Hospital level analysis to improve patient flow

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1 Hospital level analysis to improve patient flow Sankalp KHANNA a, Justin BOYLE a, Norm GOOD a, Simon BUGDEN b, and Mark SCOTT b a The CSIRO Australian e-health Research Centre, Brisbane, Australia You can change this image to be appropriate for your topic by inserting an image in this space or use the alternate title slide with lines. Note: only one image should be used and do not overlap the title text. Enter your Business Unit or Flagship name in the ribbon above the url. Add collaborator logos in the white space below the ribbon. [delete instructions before use] b Caboolture Hospital, Queensland Health, Brisbane, Australia 16 July 2013 THE AUSTRALIAN E-HEALTH RESEARCH CENTRE

2 Motivation Overcrowding in Hospitals: an International Crisis ED admissions Elective surgery Increased wait times. Increased medical errors. Increased length of stay. Increased medical negligence claims. Increased walkouts. Ambulance diversion. Patient safety at risk. Unnecessary deaths. 2

3 Motivation The Magic Fixes 3

4 Capacity (Number of Beds) Occupancy (%) Motivation S. Khanna, J. Boyle, N. Good, and J. Lind, Unravelling relationships: Hospital occupancy levels, discharge timing and emergency department access block, Emergency Medicine Australasia, vol. 24, no. 5, pp , Capacity : Group 1 (> 900 Beds) Capacity : Group 2 (900 <= Beds >= 300) Capacity : Group 3 (< 300 Beds) Occupancy (March 2010 quarter) 140% 130% 120% 110% 100% % 80% % Choke Point A : increasing patient turnover Choke Point B : excess ED inflow Choke Point C : excess inpatient ward inflow % 50% 40% Analysis Period : 1st October 2007 to 31st March 2010 (913 days) 4

5 Motivation S. Khanna, J. Boyle, N. Good, and J. Lind, Unravelling relationships: Hospital occupancy levels, discharge timing and emergency department access block, Emergency Medicine Australasia, vol. 24, no. 5, pp , QLD Group 3 Hospital Chokepoints : A : 98% B : 102% C: 106% 5

6 Caboolture Public Hospital 6

7 ACCESS BLOCK CASES Caboolture Public Hospital Average Access Block Cases per hour Max Access Block Cases per hour Proportion of Access Block cases Data Ordinary Least Squares Robust Regression OCCUPANCY Occupancy Access Block Vs Inpatient Occupancy Robust Regression : Access Block Vs Inpatient Occupancy S. Khanna, J. Boyle, N. Good, J. Lind, K. Zeitz. Time Based Clustering For Analyzing Acute Hospital Patient Flow. In Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE, pp ,

8 Analysis Methodology Analysis period : 1 Oct 2007 to 31 March 2010 (913 days). Data Sources : Hospital Based Corporate Information System (HBCIS) Emergency Department Information System (EDIS) 3 points of performance change identified : Choke Point A : increasing patient turnover Choke Point B : excess ED inflow Choke Point C : excess inpatient ward inflow Compared to similarly sized QLD hospitals In Addition : Analysis groups : for all days together. for weekdays. for weekend days. 8

9 The findings Caboolture Hospital Chokepoints : 100%, 106%, and 113% QLD Group 3 Hospital Chokepoints : 98%, 102%, and 106% 9

10 The findings Weekdays Vs Weekends Caboolture Hospital WEEKDAY Chokepoints 100%, 108%, and 113% Caboolture Hospital WEEKEND Chokepoints Choke Point B : 106% 10

11 In Conclusion Do the science Know the your service Understand how the science applies to your service 11

12 Patient AEHRC Enabling hospitals to better manage their resources & hence reduce waiting times 12

13 Thank you CSIRO Australian e-health Research Centre Sankalp Khanna Postdoctoral Research Fellow t e w CSIRO Australian e-health Research Centre Justin Boyle Research Scientist t e w THE AUSTRALIAN E-HEALTH RESEARCH CENTRE

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