Top 10 AI Prompts and Use Cases and in the Healthcare Industry in Los Angeles

Los Angeles health systems are already testing concrete AI tools - UCLA Health and Cedars‑Sinai deploy chatbots and EHR documentation support and are integrating imaging and text data into clinical workflows - yet national research shows adoption stalls unless use cases map directly to clinician workflow: HIMSS finds 86% of organizations use AI and notes both promise and governance concerns, while the Healthcare AI Adoption Index reports only ~30% of pilots reach production despite AI budgets outpacing IT spend.

That gap makes LA a high‑impact place for ready‑to‑use prompt libraries and workflow‑anchored examples (triage, documentation scribe templates, imaging summarization) that reduce clinician time in the EHR and surface diagnostic patterns earlier; practical prompt-writing programs such as Nucamp's AI Essentials for Work course syllabus can equip teams to turn these POCs into repeatable, safe deployments.

For local leaders, the priority is clear: pair targeted prompts with governance and measurable ROI to move from experimentation to production.

Attribute	Information
Program	AI Essentials for Work
Length	15 Weeks
Focus	Write effective prompts & apply AI across business functions
Cost (early bird)	$3,582
Syllabus	AI Essentials for Work course syllabus - Nucamp (15‑week)

Selection prioritized prompts that map directly to clinician workflow, show peer‑reviewed performance, and remain portable across hospital systems - criteria designed to help Los Angeles teams move beyond pilots into production.

Each candidate prompt needed evidence of clinical benefit (for example, the UCLA MEME study that converted tabular EHRs into “pseudonotes” and outperformed existing approaches across more than 1.3 million emergency visits), alignment with prompt‑engineering best practices summarized in the prompt engineering scoping review, and fit with local training and governance paths reflected in recent medical‑education mapping.

Prompts were scored on (1) workflow fit (triage, documentation, imaging), (2) measurable impact on decision support or admin time, (3) portability across coding standards, and (4) ease of clinician retraining; teams can reproduce the process using the linked UCLA MEME study, the prompt‑engineering review, and the BMC education mapping as implementation guides.

Criterion	Evidence
Clinical performance	UCLA MEME: outperformed on >1.3M ED visits
Prompt engineering	JMIR prompt engineering scoping review
Education & governance	BMC mapping of AI in medical education (Apr 12, 2025)

"This bridges a critical gap between the most powerful AI models available today and the complex reality of healthcare data. By converting hospital records into a format that advanced language models can understand, we're unlocking capabilities that were previously inaccessible to healthcare providers." - Simon Lee, PhD student at UCLA Computational Medicine

Freenome's multiomics blood test is a pragmatic diagnostic‑assistance use case for Los Angeles clinics and population‑health programs because it pairs noninvasive screening with real‑world integration opportunities: the PREEMPT CRC registrational study enrolled 48,995 average‑risk adults and reported 79.2% sensitivity and 91.5% specificity, while a recent commercial agreement signals broader EMR and payer reach for U.S. health systems; see Freenome's PREEMPT CRC results and the exclusive license agreement with Exact Sciences.

Practical prompts for LA deployments include: “Given this patient's age, last screening date, and Freenome test result, recommend next steps aligned to USPSTF guidance and flag patients for outreach,” and a clinician‑note prompt that converts a positive test into a concise action checklist (referral, scheduling, patient language).

The so‑what: a validated blood option that can be embedded in outreach and EHR workflows helps safety‑net and private practices increase screening rates among patients who decline colonoscopy, accelerating early detection without changing existing clinic infrastructure.

