Top 10 AI Prompts and Use Cases and in the Healthcare Industry in St Louis

St. Louis healthcare leaders are watching AI shift from promising research to practical impact: algorithms that scan radiology images for earlier detection, predictive models that flag readmission risk, and automation that trims administrative hours so clinicians spend more time with patients.

ForeSee Medical's review shows AI reshapes diagnosis, treatment and workflows - see ForeSee Medical analysis of artificial intelligence in healthcare: practical applications and outcomes.

Accessible primers outline machine learning, natural language processing, and robotic assistance that speed care and reduce errors - read a beginner overview of AI in healthcare.

Local innovation hubs like the Washington University Center for Health AI are turning these tools into Missouri pilots to cut costs and improve outcomes, delivering concrete wins - faster reads, smarter scheduling, and fewer billing headaches - that make better care more achievable across the region.

Bootcamp	Key details
AI Essentials for Work	Length: 15 weeks; Description: practical AI skills for any workplace; Early bird cost: $3,582; Syllabus: AI Essentials for Work syllabus and course outline; Register: Register for AI Essentials for Work

“we expect to see limited use of AI in clinical practice within 5 years and more extensive use within 10” (NCBI)

Selection of the top 10 AI prompts and use cases centered on practical benefit for Missouri patients and clinics, not novelty alone: criteria included demonstrated local research (projects and FDA-designated tools emerging from Washington University's AI for Health Institute), measurable adoption patterns across the region, and community readiness.

Researchers scanned WashU's AI for Health updates and launch coverage to capture clinical priorities and imaging, wearables, and NLP work from local teams (Washington University AI for Health Institute research), paired that with community sentiment from the iHeardSTL survey that found only 8% of St. Louis adults report a strong grasp of AI, and reviewed state-level hospital adoption data from the St. Louis Fed's Eighth District analysis to flag high-impact, implementable ideas for Missouri systems (iHeardSTL community AI literacy survey, St. Louis Fed Eighth District AI in health care analysis).

Prompts were then scored for clinical value, feasibility, equity/fairness, workforce impact and evidence needs - drawing on implementation research guidance - to prioritize items likely to improve outcomes in St. Louis hospitals while addressing gaps in literacy, trust and deployment capacity.

Metric	Value
St. Louis adults reporting strong AI understanding	8%
Missouri responding hospitals reporting any AI use	100%

“This is the new frontier of health care.”

Medical imaging in St. Louis can leap from time‑consuming manual reads to streamlined, research-ready output with a Clinical Imaging Assistant prompt that mirrors tools like Siemens Healthineers' AI‑Rad Companion Chest CT, which automatically measures, highlights chest structures and flags potential abnormalities while producing quantified images and structured reports importable into hospital databases (Siemens Healthineers AI‑Rad Companion Chest CT product page); pairing that capability with targeted radiology prompts - for CT scan analysis, report generation, or chest X‑ray interpretation - helps translate pixel-level work into concise findings the care team can act on (Radiology AI prompt examples and templates).

Local innovation hubs are already bridging these pieces to cut turnaround and support research workflows, so a single, well-crafted Clinical Imaging Assistant prompt can turn repetitive image measurements into a structured dataset ready for clinical decision support and academic studies - freeing clinicians to focus on complex diagnoses and patient conversations rather than manual slice counting (WashU Center for Health AI St. Louis collaboration overview).

Predictive analytics can turn the chaotic end-of-stay scramble into a targeted safety net: models that flag patients most likely to bounce back into hospital enable earlier, focused discharge planning and resource allocation.

Saint Louis University work showed that forecasting medication nonadherence - and related readmission risk for the first three months after a fill - reveals actionable causes (33% missed doses from inattention, 16% from cost) that a care team can address with reminders or medication access programs (Saint Louis University medication adherence and readmission prediction study); complementary research demonstrates that nursing data collected at admission meaningfully boosts early prediction (test AUROCs ~0.62–0.64) and can flag one in six patients at risk within 30 days (JMIR Medical Informatics early readmission prediction using nursing data).

Proven implementations bring the score to the bedside and the morning huddle - CHOC integrated their readmission probability into the EHR for daily use and improved AUC performance, while Mission Health made a production risk score available by 8:00 a.m.

after discharge and achieved an AUC ~0.78 - showing that timely, interpretable risk signals can cut readmissions when paired with care coordination and SDOH follow‑up.

One vivid payoff: a risk score on the clinician's dashboard can transform a single high‑risk patient from “mysterious return” into a scheduled, preventive phone call or transportation voucher before trouble starts.

