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Patient Transfer reports

Patient Transfer reports. Zen Sigma team – progress report. Kawa Shwaish, Hayzel Criollio , Kulbhusian Sinha , Fatih Yilmazer Devin Leibert , Amy Nguyen. Background.

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Patient Transfer reports

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  1. Patient Transfer reports Zen Sigma team – progress report Kawa Shwaish, HayzelCriollio, KulbhusianSinha, FatihYilmazer Devin Leibert, Amy Nguyen

  2. Background • UPMC’s Patient Transfer call center facilitates provider-to provider communication in order to assure bed assignment for patients. • UPMC determined that this was a problem when mgmt system was producing un-useful or incomplete reports.

  3. Define • Measure • Analyze • Improve (make recommendations) • Control (suggestion for how to control)

  4. D Problem and Defect • Problem Statement: The reports generated by the patient transfer system include significant inconsistencies in the data which reduces the reliability and usefulness of the report. • The Defect: Missing or inaccurate data in daily report to case managers

  5. D DEFINE • Objective • Understand the extent of defect and the transfer center business processes contribute to the defect. • Identify possible changes within the transfer center that can reduce the defect. • Projected Benefits • Metric • Area of Focus

  6. D Level 1 Process Map

  7. M Measure • Data Collection • Observation • Meetings and phone conferencing • Files (csv, pdf, excel) • Email • Paper forms and templates • QA of two weeks of reports • Given vs. Calculated

  8. M Measurement System Analysis • Where is the data coming from • How reliable is the data • Assumption and Concerns • The QA process performed by the case managers is fairly accurate • The data is not sufficient to run statistical analysis such as regression and control charts • The four hospitals selected are representative of the 24 hospitals in the network • Data related to # of calls per coordinator does not include calls they might have processed to other hospitals

  9. M Shift data

  10. M Data Collection • Transform to unique shifts • i.e. If shift end is after midnight add a day • Cross reference to identify coordinators • Coordinator • Start time • End time

  11. M Data Collection • Linking the different data sources • Annotated report • Data dump • Unique shifts

  12. M Defect detection • If not marked ok by coordinator • If coordinator missed the fact that there is no time stamp • If elapsed time was negative • IIf(([Sheet1].[cs_status_id]=2) • And (([sheet2].[comments]<>"OK") • Or ([sheet2].[Bed Assigned] Is Null) • Or ([Calculated Elapsed Time]<0)),1,0) • AS [Defect of completed]

  13. A Analyze • Fishbone Diagram (start from the flow chart) • Receiving Call • Identifying Availability • Conferencing Dr.s • Conferencing Nrs. • Data entry • Report generation

  14. A Fishbone diagram

  15. A Analyze • Metrics to measure dependent variable • Defect rate (actual counts of defect) • Number of defects per call per hour • Metrics to measure independent variables • Call traffic • Average number of coordinators per hour • Number of coordinators on duty per call

  16. A Coordinator effectiveness • Out of 701 Sample calls analyzed over two weeks, the number of defects ASTRONOMICAL: 55.9 % • The correlation between the number of calls received and defects occurring: 99.9 %

  17. A Trend of defect vs. No. of coordinators

  18. A Trend of Defect rates by hour

  19. A Variation of defect with available coordinators

  20. A Time of day

  21. A Analyze • Correlation levels for: • Defect Rate/call/hour vs. Average number of coordinators per hour= -0.72 • Correlation between number of calls per hour and the average number of coordinators per hour= 0.865

  22. A Analyze

  23. A Analyze • So, almost 52 percent variability can be explained by the model. • So, the regression equation line, y = -12.04x + 12.66 Example: To contain the defect rate/call/ hour at 0.1, the average number of coordinators required= 11.456 Decide between 11 and 12 coordinators!

  24. A COORDINATOR CORRELATION • We analyzed, whether number of coordinators available during a call has an effect on defect and we found a weak correlation of -28.87 percent between available coordinators and number of defect. • The negative sign re-emphasizes intuition that with the increase in number of coordinators on duty, defects will go down.

  25. A Coordinator effectiveness

  26. A Defect Percentage per operator

  27. A Referring facility

  28. A Referring Unit type

  29. A Admit service

  30. A Admit service ratio

  31. A Day of the week

  32. A Bed type

  33. A defect Rate / bed type

  34. A Defect based on Time of day

  35. A Variation of defect with available coordinators

  36. A TRENDS (10/15–10/27)

  37. A Defects Per Receiving Hospital

  38. A Time of day Defect Ratio

  39. A Time of day defects Evening Night Day

  40. A Daily Defects P-Chart for Daily Defects

  41. I RECOMMENDATIONS • Night shift’s performance should be improved • Training • Performance based evaluation for their Human Capital Management Process • Reduce number of simultaneous calls handled by coordinators • Rework the phone call routing • Headset • Merge phone lines • Review process of ED transfers • Optimize the scheduling of the coordinators • Improve information infrastructure

  42. C Control

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