The What, When, Where and How of Natural Language Processing
There’s a mystique that surrounds natural language processing (NLP) technology, regarding how it works, and what it can and cannot do. Although the healthcare industry is buzzing with talk of NLP, discussions often refer to different technologies – whether speech recognition, computer-assisted coding (CAC), or analytics.So, what is NLP and how can healthcare organizations leverage its capabilities? The following information defines NLP, how it can empower downstream applications, and how healthcare organizations can leverage NLP technology to reduce expensive manual workflow, improve analytics capabilities, and increase revenue.
What is NLP, and What Technologies are Confused for NLP?
NLP is an enabling technology that allows computers to derive meaning from human, or natural language input. Text passes into an NLP system and coded features are returned. For example, clinical documentation from a patient encounter that is analyzed by NLP technology may produce the following: Clinical Documentation NLP Analysis
The patient has a fracture of the left femur with no underlying arterial injury. Pain was controlled with 5 mg of Morphine iv.
Problem:
primaryTerm: fracture bodyLocation: femur bodySide: left
ICD-9-: 821.0 (fracture of unspecified part of femur) ICD-10: S72-92XA (fracture of left femur)
SNOMED: 71620000 (fracture of femur) Problem:
primaryTerm: injury bodyLocation: artery
notExperiencedIndicator: true (negated) Medication:
primaryTerm: Morphine dose: 5 mg
route: iv
RxNorm: 894807
NLP technology is commonly used to empower downstream applications, including:
Computer Assisted Coding (CAC) – NLP is used to “read” and analyze clinical documentation to recommend codes, such as ICD-9 or ICD-10, that may feed a CAC billing application
Quality Measures – NLP capabilities can be used to classify a patient according to applicable measures, such as poorly controlled diabetes mellitus, to feed a quality reporting tool
Analytics – NLP may be used to populate a data warehouse and feed analytics software to provide descriptive or predictive modeling, such as the likelihood of a patient being readmitted within 30 days of discharge
Speech Recognition – NLP can be used to empower voice-based data entry, working closely with voice recognition software to translate converted text into ideas by examining context, patterns and phrases.
The use of NLP is unrelated to input technology or format. In today’s healthcare settings, most input is through an electronic health record (EHR), with the information in either free-text or template format. Some information flows through a health information exchange (HIE) or from a data warehouse. Some information is captured through dictation, of which a small portion is automated speech recognition. All these formats work well as input for NLP.
Content that is not electronic, such as scanned documents, handwritten notes or PDFs without embedded text, does not contain machine readable information and is not appropriate input for NLP. Who Needs NLP?
Organizations that are working with unstructured data can benefit from NLP to reduce manual workflow and empower more robust use of data. These include software vendors providing high-end applications that use, or want to use, unstructured data to support automated workflow. It also includes healthcare organizations with a desire to implement best-of-breed data architecture that links EHR systems to a data warehouse for deep analytics capabilities that leverage full clinical data.
Conversely, there are organizations that may not benefit from NLP, such as software vendors with applications that rely exclusively on manual coding, such as using dropdown menus, textboxes and checkboxes. In addition, most modern EHRs, billing software systems and analytic solutions require primarily manual processes (although solutions that leverage automated workflow have been far more successful in the market in recent years).
In between the two sets of organizations is a third type – organizations that may think they want NLP, but more likely want an NLP-enabled application. For example:
Healthcare organizations that desire an automated billing workflow, actually need an NLP-enabled CAC solution
Healthcare organizations that want search and query capabilities on full data sets, actually need a data warehouse with NLP-enabled unstructured data capture
Physicians desiring voice-based data input, actually need a dictation service or an automated speech recognition solution
How Can an NLP Solution be Evaluated?
There are numerous components to an NLP solution that impact functionality, performance and usability. The core capabilities to evaluate include:
Criteria What to Evaluate
Breadth of clinical specialties From primary care to other specialties and sub-specialties
Speed and performance Is output returned quickly, or is there substantial wait time involved? Depth of coding Which terminologies are mapped, such as 9, 10 CM and
ICD-10 PCS, SNOMED CT, LOINC, CPT, RxNorm, etc.? Quantity of output Problems, findings, medications, labs, procedures, etc.
