EHR Enablement for Data Capture

Transcription

1 EHR Enablement for Data Capture Baylor Scott & White (15 min) Bonnie Hodges, RN University of Chicago Medicine(15 min) Susan M. Sullivan, RHIA, CPHQ Kaiser Permanente (15 min) Molly P. Clopp, RN Tammy Peacock, RN Cleveland Clinic (15 min) Nancy Anzlovar, RN, BSN NSQIP Program Manager Rocky Oppedisano, CPM, MBA IT Project Manager Allan Siperstein, MD NSQIP Surgeon Champion Panel Discussion (30 min)

2 2014 ACS NSQIP Conference Disclosure The authors and presenters have no relevant financial relationships with commercial interests or conflicts of interest to disclose.

3 Perspective Opportunities Surgeon Champion - How to provide, measure, and improve quality surgical care? - How to get relevant real time outcomes information? Surgical Clinical Reviewer (SCR) - How to effectively collect, review, and report surgical outcomes data to both ACS NSQIP and within your organization? Information Technology (IT) - How to leverage technology to better facilitate ACS NSQIP outcomes reporting process? - What level of automation is right for your organization considering organizational constraints? 2014 ACS NSQIP Conference

4 Great Minds Working as One Automating data extraction, improving work flow, and outcome reporting within Baylor Scott & White Memorial Hospital Bonnie Hodges, R.N. ACS NSQIP National Conference New York, NY July 26 29, 2014

5 Background Baylor Scott & White Memorial Hospital Tertiary Referral Hospital (636 bed) Academic teaching institution (Texas A&M College of Medicine) FY ,808 operations NSQIP Program Participation History GS/Vascular Multispecialty Bariatric Automated Upload Process 3360 Cases Reviewed Procedure Targeted 5

6 SCR Chart Review SCR s Pre-Automation Approach Standard Variables Process Demographics Admit Date DOS Chart MRN Specialty Date of Discharge Review Multiple EMR Surgeon Anesthesia Type ASA Manual data entry to NSQIP CPT Code Lab Values OR Times Time! 6

7 Time Consuming Data Automation SCR s Approach to becoming a lean, mean, machine! Before automation!! o Multiple Electronic Medical Records o Delayed data entry o Delayed reports to surgeons o Extended hours at work & home Solution Data Automation! 7

8 Your Journey into Automation The Basics Identify Key members IT NSQIP (Quintiles) Resource SCR Communication Institutions IT Department Where e-data housed? How data formatted? Can data be converted for extraction? Identify variables for extraction Communication with team ACS NSQIP (Quintiles) Discuss automation needs with ACS NSQIP Technical Team Communication! SCR Designate lead SCR for automation initiatives Communication between IT & NSQIP Know your data Know your variables Automation will not happen overnight VALIDATION Lessons Learned Upload one cycle at a time validate! Validate pre and post upload process Communicate any changes during process Automation takes teamwork and time Communication 8

9 Data Automation SCR s Approach to becoming a lean, mean, machine! Each SCR reviewed 32 like cases All multispecialty reviewed 14.3 min saved per case x 3668 cases/year hours per year saved! (0.5 FTE)

10 Reporting Process Monthly reports Surgeon Champion Specialty Surgeon Quality Leaders Quality Improvement Projects Multidisciplinary Teams Accountability Empowerment 10

11 Reporting Hospital Quality Council Surgeon Champion NSQIP Nurse Leader Surgical Quality Improvement Team Educator Statistician Data Management Team SCIP OR Personnel Surgeon NSQIP SCR Infection Control NSQIP SCR s IT Support Quality Dept. Surgeon Analysis and QI Initiatives Data and Results Educate and Implement Staff Residents 11

12 Data Automation Advantages Streamlined review QI and NSQIP initiatives Real time reports Paperless Efficiency! Data Automation SCR Satisfaction 12

13 Future Goals Increase # of extracted variables Enhance Reporting Process Improve Surgical Outcomes Streamline Data Extraction A B Real time Functional and effective Continual Improvement Patient Care Increase QI Initiatives Perform more efficiently C Enhance SCR satisfaction 13

14 Conclusions Data automation can improve efficiency Requires extensive planning and communication between IT, NSQIP and SCRs. Time saved can be used to focus on QI projects, and enhance data reporting process If SCR is happy then the Surgeon Champion is happy! 14

15 Key To Success Identify key internal IT resources Communication & Team work Validation of source data & uploaded data Support from Surgeon Champion 15

