishing the automatic identification of clinical trial cohorts from electronic health records
Journal / Date	Scientific Reports, 2024 Oct 25;14(1):25387
DOI / PMID / PMCID	10.1038/s41598-024-77447-x · PMID: 39455879 · PMCID: PMC11511882

Cleveland health systems must treat generative AI like any other regulated medical system: protect PHI, prove controls, and stop faulty model outputs before they reach clinicians.

Amazon Bedrock Guardrails offers policy-driven safeguards - sensitive‑information filters, denied topics, contextual grounding checks and Automated Reasoning - to redact PII, block unsafe prompts, and catch hallucinations in RAG workflows, and AWS tools (IAM, Audit Manager, AWS Config, CloudWatch/S3 logs) provide the audit trails needed for HIPAA‑eligible deployments in the US; practical metrics matter here: Guardrails has been shown to block up to 88% more harmful content and filter over 75% of hallucinated RAG responses, and its test traces let teams prove why a prompt was blocked or masked.

For Ohio pilots, ensure guardrails are applied to both Bedrock and external models via the ApplyGuardrail API and align policies with institutional compliance processes.

See AWS guidance on safeguarding healthcare data with Bedrock Guardrails for step‑by‑step setup and the Bedrock FAQs for region, HIPAA eligibility, and encryption details.

Capability	Relevance for Ohio healthcare
Sensitive Information Filters (PII)	Redact/mask PHI to support HIPAA‑eligible workflows
Contextual Grounding & Automated Reasoning	Detects hallucinations in RAG and provides verifiable checks before clinical use
ApplyGuardrail API & IAM enforcement	Central governance across Bedrock, self‑hosted or third‑party models
Logging & Audit (CloudWatch / S3 / Audit Manager)	Creates auditable traces for compliance reviews and incident response

Amazon Bedrock Guardrails how-to for safeguarding healthcare data privacy | Guide to building responsible AI applications with Amazon Bedrock Guardrails | Amazon Bedrock FAQs: HIPAA eligibility, regions, and security details

Getting started with AI prompts in Cleveland healthcare means pairing rapid pilots with ironclad privacy and clinician buy‑in: begin by selecting one measurable use case (ambient scribing, triage, or a radiology worklist) and run a short, focused pilot with frontline clinicians - Cleveland Clinic's selection process tested five scribes across 80+ specialties with pilots lasting three to five months and 25–35 clinicians per evaluation to ensure fit and adoption - so what: that cadence reveals usability issues before wide rollout (AHA analysis of the Cleveland Clinic AI scribe pilot).

Lock governance in early: map HIE participation (UH's Ohio Health Information Partnership / CliniSync) and HIPAA controls, document opt‑out and PHI procedures with your privacy officer, and keep auditable trails for every prompt and response (University Hospitals HIPAA Privacy Notice and CliniSync HIE details).

Finally, train prompt writers and operational owners before scale - enroll care managers or analysts in a focused curriculum like the 15‑week AI Essentials for Work to move from pilot prompts to production guardrails and measurable impact (Nucamp AI Essentials for Work bootcamp registration and syllabus (15-week AI program)).

Resource	Practical use
University Hospitals HIPAA Notice / CliniSync	Defines PHI protections, HIE participation and patient opt‑out process
Cleveland Clinic AI Scribe pilot (AHA)	Pilot cadence and clinician engagement model (3–5 months; 25–35 clinicians)
Nucamp - AI Essentials for Work (15 wks)	Hands‑on prompt writing, workflow integration, and operational skills

“You want the company to have passion for health care. This is not a technology play when all is said and done; this is a health care play.” - Rohit Chandra