Top 10 AI Prompts and Use Cases and in the Healthcare Industry in Winston Salem

AI matters in Winston‑Salem because it tackles two everyday pressures on local health systems - faster, more accurate diagnostics and relentless administrative burden - without replacing clinical judgment.

Tools that prioritize radiology findings and integrate into hospital IT can boost throughput and follow‑up, and local analyses highlight an economic case for radiology AI in the Triad region (Triad Radiology AI case study for Winston‑Salem healthcare), while enterprise platforms and triage AI are already used to speed image review and care coordination.

AI also automates scheduling, messaging and record synthesis - examples include systems that can review mammograms

30 times faster

with high accuracy and cut administrative work so clinicians spend more time with patients (AI in healthcare: appointment, triage, and imaging use cases).

For Winston‑Salem administrators and clinicians ready to lead adoption, practical training like Nucamp's Nucamp AI Essentials for Work bootcamp (registration) teaches usable prompt writing, workflow design, and non‑technical skills to deploy AI safely and effectively.

Bootcamp	Details
AI Essentials for Work	15 Weeks - $3,582 early bird; courses: AI at Work: Foundations, Writing AI Prompts, Job Based Practical AI Skills - AI Essentials for Work registration | AI Essentials for Work syllabus

Selection for the top 10 AI prompts and use cases was driven by local evidence of patient impact, technical maturity, and operational fit - criteria visible in Winston‑Salem research and system deployments.

Priority went to tools backed by the Wake Forest Center for Artificial Intelligence Research (CAIR) that emphasize explainability and cross‑disciplinary collaboration (Wake Forest Center for Artificial Intelligence Research (CAIR)), real‑world use cases cataloged across North Carolina that show both clinical benefit and workflow gains (for example, a Virtual Nodule Clinic that augments biopsy decisions and an app that cut post‑op messages and calls by ~70%) (North Carolina Health News: AI in North Carolina Healthcare examples), and system‑level innovation from health systems investing in pilots and centers of excellence (Novant Health Institute of Innovation and Artificial Intelligence).

Additional filters included data security, potential for measurable outcomes, and whether the technology was designed to augment clinical judgment and fit into existing workflows - so projects that showed explainable models, clear escalation paths, or measurable reductions in clinician burden were preferred.

Selection Criterion	Supporting Local Source
Explainability & collaboration	Wake Forest Center for Artificial Intelligence Research (CAIR)
Demonstrated clinical or operational impact	North Carolina Health News AI examples (Virtual Nodule Clinic, Medical Brain)
Organizational adoption & innovation capacity	Novant Health Institute of Innovation and Artificial Intelligence

“AI development requires a village.”

Optellum's Virtual Nodule Clinic (VNC) is already reshaping early lung‑cancer pathways relevant to North Carolina health systems by turning incidental chest CTs - found in roughly 30% of scans - into actionable follow‑up rather than missed opportunities; the FDA‑cleared, EHR‑ and PACS‑integrated platform runs automated overnight searches, produces a Lung Cancer Prediction (LCP) score in seconds, and is reported to achieve 100% sensitivity and 98% specificity while improving throughput (installed at Wake Forest Baptist Health in Aug 2020 and used for hundreds of patients annually).

Real‑world results cited by the vendor include 71% more interventional procedures, 85% more early‑stage cancers identified, and a Medicare reimbursement pathway (~$650 per use), making the business and clinical case clearer for systems looking to reduce lost‑to‑follow‑up nodules; Optellum provides a provider overview for technical and reimbursement details, and GE HealthCare has reviewed how partnerships scale CT+AI deployment, while ongoing trials (including a Vanderbilt trial listing) are evaluating radiomic prediction scores against usual care.

For Winston‑Salem clinicians and administrators, VNC offers a tested route to catch the nodule that would otherwise slip through - a single incidental CT can now trigger an automated, guideline‑driven pathway that meaningfully shifts stage at diagnosis.

Metric	Reported Value / Feature
Sensitivity / Specificity	100% / 98% (Patient Discovery AI)
Early‑stage cancer increase	85% more early‑stage lung cancers (vendor data)
Interventional procedures	71% increase
Medicare reimbursement	~$650 per use
LCP score	1–10 risk scale (1≈0.2% to 10≈93% reported ranges)

“The decision to implement the Optellum Virtual Nodule Clinic into the practice has enhanced our ability to address these diverse health challenges and ensure that patients in our region receive timely and comprehensive care.”

