Medical scenario Report simplification
Example GPT-4 prompt (assuming model is trained on current medical data, e.g., OpenAI/Epic collaboration)
“You’re an expert radiologist, frequently analyzing complex scans. Your task is to translate a complex radiology report into simpler terms, suitable for an 8th grader. You goal is not just to simplify medical jargon, but also explain the diagnosis in a reassuring and understandble way that minimizes anxiety.” [INSERT RADIOLOGY REPORT]
Appeal of pre- authorizationd deal
“As a medical billing specialist, you’ve received a denial for preauthorization for a uterine fibroid embolization procedure. Your challenge is to write an appeal letter that argues the medical necessity of UFE for the patient. Use only provided medical evidence, provided patient history, and provided appropriate guidelines to counter the denial. Your aim is to ensure the patient receives this critical care while convincingly communicating to the insurance company that this procedure is essential for the patient’s well-being. Patient’s symptoms include: [INSERT PATIENT SYMPTOMS AND RELEVANT GUIDELINES/LITERATURE”
Patient education prompt
“As a patient, you have recently been diagnosed with [INSERT MEDICAL CONDITION] and you’re interested in learning more about the condition. You want to understand the nature of the disease, its symptoms, potential complications, and the range of treatment options available, ranging from conservative to surgical. These treatment options should include detailed descriptions, duration, and potential side effects, formatted in a table.”
Figure 3. Useful beginner prompts for GPT-4
can constrain LLMs to datasets (such as a medical chart) wherein unknown answers produce an admission of “not known” rather than a hallucination.14
Trained data limitation LLMs are limited to the data that they are trained on. Therefore, novel innovations or discoveries will not be reported by the model if they occurred following its training cutoff. However, plugins such as Microsoft Bing browser in ChatGPT and various addons enable LLM models to actively search the web and relay information that was produced after the end of its training period.
The origin of training data may pose a potential limitation, too. Large volumes of training information come from well-funded institutions in wealthy and predominantly English-speaking countries, presenting a risk for bias.15
Conclusions LLMs have the potential to profoundly impact healthcare. By improving efficiency, solving complex problems and streamlining tasks, LLMs can enhance patient care and their experience with the healthcare system.
14 IRQ | FALL 2023
Their potential applications in clinical management, physician–patient communication, procedural planning and charting are promising. It is important that we continue to develop LLMs, particularly in IR, to fully harness this technology and enhance how we practice medicine.
References
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https://trust.openai.com.
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