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

Mesa, Arizona matters for AI in healthcare because it sits inside a complex U.S. system - where public programs like Medicare and Medicaid coexist with employer-based and marketplace coverage and roughly 9% of Americans remained uninsured as of 2019 - creating varied payer workflows and data sources that AI can help unify (U.S. healthcare system overview (ISPOR)).

Locally, the City of Mesa publishes Cigna machine-readable files under the federal Transparency in Coverage rule - data formatted so researchers and application developers can analyze negotiated rates and out‑of‑network allowed amounts - which gives prompt-driven tools a concrete input for cost-translation and prior‑authorization automation (City of Mesa employee benefits (machine-readable files)).

With brokers and plans in Mesa serving Medicare, Medicaid, employer, and individual markets, AI prompts that standardize billing codes, triage telehealth demand, or surface patient-facing cost estimates are especially relevant; see local use cases and telehealth benefits explored for Mesa providers and patients (Telehealth and remote monitoring use cases for Mesa healthcare providers).

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The Top 10 prompts and use cases were selected by blending proven prompt‑writing practices with practical tool‑evaluation and local priorities: prompt engineering heuristics such as the Role‑Goal‑Instruction method and iteration to sharpen clinical intent (see the Physiopedia AI Assistant Prompt Writing Guide Physiopedia AI Assistant Prompt Writing Guide for clinicians), a checklist to vet whether an AI tool actually improves care and limits risk (Checklist: How to evaluate AI tools for healthcare AI tool evaluation checklist for healthcare), and real-world examples where chart‑centric LLMs speed information retrieval so clinicians spend less time searching records (Stanford's ChatEHR pilot Stanford ChatEHR pilot study and results).

Prioritization favored prompts that (1) map cleanly to local Mesa workflows such as telehealth triage and payer authorizations, (2) can be iteratively refined with RGI-style prompts, and (3) pass basic safety and usability checks - so the list emphasizes actionable templates clinics can test quickly to reduce administrative friction and surface timely clinical information at the point of care.

Article	Journal	Published
From prompt to platform: an agentic AI workflow for healthcare simulation scenario design	Advances in Simulation	16 May 2025

“AI can augment the practice of physicians and other health care providers, but it's not helpful unless it's embedded in their workflow and the information the algorithm is using is in a medical context … ChatEHR is secure; it's pulling directly from relevant medical data; and it's built into the electronic medical record system, making it easy and accurate for clinical use.” - Nigam Shah

A practical Chest CT/X‑ray triage prompt for Mesa EDs directs an image‑analysis model to flag radiographic patterns linked to acute coronary syndrome, pulmonary embolism, aortic dissection, or other high‑risk findings and to combine that image score with age, sex, and available biomarkers to produce a concise disposition recommendation (urgent imaging/consult, admit, or safe discharge) with an explicit sensitivity target; in a Radiology study an open‑source deep‑learning chest X‑ray model improved 30‑day adverse‑outcome prediction beyond age/sex and biomarkers and - at a 99% sensitivity threshold - deferred additional testing in 14% of patients versus 2% for traditional models, demonstrating how a prompt‑driven workflow can safely reduce unnecessary downstream testing while prioritizing scarce ED beds, and real‑world validation of CXR triage software showed AI reduced radiology turnaround times by roughly 77%, cutting median review from hours to minutes - benefits that translate to faster decisioning for Mesa's mixed urban/rural patient flows.

For technical background, see the RSNA deep‑learning triage study, the real‑world CXR Triage validation, and a broad scoping review of AI performance in emergency imaging.

Study	Key metric
RSNA MGH/Brigham deep‑learning chest X‑ray triage study	Deferred testing in 14% vs 2% at 99% sensitivity for 30‑day composite outcomes
Real‑world CXR triage validation study reducing radiology turnaround time	Turnaround time reduced ~77%; median AI TAT 8.5 min vs radiologist 432.1 min
Scoping review of AI performance in emergency imaging	Imaging AI commonly achieves ~85–90% accuracy across X‑ray/CT studies

“Analyzing the initial chest X‑ray of these patients using our automated deep learning model, we were able to provide more accurate predictions regarding patient outcomes as compared to a model that uses age, sex, troponin or d‑dimer information. Our results show that chest X‑rays could be used to help triage chest pain patients in the emergency department.” - Dr. Márton Kolossváry

Design an EHR prompt that continuously fuses vitals, labs, nursing notes, and a rapid host‑response score to triage sepsis risk for Mesa EDs and wards - automatically escalating a Sepsis Alert or recommending Code Sepsis activation when objective criteria and a high‑risk biomarker band coincide.