Medical Brain-style smartphone follow-up programs - exemplified by OrthoCarolina's long-term outcome tracking - turn a one-time discharge into an ongoing recovery partnership by using structured online surveys and remote monitoring at intervals (before surgery; 3 months, 6 months, 1 year, 5 years, 10 years and beyond) and, remarkably, plans to follow patients for up to 30 years to catch late problems and measure real function over time (OrthoCarolina patient follow-up program improving outcomes after surgery).

That model aligns with growing evidence that telemonitoring and wearable-supported programs improve early functional recovery and patient engagement after joint and cancer surgery - randomized and observational studies report greater step counts, less symptom interference, and measurable benefits to recovery trajectories (Remote monitoring validation for total knee arthroplasty (J Arthroplasty 2019); PCORI study on postoperative remote monitoring for cancer patients).

For Winston‑Salem practices, a smartphone-driven “Medical Brain” can flag early deviations from expected recovery, reduce avoidable calls and visits, and turn routine follow-up into data that drives better surgical decisions and patient-centered care.

Viz.ai's stroke tools - most notably Viz LVO and Viz CTP - demonstrate how real‑time AI alerts can transform ED triage for suspected large‑vessel occlusion (LVO) stroke by automating detection, team notification, and perfusion interpretation so care teams act faster and more reliably; the Viz LVO knowledge base describes rapid CTA analysis, seamless integration into imaging workflows, and built‑in communication features for on‑call teams (Viz.ai Viz LVO product overview).

Published analyses of hub‑and‑spoke systems show dramatic workflow gains: AI‑flagged cases shortened median CTA‑to‑team notification from 26 to 7 minutes and cut door‑in‑to‑puncture times by about 86.7 minutes while also improving adequate reperfusion rates, findings that a 2025 systematic review cataloged across multiple centers (Viz.ai study on AI stroke workflow improvements, Translational Stroke Research 2025 systematic review of AI in stroke care).

For emergency departments weighing AI, those numbers are tangible: shaving more than an hour off time‑to‑treatment - paired with higher reperfusion - means faster thrombectomy decisions and a clearer path to better outcomes for time‑sensitive stroke patients.

Metric	Result / Source
CTA → team notification (median)	26 min → 7 min (AI vs usual care)
Door‑in to puncture (mean)	206.6 min → 119.9 min (−86.7 min)
Door to arterial puncture (transfer patients)	185 min → 141 min (AI vs usual care)
Adequate reperfusion rates	Significantly higher post‑AI implementation (p = 0.036)

AI-assisted patient messaging and triage - drafting replies, prompting clearer patient questions, and routing messages to the right clinician - offers a practical way for North Carolina systems such as Atrium Health and WakeMed to cut clerical load without losing the human touch: a Duke patient survey found higher raw satisfaction for AI‑written portal messages (Duke patient survey on AI-generated patient portal messages: higher satisfaction for AI-authored messages Duke patient survey on AI-generated portal messages) though satisfaction fell when patients were told a message was AI‑authored (pilot programs pairing nurses and dedicated “inboxologists” with automation have shrunk primary‑care in‑basket burden and reduced time‑to‑resolution in some programs: inboxologist pilot and routing workflows pilot showing 41% fewer messages handled by PCPs and 93% reduced time-to-resolution).

Academic pilots show LLM drafts lower cognitive burden for clinicians when reviewed before sending, arguing for a “human‑in‑the‑loop” model and role‑aware tuning so replies match the recipient's complexity and scope (Stanford Medicine study on AI‑drafted clinical messages and clinician cognitive burden Stanford Medicine study on AI‑drafted responses).

The takeaway for Winston‑Salem: combine AI drafting with clear disclosure, clinician review, and nurse‑led triage to save time while preserving trust and empathy - an approach that can turn an overflowing inbox into a predictable, safe workflow.

“We're always trying to find ways to have the electronic health record work with clinicians through automation. Already, clinicians are noting a reduction in cognitive burden – and the AI is only going to improve from here.”

An Electronic Cognitive Health Index for Alzheimer's risk stratification could help Winston‑Salem clinicians turn scattered cognitive tests, medication lists, and chart notes into a single, prioritized flag that routes high‑risk patients to memory clinics before delays compound - think of it as a digital yellow sticky‑note that surfaces the patients most likely to benefit from early intervention.

