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

To compile the Top 10 AI prompts and use cases for Carmel healthcare organizations we applied a locally grounded, evidence-first methodology that prioritized clinical impact, real-world readiness, and governance - drawing directly from Regenstrief's AI research programs and the themes surfaced at the Regenstrief Healthcare AI Conference.

We screened candidate prompts against three lenses: (1) alignment with Regenstrief‑tested domains such as clinical decision support, NLP for EHRs, and public‑health case detection, (2) operational feasibility in mid‑sized systems and physician workflows, and (3) ethical, privacy and governance safeguards emphasized at regional convenings.

We favored prompts with peer‑reviewed or registry‑tested performance, clear integration paths into existing EHR/HIE infrastructure, and training pathways for local staff (see Nucamp's practical guidance for explainable and privacy‑preserving AI in Carmel).

Selection Criterion	Relative Weight
Clinical impact / patient safety	30%
Evidence & readiness (Regenstrief projects)	25%
Governance / trustworthiness (conference guidance)	20%
Operational feasibility / ROI	15%
Workforce & training needs	10%

Read more about the research foundations at Regenstrief AI and machine learning research (Indianapolis), review the conference program at Regenstrief Healthcare AI Conference 2025 program and panels, and consult implementation best practices in our local guide: Nucamp guide: Complete guide to using AI in Carmel healthcare (2025).

DAX Copilot (Nuance Dragon Ambient eXperience) brings ambient conversational AI directly into Epic workflows to draft clinical notes, reduce documentation time, and help clinicians focus on patient care; Nuance announced general availability of the Copilot embedded in Epic in January 2024, positioning it for broader health-system rollouts (Nuance DAX Copilot general availability announcement).

The capability builds on earlier DAX Express integrations that extend ambient documentation across encounters and modalities, enabling smoother capture of visit context and faster note generation (Nuance DAX Express integration with Epic).

Importantly for Carmel and greater Indianapolis providers, Indiana systems are already adopting Nuance tools - Community Health Network reported expanded deployment of DAX/Dragon platforms as part of its documentation modernization effort - showing local feasibility and a pathway for pilot implementations with governance, privacy safeguards, and clinician feedback loops (Community Health Network DAX/Dragon deployment in Indiana).

Practical next steps for Carmel: start small with Epic-integrated pilots, capture time‑saved and accuracy metrics, and train staff on prompt‑level controls and documentation review workflows.

Ada Health's patient‑facing triage and symptom‑checking chatbots offer Carmel health systems a pragmatic digital front door: they guide patients through structured symptom flows, prioritize urgency, and route appropriate next steps (self‑care, primary care, telehealth or ED), helping reduce low‑acuity ED visits and improving scheduling efficiency when integrated with local EHRs and scheduling platforms.

Vendor	Primary use	Security / Notes
Clearstep	Virtual triage & symptom navigation	Patient engagement / EHR integrations
Ubie	AI symptom checker & triage	HIPAA‑compliant, clinically referenced
Xund	Conversational symptom & illness checker APIs	ISO 27001; API/SDK for integration

Vendors in the administrative and patient‑engagement landscape (see the AI‑powered symptom checker listings) commonly provide HIPAA and ISO controls and developer toolkits for EHR writeback, so Carmel organizations should pilot Ada‑style triage with measurable metrics (avoidance of unnecessary visits, time‑to‑triage, and patient satisfaction), defined governance, and clinician review workflows to maintain safety and explainability.

For local context on how revenue‑cycle and access improvements pair with triage automation in Carmel, review practical examples from our region, and consult guidance on explainable, privacy‑preserving deployments before scale‑up.

Implement Ada‑style chatbots in Carmel by starting with small, monitored pilots that link triage outcomes to scheduling and revenue‑cycle processes, use human‑in‑the‑loop review, and follow local best practices for explainability and privacy.

Storyline AI reconceives telehealth for high‑touch, behavioral and chronic‑care workflows by combining precision care pathways, automated triggers, a growing library of clinically validated assessments, and military‑grade HIPAA/GDPR security - making it a practical option for Carmel clinics that need scalable, patient‑centered virtual programs rather than simple video visits; evaluate the platform and clinical library on the Storyline AI behavioral telehealth platform (Storyline AI behavioral telehealth platform) and review pricing tiers and pilot options on the Storyline telemedicine pricing and plans page (Storyline telemedicine pricing and plans).