Quality of output Accuracy, sensitivity/specificity, or precision/recall Ease of integration Clinical modeling of data for retrieval, SDK, API
Scalability Deployment model, speed of return, ability to scale throughput Flexibility of architecture Ability and speed in addressing errors and enhancements Security HIPAA compliance and other intrusion preventions
What are the Best Practices for Evaluating NLP Solutions for Your Use Case?
Reviewing peer-reviewed journals is a good place to begin evaluation of NLP solutions. Most high-end commercial systems can provide references to independent academic groups that offer non-biased insights into the solution. Also, white papers and other literature on the systems are typically available for review. Most importantly, organizations should request access to the vendor’s solution to support the testing of their organization’s data and the evaluation of output for their specific use case. As with any system evaluation, there are “red flags” that should alert organizations to potential short-comings. These include companies that provide accuracy statistics in their own white papers, but have no peer-reviewed independent literature to verify the claims. Also, beware of companies that only offer demonstration systems or “slideware” for evaluation. Finally, avoid companies that will not allow organizations to test the system using their own data with their own access credentials. Vendors that request organizations send them test data so they can process it – as opposed to letting the organization process the data using the system directly – are potentially hiding serious limitations.
Who are the NLP Industry Leaders and Why Aren’t There More?
Healthcare data transformation breaks into three categories, which can be confusing to the customer. These include NLP (understanding of human language), terminology mediation (mapping of discrete data terms), and health information exchange (sharing of machine-consumable data). Confusion arises because these functions are complementary in certain use cases, and industry leaders often work together. An NLP company should not be expected to perform HIE, nor should a terminology mediation company be expected to perform NLP.
Category Function Leaders
NLP Parsing and modeling of narrative text
Example: Patient describes a worsening diabetic ulcer on the left leg, which may become:
Problem: primaryTerm diabetic ulcer bodyLocation: leg
bodySide: left
temporalStatus: worsening
Leading NLP companies will also provide codes via terminology mediation, such as:
ICD-9 code: 250.8 (diabetes with other specified manifestation)
ICD-10 code: E11.622 (diabetes with skin ulcer)
SNOMED code: 422183001 (diabetes with
Health Fidelity Nuance M*Modal
skin ulcer)
IMO code: 716104 (diabetes with ulcer of leg)
Terminology mediation
Maps a discrete concept to a code
Example: Left leg ulcer becomes ICD-9 707.1
Intelligent Medical Objects (most commercial
implementations) Health Language Apelon
HIE Transfers data in a usable format Example: Structured continuity of care document (CCD)
Medicity
Optum (through its acquisition of Axolotl) Orion
dbMotion
There are few companies within the NLP marketplace because the technology typically takes 10 to 20 years to develop. The companies must have an extensive knowledge database, disambiguation
capabilities, negation capabilities, data modeling, and terminology mapping. All of this requires detailed curation to optimize. Healthcare language is complex and incorporates inferred clinical knowledge, making it poorly suited to a machine-learned or statistical approach.
Distinguishing Traits of Leading NLP Companies Health Fidelity
Product: REVEAL
Largest breadth of clinical specialties and depth of
terminologies (ICD-9, ICD-10, SNOWMED, RxNorm, LOINC, CPT-4)
Leading modeling for rapid integration and tooling for scalability
Robust data warehouse integration Nuance
Product: Clinical Language Understanding (CLU)
Industry-leading voice capabilities with excellent NLP integration
Extensive commercial implementations M*Modal
Product: Fluency
Excellent voice capabilities with strong NLP integration Robust EHR integration
Due to the required financial investment, some organizations have elected to grow their own use-case specific systems, using NLP for a narrow clinical domain, a narrow terminology set, and a single-use case. This approach is often used for specialty specific CAC solutions. These companies can have excellent targeted products, such as Optum A-Life Medical LifeCode and 3M CodeRyte, both serving the CAC market and confined to specific specialties.
