Wolters Kluwer Helps Payers Accelerate Medical Chart Review with Clinically Intelligent NLP
Clinician-friendly solution goes beyond search results to deliver quick, actionable answers
WALTHAM, Mass.–(BUSINESS WIRE)–Wolters Kluwer, Health announced today it has expanded its portfolio of clinical natural language processing (cNLP) solutions with Chart Review Accelerator. The new software intelligently scans patient medical records, extracting clinically important information such as conditions, medications, procedures, allergies, and lab results to efficiently identify gaps in care. The solution is part of the Health Language platform and is designed specifically for clinician end-users, accurately informing medical necessity reviews, HEDIS quality reporting and risk adjustment directly within their workflow.
Going from search to answers
Patient medical records range from one to hundreds to thousands of pages, depending on the patient. Manually reviewing these records, which are often highly complex and out of order, is an onerous, costly, and error-prone process. It also wastes valuable clinical resources that are better spent delivering patient care. While search-based technology has become available, the incremental improvements offered to date continue to fall short of what clinicians really need.
“Traditional technologies deployed for chart review simply search records for relevant terms and produce a long list of results that clinicians must sift through to get the information they need,” said Frank Jackson, vice president and general manager of Health Language at Wolters Kluwer, Health. “In contrast, Health Language Chart Review Accelerator was purposely designed to summarize search results with rich clinical context so clinicians can focus on relevant information and more easily and quickly answer the specific questions they are trying to answer.”
The new solution supports several important uses cases, including:
- Risk adjustment: Extracts clinical findings relevant to the most common HCCs for accurate risk adjustment
- Quality measure reporting: Surfaces key information relevant to the most common HEDIS measures from clinician notes to report more complete, accurate data
- Medical chart review: Speeds up patient chart review for medical necessity and pre-authorization purposes
- Predictive analytics: Enables the creation of structured, normalized data that powers predictive analytics, rules engines and machine learning models
By combining cutting-edge data science with deep domain expertise, Wolters Kluwer’s cNLP Chart Review Accelerator delivers the insights clinicians need to act. Wolters Kluwer’s exclusive Provider Friendly Terminology with over one million clinical synonyms, abbreviations, and related terms, is embedded in this cNLP solution to help clinicians optimize reporting and analytics, improve quality of care, and maximize reimbursement – all without adding to their administrative burden.
About Wolters Kluwer
Wolters Kluwer (WKL) is a global leader in professional information, software solutions, and services for the clinicians, nurses, accountants, lawyers, and tax, finance, audit, risk, compliance, and regulatory sectors. We help our customers make critical decisions every day by providing expert solutions that combine deep domain knowledge with advanced technology and services.
Wolters Kluwer reported 2020 annual revenues of €4.6 billion. The group serves customers in over 180 countries, maintains operations in over 40 countries, and employs approximately 19,200 people worldwide. The company is headquartered in Alphen aan den Rijn, the Netherlands.
Wolters Kluwer provides trusted clinical technology and evidence-based solutions that engage clinicians, patients, researchers and students in effective decision-making and outcomes across healthcare. We support clinical effectiveness, learning and research, clinical surveillance and compliance, as well as data solutions. For more information about our solutions, visit https://www.wolterskluwer.com/en/health and follow us on LinkedIn and Twitter @WKHealth.
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