Making effective use of available data. And prioritizing!
Healthcare is a business. A business with a huge, important task: to provide quality care to patients, while also dealing with ever higher patient loads and facing their own increasing physician burnout rates. Clinicians are faced with both administrative and clinical priorities - some of which conflict. So how do you prioritize what should come first? Like the old adage “which came first: the chicken or the egg?”, one can’t exist without the other. The provider can’t exist if the healthcare establishment goes bankrupt - and the healthcare business is nothing if it doesn’t have providers. Data and technology are potential ways to help ease this burden on clinicians and provider teams, enabling them to understand their patients better and streamline access to payer approvals.
Five ways natural language processing can help healthcare providers
You may be aware of Natural Language Processing (NLP) and its potential to augment the intelligence of the clinical workforce. Below we list just a few areas where NLP technology can help improve patient care while reducing administrative and reporting burdens:
- Improve the efficiency of quality reporting and disease registry processes, reducing manual chart review - An Accountable Care Organization (ACO) utilized Linguamatics NLP to reduce manual review from the previous 1,000 records per care gap identified to only six records per care gap.
- Augment patient safety and quality initiatives such as screening radiology reports for early signs of cancer - A missed radiology cancer diagnoses could cost millions in lawsuits. So from an ethical and fiscal point of view - patients need to get diagnosed and treated early.
- Automate prior authorization to streamline the payer approval process - Surely your clinical staff have better things to do with their time?
- Power predictive and risk stratification models with social determinants of health (SDOH) features. - Nurture really does make a difference on population risk, hospital readmissions, etc. For example, it’s a lot more difficult for someone living alone to recover from a condition that required a hospital admission if they are having mobility issues. This information is usually hidden in clinical notes, but can be found quickly using NLP.
- Enhance clinical research and real-world evidence projects with faster cohort selection and analysis. Approximately 85% of clinical trials fail due to recruitment and retention issues- why not let NLP help?
Find out more about NLP
Want to learn more about how you can help in providing better care and help your institution “recoup” some of its expenditures? Of course you do. Our NLP webinar is a good start.
