Hospitals slow to adopt ambient AI in acute care: KLAS Research
CHICAGO, ILLINOIS — Ambient AI is rapidly gaining ground in healthcare, primarily due to the rising burden of charting and clinician burnout; yet, its adoption in acute care settings remains slow and fragmented.
According to the latest KLAS Ambient Speech 2025 report, 95% of ambient AI deployment is concentrated in ambulatory care, leaving acute care with a mere 5% share.
This imbalance prompts health system leaders and hospital Chief Medical Information Officers (CMIOs) to seek solutions adapted to emergency care and hospital medicine workflows, where clinical contacts might last hours and require swift adaptability to patient acuity and changing conditions.
Why acute care is harder for ambient AI
The hesitancy to adopt AI in acute care primarily stems from the multifaceted nature of emergency departments and inpatient medicine.
Multiple touchpoints, including triage, assessments, diagnostic tests, consultation loops, and urgent re-evaluations, demand technology that models ever-changing workflows and integrates seamlessly with electronic medical records.
According to Becker’s Hospital Review, unlike ambulatory settings, notes in acute care must capture clinically defensible reasoning and satisfy stringent requirements from quality, risk, and revenue cycle management, as well as local hospital guidelines.
Furthermore, solutions must be deeply tailored, reflecting the nuances of each site, from stroke triggers to the privileges of advanced practice providers, and the solution cannot rely on simple speech-to-text technology.
Measuring impact: Beyond documentation speed
Hospital leaders are urged to pilot AI solutions against meaningful benchmarks, focusing on both quantitative and qualitative dimensions.
Success in acute care is not just about saving time in documentation but also about improving quality and risk compliance, such as SEP-1 sepsis care metrics and MIPS measures clinical adoption rates, operational efficiency, and financial outcomes, including fewer coding queries and high-fidelity diagnosis capture.
Efficient AI systems are to empower clinicians to articulate their decision-making, enable compliance with critical care pathways, and drive quality patient outcomes while reflecting the individualized tone and style of each doctor’s documentation.
Future outlook: From transcription to intelligent support
As ambient AI matures, its true promise in acute care lies in supporting clinicians through intelligent workflow modeling and decision support, rather than just automated transcription.
The healthcare sector aims to evaluate vendors rigorously, prioritize pilot deployments with robust impact metrics, and demand technology that enhances patient care and clinician experience, transforming AI from a charting tool into a catalyst for safer, faster, and more effective medicine.
