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

Wilmington health systems stand at the edge of a practical AI revolution that promises real wins: faster, more accurate imaging interpretations (AI has been shown to be “twice as accurate” on some stroke scans) and fewer missed fractures - urgent care doctors miss broken bones in up to 10% of cases - while automation trims the heavy admin load that eats clinician time.

National analyses suggest broader savings - an estimated 5–10% reduction in U.S. health spending, roughly $200 billion - if AI is adopted responsibly, and federal guidance and pilots are already shaping safer rollouts; see the World Economic Forum's overview of AI in health and AHRQ's webinar on clinical AI tools for implementation and effectiveness.

For Wilmington clinicians, care teams, and administrators who want practical upskilling, the AI Essentials for Work bootcamp offers a hands-on path to prompt-writing and workplace AI skills that help turn promising pilots into everyday improvements.

Program	Length	Early-bird Cost	Register
AI Essentials for Work	15 Weeks	$3,582	Register for AI Essentials for Work (Nucamp)

“AI digital health solutions hold the potential to enhance efficiency, reduce costs and improve health outcomes globally.”

Methodology: to pick the top 10 AI use cases for Wilmington, the team prioritized real-world impact, regulatory readiness, and operational feasibility - with a bias toward examples already delivering measurable gains in U.S. systems and relevance to North Carolina providers (for example, UNC Lineberger's AI-aligned oncology work).

Evidence-weighting favored: (1) documented time or outcome improvements (Applied Clinical Trials report on the impact of AI-enabled solutions shows an average 18% cycle-time reduction in drug-development activities and patient-monitoring approaches showing up to 75% time savings), (2) proven clinical deployments and cross-specialty applicability (a Medwave roundup of 12 real-world AI healthcare use cases shows concrete gains across radiology, sepsis detection, and oncology), and (3) governance and prioritization frameworks that make pilots scalable (Info‑Tech's practical roadmap for prioritizing health-insurance and provider use cases).

Each candidate use case needed at least one peer-reviewed or industry report of measurable benefit, a clear data-governance path, and an operational owner who could run a quick pilot aligned to local workflows - because an AI idea only matters when it saves time or prevents harm, not just when it sounds smart.

Criterion	Supporting evidence
Measured impact	Applied Clinical Trials report on AI-enabled solutions impact (avg 18% cycle reduction; patient monitoring up to 75%)
Real-world deployment	Medwave article on 12 AI healthcare use cases (includes examples such as UNC Lineberger)
Governance & prioritization	Info‑Tech frameworks for AI roadmap and prioritization

“There's currently a real delta between the numerous proofs of concept and full-blown AI strategies.” - Paul McDonagh-Smith (Info‑Tech / MIT Sloan)

For Wilmington hospitals facing time-sensitive strokes, Viz.ai's AI-assisted imaging and urgent-case triage can be a practical accelerator: the Viz LVO tool can auto-detect suspected large vessel occlusions within seconds, with 90% of alerts reviewed by the intended specialist within five minutes, and the platform is already backed by real-world evidence - the multi-center VALIDATE study found a 39.5‑minute reduction in notification time versus non‑AI centers - and a recent systematic review and meta-analysis documents workflow and outcome improvements tied to Viz.ai deployments; see Viz LVO for features and Viz's launch of the new Viz 3D CTA, which adds AI-enhanced, skull-stripped 3D views to speed interpretation and planning.

Those minutes matter in concrete ways: faster door-to-puncture, shorter transfers, and measurable drops in length of stay have been reported, making Viz.ai a compelling candidate for Wilmington health systems that need to tighten stroke pathways without reinventing local workflows.

Metric	Reported impact
Notification time (VALIDATE)	39.5 minutes faster (AI vs non-AI)
Time-to-treatment decision	73% faster
Hospital length of stay	2.5 day reduction
Faster than standard care	52 minutes

“In the world of stroke treatment, the saying ‘time is brain' comes from the fact that when brain tissue is deprived of oxygenated blood, approximately two million neurons die every minute. VALIDATE data show that use of the Viz platform resulted in a clinically important decrease in the time it took for large blood vessel occlusion recognition and contact with the interventional team, which in turn may translate to fewer neurons dying and, ultimately, better patient outcomes.”

