1 / 37

Computer Assisted Coding (CAC) Progressing to the Future

Computer Assisted Coding (CAC) Progressing to the Future. Lou Ann Wiedemann, MS, RHIA, CDIP, FAHIMA, CPEHR. Learning Objectives. Explore current drivers and technology Learn what natural language processing (NLP) is and how it is being used in conjunction with CAC

lemuel
Download Presentation

Computer Assisted Coding (CAC) Progressing to the Future

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Computer Assisted Coding (CAC) Progressing to the Future Lou Ann Wiedemann, MS, RHIA, CDIP, FAHIMA, CPEHR

  2. Learning Objectives • Explore current drivers and technology • Learn what natural language processing (NLP) is and how it is being used in conjunction with CAC • Discuss operational benefits and challenges

  3. The Need…the need for speed

  4. Data • More Data Please…..

  5. Data – The New Bottom Line

  6. Data Center for Disease Control. “Ambulatory Medical Care Utilization Estimates for 2007.” April 2011. http://www.cdc.gov/nchs/data/series/sr_13/sr13_169.pdf • There are an estimated 1.2 billion ambulatory care visits in the United States per year • Physician Offices • Hospital Outpatient • Emergency Department

  7. Data ASTM International. ASTM E 2117-06 Standard Guide for Identification and Establishment of a Quality Assurance Program for Medical Transcription. West Conshohocken, PA. • Governance Focused • To ensure that documentation is: • Clear • Consistent • Accurate • Complete • Timely • And satisfies stated or implied requirements for documentation of patient care.

  8. Data Focus on delivering the correct information when and where it is needed while maintaining the highest standards of data integrity, confidentiality and security There will be an increased need for accurate and available health information

  9. Capturing the Data

  10. Capturing the Data “Hidden information” • 80% of information today is unstructured and based on natural language. **2011 – IBM Watson and Medical Record Text Analytics

  11. NLP Synergies CAC 813.52 Transcription System “patient had an open fracture, distal end of the radius”.

  12. Capturing the Data Computer-Assisted Coding (CAC) is the use of computer software that automatically generates a set of medical codes from documentation provided by healthcare practitioners and presents the results to the coder for review and validation.

  13. Capturing the Data Cannon, Jay; Lucci, Susan. "Transcription and EHRs: Benefits of a Blended Approach." Journal of AHIMA 81, no.2 (February 2010): 36-40.

  14. Capturing the Data • Speeds the cascading of information • Once discrete data elements are coded or structured documents are created, they can be repurposed: • CAC • EHR • Clinical DSS

  15. Capturing the Data CAC technology can create alerts signaling certain documentation needed for reimbursement and quality. Improve concurrent documentation processes such as CDI programs for better data collection for overall benchmarking to improve care.

  16. NLP Natural Language Processing (NLP) is a computer process that extracts implied facts from the text. It analyzes text and “extracts” the facts from the text as very carefully constructed and coded facts. Such facts are often referred to as “structured data.”

  17. NLP • NLP is a “true marriage of linguistics, computation statistics, and computer engineering”. AHIMA e-HIMTM Work Group on Computer-Assisted Coding. "Delving into Computer-assisted Coding. Appendix A: Primer on NLP for Medical Coding" Journal of AHIMA 75, no.10 (Nov-Dec 2004): web extra.

  18. Technology • Natural Language Processing (NLP) – originated in the 1950s, exploded in the 1990s • Factors driving technology: • EHR adoption • National health information infrastructure • ICD-10-CM/PCS • Increase coder productivity

  19. NLP Analyzes and converts words in transcribed text into standard coded terminology using a controlled medical vocabulary such as SNOMED-CT, LOINC and RxNORM.

  20. NLU Used in conjunction with NLP Analyzes phrases with narrative and produces structured data And… brings meaning to the narrative phrases to extrapolate into coded data

  21. NLP/NLU Approaches • Rules or Knowledge-based • Building an understanding of words and phrases with associated codes • Software’s ability is based on knowledgeable person who provides complex rules • Statistics-based or data driven • Based on statistics from large body of reports • Software makes prediction of proper codes for a given word or phrase

  22. The Consolidated CDA “The Consolidated CDA is the most appropriate standard to achieve this goal because it was designed to be simpler and more straightforward to implement and… its template structure can accommodate the formatting of a summary care record that includes all of the data elements that CMS is proposing be available to be populated in a summary care record”.

  23. The Consolidated CDA http://www.hl7standards.com/blog/2012/03/22/consolidated-cda/ • HL7 says the consolidated CDA guide as the “single source for implementing the following CDA documents: • Continuity of Care Document (CCD) • Discharge Summary • And many others”

  24. Health Story Project

  25. Operations CAC software performs initial screening of documentation and produces a set of preliminary codes Coding professional reviews/edits the list of codes and generates the final list of codes

  26. Operations Increased coding productivity Increased efficiency; frees professional from mundane tasks Comprehensive code assignment Consistent application of rules

  27. Coding Workflow • Electronic coding audit trail • Routine audits to maintain coding integrity • Ongoing assessment to identify: • Workflow problems • Bottlenecks • Editing vs. time to code

  28. Operations Cost of CAC hardware and software Complexity, quality, and format of health record documentation User resistance to change Technological limitations Potential increase in errors in the coding process Lack of industry standards

  29. Operations NLP coupled with CAC and clinical documentation improvement (CDI) will help move processes to point of service Integrated voice prompts can help fill gaps and ambiguities in the dictated note

  30. Operations • Improve computer aided abstracting • Reporting Requirements such as TJC and/or MU • Create and maintain problem lists • Time savings, enhances documentation detail

  31. Operations • Aid Research • Helps satisfy requests for information on patients and cases. • Offers Clinical Decision Support • Mine clinical data, combine with other data to enable analysis • Includes sending information to various systems

  32. Operations ICD-10 -CM/PCS Training CDI Workflow and Productivity Core Measures Patient Problem List Abstraction Coder Productivity Streamlining Coding/Billing Process

  33. Operations • Reporting: • Enterprise-wide data collection to ensure high quality standards. • Access to more robust data • Report on public health matters more effectively • Track outcomes, trends, quality metrics • Stronger pay-for-performance reimbursements

  34. Operations Increased dictation narrative? Query volume? Increased AR Days? Productivity decrease?

  35. Operations • Creating strategic roadmaps that include NLP/NLU, CAC and CDI. • Plan now to review business processes for: • Revenue Cycle acceleration • Productivity improvement • Reimbursements projection

  36. Questions?

More Related