For Carmel providers aiming to expand telepsychiatry, remote monitoring and longitudinal care pathways, Storyline's automation and analytics can reduce low‑value administrative work (Storyline reports a 4× productivity gain), enable monetizable programs, and link patient journeys to scheduling and payment flows - complementing regionally relevant digitally enabled care models described by national case studies (AMA case studies of digitally enabled care).

“Storyline lets us build robust care pathways that extend beyond the clinic to support clinical interventions and patients.”

Outcome	Storyline Metric
Team productivity	4×
Patient recommendation	97%
Revenue uplift	+17%

Start in Carmel with a small, monitored pilot tied to EHR scheduling, clinician review flows, and measurable access and outcome metrics before scaling.

Medical imaging AI can improve diagnostic support in Carmel by combining interoperable standards, curated local datasets, and clinician‑in‑the‑loop validation so models assist - not replace - radiologists; historic collaborations to unify radiology procedure names and codes make training data more consistent and comparable across systems, a key step for accurate computer‑vision models (RSNA–Regenstrief radiology procedure standardization collaboration).

Local partners such as Regenstrief are positioned to help Indianapolis‑area providers access shared infrastructure for secure model development and evaluation through the NAIRR pilot, which supports privacy‑preserving resources and interoperable software for healthcare AI (Regenstrief collaboration on the NAIRR pilot for healthcare AI).

Practical steps for Carmel health systems include piloting narrow, validated imaging AI for specific reads (e.g., chest x‑ray triage), instrumenting performance and safety metrics, and using regional convenings to align governance - the Regenstrief Healthcare AI Conference highlights applied imaging panels and operational lessons for deployable models (Regenstrief Healthcare AI Conference 2025 imaging panel and operational lessons).

“Future healthcare‑focused AI innovation is anchored in the ethical sourcing of real‑world data and the synergy between a diverse consortium of institutions…”

NAIRR Focus	Role for Imaging AI
NAIRR Open	Broad access to diverse imaging research resources
NAIRR Secure	Privacy‑preserving datasets for model validation
NAIRR Software	Interoperable tools and deployment pipelines
NAIRR Classroom	Training clinicians on safe AI use

AIDDISON, MilliporeSigma's generative‑AI drug discovery platform from Merck, combines generative models, machine learning and computer‑aided drug design to search billions of virtual compounds and propose manufacturable synthesis routes - a capability that could help Indianapolis‑area and Carmel biotech teams move faster from target to testable candidate and align discovery with local manufacturing partners.

The platform integrates the Synthia retrosynthesis API and is trained on decades of experimental R&D to screen >60 billion chemical possibilities and prioritize properties like non‑toxicity, solubility and stability, while proposing synthesis routes that improve manufacturability for regional contract labs and university spin‑outs (AIDDISON generative AI drug discovery platform - Merck product page).

Early coverage and product details note potential time‑and‑cost savings and SaaS delivery for smaller labs (MilliporeSigma AIDDISON product launch and technical brief), and industry reporting highlights the platform's role in bridging virtual design to real‑world synthesis (Biopharm International coverage of MilliporeSigma AI solution).

“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.”

Attribute	AIDDISON
Chemical space screened	>60 billion compounds
Training data	20+ years of validated R&D
Estimated industry impact	>$70B savings by 2028; up to 70% time/cost reduction

Merative's population‑health and risk‑stratification toolset is a practical match for Carmel health systems seeking to move from reactive care to targeted, measurable interventions: these platforms ingest EHR and claims data to flag high‑risk patients, prioritize transitional‑care resources, and generate registries that primary‑care teams can action.

Peer evidence shows the value of combining clinician‑driven features with machine‑learned representations - one multicenter study achieved a tuned GBM AUC ≈ 0.83 using manual + Word2Vec features, a notable improvement over the LACE baseline (BMC study on machine‑learned readmission prediction).