Study / Source	Setting	Key finding
Saint Louis University medication adherence and readmission prediction (Okoye)	Chronic disease cohort	Predicted nonadherence and readmission in first 3 months; 33% nonadherence from inattention; cost ~16%
JMIR Medical Informatics early readmission model using nursing admission data (Yonsei team)	Tertiary hospital retrospective EHR	Early prediction models (using nursing data) test AUROC ≈ 0.62–0.64; 30‑day readmission 16.5%
CHOC pediatric readmission predictor case study (HIMSS)	Pediatric readmissions	Integrated EHR score used daily; AUC improved from 0.79 to 0.822; 7‑day readmissions fell ~3.8%→3.3%
Mission Health machine learning to reduce readmissions (Health Catalyst case study)	Regional health system	Deployed ML predictor with AUC 0.784; score available by 8:00 a.m. post‑discharge to guide follow‑up

Translating Insilico Medicine–style screening into St. Louis workflows means using AI to find the needles in the genomic haystack - automated target ID, multi‑omics ranking and generative chemistry that can turn a promising protein into a testable molecule far faster than traditional funneling.

Platforms like PandaOmics fuse omics, literature and druggability metrics to surface candidates (Insilico's course and demo explain the methods and case studies), and real‑world pipelines show the payoff: an AI‑discovered program that completed Phase 1 and a team that reportedly designed a candidate hepatocellular‑carcinoma molecule in about 30 days, illustrating how speed and hypothesis trimming matter when lab time and grant cycles are tight.

Complementary computational work - such as a pipeline that flagged IL12B as a small‑molecule opportunity in autoimmune disease - shows the same approach can reveal otherwise overlooked targets for conditions relevant to regional research priorities.

For St. Louis investigators and health‑tech partners, an “Insilico‑style” screening prompt can prioritize targets, shrink initial chemistry workloads, and make local translational projects more competitive for national funding.

Learn more about the methods and case studies at the Insilico Medicine course on AI-driven drug discovery (Insilico Medicine course on AI-driven drug discovery) and the industry write-up on AI-driven discovery case studies (industry write-up on AI-driven drug discovery case studies).

Virtual health assistants and symptom‑triage chatbots can widen access across Missouri by giving patients instant, clinically guided choices - self‑care advice, a same‑day televisit booking, or an emergency‑room referral - so rural residents and busy families aren't left waiting for the morning clinic line; a rapid review catalogues these varied roles and cautions about limits and oversight (JMIR rapid review of chatbots in healthcare).

Practical pilots show real benefits - 24/7 availability that reduces needless ED visits and automates scheduling and medication reminders - yet adoption remains mixed: about 19% of U.S. medical group practices had integrated chatbots by 2025 while only ~10% of patients felt comfortable with AI diagnoses, underscoring the trust gap that implementation must close (Coherent Solutions report on AI chatbots in healthcare).

For Missouri systems, design priorities are clear: validated triage logic, seamless EHR handoffs, transparent escalation to clinicians, and strong privacy controls - so the chatbot becomes a reliable morning‑huddle flag, not a replacement for clinical judgment (Patient symptom‑tracking chatbot evidence and best practices (Shadhinlab)).

Metric	Value / Source
Medical group practices with chatbots (2025)	≈19% (Coherent Solutions report on AI chatbots in healthcare)
US patients comfortable with AI diagnoses	≈10% (Coherent Solutions report on AI chatbot patient comfort)
Chronic patients reporting greater connection with care via chatbots	≈67% (Shadhinlab patient symptom‑tracking chatbot outcomes)

A Surgical Assistance Planning prompt for St. Louis systems would turn preoperative CT/MRI into a precise, actionable plan that semi‑active and image‑based robots can follow in the OR - linking 3D anatomy, implant choice and intraoperative guidance so surgeons spend less time on manual alignment and more on complex decisions; the recent SurgiColl review shows these systems rely on high‑resolution imaging, improve anatomical accuracy across hip, knee and spine work, and can even cut per‑screw placement times to roughly 90 seconds in spine cases (SurgiColl review of robotic orthopaedic surgery and surgical robotics advancements).

For Missouri hospitals the prompt's “so what?” is concrete: when a preop plan and device match reduce intraoperative variability, high‑volume centers can justify the technology despite upfront cost, and local innovation partners like the WashU Center for Health AI are already helping translate imaging‑driven workflows into pilot projects that improve precision and throughput in regional practices (WashU Center for Health AI collaboration overview and St. Louis healthcare AI initiatives).

With the global surgical robotics market projected to reach about $7.42 billion by 2030, a carefully validated planning prompt - built around image fidelity, surgeon control level (active/semi‑active/passive) and clear cost‑benefit thresholds - gives St. Louis teams a pragmatic path to bring robotic accuracy into routine care.

