How AI Is Helping Healthcare Companies in Charleston Cut Costs and Improve Efficiency

In Charleston and across South Carolina, AI is moving from pilot to measurable ROI at MUSC Health: generative and predictive models are improving diagnostics and cybersecurity while AI agents and ambient scribing shave clinician admin time and automate access workflows; MUSC's webinar outlines applications from predictive analytics to genomics (MUSC Health webinar on AI in healthcare applications), and an expanded Notable and MUSC patient access transformation case study shows operational wins - reallocating 5,000+ staff hours per month, deflecting ~17% of patient-access calls, and delivering roughly $3.3M in annual value - so Charleston providers can reduce no-shows, speed authorizations, and restore clinician time for direct care.

Bootcamp	Length	Early-bird cost	Register
AI Essentials for Work	15 Weeks	$3,582	Register for the AI Essentials for Work bootcamp

“Start small, then scale.” - Crystal Broj, Enterprise Chief Digital Transformation Officer, MUSC Health

Charleston's clinical front lines are already seeing tangible gains as AI moves from theory to bedside: imaging algorithms in comparative studies have shown a 30% higher positive predictive value and a 1.25% greater negative predictive value versus peers, helping radiologists prioritize true positives faster (Aidoc AI imaging comparative study showing improved PPV and NPV), while MUSC's surgical and informatics teams run some 20–25 active AI projects - from a wound‑photo triage prototype that tells patients whether to call a clinician or seek emergency care to real‑time predictive analytics that flag patients at risk of deterioration or sepsis, creating an “AI safety net” for clinicians (MUSC AI patient outcomes and active AI projects overview).

The practical payoff: faster, more accurate diagnosis in radiology and surgery and earlier interventions driven by continuous risk scoring - so Charleston providers can reduce delayed care and focus scarce clinician time on the patients who need it most.

Area	Measured gain / example
Imaging accuracy	+30% PPV, +1.25% NPV (comparative study)
Predictive analytics	Real‑time alerts for deterioration/sepsis (MUSC models)
Surgical innovation	20–25 active AI/ML projects (MUSC Surgical Innovation Center)

“Even if you're using the same data set, machine learning algorithms can often improve predictive performance compared with traditional approaches.”

Operational automation in Charleston is already translating to measurable savings and capacity: MUSC's deployment of Notable digitized the digital front door - automating scheduling, registration, and clinical intake - and delivers roughly $3.3M in annual value while reassigning 5,000+ staff hours per month, avoiding about 14,500 no‑shows and running over a million repetitive workflows daily (Notable MUSC automation case study); meanwhile, MUSC's QuicksortRx procurement tools have tracked $15M+ in savings and flagged a summer 2022 price spike that would have added more than $1.2M in annual costs, demonstrating how targeted automation in revenue cycle and supply chain stops waste before it hits the budget (QuicksortRx MUSC procurement case study).

These systems perform EHR tasks like logging notes and submitting orders, deflect routine phone work, and free staff to focus on direct patient care - so the “so what” is concrete: millions saved, thousands of missed appointments prevented, and thousands of administrative hours reclaimed for clinical work.

Program	Key operational metrics
Notable - MUSC automation	$3.3M annual value; 5,000+ staff hours/month reallocated; ~14,500 no‑shows avoided; 15% touchless copay collection
QuicksortRx - Procurement	$15M+ tracked savings; prevented a projected >$1.2M price increase (Summer 2022)

“I'm essentially in QuicksortRx all day. I review our purchases from the day before, identify areas for improvement, and communicate what I learn with our different departments. The ability to see real-time data on enterprise-wide purchases at MUSC is incredibly valuable.” - Frank Giuliano, Clinical Pharmacy Specialist, Procurement Lead

MUSC Health's extended partnership with Notable has turned patient access into an automated, measurable advantage for Charleston: Notable's Front Desk and Call Center AI Agents complete about 110,000 digital registrations a month, support care‑gap closure, scheduling and revenue‑cycle tasks, and generate roughly $3.3M in annual value while reassigning 5,000+ staff hours per month and preventing ~14,500 no‑shows - so the “so what” is clear: clinics reclaim clinician time for higher‑value care, reduce missed visits, and collect an estimated 15% of copays touchless, with documented gains in equity (a 30% lift in Spanish‑language digital intake) and high patient satisfaction (MUSC Health automation case study by Notable, Notable announcement on expanded MUSC partnership).

