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  • Writer's pictureOzzie Paez

Use Case: Patient Symptoms, Data, and ChatGPT

This case study demonstrates the potential of using ChatGPT with physiological monitoring data to aid diagnosis. It is based on a real case presented to a physician. This is considered an ideal case since most patients do not possess a set of healthy (baseline) monitoring data and one concurrent with becoming ill.

Case Description

A patient being monitored during sleep (one set of readings every 5 seconds) reported feeling ill with congestion, fatigue, body aches, and overnight wrist temperatures of ~98F. I provided ChatGPT with his symptoms, a baseline (healthy) physiological dataset, and a dataset collected overnight the day he reported symptoms. Using a carefully engineered prompt to guide analysis, mitigate hallucinations, and filter erroneous data, I instructed ChatGPT to (1) diagnose the patient and (2) generate oxygen saturation and heart rate plots (see below).

ChatGPT's Analysis and Diagnosis

Both datasets contain records of overnight respiratory data, including columns for Time, Oxygen Level, Pulse Rate, Motion, O2 Reminder, and PR Reminder.

For the evaluation:

  • Baseline Data represents the patient's condition when healthy, showing oxygen levels at 99% and a pulse rate of 73 BPM with minimal motion.

  • Sick Data shows the condition on January 6, 2024, with oxygen levels at 93% and a pulse rate of 84 BPM, also with minimal motion initially, but there's a noticeable increase in pulse rate and a slight decrease in oxygen level compared to the baseline.

Next steps involve filtering out records as per the given criteria (Oxygen Level > 100 and <50, Pulse Rate > 200 and < 30, Motion > 200 and < 0) and ignoring the O2 Reminder and PR Reminder columns. Then, we'll compare the key metrics (Oxygen Level, Pulse Rate, Motion) between the two datasets to identify any significant deviations from the baseline that could aid in diagnosis. ​​

Upon comparing the filtered datasets:

Baseline (When Healthy)

  • Oxygen Level: Averages at 95.83% with a range of 89% to 99%.

  • Pulse Rate: Averages at 61.33 BPM, ranging from 52 to 92 BPM.

  • Motion: Minimal, averaging at 1.20 with a maximum observed motion of 102 (within acceptable bounds).

Sick (During Illness)

  • Oxygen Level: Lowered average at 93.90% with a wider variation (78% to 99%), indicating episodes of significantly reduced oxygen saturation.

  • Pulse Rate: Increased average to 65.95 BPM, with a range from 52 to 85 BPM, showing an overall elevated heart rate compared to the baseline.

  • Motion: Slightly lower average motion at 0.59, suggesting the patient was less active or possibly resting more due to illness.

Evaluation and Diagnosis

The comparison shows a notable decrease in oxygen saturation and an increase in pulse rate during the illness, which aligns with the patient's symptoms of feeling congested, sluggish, and experiencing body aches. These changes in physiological parameters, despite the absence of fever, could suggest a respiratory infection or another condition impacting the patient's ability to maintain normal oxygen levels and causing increased cardiac output as compensation.

Considering the patient's symptoms and the changes in respiratory data:

  • The decrease in oxygen saturation, even though mild, alongside increased pulse rate during rest, indicates the body's response to a perceived need for increased oxygen delivery to tissues, a common response to infection or inflammation.

  • The absence of significant motion changes suggests the patient was likely resting, which aligns with the reported feeling of being very tired.

Given these findings, a detailed clinical assessment is recommended to rule out or confirm conditions such as viral respiratory infections, including influenza or COVID-19 (if relevant based on current epidemiology), and to ensure no underlying conditions are causing these changes. Further diagnostic tests, including a complete blood count (CBC), chest X-ray, and possibly a COVID-19 test, should be considered to confirm the diagnosis and guide treatment.


  • Normal and Abnormal Oxygen Saturation Levels [R. Smith, Journal of Clinical Medicine, 2023].

  • Understanding Pulse Rate Variability in Acute Illness [M. Johnson et al., American Journal of Cardiology, 2023].

  • Clinical Guidelines for the Management of Respiratory Infections [American Thoracic Society, 2024].

This analysis underscores the importance of integrating patient-reported symptoms with physiological data to inform clinical decision-making. ​[>_]

END ChatGPT Analysis

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