Traditional rule sets (SIRS, SOFA, Red Flag) can either miss subtle cellular dysregulation or generate false alarms, so the prompt should weight a fast, objective test like Cytovale's IntelliSep alongside trend‑based vitals and lactate to produce a clear risk band and an action (notify provider, bundle order set, or watchful recheck) with timestamps for SEP‑1‑aligned workflows (Cytovale IntelliSep rapid sepsis risk scoring and Code Sepsis detection).

Embed that logic in a hospital sepsis program framework that assigns leaders, tracks metrics, and closes the loop on alerts per the CDC Core Elements (CDC Hospital Sepsis Program Core Elements and implementation guidance), and pilot with an AI‑driven CDS workflow to minimize alert fatigue and speed action - because sepsis mortality rises roughly 8% for every hour it goes unrecognized, yet IntelliSep-style tests can return risk bands in about eight minutes, a tangible window to change outcomes (AI‑driven sepsis clinical decision support to improve early detection and intervention).

For Mesa hospitals, a discharge‑planning prompt that integrates social determinants of health (SDOH) moves readmission prevention from checklist to action: short, targeted SDOH screens - like the six‑question tool Encompass Health embeds in the EHR - flag barriers (transportation, food access, utilities) and automatically surface tailored interventions (telemedicine, community referrals, home‑health coordination) before a patient leaves the hospital (Encompass Health SDOH readmission risk study).

Peer‑reviewed work shows adding individual and neighborhood SDOH can meaningfully improve predictive performance for vulnerable subgroups (C‑statistic gains from 0.70→0.73 for Medicaid patients and similar lifts for older adults), helping case managers target resources where they change outcomes (Weill Cornell PLoS One study on SDOH impact on predictive models).

Real deployments that operationalize SDOH into EHR scoring and workflows report measurable model gains (AUC 0.79→0.822) and lower 30‑day readmissions, underscoring a practical takeaway: a brief, EMR‑recorded SDOH prompt can turn social risk signals into concrete post‑discharge plans for Mesa patients (HIMSS / CHOC readmission predictor case study).

Program / Study	Key metric
Encompass Health	Six‑question SDOH assessment entered into the EMR to trigger interventions
PLoS One (Weill Cornell)	C‑statistic improvements for subgroups: Medicaid 0.70→0.73; 65+ 0.66→0.68; obese 0.70→0.73
HIMSS / CHOC case study	AUC for 30‑day readmission improved 0.79→0.822; 30‑day readmission 12.3%→11.0%

“A large part of what impacts a person's health is not clinical; it's more behavioral, cultural or environmental." - Dina Walker, national director of case management, Encompass Health

A Mesa-ready prompt for personalized medication optimization fuses a patient's pharmacogenomic profile with local formulary rules and payer constraints to recommend safer, cost-effective prescriptions - flagging TPMT or CYP2D6 variants that alter dosing or suggest alternate agents when a plan's formulary creates barriers.

The prompt instructs the EHR or CDS agent to (1) pull lab/genetic results and a current medication list, (2) apply CPIC-style pharmacogenomic rules and known examples (warfarin, TPMT, CYP2D6) to recommend dose or drug swaps, (3) cross-check the City/plan machine‑readable negotiated rates and generate a payer‑ready prior‑authorization draft if needed, and (4) surface a concise rationale and monitoring plan for the prescriber.

Clinically meaningful impact is clear: genomically matched care has shown up to an 85% improvement in patient outcomes in recent analyses, and whole‑genome sequencing can now be lab‑certified at roughly $1,000 - figures that make a genomics+formulary prompt both clinically powerful and increasingly practical for Mesa practices that must balance efficacy, safety, and cost (Genomics and Personalized Medicine clinical evidence - GlobalRPH, Genomic Medicine and the Future of Health Care - University of Utah).

Incorporate AI models that prioritize actionable alerts and generate pharmacist‑review tasks to avoid alert fatigue while closing the loop on safer prescribing.

Metric	Value
Reported improvement with genomically matched treatments	≈ 85% better patient outcomes
Lab‑certified whole‑genome sequencing cost	≈ $1,000 per sequence

“Personalized medicine, also called genomic medicine, uses information encoded within each person's genome - our complete set of DNA instructions - to tailor their health care.”

