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

Greensboro's hospitals and clinics are already feeling the practical edge of AI: statewide reporting shows tools such as Atrium's Virtual Nodule Clinic that score lung nodules for earlier cancer detection, DAX Copilot's ambient scribing that returns hours to clinicians, and AI‑drafted patient‑portal messaging that reduces daily inbox burden - all workflows primed to land in the Triad as Novant and Atrium expand locally and Cone Health reshapes care delivery (see North Carolina Health News and BusinessNC).

For administrators and staff who must translate these pilots into safe, auditable practice, targeted upskilling matters - Nucamp's 15‑week AI Essentials for Work bootcamp teaches prompt writing, tool use, and workplace AI integration to help Greensboro teams adopt AI without losing human oversight.

Bootcamp	Length	Early‑bird Cost	Register
AI Essentials for Work	15 Weeks	$3,582	Register for Nucamp AI Essentials for Work bootcamp

“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.”

Selection prioritized AI prompts and use cases that matter in North Carolina's clinical and policy landscape: local footprint (systems planning new capacity like Atrium's approved Greensboro Medical Center), measurable clinical benefit (tools that shorten time‑to‑treatment or avoid admissions such as sepsis prediction and lung‑nodule risk scoring), deployment readiness (pilot vs.

production), regulatory exposure, and workforce impact for clinicians and support staff. Sources tracking Carolina trends and real deployments guided ranking - Maynard Nexsen's roundup of 2025 issues and concrete AI examples across Atrium, OrthoCarolina, Novant and Duke helped identify high‑leverage prompts, while the Greensboro CON decision highlighted where scale‑up could matter most for the Triad's care mix.

Priority use cases therefore favored prompts that integrate with existing imaging, EHR, or patient‑portal workflows and those that reduce clinician inbox time or prevent inpatient stays - outcomes aligned with North Carolina Healthcare Association workforce and access priorities.

“With the integration of RealizedCare's groundbreaking triage tool and therapeutic solutions, we are building the world's largest XR healthcare platform.”

Wake Forest Baptist's Virtual Nodule Clinic applies AI-driven nodule scoring (reported as a 1–10 cancer‑risk scale) and automated outreach to flag patients who miss prescribed follow‑up lung scans, turning discrete radiology findings into a prioritized tracking workflow that helps pulmonology teams in the Triad focus limited clinic slots on higher‑risk cases; Wake Forest began using the tool in March 2023 and clinicians report the score can inform conservative management versus biopsy decisions.

Read more in the North Carolina Health News coverage of AI nodule scoring and the NC Medical Society summary of statewide AI use.

“The right thing to do is to just be conservative, which you can imagine could be pretty hard for a patient if they're very concerned and there's the uncertainty about what this nodule is.”

OrthoCarolina's strategic partnership to deploy Medical Brain® brings a post‑surgical digital assistant to a network of more than 300 providers across nearly 40 locations, using the Medical Brain mobile app to deliver 24/7 personalized clinical guidance and real‑time care orchestration for recovery follow‑up; the platform's first entry into orthopedics promises continuous patient monitoring and automated escalation so teams can prioritize in‑person visits for the highest‑need patients, improving outcomes while significantly reducing provider and staff workload.

Learn more in the Medical Brain® announcement and review practical governance steps for safe AI deployment as Greensboro providers consider adoption.

Imaging‑first AI platforms such as Viz.ai (Viz LVO) and peer systems like Aidoc are already changing acute triage across North Carolina by automatically analyzing CT scans for large‑vessel occlusion strokes, intracranial hemorrhage, pulmonary emboli and selected fractures, then sending images and prioritized alerts to clinicians' phones so teams can act faster; Novant Health was the first system in the Carolinas to deploy Viz.ai across its comprehensive stroke centers and New Hanover Regional has used Viz LVO to speed transfers and specialist review, while Novant's adoption of Aidoc expands automated flagging for bleeds and emboli to reduce ED delays.

The result is measurable time savings - company and local reports cite image‑to‑notification times measured in minutes (one study average ~7 minutes) and workflow reductions from roughly 123 to 34 minutes - which matters in North Carolina's Stroke Belt where “every minute” can mean millions of brain cells lost.

