The Most Critical NLP Use Cases in Healthcare

According to a MarketsandMarkets report, the global market for natural language processing (NLP) in healthcare will reach $7.2 billion by 2027. Such growth is driven by the technology’s widespread adoption across multiple healthcare applications, primarily for digitizing extensive medical documentation and innovating clinical practices. Let’s check these use cases!?

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Clinical Documents

NLP reduces the manual effort involved in EHR management, providing clinicians more time for patient care. Solutions, like Nuance and M*Modal , integrate team collaboration and advanced speech recognition to facilitate real-time structured data capture at the point of care. Moreover, NLP tools transform healthcare by enabling the analysis of extensive public datasets and social media to assess social determinants of health (SDOH) and policy effectiveness.

Speech Recognition

NLP has enhanced speech recognition capabilities, enabling direct transcription of doctor’s notes into EHRs. This tech captures spoken details at the point of care and employs back-end systems to refine these inputs by correcting errors before they are finalized. Despite a saturated market, innovative startups are improving speech recognition with deep learning algorithms.

Computer-Assisted Coding (CAC)

CAC systems, powered by NLP, streamline the coding process by capturing comprehensive details from medical procedures and treatments for billing purposes. While these tools improve claim accuracy, their continuous maintenance is a shared responsibility due to the evolving nature of medical billing codes and regulations, making your role crucial.

Data Collection Research

Combining data mining with healthcare techs and predictive analytics minimizes subjective decision-making by providing actionable insights from vast medical datasets. This continuous knowledge extraction cycle helps organizations refine their patient care delivery strategies.

Registry Reports Automation

NLP facilitates the automation of registry reports by ensuring the accurate and reliable extraction and storage of crucial medical data that is not typically saved as discrete values. Implementing that requires precise identification and documentation of every relevant data point to be effectively used in healthcare analytics.

Clinical Decision Support (CDS)

NLP is improving clinical decision support systems (CDSS) by ensuring more precise and efficient tools for diagnosing and managing medical conditions. Techs like those developed by Isabel Healthcare leverage NLP to enhance infection detection and support diagnostic accuracy.

Clinical Trial Matching

The tech increases the efficiency of clinical trials by optimizing patient-matching processes, enhancing recruitment and overall trial management. Thus, modern companies like Inspirata demonstrate the great benefits of using NLP to support complex trials, mainly in oncology.

Prior Authorization

Automating prior authorization processes through NLP reduces administrative burdens and expedites patient care. Systems developed by companies like Elevance Health can streamline that across payer networks, improving the speed of service delivery and reimbursement.

AI-Based Chatbots and Virtual Assistants

NLP is increasingly used to create AI chatbots and virtual assistants that reshape clinical documentation and patient interaction. Techs like Woebot can facilitate more natural patient communication via Facebook Messenger and support efficient clinical data management.

Risk Adjustments with Hierarchical Condition Categories (HCC)

NLP supports risk adjustment models like the Hierarchical Condition Category by providing accurate risk score assignments, which are crucial for forecasting healthcare costs in value-based payment models.

Computational Phenotyping

Advances in NLP allow for more accurate patient phenotyping in clinical trials. That aids in diagnosing and managing complex health conditions through detailed analysis of speech patterns and other diagnostic markers. Companies like BeyondVerbal are collaborating with institutions such as the Mayo Clinic to identify vocal biomarkers for coronary artery disease.?

Review Management

NLP tools are vital for managing and analyzing patient reviews. They provide insights into patient satisfaction and compliance with health regulations and excel at interpreting the reviews’ sentiment and context. That helps medical providers better understand patient perceptions.

Dictation and Electronic Medical Records (EMRs)

Transitioning from manual to voice-recorded documentation, NLP systems integrate spoken notes into EMRs efficiently. That allows medical providers to spend more time on patient care. NLP can parse such voice notes and extract pertinent data from different clinical documents, guaranteeing detailed and up-to-date patient health records.

Root Cause Analysis via Predictive Analytics

Predictive analytics powered by NLP help uncover the root causes of health disparities by analyzing extensive medical records to identify demographic and geographic trends. Thus, the technology facilitates the discovery of socio-cultural factors that affect health and properly explains fundamental causes that provoke patient issues and adverse outcomes.?

Are you curious about leveraging NLP techs to improve clinical decision-making processes? Write to me at [email protected] , and we will gladly assist you with your healthcare project.

Finally, NLP is reshaping care delivery by enabling faster, more accurate medical information processing, aligning with clinical needs and regulatory requirements. Since the tech continues to advance, its integration into medical practices becomes crucial for enhancing patient outcomes.