SOAP Note Agent for Clinical Documentation

• SOAP Note Agents are AI systems that create structured clinical documentation using the SOAP format, integrating natural language processing and domain-adaptive training.
• They employ hierarchical encoder-decoder models, bidirectional LSTMs, multitask decoders, and techniques like LoRA/QLoRA to efficiently process clinical data, including noisy ASR outputs.
• Evaluated with lexical, semantic, and clinical metrics, these agents enhance documentation consistency, reduce manual effort, and address fairness and specialty-specific challenges.

A SOAP Note Agent is an AI system designed to automate, assist, or enhance the creation of structured clinical documentation using the SOAP (Subjective, Objective, Assessment, Plan) note format. Such agents employ advances in natural language processing, multimodal learning, and domain-adaptive training to generate, classify, or complete SOAP notes from doctor–patient conversations, sparse clinical data, or other clinical artifacts. Modern developments encompass hierarchical context modeling, parameter-efficient fine-tuning, multimodal integration, and rigorous fairness and quality evaluation.

1. Foundational Datasets and Structured Representations

The construction and evaluation of SOAP Note Agents depend critically on extensive annotated datasets that pair clinical encounters with corresponding SOAP notes and structured metadata. Notable datasets include:

• A proprietary corpus (8,130 doctor–patient encounters, 10,000 hours of audio) pairing human and ASR transcripts with annotated SOAP sections (Schloss et al., 2020). Each SOAP note comprises observations mapped to Subjective, Objective, Assessment, and Plan categories, with additional “None” labels for unsupported utterances.
• CliniKnote: a dataset of 1,200 complex simulated doctor–patient dialogues, each paired with a full clinical note following the K-SOAP (Keyword, Subjective, Objective, Assessment, Plan) structure (Li et al., 26 Aug 2024). Keywords are labeled using advanced NER and relation extraction, refining clinical aggregation.
• Pediatric rehabilitation datasets: 432 SOAP notes—human, Copilot (commercial LLM), and KAUWbot (custom domain-tuned LLM) generated—from actual or simulated short clinician summaries (Amenyo et al., 4 Feb 2025).

Data curation processes typically involve transcription (human or low-WER ASR), manual annotation of speaker roles and SOAP sections, utterance segmentation (maximum token constraints, punctuation restoration), and the extraction or annotation of key clinical entities. The inclusion of both clean transcriptions and noisy ASR output enables the development of robust, deployable agents.

2. Architectural Paradigms for SOAP Note Agents

SOAP Note Agent architectures synthesize state-of-the-art neural representations with context modeling and multitask learning:

• Hierarchical Encoder–Decoder Models: First, token-level word representations use pretrained embeddings (e.g., ELMo), enhanced via attention across embedding layers and inner tokens. Formally, for token $t_{ij}$ in utterance $i$:

$$E_{ij} = \mathrm{ELMo}(t_{ij}); \qquad a^L_{ijk} = \mathrm{softmax}(\mathrm{dot}(w_L, e_{ijk}))$$

The combined token representation and utterance embedding $u_i$ are produced by successive attention and aggregation.

• Bidirectional LSTM for Context: Utterance embeddings $u_i$ are processed by stacked bi-LSTM layers, explicitly capturing contextual dependencies across utterances. This enforces modeling of both intrasentential and intersentential context—crucial for conversational medical language (Schloss et al., 2020).
• Multitask Decoders: Outputs are structured via task-specific unidirectional LSTMs for speaker role and SOAP section prediction:

$$P(\mathrm{section}_i \mid c_i, c_1, \ldots, c_{i-1}) = \mathrm{softmax}(w_{\mathrm{sect}} \cdot \mathrm{LSTM}_{\mathrm{sect}}(c_i) + b_{\mathrm{sect}})$$

