New Breakthrough in Medical AI: Large Models Convert Patient Experience Text into Structured Health Signals

The PREM-LLM project explores using large language models (LLMs) to convert free-text patient-reported experiences into structured, actionable health signals. It addresses the problem that traditional patient experience data (such as standardized questionnaires) struggle to capture rich, real feedback, helping healthcare information systems effectively utilize patients' voices and improve the quality of medical services.

Importance of Patient Experience Data

Patients' experiences described in natural language (e.g., anxiety while waiting, unclear discharge instructions) contain key clues for improving medical services, but the free-text format makes it difficult for information systems to utilize them.

Definition and Core Dimensions of PREM

PREM (Patient-Reported Experience Measurement) focuses on the experience during medical visits, with core dimensions including accessibility, communication quality, respect and dignity, environmental comfort, coordination, and continuity.

Limitations of Traditional PREM

Traditional PREM relies on standardized questionnaires, which struggle to capture rich and diverse real experiences; open-text feedback has large information volume but is time-consuming and labor-intensive to process.

Technical Challenges

Converting free text faces issues such as the complexity of medical language, subtlety of emotions, context dependency, and multilingual dialects.

Advantages of Large Models

LLMs have strong capabilities in semantic understanding, emotion recognition, context reasoning, and few-shot learning, which can address the above challenges.

Project Architecture

Modular design: Data preprocessing (cleaning, language detection) → LLM annotation engine (topic classification, sentiment polarity, etc.) → Structured output (FHIR-compatible) → Analytical visualization (trend and hotspot identification).

Annotation Framework

Hierarchical annotation system (document/paragraph/entity level) + medical experience ontology + quality control through multi-model integration and manual verification.

1. Real-time Experience Monitoring: Integrate feedback systems to detect severe negative feedback in real time and trigger alerts;
2. Service Quality Improvement: Identify process pain points (e.g., discharge instruction issues) and optimize them targeted;
3. Employee Performance Evaluation: Aggregate feedback related to medical staff as a reference for performance assessment;
4. Competitive Benchmarking: Analyze experience data from different institutions to learn best practices;
5. Policy-making Support: Provide large-scale patient experience insights for health policy formulation.

Data Desensitization

Automatically remove personal identification information, anonymize mentioned individuals, and follow the principle of data minimization.

Informed Consent

Ensure patients understand the purpose of feedback, provide an opt-out mechanism, and clarify the scope and duration of data use.

Fairness and Transparency

Monitor performance differences of the model across different populations to avoid bias; record decision-making basis and provide explanation and appeal mechanisms.

Current Limitations

Annotation accuracy may be incorrect in complex scenarios; long text processing is limited by the model's context window; commercial LLM API costs are relatively high.

Future Directions

Multimodal expansion (integrating voice/video); real-time streaming processing; personalized insights; causal inference (identifying root causes of problems).

PREM-LLM does not replace medical staff's judgment; instead, it amplifies patients' voices. Through structured data conversion, it allows medical institutions to timely capture improvement opportunities and promote the continuous enhancement of patient-centered medical services. In the future, this technology is expected to make every patient's feedback be taken seriously, helping medical quality progress in a data-driven way.