How Doc-Rocket Streamlines Clinical Workflows in 2025

Doc-Rocket Case Study: Reducing Documentation Time by 50%—

Executive summary

Doc-Rocket helped a mid-sized outpatient clinic reduce clinical documentation time by 50% over a 6‑month rollout. The clinic achieved faster charting, improved coding accuracy, and higher clinician satisfaction while maintaining compliance with privacy and regulatory requirements.

Background

The clinic — a 25‑provider multispecialty outpatient center serving ~40,000 patient visits annually — struggled with lengthy documentation workflows. Providers reported an average of 3.2 hours per day on after‑clinic charting, leading to overtime, burnout, and delayed billing.

Key challenges:

• Paper and free‑text notes mixed with EHR templates, causing redundancy.
• Variable documentation quality across clinicians, complicating coding.
• Time lost navigating multiple systems (EHR, billing, imaging).
• High turnaround time for completed encounter notes, affecting revenue cycle.

Goals

1. Reduce provider documentation time by at least 40%.
2. Improve completeness and coding accuracy of notes.
3. Maintain or improve clinician satisfaction.
4. Achieve measurable ROI within 9 months.

Intervention: Doc-Rocket implementation

Doc-Rocket is an AI‑assisted documentation platform that integrates with existing EHR systems to automate note generation, code suggestions, and workflow routing. The implementation included:

• Integration with the clinic’s EHR via API for patient demographics, encounter context, and problem lists.
• Templates configured to specialty workflows (family medicine, endocrinology, orthopedics).
• Voice dictation and real‑time suggested note completion using clinician prompts.
• Coding assistance that mapped clinical content to accurate ICD‑10 and CPT codes.
• Training: three half‑day sessions per provider + on‑site superuser support for 8 weeks.
• Privacy review and BAAs executed prior to go‑live.

Methods and metrics

Primary outcome: average provider documentation time per clinic day (self‑reported and EHR timestamped).
Secondary outcomes: note completion time (minutes from encounter end to finalized note), coding accuracy (audited sample), clinician satisfaction (survey), billing turnaround, and net revenue per visit.

Baseline measurements (3 months pre‑implementation):

• Average documentation time: 3.2 hours/day.
• Note completion time: 36 hours post‑encounter.
• Coding error rate: 12% (sample audit).
• Clinician satisfaction score: 3.⁄₅.

Data collection during rollout and for 6 months post‑go‑live included automated EHR logs, monthly audits of 200 random charts, and clinician surveys at 1, 3, and 6 months.

Results

• Documentation time decreased from 3.2 hours/day to 1.6 hours/day (50% reduction).
• Median note completion time fell from 36 hours to 4 hours.
• Coding error rate dropped from 12% to 4%.
• Clinician satisfaction improved to 4.⁄₅ at 6 months.
• Billing turnaround accelerated, leading to a 6% increase in net revenue per visit in months 4–6.
• ROI breakeven reached month 7 due to reduced overtime and fewer coding denials.

What drove the improvement

• AI‑assisted draft notes reduced manual typing and repetition.
• Specialty templates cut time spent structuring notes.
• Real‑time coding suggestions lowered downstream edits and denials.
• EHR integration eliminated duplicate documentation across systems.
• Focused training and superusers smoothed adoption and built trust.

Challenges and mitigations

• Initial clinician skepticism: addressed via peer champions and rapid feedback loops.
• Integration edge cases (e.g., third‑party imaging orders): handled with custom middleware scripts.
• Occasional incorrect AI suggestions: kept clinicians in control with easy edit and undo functions; continuous model fine‑tuning reduced errors over time.

Lessons learned

• Engage clinicians early to configure templates that match workflow.
• Start with a pilot group to refine templates and training before full rollout.
• Monitor both objective EHR timestamps and subjective satisfaction to get a full picture.
• Ensure strong privacy and contracting (BAA) workup before exchanging PHI.

Conclusion

The clinic’s adoption of Doc-Rocket delivered a 50% reduction in documentation time, faster note completion, fewer coding errors, improved clinician satisfaction, and a positive financial return within the first year. The case highlights that combining targeted AI assistance with solid integration, training, and governance produces measurable operational and clinical benefits.

If you’d like, I can adapt this case study into: a slide deck, a one‑page executive brief, or a version tailored for a specific specialty.