Read Novant's patient‑care summary and the Novant–Aidoc partnership for implementation details.

“Time is very critical for the brain and we need to shave off minutes every opportunity we can,” - Dr. Laurie McWilliams, Novant Health neurointensivist

WakeMed and Atrium Health are using generative AI inside patient portals to draft replies that clinicians then review, a workflow North Carolina Health News reports has cut WakeMed's daily inbox by about 12–15 messages per provider, materially easing administrative burden for busy Triad teams; systems typically deploy the feature inside Epic/MyChart so drafts pull context from the record, but national reporting warns many patients don't know when AI helped compose a message and experts urge careful oversight and disclosure (North Carolina Health News: WakeMed and Atrium use AI in patient portals, New York Times: AI drafting patient messages in MyChart).

Local pilots should pair AI drafting with role‑based triage (nurses and inbox specialists handle routine replies while clinicians edit higher‑risk notes) and track patient experience metrics - surveys show AI drafts can feel more empathetic for many patients but reactions shift when authorship is disclosed (Study on patient satisfaction with AI-generated patient portal messages).

“AI could help break ‘writer's block' by providing physicians an empathy‑infused draft upon which to craft thoughtful responses to patients.”

Wake Forest's work on cognition - anchored by the multi‑site U.S. POINTER trial and local studies linking neighborhood disadvantage to cardiometabolic risk and lower test scores - provides the evidence base that informs electronic screening ideas like a Cognitive Health Index for North Carolina clinics: POINTER enrolled 2,111 older adults across five centers and showed both structured (38 facilitated team meetings over two years) and self‑guided lifestyle interventions improved cognition, with the structured program yielding larger gains, while Wake Forest research also ties social determinants to higher blood pressure and reduced cognitive performance in older adults; together these findings and Wake Forest's biomarker funding create a concrete pathway for Triad primary‑care teams to prioritize patients for higher‑intensity prevention, targeted monitoring, or referral to community programs.

Read the POINTER results and the neighborhood study for details on study design and local implications: Wake Forest U.S. POINTER results - lifestyle changes significantly improve brain health, Wake Forest study - neighborhood disadvantage linked to higher blood pressure and reduced cognition.

“The potential to improve cognition with fewer resources and lower participant burden is compelling. It highlights that while not everyone has the same access or ability to adhere to more intensive behavior interventions, even modest changes may protect the brain.” - Laura D. Baker, Ph.D.

Duke Health's Sepsis Watch deploys a deep‑learning early‑warning system that monitors EHR data in near real time - sampling dozens of variables every few minutes - to flag patients at risk of sepsis and route those alerts into a rapid‑response workflow so clinicians can act before full clinical deterioration; the model's median prediction lead time is about five hours and Duke estimated the pilot could prevent roughly eight deaths a month while doubling 3‑hour SEP‑1 bundle compliance, outcomes that helped cut sepsis mortality across Duke hospitals in later reports.

Local Greensboro systems considering predictive sepsis alerts should note the program's emphasis on clinician‑centered design, governance, and workflow integration - key to avoiding alert fatigue and turning predictions into timely treatments.

Read Duke's project summary and a North Carolina case study on the program's outcomes for implementation details.

Metric	Value
Median prediction lead time	~5 hours
Training data	~50,000 records / ~32 million data points
Reported mortality reduction	27%–31% (Duke reports 27%; HIMSS case study cites 31%)

“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 runs a random‑forest machine‑learning algorithm natively inside the electronic health record to analyze routinely collected chart data and flag patients at heightened risk of suicide in near‑real time, giving clinicians an immediately visible, prioritized cue for outreach and escalation; the program - developed with mental‑health, emergency‑medicine and psychiatry teams - aims to shift detection from sporadic chart review to continuous surveillance so staff can intervene during the same encounter instead of after a delayed manual review.

Read the technical overview of Novant Health's BHAR implementation (Foundry event summary) and the North Carolina Health News report on how systems across the state are using machine learning to identify high‑risk patients and speed intervention.

