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

Chicago's hospitals, medical schools, and research centers are turning AI from a promising concept into operational tools for care coordination, diagnostics, and clinical education - anchored by programs like UIC AI.Health4All program and Northwestern's Institute for AI in Medicine and the Center for Collaborative AI in Healthcare - while state policy now shapes what's allowed: Governor Pritzker signed the Wellness and Oversight for Psychological Resources Act on August 4, 2025, which prohibits AI-driven therapy but permits administrative and supplementary AI support for licensed behavioral health professionals (Illinois AI therapy legislation summary); the practical implication for Chicago providers and startups is clear - build human-centered, equity-focused AI and upskill teams now, for example through targeted training like the AI Essentials for Work bootcamp syllabus, a 15-week course that teaches promptcraft and workplace AI use so technical and clinical teams can deploy safe, compliant solutions.

Program	Details
Program	AI Essentials for Work
Length	15 Weeks
Courses Included	AI at Work: Foundations; Writing AI Prompts; Job Based Practical AI Skills
Cost (Early Bird / Regular)	$3,582 / $3,942
Syllabus	AI Essentials for Work bootcamp syllabus
Registration	Register for the AI Essentials for Work bootcamp

“How we soak up AI and how we use it for good in a way that recognizes the dangers but at the same time does find the right level is going to be part of the game.” - Dr. Martin V. Pusic

Criteria focused on real-world impact for Illinois care teams: prioritize projects with local clinical leadership, measurable cohorts, federal backing, multidisciplinary inputs, and open tooling that can be validated in Chicago workflows; examples include the UIC ARPA‑H project to build “all‑team” health datasets and the CAIDF hackathon in Chicago that exposes deidentified data to developers and clinicians for rapid prototyping and evaluation.

Selection weights: (1) clinical relevance to team‑based care (nursing, PT/OT, SLP, physicians), (2) dataset scale and specificity (the CAIDF effort represents over 100,000 patients with focused cohorts such as Falls N=87,922 and NICU N=14,021), (3) operational readiness shown by technical partners and secure enclaves (Microsoft, Tackle AI, secure data enclave), and (4) commitment to open-source outputs and clinician-centered summaries that support adoption in Chicago hospitals.

The practical consequence: chosen prompts and use cases were filtered to those that can be tested against large, multidisciplinary records and iterated at events like the CAIDF hackathon to shorten the path from model to bedside in Illinois health systems.

Read more about the UIC ARPA‑H project and the Chicago hackathon for implementation details and datasets.

Item	Detail
Federal funding	$10 million from ARPA‑H
Hackathon	CAIDF Hackathon - May 30–31, 2025, Crowne Plaza Chicago (CAIDF hackathon event details and schedule)
Key cohorts	Falls N=87,922; NICU N=14,021; >100,000 patients total
Partners	UIC, Univ. of Iowa, Univ. of Missouri, Loyola, Microsoft, Tackle AI

“Other professions see patients more frequently and provide very high-fidelity data that gets closer to the reality of the patient, instead of just the brief snapshots in time that you get from data documented by physicians.” - Andrew Boyd

DAX Copilot (Nuance Dragon Ambient eXperience) is now a practical tool for Chicago health systems because it wraps ambient conversation capture, generative AI, and EHR integration into a single workflow that automates note creation and orders inside Epic - reducing documentation burden so clinicians can spend more time with patients and less time on screens.

Integrated builds with Epic and Microsoft's Dragon Copilot platform enable multiparty, multilingual encounter capture, specialty‑specific draft notes, automatic order capture, and after‑visit summaries, with early adopters in the region reporting measurable operational gains; Northwestern Medicine clinicians using DAX saw an average of 11.3 additional patients per month and 24% less time on notes while deployment studies report strong ROI and service‑level improvements.

For Chicago organizations evaluating ambient documentation, the combination of Epic integration, Microsoft's responsible‑AI controls, and peer outcomes makes DAX Copilot a concrete option to reduce “pajama time” and improve throughput without major workflow rewrites.

Metric	Northwestern Medicine / Study
Additional patients per month	11.3
Time on notes	24% less
“Pajama time” (after-hours work)	17% decrease
Reported ROI	112%
Service-level increase	3.4%

“Northwestern Medicine is committed to providing a superior work environment that promotes well-being, and implementing DAX Copilot will allow our physicians to spend more quality time with our patients, focusing on their needs rather than on paperwork and data entry.” - Dr. Gaurava Agarwal

Patient-facing triage and self‑assessment chatbots like Ada Health are practical digital “front doors” that can expand access outside clinic hours, gather structured histories, and steer patients to the right level of care - findings that matter for Chicago's crowded EDs and safety‑net clinics.

