Generating Faithful and Complete Summaries of Patient Hospital Stays from Electronic Health Records

Основные понятия

Generating hospital-course summaries that accurately and comprehensively represent the patient's medical history and care during a hospital stay is critical for continuity of care and patient safety, but is a challenging task due to the large volume of documentation in electronic health records.

Аннотация

The content describes the challenge of generating faithful and complete hospital-course summaries from the electronic health record (EHR). The EHR contains critical information about a patient's medical history and healthcare interactions, but the large volume of documentation, especially during a hospital stay, makes it difficult for clinicians to synthesize this information.

The author proposes using automatic summarization to support clinicians in making sense of a patient's longitudinal record and specific hospital visits. The focus is on hospital-course summarization, which involves faithfully and concisely summarizing the EHR documentation for a patient's inpatient visit.

The key challenges include:

• Identifying relevant problems, symptoms, procedures, medications, and observations and linking them together while adhering to temporal and problem-specific constraints
• Reviewing a high number of clinical notes and reports entered during the patient stay and synthesizing them into a concise summary paragraph
• Ensuring the summary is faithful to the actual hospital course and does not contain errors or omissions that could impact patient care

The author outlines the structure of the thesis, which addresses these challenges through data analysis, improving faithfulness of summaries, developing protocols for measuring faithfulness, and generating complete and grounded summaries using large language models.

Статистика

"The rapid adoption of Electronic Health Records (EHRs)–electronic versions of a patient's medical history–has been instrumental in streamlining administrative tasks, increasing transparency, and enabling continuity of care across providers."
"Time spent maintaining and making sense of a patient's electronic record is a leading cause of burnout."
"Given the documentation authored throughout a patient's hospitalization, hospital-course summarization requires generating a lengthy paragraph that tells the story of the patient admission."

Цитаты

"The presence of highly abstractive, entity dense references, coupled with the high stakes nature of text generation in a clinical setting, motivates us to focus on faithfulness and adequate coverage of salient medical entities."
"Automatically generated summaries can exhibit many errors, including incorrect claims and critical omissions, despite being highly extractive."
"Fine-tuned LLMs (Mistral (Jiang et al. 2023) and Zephyr (Tunstall et al. 2023)) are highly prone to entity hallucinations and cover fewer salient entities."

Ключевые выводы из

Generating Faithful and Complete Hospital-Course Summaries from the Electronic Health Record

by Griffin Adam... в arxiv.org 04-02-2024

https://arxiv.org/pdf/2404.01189.pdf

Дополнительные вопросы

How can the hospital-course summarization task be extended to support longitudinal patient care beyond a single hospital admission?

In order to extend hospital-course summarization to support longitudinal patient care, several key strategies can be implemented:

Incorporating Data from Multiple Admissions: Hospital-course summarization can be expanded to include information from multiple hospital admissions for the same patient. By aggregating data from different visits, clinicians can gain a comprehensive view of the patient's medical history, treatment outcomes, and disease progression over time.

Integration with Outpatient Records: Hospital-course summaries can be linked with outpatient records, including primary care visits, specialist consultations, and diagnostic tests conducted outside the hospital setting. This integration provides a holistic view of the patient's healthcare journey and enables better care coordination.

Longitudinal Analysis Tools: Implementing tools that analyze trends and patterns in the hospital-course summaries over time can help identify changes in the patient's health status, response to treatments, and risk factors. These tools can support proactive interventions and personalized care plans.

Patient Engagement and Education: Hospital-course summaries can be used as educational tools for patients, providing them with a clear understanding of their medical history, treatment plans, and follow-up care instructions. Engaging patients in their care can improve adherence to treatment and promote better health outcomes.

Interoperability and Data Sharing: Ensuring interoperability between different healthcare systems and data sharing platforms is essential for longitudinal patient care. Hospital-course summaries should be easily accessible to all healthcare providers involved in the patient's care to facilitate seamless information exchange.

By implementing these strategies, hospital-course summarization can evolve into a comprehensive tool for supporting longitudinal patient care, enabling healthcare providers to deliver personalized, evidence-based care across multiple healthcare settings.

What are the potential ethical and privacy concerns around deploying automated hospital-course summarization systems in clinical practice, and how can these be addressed?

The deployment of automated hospital-course summarization systems in clinical practice raises several ethical and privacy concerns that need to be carefully addressed:

Patient Privacy: Automated systems processing sensitive patient data in hospital-course summaries must adhere to strict privacy regulations, such as HIPAA in the United States. Ensuring data security, encryption, and access controls is crucial to protect patient confidentiality.

Data Accuracy and Bias: Automated systems may introduce errors or biases in summarizing patient information, leading to incorrect clinical decisions. Regular validation, auditing, and transparency in the system's decision-making process can help mitigate these risks.

Informed Consent: Patients should be informed about the use of automated systems in generating hospital-course summaries and have the opportunity to consent to the processing of their data. Transparent communication about how their information is used is essential for building trust.

Algorithmic Fairness: Ensuring that automated systems do not perpetuate biases based on factors such as race, gender, or socioeconomic status is critical. Regular bias assessments and algorithmic audits can help identify and address any discriminatory patterns in the summaries.

Clinician Oversight: While automated systems can assist clinicians in summarizing patient data, the final decision-making should always rest with the healthcare provider. Clinicians should review and validate the summaries to ensure accuracy and appropriateness of the information.

Addressing these ethical and privacy concerns requires a multi-stakeholder approach involving healthcare providers, data scientists, policymakers, and patients. Collaboration to establish clear guidelines, robust data governance frameworks, and ongoing monitoring mechanisms can help ensure the responsible deployment of automated hospital-course summarization systems in clinical practice.

What insights from the hospital-course summarization domain could be applied to improve faithfulness and completeness in other specialized text generation tasks, such as scientific paper summarization or legal document summarization?

Insights from the hospital-course summarization domain can be leveraged to enhance faithfulness and completeness in other specialized text generation tasks, such as scientific paper summarization or legal document summarization:

Entity Recognition and Linking: Hospital-course summarization often involves identifying and linking medical entities across multiple documents. This approach can be applied to scientific paper summarization to ensure accurate representation of key concepts and findings. Similarly, in legal document summarization, recognizing and linking legal terms and references can improve the fidelity of the summaries.

Temporal and Contextual Understanding: Hospital-course summaries require capturing temporal relationships and contextual nuances to provide a coherent narrative. This aspect can be beneficial in scientific paper summarization to highlight the evolution of research findings over time. In legal document summarization, understanding the chronological sequence of events and legal arguments can enhance the completeness of the summaries.

Fine-Grained Error Analysis: Conducting detailed error analysis, as done in hospital-course summarization, can help identify common pitfalls and areas of improvement in other text generation tasks. By annotating system errors, refining evaluation metrics, and training models on error-specific data, the faithfulness and completeness of summaries can be enhanced across domains.

Integration of Domain Knowledge: Incorporating domain-specific knowledge bases and ontologies, as seen in hospital-course summarization, can enrich the understanding of complex terminology and relationships in scientific and legal texts. Utilizing domain-specific resources can improve the accuracy and relevance of the generated summaries.

By transferring methodologies and best practices from the hospital-course summarization domain to other specialized text generation tasks, researchers and practitioners can advance the quality and reliability of automated summarization systems in diverse domains.