Is this a real physiotherapy clinic case study?

No. This is a simulated proof-of-method asset built with synthetic sample rows. It is not a real clinic, patient-data analysis, PHI, client outcome, testimonial, medical advice, privacy advice, DPDP compliance claim, revenue claim, booking claim or ROI claim.

What does the diagnostic demonstrate?

It demonstrates how AICS would turn fragmented clinic enquiries from Google Business Profile, WhatsApp, referrals, Instagram, paid search, camps, phone and web forms into evidence queues for source capture, callback ownership, notice prompts, opt-in evidence, AI boundary disclosure, clinical escalation and no-show recovery.

Can this guarantee patient bookings or compliance?

No. The numbers are synthetic arithmetic outputs only. A real pilot would need permissioned data, clinic approval and qualified legal, privacy and clinical review before publishing any outcome or compliance statement.

Simulated proof asset · India Healthcare GrowthOS

Simulated India physiotherapy lead-source + no-show diagnostic

This no-fake-client proof asset shows how AICS can inspect physiotherapy patient-growth leakage before a clinic scales more WhatsApp, Google, referral, Instagram, paid-search or camp-list follow-up. It is synthetic only: no real clinic, patient, PHI, medical outcome, DPDP compliance, booking, revenue or ROI claim is made.

Request a clinic workflow fit check View no-show checklist Back to proof hub

Important claim boundary: this page is a simulated proof-of-method demonstration. It is not a customer case study, not a testimonial, not a patient-data analysis, not legal, privacy, security, medical or compliance advice, not DPDP attestation, and not a revenue, booking, ranking or ROI promise.

Synthetic enquiries1,55912 synthetic workflow/channel rows

After-hours enquiries254sample events needing next-step ownership

Missed calls109sample phone leakage events

Callback coverage proxy32.0%after-hours + missed-call events with callback in 4 hours

Appointment arrival proxy70.7%401 arrivals from 567 booked appointments

No-show recovery gap1,397synthetic enquiries in rows lacking a recovery sequence

DPDP notice prompt gap1,202records requiring evidence before claims

Source capture gap521enquiries where channel evidence needs repair

Diagnostic method

What a physiotherapy clinic owner can inspect before scaling growth spend

The diagnostic converts enquiry rows into operating queues: source tagging, callback ownership, consent/notice evidence, AI/admin boundary disclosure, clinical escalation routes, closure reasons and no-show recovery.

Evidence/control area	Synthetic enquiry volume with gap	Why AICS would flag it
Owner assigned	837	Unowned enquiries make callbacks and no-show recovery hard to enforce.
Source captured	521	Owners cannot compare Google, WhatsApp, referrals, paid search and camp lists reliably.
DPDP notice prompt present	1,202	The workflow lacks evidence that data-use notice language was presented; this is an operational prompt, not compliance advice.
WhatsApp opt-in evidence	1,034	Follow-up templates should distinguish permissioned messages from ad hoc staff messaging.
AI boundary disclosure	956	Patients/prospects should not confuse automated triage/admin assistance with clinician judgment.
Clinical escalation route	393	Administrative automation needs a route for red-flag symptoms or clinical questions.
No-show recovery sequence	1,397	Booked-but-not-arrived records need same-day/next-day recovery ownership.
Closure reason captured	1,339	Without closure reasons, owners cannot separate price objections, timing issues, duplicates and clinical ineligibility.

Before diagnostic

Reception logs enquiries in separate WhatsApp chats, missed-call lists, web forms and ad-platform exports.
The owner reviews total bookings but cannot trace where no-shows and unresolved enquiries originated.
Consent/notice wording, opt-in evidence and AI/admin boundary prompts are inconsistent.
Clinical escalation relies on staff memory instead of a documented route.

After diagnostic operating rule

Each channel becomes a lightweight evidence row with source, owner, status, callback time, consent/notice prompt and closure reason fields.
A weekly owner memo shows missed-call backlog, no-show recovery backlog, source attribution gaps and evidence prompt gaps.
Automation is constrained until WhatsApp opt-in, AI/admin boundary wording and clinical escalation routes are reviewed.
Source hygiene, same-day callbacks, closure tagging and no-show recovery SOP tasks are assigned visibly.

Evidence needed before publishing any real clinic outcome

A real pilot should collect only permissioned, minimized operational exports where possible; define callback SLA and owner rules; document appointment/no-show status, notice/purpose prompts, WhatsApp opt-in handling, AI/admin boundary wording and clinical escalation routes; and obtain explicit clinic approval plus qualified legal/privacy/clinical review before any public patient, DPDP, booking, revenue or ROI statement.

Request clinic workflow fit check Use the no-show checklist

Reproducibility

Internal synthetic artifact: /home/agent/.hermes/aicloudstrategist/case-studies/simulated-india-physiotherapy-clinic-lead-source-no-show-dpdp-2026-08-19/. The generator regenerated this report with input SHA256 ddca3874d6e42bc87c53a23764a23eaea2fad6e37727294149c0094fa3945061, report SHA256 622ebe54be7bf8872e9177df9f8075f3bb05477ea6cb3c0820588fed265c30e4 and generator SHA256 fdad001a2e22158be854e868beccf4c7744737bb672c0bf9e96838a83b79c9dc.

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