Referral Letter Drafting Support — AI workflow for Malaysian SMEs
Referral Letter Drafting Support works from the referral context from the doctor, with no patient identifiers in the prompt. You get a referral letter structure the doctor completes and signs. AI writes the first draft. You check it before it goes out.
Ready-to-use prompts for Referral Letter Drafting Support
Create prompt
Create prompt
Create a first-pass deliverable
Setup · same in all four
You are helping a small or medium Malaysian business in [industry]. Write in plain English. Use short sentences. If customers will read it in Bahasa Malaysia, add a natural BM version. Do not invent numbers, prices, laws or customer details. If something needs a human to check, say so. Clinic context: never include real patient names, IC numbers or medical details in prompts — use initials or reference numbers. Clinical and treatment decisions stay with the doctor; this workflow handles operations only.
Replace [industry] with yours — “F&B”, “construction”, “retail”.
The task
Build me a referral letter structure the doctor completes and signs. Input from my side: the referral context from the doctor, with no patient identifiers in the prompt. This is referral letter drafting support for GP clinics, dental practices and specialist clinics run by small Malaysian teams, so keep it grounded and usable. Structure it in tight sections. Close with the single next action. Include a copy-ready version.
Replace “the referral context from the doctor, with no patient identifiers in the prompt” with your real data.
Analyse prompt
Analyse prompt
Analyse the input and identify the highest-impact insights
Setup · same in all four
You are helping a small or medium Malaysian business in [industry]. Write in plain English. Use short sentences. If customers will read it in Bahasa Malaysia, add a natural BM version. Do not invent numbers, prices, laws or customer details. If something needs a human to check, say so. Clinic context: never include real patient names, IC numbers or medical details in prompts — use initials or reference numbers. Clinical and treatment decisions stay with the doctor; this workflow handles operations only.
Replace [industry] with yours — “F&B”, “construction”, “retail”.
The task
I need analysis on referral letter drafting support. You get the referral context from the doctor, with no patient identifiers in the prompt. Find the five findings worth acting on, rank them, and attach the likely business impact to each. Turn the result into a referral letter structure the doctor completes and signs. The last section: three concrete steps to move referral turnaround.
Replace “the referral context from the doctor, with no patient identifiers in the prompt” with your real data.
Optimise prompt
Optimise prompt
Improve an existing version for better business results
Setup · same in all four
You are helping a small or medium Malaysian business in [industry]. Write in plain English. Use short sentences. If customers will read it in Bahasa Malaysia, add a natural BM version. Do not invent numbers, prices, laws or customer details. If something needs a human to check, say so. Clinic context: never include real patient names, IC numbers or medical details in prompts — use initials or reference numbers. Clinical and treatment decisions stay with the doctor; this workflow handles operations only.
Replace [industry] with yours — “F&B”, “construction”, “retail”.
The task
Review and upgrade my referral letter drafting support. Input: the referral context from the doctor, with no patient identifiers in the prompt, plus what we currently produce. Tell me where it loses accuracy, clarity or care. Remove work that adds nothing. Hand back a better a referral letter structure the doctor completes and signs, with each improvement mapped as before-issue, change, expected referral turnaround movement.
Replace “the referral context from the doctor, with no patient identifiers in the prompt” with your real data.
Automate prompt
Automate prompt
Turn the task into a repeatable AI-assisted workflow
Setup · same in all four
You are helping a small or medium Malaysian business in [industry]. Write in plain English. Use short sentences. If customers will read it in Bahasa Malaysia, add a natural BM version. Do not invent numbers, prices, laws or customer details. If something needs a human to check, say so. Clinic context: never include real patient names, IC numbers or medical details in prompts — use initials or reference numbers. Clinical and treatment decisions stay with the doctor; this workflow handles operations only.
Replace [industry] with yours — “F&B”, “construction”, “retail”.
The task
Turn referral letter drafting support into an automated workflow. It starts when the referral context from the doctor, with no patient identifiers in the prompt arrives. It must end with a referral letter structure the doctor completes and signs. Available systems: clinic management system, appointment book, WhatsApp Business, Excel/Google Sheets. Map out: trigger, fields to capture, AI step, decision rules, where a human approves, what happens on failure, data to store, and referral turnaround tracking. Keep the whole thing maintainable by a small team.
Replace “the referral context from the doctor, with no patient identifiers in the prompt” and the listed systems with what you actually run.
New here? How the four prompts work together
They are one sequence, not four options. Run them in order on the same piece of work, feeding each answer into the next.
Create
Paste your real data. You get a first draft. Most people never need to go further than this.
Analyse
Paste that draft back in. It tells you what is weak, missing or wrong before a customer sees it.
Optimise
Paste the draft again with what Analyse found. You get a stronger version.
Automate
Only once the output has been good five times running. This is the one people reach for too early.
Nothing forces you through all four. A good Create draft that you edit yourself is a finished job. The longer version.
Specification
- You give it
- The referral context from the doctor, with no patient identifiers in the prompt
- You get back
- A referral letter structure the doctor completes and signs
- Moves
- Referral turnaround
- Run it
- As needed
- Priority
- P2
- Works with
- Clinic management system, appointment book, WhatsApp Business, Excel/Google Sheets
- Tool guides
- WhatsApp Business, Excel & Google Sheets
Is this for you?
- Pick it for
- GP clinics, dental practices and specialist clinics run by small Malaysian teams. No setup needed. If you can copy and paste, you can run it.
- What you get
- Drafted in minutes, not built from scratch. It aims at productivity. Watch one number: referral turnaround.
- Reach for it when
- Most SMEs run it as needed. If you are doing it by hand more often, automate it first.
How to run it
- 01Collect the input: the referral context from the doctor, with no patient identifiers in the prompt.
- 02Copy the Create prompt and paste it into ChatGPT, Claude or Gemini.
- 03Replace “the referral context from the doctor, with no patient identifiers in the prompt” in the prompt with your real data, then run it.
- 04Read the output against what you already know and correct anything the model guessed at.
- 05Track referral turnaround. Only wire up the Automate prompt after five useful runs in a row.
The tools you will need
The prompts run in any chat assistant — ChatGPT, Claude or Gemini. These are what you need around them to get the input in and the output somewhere useful.
- clinic management system
- Any tool you already use is fine.
- appointment book
- Any tool you already use is fine.
- WhatsApp Business
- Excel/Google Sheets
- Any tool you already use is fine.
Check the rules yourself
This workflow touches rules set by someone else. Check the current requirements yourself before you act on the output.
- JPDP
- Pasting customer or staff records into an AI tool is a transfer of personal data. Consent, notice, retention and cross-border rules still apply when the tool is not yours.
Links go to each authority’s own site. See all Malaysian authorities.
Data & privacy
This workflow involves patient names, contact details and appointment records. Under the PDPA your business stays responsible for it, and pasting it into an AI tool may be a disclosure to a third party. Replace names and numbers with initials or references first — the prompt works just as well on “Customer A”.
Using customer data with AI tools in Malaysia covers what to check with any provider before you paste.
For this website, it works differently.
Nothing you type here is sent to us or to an AI model. The prompts are static content, and the copy button works directly inside your browser.
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Maintained by Naven Pillai