• Modular ASR Alignment: Label smoothing and probabilistic alignment adapt robustly to ASR-induced noise. Each ASR word is assigned a per-category probability based on alignment with human transcripts, providing resilience to high ASR error rates (∼40%) (Schloss et al., 2020).
• LLMs and LoRA/QLoRA: Recent systems employ parameter-efficient techniques such as Low-Rank Adaptation (LoRA) and Quantized LoRA (QLoRA) for fine-tuning transformer LLMs. Formally, model weights are updated as $W' = W + BA$, where $B$ and $A$ are trainable low-rank matrices (Li et al., 26 Aug 2024, Kamal et al., 12 Jun 2025). Adapter approaches allow for computationally efficient deployment and modular plug-in capabilities.
• Multimodal Input Processing: In dermatology, agents combine sparse clinical text and lesion images. Input features are first converted to captions (e.g., via GPT-3.5), then augmented with context (retrieval-augmented generation), before multimodal transformer processing (such as Vision-LLaMA 3.2), supporting SOAP note generation from combined modalities (Kamal et al., 12 Jun 2025).

3. Evaluation Methodologies and Quality Metrics

The effectiveness and reliability of SOAP Note Agents are measured using a suite of lexical, semantic, and clinical relevance metrics:

Metric Type	Examples	Purpose
Lexical Match	ROUGE-1, 2, L, Lsum	$n$-gram overlap with reference notes
Semantic	BERTScore, BLEURT	Contextual and semantic correspondence
Clinical-Specific	MedConceptEval, ClinicalBERT F1	Domain alignment with gold clinical concepts
Custom/Qualitative	Rubric (clarity, conciseness, relevance, organization)	Clinician-blinded rating (Amenyo et al., 4 Feb 2025)

Novel metrics such as MedConceptEval (measuring cosine similarities using ClinicalBERT between generated sections and curated descriptor banks) and Clinical Coherence Score (quantifying alignment between captioned inputs and generated SOAP sections) extend validation beyond surface similarity to clinical content fidelity (Kamal et al., 12 Jun 2025).

Statistical significance in human-in-the-loop studies is frequently established with ANOVA. For instance, the comparison of mean rubric scores across human and AI-generated SOAP notes yields an $F$-statistic:

$F = \frac{MS_{\text{between}}}{MS_{\text{within}}}$

Results indicate no significant difference (e.g., $F = 0.96, p = 0.45$), suggesting clinical acceptability of AI-generated documentation (Amenyo et al., 4 Feb 2025).

4. Fairness, Specialty-Specific Performance, and Language Variation

Algorithmic fairness is a critical area in the application of SOAP Note Agents, with explicit metrics for group disparities:

• Average Odds Difference (AOD):

$$\mathrm{AOD} = \frac{1}{2}[(\mathrm{FPR_{disadv}} - \mathrm{FPR_{adv}}) + (\mathrm{TPR_{disadv}} - \mathrm{TPR_{adv}})]$$

AOD near zero indicates parity; deviations $< -0.1$ or $> 0.1$ denote systematic group differences.

• Equal Opportunity Ratio (EOR):

$$\mathrm{EOR} = \frac{\mathrm{TPR_{disadv}}}{\mathrm{TPR_{adv}}}$$

EOR indicates true positive rate balance; values outside [0.8, 1.25] imply unequal benefit.

• False Omission Rate Ratio (FORR):

$$\mathrm{FORR} = \frac{\text{FN}/(\text{FN}+\text{TN})_{\text{adv}}}{\text{FN}/(\text{FN}+\text{TN})_{\text{disadv}}}$$

FORR highlights disparities in omission of beneficial information (e.g., Plan sections).

Empirical analysis shows that disparities—particularly in the Plan and Objective sections—are often driven by differences in appointment-type or specialty, with lexical cues (e.g., “blood work”) underrepresented in certain demographic groups’ records (Ferracane et al., 2020). Language analysis using local mutual information metrics reveals that rare or absent section-indicative n-grams can degrade classifier performance, emphasizing the value of stratified data or specialty-informed modeling.

