| Process |
Digital charting with forms, templates, and manual entry. |
Use NLP and machine learning to auto-draft notes, extract data, and suggest codes. |
| Data capture |
Structured fields need manual clicks and drop-downs. |
Captures from voice, free text, images, faxes, and device feeds. |
| Speed |
Slower and limited by typing and template switching. |
Faster intake, summarization, and order shortcuts. |
| Accuracy |
Prone to typos and copy-paste; varies by user. |
High on routine tasks when trained; still needs human review. |
| Consistency |
Varies by user and site build. |
Applies the same logic and style across encounters. |
| Scalability |
Scaling demands more staff time. |
Scales with compute and handles spikes with little friction. |
| Cost |
Lower upfront for basic setups; higher ongoing staff time. |
Higher upfront and compute; lowers per-encounter labor over time. |
| Compliance and updates |
Vendor pushes code sets and rules on a schedule. |
Models and rules can update quickly with monitoring and QA. |
| Error handling |
Relies on users to catch interactions, duplicates, and missing data. |
Flags drug interactions, duplicate orders, coding gaps, and missing labs. |
| Decision support |
Rule-based alerts and standard order sets. |
Predictive risk scores and context-aware suggestions. |
| User experience |
Many clicks and rigid workflows. |
Voice, chat, and summarization that cut clicks and screen time. |
| Interoperability |
HL7, FHIR, and CCD exchange with limited context. |
Normalizes inbound data and enriches context with mapping. |
| Search and retrieval |
Keyword search and filters with variable relevance. |
Semantic search with patient-level Q&A. |
| Documentation burden |
Long note writing and inbox triage by hand. |
Auto-draft notes, letters, and inbox replies. |
| Burnout impact |
Higher after-hours charting and click fatigue. |
Lower charting time and inbox load. |
| Patient engagement |
Portal messages and generic education. |
Personalized summaries, outreach, and education. |
| Security and privacy |
Mature controls with role-based access and audit logs. |
Same controls plus model-specific safeguards and prompt hygiene. |
| Audit and reporting |
Standard reports and SQL data marts. |
Real-time trends, anomaly detection, and auto-audits. |
| Implementation time |
Weeks or months for build, training, and go-live. |
Adds AI services on top of the EHR; pilot in phases. |
| Training needs |
Classroom sessions and elbow support. |
Short learning curve for voice and chat with in-workflow tips. |
| Maintenance |
Template and rule upkeep and periodic upgrades. |
Monitor model drift and quality; update prompts and data pipelines. |
| Resilience and downtime |
Downtime procedures with read-only modes. |
Depends on cloud services; requires fallbacks and safe degradation. |
| Customization |
Heavy configuration per site and specialty. |
Learns patterns and adapts without many hard rules. |
| Bias and safety |
Human bias and copy-forward risks. |
Model bias possible; needs fairness checks and human oversight. |
| Governance and oversight |
Change control via IT and clinical leadership. |
Add model governance with review of outputs and acceptance rates. |