Metric	Value
PREEMPT CRC participants	48,995
CRC sensitivity	79.2%
Specificity (non-advanced neoplasia)	91.5%

“We are excited to enter into this agreement with Exact Sciences, which represents a pivotal moment in our mission to detect cancer in its earliest, most treatable stages.” - Aaron Elliott, Ph.D., CEO, Freenome

Mayo Clinic's work with Google's Vertex AI shows how clinical decision support for Los Angeles hospitals can move from experimental prompts to production workflows: Mayo Clinic uses Vertex AI Agent Builder and Vertex AI Search to index and query massive clinical stores - Google reports the system can search over 50 petabytes of clinical data - and Mayo's OPUS platform taps 25 databases from a record base of more than 11 million patients while annotating datasets at scale (for example, 16 million retinal photos) to build research cohorts and train models; those grounded datasets and RAG-style footnoting enable prompt templates that generate concise, source‑linked case summaries, nurse‑handoff briefings, and condition‑timeline snippets that cite the originating notes, images, or FHIR entries.

For LA care teams the clear payoff is operational: prompts that synthesize EHR data into short, actionable checklists can shrink documentation time and make specialty consults and trial‑eligibility screening reproducible across health systems.

Google's Vertex AI Agent Builder and Mayo Clinic's OPUS provide further details on agent workflows and clinical scale.

Attribute	Value
Searchable clinical data (Vertex AI)	~50 petabytes
Mayo Clinic patient records	11 million+
OPUS source databases	25
Retinal photos annotated (Ophthalmology)	16 million
Algorithms under development (Mayo)	~250

"OPUS is a powerful AI‑bioinformatic system that allows us to search for specific patient cohorts in our medical records and build databases for AI training." - Raymond Iezzi Jr., M.D., Mayo Clinic

For Los Angeles biotech teams focused on therapeutic discovery, Cradle's AI protein‑design platform turns iterative wet‑lab cycles into promptable workflows - examples include “generate antibody variants that increase EGFR binding while preserving thermostability and expression” or “prioritize enzyme sequences for activity at 37°C and manufacturability constraints” - so researchers can move from hypothesis to lab‑ready candidates in fewer rounds.

Cradle couples generator and predictor models with a lab‑in‑the‑loop benchmarking pipeline that customers use to upload assays, track round status, and refine models privately, yielding concrete speedups (teams report 1.5–12x faster timelines) and competition wins like an 8x improvement in EGFR binding; details at Cradle's platform page and their benchmarking write‑up.

The practical payoff for LA academic labs, CROs, and startups is measurable: more candidates per experiment, reproducible model provenance for grant and regulatory workflows, and SOC‑2 level data protections that keep IP private while models improve with each upload.

Metric	Value / Example
Reported development speedup	1.5–12× faster timelines
Adaptyv Bio competition result	8× improvement in EGFR binding (case study)
Security & privacy	SOC 2 compliant; private customer models

“Alphafold takes a sequence and predicts what the protein will look like. We're the generative brother of that: You pick the properties you want to engineer, and the model will generate sequences you can test in your laboratory.” - Stef van Grieken, Cradle

Los Angeles care teams can translate HCA's real-world work on virtual caregivers into concrete prompts that reduce bedside burden: HCA's collaboration with Google Cloud shows generative AI powering nurse‑centric tools that cut repetitive handoff work (nurse handoffs average about 40 minutes per shift, adding up to roughly 10 million nursing hours annually across the system), while HCA's virtual nursing pilots highlight high‑value tasks such as admissions, medication history capture, discharge teaching, and rounding; see the HCA Healthcare and Google Cloud generative AI collaboration and the Google Cloud Nurse Handoff AI case study.

Practical LA prompts derived from these deployments include: “Summarize overnight events, vitals trends, and two immediate nursing actions (meds to reconcile, pending imaging) in ≤4 bullets for shift handoff,” and “Create a 30‑sec patient‑facing SMS in Spanish/English that reminds this patient about discharge meds and schedules a virtual check‑in.” Bennie Health's Vertex AI entry in Google's use‑cases list shows related opportunities for employee and population‑level monitoring tied to benefits and outreach.