Peer-reviewed evaluations show high coverage and safety (Ada reported up to 99.5% condition coverage in one study and 97% disposition safety versus app averages), real‑world ED validation found 94.7% urgency‑advice safety and suggested 43.4% of low‑acuity walk‑in patients could safely use lower‑intensity care, and nearly half of assessments were completed outside primary‑care hours, improving access and triage efficiency; learn more in Ada Health clinical research studies (Ada Health clinical research studies) and a review of chatbot-based mobile mental health apps that shows mobile conversational tools can feasibly deliver evidence‑based therapies when clinically validated (Review of chatbot-based mobile mental health apps (PMC)).

For Chicago health systems, the practical “so what” is clear: validated symptom checkers can document problems before visits, reduce low‑acuity ED demand, and free clinicians for higher‑acuity care - if deployments include local validation, equity checks, and governance for transparency and privacy.

Metric	Value (reported)
Condition coverage	99.5%
Disposition / advice safety	97% (PLOS ONE)
ED urgency‑advice safety	94.7%
Assessments outside clinic hours	46.4%

“Ada was 'by far the best' of the 4 tested, asking clear questions and providing the best condition suggestions.”

Predictive analytics for risk stratification turns EHR data into actionable flags that Chicago care teams can use at the bedside: near‑real‑time scores pushed into clinical workflows let case managers and social workers prioritize follow‑up, schedule PCP visits, and address social determinants before discharge - an approach that helped one program reduce seven‑day pediatric readmissions from roughly 4% to about 3% after operationalizing a daily probability score and multidisciplinary huddles (HIMSS CHOC readmission case study).

Best practices for Illinois systems include cloud‑enabled model development to shorten preprocessing, clinician‑led variable selection, and embedding scores into morning huddles and EHR dashboards so risk translates to specific actions (appointment scheduling, telehealth check‑ins, medication access, SDOH referrals), as seen in other health systems deploying predictive tools to focus transition‑of‑care resources (Corewell Health case summary).

For Chicago hospitals exploring capacity and throughput benefits, couple risk stratification with operational prompts (e.g., predictive bed management) to free beds and reduce low‑acuity returns (predictive bed management for Chicago hospitals); the practical payoff is measurable: small percentage improvements in readmission rates translate to fewer avoidable returns, better patient continuity, and lower local system costs.

Metric	Reported Value
Seven‑day readmission (before → after)	~4% → ~3% (CHOC)
Model AUC (improvement)	0.79 → 0.822
Risk thresholds	High ≥0.22; Medium 0.11–0.22; Low <0.11

The University of Illinois Chicago's ARPA‑H funded effort is building NLP pipelines to stitch together structured EHR fields and the rich, narrative notes from nurses, physical/occupational therapists and speech‑language pathologists so Chicago care teams get an “all‑team” patient narrative instead of isolated physician snapshots; the project (up to $10M from ARPA‑H) partners with Microsoft and Tackle AI to develop open‑source text‑mining and LLM tools and has already driven local prototyping at the CAIDF hackathon using >100,000 deidentified records to test fall‑risk and NICU‑to‑home summaries - so what this means on the ground is concrete: clinicians can receive concise, multidisciplinary synopses that surface missed fall history, balance assessments, and therapy progress notes that improve risk detection and smoother transitions of care.

Read the UIC ARPA‑H project overview and the CAIDF hackathon report and datasets for implementation details and datasets.

Item	Detail
Funding	$10 million (ARPA‑H)
Deidentified records used	>100,000 (CAIDF hackathon)
Key cohorts	Falls N=87,922; NICU N=14,021
Partners	UIC, Univ. of Iowa, Univ. of Missouri, Loyola, Microsoft, Tackle AI

“Health care is an interdisciplinary process, but existing data tools and infrastructure ignore most of the team.” - Andrew Boyd

Generative models such as Anthropic's Claude are reshaping patient communication in Chicago by acting as a “third agent” that can translate, summarize, and tailor medical information for lower‑literacy and multilingual populations while also preparing concise previsit summaries for clinicians; a documented case using Claude 3 Opus combined clinical records and photos to generate a differential, a multipronged care plan translated into Portuguese, and - after clinician validation - the patient's rash markedly improved within 10 days, illustrating a concrete payoff for faster, more equitable outpatient triage (see the Journal of Participatory Medicine discussion of LLMs as agents in the clinic: Journal of Participatory Medicine article on Generative AI as a Clinical Third Agent, and the practical resource: Complete guide to using AI in Chicago healthcare (coding bootcamp Chicago healthcare guide)).

