Top 10 AI Prompts and Use Cases and in the Healthcare Industry in San Francisco

In San Francisco's fast-moving health systems, carefully crafted AI prompts are the bridge between messy clinical notes and safe, actionable care; for California clinicians and health‑tech teams, learning to write targeted prompts is now a core skill - one that shortens the path from idea to validated model and helps avoid bias, privacy, and interpretability pitfalls.

UCSF Introduction to Clinical Artificial Intelligence (EPI 233)

UCSF highlights how prompt engineering and generative AI projects can turn unstructured documentation into cohort discovery, outcome labels, or structured data for research and operations, while practical guides stress that a well-crafted prompt reduces false results and improves reliability: Gleeson Library guide: Crafting Prompts for Generative AI.

Those ready to build workplace-ready prompt skills can follow a structured curriculum like Nucamp's AI Essentials for Work registration and course details to learn prompt-writing, LLM workflows, and hands-on use cases that make AI tools trustworthy and usable in Bay Area clinics.

Program	Details
AI Essentials for Work	Gain practical AI skills for any workplace; learn to use AI tools and write effective prompts (no technical background required)
Length	15 Weeks
Courses included	AI at Work: Foundations, Writing AI Prompts, Job Based Practical AI Skills
Cost	$3,582 (early bird); $3,942 afterwards - paid in 18 monthly payments, first payment due at registration
Syllabus	AI Essentials for Work syllabus
Registration	AI Essentials for Work registration

Selection favored prompts and use cases that align with three practical, legally grounded filters: relevance, proportionality, and privacy - principles drawn from guidance on the discoverability of AI prompts and outputs - so that a suggested prompt is not only useful in a clinic but defensible in California litigation and compliance conversations.

Relevance meant choosing prompts that tend to change the probability of a clinical or operational fact (for example, whether an author's interactions with AI actually bear on state of mind or a claim); proportionality required weighing the technical effort and data volume against likely benefit; and privacy demanded attention to personally sensitive chatbot exchanges and accessibility needs.

Accessibility and inclusive design were scored using the W3C AI and Accessibility research as a check on data diversity and user agency, while local deployment risk considered FDA and state regulation impacts on healthcare AI in California.

In practice this produced a short list of high-impact prompts that (1) generate explainable, auditable outputs, (2) minimize unnecessary data collection, and (3) support accessible outputs for diverse users - avoiding scenarios where a single misplaced search or chat becomes the linchpin of a case.

Put simply, the relevance of AI inputs or outputs can depend only on whether they tend to make a fact more or less probable than it would be without the evidence.

Kaiser Permanente's push to reduce diagnostic errors pairs practical education with point‑of‑care decision support: the Diagnostic Excellence hub outlines what causes mistakes and what clinicians can do to improve decision‑making, from better differential generation to systems that surface evidence at the bedside (Kaiser Permanente Diagnostic Excellence hub).

In operational practice, tools like VisualDx - integrated into KP workflows and HealthConnect - give clinicians and patients fast, visual second opinions (its DermExpert can analyze a lesion photo and build a custom differential in seconds), turning a blurry clinical hunch into a teachable, evidence‑linked workup (VisualDx clinical decision support at Kaiser Permanente Northwest).

Real-world adoption data reinforce that the technology alone isn't enough: an EHR‑embedded clinical decision support study showed overall activation in 55% of eligible encounters, but sites with active champions, training, and iterative feedback drove activation to 68% versus just 13% at passive sites - proof that governance, training, and workflow integration matter as much as the algorithm when translating AI into safer care (Kaiser Permanente Division of Research study on clinical decision support activation).

Metric	Value
Overall CDSS activation	55%
Active sites (with champions/education)	68% (346/512)
Passive sites	13% (20/150)

UC San Francisco is piloting ambient clinical documentation and AI scribes with a clear California focus: reduce the hours clinicians spend typing and restore time with patients while keeping oversight tight.

Early multi‑site evidence summarized by UCSF's DoC‑IT - drawing on a JAMA Network Open analysis - shows promise (burnout fell from 51% to 29% and nearly half of clinicians adopted the tool for most visits), but also underscores the need for rigorous, sustainable rollouts; UCSF Health's contract with Ambience Healthcare to trial real‑time AI scribing in ambulatory clinics and pediatric EDs in Oakland and Mission Bay is a practical example of this measured approach (UCSF DoC-IT summary of JAMA Network Open findings on ambient documentation, UCSF Health Ambience Healthcare pilot for real-time AI scribing).

The most vivid payoff reported by peers was literal: many clinicians said they got “nights and weekends back,” but researchers caution that effects may vary by specialty and that careful governance, clinician review of drafts, and equity analyses remain essential before scaling across California systems.