16 Electronic Health Record Enablement for Data Capture 2014 ACS NSQIP National Conference

17 The University of Chicago Medicine Medical Center Information: Not-for-profit academic medical center Adult & pediatric hospitals, outpatient center for advanced medicine, multiple outpatient locations throughout Chicago area 606 licensed beds 34 operating rooms, 18,000+ procedures/year 700+ physicians ACS NSQIP Information: July 2012 Adult Essentials Multispecialty 1, cases reviewed annually 1.3 Surgical Clinical Reviewers July 2014 Adult Targeted Multispecialty Program reports through quality division

18 NSQIP Program Goals at UCM Year 1 Implement Multispecialty Essentials and automate capture of discrete data variables from electronic health record (EHR) Year 2 Streamline internal multispecialty outcomes reporting to reduce processing time and allow time to identify opportunities for improvement Years 2 and 3 Improve capture and validity of discrete NSQIP variables with anticipated move to Multispecialty Targeted

19 Year 1: Automation Case Selection: Generated Excel report from operating room log Data Variable Capture: Multi-disciplinary team decided source of each discrete point-of-care variable Automated 99 of 112 considered variables Variables: Preop risk factors, preop labs, intraop data, discharge information, mortality, readmissions and returns to operating room IT developed Excel report of data variables for selected cases Upload Process: Used free ACS NSQIP software Validation: SCR validates, completes abstraction, and submits

20 Year 2: Streamlined Reporting Obtained support, staffing and resources Revamped multispecialty reporting process: Old Process (SCR) 1. Run 30-Day Postop Occurrence Reports 2. Cut and paste data into Excel template 3. Add benchmarks and validate 4. Repeat process for quarterly and annual time periods (50 in total) Tested and released new reports: New Process (Data Analyst) 1. Selects files from NSQIP Data Download 2. Uploads to SQL database and runs program 3. Produces reports for each specialty using reporting software Validated using online 30-day Postop Occurrence Summary Reports

21 Years 2 and 3: Data Improvements Improve capture of preoperative risk variables from EHR: Surgeon Champion developed form to capture discrete preop risk variables Tested with general surgery, spread change to other specialties Embedded links to NSQIP data definitions to improve accuracy Improve validity of wound class variable: Problem: Wound class (WC) is auto-assigned at time of case scheduling but not always updated at postop debrief Solution: Staff educated on WC variable Documentation work flow redesigned Embedded link to WC definitions/examples within patient record Feedback given to staff when wound class is missing or incorrect

22 Automation: Program Results Saved time: 30 minutes/case, 840 hours/year, 0.4 FTE/year Improved data accuracy by eliminating data entry errors Streamlined Reporting: Time savings of 67% (32 hours 10 hours); use time saved to reduce reporting time lags and analyze data Minimal effort to produce additional reports (for outcomes by specialty, outcomes by CPT groups, and patient lists) Potential to merge the data download with other medical center data marts Data Improvements: Standardized capture of NSQIP data variables Fewer chart locations to search for data

23 Current Challenges Limited IT resources; requests are prioritized Documentation changes made in silos by clinical staff without understanding the impact on end-users Limited SCR time to work with clinical, QI, and IT staff to identify documentation and work flow improvements to impact NSQIP discrete data collection Change to Multispecialty Targeted increases number of variables to be captured

24 Keys to Success Strong Surgeon Champion support Ownership of program by surgery specialties Appropriate resources (health information management, coders, perioperative staff, IT, clinical staff) Get in the IT queue Communication of mission Integrated team of cross functional resources

25 Kaiser Permanente Northern California NSQIP Collaborative Tammy Peacock, RN, MAPSY Clinical Practice Consultant, NSQIP Improvement Advisor Regional Risk & Patient Safety Northern California Region Kaiser Permanente July 28, 2014 Facilitating exemplary care across the continuum to the surgical patients of Kaiser Permanente

26 Northern California Kaiser Permanente Consists of: 18 licensed hospitals on 21 campuses, including 12 ambulatory surgery units All ACS NSQIP participants 67 medical offices Serving 3.4 million Kaiser Permanente members plus those in the communities we serve. Annual Statistics: Over 230,000 hospital discharges Over 32,000 deliveries 25.5 million prescriptions dispensed 30 million lab orders processed 17 million office visits

