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Use cases for NLP in Healthcare with Linguamatics

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The global healthcare Natural Language Processing (NLP) market is expected to grow from $1.10 billion in 2015 to $2.67 billion by 2020, according to a report from Markets and Markets. This growth is driven by the need to better understand and manage the health and wellness of patient populations in a changing healthcare landscape. But with 80% of healthcare data in unstructured Big Data, so much insight is trapped in text. How are payers and providers to extract these insights and apply them in statistical and machine learning models?

This Cloudera webinar with Linguamatics explores NLP techniques for data discovery and information extraction to enhance predictive risk models, improve population stratification and automate capture of quality measures.

3 things to learn:
Enhance predictive risk models for healthcare payers and plan providers
Improve population stratification
Automate capturing of quality issues for plan providers

Veröffentlicht in: Technologie
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Use cases for NLP in Healthcare with Linguamatics

  1. 1. 1© Cloudera, Inc. All rights reserved. Use Cases for NLP in Healthcare with Cloudera certified partner Linguamatics
  2. 2. 2© Cloudera, Inc. All rights reserved. Agenda Simon Beaulah, Senior Director, Healthcare, Linguamatics Shawn Dolley, Industry Leader, Health & Life Science, Cloudera Use cases for NLP in Healthcare Implementing NLP in Healthcare
  3. 3. 3© Cloudera, Inc. All rights reserved. Cloudera customers in health & life science
  4. 4. 4© Cloudera, Inc. All rights reserved. One data platform for many health use cases Data Streams Batch or Real- time Data Streams Applications ● Single place for all data ● Easy in, easy out ● Highly governed ● Shared data experience Structured Semi-Structured Unstructured Claims Drug Research Genomics Monitoring Social Media Supply Chain Medical Imaging Patient-Centered EHR ● Data discovery ● Democratize NLP ● Real-time processing
  5. 5. 5© Cloudera, Inc. All rights reserved. NLP is not a use case ● Natural language processing is an approach to parsing clinical text ● Clinical text is how our clinicians add, engage, and interpret data ● It’s how our patients, administrators, researchers add, engage, and interpret data ● Text is the data source, or ‘rides along’ with every data source ● NLP is not a use case, it is every use case’s associate, friend….or BFF
  6. 6. 6© Cloudera, Inc. All rights reserved. Top use cases for big data and NLP in Healthcare Patient stratification Predict outcomes Clinical document improvement Quality measures Identify financial risk
  7. 7. 7© Cloudera, Inc. All rights reserved. Poll #1 What is your top use case for NLP? (pick one) 1. Patient stratification 2. Clinical document improvement 3. Identify financial opportunity 4. Predict outcomes 5. Quality measures 6. All of the above 7. None of the above
  8. 8. 8© Cloudera, Inc. All rights reserved. Key challenges in clinical text Lack of clinical terminology sets Understand context, family history vs problem list Multiple sources of unstructured data Limited availability of NLP skills Deal with variability of language & format, needs to be data driven Support retrospective and prospective analysis
  9. 9. 9© Cloudera, Inc. All rights reserved. Why NLP - a total view of the patient
  10. 10. 10© Cloudera, Inc. All rights reserved. Linguamatics I2E Transforms Text into Structured Data
  11. 11. 11© Cloudera, Inc. All rights reserved. Top 5 Health Plan needed to improve population stratification to better care for members. Wanted to integrate unstructured data into analytics infrastructure to access insights trapped in notes • Powerful NLP capabilities for multiple applications • Agile, robust solution • Enterprise integration with cloudera data lake Production system deployed based on Linguamatics I2E to risk stratify CHF and End of Life populations: • Source data 1.5TB of unstructured data stored in Cloudera data lake • C-CDA HL7, OCR patient documents, call center notes • Information extracted: Smoking status, family history, problem list, labs and medications Results in faster model development and more detailed population stratification models Use Case #1 Population Stratification Using NLP
