How can Natural Language Processing help doctors? 7 ways NLP supports providers

For doctors, natural language processing (NLP) can optimize costs and help provide better quality care. It helps physicians by automating other manual processes and gives them more time with patients by freeing them from labor-intensive requirements of extracting and inputting data into EHRs to inform claims management, care delivery, quality reporting, and other key aspects of treatment.

NLP technologies recognize speech patterns and process verbal communications to help clinicians gain access to structured data generated at the point of care. The collection of information is available for later use, such as extracting relevant data from a large database to inform analytics for value-based care (VBC) or public health management (PHM) initiatives.

Physicians are able to employ these solutions at the point of care to capture patient data during a care encounter. This lets them engage and remain engaged instead of trying to capture data alongside the patient and, later, dictate patient notes into the patient health record.

In addition, NLP technology detects errors and corrects them before passing the transcription on for proofing.

Here are 7 ways doctors leverage natural language processing

To reduce subjectivity

Healthcare organizations can reduce subjectivity in decision-making by integrating data mining. This provides practical medical knowledge such as diagnoses and symptom checking.

For clinical trial design

Some healthcare organizations use NLP to recognize patients best suited for clinical trials. The newest NLP technology can automate trial matching to make the recruitment process easier. In fact, a few organizations have devoted massive resources from NLP to support oncology trials.

For prior authorizations

Obtaining prior authorizations from a health plan can increase a practice’s overhead and delay care delivery. Thanks to NLP, the question of whether payers will approve compensation may soon be a thing of the past. NLP can be used in the payer’s network to authorize procedures and medications promptly.

To support Hierarchical Condition Category coding

A risk adjustment model known as Hierarchical Condition Category was designed to predict future patient care costs. NLP can also assign patients a risk factor to predict healthcare costs.

HCCs are increasingly vital to provider’s revenue cycles. For more than 20 years, they have been used in various forms across all government programs (Medicare Advantage, Commercial ACA, and managed Medicaid).

Risk adjustment is primarily focused on the financial aspect of member care – ensuring health plans receive appropriate funding to care for their members based on accurate documentation of member conditions. Quality rating programs focus on the clinical aspect of member care – measuring health plan performance on a wide range of quality measures and incentivizing accountability and care quality improvement through consumer transparency and financial bonus arrangements.

The distinct purpose and goals of these programs lend to the siloed organizational structure frequently seen today. Different objectives result in separate and unique data needs to drive these programs, with different annual timelines tied to data collection to support federal reporting requirements. Add in ongoing challenges tied to organizational culture, healthcare interoperability, regulatory hurdles, health information exchange (HIE) data limitations, and provider hesitancy around data sharing – and it’s no wonder most health plans continue to operate separate and distinct quality and risk programs.

However, NLP can play a significant role in HCC processing, helping health plans overcome their quality and risk silos, which benefits their provider networks and the patients they serve.

To validate diagnostics and improve care delivery

Physicians can validate diagnostic potential using NLP, including for Alzheimer’s, dementia, and psychological and cardiovascular disorders. Eventually, NLP will significantly improve care delivery, physician decision-making, and patient health outcomes. For example, using speech-to-text and extracting critical data from EHR at the end point of care is hugely beneficial.

NLP ensures that clinical documentation is accurate and that physicians can focus on providing the best patient care. There is no time like the present to implement a great NLP program to improve patient outcomes, data gathering, and diagnostics.

To automate EHR data collection

Many physicians are switching from written notes to voice notes because NLP can easily and quickly analyze patient information and add it to their EHR. This allows physicians to commit more time to patient care. NLP can also extract details from physicians’ letters and reports to ensure that all critical information has been appropriately uploaded to a patient’s health profile.

To power predictive analytics

NLP can assist with predictive analysis by going through vast numbers of digital medical records to identify racial groups, geographic regions, and other population statistics, each with its health discrepancies. It also can pinpoint patient outcomes.

Using analytics alongside NLP can help identify high-risk patients and improve the diagnostic processes. Likewise, when encountering unstructured data, NLP can detect and prevent hospital-acquired infections. An NLP program has also been used to identify hospitalized patients at risk of heart failure.

Specialized NLP predictive algorithms can predict specific conditions, too, which adds to the potential for considerable potential for improving care delivery and streamlining workflow.

How can doctors best leverage NLP?

Doctors can improve interactions with patients to bridge the gap between patient understanding and complex medical terms. Most patients need help understanding the data within their health records to make health decisions. Machine learning solutions such as NLP, can change that.

For doctors, NLP empowers them to more effectively improve care quality and evaluate their performance. By leveraging the data provided by NLP and sophisticated algorithms, they can identify gaps in care, potential errors, and find the right tools for patients with complex issues.

NLP can accelerate healthcare value. For more information on how this technology is driving improvements in health outcomes and economics, check out this blog on using natural language processing.

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