A Mesa‑ready prior authorization drafting prompt turns a fragmented workflow into a single, payer‑ready packet: instruct the agent to pull patient identifiers, recent labs/imaging, the exact CPT/ICD codes, clinician notes that establish medical necessity, and any local machine‑readable plan constraints, then auto‑format those elements into the payer's required fields, append succinct evidence citations, and draft an appeals/peer‑to‑peer script when appropriate - so schedulers no longer chase portals or fax cover sheets.

Built this way, the prompt operationalizes best practices (centralizing documentation, verifying benefits, and using ePA pathways) and targets the administrative harms documented nationally - physicians and staff spend on average 14 hours weekly managing prior authorizations and many report that delays lead patients to abandon recommended care - meaning a precise, payer‑ready draft can directly cut care delays and denial rework.

For implementation guidance and templates, see the AMA prior authorization practice resources and the ACP toolkit for reducing administrative burden, both of which emphasize standardized ePA workflows and clear clinical rationale to improve approval rates and clinician time.

Design a Mesa‑ready "Type 2 diabetes 30‑day plan" prompt that blends brief daily coaching tasks with remote glucose checks and escalation thresholds - because evidence shows health coaching coupled with remote monitoring improves diabetes control and self‑management (JMIR scoping review: health coaching with remote monitoring (2025)) and systematic reviews report a pooled A1C reduction (≈ −0.32%) with diabetes health coaching (Canadian Journal of Diabetes systematic review: diabetes health coaching pooled A1C reduction).

Early validation work on virtual coaches supports psychoeducational, counseling‑style interactions that encourage coping and behavior change, so a practical 30‑day prompt can standardize daily goal setting, reminders for medication and SMBG/CGM checks, and clear escalation language for out‑of‑range readings while leveraging telehealth channels that benefit Mesa's mixed urban and rural populations (Telehealth and remote monitoring use cases for Mesa healthcare providers), turning proven coaching effects into a deployable, monitorable workflow clinics can pilot quickly.

A Mesa-ready clinical trial matching prompt tells an AI agent to fuse a patient's molecular profile, recent pathology and treatment history, and electronic health record eligibility fields with active trial criteria to produce a prioritized, location‑aware shortlist and a payer‑ready enrollment packet; this approach mirrors capabilities from industry leaders that combine genomic profiling, EHR integration, and automated trial matching to accelerate options for patients.

Use a stepwise prompt: (1) pull genomic/reported biomarkers and prior therapies, (2) query curated trial registries and local research networks for molecular eligibility, (3) rank matches by fit, distance, and trial phase, and (4) generate next‑step tasks (consent contact, genomic re‑test, or referral) with templated language for site coordinators.

Arizona-specific infrastructure already supports this workflow - the University of Arizona Cancer Center and Banner system participate in precision oncology partnerships, and commercial platforms report large-scale matching and enrollment capability - so a tested prompt can turn a Mesa clinic's sequencing result into concrete trial opportunities quickly (Tempus clinical trial matching and genomic profiling, Caris Life Sciences and UArizona Cancer Center precision oncology alliance, University of Arizona Precision Medicine clinical trials resources).

Metric	Value
Academic medical centers connected to Tempus	~65%
Oncologists connected via sequencing/matching	50%+
Patients identified for potential trial enrollment	30,000+
Biopharma partnerships	200+
Data storage footprint	350+ petabytes

“We look forward to working with Caris Life Sciences and the Precision Oncology Alliance to further advance molecular profiling and precision medicine.” - Andrew Kraft, M.D.

An automated "medical scribe / visit summary" prompt for Mesa clinics should instruct an ambient agent to transcribe the encounter, segment content into SOAP/visit‑summary sections, propose CPT/ICD suggestions, and produce both a clinician‑ready EHR draft and a patient‑facing after‑visit summary (including Spanish where supported) so documentation is reviewable and signable within minutes; real vendors demonstrate this workflow - Heidi Health and Sunoh automate transcription, templating, and customized outputs to reduce administrative burden and restore eye contact, and DeepScribe adds context‑aware, specialty‑specific notes and coding intelligence - so Mesa practices can reclaim roughly 1–3 hours of clinician time per day reported in published vendor and guidance materials, cut after‑hours charting, and generate payer‑ready text for referrals or prior authorizations at the point of care.

