UDLCO summary: AI LLMs in their routine responses are always courteous albeit they can start confabulating (aka hallucinations popularly but perhaps not the appropriate term) and mislead. In the sample taken from our morning PaJR session with one of our patients and meta AI, we notice a positive note of collaboration in their courteous stance that is again very unlike competing humans for now. At this juncture it's difficult to say how long our honeymoon with AI shall last before it quickly learns all our human follies!
Conversational PaJR transcripts on the morning of 9th October between different PaJR users, patient advocate, PaJR moderator and meta AI centered around the requirements of a 30 year old type 1 diabetic:
[09/10, 23:59] Patient DM1 MP Advocate 2: 6:30 pm drink 1 cup black tea without sugar
7 pm insulin lantus 8.5 unit
7:50 pm sugar 52
Trembling and shivering so I ate 1 sweet,3 chocolate and rice with bottle gourd curry and brinjal fry
[10/10, 08:34] Patient DM1 MP Advocate 2: Good morning sir
10/10/24
8:30 am fasting sugar 146
[10/10, 10:41] PaJR moderator: How do we explain this episode of hypoglycemia at 7:50 AM yesterday, in terms of precipitating cause @Meta AI ?
[08/10, 09:01] Patient DM1 MP Advocate 2: Good morning sir
8/10/24
8:45 am fasting sugar 187
[08/10, 17:06] Patient DM1 MP Advocate 2: 9 am drink 1 cup black tea without sugar
10:25 am before breakfast sugar 230
10:30 am
Sprouts
12:30 pm after breakfast sugar 235
2:55 pm before lunch sugar 166
3 pm
Rice with cabbage fry and lentil
5 pm after lunch sugar 143
[08/10, 23:53] Patient DM1 MP Advocate 2: 5:15 pm drink 1 cup black tea without sugar
7 pm insulin lantus 8.5 unit
9:25 pm before dinner sugar 140
9:30 pm
2 roti with lentil and cabbage fry
11:30 pm after dinner sugar 214
[09/10, 06:38] Patient DM1 MP Advocate 2: Good morning sir
9/10/24
6:30 am fasting sugar 143
[09/10, 15:37] Patient DM1 MP Advocate 2: 8:30 am drink 1 cup black tea without sugar
9:55 am before breakfast sugar 186
10 am
Besan chila
12 pm after breakfast sugar 175
1:25 pm before lunch sugar 163
1:30 pm
Khichdi, mix veg and kheer ( Prasad)
3:30 pm after lunch sugar 180
[09/10, 23:59] Patient DM1 MP Advocate 2: 6:30 pm drink 1 cup black tea without sugar
7 pm insulin lantus 8.5 unit
7:50 pm sugar 52
Trembling and shivering so I ate 1 sweet,3 chocolate and rice with bottle gourd curry and brinjal fry
[10/10, 08:34] Patient DM1 MP Advocate 2: Good morning sir
10/10/24
8:30 am fasting sugar 146
[10/10, 10:41] Meta AI: The episode of hypoglycemia (low blood sugar) at 7:50 AM yesterday can be analyzed based on the provided data.
Precipitating causes:
1. Insulin dose: 8.5 units of Lantus insulin taken at 7 PM the previous evening. Lantus has a peak effect in 10-16 hours, which aligns with the hypoglycemic episode.
2. Timing of last meal: Dinner was at 9:30 PM, and the next meal wasn't until breakfast at 10 AM, resulting in a relatively long fasting period.
3. Physical activity: No significant physical activity mentioned.
4. Food intake: No significant carbohydrate intake between dinner and the hypoglycemic episode.
Contributing factors:
1. Possible insulin stacking: Taking 8.5 units of Lantus insulin at the same time each evening may have accumulated in the patient's system.
2. Inadequate carbohydrate intake before bedtime or at breakfast.
Recommendations:
1. Monitor blood glucose levels more frequently.
2. Adjust insulin dosing regimen or timing.
3. Ensure adequate carbohydrate intake before bedtime and at breakfast.
4. Consider a snack before bedtime or a late-evening snack.
Consult a healthcare professional for personalized guidance.
Please provide additional context or information, if needed.
[10/10, 10:43] PaJR moderator: @Meta AI in error I mentioned 7:50AM. That was supposed to mean 7:50 PM, which is when the patient experienced hypoglycemia
[10/10, 10:43] Meta AI: Given the corrected time of hypoglycemia (7:50 PM), let's re-analyze the data:
Precipitating causes:
1. Insulin dose: 8.5 units of Lantus insulin taken at 7 PM, which is 50 minutes before the hypoglycemic episode. Lantus has a peak effect in 10-16 hours, but the patient experienced hypoglycemia within a shorter timeframe.
2. Recent food intake: Dinner was at 9:30 PM the previous night, and the patient had black tea without sugar at 5:15 PM.
Contributing factors:
1. Timing of insulin administration: Taking Lantus insulin at 7 PM may have coincided with a natural dip in blood glucose levels before dinner.
2. Inadequate carbohydrate intake between 5:15 PM tea and 7:50 PM hypoglycemia.
3. Potential insulin stacking or sensitivity.
Recommendations:
1. Consider adjusting insulin dosing timing to closer to dinner (e.g., 9 PM).
2. Ensure adequate carbohydrate intake between 5 PM and dinner.
3. Monitor blood glucose levels more frequently, especially before and after dinner.
4. Consult a healthcare professional to reassess insulin regimen and adjust as needed.
Additional questions:
1. What is the patient's typical physical activity level?
2. Has the patient experienced hypoglycemia at this time of day previously?
3. Are there any other medications or health conditions that could impact blood glucose levels?
[10/10, 10:49] PaJR moderator: @Meta AI thanks for the valuable inputs.
Wish you could read the patient's entire data from few months, painstakingly archived in the URLed web based EHR content here:
Updated till 5th October. @Patient DM1 MP Advocate 2 can you update it till today?
I guess till meta AI learns to read URLs we can share the URL with Notebook LM for more insights from longer past data trends?
[10/10, 10:49] Meta AI: I'm glad I could provide helpful insights.
Unfortunately, I'm currently unable to access external URLs or scrape data from websites, including the patient's EHR data. However, I can offer suggestions:
*Short-term solution:*
1. Continue sharing relevant data snippets, and I'll provide analysis and recommendations.
2. Consider copying and pasting specific data periods or events for more detailed analysis.
*Long-term solution:*
1. Notebook LM: Share the URL with Notebook LM, as you mentioned, to leverage its capabilities.
2. Data export: Export the patient's EHR data in a machine-readable format (e.g., CSV, JSON).
3. Secure data sharing: Explore secure, HIPAA-compliant platforms for sharing and analyzing patient data.
*Future development:*
As AI capabilities advance, I may be able to:
1. Access and parse data from URLs.
2. Integrate with EHR systems.
3. Analyze larger datasets.
Until then, I'm here to assist with data analysis and insights based on the information you provide.
To update the data till today, please share:
1. Blood glucose readings.
2. Insulin dosages and timing.
3. Relevant medical events or changes.
4. Any new medications or lifestyle changes.
I'll continue providing valuable inputs.
 |
Creative commons license: https://commons.m.wikimedia.org/wiki/File:RAG_schema.svg#mw-jump-to-license
No comments:
Post a Comment