The economic case that supports radiology AI in the Triad - better follow‑up, clearer reimbursement paths, and demonstrable throughput gains - applies to cognitive‑risk tools as well (Economic case for radiology AI in the Winston‑Salem Triad), and successful deployment depends on role redesign so triage assistants and inbox managers evolve into skilled navigators and escalation experts (Role evolution for triage assistants and inbox managers in healthcare AI).

Local training and pipeline development - internships, CAIR openings, and targeted up‑skilling - are practical next steps to make an index clinically useful and operationally sustainable (Local AI training, internships, and CAIR openings in Winston‑Salem), so tools meaningfully reduce missed diagnoses rather than add noise to busy clinics.

Duke Health's Sepsis Watch brings a proven, locally relevant model for Winston‑Salem EDs and hospitals by turning continuous EHR streams into timely action: the deep‑learning system samples records every five minutes, analyzes 86 variables, and typically flags patients about five hours before clinical presentation - roughly the length of a work shift to intervene and alter a patient's course.

Implemented across Duke's EDs since 2018, the program doubled SEP‑1 bundle compliance, is associated with a 27% drop in sepsis‑attributed deaths, and is estimated to save about eight lives per month in its original setting, showing how a centralized rapid‑response workflow (RRT nurses + bedside clinicians + a simple risk dashboard) can translate predictive signals into faster treatment and measurable outcomes.

For Winston‑Salem systems weighing predictive alerts, Duke's playbook - detailed by the Duke Institute for Health Innovation and Duke physicians - offers practical steps for EHR integration, stakeholder engagement, and operational governance to keep alerts actionable and trusted.

Metric	Value / Feature
Data sampling cadence	Every 5 minutes
Variables analyzed	86 clinical variables
Median prediction lead time	5 hours
Estimated lives saved	~8 per month
Reported outcomes	27% drop in sepsis deaths; doubled 3‑hour SEP‑1 compliance

“Sepsis is very common but very hard to detect because it has no clear time of onset and no single diagnostic biomarker.”

Novant Health's Behavioral Health Acuity Risk (BHAR) model brings a locally developed, evidence‑driven way to spot patients at elevated suicide risk by analyzing routine chart data and estimating the likelihood of suicidal behavior - work that was developed and validated by Novant teams in Winston‑Salem (see the BHAR validation study on medRxiv: BHAR validation study on medRxiv).

Built by mental‑health, emergency‑medicine, and psychiatry experts, the model uses a random‑forest approach that can be hosted directly inside the electronic health record and update in near‑real time so results are immediately visible to clinicians (Foundry Events overview of the Novant BHAR risk model: Foundry Events overview of the Novant BHAR risk model).

In practice it produces a simple, color‑coded risk score in the chart - a clear visual flag that helps teams prioritize brief interventions and referrals - and figures among state examples of AI tools that surface high‑risk patients for faster, actionable follow‑up (NCMS: AI uses in North Carolina healthcare - NCMS summary of AI uses in North Carolina healthcare), turning scattered notes into an operational safety net.

Operating rooms are one of a hospital's priciest bottlenecks, and Duke Health's machine‑learning scheduling model shows how AI can turn chaos into predictability: trained on more than 33,000 cases and published in the Annals of Surgery, the algorithm proved about 13% more accurate than human schedulers and nudged 3.4% more cases to within 20% of their actual length, reducing the cascade of late finishes that drive overtime and hidden costs (Duke Health study).

In practical terms the study authors noted potential overtime‑labor savings of roughly $79,000 over four months, and the model is already in use at Duke University Hospital, demonstrating immediate operational utility.

Ongoing partnerships - like Duke's collaboration with SAS to build advanced analytics and digital‑twin tools - signal that scheduling AI can soon move from a single algorithm to system‑wide process optimization across North Carolina health systems (Becker's summary; SAS announcement).

Metric	Reported Value / Source
Accuracy improvement vs. human schedulers	13% (Duke Health)
Cases analyzed	~33,815 surgical cases (study dataset)
Cases predicted within 20% of actual length	+3.4% (Becker's summary)
Estimated overtime savings	~$79,000 over 4 months (example)
Implementation	In use at Duke University Hospital; published June 2023

“One of the most remarkable things about this finding is that we've been able to apply it immediately and connect patients with the surgical care they need more quickly.”