  12. 12. 12© Cloudera, Inc. All rights reserved. ● People with chronic diseases can not be cared for effectively if they are not properly documented ● NLP identifies undocumented CHF, COPD, Obesity, Diabetes, HIV, Hep C patients for improved care ● NLP extracts ejection fraction, BMI, FEV1, A1C values to identify gaps in care ● Results in better characterization and clinical documentation of chronic disease for improved care ● Additional revenue from Medicare Advantage Use Case #2 Clinical Document Improvement Using NLP
  13. 13. 13© Cloudera, Inc. All rights reserved. Large health system wanted to improve early diagnosis of lung cancer ● Improve outcomes and reduce potential $1m per missed patient litigation risk I2E used to nightly screen radiology reports from routine ER visits for incidental findings ● 1212 cases flagged in 14 months ● 64 biopsies ordered ● 37 malignant cases identified ● 70% lung cancer, 30% other Resulted in early treatment and improved outcomes Use Case #3 Identify Financial Risk Using NLP
  14. 14. 14© Cloudera, Inc. All rights reserved. Large IDN needed to accelerate predictive model development for patient outcomes. Include insights into Social Determinants of Health into clinical models for 30-day Readmission Need to monitor for predictive factors of success or failure for clinical outcomes ● Social determinants, ambulatory status and living location I2E used to explore 700,000 discharge summaries to extract attributes for statistical and Machine Learning modelling. Resulted in: ● Well characterized, consistent and well populated data for ML without huge manual curation effort ● Faster identification of patient cohorts and predictive model development Use Case #4 Predict Patient Outcomes with NLP
  15. 15. 15© Cloudera, Inc. All rights reserved. Linguamatics Data-Driven NLP
  16. 16. 16© Cloudera, Inc. All rights reserved. Use Case #5 Quality Measures Extraction Using NLP Quality measures reporting can often be a multi-million dollar manual process of extracting Linguamatics I2E extracts quality measures from clinical notes in cloudera data lake • Examples include Diabetes Population Quality Measures Completed internal and external audits • Improved extraction of data compared to manual methods Results in significant reduction in manual processes and constant insight into population wellness
  17. 17. 17© Cloudera, Inc. All rights reserved. Quality Measures Extracted in I2E
  18. 18. 18© Cloudera, Inc. All rights reserved. Integrated cloudera and Linguamatics Architecture
  19. 19. 19© Cloudera, Inc. All rights reserved. Linguamatics Key Capabilities
  20. 20. 20© Cloudera, Inc. All rights reserved. Multiple high value applications from the same big data platform • cloudera and Linguamatics infrastructure supports risk stratification, quality measures and risk adjusted diagnosis Transforming unstructured data • Linguamatics I2E is advancing risk stratification to a much more detailed level of insights • Supports future areas such as behavioral health Flexibility and fast time to value • Powerful NLP engine for data-driven development and extraction • Drag and drop NLP system for non-experts opens up use Cloudera and Linguamatics Business Impact
  21. 21. 21© Cloudera, Inc. All rights reserved. Poll #2 What describes you best? (pick one): 1. Have never used NLP but have a use case in mind 2. Have tried DIY NLP and failed :( 3. Have tried NLP with packaged software 4. Using NLP successfully today but room for improvement
  22. 22. 22© Cloudera, Inc. All rights reserved. Demo
  23. 23. 23© Cloudera, Inc. All rights reserved. Congestive Heart Failure Dashboard
  24. 24. 24© Cloudera, Inc. All rights reserved. CHF Comorbidities
  25. 25. 25© Cloudera, Inc. All rights reserved. CHF Social Determinants and Raw Data
  26. 26. 26© Cloudera, Inc. All rights reserved. Cancer Pathology Extraction
  27. 27. 27© Cloudera, Inc. All rights reserved. Pathology Demographics
  28. 28. 28© Cloudera, Inc. All rights reserved. Cancer Biomarkers and Stage
  29. 29. 29© Cloudera, Inc. All rights reserved. Resources Cloudera Shawn Dolley sdolley@cloudera.com Healthcare Website: https://www.cloudera.com/solutions/healthcare.html Linguamatics Simon Beaulah simon.beaulah@linguamatics.com Website: www.linguamatics.com/healthcare
  30. 30. 30© Cloudera, Inc. All rights reserved. Thank you

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