Use Case 4: Predictive Scoring with Smart Summaries
Challenge
Selection committees must review thousands of applications containing both quantitative metrics and qualitative materials. Manual review is time-intensive, yet purely data-driven approaches miss important context from written materials.
Solution
Combine machine learning (ML) predictions based on structured data with large language model- (LLM-) generated summaries of unstructured content to provide a comprehensive yet efficient review tool.
How it Works
Step One: Structured Data Analysis
- Build prediction model using historical data (USMLE scores, publications, research experience).
- Generate an "Interview Likelihood Score" (0-100) based on past successful candidates.
- Identify key statistical factors influencing predictions.
Step Two: LLM Summary Generation
- Process personal statements, activity descriptions, and letters.
- Programs first define priority areas for summarization.
- Create targeted summary highlighting, for example:
- Program value alignment (e.g., community service, research excellence).
- Socioeconomic context (e.g., education background, financial circumstances).
- Key experiences and achievements.
- Notable characteristics or qualities.
- Unique background elements.
- Include relevant quotes as evidence.
Step Three: Example Output for Alex Rivera
- Interview Likelihood Score: 85/100.
- Statistical factors.
- USMLE Step 1: 245 (top 15% of past interviewees).
- USMLE Step 1 attempts: 2.
- Research: 2 first-author publications.
- Clinical experience: 1,000-plus hours.
- Program value alignment.
- Community focus: “Created mobile health clinic for underserved areas.”
- Found in: Activities section, entry #3.
- Reasoning: Demonstrates initiative in addressing health care access.
- Research excellence: “Led quality improvement study on ED wait times.”
- Found in: CV research section and personal statement paragraph 2.
- Reasoning: Shows both leadership and research methodology skills.
- Educational innovation: “Peer tutoring program for premed students.”
- Found in: Activities section, entry #7.
- Reasoning: Indicates commitment to medical education.
- Community focus: “Created mobile health clinic for underserved areas.”
- Context and Background.
- First-generation college student.
- Found in: Secondary application essay #2.
- Reasoning: Explicitly stated in response about challenges.
- Worked 20-plus hours per week during undergrad.
- Found in: CV employment history and referenced in personal statement.
- Reasoning: Indicates financial need and time management skills.
- Rural health care experience in medical desert region.
- Found in: Personal statement opening paragraph and activities #4.
- Reasoning: Shows exposure to underserved health care settings.
- First-generation college student.
- Key Experiences.
- ED quality improvement project leader
- Found in: Research experience section and LOR from ED director.
- Reasoning: Major leadership role with measurable impact.
- 3 years EMT experience.
- Found in: CV clinical experience section.
- Reasoning: Sustained clinical commitment in premedical school.
- Health care disparities research focus.
- Found in: CV research section and personal statement theme.
- Reasoning: Consistent thread across multiple experiences.
- ED quality improvement project leader
Key Takeaways
Core Benefits
- Hybrid analysis. Combines predictive scoring with qualitative insights.
- Adaptable framework. Updates with evolving priorities and fresh analysis.
- Evidence-based. Clear sourcing and reasoning for all insights.
- Efficient review. Streamlines document analysis while maintaining depth.
Resource Requirements
- Technical: Both ML and LLM infrastructure.
- Personnel: Technical team, SMEs for evaluation standards.
- Effort: High initial setup for both prediction models and LLM framework.
Challenges, Solutions, and Information Triangulation
Building on Table 1's challenges around example libraries and expert consensus, Table 3 provides a non-exhaustive list of challenges building with LLMs. The key to success lies in systematic human evaluation — the same careful approach needed for building reliable example libraries and achieving expert consensus.
Best suited for
- Programs seeking both efficiency and depth in review.
- Institutions with resources for dual AI implementation.
- Teams wanting fresh analysis beyond historical patterns.
- Programs handling large application volumes.