Top 10 AI Prompts and Use Cases and in the Healthcare Industry in Des Moines

Des Moines healthcare leaders confront an aging population, limited primary-care capacity, and access gaps that generative AI can directly address by enabling proactive clinical decisions, personalized patient outreach, and reduced clinician burden; local pilots point to measurable wins - predictive staffing models, for example, can cut overtime and improve patient flow - so hospitals and clinics can convert AI from rhetoric into day‑to‑day improvements (Lumeris interview about AI in primary care and engagement: Lumeris interview on AI in primary care, comprehensive local guide: Complete guide to using AI in Des Moines healthcare in 2025); practitioners and administrators who want to lead deployments can gain practical, job‑ready prompt-writing and implementation skills through short courses like the AI Essentials for Work bootcamp - practical AI skills for any workplace, moving pilots toward safer, more efficient patient care.

Bootcamp	Length	Early-bird cost	Registration
AI Essentials for Work	15 Weeks	$3,582	AI Essentials for Work bootcamp registration

“We're thinking about primary care as a massive problem that needs to be solved.” - David Carmouche

Selection prioritized prompts and use cases that balance clinical impact in Iowa with realistic governance and technical constraints: tools were chosen from leading market lists (see the TechTarget roundup of the “10 top AI tools in healthcare for 2025” for vendor context) and filtered by four practical criteria - (1) direct benefit to Des Moines care gaps (triage, predictive staffing, documentation) and measurable ROI, (2) HIPAA and data‑privacy risk mitigations (follow the best practices and controls summarized in TechTarget's “AI and HIPAA Compliance” guide), (3) integration feasibility with common clinical stacks (Epic integrations, edge/on‑device options, private LLM deployment), and (4) operational security and governance (zero‑trust, vendor contracts, and audit trails).

Weighting favored solutions already proven in clinical workflows (ambient documentation, clinician‑facing summarizers, telehealth personalization) and those that can be sandboxed during pilots.

One concrete data point shaped prioritization: a 2025 AMA survey cited in the compliance literature shows two‑thirds of U.S. physicians now using AI - so Des Moines providers must move quickly to operationalize governance to capture efficiency gains without amplifying privacy or safety risks.

Criterion	Why it mattered
Clinical impact	Targets access and staffing shortfalls in Des Moines
Privacy & HIPAA	Reduces legal and patient‑risk exposure
Integration	Ensures EHR/operational fit and pilot speed
Security & ops	Controls for cloud, serverless, and vendor risk

"ChatGPT is receiving increasing attention and has a variety of application scenarios in clinical practice."

Ada-style patient-facing triage and symptom‑assessment prompts turn ad‑hoc phone screens into a consistent, guided intake that flags higher‑risk presentations and routes low‑acuity concerns to self‑care or virtual visits, preserving scarce primary‑care slots in Iowa; when those standardized symptom flows feed scheduling and capacity tools they directly support the predictive staffing models shown to cut overtime and improve patient flow in Des Moines hospitals (predictive staffing models for Des Moines hospitals).

The so‑what is concrete: reliable, patient-facing triage reduces last‑minute clinic demand spikes that drive expensive overtime, and local implementation guidance and workforce implications are covered in the Complete Guide to Using AI in Des Moines Healthcare in 2025, which outlines steps for safe pilot integration and staff training.

DAX Copilot (Nuance) converts ambient clinician–patient conversations into specialty‑specific notes that can populate Epic's smart data elements, automatically capture orders, and produce after‑visit summaries - a workflow shown to cut documentation time by roughly 50% (about 6–7 minutes saved per encounter) and reduce late‑night charting, which directly addresses staffing and burnout pressures facing Des Moines clinics; see Epic's announcement of DAX Express integration and Microsoft's Dragon Copilot capabilities for how voice capture, multilingual support, and EHR embedding work in practice (Epic DAX Express integration announcement and details, Microsoft Dragon Copilot clinical workflow overview), while a technical deep dive describes the growing ability to populate granular Epic fields from ambient voice (Healthcare IT Today technical deep dive into Epic, Microsoft, and Nuance integration).

The so‑what is pragmatic: per‑visit minute savings scale across clinics to reduce overtime, expand access, and free clinicians for higher‑value patient time.

Metric	Reported value
Documentation time reduction	~50% / 6–7 minutes saved per encounter
System outcomes (reported)	24% decrease in time on notes; 17% reduction in late‑night admin tasks
Throughput impact	~11.3 additional patients/month for frequent users

“Northwestern Medicine is committed to providing a superior work environment that promotes wellbeing, 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.” - Gaurava Agarwal

Doximity GPT offers Des Moines clinicians a HIPAA‑compliant generative‑AI copilot that accelerates routine communication and documentation - drafting insurance appeals, patient letters, discharge instructions, instant notes, and even rapid translations - so clinicians spend less time on paperwork and more on care; the platform is free with unlimited access and Doximity frames this tool as capable of saving “over 10 hours a week” by automating admin tasks while providing evidence‑backed clinical references (see Doximity GPT details and capabilities).

