Top 5 Jobs in Healthcare That Are Most at Risk from AI in Columbia - And How to Adapt

AI is already reshaping South Carolina's health workforce: the Medical University of South Carolina is launching a fully AI‑integrated online Healthcare Studies degree in fall 2025 to prepare a “future‑ready, AI‑competent” workforce - more than 95% of that program's students are South Carolina residents - while Prisma Health's 10‑year partnership with Siemens Healthineers will create an Intelligence Insights Center that uses de‑identified data to speed diagnoses and streamline workflows across the state; together these initiatives show why administrative and entry‑level clinical roles that center on routine documentation and imaging interpretation face rapid change, and why upskilling matters now.

Employers and clinicians are already seeing time savings and reduced burnout from documentation copilots, so workers should pair clinical expertise with practical AI skills - start with focused programs like MUSC's AI‑integrated Healthcare Studies overview, Prisma Health's Siemens Healthineers partnership for healthcare innovation, or Nucamp's AI Essentials for Work bootcamp to stay relevant on the Columbia job market.

“By integrating AI into the program, we are providing students with the tools to drive health care innovation, improve patient care, and lead within their communities,” said Lauren Gellar, Ph.D.

Methodology combined South Carolina's official labor projections with a task‑level review of AI use cases to isolate roles most vulnerable to automation: first, the SC Department of Employment and Workforce 2032 projections identified which healthcare occupations dominate fastest growth across WDAs (for example, nurse practitioners show a statewide 61% projected increase), and those high‑growth advanced clinical roles were deprioritized as “at‑risk” because growth reflects expanding clinical demand; second, roles were scored for susceptibility based on core tasks - repetitive documentation, rule‑based coding, routine image reading, and administrative scheduling - by cross‑referencing practical AI deployments such as automated SOAP note generation and workflow optimization described in local Nucamp guides; third, geographic weight was applied using WDA breakdowns (e.g., Trident and Upstate growth ranges) so findings reflect where automation impacts will matter most across South Carolina.

The result: a focused list that targets routine, high‑volume tasks where AI yields immediate time‑savings and displacement risk, while preserving a clear pathway to reskilling for workers in Columbia and statewide.

Read the underlying projections and applied use cases here: South Carolina 2032 healthcare employment projections and automated SOAP note generation in Columbia (AI Essentials for Work).

Clinical coding specialists and billers in South Carolina face one of the clearest near‑term risks from AI because their core work - rule‑based ICD/CPT assignment, claims scrubbing, and repetitive eligibility checks - is already automatable with NLP, RPA, and autonomous coding engines; providers adopting these tools aim to cut denials and speed first‑pass claim resolution, which shifts inpatient and outpatient coders toward auditing, exception handling, and workflow oversight rather than raw data entry.

Practical deployments show the divide: vendor pilots and case studies report dramatic throughput gains (for example, certain autonomous workflows yielded an 80–90% increase in coder efficiency for infusion coding), but successful implementation hinges on people, data interfaces, and clear use cases rather than drop‑in software - meaning health systems and payroll‑conscious practices in Columbia should plan reskilling for coders into review‑and‑audit roles while investing in reliable data pipelines.

For context on operational tradeoffs and workforce strategy, see the AGS Health analysis of AI and automation in medical coding and the Medaptus webinar on AI coding automation, and review the market forecast for AI in medical coding for scale and investment trends.

“I think about augmenting skilled human labor with automation and AI.”

Radiology technologists and entry‑level image readers in South Carolina face a medium–high automation risk because contemporary AI is moving beyond simple flagging into procedure planning, automated acquisition, dose‑optimization, and rapid post‑processing - tasks that form much of an entry‑level reader's daily workload; research shows AI can significantly streamline chest X‑ray interpretation and speed radiologist analyses, and diagnostic‑imaging studies map clear automation targets across acquisition, protocol selection, and segmentation that vendors are already turning into clinical tools (BJR study on automated imaging acquisition and dose optimization, and a recent review that documents reduced chest X‑ray interpretation time with AI (Diagnostics review on AI‑assisted chest X‑ray interpretation)).

So what: hospitals and imaging centers in Columbia that adopt triage and automated post‑processing will free technologist hours previously spent on routine reads and repeats - creating immediate demand for workers who can do AI quality assurance, protocol oversight, patient communication, and AI‑assisted reporting rather than just first‑look reads - making targeted upskilling the most practical defense for entry‑level staff.

Clinical documentation specialists and medical transcriptionists in Columbia and across South Carolina are among the highest‑risk roles because ambient‑listening AI and scribe copilots now draft structured, specialty‑specific notes, extract orders, and populate EHR fields - tasks that once anchored these jobs.