The so‑what for Los Angeles: prompts that produce short, source‑linked handoffs and automated check‑in messages can immediately free bedside time, standardize cross‑site continuity, and scale outreach across safety‑net and private practices without new clinical staff headcount.

Metric / Pilot	Value / Note
Nurse handoff time	≈40 minutes per nurse shift
Aggregate nurse hours	~10 million hours/year (systemwide)
HCA footprint cited	190 hospitals & ~2,400 ambulatory sites
Virtual nursing tasks	Admissions, med history, discharge teaching, rounding
Bennie Health	Vertex AI for employee health benefits insights (use‑case)

“The smoother the handoff, the more time we have to spend with patients.” - Samantha Hall, RN

For Los Angeles clinical‑trial teams, CytoReason's Disease Model Platform turns disparate omics and trial data into promptable workflows that speed cohort selection, biomarker prioritization, and trial‑risk assessment by mapping treatments, patient groups, and disease mechanisms and by accepting secure, user‑uploaded datasets; see CytoReason's overview of the CytoReason Disease Model Platform overview.

Combining CytoReason's multi‑layer disease models with automated literature curation (a retrieval‑augmented generation pipeline powered by NVIDIA NIM) collapses manual review from days to hours, extracting 99 gene findings with 70 overlapping manual curation results and reporting ~96% evidence accuracy - concrete gains that help Los Angeles sponsors and contract research organizations prioritize targets, stratify trial arms, and shorten development cycles without reworking existing data pipelines; see the NVIDIA write‑up on NVIDIA NIM RAG-enabled literature curation case study.

Metric	Value
Literature curation time	Days → a few hours (NVIDIA NIM RAG)
Genes extracted (case study)	99
Overlap with manual curation	70 genes (plus 29 validated new findings)
Evidence accuracy	~96%

“The difference between using CytoReason technology and not using it, is the difference between engineering and art.” - Mike Vincent, SVP Immunology & Inflammation

Covered California's deployment of Google Cloud's Document AI, built with Deloitte support, automates eligibility document intake and verification so California applicants can upload files and receive near‑real‑time acceptance status - pilot results show an average 84% automated verification (80–96% by document type) versus the legacy 18–20% completion rate, enabling the exchange to scale to roughly 50,000 documents per month and shift many cases from multi‑day manual review to seconds; read the official Covered California announcement about Document AI deployment and Google Cloud's customer case study on Covered California Document AI implementation and security for implementation and security details (Assured Workloads, FedRAMP, and Google Security Operations).

The practical payoff for Los Angeles health teams and enrollment navigators is concrete: fewer paperwork bottlenecks during the October open‑enrollment surge (which drives ~75% of traffic), faster subsidy determinations, and reallocated staff time toward complex eligibility counseling and outreach to underinsured communities.

Metric	Value
Average automated verification	84% (pilot)
Pilot range by document type	80%–96%
Legacy automated completion	18%–20%
Documents processed (scale)	~50,000 per month
Open‑enrollment traffic	~75% of annual traffic
Security/compliance	Assured Workloads / FedRAMP; Google Security Operations

“The effectiveness of Google Cloud's solutions on its open‑cloud infrastructure enabled our team to automate document verification, meet high validation thresholds and consistently keep up with shifting state legislation. We are very pleased with these initial results; Document AI has reliably fulfilled its promises and we are confident in our ability to effectively leverage it.” - Karen Johnson, Chief Deputy Executive Director, Covered California

WellSky's predictive analytics - built from millions of patient episodes - offers Los Angeles providers concrete prompt opportunities to move population health work from dashboards into action: for example, a prompt that generates a prioritized outreach list of patients with the highest predicted 60‑day hospitalization risk, another that drafts a one‑page, clinician‑ready care plan linking risk drivers (including social determinants) to recommended home‑health interventions, and a third that produces exportable performance snapshots for referral partners to support value‑based contracting.