Metric	Result
Monthly digital registrations	110,000
Annual value	$3.3M
Staff hours reallocated per month	5,000+
No‑shows avoided	14,500
Touchless copay collection	≈15%
Patient satisfaction (since go‑live)	98%

“A successful partnership for MUSC Health goes well beyond technology; it also requires a deep understanding of health care operations and the ability to facilitate change management to drive the desired outcomes.” - Crystal Broj, Chief Digital Transformation Officer, MUSC Health

Population health in Charleston is improving as AI makes access faster and the patient experience smoother: MUSC's notable deployments automate front‑door tasks - 110,000 monthly digital registrations, roughly $3.3M in annual value, 5,000+ staff hours reallocated per month and about 14,500 no‑shows avoided - so clinics can focus outreach on high‑risk patients and close care gaps (Notable Health MUSC automation case study demonstrating digital registration and value); voice and chat agents now handle routine scheduling and questions - MUSC's “Emily,” powered by the Amelia Patient Engagement solution integrated with Epic, processed more than 100,000 calls in August and verified thousands of appointments, reducing call volumes and wait times (SoundHound AI and Amelia partnership announcement with MUSC Health describing the Emily virtual agent); coupled with ambient scribing pilots that cut documentation time by ~20%, these tools free clinicians to spend more face‑to‑face time on complex care - so the “so what” is measurable: faster access, fewer missed visits, and more clinician time for patients (MUSC DAX Copilot documentation results and pilot outcomes).

Metric	Result
Monthly digital registrations	110,000
Annual value (Notable)	$3.3M
Staff hours reallocated / month	5,000+
No‑shows avoided	14,500
Emily call volume (Aug)	>100,000 calls (16% of volume)
Documentation time reduction (DAX)	~20%

“MUSC Health is committed to delivering best‑in‑class service to our patients. Thanks to Amelia's integration with Epic, we are able to build and deploy a robust digital assistant that is already having a positive impact on patient access.” - Crystal Broj, Enterprise Chief Digital Transformation Officer, MUSC Health

Scaling AI across Charleston and wider South Carolina can convert pilot gains into system‑level savings by automating low‑value work, tightening supply‑chain spend, and improving capacity planning - an approach already delivering roughly $3.3M in annual value and reassigning 5,000+ staff hours per month at MUSC through Notable‑driven access automation (Notable MUSC automation case study); when paired with predictive analytics that reduce avoidable admissions and optimize staffing, those operational wins become reproducible across regional health systems (see local examples in the Nucamp AI Essentials for Work predictive analytics guide for Charleston).

Policymakers and finance leaders should also track macro shifts: AI/ML is accelerating structural changes in how services and capital flow, which affects funding models and reimbursement strategies for health systems (Congressional Research Service report on AI/ML economic implications) - so the “so what” is tangible: invest in scalable automation now to capture millions in annual value and protect margins as the broader economy digitizes.

Metric	Result
Annual operational value (MUSC, Notable)	$3.3M
Staff hours reallocated per month	5,000+
No‑shows avoided (annual)	~14,500
Monthly digital registrations	110,000

Charleston's AI ecosystem combines national vendors with deep local partnerships to turn pilots into operational gains: Notable's AI workforce is embedded at MUSC to automate registration, scheduling, referrals and care‑gap closure - processing roughly 110,000 digital registrations a month and reassigning 5,000+ staff hours per month while producing about $3.3M in annual value (Notable MUSC automation case study – Notable Health) - while strategic alliances with Siemens Healthineers, Medtronic, Helix and others bring device, imaging, genomics and population‑health capabilities to the state (see MUSC partner list and initiatives) including a statewide roll‑out of Siemens' syngo Virtual Cockpit that lets technologists in Charleston support remote MRI scans across multiple sites to reduce travel and level access (MUSC strategic partnerships and initiatives – MUSC).

The practical payoff for Charleston systems: fewer missed appointments, faster imaging access, and genomic‑scale research (In Our DNA SC aims to enroll 100,000 participants) that make AI investments immediately actionable rather than speculative.

Vendor/Partner	Local role / impact
Notable	Front‑door automation; 110,000 monthly registrations; ~$3.3M annual value
Siemens Healthineers	syngo Virtual Cockpit for remote MRI scanning statewide
Helix (In Our DNA SC)	Population genomics initiative; target 100,000 participants

“With syngo Virtual Cockpit, MUSC is addressing a primary challenge for many health care institutions: providing equal access to quality imaging services in remote and decentralized locations, using technology that also helps to solve for staffing challenges and improve the patient experience.” - David Pacitti, President, Siemens Medical Solutions USA Inc.

Charleston health leaders adopting on‑demand tools and generative systems must balance clear governance with real clinical risks: scalable mental‑health chatbots like on‑demand mental health chatbots Wysa and Woebot and automated patient messaging increasingly handle sensitive conversations, while generative models that now draft patient outreach create new points of failure and ethical concern - highlighted by work on patient education and content automation in healthcare; at the same time, clinical predictive analytics deployed to anticipate deterioration demand robust validation and monitoring to avoid misclassification or capacity errors (predictive analytics for Charleston hospitals).

