Medical records automation for hospitals and healthcare networks
At a cross-border hospital group, most clinical input still arrived on paper or as PDF scans, some of it handwritten, in 3 languages. Reading every document into structured clinical fields, suggesting codes with the supporting evidence and drafting notes from the consultation clears coding backlogs, lifts accuracy and gives clinicians time back.
What is medical records automation?
Medical records automation reads clinical documents, from discharge summaries and lab reports to handwritten referral notes, and extracts structured fields such as diagnoses, procedures, medications and allergies, each traceable to its source. Those fields feed coding, a searchable patient record and the EHR. Suggested codes come with the evidence behind them, coders confirm the less certain ones, and clinicians review drafted notes before saving them.
- A cross-border hospital group lifted clinical coding accuracy from 87% to 99.2% across 9 hospitals and cleared its coding backlog.
- The same group now processes 14,200 documents a day, up from about 3,500, and more than 96% of encounters the same day.
- An Indian hospital group digitised about 8 million legacy records in 14 languages, with 96.8% field-level extraction accuracy.
- At a UK healthcare network, documentation time per encounter fell from about 22 minutes to 11, and 94% of drafted notes are saved unedited.
Medical records and clinical notes, step by step
Read every incoming document
Referrals, external lab results, imaging reports and discharge summaries from other hospitals arrive as paper, PDF scans and handwriting. At one hospital group, discovery found 211 document types. OCR runs inside each hospital, with a handwriting model trained on about 65,000 labelled examples from the group's own records.
Extract structured clinical fields
Each document is read into a standard set of clinical fields, such as diagnoses, procedures, medications, allergies, comorbidities, lab values and length of stay. Phrasing in any supported language maps to the same SNOMED CT concepts. At the Indian group, every field keeps its source document, confidence score and language.
Suggest codes with the evidence
Suggested ICD-10 and CPT codes come with a confidence score and the passage that justifies each. Cases where every code scores above 96% go straight to billing; between 80% and 96%, a coder confirms with one click; below that, a coder works up a pre-filled draft.
Draft notes from the consultation
With the patient's consent, the clinician records the consultation. The audio is transcribed in the UK, and a note is drafted in the network's structure, from presenting complaint to follow-up, with each paragraph linked to the transcript. The clinician reviews it in the EHR's usual note screen and saves it.
Write back and make it searchable
Coded records go back into each EHR and the billing system through FHIR-based adapters built per hospital, or a database-level adapter where an in-house EHR needed one. At the Indian group, one record across 3 commercial EHRs and 2 in-house builds is searchable from clinicians' own EHR, with 95% of lookups under 2 seconds.
Coders and clinicians keep the final say. Coders confirm mid-confidence codes and work up the uncertain cases from a pre-filled draft, and clinicians review every drafted note before saving it. At the hospital group, time freed from coding went into documentation improvement with physicians, which now covers 81% of inpatient encounters, up from 22%.
Results from 3 deployments
Every figure below comes from the case study it links to.
Hospital Group EMEA
Medical Records Processing
- 4× processing speed
- 14K documents per day
- 23% revenue cycle uplift
Indian Multi-Specialty Hospital Group
Indian Hospital Medical Records
- 14 indian languages supported
- 96.8% extraction accuracy
- <2s record-lookup latency
UK Healthcare Network
Ambient Clinical Documentation
- 94% note acceptance with zero edits
The accelerators behind it
Pre-built accelerators do the work, configured to your documents, rules and systems. Delivery took 20 to 56 weeks in the case studies above.
Medical Records Parser →
Extracts structured clinical data from PDFs, faxes and scans.
Medical Coding Assistant →
ICD-10 / CPT coding co-pilot for revenue cycle teams.
DocuMage →
Flagship IDP — OCR + ICR + LLM for any document type, replaces legacy OCR.
Summarization Wizard →
Long-document, meeting and email summarisation.
Document processing ROI calculator
Enter your document volumes, handling times and current straight-through rate to see the three-year return, then email yourself the PDF.
Medical records and clinical notes: the questions buyers ask
How accurate is automated clinical coding?
At a cross-border hospital group, coding accuracy rose from 87% to 99.2% on a rolling 30-day measurement. Codes go to billing automatically only when every code on the case scores above 96% confidence, and a second model checks them against each country's coding rules. Denials on first claim submission fell from 14% to 4.6%.
Can it read handwriting and records in other languages?
Yes. At the hospital group, a handwriting model trained on about 65,000 labelled examples from its own records reads referral notes and physician annotations, and one pipeline handles Arabic, English and German. The Indian group's records span 14 languages; extraction quality is consistent across them, with only a modest drop for smaller regional languages against English and Hindi.
How quickly does it clear a coding backlog?
At the hospital group, the coding backlog averaged 4 working days. Throughput rose from about 3,500 to 14,200 documents a day, the backlog was eliminated, and more than 96% of encounters are now processed the same day.
Does it work with our existing EHRs?
Yes, it writes into them rather than replacing them. At the hospital group, each hospital's EHR connects through a FHIR-based adapter, with a database-level adapter where an in-house EHR didn't support FHIR cleanly; coded records go back into the EHR and billing system. At the UK network, drafted notes appear in each EHR's own note screen, so the clinician's workflow is unchanged.
Where does patient data stay?
Where each country's rules say it must. At the hospital group, ingestion and OCR run inside each hospital, and records are pseudonymised before anything crosses a border to the central models, a pattern each of its data-protection officers approved. The Indian group's data stays in India. At the UK network, patient audio never leaves the UK and is not kept in raw form after processing.
How long does a deployment take?
Delivery took 20 weeks at the hospital group, starting with a 6-week sprint to catalogue every document type, and 22 weeks for ambient documentation at the UK network. At the Indian group, the archive of about 8 million records went through a 14-month ingestion campaign, with active patients' records first.
How much time does ambient documentation save clinicians?
At a UK healthcare network, documentation time per encounter fell from about 22 minutes to 11. Clinicians save 94% of drafted notes without edits and lightly edit the rest. A 30-minute consultation is transcribed in about 90 seconds, and the clinician starts and stops each recording with the patient's consent.
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