CASE STUDY · HEALTHCARE AI
Healthcare AI & clinical workflow automation: less documentation, same clinical judgment.
Illustrative scenario · Sector: Healthcare and HealthTech · Solution partner: Brandsmashers Tech
- ~163 hrsModeled documentation time released per week
- ~33%Modeled reduction in documentation time per encounter
01 · PROJECT OVERVIEW
Workflow assistance, not autonomous decisions.
Healthcare organisations generate enormous amounts of information during routine clinical workflows. Physicians may spend significant time documenting encounters, preparing notes, organising information and completing administrative tasks.
The opportunity for AI is therefore not necessarily to make autonomous clinical decisions. A more practical starting point is workflow assistance.
- Designing clinical documentation assistance and speech-to-text workflows
- Extracting medical information and summarising patient records
- Integrating with EHRs through secure APIs with role-based access
- Keeping audit trails and clinician review on every output
- 1Documenting each patient encounter.
- 2Preparing notes and organising information.
- 3Completing administrative tasks around care.
02 · THE CHALLENGE
Documentation at scale.
Consider a healthcare organisation with 35 physicians, each seeing 70 patient encounters a week: 2,450 encounters every week. If documentation takes about 12 minutes per encounter, that is 29,400 minutes, roughly 490 hours of documentation effort a week.
- PROBLEM 01Repetitive documentation
Every encounter produces notes that are written, structured and filed largely by hand.
- PROBLEM 02Scattered information
Relevant history sits across records and systems, and takes time to find and summarise.
- PROBLEM 03Administrative friction
Appointments, routing and paperwork add work around each visit.
- PROBLEM 04High stakes
Any automation has to handle sensitive data securely and leave the clinician in charge of the final record.
Automate documentation and workflow friction. Keep clinical judgment human.
03 · THE APPROACH
AI drafts and organises; the clinician reviews and approves.
A human-in-the-loop architecture: AI assists with documentation and information processing, and the healthcare professional remains responsible for reviewing and approving the final information.
- ADocumentation assistance
- Clinical documentation assistance
- Speech-to-text workflows
- Patient record summarisation
IMPACTLess time writing, more time with patients.
- BInformation handling
- Medical information extraction
- Faster retrieval of relevant history
- Appointment workflow automation and intelligent routing
IMPACTThe right information in front of the clinician sooner.
- CSecure integration
- EHR integration through secure APIs
- Role-based access to sensitive data
- Audit trails on every action
IMPACTFits existing systems and protects patient data.
- DHuman review
- Clinician reviews and approves every AI draft
- Clear edits and sign-off before anything is saved
- Adoption and accuracy measured before scaling
IMPACTClinical judgment stays with the clinician.
- 1→Encounter
- 2→Speech to text
- 3→AI draft
- 4→Clinician review
- 5→Approved record
- 6EHR updated
04 · RESULTS
The modeled difference.
If an AI-assisted workflow reduces the documentation component from 12 to 8 minutes per encounter, the modeled weekly effort falls from about 490 hours to about 327.
| MEASURE | TODAY | AI-ASSISTED |
|---|---|---|
| Minutes per encounter | 12 minutes | 8 minutes |
| Weekly minutes (2,450 encounters) | 29,400 minutes | 19,600 minutes |
| Weekly hours | ~490 hours | ~327 hours |
The scenario is modeled for demonstration and is not a clinical outcome. Healthcare workflows and regulatory requirements must be validated for each implementation.
- 35Physicians in the modeled organisation70 encounters each per week.
- 2,450Encounters per week35 × 70.
- ~163 hrsDocumentation time released per week490 − 327 modeled hours.
- ~33%Reduction in modeled documentation timeFrom 12 to 8 minutes per encounter.
- Less repetitive documentation
AI drafts the routine parts of each note for review.
- Faster information retrieval
Summaries and extraction bring history forward.
- More consistent workflows
Routing and appointments follow the same steps every time.
- Better administrative productivity
More time returned to healthcare professionals.
05 · DELIVERABLES
How Brandsmashers would build it.
- Documentation assistanceSpeech-to-text and AI drafting of clinical notes for review.
- Information extractionMedical information extraction and patient record summarisation.
- EHR integrationSecure APIs into existing electronic health record systems.
- Workflow automationAppointment workflows and intelligent routing.
- Security and auditRole-based access, audit trails and careful handling of sensitive data.
- AI
- Speech-to-textLLM summarisationInformation extraction
- Integration
- EHR integrationSecure APIs
- Security
- Role-based accessAudit trailsData protection
- Engineering
- Workflow designData managementCloud
TAKEAWAYS
Why Brandsmashers.
- 01Start with workflow assistance, not autonomous clinical decisions.
- 02Keep the clinician as the reviewer and approver of every record.
- 03Model the time saved before building, then measure it in practice.
- 04Treat security, integration and data handling as core requirements.
- Hospitals
- Clinics
- HealthTech products
- Telehealth
- Diagnostics
YOUR TURN
Reducing documentation burden in healthcare?
Healthcare AI requires more than an LLM: secure engineering, API integration, data management, workflow design and careful handling of sensitive information. Brandsmashers can provide engineering capacity across all of these.