Operational playbooks emphasize embedding risk scores into family‑medicine workflows so clinicians can schedule early follow‑up and reconcile medications (MGH opinion on leveraging predictive analytics to transform hospital readmissions).

Regional implementations offer practical lessons: a Health Catalyst–assisted deployment reported an AUC of 0.784 and faster availability of risk scores to drive post‑discharge outreach (Health Catalyst Mission Health machine‑learning readmission case study).

“Future healthcare‑focused AI innovation is anchored in the ethical sourcing of real‑world data and the synergy between a diverse consortium of institutions, researchers, medical professionals, and both public and private stakeholders.”

Model / Source	AUC	Implication for Carmel
BMC multicenter ML (GBM tuned)	0.83	Combine manual + learned features
Mission Health (Health Catalyst)	0.784	Operational integration + daily availability
LACE baseline	0.655	Insufficient for targeted outreach

Start small in Carmel: validate models on local data (Regenstrief/HIE partnerships), route scores into primary‑care workflows, measure reductions in 30‑day readmissions and equity impacts, and maintain clinician oversight for explainability and governance.

Read the BMC study on machine‑learned readmission prediction for methodology details: BMC study on machine‑learned hospital readmission prediction and methods.

For practical guidance on embedding predictive analytics into primary care workflows, see the MGH opinion piece: MGH opinion on leveraging predictive analytics to reduce readmissions.

For a regional implementation case study on reducing readmissions with machine learning, see the Health Catalyst Mission Health success story: Health Catalyst Mission Health machine‑learning readmission case study.

Moxi from Diligent Robotics is a practical, deployable robotics assistant that Carmel hospitals can use to automate non‑patient‑facing logistics - running patient supplies, delivering lab samples, fetching items from central supply and distributing PPE - so nurses spend more time at the bedside and less on routine errands (see the Moxi healthcare robot product page - Diligent Robotics: Moxi healthcare robot product page - Diligent Robotics).

Diligent reports fleet milestones and operational metrics that matter for mid‑sized systems evaluating pilots:

Metric	Value
Deliveries across fleet	1,000,000+
Clinical staff hours saved	~575,000 hours
Autonomous elevator rides	125,000+
Partner health systems	23 systems (31 hospitals)
Average task time	20–26 minutes

Those outcomes - fewer steps, measurable time savings, and rapid site rollout without infrastructure buildout - align with Carmel's focus on practical, governed AI pilots.

As Diligent's CEO, Ludo Fourrage, noted:

“This milestone is a testament to Diligent Robotics' leadership in healthcare automation and our commitment to delivering impactful solutions that support clinical teams.”

For Carmel leaders planning a pilot, review the fleet milestone and implementation lessons (Diligent Robotics one million deliveries milestone blog post) and pair technical pilots with local governance and workforce‑training guidance from our regional playbook (Nucamp AI Essentials for Work syllabus - Guide to using AI in Carmel healthcare (2025)).

Doximity GPT offers Carmel clinicians a practical, HIPAA‑aligned way to speed care coordination and boost patient engagement by automating routine documents (insurance letters, patient instructions), summarizing charts, and generating patient‑friendly education in multiple languages - capabilities that free clinician time for direct care while supporting secure communication via Doximity's platform (Doximity GPT HIPAA-compliant workflow details).

Its clinical‑focused outputs (instant notes, diagnostic workups, treatment pathways) pair well with local pilots that target high‑volume administrative tasks and care‑transition workflows; start by measuring time‑saved, accuracy, and patient comprehension, and require human review before signing.

Doximity also documents SOC2/HIPAA controls and offers BAAs for institutional and individual users, a key procurement check for Indiana health systems (Doximity security and BAA information for healthcare organizations).

For organizations evaluating LLMs, follow established HIPAA guidance on de‑identification, BAAs and secure deployment to avoid exposure of PHI (Guide to HIPAA-compliant use of LLMs in healthcare).

“This tool has been a game-changer for my charting process… It provides accurate, comprehensive support that saves me time.”