How Can NLP be Leveraged Today?
Healthcare is data intensive, from both clinical and business perspectives. The industry’s transition to electronic data collection and storage in recent years has multiplied the information stores that are available. Although data volumes have increased, up to 80 percent of it remains unusable because it is unstructured, meaning that it is in a format that cannot be easily searched or accessed electronically. Without the right tools, combing through the data and extracting meaning to make decisions must be done manually, which is expensive, limiting, and error prone.
Healthcare organizations that desire to improve clinical quality and leap ahead of competitors can employ NLP in numerous ways, starting with engaging a vendor with robust software, talent, and data integration capabilities to empower applications with unstructured data. Also, organizations desiring robust data capabilities and an expansible platform can create a best-of-breed data architecture that incorporates:
Structured and unstructured data, which feed:
o Data integration, identity resolution, terminology mediation, and NLP, which feed: Data warehouses, data marts, mining and analytics, which feed:
Financial, clinical, and operational applications
NLP can be leveraged to drive improvements in the three main components of today’s healthcare system:
Financial – The rapidly approaching transition to the ICD-10 code set is an ideal situation to incorporate CAC solutions to improve coding compliance and the specificity needed for quality measurements. Additionally, NLP-empowered solutions can help automate data extraction
processes used for financial auditing and revenue cycle analytics. As coders are learning in revenue cycle management, effective measurements don't necessarily require manual generation of more data. Instead, they require better use of the data that already exists.
Clinical – With improved tracking of clinical quality measures (CQMs), the healthcare industry has numerous opportunities to improve outcomes while decreasing healthcare costs — effectively elevating the quality of care that is delivered. Unfortunately, the leading U.S. hospitals are
addressing only about 15 CQMs (e.g., hypertension, diabetes, heart disease, specific types of cancer, etc.) out of nearly 600 industry recognized CQMs. There is clearly an opportunity to support quality measurements and quality improvement through automated, scalable processes with clear audit trails.
Operational – NLP provides the technology for organizations to engage in descriptive and predictive modeling, whether it’s used to assess population health, or model new approaches and care
management strategies that address chronic conditions. The widespread changes that will be enacted in 2014 under the Affordable Care Act will require healthcare organizations to better analyze the influx of patients entering the marketplace and understand who will require or benefit from additional resources – both of which are ideal instances for NLP usage. Additionally, NLP can help with the gathering and analysis of data used to measure productivity among clinicians and non-clinical staff.
The future of healthcare is a data-driven environment where data for hundreds of quality measures and key performance indicators are automatically extracted and analyzed, rather than relying on manual processes. NLP is a key enabling technology to drive the industry’s transformation.
Coding for billing and coding for quality will ultimately require a common infrastructure, which will include the use of NLP and expert human intervention to extract meaning and value from all the data feeding analytic solutions used in care quality measurements. Leveraging NLP technology can empower the healthcare industry to deliver higher quality care that is more personalized, more efficient, and more cost effective.
About Health Fidelity
Health Fidelity, Inc., based in Palo Alto, Calif., is a healthcare big data company providing industry leading cloud-based healthcare natural language processing (NLP) and unstructured data management solutions. Leveraging the longest standing clinical NLP technology with the most peer-reviewed literature and scientific citations, the company’s REVEAL product is the world’s most accurate, reliable and studied NLP technology in healthcare. It enables application developers and healthcare
organizations to utilize vast amounts of unstructured clinical data to improve quality and efficiency of the care delivery process. Health Fidelity draws on its deep domain knowledge and understanding of medical language to unlock actionable information from this unstructured data.
For more information about Health Fidelity and its solution partner program, visit
www.healthfidelity.com. For more information about REVEAL, please contact Health Fidelity at [email protected].
Health Fidelity and REVEAL are trademarks of Health Fidelity, Inc. in the United States and/or other countries.