Robotic System	Primary Specialty
Mako	Knee, Hip (semi‑active)
ROSA	Knee, Hip, Spine, Shoulder (semi‑active)
ExcelsiusGPS	Spine (passive)
da Vinci	Soft‑tissue procedures (teleoperated)

Administrative Workflow Automation in Missouri hospitals can turn paperwork into a force-multiplier: prompts that drive HCC chart review, automated pre‑visit summaries, care‑gap outreach and EHR‑aware AI agents promise to shrink the daily slog that fuels burnout and turnover.

Local systems can borrow proven tactics - real‑time HCC spotting and care coordination agents that closed care gaps in a Montage Health example (14.6%) - to free clinicians for patient care, not data entry; clinicians already report spending a large slice of their day on EHR tasks, and physicians consistently say AI's clearest win so far is cutting that administrative load (Notable Health: reducing administrative burden and physician burnout with AI analysis, American Medical Association: physicians' greatest use of AI to cut administrative burdens).

Practical prompts prioritize high‑value automation (prior auths, medication reconciliation, scheduling, HCC coding) and measurable handoffs to human teams so time saved - sometimes as much as roughly eight minutes per visit that scales into nearly two extra clinician hours in a busy clinic - becomes protected patient‑facing time rather than more inbox work (Innovaccer: addressing clinician burnout by fixing the EHR).

Metric	Value / Source
Clinician burnout symptom rates	Emotional exhaustion 38.8%; depersonalization 27.4%; one symptom 44.0% (Notable Health burnout statistics and analysis)
Annual turnover costs tied to burnout	$4.6 billion (Notable Health: cost of clinician turnover)
Portion of working hours on administrative tasks	≈17% (Innovaccer: percentage of clinician time on administrative tasks)
Primary care clinician workday length	Over 11 hours/day (ClinicalAdvisor summary)
Reported care gap closure (example)	14.6% (Montage Health example, reported in Notable Health analysis)

“Whether powered by AI or by pen and paper, meaningful solutions for primary care clinicians will need to help where they need it most: lightening the workload.”

A Personalized Oncology Recommendation prompt can turn St. Louis oncology care from one‑size‑fits‑many into precision‑guided treatment by marrying tumor sequencing, liquid biopsy signals and AI‑driven interpretation to produce concise, actionable recommendations - think of it as turning a tumor's genomic fingerprint into a tailored shopping list of drug options and clinical trials.

Sources show genomic profiling and next‑generation sequencing already drive targeted choices (EGFR, BRAF, HER2) and that circulating tumor DNA enables real‑time monitoring, while AI helps annotate variants, predict response and prioritize trial matches (genomic insights translating into targeted cancer therapies - ATM Genome Medicine, evolution of personalized cancer care with molecular profiling - Targeted Oncology).

For Missouri systems, the practical prompt would package sequencing results, predicted drug sensitivity, and eligible trials into an EHR‑ready recommendation, backed by local implementation support from the WashU Center for Health AI to address workflow, cost and equity barriers; without that operational bridge, genomic promise can stall at interpretation and access.

The payoff is concrete: earlier, better‑matched therapies, fewer toxicities, and the ability to flag recurrence from a single blood draw long before symptoms emerge.

Reference	Type / Published	Metrics
Clinical trial design in the era of precision medicine (Genome Medicine)	Review / 31 August 2022	~33k accesses; 200 citations; Altmetric 39

“For many years, cancer was completely defined by what it looked like under the microscope. We still do that, but now we have additional information that we can get by sequencing these tumors and learning what causes them to be the genetic drivers. It can tell us things about how they are going to respond to therapies and it can make them be able to use certain therapies that are targeted to these specific mutations.” - Alec Kimmelman, MD, PhD

An NLP-powered Clinical Documentation Summarizer prompt can turn sprawling charts and long clinic notes into crisp, EHR‑ready timelines and SOAP templates so St. Louis clinicians see the story at a glance rather than hunting through pages: modern approaches pair extractive and abstractive summarization techniques and retrieval‑augmented generation to preserve critical details while pruning noise (Text summarization techniques overview and use cases).

Practical pipelines handle messy inputs - OCR for scanned PDFs, handwriting localization, and chunking strategies - to produce a single patient timeline or a structured SOAP note that slots into Epic or Cerner, reducing the documentation burden that sends nurses into 25–50% of shift time on notes and costs physicians roughly 15.5 hours per week in paperwork (Automated SOAP note generation impact on clinical documentation).

