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Performance Improvement and Risk Mitigation in Transplantation

Performance Improvement and Risk Mitigation in Transplantation. Randy Sung, MD Associate Professor of Surgery University of Michigan Tony Dawson RN, MSN Vice President of Operations New York Presbyterian Hospital. Speakers. Pam Gillette, MPH, RN, FACHE Medical City Dallas

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Performance Improvement and Risk Mitigation in Transplantation

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  1. Performance Improvement and Risk Mitigation in Transplantation Randy Sung, MD Associate Professor of Surgery University of Michigan Tony Dawson RN, MSN Vice President of Operations New York Presbyterian Hospital

  2. Speakers Pam Gillette, MPH, RN, FACHE Medical City Dallas Ajay Israni, MD, MS SRTR Linda Munro, RN, MSN Henry Ford Hospital Sandra Shwantz, MPT Mayo Clinic Rochester

  3. Surviving the Rough Waters of an SIA Pam Gillette, MPH, RN, FACHE Vice President, Transplant Services Medical City Dallas

  4. What is an SIA? • Systems Improvement Agreement • Alternative step to decertification • Agreement between Transplant Hospital and CMS • Triggers for SIA

  5. Mitigating Circumstances • TXP Program may request the above based on these factors: • Extent to which the outcomes measures were not met • Availability of other approved transplant Centers in the area • Extenuating circumstances having a temporary effect on outcomes • Successful requests include: • Significantly diverse populations from the national average • Plan of Correction already in place • Significant improvement in outcomes currently

  6. Elements of SIA • Binds Hospital to improvement activities • Hospital must contract with outside monitor • Hospital pays for the team’s time & expenses • Monitor assists developing a Plan of Action • Monitor provides onsite support for plan implementation

  7. Transplant Center’s Responsibilities • Commitment by Hospital: • To provide resources requested by Monitor team • To make sustainable behavioral changes • To maintain a Leadership focus on the program

  8. Root Cause Analysis • Determine what happened • Determine why it happened • Figure out what to do to reduce the likelihood it will happen again • http://psnet.ahrq.gov/primer.aspx?primerID=10 • http://www.patientsafety.gov/CogAids/RCA/index.html#page=page-1

  9. Steps to Success • Transplant Team and Hospital make commitment to changes: • Organization structure • Process Improvement • Focused review of every graft loss or patient death • Re-training where needed • Standardization of immunosuppressant protocols

  10. Steps to Success • Transplant Team and Hospital make commitment to changes: • Allocation of time for Waitlist Management • Continuing education for Transplant Coordinators • Dedicated IT support for transplant needs

  11. Steps to Success • Transplant Team and Hospital make commitment to changes: • Protected time for Medical Directors • Replacing low performers at any level • No longer doing business as usual

  12. How to Reach Success • Follow the Plan of Correction to the letter • On-site Monitor for assistance • Fully participate in required interim CMS conference calls • Meet every goal set by the Transplant Center

  13. How to Reach Success • Keep team motivated during process • Meet interim goals set by your team • Call your Monitor Team with questions • Remember to celebrate when the storm is over!

  14. Contact Information • Pamela.gillette@hcahealthcare.com • 972.566.7325 office • 602.692.1190 cell

  15. Performance Improvement and Risk Mitigation in Transplantation: Making the Most of Tools and Time ProvidedAjay Israni, MD, MS Deputy Director, SRTRAssociate Professor of MedicineHennepin County Medical Center, University of Minnesota

  16. Outline • Scientific Registry of Transplant Recipients (SRTR) Activities as per Final Rule • Sources of SRTR data • Phases in the Program Specific Report Cycle • Missing Data Reports • Expected Survival Worksheets

  17. SRTR Activities as per Final Rule Reporting Requirements …data shall include the following measures of inter-transplant program variation: • risk-adjusted total life-years pre- and post-transplant, • risk-adjusted patient and graft survival rates … • risk-adjusted waiting time, and • risk-adjusted transplantation rates, • …as well as data regarding patients…who were inappropriately kept off a waiting list or retained on a waiting list. • Final Rule implemented in 2000

  18. SRTR Data Sources for the Program Specific Reports: OPTN Data • Transplant Candidate Registration (TCR) • Waitlist data collection • For example, MELD or LAS components • Transplant Recipient Registration (TRR) • Histocompatibility Form • Deceased Donor Registration • Living Donor Registration • Recipient Follow-Up and Death Reporting

  19. SRTR Data Sources for the Program Specific Reports: External Data • Centers for Medicare and Medicaid Services (CMS) • For example, years on dialysis • Social Security Death Master File (SSDMF)

  20. Phases in the Program Specific Report Cycle

  21. Phases in the Program Specific Report Cycle: Spring/Summer Example

  22. Phases in the Program Specific Report Cycle: Spring/Summer Example OPTN Data cut for draft release. –Data current up to this day.

