⚡ Quick Summary
This study utilized natural language processing (NLP) to analyze 18,609 eConsult orders from a single academic medical center, revealing key insights into the common uses of telehealth technology across various specialties. The findings highlight the potential of NLP in extracting valuable information from free text fields in electronic health records.
🔍 Key Details
- 📊 Dataset: 18,609 eConsult orders
- 🧩 Features used: Free text fields from eConsult orders
- ⚙️ Technology: N-gram frequency analysis
- 🏆 Specialties analyzed: 28 subspecialties
🔑 Key Takeaways
- 📊 High volume: eConsult orders ranged from 12 to 3,839 across specialties.
- 💡 Median character length: Clinical questions averaged 189 characters, while specialist responses averaged 1,393 characters.
- 👩⚕️ Specialty insights: The most common clinical question in Endocrinology was “thyroid nodule” (190 occurrences).
- 🏥 Response frequency: The term “ref range” appeared 3,139 times in specialist responses from Endocrinology.
- 🔍 Variability: The frequency of clinical questions varied significantly by specialty.
- 🛠️ Pre-processing needed: Effective NLP insights required pre-processing of the text data.
- 🌍 Study conducted: At a single academic medical center.
📚 Background
The integration of telehealth technologies has transformed the way healthcare is delivered, particularly in primary care settings. eConsults allow primary care providers to seek specialist advice efficiently, but the free text fields within these orders often contain rich, descriptive information that remains underutilized. By applying natural language processing, we can unlock valuable insights from these text fields, enhancing our understanding of common clinical queries and improving healthcare delivery.
🗒️ Study
This study focused on analyzing eConsult orders placed within a single academic medical center. By extracting text data from the electronic health record, researchers employed N-gram frequency analysis to examine the content of clinical questions and specialist responses. The goal was to identify common themes and trends across various subspecialties, providing a clearer picture of how eConsults are utilized in practice.
📈 Results
The analysis revealed a total of 18,609 eConsult orders, with significant variability in order volume across the 28 subspecialties. The median character length for clinical questions was found to be 189 characters, while specialist responses averaged 1,393 characters. Notably, the term “thyroid nodule” was the most frequently mentioned clinical question in Endocrinology, while “ref range” dominated specialist responses in the same specialty, indicating a focused area of inquiry.
🌍 Impact and Implications
The findings from this study underscore the potential of natural language processing in enhancing our understanding of telehealth applications. By extracting and analyzing data from eConsult orders, healthcare providers and administrators can gain valuable insights into common clinical queries, ultimately leading to improved patient care and more efficient use of specialist resources. This approach could pave the way for more data-driven decision-making in healthcare settings.
🔮 Conclusion
This study highlights the transformative potential of natural language processing in the realm of telehealth. By effectively analyzing eConsult orders, we can derive meaningful insights that inform clinical practice and administrative strategies. As telehealth continues to evolve, leveraging technologies like NLP will be crucial in optimizing healthcare delivery and enhancing patient outcomes. Further research in this area is encouraged to explore the full capabilities of NLP in healthcare.
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Natural language processing to describe primary care requests for eConsult specialty care: A simple and practical application.
Abstract
Objective: Free text fields embedded within electronic consultation (eConsult) orders serve as rich sources of descriptive information regarding common uses of this novel telehealth technology. Simple text mining and language processing may efficiently extract key insights that help inform providers and administrators. Methods: Text data from eConsult orders placed within a single academic medical center were extracted from the electronic health record and examined. N-gram frequencies were used to describe the content of eConsult clinical questions and care recommendations. Results: 18,609 eConsults were ordered, with volumes ranging from 12 to 3839 orders across 28 subspecialties. Median character length for the clinical question was 189 and 1393 for specialist response text. Frequency count for top bigram varied greatly by specialty, with a high of 190 (“thyroid nodule”) in Endocrinology and a low of 6 (“shoulder pain”) in Orthopedics for clinical questions, and a high of 3139 (“ref range”) in Endocrinology and a low of 6 (“surgical oncology”) in Medical Oncology for specialist response. Discussion: Descriptive word sequences from NLP may provide limited insight into common use cases for eConsult across many subspecialties, though pre-processing was required to generate meaningful results.
Author: [‘Grim S’, ‘Fuhlbrigge A’, ‘Thomas JF’, ‘Kessler R’]
Journal: Health Informatics J
Citation: Grim S, et al. Natural language processing to describe primary care requests for eConsult specialty care: A simple and practical application. Natural language processing to describe primary care requests for eConsult specialty care: A simple and practical application. 2025; 31:14604582251345319. doi: 10.1177/14604582251345319