Realized locally, a reliable summarizer (with validated accuracy checks and privacy safeguards) can transform multi‑page histories into one clear action list for rounds - so the morning huddle focuses on care, not searching for a missed diagnosis in the chart (Patient record summarization pipelines and timeline examples).

A Remote Monitoring Alert Prompt turns the steady stream of wearable vitals into timely, clinic-ready signals for St. Louis care teams - filtering noise, prioritizing true deterioration, and nudging coordinated responses for patients who live hours from the nearest hospital.

By combining device feeds (from blood‑pressure cuffs and CGMs to pulse oximeters and wearable ECGs) with AI‑based anomaly detection and federated privacy techniques, the prompt can surface a high‑risk alert - say, an arrhythmia flagged by a smartwatch - and trigger a nurse callback or televisit before an ER trip becomes necessary, a concrete “so what?” that saves time, anxiety and cost.

Industry guides show RPM supports chronic care, post‑op recovery and population health while highlighting interoperability and privacy hurdles (Oracle remote patient monitoring guide); recent reviews map AI and IoT solutions that boost accuracy and trustworthiness for clinical use (Journal of Cloud Computing AI and IoT integration paper).

With the RPM market and device penetration rising rapidly - creating new chances to lower readmissions and extend specialist reach - St. Louis systems can prioritize validated alerts, clear escalation paths, and patient training so remote monitoring becomes a reliable clinical ally, not a background whisper.

Common RPM Device	Typical Clinical Use
Blood pressure cuff	Hypertension management and cardiovascular risk tracking
Continuous glucose monitor / glucometer	Diabetes control and medication titration
Pulse oximeter	Respiratory monitoring and triage (COPD, post‑COVID)
Wearable ECG / smartwatches	Arrhythmia detection and early cardiology referral
Wireless scale	Heart failure weight/fluid‑retention surveillance
Activity trackers / sensors	Engagement, fall risk and rehabilitation progress

A Population Health Risk Stratification prompt gives Missouri health systems a practical map for turning messy, fragmented patient lists into prioritized, action‑ready cohorts: by classifying patients according to projected care needs - as described in recent consensus work on risk stratification - teams can focus outreach where it will move the needle most (BMC Primary Care article on stratifying the population based on health risk (2025)).

Practical design must heed known hurdles - complete data sets, mental‑health integration, measurable ROI, and transparent, customizable algorithms - so care managers trust and act on the scores (HealthCatalyst guide to effective patient stratification and four solutions).

Low‑code cohort engines that feed dynamic dashboards and targeted campaigns make the “so what?” obvious: instead of reacting to 1,000 alerts, a team sees a few high‑impact cohorts and launches automated outreach or resource bundles that prevent costly readmissions and concentrate scarce community supports where they matter most (blueBriX on real-time risk stratification and cohort management).

Source	Key point
BMC Primary Care article on population risk stratification (2025)	Defines risk stratification to classify patients by projected care needs
HealthCatalyst guide to four practical stratification solutions	Four practical solutions: consider behavioral health, prove ROI, complete data, transparent tech
blueBriX on real-time stratification and cohort dashboards	Real‑time stratification and cohort dashboards enable targeted preventive campaigns

Getting started with AI in St. Louis health care means pairing bold pilots with ironclad safeguards: adopt a clear, locally‑aligned governance framework (the National Academy of Medicine's Health Care Artificial Intelligence Code of Conduct lays out safety, equity, transparency and lifecycle roles), embed transparency and data‑reuse practices taught in NIH workshops, and move from experiments to routine care only after rigorous validation, patient consent and strong vendor contracts; local momentum is already visible (BJC, Mercy and WashU pilots, for example, have tested ambient scribes and risk scoring with high clinician and patient uptake, one pilot reporting 97% positive patient response).

Practical first steps are governance, clinician training and small, monitored pilots that measure equity and outcomes - pairing WashU‑style research with community ethics conversations like Saint Louis University's Bander Center events helps avoid widening disparities while accelerating benefit.

For teams ready to build workplace AI skills, structured training such as the AI Essentials for Work 15‑week bootcamp syllabus provides a 15‑week path to practical prompt writing and deployment skills so hospitals and vendors can ship safer, usable tools rather than black boxes.

Program	Length	Early bird cost	Syllabus / Register
AI Essentials for Work	15 weeks	$3,582	AI Essentials for Work syllabus (15 weeks) • AI Essentials for Work registration

“People are scared of dying, they're scared of losing their mom, they're scared of not being able to parent and walk their child down the aisle. How can we start using the power of these tools, not through a lens of fear and reluctance, but to create a culture change from ‘doctor knows best' or ‘patient knows best' to ‘person powered by AI knows best'?” - Grace Cordovano, NAM