Metric	Value
Burnout prevalence (before → after)	51% → 29%
Adoption (tool used for most visits)	Nearly 50%
MGB measured burnout reduction (84 days)	21.2% absolute reduction

“Burnout adversely impacts both providers and their patients who face greater risks to their safety and access to care. This is an issue that health systems nationwide are looking to tackle, and ambient documentation provides a scalable technology worth further study.” - Lisa Rotenstein, MD, MBA

NYU Langone's work on generative AI shows a practical California-relevant path for turning dense discharge notes into patient-friendly language without losing clinical fidelity: a JAMA Network Open study summarized by NYU's guides found AI-generated summaries dropped average readability from an 11th‑grade level to about a 6th‑grade level and raised understandability (PEMAT) from 13% to 81%, though clinicians flagged accuracy gaps and omissions that must be managed in workflow design - so these tools are promising for improving patient access and follow‑up, but not ready for unsupervised rollout (NYU Langone generative AI study summary in Inside Precision Medicine).

Practical support for teams experimenting locally is available through NYU's AI in Healthcare tutorials and patient‑education resources, which include prompt engineering tutorials and examples for safer, clinician‑reviewed deployments (NYU Health Sciences Library AI Studio video tutorials on AI in healthcare, NYU patient education and prompt engineering resources), highlighting the tradeoffs between readability, completeness, and safety that California systems must govern carefully.

Metric	Study result
Readability (Flesch‑Kincaid)	AI summaries 6.2 vs originals 11
Understandability (PEMAT)	AI summaries 81% vs originals 13%
Physician‑rated accuracy	54 of 100 reviews scored 6 (100% accuracy)
Safety concerns raised	18 reviews
Incompleteness	44% of AI‑generated statements rated incomplete

“Increased patient access to their clinical notes through widespread availability of electronic patient portals has the potential to improve patient involvement in their own care, as well as confidence in their care from their care partners.”

MetroHealth's pragmatic approach - embedding Epic's “Risk of Patient No‑Show” model into scheduling workflows and then adding targeted, live phone outreach - offers a California‑relevant template for safety‑net systems seeking equitable access: patients above a 15% calculated no‑show risk were randomized (Jan–Sep 2022) and schedulers called to confirm appointments and offer low‑tech fixes like transportation or telehealth, which disproportionately helped Black patients who were more likely to pick up the phone; the peer‑reviewed quality‑improvement study and MetroHealth summary both show meaningful gains and caution that careful localization and validation are essential before scale-up (MetroHealth CWRU press release on AI improving clinic access for minorities, Journal of General Internal Medicine article on AI‑guided outreach reducing no‑shows).

The operational payoff is intuitive and measurable - a single avoided no‑show for roughly every 29 calls made - so systems can weigh added staff time against recovered visits while guarding against workflows that could unintentionally widen disparities.

Metric	Value
Outreach threshold	Calculated no‑show likelihood ≥ 15%
Overall no‑show reduction	9.4% decrease (study population)
Black patients (no‑show rate)	With call 35.8% vs without 42.1% (15.0% reduction)
Operational impact	~1 no‑show prevented per 29 calls; ~1 hour of caller work per conversion

“We used the AI technology to figure out who needed additional support or an alternative low‑tech outreach solution.” - Yasir Tarabichi, MD

California health systems aiming to operationalize algorithmovigilance can look to Duke Health's playbook: the Duke Health AI Evaluation & Governance (E&G) Program pairs a Duke Health ABCDS Oversight initiative that enforces clinical value, fairness, usability, and lifecycle monitoring with evaluation frameworks - like the SCRIBE and JAMIA approaches - that test accuracy, fairness, coherence, and resilience before and after deployment.

Duke's pragmatic “Model Facts” labels (first used for the Sepsis Watch model and now updated for HTI‑1 compliance) illustrate how a concise, shareable factsheet can surface provenance, performance, and the 31 source attributes regulators now expect, helping teams in San Francisco document audits, validate local performance, and meet EHR vendor disclosure rules; see Duke's overview of their work and download the updated label template for practical implementation.

The result is governance you can operationalize: a set of checklists, validation tests, and continuous monitoring hooks that turn abstract guardrails into day‑to‑day practices - so that a vendor demo doesn't become the only record clinicians rely on when lives are at stake.

Tool / Initiative	Purpose
Duke Health ABCDS Oversight program for clinical AI governance	Governance and lifecycle monitoring for algorithm‑based clinical decision support
SCRIBE & JAMIA frameworks	Structured evaluation of ambient scribing and LLM replies (accuracy, fairness, resilience)
Model Facts v2 label for HTI‑1 compliance and transparency	Product label template for transparency and HTI‑1 source‑attribute disclosure
Health AI Maturity Model	Readiness framework to benchmark governance, data quality, and monitoring

“Ambient AI holds real promise in reducing documentation workload for clinicians. But thoughtful evaluation is essential. Without it, we risk implementing tools that might unintentionally introduce bias, omit critical information, or diminish the quality of care. SCRIBE is designed to help prevent that.” - Chuan Hong, Ph.D.

