Infrastructure Initiatives SLU Approach To Data System Implementation

1. Infrastructure Initiatives SLU Approach To Data System Implementation Jack M Lionberger, MD.,PhD. 09/19/2016 OTTR Users Conference

2. Disclosure I have no financial relationships to disclose. I am employed solely by Saint Louis University, but I do take orders from my wife.

3. Data Plan – Global CLINICAL DATA EHR Chemo Lab Pharmacy Laboratory GMP, Inventory Legacy Data Source (Excel? Access? SQL?) Clinical Data System CIBMTR Back-Download (x1) Workflow Management CIBMTR Report BMT CTN Quality IRB Research

4. Types of Legacy Data, Or New Vistas Legacy Data Source (Excel? Access? SQL?) Master List ALL BMT Patients CRID # OTLs CRInf Chimerisms HCT-CI GI Molecular Markers aGVHD Cyto/FISH cGVHD Cdiff Other Table Links: OTL Prospective In Progress, POC Retro finis Prospective In Progress, POC Retro finis Pending In Progress, POC Retro/Pro Prospective In Progress, POC Retro finis In Progress, POC Retro/Pro In Progress, POC Retro/Pro No LLE perhaps with SSM Link Pharm No LLE LLE in time NDDL NDDL NDDL NDDL NDDL NDDL NDDL NDDL Retro-spective project (NDDL) Prospective project (LLE via Flow sheets)

5. Goals & Needs: Multiple Perspectives A Project like this has multiple stakeholders, multiple perspectives, multiple goals and needs. You get to satisfy them all! “Need publishable data for my Center and my Career. I want the data as easy and perfect as possible.” Academician “Need data for policy development for compliance, certification, outcome and defined goals by Center” Quality Team (s) “Need the maximum value for system, implementation, and maintenance. Not a content expert, rely on advice for goal completion Sponsor (s) “Need accurate data with minimum fuss and re-validation. Gotta get my forms done on time, the first time” Registry “Need to do this well, quickly and with highest value algorithm for the company and the client. Software Content expert, not an expert on your system per se.” Vendor (s) IT, Clinic Administration: “Need clear implementation algorithm. Need plans for approach, initiation and maintaining with minimal interruption to workflows” Sponsor Support “Improve Quality, but please don’t slow my day down” Clinician (s)

6. The Art of Compromise Making everyone unhappy with you, but nobody homicidal towards you.

7. Quality Improvement – Data System We have viewed the Data System Implementation as a step wise Quality Improvement Plan. Piece by piece • Quality Improvement • Multistep process to improve: • Knowledge • Documentation consistency • Therapy accuracy • Patient outcomes • Steps: • Retrospective Data Analysis • Quality Improvement Intervention • Prospective Evaluation A B C D E Retrospective Data Collection Intervene with Education and Tools Collect Prospective Data Real time Clinical Validation of Data Report back to Group for QI

8. Our OTTR Timeline Concept of Data System: Hutch – Home Grown (Fractured); Vanderbilt - Vendor Review Vendors, Weigh Pros-Cons. Decided on OTTR Administration Approval, PO, Contract What Is Project Scope? Huh. Probably should have thought of that… Scope of Project: Number of Columns? What that means is, how many places does the OTTR data conversion have to worry about in the database. In its simplest conception, this is something like “WBC”. There might be several sources of WBC, and each have to be converted to a specific discrete (defined) type of information from the original source information. More complicated: Data that must be moved from a legacy data set (electronic, paper, etc) that must be converted into a unified data set, then converted to OTTR, then validated. This is very center specific. An example is your GVHD data.

9. Our OTTR Timeline Concept of Data System: Hutch – Home Grown (Fractured); Vanderbilt - Vendor Review Vendors, Weigh Pros-Cons. Decided on OTTR Administration Approval, PO, Contract Introduce the system and goals Kick-Off – Dec. 2015 Seek Early Wins: What are the tasks you can work on that will facilitate your stakeholder recognizing the value of the system? What is the timeline of goal completion for each stakeholder’s needs? What are the burning planks that must be resolved? Are these burning planks opportunities or deal breakers? What are your institution and system limitations? Do you know your system? “An EHR will not fix all of your problems, but implementing an EHR will identify all of your problems” ErronSwickPharm D.

10. Burning Plank: Paper Burns!! Thank Goodness for those nice looking people in our Data Team!! Ouch! Yikes! Regulatory (FACT) The Way We have Always Been Clinical Data System Opinion: It is going to be harder and harder to comply with regulatory groups without a central and electronic data plan.

11. Data: Clinical Work Drives Outcome Legacy Formats Registry Local Quality / Academic You must comply with Registry Data Collection and Reporting, even though these are sometimes dated. You will likely want State of The Art transplant Approaches / Protocols / Data Collection. Especially as a Small to Medium sized Transplant Program. Lets use the protocols from the Best of the Best Not always the Same Fields CIBMTR: Data Back to Center

12. aGVHD: Standardization Consensus State of the Art Ravulapati, Lionberger, et.al. BBMT; March 2016, (22), (3), Supp., P: S282–S283

13. aGVHD: Retro/Prospective & Validation Ravulapati, Lionberger, et.al. BBMT; March 2016, (22), (3), Supp., P: S282–S283