Duke Health's Sepsis Watch offers Wilmington hospitals a concrete model for early, actionable sepsis detection: the system polls the EHR every five minutes and analyzes 86 clinical variables to flag at‑risk patients a median of five hours before clinical presentation, an advance that Duke estimates could translate to roughly eight lives saved per month and has helped double 3‑hour SEP‑1 bundle compliance since its 2018 launch - making it more than a research toy, but a workflow tool that pairs AI with rapid‑response nurses and ED teams to start 3‑ and 6‑hour treatment timers and monitor completion; learn more from the Duke Institute for Health Innovation's Sepsis Watch overview and Duke's physicians page describing how the tool samples data and integrates into bedside decision‑making, or read about the underlying Deep Sepsis model and its licensing as an example of how regional systems might adopt or adapt the approach.

Metric	Reported value
Median prediction lead time	5 hours before clinical presentation
Estimated lives saved	≈8 per month
EHR polling frequency	Every 5 minutes
Variables analyzed	86 (vitals, labs, comorbidities, demographics)
Training data	50,000 patient records (≈32 million data points)
SEP-1 bundle compliance	Doubled (post-deployment)

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

OrthoCarolina's rollout of Medical Brain pairs the practice's unusually long-term outcomes mindset - patients are tracked “for 30 years” after surgery - with an AI-first layer that automates post-op check-ins, flags care gaps, and delivers 24/7 personalized clinical guidance via a mobile app; the result reads like a scalable model for North Carolina systems that want smarter, less-burdensome follow-up that helps patients stay on track and clinicians focus on higher‑value tasks.

The partnership brings Medical Brain's real-time care orchestration to a network of over 300 providers at nearly 40 locations across the Southeast, combining OrthoCarolina's patient-reported outcome surveys and structured follow-up cadence with automated monitoring and risk detection that identifies emerging issues before they escalate - reducing avoidable calls and smoothing rehab pathways.

Learn more from OrthoCarolina's outcomes program and the announcement of the Medical Brain strategic partnership to see how automated follow-up can move recovery conversations from one-off appointments to continuous, data-informed care.

Metric	Reported value / feature
Provider network	Over 300 providers
Locations	Nearly 40 (Southeast)
Patient-facing feature	Medical Brain mobile app - 24/7 personalized clinical guidance
Operational benefits	Automated clinical follow-up, identifies care gaps, reduces provider workload

“For decades, OrthoCarolina has been committed to providing patient-first comprehensive care across a wide array of orthopedic specialties, and the integration of Medical Brain® into our care continuum will help us to better meet patients' real-time needs while also accelerating our organizational value-based care goals.”

For Wilmington clinics grappling with clinician burnout and crowded schedules, DAX Copilot (Nuance) and Microsoft's Dragon Copilot offer a practical way to shrink documentation time by capturing ambient, multiparty conversations and turning them into specialty-specific, EHR-ready notes, after-visit summaries, and even referral letters; Microsoft reports Dragon Copilot is generally available in the U.S. and is trained on more than 15 million encounters to produce customizable, high-quality documentation that can auto-populate orders into systems like Epic, while an academic cohort study of Nuance DAX's ambient listening approach evaluated real-world impacts on outpatient documentation and workflows - useful evidence when Wilmington health systems weigh pilots.

These tools promise modest but tangible gains (reports show clinicians saving minutes per encounter and measurable drops in admin burden), better patient-facing summaries in multiple languages, and built-in security and compliance on Azure - making them a sensible first pilot for North Carolina practices that want faster notes, fewer after-hours charting nights, and smoother revenue capture; see Microsoft's Dragon Copilot overview and the Nuance DAX cohort study for rollout details and outcomes.

Reported metric / fact	Source
Trained on >15 million ambient encounters	Microsoft Dragon Copilot overview: trained on over 15 million encounters
General availability in U.S. (May 1, 2025)	Microsoft announcement: Dragon Copilot U.S. availability details
Peer-reviewed cohort study on DAX ambient listening	PMC (AMIA) cohort study of Nuance DAX ambient listening

“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

Duke's suite of surgical ML tools shows how AI can turn opaque OR schedules into predictable, money‑saving workflows for North Carolina hospitals: models that predict post‑op length of stay (81% accuracy) and discharge disposition (88%) are now part of a broader toolkit that already improved case‑time forecasting (machine‑learning estimates were ~13% more accurate than human schedulers) and ran prospectively on 33,815 cases at Duke, helping schedulers hit targets more often and trimming median daily error per room by roughly 18–20 minutes - enough time to finish a short case or avoid costly overtime.