Duke Health's operating‑room duration model - built from three machine‑learning models trained on thousands of cases and now used in more than 33,000 Duke procedures - proved about 13% more accurate than human schedulers at predicting how long each surgery would occupy an OR, a practical boost that translated into workflow gains and lower labor costs (one estimate: roughly $79,000 in reduced overtime over a four‑month period) and is now embedded in Duke University Hospital scheduling workflows; the system is designed to assist, not replace, human schedulers and was implemented alongside Duke's algorithm governance to ensure safety and equity.

Read the Duke Health study and the School of Medicine analysis for details on accuracy, deployment, and oversight: Duke Health algorithm study, Duke Med overview of AI use and governance.

Metric	Value
Improvement vs. human schedulers	~13% more accurate
Cases used / deployed on	Used on >33,000 cases
Estimated overtime savings	~$79,000 over 4 months
Deployment	In use at Duke University Hospital

“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.” - Daniel Buckland, M.D., Ph.D.

UNC Health's internal generative‑AI chatbot (branded internally as an “AI virtual assistant” or Ava) is a secure, governed conversational tool built with Azure OpenAI Service and piloted as part of Epic's generative‑AI program to cut administrative friction across a large Chapel Hill‑based network; the bot was fed UNC's training and education libraries so teammates can ask how‑to questions about Epic and other digital tools instead of combing hundreds of documents, letting clinicians and staff get immediate, record‑specific recommendations and spend more time with patients.

The pilot began in June 2023 with a small cohort of clinicians and administrators (reports cite roughly 30 initial staff), with broader availability planned after testing and governance checks.

Read UNC Health's pilot announcement and the clinical‑informatics summary for rollout and governance details.

Pilot start	Initial participants	Platform	UNC network
June 2023	~30 clinicians/administrators	Azure OpenAI / Epic integration	15 hospitals, 19 campuses, 900+ clinics

“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.”

Ambient documentation and clinician‑facing LLM tools - most prominently Nuance's DAX/Dragon Copilot family integrated into Epic workflows - capture multiparty patient conversations, draft specialty‑specific notes, and surface orders so clinicians spend less time charting and more time at the bedside; Epic frames DAX Express as a “copilot” to reduce administrative workload and improve access to care, while peer‑reviewed work observed positive trends in provider engagement with no signal of harm to patient safety (Epic DAX Express ambient documentation integration overview, JAMIA cohort study evaluating DAX impact on clinician workflow and safety).

Reported outcomes range from roughly 5–7 minutes saved per visit to 24%–50% reductions in documentation time, gains that in Greensboro could translate to extra patient slots or meaningful reductions in after‑hours charting for overburdened primary‑care and specialty teams; local pilots should pair EHR integration with clear governance, disclosure, and clinician review to preserve accuracy and trust.

Reported outcome	Range
Time saved per patient visit	~5–7 minutes
Documentation time reduction	~24%–50%

“Dragon Copilot helps doctors tailor notes to their preferences, addressing length and detail variations.”

For Greensboro healthcare beginners ready to move from curiosity to action, start with three focused, measurable moves: 1) require governance and bias audits before any deployment - North Carolina reporting shows oversight gaps and real risks from biased models, so document data sources and monitoring plans (North Carolina Health News oversight on AI in NC health care); 2) invest in staff training and just‑in‑time workshops so clinicians and admins can write safer prompts and verify outputs (UNCG's central AI Hub and campus generative‑AI workshops provide local, governed training: UNCG AI Hub and generative‑AI training); and 3) pilot low‑risk productivity tools first and measure concrete outcomes already reported locally - examples include inbox reductions of ~12–15 messages per provider and documentation savings of ~5–7 minutes per visit - while pairing each pilot with clear disclosure, clinician review, and a rollback plan.

For practical skills to run and evaluate these pilots, consider a targeted course such as Nucamp's AI Essentials for Work to build prompt and governance literacy before wider roll‑out: Register for Nucamp AI Essentials for Work.

Step	Action	Local Resource
Governance	Bias audits, vendor review, disclosure policy	NC Health News reporting / state guidance
Training	Prompt writing, tool verification, clinical review	UNCG AI Hub workshops
Pilot & Measure	Start with portal or scribing tools; track inbox & time metrics	Nucamp AI Essentials for Work

“Not only do I truly believe that AI can really improve health care and health, I also believe we need AI to improve health care and improve health. We are looking at an aging population and an overburdened workforce that's only going to get worse.” - Christina Silcox