Manual entry of faxed PDFs overwhelmed staff, causing errors and losing patients. Now, AI extracts data instantly to clear the backlog.
A Florida-based sleep diagnostics clinic managing roughly 400 patient referrals per month with a five-person manual intake team.
Manual processing of faxed PDFs created a one to two week referral turnaround time, causing the clinic to lose patients who required contact within...
“They would go too fast and end up deleting referrals instead of entering them. At which point we’d just have to hope the provider would send another fax.”
Sleep disorder diagnostic and treatment center.
AI-powered healthcare operations platform that automates referral management, fax processing, and document intake for medical practices.
Total Sleep Management's Patient referral processing is part of this use case:
Related implementations across industries and use cases
Manual reviews created a persistent 140-document backlog. AI now routes standard referrals, freeing staff for complex cases.
A 10-person ops team manually transcribed faxes into EMRs. Now, an AI model extracts and routes data directly, clearing the backlog.
Staff manually keyed 15,700 fax pages a month. AI now extracts the data, letting the team validate records rather than type.
Manual reviews created a persistent 140-document backlog. AI now routes standard referrals, freeing staff for complex cases.
A 10-person ops team manually transcribed faxes into EMRs. Now, an AI model extracts and routes data directly, clearing the backlog.
Manual scheduling took 3 days, slowing hiring. An AI agent now screens and books interviews in 29 minutes, doubling scheduled volume.
Clinicians spent evenings buried in manual notes. AI now generates charts from audio, scaling from primary care to oncology.
Manual maintenance reports and routine purchases bottlenecked staff. AI agents now accurately classify faults and recommend suppliers.
QA teams manually searched vast incident logs to assess deviations. Now, multi-agent AI synthesizes past cases into verified summaries.
Manual entry of faxed PDFs overwhelmed staff, causing errors and losing patients. Now, AI extracts data instantly to clear the backlog.
A Florida-based sleep diagnostics clinic managing roughly 400 patient referrals per month with a five-person manual intake team.
Manual processing of faxed PDFs created a one to two week referral turnaround time, causing the clinic to lose patients who required contact within...
“They would go too fast and end up deleting referrals instead of entering them. At which point we’d just have to hope the provider would send another fax.”
Sleep disorder diagnostic and treatment center.
AI-powered healthcare operations platform that automates referral management, fax processing, and document intake for medical practices.
Total Sleep Management's Patient referral processing is part of this use case:
Related implementations across industries and use cases
Manual reviews created a persistent 140-document backlog. AI now routes standard referrals, freeing staff for complex cases.
A 10-person ops team manually transcribed faxes into EMRs. Now, an AI model extracts and routes data directly, clearing the backlog.
Staff manually keyed 15,700 fax pages a month. AI now extracts the data, letting the team validate records rather than type.
Manual reviews created a persistent 140-document backlog. AI now routes standard referrals, freeing staff for complex cases.
A 10-person ops team manually transcribed faxes into EMRs. Now, an AI model extracts and routes data directly, clearing the backlog.
Manual scheduling took 3 days, slowing hiring. An AI agent now screens and books interviews in 29 minutes, doubling scheduled volume.
Clinicians spent evenings buried in manual notes. AI now generates charts from audio, scaling from primary care to oncology.
Manual maintenance reports and routine purchases bottlenecked staff. AI agents now accurately classify faults and recommend suppliers.
QA teams manually searched vast incident logs to assess deviations. Now, multi-agent AI synthesizes past cases into verified summaries.