14. aGVHD Workflow APP: We started Prednisone on Mr. SuchandSuch for GVHD. Data is still pending. Data Team: “Treatment” matches our definition of a new GVHD case. I will “drop” a form for APP to fill out. APP: Mr. SuchandSuch has a new rx for GVHD (or a new Organ involved) Data Team: “New Treatment” and “new Organ system” matches our definition of new GVHD case. I will “drop” a form for APP to fill out. 4 wks later APP finishes and files to Data Team APP finishes and files to Data Team Weekly Clinical Mtg 1 Month of Data APP NP/MD Co-Validation One Pt, One NP, One Attending – One Data Set One Pt, One NP, One Attending – One Data Set One Pt, One NP, One Attending – One Data Set MD APP Used to Populate D100, etc forms Adds Data to Excel Sheet MD At Data Meeting zzzzz Summary Forms Validate Summary CIBMTR

15. General Plan: Validation In OTTR Data Event Occurs Record Data Clinical Mtg Milestone Passes Coordinator Clinician NP/MD Co-Validation NP MD CIBMTR QIP Academics

16. Types of Legacy Data, Or New Vistas Legacy Data Source (Excel? Access? SQL?) Master List ALL BMT Patients CRID # OTLs CRInf Chimerisms HCT-CI GI Molecular Markers aGVHD Cyto/FISH cGVHD Cdiff Other Table Links: OTL Prospective In Progress, POC Retro finis Prospective In Progress, POC Retro finis Pending In Progress, POC Retro/Pro Prospective In Progress, POC Retro finis In Progress, POC Retro/Pro In Progress, POC Retro/Pro No LLE perhaps with SSM Link Pharm No LLE LLE in time NDDL NDDL NDDL NDDL NDDL NDDL NDDL NDDL Retro-spective project (NDDL) Prospective project (LLE via Flow sheets)

17. Our OTTR Timeline Concept of Data System: Hutch – Home Grown (Fractured); Vanderbilt - Vendor Review Vendors, Weigh Pros-Cons. Decided on OTTR Administration Approval, PO, Contract Introduce the system and goals • Weekly Meetings: • Overall Strategy • Interface • Data Conversion • Hardware • Go-Live: • Who is trained • When • How do they visualize their goals Use OTTR Project Management as basis for local Project Management Kick Off December 2015 • Regular Reports: • Sub-meetings • Training • Staff Management • Timeline • Optimization • Management • Resources

18. Our Data System Go-Live Carbon Based Virtues and Liabilities Silicon Based Virtues and Liabilities Concept Based Virtues and Liabilities People don’t like to change workflows We had interviewed our workforce and discussed Project regularly. Orchestrated Global Mtgs Training for the Post Go-Live is difficult without seeing the final product (Chicken and the Egg) Establish concept of integrated workflows (conduits of communication) Vendor training team is diligent and thoughtful Not all stakeholders understand the point of the Data Program Management. Not all workflows are solid Continued communication is absolutely central, and establishing specific milestone Post Go-Live. You must be the visionary with thick skin.

19. Our Data System Go-Live Carbon Based Virtues and Liabilities Silicon Based Virtues and Liabilities Concept Based Virtues and Liabilities A combined University and Hospital system have overlapping IT departments with varied Jurisdictions Centralized and regular meetings facilitate identifying responsible parties to engage in process, even if it does not ward off all problems Review of IT stakeholder’s goals may indicate in your case that you need more personnel support Honest and open conversations with your IT Team (s) will facilitate this clarity earlier in the process. IT Teams seem to appreciate engagement with the new processes

20. Our Data System Go-Live Carbon Based Virtues and Liabilities Silicon Based Virtues and Liabilities Concept Based Virtues and Liabilities No one vendor does all things, so you must manage several entities. We chose this Vendor because of the history at SLU of using the vendors successfully, and using HL-7. Our local system has grown organically with “work arounds” in electronic and in paper formats. Data is central to Transplant, as shown in recent publications (Marmor et. al.) and outcomes. The process of Data System Implementation supports the goal of improved outcomes Politics may cause short term blocks to productivity Yeah, your on your own there.

21. Data System: Roll Out Week Pre Transplant Patients - Overview backfill as part of “Go-live Backfilled and Cross-walked for both hospitals. Historical Up to 7/27/16 Limited to Post Transplant Patients = = x SLUH/Glennon Data in Data System Referral - Listed Data Review Conditioning Social Work Prospective manage ‘p “Go-live Scheduling Transplant Financial Arrival TOC Referral - Listed Financial IDM/Organ Fxn Social Work Scheduling TOC Arrival Data Review Conditioning Transplant

22. Data System: Roll Out Week Backfilled and Cross-walked for both hospitals. Historical Up to 7/27/16 Limited to Post Transplant Patients Milestone: Are all pre transplant patients in? SLUH/Glennon Data in Data System Milestone: 8/12/16: Did all new pts get in? Milestone: 8/19/16: Are all pre transplant patients in? Milestone: Weekly report/review thereafter. During Pre Transplant Mtg Weekly Check: Is data flowing to down stream algorithm functions? Financial Arrival Social Work TOC Scheduling

23. Post Go-Live: Success & Milestones We see this process as being completed in about 12 months. The reason it takes time is because each workflow must be carefully extracted from our personnel, redesigned to be fully functional and then carefully placed into the data system and then fully validated. That is the process not of putting in a data system. That is the process of building a solid program.

24. Good Luck I will leave you with a profound sentiment from Linda Laub, RN: “You don’t suddenly lose your expertise in your job just because a data system is being implemented. You were an expert last week, you are still an expert” That is a great mantra for your folks who are nervous. Thanks Linda, Marita, Shyla, Doug, Todd, Kathleen, Michelle and the Whole Vendor Team!!!! You are very good at yourjob, and we appreciate your work!