The so‑what: without documented model performance, data‑governance rules, and clear accountability for automated communications, gains in efficiency risk amplifying clinical and workforce harms - local policy, procurement, and clinical leaders should require validation, transparency, and ongoing oversight before scale.

Charleston hospitals are pairing targeted training with deliberate change management to convert AI from a novelty into everyday clinical capacity: MUSC's playbook emphasizes interoperability, clear expectations, and incremental accuracy goals so staff see automation as a workflow default rather than a threat (see the Notable change‑management principles for AI Agents), while point‑of‑care pilots prove the payoff - ambient scribing with DAX Copilot cut after‑hours documentation by about 20%, and a Virtual Nursing pilot at MUSC Lancaster saved roughly 470,580 minutes (≈7,843 hours, or ~326.8 nursing days) by shifting routine admission and discharge education to a virtual team; training curricula are being updated accordingly, and device partnerships like Butterfly Blueprint ensure students and clinicians get hands‑on practice with AI‑enabled tools at the bedside.

The practical result: fewer administrative tasks per clinician, measurable time reclaimed for direct care, and change programs that reframe automation as capacity‑building rather than headcount replacement, so leaders can redeploy talent to the most complex patient needs (Notable AI Agents change-management case study, MUSC DAX Copilot ambient scribing documentation pilot, Butterfly Blueprint AI-enabled bedside training partnership).

Program / Pilot	Measured impact
DAX Copilot (ambient scribing)	~20% reduction in after‑hours documentation
Virtual Nursing (MUSC Lancaster)	470,580 minutes saved (≈7,843 hours / 326.8 days)
Notable AI Agents (change management)	Pre‑visit completions +88%; no‑show rate reduced from 14% to 8%

“It increases our efficiency. Patients are reporting that they are getting more education, and that they're understanding more when it comes to discharge instructions. That process has improved by a long shot.” - Maria Moore, R.N., MUSC Health Lancaster

Begin with high‑value, low‑risk workflows that show quick ROI: target prior authorizations and front‑door automation where MUSC Health's Notable AI agents now complete about 40% of prior authorizations and reduce a task that took roughly 30 minutes to about 1 minute, directly enabling faster scheduling and access (MUSC Health AI agents case study on prior authorizations).

Define success metrics up front (time per transaction, touchless completion rate, scheduling conversions), require vendor‑to‑EHR and payer‑portal integrations, run short pilots with built‑in validation and monitoring, and align training and change management so digital check‑in adoption scales (MUSC doubled digital check‑ins toward ~50%).

Track clinical downstream effects too - automated outreach scheduled 129 mammograms overnight and over 2,000 to date - to decide what to scale next (predictive analytics and scaling guide for Charleston hospitals), and ensure governance and clinician oversight are part of every rollout to protect access while realizing savings.

Metric	Result / example
Prior authorizations completed by AI	40%
Prior authorization time (manual → AI)	~30 minutes → ~1 minute
Digital check‑in adoption	Doubled to nearly 50%
Mammograms scheduled via outreach	129 overnight; >2,000 total

“At the end of the day, it's about access.” - Crystal Broj, MUSC Health Chief Digital Transformation Officer

Charleston's trajectory makes clear that AI is no longer a speculative tool but a pragmatic lever: MUSC's Notable deployment alone shows roughly $3.3M in annual value, 110,000 monthly digital registrations, and 5,000+ staff hours reallocated per month - proof that targeted automation can reclaim clinician time and protect margins while improving access (Notable MUSC Health case study demonstrating $3.3M operational value).

That upside, however, sits alongside fast‑moving policy and funding shifts that leaders must monitor and design for - Medicaid and reimbursement pressures, CON changes, and emerging AI oversight are already shaping what can be scaled in 2025 (Key health care policy issues to track in the Carolinas for 2025).

Practical next steps: start with high‑value, low‑risk front‑door and prior‑auth pilots, require EHR and payer integrations, and pair deployments with workforce reskilling so savings convert to sustained capacity - training programs like Nucamp's AI Essentials for Work offer a pragmatic path to build those skills locally (Nucamp AI Essentials for Work bootcamp: practical AI skills for the workplace).

The so‑what is direct: by combining disciplined governance, clinician oversight, and focused training, Charleston systems can convert pilots into recurring millions in value and thousands of clinician hours reclaimed for patient care.

Metric	Result
Annual operational value (Notable @ MUSC)	$3.3M
Monthly digital registrations	110,000
Staff hours reallocated / month	5,000+
No‑shows avoided (annual)	≈14,500

“Start small, then scale.” - Crystal Broj, Enterprise Chief Digital Transformation Officer, MUSC Health