Opportunity	Concern
Multilingual summaries & previsit prep	LLM hallucinations → human review required
Lowering literacy barriers & translating care plans	Digital divide and unequal access to high‑capacity models
Reduce clinician documentation burden	Risk of eroding therapeutic alliance without safeguards

“Keyboard liberation”: LLM-driven scribing reduces EHR burden, may restore clinician attention to patients.

AIDDISON™ brings generative AI and advanced CADD into a cloud‑native, SaaS workspace that Chicago medicinal chemists, university labs, and biotech startups can use to compress early discovery cycles: de novo design powered by REINVENT 4.0, integrated ligand‑ and structure‑based workflows, and rapid searches across >60 billion virtual and known molecules let teams generate and prioritize novel, synthesizable candidates in minutes rather than weeks, while built‑in predictive ADMET and retrosynthesis planning (SYNTHIA™ rules) reduce costly downstream failures; the platform is ISO‑27001 certified for IP protection and supports custom ML models trained on proprietary data for local projects.

For Illinois innovators looking to partner with pharma or spin out lead candidates, AIDDISON's explainable models and cloud scalability make it practical to iterate on multi‑parameter objectives (potency, QED, ADMET) without heavy on‑prem infrastructure - learn more on the Merck AIDDISON overview and the AIDDISON product page for technical features and deployment options.

Capability	What it does
De novo design	Generative AI (REINVENT 4.0) for novel, synthesizable molecules
Large‑space search	Searches >60 billion virtual/known molecules in minutes
Predictive ADMET	ML models to flag toxicity and PK liabilities early
Retrosynthesis	Integrated SYNTHIA™ rules and synthetic accessibility scoring
Security & deployment	Cloud‑native SaaS with ISO‑27001 data protection

“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

Oracle GenAI on Oracle Cloud Infrastructure (OCI) is positioned to augment Chicago imaging departments by automating image triage, highlighting suspicious findings, and integrating AI‑derived measurements and summaries directly into radiology workflows so technologists and radiologists can prioritize urgent cases and close reports faster; Oracle Health's imaging solutions emphasize efficiency and informed decisions for radiology teams (Oracle Health Radiology and Imaging solutions) and Oracle's AI healthcare overview details diagnostic imaging as a key use case for earlier problem detection and streamlined documentation (Oracle AI in Healthcare diagnostic imaging overview).

Local validation matters: Northwestern Medicine's real‑world deployment demonstrated measurable gains - analyzing nearly 24,000 reports and producing templates that were 95% complete while boosting some radiologists' productivity up to 40% - so Chicago systems can realistically expect faster detection, reduced backlog, and quicker ED throughput when Oracle GenAI is paired with rigorous clinical governance and the ACR's vetted use‑case practices for safe integration (Northwestern Medicine real-world AI deployment in radiology).

Metric	Value / Source
Radiology reports analyzed	~24,000 (Northwestern deployment)
Productivity boost	Up to 40% for some radiologists (Northwestern)
Template completeness	95% complete, personalized reports (Northwestern)

“This is, to my knowledge, the first use of AI that demonstrably improves productivity, especially in health care… I haven't seen anything close to a 40% boost.” - Dr. Mozziyar Etemadi

Premier's June 30, 2025 acquisition of IllumiCare folds real‑time cost attribution into an EMR‑agnostic clinical decision support (CDS) layer that is already used by over 82,000 providers and compatible with more than 50 EHR systems - a practical fit for Illinois hospitals and ambulatory networks that can't afford large EHR rewrites; by surfacing the ordering provider's actual supply cost and flagging inpatient meds or diagnostics unlikely to provide clinical benefit, the platform drives targeted “nudges” at the point of care, supports upstream formulary and order‑set changes, and aims to improve value‑based revenue performance and trial recruitment while reducing low‑value care with reported ROI claims up to 10:1.

For Chicago health systems balancing margins and quality, the combined Premier–IllumiCare CDS (to be marketed under Stanson Health) offers a turnkey path to embed cost‑aware guidance into clinician workflows without rebuilding core EHR infrastructure - accelerating decisions that save money and keep clinical focus on patients (Premier press release on the IllumiCare acquisition, Healthcare Innovation article on Premier's IllumiCare acquisition).