Kaiser Permanente is translating cautious optimism about AI into outcomes‑focused operational trials that test whether tools actually improve patient care at scale - not just models in a lab.

Northern California's Division of Research secured a $5 million American Heart Association grant to fund a prospective, multi‑center trial that will evaluate whether AI can turn routine, inexpensive, portable echocardiograms into opportunistic screens for liver and kidney disease and prompt appropriate referrals (AHA-funded Kaiser Division of Research cardiovascular AI study).

That work sits alongside the AIM‑HI program, which has awarded up to $750,000 per project to run rigorous, real‑world demonstrations (from randomized prompts for sepsis resuscitation to diabetic retinopathy point‑of‑care screening) and to develop best practices for deployment (AIM-HI program project portfolio and deployments).

Early evidence from a JAMA Internal Medicine evaluation of an AI‑enabled deterioration alert showed a meaningful -10.4 percentage‑point absolute reduction in escalations of care at the score threshold, proving that well‑designed operational trials can move AI from promise to measurable harm reduction (JAMA Internal Medicine evaluation of AI-enabled clinical deterioration alert).

The approach emphasizes pragmatic designs, integrated EHR leverage (as in virtual MITIGATE trials), and lifecycle monitoring so California systems can judge benefit, equity, and safety before broad rollout.

Metric	Value
AHA research grant to KP DOR	$5,000,000
Max AIM‑HI award per grantee	Up to $750,000
JAMA AI intervention effect	-10.4 percentage points reduction in escalations of care
MITIGATE trial planned follow‑up	1,500 treated vs 15,000 control (electronic follow‑up)

“The work will show whether AI can help doctors make each clinical encounter an opportunity to provide more comprehensive care.” - Rachel Ramoni, DMD, ScD

Vanderbilt Health is pairing generative AI with deep genomics to fast‑track drug repurposing for hard‑to‑treat conditions relevant to California clinicians and researchers: the Accelerating Drug Discovery and Repurposing Incubator (ADDRI) mines BioVU's linked DNA and electronic health records using PheWAS to surface gene‑disease links, while large language models (LLMs) like ChatGPT have been tested as rapid hypothesis engines to prioritize candidates for Alzheimer's and other diseases.

Pilot work shows LLMs can accelerate literature review and produce sensible shortlists that are then validated against real‑world datasets (VUMC and NIH All of Us), yielding promising associations such as metformin (−33% AD risk), losartan (−24%), and simvastatin (−16%) in combined analyses and a strong minocycline signal in the VUMC sample - a workflow that saves weeks of manual sifting but still requires rigorous EHR validation and downstream trials before clinical use.

Approach	Key result
LLM‑driven hypothesis generation	Top‑20 candidate lists for Alzheimer's (pilot study)
Validation datasets	VUMC Synthetic Derivative & NIH All of Us
Notable associations	Metformin −33%, Losartan −24%, Simvastatin −16% (combined); Minocycline −66% (VUMC)

“When I first saw the list of candidates, I was shocked. The list was surprisingly rational, with some of the drugs already being studied for potential use in treating Alzheimer's.”

UC San Diego's large‑scale Epic integration - where almost 40,000 student records were migrated into a HIPAA‑ and FERPA‑protected Epic environment - offers a concrete California example of how secure, interoperable EHR connections can underpin enterprise GenAI platforms and trusted LLM gateways; at go‑live UCSD gained access to 93,000 unique documents from 262 health systems and shared roughly 250,000 documents across multiple states, while rapid pandemic workflows (including same‑day video visits and a shift to >95% video care for students) proved the value of tightly integrated data and workflows (UC San Diego Epic migration case details).

Operationally, the UCSD + Luma Health collaboration - built on Epic - sent more than 4 million outreach messages with an 89% engagement rate, a vivid reminder that an enterprise GenAI layer must sit on both large, trusted datasets and robust communication channels to be useful in California health systems (Luma Health and Epic UCSD case study).

