Natural language processing (NLP), a subfield of artificial intelligence (AI), allows computers to comprehend and process human language. In healthcare, this technology is often employed to extract insights from textual data such as clinical notes, patient records, and research papers. Those insights, in turn, can be leveraged for a variety of activities like automated clinical coding, document summarization, and enriching real-world evidence data sets.
Recognizing the numerous capabilities and benefits of NLP in healthcare is a fairly straightforward exercise. Extracting clinical and biomedical concepts from free text makes that information easier to search, analyze, and share. And mining data from disparate electronic health record (EHR) and health IT systems can help create a unified “language” to facilitate greater interoperability and data analysis. But knowing which NLP solution – and which vendor – is the best fit for your organization may not be so simple.
In The data quality toolkit: Considerations for clean, complete, and usable data, we provide a checklist of factors to weigh when exploring NLP options. Understanding your goals and priorities, how NLP models are trained, specifications for deployment and integration, and the potential for scalability are just a few of the details to evaluate before signing on the dotted line.
If you see NLP in your organization’s future, scroll to check out the checklist below. But if other technologies like value set management and data normalization tools are of interest (and spoiler alert…they probably should be), click the button to download the eBook now.
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Data quality toolkit: Considerations for clean, complete, and usable data
Without the right tools, accurate and reliable clinical data can be elusive.
Before you invest: An NLP in healthcare checklist
- Define needs and goals: Clearly identify the specific clinical or operational challenges you aim to address with NLP and align those goals with the capabilities of different NLP solutions.
- Model selection and validation: Choose NLP models suited for specific use cases and rigorously validate their performance for accuracy and effectiveness.
- Technical deployment, hosting, and integration: Understand integration requirements (API vs. batch uploads; self-hosted vs. 3rd party hosted) and how the technology does or does not supplement existing HIT infrastructure and workflows.
- NLP model training: Seek out a vendor that provides comprehensive model training with robust clinical terminologies and is supported by clinical informaticists, data scientists, and other domain experts.
- Privacy and bias: Patient privacy is paramount, so prioritize solutions that comply with relevant regulations and ethical guidelines. Model performance should also be evaluated across diverse patient populations and settings to avoid unwanted biases.
- Transparency: Choose solutions that provide clear explanations for their predictions, helping clinicians to understand and trust the reasoning behind the output.
- Cost and return on investment (ROI): Conduct a thorough cost-benefit analysis, considering the potential impact on outcomes, operational efficiency, and financial performance. Look for solutions that offer long-term value and a clear path to ROI.
- Usability: NLP solutions should complement existing workflows and minimize disruption. Seek out user-friendly interfaces and intuitive design – they are essential for buy-in and successful implementation.
- Infrastructure and resources: Assess hardware, software, and computational resources needed to support NLP applications and consider cloud-based solutions for scalability and cost savings.