UNC Health's internal generative AI chatbot - built with Microsoft Azure OpenAI as part of Epic's pilot - offers a practical, securely governed assistant that answers UNC‑specific questions and helps clinicians spend less time hunting through training libraries and hundreds of “how‑to” documents, freeing minutes for patient care; the tool began a limited rollout in June 2023 to a small group of clinicians and administrators (Becker's notes an initial 5–10 physicians) and is designed to scale across UNC's network of 15 hospitals, 19 campuses and roughly 900 clinics once pilots clarify safe use cases.

Early aims include drafting administrative replies, surfacing reference materials by conversational query, and identifying additional workflow savings during testing - steps local leaders say are part of a responsible, role‑aware approach to reduce friction while keeping clinicians in control (see UNC Health pilot details).

Item	Detail / Source
Platform	UNC Health Azure OpenAI generative AI chatbot pilot details
Initial rollout	June 2023, small group (5–10 physicians reported)
System size	15 hospitals, 19 campuses, ~900 clinics (UNC Health)
Primary use cases	Administrative drafting, quick reference, workflow triage

“This is just one example of an innovative way to use this technology so that teammates can spend more time with patients and less time in front of a computer.”

For Winston‑Salem clinicians juggling overflowing inboxes and after‑visit notes, Nuance's DAX Copilot (and Microsoft's integrated Dragon Copilot experience) offers a practical route to reclaim time: ambient capture and generative AI listen to multi‑party encounters, transcribe conversations, extract orders and key findings, and produce specialty‑specific draft notes and patient summaries in seconds so providers review and sign instead of typing.

Vendor and platform reports cite meaningful per‑encounter savings (Microsoft notes roughly 5 minutes saved per clinician; vendor summaries report ~7 minutes) while highlighting features that matter locally - EHR integration, multilingual encounter capture, automated referral letters and after‑visit summaries, and HITRUST‑backed Azure security - so practices can increase throughput, reduce documentation‑driven burnout, and improve coding accuracy.

Built from millions of real encounters and designed for role‑aware workflows, these tools aim to shift clinicians back to the bedside: imagine turning a 20‑minute visit into a ready‑to‑sign note in seconds and a clearer path to timely reimbursement and follow‑up.

Learn more from the Microsoft Dragon Copilot overview and the Nuance DAX Copilot infographic for implementation details and reported outcomes. Microsoft Dragon Copilot overview and healthcare AI solutions and Nuance DAX Copilot infographic and Dragon Ambient eXperience information.

“Dragon Copilot is a complete transformation of not only those tools, but a whole bunch of tools that don't exist now when we see patients. That's going to make it easier, more efficient, and help us take better quality care of patients.” - Anthony Mazzarelli, MD

Winston‑Salem health leaders ready to turn AI pilot projects into lasting improvement should follow a few practical, local steps: begin with clinician‑led, explainable pilots focused on high‑value problems already proven in the state (lung‑nodule triage, sepsis prediction, stroke imaging alerts, OR scheduling), measure clear ROI and outcomes from day one, and invest in workforce re‑skilling so new tools become operationally sustainable rather than a fleeting experiment.

Insist on “human‑in‑the‑loop” workflows and strong governance - advice echoed across Wake Forest's research priorities and the broader North Carolina reporting on deployed tools - then use an adoption playbook to prioritize cases with rapid, measurable impact (see NC Health News' roundup of statewide AI use and the Healthcare AI Adoption Index for guidance).

Pairing pilots with targeted training - practical up‑skilling like Nucamp AI Essentials for Work bootcamp registration - helps hospitals and clinics keep clinicians at the decision point while freeing time for patient care.

Start small, prove value, scale with security and transparent oversight, and the result will be safer care, measurable savings, and a workforce ready to steward AI for the community.

Next step	Why / Source
Run clinician‑led pilots with clear metrics	Dr. Tim O'Connell / AJHCS guidance on rigorous, human‑centered pilots
Prioritize high‑ROI, production‑ready use cases	NC Health News examples; BVP Healthcare AI Adoption Index on choosing entry points
Invest in local training & governance	Wake Forest research emphasis on community adoption; Nucamp AI Essentials for Work bootcamp registration for practical up‑skilling

“our healthcare leaders and organizations, institutions, payers - wherever they are - need to remain the gatekeepers to ensure that implementing new technologies doesn't happen too quickly or for the wrong reasons and that it adversely affects patient care.”