Ophthalmology patient-growth and DPDP evidence readiness example
A transparent synthetic proof-of-work showing how AICloudStrategist maps eye-clinic enquiries, cataract counselling, LASIK eligibility, retina triage, post-op follow-up, eye-camp lists and reactivation workflows into owner-visible growth and evidence queues.
Honesty label
This is a simulated internal proof-of-work asset. It uses synthetic ophthalmology workflow counts only. It is not a real clinic, eye hospital, patient, PHI dataset, client, production export, medical advice, legal advice, privacy advice, security advice, DPDP compliance attestation, audit certification, appointment booking, ad-platform performance, revenue result, ROI result, testimonial, logo, ranking result, patient acquisition result or clinical-accuracy claim.
Why this asset exists
Eye clinics often run growth across paid search, WhatsApp, phone calls, Instagram DMs, screening camps, CRM reactivation and post-op reminders. This page demonstrates the operating evidence AICS would assemble first: response SLA, source attribution, owner assignment, DPDP notice evidence, WhatsApp opt-in evidence, counselling handoff, red-flag escalation and no-show follow-up.
Synthetic input summary
| Metric | Result from synthetic sample | Diagnostic meaning |
|---|---|---|
| Workflow rows reviewed | 10 | Small synthetic sample for demonstrating method, not a real clinic export. |
| Synthetic monthly enquiries | 1,766 | Volume represented across calls, WhatsApp, paid search, Instagram, website, CRM, walk-in and eye-camp workflows. |
| Modelled confirmed bookings in sample | 341 | Synthetic booking field used to show leakage mapping only; not an AICS booking result. |
| After-hours enquiries | 320 | Rows where callback ownership and next-day queueing would need review. |
| Response time above 60 minutes | 8 workflows | Workflows needing response-SLA owner review. |
| Paid media spend represented | ₹143,000 | Synthetic spend field for source leakage diagnostics only; not ad-platform performance proof. |
| Paid enquiries represented | 737 | Synthetic source-attribution count for paid-search and social workflows. |
| Sample paid cost per enquiry | ₹194.03 | Calculated from synthetic inputs; not a benchmark, guarantee or customer result. |
Evidence gaps quantified from synthetic rows
| Evidence gap | Synthetic records | Share of sample |
|---|---|---|
| DPDP notice/purpose evidence gap | 1,482 | 83.9% |
| WhatsApp opt-in evidence gap | 1,482 | 83.9% |
| Owner assignment gap | 940 | 53.2% |
| Patient counselling content not sent/recorded | 1,111 | 62.9% |
| Doctor/escalation rule gap | 95 | 5.4% |
| 24-hour follow-up gap | 1,482 | 83.9% |
| No-show follow-up evidence gap | 1,670 | 94.6% |
| Source attribution gap | 1,036 | 58.7% |
Highest-attention synthetic workflow flags
| Workflow | Channel | Monthly enquiries | Response minutes | Selected flags |
|---|---|---|---|---|
| Second-opinion report upload | 41 | 420 | DPDP notice, WhatsApp opt-in, owner, clinical escalation, follow-up SLA and source-attribution gaps. | |
| Retina red-flag triage | Missed call | 54 | 310 | Consent basis, escalation owner and automation wording need review before reminders. |
| Corporate eye-camp leads | Spreadsheet upload | 320 | 1,440 | DPDP notice, opt-in, owner, follow-up SLA and source-attribution gaps. |
| LASIK eligibility - Instagram | Instagram DM | 135 | 240 | Opt-in, counselling handoff, owner and source evidence gaps. |
| General appointment call queue | Phone | 390 | 130 | Notice, opt-in, owner, follow-up and attribution gaps. |
| Diabetic eye-check reminders | CRM reactivation | 260 | 720 | Consent basis, follow-up SLA and clinical escalation wording need review. |
Owner-dashboard proof pack
- Channel inventory for phone, WhatsApp, website, Instagram, paid search, CRM/reactivation, walk-ins and screening-camp spreadsheets.
- DPDP notice, purpose, WhatsApp opt-in and opt-out evidence status by workflow and enquiry volume.
- Patient-growth queue for source attribution, first response SLA, booking status, no-show follow-up and owner assignment.
- Counselling handoff evidence for cataract, LASIK, retina, diabetic-screening, pediatric and post-op workflows.
- Clinical safety boundary showing red-flag escalation script, doctor/nurse handoff and no automated suitability claims.
- Written approval gate before any real case study, testimonial, logo, patient outcome, booking, revenue or ranking claim.
Reproducible artifact
The source model and generated markdown report are stored internally at /home/agent/.hermes/aicloudstrategist/case-studies/simulated-india-ophthalmology-clinic-cataract-counselling-dpdp-2026-07-13/. Inputs are explicit in sample_ophthalmology_patient_growth_workflows.csv; calculations are deterministic and labelled as simulated.
Verification: python3 ophthalmology_growth_dpdp_diagnostic.py regenerated the report with rows=10, total_enquiries=1766, booked=341, after_hours=320, consent_gap_records=1482, whatsapp_gap_records=1482, owner_gap_records=940, counselling_gap_records=1111, escalation_gap_records=95, followup_gap_records=1482, no_show_gap_records=1670 and source_gap_records=1036. Input SHA256: e106652f82466ee35c028ee4bb8a5206e2a0ce7f6b72a9a811be3939dfafa098.
Claim boundary
No real ophthalmology clinic, eye hospital, patient, PHI, client, production data, Data Protection Board filing, DPDP compliance status, audit attestation, security audit, legal advice, medical advice, privacy advice, security advice, appointment booking, patient acquisition outcome, ad-platform performance, revenue outcome, ROI, regulator outcome, platform partnership, logo, testimonial, ranking or clinical-accuracy claim is made. Real implementation would require explicit scope, data-handling approval, organisation-approved policies, payment route and written authorization before any public proof use.