Fast, Structured Clinical Documentation via Contextual Autocomplete (2007.15153v1)

Abstract: We present a system that uses a learned autocompletion mechanism to facilitate rapid creation of semi-structured clinical documentation. We dynamically suggest relevant clinical concepts as a doctor drafts a note by leveraging features from both unstructured and structured medical data. By constraining our architecture to shallow neural networks, we are able to make these suggestions in real time. Furthermore, as our algorithm is used to write a note, we can automatically annotate the documentation with clean labels of clinical concepts drawn from medical vocabularies, making notes more structured and readable for physicians, patients, and future algorithms. To our knowledge, this system is the only machine learning-based documentation utility for clinical notes deployed in a live hospital setting, and it reduces keystroke burden of clinical concepts by 67% in real environments.

• The paper presents a hierarchical language model that uses contextual autocomplete to reduce keystroke burden by 67% in real-time clinical environments.
• It employs a dual-branched shallow neural network combined with TF-IDF feature extraction from triage texts and EHR data for rapid clinical concept suggestions.
• Live evaluations demonstrate that the system enhances note clarity and reduces clinician workload, signaling a significant step forward in EHR optimization.

Fast, Structured Clinical Documentation via Contextual Autocomplete: An Expert Overview

The paper under discussion introduces an innovative system for enhancing clinical documentation through a mechanism referred to as contextual autocomplete. This system leverages a combination of structured and unstructured data within electronic health records (EHRs) to deliver contextually relevant clinical concept suggestions in real time. These suggestions aim to streamline the documentation process for clinicians, reducing the time burden and enhancing note readability. Notably, this system achieves a 67% reduction in keystroke burden in live hospital settings, affirming its utility in real-world clinical environments.

Core Contributions

The principal contribution of this paper lies in the introduction of a hierarchical LLM tailored to operate within the noisy and ambiguous domain of clinical notes. This model is uniquely constrained to shallow neural networks, a design choice that facilitates real-time performance crucial for dynamic clinical settings. By suggesting relevant clinical concepts drawn from standardized medical vocabularies during the note-taking process, the system enhances the structure and clarity of clinical documentation.

Furthermore, the paper emphasizes the system’s deployment in a live hospital setting, potentially marking it as the first ML-based clinical documentation utility actively used in such an environment.

Methodology

The system utilizes several key data elements available prior to clinician-patient interaction, such as the patient's triage assessment, vitals, and past EHR notes. These elements form the basis for feature extraction, where a term frequency-inverse document frequency (TF-IDF) representation for textual data and a categorical representation for vitals are used.

For autocompletion, a hierarchical, human-in-the-loop LLM is created to suggest clinical concepts categorized into conditions, symptoms, labs, and medications. Specific models are developed for each category, with the dual-branched neural network proving most effective for conditions. This network combines information from triage text and EHR to predict relevant clinical concepts rapidly.

Analytical Results

Retrospective evaluations demonstrate that the contextual autocomplete model outperforms traditional spell-based and frequency-based methods, particularly for conditions where the space of potential entries is large. The system is particularly effective for concepts frequently appearing mid-distribution in terms of patient record prevalence. Additionally, live evaluations reveal a substantial reduction in documentation workload, reaffirming the model's value in practical settings.

Implications and Speculative Outlook

This research holds significant implications for clinical workflows and EHR design. By reducing documentation time and enhancing the clarity of clinical notes, it could alleviate clinician workload and mitigate burnout, a critical issue in healthcare environments burdened by extensive EHR demands.

From a theoretical perspective, this approach presents novel insights into integrating deep learning methodologies within clinical settings. It highlights the potential for shallow networks to achieve significant impact when designed with domain-specific constraints and considerations. Moreover, the tagging of clinical concepts during documentation could facilitate new avenues for developing large-scale annotated datasets, crucial for advancing machine learning applications in healthcare.

Future developments could focus on enhancing the system's semantic capabilities, such as incorporating negation scopes or temporal information, and refining the autocompletion contexts based on real-time updates. Additionally, exploring more sophisticated sequential models for type and scope prediction, balanced against system latency requirements, could further elevate the utility of such systems in clinical settings.

In conclusion, this paper contributes a practical and theoretically sound framework for improving clinical documentation through machine learning, with substantial implications for both immediate healthcare practice and longer-term AI developments in medical informatics.