These prompts map directly to outcomes shown in WellSky's three‑year study: faster, more efficient episode management (CareInsights users saw a 12% drop in 60‑day hospitalizations after one year and up to 26% lower rates for consistent users) and far fewer visits per admission for high adopters - metrics LA health systems and payer teams can use to target Medi‑Cal populations, scale outreach, and measure ROI. See the full WellSky CareInsights study detailing predictive analytics outcomes and the WellSky transformative analytics overview with implementation guidance for implementation details and solution scope.

Metric	Result
60‑day hospitalization reduction (after 1 year)	12% lower risk‑adjusted rate
Visits per admission (after 1 year)	8% fewer visits
60‑day hospitalization reduction (top users, 3 years)	26% lower rate
Visits per admission (top users, 3 years)	45% fewer visits (top users)

“Ultimately, providers are not aiming for fewer visits but better episode management. This, coupled with lower hospitalization rates and improved patient outcomes, becomes a meaningful measure of efficiency.” - Tim Ashe, Chief Clinical Officer, WellSky

Los Angeles radiology teams can use Bayer's cloud‑native imaging platform to turn repeatable prompts into operational gains - examples include automated worklist prioritization for suspected critical findings, RAG‑style image+report summarization that cites source files, and batch quantification prompts for follow‑up measurement consistency - because the platform couples Google Cloud infrastructure and generative AI tooling with curated imaging data and app orchestration.

Practical LA deployments gain traction from three concrete enablers: Bayer's partnership with Google Cloud to accelerate AI apps for radiologists (Bayer and Google Cloud radiology AI platform), the integration of Segmed's de‑identified repository (bringing ~100 million real‑world imaging studies) to power development cohorts (Segmed 100M de-identified imaging studies integration), and Calantic's PACS/RIS‑friendly deployment model that bundles multiple validated AI apps into one interface for secure clinical rollout (Calantic Digital Solutions PACS integration and deployment model).

The so‑what for LA: hospitals can prototype clinician‑facing prompts against large, diverse imaging cohorts and ship standardized, audit‑ready inference apps - Bayer plans extended testing in the EU and U.S., shortening the path from pilot to production.

Attribute	Detail
Cloud stack	Google Cloud (Vertex AI, BigQuery, Healthcare API, Chronicle)
Data	Segmed RWiD ≈100 million imaging studies
Deployment	Calantic: PACS/RIS integration, single‑interface app management
Testing scope	First extended testing planned in EU and U.S.

“Radiology plays a vital role in healthcare, and the need to efficiently and accurately uncover insights and deliver solutions at scale that can improve patient outcomes has never been greater.” - Nelson Ambrogio, President, Radiology at Bayer

Adapting omnichannel donor‑chatbot flows and clinic triage prompts (as in the Hemominas donor chatbot and Family Vision Care prompt templates) to Los Angeles practices creates a fast, consistent front door that surfaces red‑flag answers and routes patients where they need to be - same‑day clinic, urgent care, or ED - without adding staff headcount.

Build prompts from concise, high‑value questions used in eye triage - e.g., “Is the white of the eye red or the eyelid?”; “Is it one eye or both?”; “When did you first notice double vision?”; and “Any pain with eye movement?” - so that a chatbot or SMS flow can flag likely retinal detachment, chemical injury, or cranial‑nerve palsy for immediate evaluation (Optometry Times).

Pair those scripted questions with triage thresholds from specialist clinics - severe pain, sudden vision loss, or inability to open the eye should trigger an automated escalation to an ophthalmology triage line or ED referral (Moran Eye Center) - and integrate with LA's existing AI tooling and clinician workflows (for example, automated messages and documentation templates used in local AI deployments) so the system records the exact responses, language preference, and next‑step appointment suggestion for quick follow‑up.

The so‑what: one well‑designed triage flow that asks two targeted questions (e.g., pain with movement + sudden vision change) can convert ambiguous “FYI” calls into same‑day evaluations that prevent vision‑threatening delays while keeping routine concerns in scheduled care.