The approach pairs a “similarity cascade” of historical medians with production pipelines that push predictions into Epic OpTime each morning, supporting smarter case scheduling, bed allocation, and earlier discharge planning; read the implementation details in the Annals of Surgery study, Duke's scheduling algorithm overview, and the Duke Surgery report on LOS/DD models for practical takeaways on deploying similar pilots in Wilmington systems.

Metric	Reported value
Length of stay prediction accuracy	81%
Discharge disposition prediction accuracy	88%
Case‑time prediction vs human schedulers	~13% more accurate
Prospective cases evaluated	33,815
Median error time reduced per room per day	18 min (inpatient); 20 min (ambulatory)

“This study is important because it shows that machine learning can be used to improve hospital efficiency and patient care.” - Daniel Buckland, MD, PhD

Chat-style models like ChatGPT are already a practical tool for Wilmington clinics that need a reliable, conversational assistant for triage, appointment booking, portal drafting, and routine patient FAQs - think of a 24/7 virtual front desk that reduces hold times and nudges no-shows with automated reminders while freeing staff for higher‑value work; Nextech reports chatbots can handle as much as 85% of routine conversations and cites $3.6 billion in industry savings in 2022 from similar automation.

These LLMs excel at summarizing records, drafting clear portal messages, and guiding patients through pre/post‑procedure instructions, but they are not a replacement for clinicians and carry well‑documented risks (hallucinations, privacy gaps), so local pilots should include HIPAA guardrails, BAAs or private deployments, and verification workflows before any clinical use.

For practical ideas and limits, see the Medical Futurist rundown of ChatGPT medical use cases and Topflight's guide on deployment and compliance to help Wilmington teams choose where conversational AI can safely trim admin burden and improve access without eroding trust.

Use case	Why it matters (evidence)
Appointment scheduling & reminders	24/7 booking, reduces no-shows and front‑desk load (Nextech benefits of chatbots in healthcare)
FAQ & symptom triage	Rapid answers and initial triage in underserved or busy settings (Medical Futurist ChatGPT medical use cases)
Administrative savings	Can handle up to ~85% of routine conversations; industry estimates cite $3.6B saved in 2022 (Nextech chatbot ROI and savings)

Novant Health's Behavioral Health Acuity Risk (BHAR) model is a practical, locally built tool for suicide-risk detection that turns routine chart data into an actionable alert - developed by Novant clinicians and data scientists in Winston‑Salem and Charlotte, the model analyzes information already in the medical record to estimate suicide risk and surfaces a simple, color‑coded risk score directly in the electronic chart so care teams can prioritize high‑risk patients during normal workflows; the approach was described in a development and validation study (see the BHAR model validation study at medRxiv BHAR model validation study), and Novant's technical overview notes the model uses random forests and can be natively hosted in the EHR to update in near‑real time (Novant Health technical overview for the BHAR risk model Novant Health BHAR technical overview); North Carolina health leaders have highlighted this kind of EHR‑integrated, color‑coded risk flag as a clear example of AI helping clinicians spot patients who need immediate attention without adding extra screens or separate apps (North Carolina Medical Society roundup: AI use cases in healthcare NCMS AI in healthcare roundup), so Wilmington systems exploring behavioral‑health pilots can see a concrete path from data to bedside action.

Attribute	Reported detail
Model	Behavioral Health Acuity Risk (BHAR)
Developer / location	Novant Health Cognitive Computing; Winston‑Salem & Charlotte, NC
Method	Random forests (machine learning)
Deployment	Natively hosted in the EHR; near‑real‑time updates
Output	Simple, color‑coded suicide‑risk assessment visible in the electronic medical record

For North Carolina's translational labs and Wilmington biotech startups, the AIDDISON / BioMorph pairing represents a practical way to speed and de-risk early-stage therapeutics: Merck's AIDDISON platform combines generative AI, ML and CADD to virtually screen a universe of more than 60 billion chemical targets, then proposes synthesis routes via Synthia so medicinal teams can go from hit identification to actionable leads far faster than hopping between legacy tools (see the AIDDISON overview).

At the same time, predictive models like BioMorph give interpretable, image‑based signals about cellular toxicity and pharmacokinetics so teams can “fail fast” on unsafe candidates before costly lab work begins (Broad Institute write-up).

The result for a regional system: imagine shrinking a months‑long compound triage down to a few iterative in‑silico cycles, letting researchers reserve precious bench time for the clearest, safest candidates and accelerating partnerships with contract labs and local CROs.