Because local health systems must balance speed with safety, use Doximity GPT inside HIPAA‑governed workflows and BAAs and follow the recommended human‑oversight step to review every AI output before signing (see guidance on HIPAA controls and practical cautions from clinicians).

The so‑what: reclaiming that administrative time at small and mid‑sized Des Moines practices can reduce evening charting burdens and free clinician time for same‑day visits or care coordination.

Feature	Detail
Access	Free, unlimited
Compliance	HIPAA‑compliant; BAA support and enterprise security controls
Claimed impact	Save over 10 hours/week on admin tasks
Common uses	Instant notes, insurance letters, patient education, chart summaries, translations

"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

ChatGPT‑4 can draft coherent SOAP notes quickly, but peer‑reviewed evidence shows significant and unpredictable errors that matter for patient safety and billing: a JMIR comparative study found an average of 23.6 errors per case (omissions 86.3%, additions 10.5%, incorrect facts 3.2%), only about 53% of data elements were consistently correct across three replicates, and mean PDQI‑9 quality scores were 29.7 - leading the authors to conclude these notes do not yet meet clinical‑use standards (JMIR study on ChatGPT‑4 SOAP notes accuracy).

A parallel systematic review similarly cautions that AI improves structuring and workflows but that end‑to‑end assistants show moderate accuracy and need targeted deployment (Systematic review of AI for clinical documentation improvement).

So what: Des Moines health systems should pilot ChatGPT as a drafting tool focused on objective fields (highest accuracy in the study) with mandatory human verification, constrained prompts, and monitoring before any automated signing or coding workflows are adopted.

Metric	Value (JMIR study)
Average errors per case	23.6
Omissions	86.3%
Additions	10.5%
Incorrect facts	3.2%
Consistently correct data elements	~53%
Mean PDQI‑9 score	29.7
Objective section median accuracy	86.9%

Claude can serve as a privacy‑focused copilot for Des Moines clinics by producing concise patient summaries, cleaning medical transcriptions, and generating tailored education materials when run inside a HIPAA‑controlled environment; one analysis even reported the model achieved a 99.1% HIPAA‑compliance rate when generating radiology reports (Analysis: Claude 3.7 Sonnet in healthcare and radiology report HIPAA compliance).

That capability is useful locally for community hospitals and primary‑care practices that need readable discharge instructions and literacy‑adjusted patient handouts, but Claude is not HIPAA‑compliant by default - compliance depends on deployment choices, BAAs, encryption, access controls, and audit logging (Feather guide to Claude AI HIPAA compliance considerations).

Best practice for Des Moines health systems is to run Claude via HIPAA‑eligible platforms or vetted vendors and to retain human review of outputs; implemented this way, Claude‑based prompts can reduce tedious documentation steps and reliably produce patient-facing content without widening privacy risk (Comprehensive guide to HIPAA‑compliant large language models).

Moxi, the nurse‑assisting robot from Diligent Robotics, automates non‑patient‑facing logistics - supply and specimen deliveries, linen collection, and route learning - so clinical staff spend less time walking and more time at the bedside; deployments have been credited with nearly one million deliveries and roughly 1.5 billion staff steps saved across early adopters, and operational reports show a single robot can save about 30 hours of nursing work per week, freeing time for direct patient care and easing overtime pressure in smaller systems like those in Des Moines (Humanoid Robots in Healthcare: analysis of Moxi deployments and metrics, Moxi fleet expansion and nursing impact - AI innovations in nursing); the so‑what: redeploying three to four robot‑hours each day can meaningfully reduce evening charting and overtime costs while preserving nurse time for complex clinical tasks, making logistics automation a pragmatic pilot for Des Moines hospitals.

Metric	Reported value
Completed deliveries	Nearly 1,000,000
Staff steps saved	~1.5 billion
Time saved per robot	~30 hours/week
Deployment footprint	Dozens of health systems / reports of 100 hospitals in 28 states

Storyline's behavioral‑AI telemedicine platform combines precision care pathways, a growing library of clinical assessments, automated triggers, and unified messaging to personalize telehealth and remote monitoring for community systems; it advertises military‑grade encryption and HIPAA/HITECH readiness plus features that let clinics run one‑to‑one and one‑to‑many programs without separate apps, so Des Moines practices can stand up standardized post‑visit pathways, automated check‑ins, and literacy‑adjusted patient education that maintain continuity at scale.

The practical payoff is concrete: Storyline reports a 4x productivity gain from intelligent workflows and a library that accelerates access to new evidence‑based tools - an efficiency boost that, when paired with the access benefits shown in a systematic review of AI and telemedicine in rural communities, points to a feasible route for local pilots to improve follow‑up, chronic‑care monitoring, and patient engagement without adding clinician hours (Storyline behavioral AI telemedicine platform, systematic review of AI and telemedicine in rural communities).