Build the prompt with clear role/format instructions (template example, required fields, and one‑line patient summary), a reviewer step for clinician sign‑off, and a multilingual flag to serve Mesa's Spanish‑preferred patients while preserving HIPAA controls and EHR integration requirements (see Heidi Health ambient AI medical scribe at Heidi Health ambient AI medical scribe, Sunoh transcription and structured clinical notes at Sunoh.ai transcription and structured clinical notes, and DeepScribe context‑aware AI medical scribe at DeepScribe context‑aware AI medical scribe).

Vendor	Notable benefit
Heidi Health	Ambient scribe, multilingual support, templates to “restore eye contact”
Sunoh.ai	Trusted by 80,000+ physicians; rapid transcription and structured clinical notes
DeepScribe	Context‑aware, specialty‑specific notes with coding intelligence (high KLAS performance)

“Allows me to get home earlier! Heidi health found me at a time when I was feeling that noting was interfering with my desire to be fully present for clients.” - Lisa Terwilliger, Therapist/US

For Mesa surgical teams, a practical "post‑op complication prediction" prompt directs a chart‑centric LLM to parse preop assessments, operative reports, and nursing notes alongside structured vitals and labs, then return a calibrated risk band, likely complications (pneumonia, VTE, infection, AKI), and a clear next step (enhanced monitoring, early imaging, ICU consult, or targeted discharge instructions) with timestamps for handoff and SEP‑1‑aligned escalation.

Grounded in evidence that ICU AI work commonly targets complication prediction (systematic review of AI prediction of ICU postoperative complications) and in recent foundation‑model research showing LLMs tuned on surgical notes can markedly improve detection, this prompt design can flag higher‑risk patients before deterioration and shorten time to intervention (WashU 2025 study predicting postoperative risks from clinical notes).

That matters in Mesa because earlier, accurate risk identification can reduce ICU admissions, avoid prolonged stays, and lower costs for a population served by mixed urban and rural systems.

Metric	Value / Source
Additional patients identified vs traditional NLP	39 per 100 patients with complications (WashU)
WashU dataset	~85,000 surgical notes (2018–2021)
MySurgeryRisk dataset	>74,000 procedures / ~58,000 patients (UF)

“Our large language model, tailored specifically for surgical notes, enables early and accurate prediction of postoperative complications. By identifying risks proactively, clinicians can intervene sooner, improving patient safety and outcomes.”

Build a Mesa‑specific population health outreach prompt that moves beyond “only high‑risk” contact and instead identifies moderate‑risk cohorts by combining EMR fields such as adherence, chronic diagnoses, lab values, ZIP code, payer, age, gender, and preferred language so outreach is preventive and equitable; operationalize CipherHealth's recommendations with inclusive multimodal channels (SMS, phone, portal) and data‑driven disparity checks to tune modality and timing per neighborhood, and embed automatic SDOH flags and tailored interventions (rides, referrals, telehealth) so social barriers are addressed before discharge.

Practical details matter: patients expect notifications (roughly 57% want appointment/medication reminders) and targeted, earlier outreach to rising‑risk groups reduces the chance they become costly, high‑risk patients - turning outreach from a cost center into a cost‑saving prevention tool.

For pitfalls and fixes, see common population health outreach mistakes and practical outreach playbooks that emphasize scale with personalization (detailed population health outreach strategy mistakes: 3 mistakes hurting population health outreach strategy, multimodal patient outreach to advance health equity: advance health equity with multimodal patient outreach, and patient outreach strategies and benefits overview: patient outreach strategies and benefits).

"One study estimates more than 80 percent of outcomes are influenced by these social determinants of health."

Getting started in Mesa means piloting a few high‑value prompts that fit local workflows, validating them with explainable models, and training staff so gains stick: prioritize administrative wins (payer‑ready prior authorizations, automated visit summaries) and one clinical pilot (imaging triage or a sepsis EHR alert) that yields rapid ROI and measurable safety gains - sepsis matters because mortality rises roughly 8% for every hour it goes unrecognized, yet rapid host‑response tests can return actionable risk bands in minutes.

Follow an implementation playbook that demands explainability and clinician oversight (Explainability for AI in healthcare), align projects to strategic priorities and near‑term ROI per the AHA action plan, and invest in staff prompt‑writing and operational skills so tools become part of workflow rather than noise (AHA build and implement an AI health care action plan).

Practical training accelerates this transition - consider cohort education like the AI Essentials for Work bootcamp: prompt design and safe AI rollout to teach prompt design, evaluation, and safe rollout so Mesa clinics can move from experiment to measurable improvements in access, cost, and outcomes.

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

“It's prime time for clinicians to learn how to incorporate AI into their jobs.”