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NLP – Key to Effective Clinical Decision Support

May 21, 2021

In the COVID-19 environment, insurance providers are taking the opportunity to strengthen the technological infrastructure needed to meet the imminent challenges to population health management. Discontent with electronic health records (EHRs) persists at an all-time high, as providers lack access to the socioeconomic, behavioral, and environmental data that would aid them in formulating actionable analytics.

The capacity to interpret and derive meaning from narrative text or other unstructured data sources is a significant portion of the current data puzzle, and Natural Language Processing (NLP) lies at the heart of this complicated assignment.

The Core of Natural Language Processing

NLP is the method of using computer algorithms to distinguish key elements in everyday language and deduce meaning from unstructured inputs. In the healthcare industry, NLP has many inherent applications. It can intensify the accuracy of EHRs by translating the unstructured text into standardized data. It can make documentation requirements easier by empowering providers to dictate their notes or generate tailor-made educational materials for ready to discharge patients.

NLP is allowing provider systems to understand spoken or typed remarks and are prompting healthcare organizations to leverage that field. In sectors such as voice-activated assistants and speech recognition programs, NLP creates better experiences by developing patient access to information, and advancing the quality of health records.

Primary NLP Applications
1. Improving Clinical Documentation

Rather than squandering valuable time manually reviewing complex EHRs, NLP practices speech-to-text dictation and formulated data entry to secure crucial data from EHR. This practice enables physicians to concentrate on equipping patients with the inherent care they need. It also ensures that clinical documentation is valid and kept up to date.

2. Accelerating Clinical Trial Matching

Using NLP, healthcare providers can automatically analyze extensive quantities of unstructured clinical and patient data and recognize eligible candidates for clinical trials. Not only does this empower patients to access experimental care that could dramatically enhance their condition and their lives, but it also supports innovation in the medical field.

3. Strengthening Clinical Decisions

NLP can be the front-runner in evaluating and amplifying healthcare quality by measuring physician performance and distinguishing gaps in care delivery. Research indicates artificial intelligence in healthcare can ease the process of physician assessment and automate patient diagnosis. It also decreases the human effort necessitated in carrying out routine assignments such as patient diagnosis.

Challenges of Integrating NLP into Clinical Care

Natural language processing technology is already embedded in products from some electronic health record vendors, but unstructured clinical notes and narrative text still present a major problem for computer scientists.

1. True Reliability and Accuracy

Several problems such as word disambiguation and splintering “doctor speak” can stump even the best of NLP algorithms. Clinical text can often be ungrammatical, consists of telegraphic phrases with insufficient context, along with making substantial use of acronyms and abbreviations, presenting them as highly ambiguous. While the human brain can normally decipher these types of differences by relying on the context of the encompassing words for clues, NLP technology still has a long way to go before it can reach the same fidelity threshold as the standard flesh-and-blood reader.

2. Illustrating Support Data

In addition to the somewhat questionable validity of results, EHR developers face difficulties in figuring out how to present clinical decision support data within the workflow. However, there is no industry standard for creating a support tool that will deliver pertinent, essential information without disordering the patient-provider relationship.

3. Unstructured Data Gaps

Using NLP to fill in the gaps of structured data on the back end is also a big challenge. Inadequate standardization of data elements, insufficient data governance policies, and an almost infinite variation in the design and programming of electronic health records have left NLP developers with a tough job to carry out.

Conclusion

The key to NLP’s success will be to advance algorithms that are accurate, intelligent, and healthcare-specific. If the healthcare industry meets the dual goals of extraction and exhibition, there is no telling what big data doors could be open up. Ultimately, natural language processing tools can bridge the gap between the unending amount of daily data generate and the limited cognitive capacity of the human mind.

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