Read the triage guidance and clinic thresholds at Optometry Times and Moran Eye Center, and see examples of AI‑enabled clinical tools in Los Angeles deployments.

“When in doubt, check it out. If you're not sure, bring the patient in to be seen by the doctor.” - Optometry Times

Los Angeles health systems rely on last‑mile partners to move specimens, meds, and bulky supplies across complex care networks, so embedding AI routing and exception‑management prompts into courier workflows can cut clinical risk and operating cost: L.E.K. highlights that mishandled specimens alone can cost an average‑sized system ~$1M/year and that more than half of nurses surveyed reported last‑mile errors that led to delays or cancellations (avg.

cost ≈ $4,500 per event), which makes routing accuracy and cold‑chain alerts a patient‑safety priority; see L.E.K.'s analysis of the true value of last‑mile logistics in healthcare.

Metric	Value / Source
Estimated cost of mishandled specimens	$1,000,000 per average‑sized system / L.E.K.
Average cost per delayed/cancelled procedure	$4,500 / L.E.K.
% nurses reporting last‑mile caused a delay/cancel	More than 50% (surveys ≈660 responses) / L.E.K.
Beep Saúde reported cancellation reduction	~10% (AI routing & order processing) / Google Cloud use cases

Two practical prompt sets for LA teams - drawn from real‑world AI logistics deployments - are: (1) Beep Saúde‑style routing prompts that reduce cancellations (10% reported in vendor case lists) by prioritizing temperature‑sensitive pickups, suggesting alternate drivers, and auto‑notifying labs/clinicians when ETAs slip; and (2) Nowports‑style port‑to‑door optimization prompts that predict container delays, recommend contingency lanes, and produce an auditable handoff log for supply‑chain and pharmacy teams; see Google Cloud's industry use‑case compendium on AI routing & logistics.

Operational partners such as MedSpeed illustrate how dedicated healthcare courier tech and teams can operationalize these prompts without adding clinical headcount - so what: a few well‑scoped prompts can prevent costly redraws, avert procedure cancellations, and reclaim clinician hours for direct care.

"We consider MedSpeed an extension of our teams in supply chain, lab, pharmacy, and even IT asset management." - Joshua S. Dritz, Senior Director, Logistics and Sterile Processing (MedSpeed testimonial)

Los Angeles healthcare teams should translate this report's use cases into a short, structured roadmap: (1) take an immediate inventory and risk‑classify deployed and shadow LLMs, (2) pick one high‑impact workflow to pilot (nurse handoffs - ≈40 minutes/shift - or imaging triage) and run a 60–90 day “prompt‑to‑production” sprint with measurable safety and time metrics, (3) implement dynamic, real‑time monitoring and granular controls - not periodic reviews - to detect hallucinations and workflow drift as recommended in the governance playbook, and (4) align deployment choices to California's evolving patchwork of state rules and federal guidance while documenting provenance for audits and payers.

Pair pilots with rapid upskilling for clinicians and operations staff (practical prompt writing, RAG checks, evaluation criteria) so teams can sustain production; for curriculum-ready training see Nucamp's AI Essentials for Work (15‑week) syllabus and registration: AI Essentials for Work - 15-week practical AI skills for work syllabus.

For governance design and monitoring architecture, review the case for agile oversight in the Chief Healthcare Executive viewpoint on LLMs and the state‑focused compliance guidance in the Kirkland/Law360 summary to ensure pilots scale safely and legally in California.

Attribute	Information
Program	AI Essentials for Work
Length	15 Weeks
Focus	Practical prompt writing & AI at work
Cost (early bird)	$3,582
Syllabus	AI Essentials for Work syllabus (15-week)
Registration	Register for AI Essentials for Work - Nucamp registration

“Healthcare organizations must evolve towards dynamic, real-time governance and risk-based management of AI tools.” - Justin Norden & Kedar Mate