Quality measures: Reduce manual chart review with Linguamatics NLP
The challenge of reviewing charts to extract quality measures
The regulatory burdens of initiatives such as i.e. HEDIS, Joint Commission accreditation, and Merit-Based Incentive Payment System (MIPS) quality measures continue to put a strain on overworked hospital departments, and payers alike. Be it member or patient, the message is clear, better outcomes for individuals is of the utmost importance for both payers and providers. Quality measures often require qualified personnel to perform extensive manual chart review to extract values from the patient/member narrative.
How can NLP reduce the burden of quality and core measures?
The Natural Language Processing solution
The Linguamatics Natural Language Processing platform provides an augmented intelligence workflow that automatically extracts key quality elements from clinical text for validation and prepares them for review - greatly reducing manual medical record review. NLP reports can then be saved to provide audit trails that will allow you to go back to the precise document and location where the information was found.
Here are just a few examples of how Linguamatics NLP can contribute to your quality measure reporting needs:
HEDIS is a vital set of healthcare quality indicators developed and administered by the National Committee for Quality Assurance (NCQA) with the goal of improving the triple aim in healthcare. One of NCQA’s goals is to move from process-oriented measures to outcome-based measures. In an outcome-based paradigm, the measurement point will recalibrate from a primarily structured process to a subjective one. Linguamatics provides NLP capabilities for hybrid measures to support medical record review, efficient data collection, enabling NCQA’s Outcome-Based Quality Measures, and so much more. Learn how a HEDIS auditor thinks NLP can help.
- Extract valuable laboratory testing information hidden within notes
Testing information such as ejection fraction and pulmonary function values for heart failure and COPD populations can be extracted for Next Generation Accountable Care Organizations (ACOs) reporting measures; to learn more, watch this webinar.
- Extract fall history and fall risk assessment information
Using NLP to extract information on fall risk assessment and history to improve patient safety, allow better care coordination and report to ACO shared savings and MIPS value-based reimbursement programs to lessen the burden of manual abstraction. According to the Center for Disease Control (CDC) if the rate of fall growth continues at the current rate, by 2030 there will be 7 deaths due to falls every hour. Read how one of our clients utilized NLP to lessen the burden.
To find out more:
Download our datasheet below to find out more on: "Linguamatics Health for Quality Measures".
