Using CDI and CAC to Improve Quality and Reimbursement

1. Using CDI and CAC to Improve Quality and Reimbursement Anne Robertucci, Director UPMC Corporate Coding

2. Conflict of Interest Disclosure Anne Robertucci (UPMC) Has no real or apparent conflicts of interest to report. UPMC has a financial interest in the OptumClinical Documentation Improvement Module

3. Learning Objectives • Discuss how physician documentation provides the only source for hospital reimbursement and quality metrics • Explore how ignored, rejected or inadequately answered physician queries limit the data integrity of the patient record and the revenue integrity of provider organizations • Outline how natural language processing enables more comprehensive medical record reviews • Describe how integrating physician queries within electronic medical record interface can improve physician response • Demonstrate how NLP technology can improve Physician Documentation, Quality measures and Hospital Reimbursement

4. UPMC Snapshot

5. UPMC Prior State:The CDI Challenge

6. What is Clinical Documentation Improvement? Why is it Important? • Clinical documentation created by physicians & allied health providers is the only source used to... • Capture quality metrics • Determine hospital reimbursement • Serves as proof of the care provided • Increasingly audited by payors and regulators • Lack of sufficient specificity results in aquery to establish the diagnosis • Query process can be cumbersome and time consuming for HIM departments and physicians • ICD-10 will require more specific clinical documentation by providers

7. Documentation Gaps in the EMR • Cut & Paste Phenomenon • new information often buried • When Doctors Type • not much information is provided • Symptoms…not diagnoses… are documented • Doctors can’t find correct diagnosis from pick-list • Communication must be within physician workflow COMMUNICATION

8. Common Ways Physicians Downplay Severity of Illness

9. Revolutionary Changes with ICD-10

10. NLP Technology at UPMC • UPMC Technology Development Center • Makes strategic investments in technology to aggregate and translate data into knowledge • Co-developed first inpatient CAC solution • Launched in 2008 • Improved CMI by 8% • External audits decreased by 50% • Saved more than $500K per year • Increased coder productivity by 20-22% • Decreased overtime by 84%

11. CDI at UPMC: The challenge CONCURRENT • No concurrent CDI program in place • 100% retrospective focus • Average 550 inpatient medical records coded per day • 5% of the total discharges result in a query with revenue impact of $1M per month • Creating, distributing, monitoring and resolving physician queries is labor intensive • Queries that are not resolved quickly impact the DNFB • This data is inclusive of 20 UPMC hospitals 100% RETRO 550 records/day 5% = $1M/mo.

12. UPMC Prior State:Drivers to Change Why a technology solution is needed for CDI and ICD-10

13. Balancing Organizational Approach with Physician Needs

14. ICD-10 Impact on Coder Productivity and Hospital Revenue CDI Key CAC Key Content derived from The Advisory Board Company. 2013

15. Staffing per Financial Benchmarking Report • Assumptions: • Total Beds Provided from UPMC Finance • Average Salary for CDIS $28.84/hr per Indeed.com • Salary marked up 22% to add benefit costs to total salary $35.18/hr • FTE of 2,080 hours per year Source: “Best-in-Class Clinical Documentation Improvement Programs.“ Financial Leadership Council - The Advisory Board Company. 2010.

16. UPMC Current State:Using Technology to Create an Electronic Concurrent Coding and CDI System

17. Transformation opportunity for CDI with NLP • Natural language processing (NLP) is transforming HIM and coding with computer-assisted coding (CAC) solutions • Benefits: Productivity, accuracy, efficiency, transparency, manageability • CDI programs shares these same goals • However CAC is not the same as CDI • Not limited to finding only “code-able” facts, but clinically significant facts that are evidence of an information gap

18. Two Types of CDI Opportunities Test NLP Capability Discrete Data Easy to Moderate High Difficulty

19. CDI for ICD-10: Using PCS Structure to Identify Gaps in Procedure Documentation Section BodySystem Root Operation BodyPart Approach Device Qualifier O D B 6 8 Z X ???? Med-Surg Excision Transorifice Intraluminal Endoscopic Diagnostic Gastrointestinal Stomach None

20. System-built queries vs. manually-built Dear Dr. What kind ofCHF is being treated?

21. CDI Module TechnologyHow it works Concurrent CDI Case Finding Business Rules Logic Continuous processing of the EMR data through NLP to both code and apply case-finding rules to each admission Passive Query Building • If a case is marked for CDI, ensure that it conforms to business rules for presentation to a user: • Financial class • Revenue code • Physician service • Location • How should it be routed • Direct to physician • Peer advisor • CDI specialist • CDI manager • Specific user coder Query passively built with minimal (if any) additional editing and update required by CDIS Presentation to physician either interfaced to EMR or Inbox or via PQRT Portal Query response returned to NLP

22. Example of Nonspecific Physician Documentation

23. Electronic Query Using NLP

24. Physician Selects Appropriate Dx

25. Physician Adds Supporting Statement

26. UPMC NLP CDI Outcomes

27. Types of Queries Sent

28. Physician Query Response RatesComparing paper query process to NLP CDI query process

29. Physician Query Average Turnaround TimeComparing paper query process vs. NLP CDI query process

30. Coding TAT to Final BillCases with a Query 2012 vs. 2013 (average, days)

31. Queries Yielding ROI

32. UPMC CC/MCC Capture Improvement • 3% Improvement CC/MCC at Presbyterian/Shadyside Hospitals • 4% Improvement CC/MCC at St. Margaret Hospital Presbyterian / Shadyside St. Margaret

33. Physician Query Related DRG Shift DRG shift related to queries that changed the MSDRG

34. CDI Audit of Discharged Patients • A CDI audit was completed on queries that were generated between November 10, 2013 and December 8, 2013 of all 5,359 discharges • 847 queries were generated for 814 patients or 15% of discharges • The results found a total of $1.32M in value from the combination of concurrent (32%) and discharge (68%) queries 271 Concurrent

35. Projected Value of Automated CDI to UPMC

36. Outcomes of NLP Driven CDI • NLP identifies more cases with missing or nonspecific documentation than manual review • Query volume increased by 5 fold • Physicians answer queries more timely and accurately when integrated with the electronic record • Time to final bill improved for cases with query • MCC/CC most frequent reason to query • Principal Dx and procedure codes also queried • Services lines most queried for MCC/CC • Cardiology, Medicine, Oncology • ROI: MS-DRG as well as APR-DRG (SOI, ROM) • Second level peer review can yield additional ROI

37. UPMC Future State:Preparing Physicians for ICD-10 and Beyond

38. Educational Videos

39. CDI Performance Metrics

40. Future State • Make current process more automated • Examples: BMI, pathology reports • Add parameters to business rules to determine course of action • Determine queries that are always clinically relevant to prompt. May not always affect reimbursement or SOI/ROM • Weigh automated decisions with the “Physician Annoyance Factor” • Apply a second level of physician review • Improve education, documentation and response to queries • Apply CDI to ICD-10 and assess risk prior to October 1, 2015

41. Questions?Thank You!