html5-img
1 / 42

Patient Transfer reports

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

darius-langley
Télécharger la présentation

Patient Transfer reports

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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

More Related