Metric	Reported Value / Detail
Announcement date	June 30, 2025
Provider footprint	Used by >82,000 providers
EMR compatibility	Compatible with 50+ EHR systems
Reported ROI	Up to 10:1 (claimed)

“Hospitals are battling rising costs and shrinking margins. IllumiCare's real-time cost attribution capabilities will be a game changer, differentiating Premier in a competitive CDS market with a smarter, more robust CDS solution.” - David Zito, Premier's President of Performance Services

Robotics are moving from novelty to daily teammate in Chicago hospitals: Diligent Robotics' Moxi fleet has passed 1 million hospital deliveries - including Moxi #119's milestone run at a top‑ranked Chicago‑area academic health system - by handling point‑to‑point logistics (pharmacy to infusion, lab specimens, supplies) so clinicians stay on the unit; city systems report concrete returns - Northwestern Memorial's four‑robot fleet completed 800+ errands and saved pharmacy teams hundreds of thousands of steps while improving infusion delivery speed - and Illinois systems like Edward‑Elmhurst have recorded thousands of Moxi deliveries that together saved nearly 9,500 staff hours over ten months.

For Chicago leaders evaluating clinical automation, the takeaway is simple: modest, repeatable delivery tasks automated by Moxi translate into measurable bedside time recovered, lower clinician interruptions, and faster medication and specimen flow across complex hospital layouts.

Learn more from Diligent Robotics' milestone post and Northwestern Memorial's deployment report.

Metric	Reported Value / Source
Fleet deliveries	Over 1,000,000 across Moxi fleet (Diligent Robotics milestone post)
Clinical hours saved (fleet)	Over 575,000 hours saved (Diligent Robotics)
Steps saved (fleet)	Over 1.5 billion steps (Diligent Robotics)
Northwestern Memorial	800+ errands; saved ~400,000 steps (Northwestern Memorial deployment report)
Edward‑Elmhurst (IL)	Edward: 7,298 deliveries, 4,125.5 hours saved; Elmhurst: 9,813 deliveries, 5,345 hours saved (NursingCECentral)

“One of the things I noticed when shadowing nurses during their day-to-day work is how often they get pulled away from patient care to go and run tasks… Moxi doing the running around for them is just super cool.” - Trish Fairbanks, Chief Nursing Officer

Telehealth enrichment and remote monitoring are moving from emergency stopgaps to durable care channels that Chicago hospitals and community clinics can operationalize - national roundups note a post‑pandemic surge in telehealth startups and at‑home diagnostics (Top 10 US health tech startups (2025) industry roundup), while local operational work (virtual primary care, hospital‑at‑home pilots) shows how remote care shifts volume out of crowded EDs and into scheduled, monitored touchpoints (CHEPS telehealth and operations summaries (2019–2022)).

Practical evidence for Chicago teams: structured, CHW‑led mHealth lessons - developed with eLearning tools such as Articulate Storyline - can deliver diabetes self‑management support at home and be paired with remote monitoring to close care gaps between visits (JMIR Diabetes: CHW‑led mHealth diabetes self‑management trial (2022)); the so‑what is concrete - combining AI‑enabled triage and passive monitoring with structured education creates predictable touchpoints that keep stable patients at home, free up clinic slots for higher‑acuity care, and provide measurable paths for equity‑focused deployments in Illinois.

Chicago's pathway from pilots to safe, scalable AI is both technical and regulatory: federal and state oversight for medical AI/ML is distributed across agencies (FDA, HHS, FTC) and benefits from pragmatic, documented controls - see the NCBI review of US regulation of medical AI/ML for governance recommendations - and Illinois providers should track the July 2025 legislative and regulatory update for HIPAA Security Rule guidance and heightened enforcement expectations.

Practical next steps for hospitals and startups are concrete: require algorithmic impact assessments for clinical deployments, use expert‑determination de‑identification when sharing datasets, embed local validation and equity checks into procurement, and pair governance with workforce upskilling (for example, the AI Essentials for Work bootcamp) so clinicians and operators can evaluate model outputs and audit trails.

The payoff is tangible: these measures lower re‑identification and compliance risk, shorten FDA/IRB handoffs for SaMD, and make AI tools operationally useful in Chicago workflows without sacrificing patient trust.

Recommended Action	Why it matters
Algorithmic impact assessments	Supports FDA/IRB review and surfaces clinical risks (NCBI: US regulation of medical AI/ML)
Expert de‑identification & local validation	Reduces re‑identification risk and improves fairness before dataset sharing (Insights Association legislative update)

“Health care is an interdisciplinary process, but existing data tools and infrastructure ignore most of the team.” - Andrew Boyd