27 NCAL Kaiser Permanente NSQIP Journey 2006 One Pioneer Facility Grant funded expansion to Six more facilities 2011 Regional Implementation 2014 Participation Model Alignment & Centralization of Abstraction Surgical Outcomes Improvement 5 medical centers multispecialty 15 medical centers multispecialty with targeted procedures in alignment with our organizational strategic goals

28 The Automation alternatives offered by IT Automobile Scooter Moped Bicycle

29 Final Product-Surgical Clinical Reviewer Abstraction Program(SCRAP)

30 SCRAP Components Data pulled from EPIC via custom report 55 variables are pulled into SCRAP Case selection process still done manually by the SCR Challenges with finding data in discrete fields One click upload to NSQIP

31 Noteworthy Results Standardized Abstraction process with Centralization and implementation of SCR playbook Reduced Variation in Abstraction Practices Instituted an Inter-Rater Reliability (IRR) process Reduced abstraction time by Leveraging Technology with Automation Improved ability to improve the care of our surgical members

32 Next Steps Continuing to work with our IT partners to further automate 35 additional variables Prioritize and standardize clinical documentation practices Addition of a coder to our central team Automate standard non risk adjusted process and outcome reports Barriers Inability to access data in an efficient manner from the Vendor from a collaborative level Variation in documentation practices Kaiser Permanente doesn t code inpatient surgeries with CPT codes.

33 Keys to Success Dedicated leadership and organizational support Know which resources are available within your organization Have a resource available for mapping and validating data Continued IT support for your data automation program Know your maintenance costs up front Vender upgrades and changes variable definitions may impact your program Patience! This does not happen overnight

34 2014 ACS NSQIP Conference The Cleveland Clinic (2013 Vital Statistics) ACS NSQIP participant since March, Currently enrolled in Targeted Procedures & MBSAQIP (Bariatric)programs Group practice model Main campus, 16 family health centers and 8 system hospitals Surgical Cases: 202,186 Physicians / Scientists: 3000 Residents / Fellows: 1793

35 2014 ACS NSQIP Conference Process Initiatives Improve surgical outcomes & patient care - Standardized the surgical episode of care process - Ensure H&P examinations are consistent with quality, accreditation and compliance standards - Incorporate ACS standards for surgical risk assessment Improve ACS NSQIP reporting - Efficient & accurate documentation through continuum of care - Ensure collection of required clinical data at point of care - Eliminate manual & labor intensive reporting processes - Development of QA process to ensure data integrity

36 Automation Initiatives An EMR centric enterprise solution that: - Effectively captures discrete NSQIP data at point of care - Automates the NSQIP case selection process - Consolidates available electronic NSQIP data - Efficiently reviews, edits, validates, and reports NSQIP data - Provides tools to improve NSQIP administration 2014 ACS NSQIP Conference

37 2014 ACS NSQIP Conference NSQIP Automated Process

38 2014 ACS NSQIP Conference Automation Features Standardization of surgical episode (Surgical Navigator) Integration of OR & Anesthesia NSQIP data Collection of discrete NSQIP data at point of care: - Pre-operative data: demographic, surgical profile, labs, risk assessment - Intra-Operative data: times, concurrent procedures, transfusions - Post Operative data: labs, discharge, mortality, readmits, re-ops Case selection and data abstraction by cycle Case validation and submission

39 Case Selection Before automation After automation!

40 2014 ACS NSQIP Conference Key Automation Results Improved surgical case documentation: currently over 80% of NSQIP data available electronically Improved data integrity: abstractors modify data 10% of the time Improved case reporting productivity & operational monitoring: - Reduced time to complete cases by 70% - Increased case reporting capacity by 3 fold - Eliminated reliance on paper charts: saved approx. $100K/yr. in paper chart ordering costs

41 2014 ACS NSQIP Conference Current Challenges & Risks Ability to standardize & collect post op 30 day follow up NSQIP specific data Ability to effectively support automated processes - ACS alignment with ever changing national standards - Effective ACS NSQIP change control process Vendor technology / tools align with industry standards

42 2014 ACS NSQIP Conference Strong Surgeon Champion & Organizational Support Involvement of multi-disciplinary team Appropriate time, resources & funding Know your data Keys To Success On-going commitment to improve process, quality, & outcomes

43 2014 ACS NSQIP Conference Keys To Success Strong Surgeon Champion & Organizational Support Involvement of multi-disciplinary team Appropriate time, resources & funding Know your data On-going program support / commitment to improve process, quality, & outcomes

44 2014 ACS NSQIP Conference Questions

45 The Automation alternatives offered by IT Automobile Scooter Moped Bicycle