Metric	Reported value
Team productivity	4x
Patient recommendation	97%
Revenue lift	17% increase

“Storyline is truly transformative!” - Joshua Schiffman MD, Pediatric Oncologist; CEO Peel Therapeutics

AIDDISON from Merck brings generative AI and integrated CADD tools to local drug discovery efforts, letting Des Moines research partnerships explore vast chemical space and design successful drug candidates in minutes - a concrete speed-up that can turn iterative medicinal chemistry cycles into same‑day hypothesis testing for university labs and small biotech teams.

The platform combines de novo molecular design, ligand‑ and structure‑based screening, and ML‑driven ADMET predictions with cloud‑native SaaS compute and robust data protections, enabling rapid virtual screening across billions of compounds and automated docking to prioritize leads before any wet‑lab work (Merck AIDDISON platform overview and capabilities, AIDDISON AI‑powered drug discovery technical details); for Des Moines this means tighter academic–industry translational loops, lower screening costs, and faster candidate nomination for locally relevant targets (analysis of turnkey AI solutions for drug discovery).

Capability	What it enables for Des Moines partners
De novo molecular design	Generate novel candidate libraries in minutes for rapid lead exploration
Ultra‑large similarity & pharmacophore search	Screen billions of virtual compounds to prioritize hits before synthesis
Cloud‑native, secure SaaS	Scalable compute with industry‑grade data protection for collaborative projects

“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 is a predictive‑analytics platform that analyzes datasets describing how compounds affect cells and forecasts which molecules are likely to achieve a desired effect on cell health, an approach TechTarget highlights as TechTarget article on top AI tools in healthcare.

“much faster than manually reviewing compound data and designing a drug”

For Des Moines‑area life‑science programs and university–industry partnerships, that speed matters because it lets scarce wet‑lab time focus on the highest‑probability leads instead of broad exploratory screens, shortening the triage cycle and reducing per‑candidate cost.

Integrating BioMorph's predictions into local translational pipelines - paired with standard validation workflows and secure data controls - gives clinical researchers a pragmatic way to tighten hypothesis testing and accelerate candidate nomination for projects tied to Iowa's public‑health priorities; see the local implementation primer for next steps on building AI‑assisted research capacity in Des Moines in the complete guide to using AI in Des Moines healthcare (2025).

Merative's Truven Health Insights packages large‑scale analytics and care‑management tools - self‑service dashboards, a Visualization Studio, and an Ad Hoc Report Writer - so Des Moines hospitals, health plans, and employer groups can turn disparate claims, EHR, and vendor data into actionable cohorts, KPIs, and program comparisons without a full data‑science team; the platform is built on Microsoft Azure and emphasizes security and compliance for HIPAA‑governed use cases (Merative Truven Health Insights healthcare analytics platform).

Local care managers can use these capabilities to stratify members, prioritize outreach, and evaluate benefit or population‑health pilots more rapidly - Merative's analytics work explicitly aims to “identify the right member, the right intervention, at the right time,” which translates into faster, evidence‑driven decisions for chronic care and behavioral‑health interventions in Iowa communities (Advanced analytics for population classification on the Merative blog).

The so‑what: by surfacing targeted cohorts and real‑time KPIs, small systems can focus scarce care coordination hours on patients most likely to benefit and better demonstrate program value to employers and payers.

Feature	What it enables
Self‑service dashboards	Aggregate vendor data, view KPIs and trends, guided drill paths
Visualization Studio	Personalize dashboards, jumpstart views with pre‑made modules, collaborate
Ad Hoc Report Writer	Detailed program comparisons and cohort evaluation for power users
Secure foundation	Azure‑based scalability, performance, and compliance support for HIPAA

“We look to Truven to help create measurement strategies to evaluate some of the benefits designs and program changes we've made over the years.” - Ethan Rush, Director of US Medical Benefits, Eaton Corporation

Des Moines healthcare leaders should treat AI as an operational program, not a point solution: establish an AI governance committee with clear policies, role‑based approvals, auditing cadence, and staff training as described in the Sheppard Mullin AI governance guidance for healthcare (Sheppard Mullin AI governance guidance for healthcare); pair that governance with a practical HIPAA 8‑step checklist to lock down BAAs, encryption, access controls, incident response, and continuous monitoring before any pilot touches PHI (HIPAA compliance 8-step checklist); and start workforce readiness now - give care teams prompt‑writing and oversight skills through a structured course like the 15‑week AI Essentials for Work bootcamp so pilots (ambient notes, triage, telehealth pathways) are safe, auditable, and scalable (AI Essentials for Work bootcamp registration (Nucamp 15-week course)).

The measurable first win: a short, governed pilot with human review that reduces evening charting and documents compliance before broader roll‑out.

Priority	First practical step
Governance	Stand up an AI governance committee and policies (committee review, approval workflows)
Compliance	Run the HIPAA 8‑step checklist and sign BAAs for vendors
Workforce & pilots	Enroll clinicians in a 15‑week AI Essentials course and launch a human‑in‑the‑loop pilot

“By establishing a dedicated AI governance program, healthcare organizations can better manage the complexities and risks associated with AI, ensuring these technologies are used safely, ethically, and effectively in patient care.”