DATA MANAGEMENT.& INTEGRITY

Transcription

1 DATA MANAGEMENT.& INTEGRITY Transplant Quality Institute Jennifer Milton Executive Director Clinical Assistant Professor Disclosures I have a relevant financial disclosure with a company called XynManagement 1

2 Objectives After this presentation you will be able to Review the methodology and use of expected survival Cite importance of a data management policy Identify common issues with data integrity List key Unet forms for data management programs O to E O to E O to E O to E O to E (Review the concept of Expected) If everyone else in the US had transplanted your patients, how would your patients have done) Category 1 Category 2 Category 3 Category 4 Series 1 Series 2 Series 3 2

3 This talk focuses on the E The E How do they get it? How is it calculated? How do you manage the E The O is kinda important too How Much Does the E Cost you? How Much can the E REALLY cost you? S.A.D. Transplant Center had 15 observed 11 expected The O/E was 1.5 The reported p value was.107 as expected COE loss for 6 weeks 46 Referrals 1.6 million lost potential revenue What if it took a year? 3

4 DATA Management What is a Beta? SRTR analyzes all data entered in the US and selects variables that are most impactful Some fields assigned weight/ risk to entered data Excluded Variables: Not Collected Not statistically relevant Proxy variables 27 page methodology Manual. I understand the table of contents 4

5 Then What Happens Race Insurance Race Donor Recipient KI-1 y DD Graft KI-1y DD Patient KI-1 y LD Graft KI-1y LD Patient NAFCYBI Kidney Waitlist LI-1y DD Graft Heart-3 y Patient NAFCYBI Insurance Private Public KI-1 y DD Graft KI-1y DD Patient KI-1 y LD Graft KI-1y LD Patient Kidney Waitlist NAFCYBI NAFCYBI LI-1y DD Graft Heart-3 y Patient NAFCYBI Some of it you don t even enter! Donor Factors by UNOS ID & the TCR to TRR Diagnosis for Re-TX Dialysis Race 5

6 Typical John Doe Previous TX Diagnosis Race BMI Age Hep C Insurance One Year Expected: 97.01% But is the patient really that risky? Team Data Entering Mr. Doe Previous TX Diagnosis Race BMI Age Hep C Insurance Malignancy Sudden Death Dialysis 2 weeks Hospitalized Zip Code One Year Expected: 98.5% 6

7 How Big Can The Problem Be? DM not captured X 50 kidneys (229 in the cohort) = SRTR Expected calculation significantly wrong John Diabetic Bob Multiethnic Lives Far Away Jane Observed to Expected Wrong Age and DCD Risk Peter Dialysis Tim But My Data is OK New Treatment for Missing Data unknown Retransplant -- connect to original TCR listed diagnosis REALLY assessing PVD, COPD, EBV mismatches Missing data when window closes? 7

8 Release of New Equations New Emphasis on TCR Kidney Heart Liver is Next Key Unet Forms Critical Uses TCR Allocation Waitlist Mortality Transplant Rate SRTR 1 y CUSUMS TRR SRTR 1y SRTR 3y CUSUMs TFF Research National Funding 8

9 The OPO is Critical Blank Diabetes DCD to SDC Zip Code Height Weight Ethnicity Data Submission Accuracy Remember That Pool? This proposal s goal is to amend Policy 18.1 to explicitly state that the data must be accurate and that members must provide documentation to support their data submissions. UCSF UAB DUH U of F EVERYONE!! 9

10 Where Should You Start? Field Label Patient Last Name Patient First Name Patient DOB Medical Record Number Gender Height (cm) Weight (kg) Center Definition Last name entered as it appears on the demographic face sheet populated at the first ambulatory visit to UTC First name entered as it appears on the demographic face sheet populated at the first ambulatory visit to UTC DOB as it appears on the demographic face sheet populated at the first ambulatory visit to TC Original Source Document Database face sheet Database face sheet Database face sheet Training that needs to be provided Include with general data training as typos can result in UNOS citations Include with general data training as typos can result in UNOS citations Include with general data training as typos can result in minimizing risk and also in UNOS citations As set in the EMR Database face sheet Include with general data Gender entered as it appears on the demographic face sheet populated at the first ambulatory visit to TC Measured height -verbal heights and missing heights not permitted. Standing scale weight upon admission for transplant event * Database face sheet If inpatient evaluation height only from Nursing Admissions Form. If called in from home, measured height at initial evaluation clinic visit. Nursing Admissions Form only. training as typos can result in UNOS citations Include with general data training- typos could result in UNOS citations although unlikely error Any staff member responsible for entering inpatient height on admission or in ambulatory transplant clinic. Any staff member responsible for entering weight on admission (bedside nurses or assistants, same day surgery staff, etc.) 1) TCR & TRR 2) Discuss data Practice 3) Focus on the Source 4) Watch data entered 5) Get QAPI engaged 6) Establish a policy 7) Go Forward Orientation and Competency 10

11 Word of Cautions EMR & Databases: Efficiency Versus Integrity Automated Data Submission Height & Weight on TCR to TCR, are you sure? Did diagnosis get entered at referral? Did it change in that liver recipient while listed? Statistically Double Whammy But he is soooo nice Compliance Age >55 is same risk as > 70 actually in some cases, its one of those negative risks Substance Abuse Frailty Six Minute Walk Test Walk up the stairs Hospitalized for Pneumonia Beyond Transplant Data ESRD The Almighty 2728 Start of Chronic Dialysis Diagnosis Dual Entitlement TJC Core Measures Heart Failure Pneumonia Acute MI Surgical Care Improvement Stroke Intermacs 11

12 Transplant Quality Institute SPONSORED BY AFDT. 12