Benefit	Example
Time saved	10+ hours/week
Access	Free, unlimited for clinicians
Compliance	HIPAA & BAA options

ChatGPT and Anthropic's Claude are powerful NLP engines for EHR summarization and clinical insights - automating chart summaries, extracting problem lists, drafting visit notes and care‑plan templates to reduce clinician documentation time and surface actionable risk signals for Carmel primary‑care and hospital teams - but safe, compliant deployment is essential: public LLMs should never receive identifiable PHI without controls and a signed BAA.

“ChatGPT is not HIPAA-compliant out of the box and cannot be used to handle Protected Health Information (PHI) without significant customizations.”

Practical, HIPAA‑aware options include self‑hosting, using HIPAA‑eligible cloud LLMs, or partnering with healthcare‑focused vendors; each approach balances privacy, cost and speed (see the TechMagic HIPAA‑Compliant LLMs guide for implementation details).

Start small in Carmel: validate summarization prompts on de‑identified Regenstrief/HIE‑sourced notes, require human‑in‑the‑loop review, log and encrypt all interactions, and track accuracy and downstream safety metrics.

Use curated prompt sets to make models productive while reducing iteration time - refer to the 100+ ChatGPT prompts for healthcare professionals for EHR and patient‑communication templates - and follow local procurement checks (BAA, SOC2, encryption).

Below is a quick deployment comparison to guide pilot choices.

Deployment	Tradeoffs
Self‑hosted LLM	Max privacy & control; high infra/ML cost
HIPAA‑eligible cloud	Fast scale, BAA options; shared responsibility
Healthcare vendors	Turnkey, compliant features; higher vendor lock‑in/cost

For an accessible primer on ChatGPT HIPAA risks and mitigations, review Topflight's analysis on ChatGPT and HIPAA compliance.

For Carmel healthcare leaders, deploying AI safely means pairing ModelOps with adversarial‑resilience and data‑supply‑chain controls: vendors like Modzy provide ModelOps-as-a-Service with built‑in audit trails, real‑time model monitoring and FedRAMP/CMMC‑oriented controls while specialists such as HiddenLayer focus on adversarial‑ML defense and model protection - see the Modzy and HiddenLayer top US AI consulting firms overview (Modzy and HiddenLayer top US AI consulting firms overview).

Complement those platform capabilities with documented governance practices from ModelOp - automated documentation, bias detection, drift monitoring and lifecycle enforcement - to ensure every model decision is auditable and reviewable (ModelOp AI governance and audit trails for model owners).

Align technical controls with national guidance: CISA's AI data‑security recommendations (data provenance, integrity checks, encryption, continuous monitoring) should be operationalized within ModelOps pipelines before any production rollout in Indiana systems (CISA AI data security guidance for system operators).

Solution	Primary capability	Compliance focus
Modzy	ModelOps, audit trails, monitoring	FedRAMP/CMMC readiness
HiddenLayer	Adversarial defense, model security	AI cybersecurity
ModelOp	Governance automation, bias/drift detection	NIST AI‑RMF / auditability

Conclusion - next steps for Carmel healthcare organizations: prioritize governance, privacy and measured pilots so AI improves care without increasing risk. Begin with a formal risk assessment and HIPAA-ready controls (encryption, BAAs, incident plans) guided by a practical checklist such as the HIPAA compliance checklist by Scytale to prepare audits and evidence; align procurement and model life‑cycle plans with federal guidance on medical AI/ML (NCBI) to clarify FDA/IRB boundaries for clinical tools and research.

Operationalize pilots that use de‑identified Regenstrief/HIE data for narrow tasks (EHR summarization, triage, imaging triage), require human‑in‑the‑loop review, instrument safety and equity metrics, and pair deployments with ModelOps/monitoring.

“Future healthcare‑focused AI innovation is anchored in the ethical sourcing of real‑world data and the synergy between a diverse consortium of institutions, researchers, medical professionals, and both public and private stakeholders.”

Priority	Milestone (90 days)
Governance & HIPAA	Risk assessment + BAAs
Pilot & Validation	De‑identified test on local data
Workforce & Ops	Training + ModelOps monitoring

Invest in workforce readiness - short applied courses and cohort training reduce rollout risk; consider team enrollment via the Nucamp AI Essentials for Work bootcamp registration to build prompt, governance and integration skills locally.

Nucamp AI Essentials for Work bootcamp registration