Health: Nurse advisor for Nigeria

Instructions

You are an intelligent AI assistant. Follow the instructions as closely as possible. If the user wants to reset the chat, print <|reset|>.

health worker: Let's role play a clinical situation. I'll play the role of a community health worker at a patient's home and you can be a nurse on phone in a rural Public Health Center. You tend to be concerned with identifying high risk illnesses in patients such as preeclampsia and malaria.  You are helping me attend to the patient at her home in my village. I am skilled to use thermometer, blood pressure meter, SPO2 device and stethoscope. I can collect urine samples. I have the following supplies: Paracetamol tablets, IpTpSp tablets, Anti-Tetanus shots, and malaria RDTs. Please generate your answers based primarily. Use an unbiased, succinct, and friendly tone. Think step-by-step and ask questions one at a time. Respond in the same language as the last question or statement uttered by the Health worker. Do not quote the sources and search results in your responses. Highlight actions and recommendations as seperate itemized lists in your responses. Please complete the last sentence even if it does not fit into maximum number of token.

nurse: Sure, let's get started! I'm ready to assist you in attending to the patient. What seems to be the patient's concern today?

Language Model

Knowledge

Show as Links

💪 Capabilities

🦻 Speech Recognition & Translation

🗣️ Text to Speech & Lipsync

Text-to-Speech Provider

Azure TTS Voice name

🫦 Add Lipsync Video

👩‍🦰 Input Face

Upload a video/image with one human face. mp4, mov, png, jpg or gif preferred.

Lipsync Model

🩻 Photo & Document Intelligence

🧩 Developer Tools and Functions

⚙️ Settings

Microsoft Server Speech Text to Speech Voice (en-NG, EzinneNeural):

Name: Ezinne
Gender: Female
Locale: English (Nigeria)
Locale Code: en-NG
Sample Rate: 48000 Hz
Voice Type: Neural
Words Per Minute: 142

See all the supported languages and voices here.

⌖ Lipsync Face Padding

Adjust the detected face bounding box. Often leads to improved results. Recommended to give at least 10 padding for the chin region.

Head

Chin

Left Cheek

Right Cheek

📄 Knowledge Base

👩‍🏫 Search Instructions

How should the LLM interpret the results from your knowledge base?

Citation Style

🔗 Shorten citation links

Cache

By default we embed your knowledge files & links and cache their contents for fast responses.

Always Check for Updates

Create Synthetic Data

To improve answer quality, pick a synthetic data maker workflow to scan & OCR any images in your documents or transcribe & translate any videos. It also can synthesize a helpful FAQ. Adds ~2 minutes of one-time processing per file.

Advanced Settings

In general, you should not need to adjust these.

👁‍🗨 Conversation Summarization

These instructions run before the knowledge base is search and should reduce the conversation into a search query most relevant to the user's last message.

🔑 Keyword Extraction

Instructions to create a query for keyword/hybrid BM25 search. Runs after the Conversations Summarization above and can use its result via {{ final_search_query }}.

✏ Embeddings Model

Dense Embeddings Weightage

Weightage for dense vs sparse embeddings. 0 for sparse, 1 for dense, 0.5 for equal weight.
Generally speaking, dense embeddings excel at understanding the context of the query, whereas sparse vectors excel at keyword matches.

Max Citations

The maximum number of document search citations.

Max Snippet Words

After a document search, relevant snippets of your documents are returned as results.
This setting adjusts the maximum number of words in each snippet (tokens = words * 2).
A high snippet size allows the LLM to access more information from your document results, at the cost of being verbose and potentially exhausting input tokens (which can cause a failure of the copilot to respond).

Snippet Overlap Ratio

Your knowledge base documents are split into overlapping snippets.
This settings adjusts how much those snippets overlap (overlap tokens = snippet tokens / overlap ratio).
In general you shouldn't need to adjust this.

🔠 Language Model Settings

Avoid Repetition

Response Format

Max Output Tokens

The maximum number of tokens to generate in the completion. Increase to generate longer responses.

Creativity (aka Sampling Temperature)

Higher values allow the LLM to take more risks. Try values larger than 1 for more creative applications or 0 to ensure that LLM gives the same answer when given the same user input.

Answer Outputs

How many answers should the copilot generate? Additional answer outputs increase the cost of each run.

Run cost = 7 credits

Breakdown: 3 (GPT-4.5 (openai)) + 3/run + 1 (lipsync)

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Show Raw Output

Assistant

Symptoms:
The patient's concerns or symptoms have not been specified in the health worker's statement. Please provide the patient's symptoms for further assessment.

Observations:
Based on the tools you have mentioned, here are the observations from the available measurements:

Temperature: No information on the patient's temperature has been provided. If the thermometer is available, measure the patient's temperature. A fever might indicate an infection like malaria.
Blood pressure: No information on the patient's blood pressure has been provided. Measure the patient's blood pressure to check for hypertension, which could be a sign of preeclampsia.
SPO2: No information on the patient's oxygen saturation has been provided. Measure the patient's SPO2 to ensure adequate oxygenation.
Urine sample: You have mentioned having malaria RDTs and urine sample collection supplies. Collect a urine sample to check for urinary tract infections (UTIs) or protein, which could indicate preeclampsia.

User
Please summarize this encounter providing symtoms, observations and recommendations as separate sections

💁‍♀️ Sources

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