5. Practical Applications, Clinical Impact, and Workflow Integration

SOAP Note Agents have demonstrated several clinically significant benefits:

• Time Efficiency: Automated pipelines for K-SOAP note generation can reduce documentation times from 10–30 minutes per case to substantially lower durations, especially when compared to manual annotation (Li et al., 26 Aug 2024).
• Information Accessibility: The integration of a keyword summary (as in K-SOAP) allows clinicians to rapidly access and review critical symptoms, diagnoses, and their contextual information, thereby improving operational decision-making.
• Consistency and Quality: AI-generated notes exhibit lower standard deviations in blind clinical evaluations, suggesting greater documentation consistency compared to fully human-authored notes (Amenyo et al., 4 Feb 2025). However, human post-editing remains essential for correcting section misallocations or nuanced content errors.
• Scalability: Parameter-efficient tuning (e.g., LoRA, QLoRA) and weakly supervised multimodal training minimize computational and data annotation overhead, enabling broader deployment across different healthcare environments (Li et al., 26 Aug 2024, Kamal et al., 12 Jun 2025).
• Use in Specialty Domains: Custom, domain-specific fine-tuning (e.g., pediatric rehabilitation for KAUWbot) yields outputs more aligned with clinical expectations and practice patterns (Amenyo et al., 4 Feb 2025). Multimodal approaches facilitate SOAP note generation in domains like dermatology, where visual and text data are both integral (Kamal et al., 12 Jun 2025).

6. Limitations, Mitigations, and Future Directions

SOAP Note Agents face limitations pertaining to fairness, data diversity, and specialty generalization:

• Group-level disparities arise from both linguistic variation and specialty-dependent dialogue structures. Mitigation strategies include ongoing fairness metric monitoring, appointment-type stratification, and targeted data augmentation (Ferracane et al., 2020).
• ASR alignment errors and noise can significantly impact downstream classification, necessitating modular label smoothing and robust alignment strategies (Schloss et al., 2020).
• Models are sensitive to lexical cue frequency; rare phrasing or indirect semantic content remains challenging.
• Weak supervision and synthetic augmentation decrease annotation burden, but human-in-the-loop review is critical to ensure clinical safety and relevance (Kamal et al., 12 Jun 2025).
• The integration of opinionated or non-authoritative data in retrieval augmentation may introduce risk; curation remains essential.
• Future directions include extending these frameworks to additional specialties, integrating with electronic health records, iterative human feedback cycles, and developing benchmarks for more complex clinical reasoning (Kamal et al., 12 Jun 2025).

7. Summary Table: Current Approaches and Benchmarks

Study/Paper	Modality	Core Methodology	Clinical Domain	Evaluation Highlights
(Schloss et al., 2020)	Text (ASR/Human)	Hierarchical Encoder–Decoder, LSTM	General Outpatient	Near human F₁ for sectioning
(Ferracane et al., 2020)	Text	Fairness Analysis, LMI	General Outpatient	Detailed disparity metrics
(Li et al., 26 Aug 2024)	Text	LoRA/QLoRA, K-SOAP, LLMs	Simulation	ROUGE, BERTScore, BLEURT
(Amenyo et al., 4 Feb 2025)	Text	Copilot, KAUWbot, Human-in-the-Loop	Pediatric Rehab	ANOVA, clinician rubric
(Kamal et al., 12 Jun 2025)	Text + Image	Multimodal, Retrieval-Augmented, QLoRA	Dermatology	ROUGE, BERT Score, MedConceptEval, CCS

References to Key Research

• "Towards an Automated SOAP Note: Classifying Utterances from Medical Conversations" (Schloss et al., 2020).
• "Towards Fairness in Classifying Medical Conversations into SOAP Sections" (Ferracane et al., 2020).
• "Improving Clinical Note Generation from Complex Doctor-Patient Conversation" (Li et al., 26 Aug 2024).
• "Assessment of AI-Generated Pediatric Rehabilitation SOAP-Note Quality" (Amenyo et al., 4 Feb 2025).
• "Towards Scalable SOAP Note Generation: A Weakly Supervised Multimodal Framework" (Kamal et al., 12 Jun 2025).