  23. Phases in the Program Specific Report Cycle: Spring/Summer Example Data cleaned and prepared. Reports created.

  24. Phases in the Program Specific Report Cycle: Spring/Summer Example Draft Reports and Missing Data Sheets are posted to the SRTR Secure Site. -Observed results only.

  25. Phases in the Program Specific Report Cycle: Spring/Summer Example 30-Day Data Review Period. -All data changes will be reflected in the final report.

  26. Phases in the Program Specific Report Cycle: Spring/Summer Example OPTN Data cut for final release. –All changes made by this date will be included.

  27. Phases in the Program Specific Report Cycle: Spring/Summer Example Data cleaned and prepared. Final reports created.

  28. Phases in the Program Specific Report Cycle: Spring/Summer Example Secure Release and Comment period begin. Reports and Expected survival worksheets are posted to the SRTR Secure Site.

  29. Phases in the Program Specific Report Cycle: Spring/Summer Example Comment period allows centers to add text that will be appended to reports.

  30. Phases in the Program Specific Report Cycle: Spring/Summer Example Public Release of Reports. Made available online at www. srtr.org.

  31. Data Review Period

  32. Missing Data Reports & Expected Survival Worksheets: Organization Relate to post-transplant outcomes only. • Patient Survival (P) • Graft Survival (G) Separates out living donor (L) and deceased donor (C) transplants. Separates out pediatric (Pe) and adult (Ad) recipients. Separates out different time periods • 1-month (1), 1-year (2), and 3-year cohorts (3) This means there are MANYworksheets in each Excel file. The abbreviations above will help you differentiate between the worksheets.

  33. Missing Data Reports & Expected Survival Worksheets: File Naming Conventions ABCD TX1 KI L 2012 03 Missing.xls ABCD TX1 LI C 2012 03 Expected.xls Organ Abbreviations Year Month Created Living-Donor Transplant Center Code Kidney Report Type Deceased-Donor Transplant Liver Heart-Lung = HL Heart = HR Lung = LU Intestine = IN Liver = LI Kidney = KI Kidney-Pancreas = KP Pancreas = PA

  34. Worksheets in the Missing Data Reports • Information • Miss_KI_C_G_Ad_1_2 • Program level data • Summarizes % flagged for review • Data_KI_C_G_Ad_1 • Recipient level data • Identifies events and key dates • Flags items to indicate need for review • BaseVars_KI_C_G_Ad_1 • Recipient level data • Text for risk adjustment components Data_KI_C_G_Ad_1 Page Type Time Period(s) Donor Type Organ Measure: Patient or Graft

  35. Worksheets in the Missing Data Reports:Information Page

  36. Worksheets in the Missing Data Reports:Miss_... Tab On all worksheets the upper left corner will state if this patient or graft survival, donor type, timeframe, and the age range. Keep in mind the cohorts for patient and graft survival are different.

  37. Worksheets in the Missing Data Reports:Miss_... Tab Percentages of data flagged for review at the center and nationwide.

  38. Worksheets in the Missing Data Reports:Data_... Tab Only the observed survival is available during the data review period.

  39. Worksheets in the Missing Data Reports:Data_... Tab A ‘1’ indicates this graft counted as a failure for this timeframe.

  40. Worksheets in the Missing Data Reports:Data_... Tab

  41. Worksheets in the Missing Data Reports:Data_... Tab Initial white columns: Items with ‘1’ flagged for review

  42. Worksheets in the Missing Data Reports:Data_... Tab Yellow columns on the right: Present data for certain items

  43. Worksheets in the Missing Data Reports:BaseVars_... Tab Data in “text” format for each transplant

  44. Missing Data Reports:What does a ‘1’ really mean? Purpose: • Identify potential data entry errors and omissions. • Present the data used for post-transplant outcomes. A ‘1’ DOES NOTnecessarily indicate missing data. It may also indicate (1) data that fall into “other” category, (2) data that are indicated to be “unknown”, and (3) the data are rare/unlikely values. Examples of data commonly flagged for review: • Race of Native American or Alaska Native • BMI > 35

  45. Reviewing Data using the Missing Data ReportsStep 1: Identify Variables for Review

  46. Reviewing Data using the Missing Data ReportsStep 2a: Compare with data on same worksheet

  47. Reviewing Data using the Missing Data ReportsStep 2b: Compare with data BaseVars Sheet

  48. Missing Data Reports:Step 3 (If necessary): Review Submitted Data Review the data your program or lab submitted: • Transplant Candidate Registration (TCR) • Waitlist data collection • Transplant Recipient Registration (TRR) • Histocompatibility Form • Living Donor Registration • Recipient Follow-Up and Death Reporting If necessary… • Coordinate with the OPO handling the donor or the OPTN Help Desk to correct any omissions or errors on the Deceased Donor Registration Form.

  49. Secure Release

  50. Expected Survival Worksheets: Purpose: • Identify cohort and data used in post-transplant outcomes. • Facilitate sub-group analysis. Unintended use: • Prediction of future expected numbers.

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