CDSS: Multi-Level Summaries for Patient Case Notes with AI

In the medical field, clinicians often need to quickly access and understand patient case notes to make informed decisions. The sheer volume of data in these notes can be overwhelming, especially when time is critical. This is where Large Language Models (LLMs) can play a transformative role by generating summaries at varying levels of detail. In this blog post, we will explore how to define and implement these levels of abstraction to meet clinicians’ needs at different stages of their decision-making processes.

Clinicians interact with patient data at various stages, each requiring a different level of detail. From initial assessments to emergency interventions, the ability to access the right amount of information quickly can significantly impact patient care. Here’s a breakdown of the stages of clinical decision-making and the specific needs at each stage:

Needs at Each Stage

StageNeedsDetail LevelTypical Actions
Initial AssessmentComprehensive overview of patient history, current complaints, and relevant background information.Moderate to HighPatient interview, physical examination, review of past medical records.
Diagnosis FormulationDetailed clinical information, diagnostic clues, examination findings, past diagnoses, lab results, and response to past treatments.HighOrdering tests, differential diagnosis, reviewing detailed case notes.
Treatment PlanningCurrent clinical status, recent lab results, current medications, and treatment responses, with relevant past medical history and treatments.HighDeveloping a treatment plan, prescribing medications, planning interventions.
Ongoing ManagementRegular updates on patient’s progress, current medications, and treatment plans, along with monitoring changes in condition.ModerateMonitoring patient’s condition, adjusting treatments, routine follow-ups.
Emergency SituationsImmediate access to crucial information for rapid decision-making, including current diagnosis, critical medications, and key lab results.MinimalLife-saving interventions, emergency diagnosis, and treatment decisions.
Follow-up and MonitoringConcise updates on patient’s current status, recent lab results, and changes in treatment, with a summary of past treatments and outcomes for guiding ongoing care.Low to ModerateRoutine check-ups, evaluating treatment efficacy, patient education.

Combining the stages and their specific needs helps validate and refine the proposed summary levels:

Summary LevelPurposeStageRationale
Detailed SummaryComprehensive detail for in-depth analysisInitial Assessment, Diagnosis Formulation, Treatment PlanningProvides exhaustive details needed for thorough understanding and planning.
Intermediate Summary (History-Focused)Significant historical detailsInitial AssessmentFocuses on past events and patient history necessary for understanding background.
Intermediate Summary (Current-Focused)Current clinical status and immediate concernsTreatment Planning, Ongoing Management, Follow-up and MonitoringFocuses on current status, essential for ongoing treatment and follow-ups without overwhelming with historical data.
High-Level SummaryConcise overview for quick referenceOngoing Management, Follow-up and MonitoringProvides a quick refresher, summarizing the most relevant recent information for routine and follow-up care.
Executive SummaryMinimal critical information for urgent decisionsEmergency SituationsDelivers only the most crucial information needed immediately in emergencies, ensuring that clinicians can make rapid, informed decisions without any delay.

To implement these summaries, one can leverage NLP AI techniques and LLMs such as GPT-4. Here’s a practical approach to structuring and implementing the system:

Data Structuring:

  • Organize patient case notes into structured data points, tagging each piece with relevant metadata.

Algorithm Development:

  • Develop algorithms that filter and select data points based on the defined levels of abstraction.

User Interface Design:

  • Create an intuitive interface allowing clinicians to select the desired summary level, with easy navigation to more detailed levels if needed.

Integration of LLMs:

  • Use LLMs to generate summaries from the structured data based on the specified prompts.

Here’s a basic example of how you might use Python with an LLM API to generate these summaries:

import openai

# Function to generate summary using OpenAI GPT
def generate_summary(case_notes, level):
    prompts = {
        'detailed': "Generate ...",
        'intermediate_history': "Generate ...",
        'intermediate_current': "Generate ...",
        'high_level': "Generate ...",
        'executive': "Generate ..."
    prompt = prompts[level]
    response = openai.Completion.create(
    return response.choices[0].text.strip()

# Example case notes
case_notes = "Patient John Doe, male with a history of ..."

# Generate a detailed summary
summary = generate_summary(case_notes, 'detailed')
print("Detailed Summary:\n", summary)


Implementing multi-level summaries using LLMs can significantly enhance the efficiency and effectiveness of clinicians. By providing the right level of detail at the right time, we can help healthcare professionals make better, faster decisions. As software professionals, our role is to ensure that the summarization system is robust, intuitive, and adaptable to the dynamic needs of the medical field.

By following the structured approach and practical examples provided, you can create a powerful tool that transforms patient data into actionable insights, ultimately improving patient outcomes.

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