Vendor data and peer‑reviewed work show major efficiency gains: DAX/Dragon family tools automate encounter capture and note generation, while LLM‑based AI scribes are explicitly designed to create medical documentation from recorded encounters.

Reported outcomes include faster, more consistent notes, up to 7 minutes saved per visit and documented reductions in documentation time of 50–72% in rollout studies, which shifts the practical job from transcription to verification, quality assurance, exception coding, and clinician review - so what: in Columbia clinics that adopt these systems, teams should reallocate headcount toward auditing AI outputs and managing edge cases rather than paying for high‑volume typing.

Prioritize retraining in AI oversight, EHR integration, and clinical validation to stay employable as documentation work moves from keystrokes to supervision.

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

Utilization‑review nurses and some physician‑extenders in South Carolina face a medium risk from AI because insurers and health systems are automating rule‑based prior‑authorization work and routine medical‑necessity scoring - tasks that historically consumed much of UM nurses' time - while still relying on clinicians for complex judgment, appeals, and patient‑centered exceptions; insurers are already piloting these tools, and NORC's project tracking shows plans to expand AI in utilization management across payers (NORC project on AI in utilization management).

Local context matters: AnMed, a 648‑bed system serving upstate South Carolina, is deploying XSOLIS's CORTEX to generate real‑time medical‑necessity scores and reduce administrative back‑and‑forth, which means a typical UM nurse in Columbia could shift from chasing routine prior auths to supervising AI outputs, managing exceptions, and improving care transitions - skills that preserve job relevance while cutting turnaround time for high‑volume cases (AnMed and XSOLIS CORTEX partnership for utilization management).

Nursing research also finds AI can support clinician mental health and care quality when used as an assistant rather than a replacement, underscoring a clear path: upskill in guideline interpretation, AI validation, and payer communication to remain indispensable (Review of AI in nursing and its impact on clinician mental health and care quality).

“This is a challenging time for provider organizations across the U.S. By providing an automated, objective view of medical necessity, XSOLIS will help increase the efficiency of our existing staff, so that we can better serve our patients.”

Pharmacy technicians and pharmacy administrative roles across South Carolina face a medium risk from AI because much of their daily work - refill processing, prior authorization coordination, inventory checks, and routine call‑center triage - is already automatable, and vendors report real, measurable gains when these tools are deployed: a recent narrative review documents community‑pharmacy pilots tied to a 40% rise in medication adherence and a 55% drop in missed refills after AI implementation (Narrative review of AI in community pharmacy (MDPI)), while industry coverage highlights pharmacies that cut prescription processing from a day to about 20 minutes using automation and workflow AI (Asembia report on pharmacy automation and workflow AI (Drug Topics)).

So what: those time savings can convert front‑counter and back‑office hours into adherence counseling, AI‑output validation, and complex prior‑auth management - roles that protect job relevance if technicians retrain for oversight, patient communication, and clinical support rather than manual dispensing.

Practical guidance and risk assessments from industry white papers show this transition is already underway and most sustainable when paired with targeted AI literacy and governance training (MedMe Health white paper on AI adoption in pharmacy).

“We as an industry are certainly starting to look at some additional tools, like AI models for example, to help really ascertain what's in the best interest of the patient,” - David Skomo, HealthDyne (Asembia / Drug Topics)

Actionable next steps for South Carolina healthcare workers and employers: audit routine tasks now (documentation, repetitive coding, prior‑auth triage) and map which can be piloted with AI while protecting clinical judgment; enroll affected staff in short, outcome‑focused training through state partners like the South Carolina Department of Employment and Workforce training programs and SC Works to access WIOA‑style upskilling and local technical college pathways (South Carolina DEW training and programs), pair clinical teams with academic partners such as the Medical University of South Carolina's AI‑integrated Healthcare Studies program that prepares a “future‑ready, AI‑competent” workforce (MUSC AI‑integrated Healthcare Studies curriculum), and require practical, role‑based AI literacy (for example, a focused 15‑week AI Essentials for Work pathway that teaches prompt engineering and real workplace use cases) so staff can move from data entry to AI oversight and exception handling (Nucamp AI Essentials for Work syllabus).

Employers should pilot narrowly, measure outcomes, and fund time for learning; workers who complete targeted programs or SC AHEC continuing‑education modules will be positioned to supervise AI, not be replaced - one concrete payoff: a single 15‑week course can convert routine documenters into AI‑validation specialists within a quarter of a year.

“Without the right skills, even sophisticated AI deployments risk failure through underuse, misalignment, or erosion of trust.”