Metric	Value
Student records migrated to Epic	~40,000
Unique documents accessible at go‑live	93,000 (from 262 systems)
Documents shared over six months	~250,000
Broadcast messages sent (Luma Health)	4,000,000+
Patient engagement with messages	89% (double MyChart engagement)
Video visit conversion for students	>95%

“After the medical record transition, we observed significant improvements to the provision of care for UC San Diego students. ... By sharing health records, we've improved the continuity of care for students through same-day video visits, improved turnaround for radiology results, and accessible COVID-19 testing.” - Christopher Longhurst, MD

The Lancet Digital Health evaluation of GPT‑4 - led in part by researchers at UCSF and Stanford - is a timely caution for California health systems exploring LLMs: the model exaggerated known demographic prevalence in 89% of diseases when generating educational vignettes, described a sarcoidosis case as a Black woman 81% of the time, and changed the top‑ranked diagnosis in 37% of NEJM case variants when only race or gender was altered, while 23% of subjective patient assessments varied by race or gender, signals that can translate into unequal care if unchecked (Lancet Digital Health study, ScienceDaily summary).

For San Francisco systems that prize equity, the practical takeaway is simple and stark: run targeted bias audits, validate LLM outputs locally with diverse clinical data, and build governance so clinicians aren't the only line of defense against subtle, systemic errors that are hard to spot case‑by‑case.

Finding	Result
Vignette demographic exaggeration	89% of diseases showed exaggerated prevalence differences
Top‑diagnosis prioritization affected	37% of NEJM case variants
Subjective patient assessment differences	23% of questions varied by race/ethnicity or gender
Notable example	Sarcoidosis vignette: described as a Black woman 81% of the time

“While LLM-based tools are currently being deployed with a clinician in the loop to verify the model's outputs, it is very challenging for clinicians to detect systemic biases when viewing individual patient cases.”

Mass General Brigham's work highlights how predictive analytics - especially natural language processing of unstructured clinical notes - can turn mountains of EHR text into actionable risk flags that prevent costly, avoidable readmissions: CMS data still show nearly one in five Medicare patients returns within 30 days, so tools that surface high‑risk patients in primary care matter.

Practical models (LACE, DSI, HOSPITAL) embedded into workflows let teams flag, for example, a congestive‑heart‑failure patient with recent ED visits for a targeted 7‑day follow‑up, medication reconciliation, or home‑health referral - exactly the family‑medicine interventions an MGH Institute primer recommends for shifting from reactive to proactive care (MGH Institute analysis: From Data to Decisions on leveraging predictive analytics).

Real‑world impact is striking: analytics and NLP‑driven process changes at Mass General reduced index PCI readmissions 40% (from 9.6% to 5.3%), showing how unstructured notes can drive measurable gains when paired with checklists, e‑consults, and care‑team workflows (Mass General quality improvement case study on PCI readmission reduction).

The opportunity is large - and urgent - because most hospital data are unstructured and underused: platforms that surface those signals let San Francisco systems prioritize patients equitably and act before the next hospital return (Avant‑garde Health analysis: features every healthcare analytics platform needs).

Metric	Value / Source
Medicare 30‑day readmission rate (approx.)	Nearly 1 in 5 (MGH Institute analysis: Medicare 30-day readmission context)
Mass General PCI readmission reduction	40% (9.6% → 5.3%) (Mass General quality improvement case study: PCI readmission reduction)
Hospital data unstructured / unused	~80% unstructured; ~97% unused (Avant‑garde Health analysis: unstructured data in healthcare)

“With clinical outcomes that are at best mediocre, we think we can do better.” - Jason H. Wasfy, MD

For beginners in California health systems, the practical next steps are clear: learn prompt craft, build governance muscle, and pilot ethically before scaling - start with concise prompt templates and best practices (see an accessible primer on prompt engineering for healthcare) and pair that skillset with a governance framework that tracks transparency, fairness, and lifecycle monitoring so deployments don't repeat fast‑fail disasters like early chatbots; a short, governed pilot with clinician review and vendor scrutiny is better than a rushed enterprise rollout.

Useful starter resources include a hands‑on prompt guide for healthcare teams (Effective Prompt Engineering for Healthcare AI guide) and a practical governance overview that emphasizes transparency, bias mitigation, and continuous auditing (AI governance best practices overview).

For workplace-ready skillbuilding, consider a structured course that teaches prompts, workflows, and human‑in‑the‑loop practices - Nucamp's AI Essentials for Work is designed exactly for non‑technical clinicians and operations staff who want immediately usable skills (AI Essentials for Work registration).

The payoff is tangible: better prompts plus clear vendor questions and routine audits turn opaque outputs into auditable, equitable tools that clinicians can trust at the bedside.

Program	Key Details
AI Essentials for Work	15 weeks; courses: AI at Work: Foundations, Writing AI Prompts, Job Based Practical AI Skills; $3,582 (early bird) / $3,942; AI Essentials for Work syllabus • AI Essentials for Work registration

“The greatest benefits are related to the work that's required for a lot of administrative repetitive tasks. There could be streamlined processes in place where AI can alleviate some of the workload and pressure regarding completing those tasks.”