Platform / tool	Key fact
AIDDISON AI-powered drug discovery platform	Virtual screens >60 billion compounds; integrates generative design with Synthia retrosynthesis
BioMorph predictive AI (Broad Institute)	ML models predict cardiotoxicity/liver injury and cell‑health effects to de‑risk candidates

“Our platform enables any laboratory to count on generative AI to identify the most suitable drug‑like candidates in a vast chemical space.” - Karen Madden, CTO (Merck / MilliporeSigma)

Moxi from Diligent Robotics is a practical, people-first way for Wilmington hospitals to shave the small but time‑consuming errands that pull nurses from bedside care - think automated delivery of medications, lab samples, PPE and supplies that runs 24/7 and can be stood up without an infrastructure rebuild (just your existing Wi‑Fi) according to the Moxi hospital delivery robot overview by Diligent Robotics (Moxi hospital delivery robot overview - Diligent Robotics).

Pilots around the U.S. show real returns: kids' hospitals and major systems report thousands of deliveries, hundreds to thousands of staff hours reclaimed, and even the delightful “heart‑eyes” moments and selfies that make Moxi a morale boost on busy units; see the Children's Hospital Los Angeles pilot where Moxi saved roughly 20–30 minutes per delivery and restored pharmacy and bedside time for clinicians in the CHLA Moxi pilot and impact report (CHLA Moxi pilot and impact - Children's Hospital Los Angeles).

For North Carolina systems wrestling with staffing shortages and high bedside turnover, Moxi's social intelligence, locked drawers for secure transfers, and rapid, human‑guided learning make it a low‑risk pilot to return measurable hours to patient care in a matter of weeks rather than months.

Metric	Reported value (source)
CHLA (just over 4 months)	~2,500+ deliveries; 132 miles traveled; ~1,620 staff hours saved (CHLA pilot data - Children's Hospital Los Angeles)
Edward / Elmhurst hospitals	Edward: 7,298 deliveries, 4,125.5 hours saved; Elmhurst: 9,813 deliveries, 5,345 hours saved (NursingCE summary - Moxi hospital deployments and outcomes)
UT Southwestern initial 3 months	6,463 deliveries in 2,859 hours; >500 deliveries/week thereafter (UT Southwestern Moxi pilot outcomes - UT Southwestern)

“Moxi's support in delivering meds has helped our staff recoup 20 to 30 minutes per delivery.” - CHLA Chief Pharmacy Officer Carol Taketomo

EpiClim‑style public‑health surveillance systems give Wilmington leaders a real-time lens on community signals that can inform when to ramp clinics, tweak schedules, or stand up targeted outreach - acting a bit like a weather radar for outbreaks so hospitals don't get blindsided while staff are already stretched thin; these systems pair especially well with the kind of administrative automation that slashes billing and scheduling burdens so clinicians can focus on care (administrative automation in Wilmington hospitals to reduce billing and scheduling burdens).

Deployments also change workforce needs, creating opportunities for coders and quality teams to upskill into roles such as clinical documentation improvement to keep work local and valuable (upskilling into clinical documentation improvement roles in Wilmington healthcare).

Because these systems touch patient data continuously, practical privacy guardrails matter up front - Wilmington clinics should follow HIPAA‑focused deployment strategies and state law guidance when integrating surveillance AI into EHR and reporting workflows (HIPAA-focused deployment strategies for protecting patient privacy in Wilmington clinics), ensuring the promise of earlier insight doesn't come at the cost of trust.

Getting started with AI in Wilmington means marrying cautious governance with practical pilots: North Carolina providers should follow the lead of local systems that already vet tools internally, inventory every AI asset, and treat vendor oversight and BAAs as non‑negotiable - especially while state leaders push for clearer rules (North Carolina Health News overview of NC AI activity and oversight) and HHS/HIPAA guidance evolves toward explicit AI governance (proposed HIPAA Security Rule: AI governance requirements).

Start with small, measurable pilots that assign a clinical owner, limit PHI access to the minimum necessary, and build audit trails for performance and bias reviews; pair those pilots with workforce upskilling so local teams can manage models and vendor risk - practical training such as the AI Essentials for Work bootcamp helps clinicians and staff learn prompt design, risk-aware use cases, and vendor assessment without needing a developer background.

In short: protect patients, measure outcomes, and scale only when governance, security, and human oversight are proven at the bedside.

Program	Length	Early‑bird Cost	Register
AI Essentials for Work	15 Weeks	$3,582	Register (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.” - Christina Silcox, Duke‑Margolis Institute for Health Policy