1. P ATIENT J OURNEY D ATA A NALYSIS Topic Code: COMP5700 Name: Letisia Odeke Supervisor: Dr Shaowen Qin

2. P RESENTATION O UTLINE Significance Related Studies Proposal Progression Analysing Wards Admission dates Timeline Conclusion

3. S IGNIFICANCE High level Objectives Overcrowding/ access blocking Improved patient care throughout a patients’ journey Patient satisfaction Hospitals are expected to increase throughput Low level Objectives Fundamental issues Analyse Patient data to id cause of prolonged patient waiting time FMC, Tertiary teaching hospital

4. R ELATED S TUDIES Many studies on Clinical redesign Focusing on patients throughout their journey Clinical Redesign in NSW (Patient Journey) [1] Mapping the journey Questioning the status quo Redesigning care at FMC [2] Lean Thinking AAU established Application of process redesign on planned arrivals [3] Planned arrivals scheduled to suit clinical preferences Waiting list management

5. P ROPOSAL Patient journey data from FMC Grouping the data Clustering is used to crystallize data supplied Exposing patterns and Trends Make comparisons between the derived data Highlights the relationship between different entities Simulation model of patient journeys can be established Analyse the following; Patient movement patterns based on wards and unit Admission dates by the months of the year, days of the week and hours of the day

6. P ROPOSAL C ONT. Outcomes vs. Expectations Hospital Analysis Geared toward operation review Measuring performance, throughput, LOS, waiting time Objectives Identify root causes using data provided Trends

7. P ROGRESSION Patient journey data 1048576 records 2003-2008 Determined the number of different units and wards 97 wards 88 units (Care personnel) Top 10 wards Determined by the number of records

8. W ARDS 6A has the most records Most number of different Units WARDSNUMBER OF RECORDS (%)NUMBER OF UNITS (%) 6A5.461 5E4.956 5C4.653 H@H4.272 Fig.1 Table depicting wards, total records and total units used

9. A DMISSION D ATES - Y EARLY Grouping according to yearly patient load Calculating the total number of patients Increase in patient load between 2003 and 2008 YEARPATIENT LOAD 2003140646 2004140253 2005155961 2006156881 2007164851 2008170900 Fig.3 Table depicting yearly patient load

10. A DMISSION D ATES - M ONTHLY Monthly increase every year June the highest rate of admission? Expected to see an increase in June as more people fall sick in the winter months Fig.4 Table showing number of patients admitted monthly MONTHSJANUARYJUNEDECEMBER 20037.8%8.3%8.2% 20047.6%8.6%8.3% 20057.7%8.3%

11. A DMISSION D ATES - D AILY Slight increase of patient load at the beginning of the week Decrease in patient load over the weekends DAY2003 (%)2004 (%)2005 (%)2006 (%)2007 (%)2008 (%) Monday1414.512.413 15.7 Tuesday14.615.6151615.616.4 Wednesday15.41615.21515.314.4 Thursday1615.716.416.31615.2 Friday1514.51515.214.5 Saturday12.5 13121312.2 Sunday1110.912.611.81211.2 Fig.6 Table showing number of patients admitted daily in the first full week of January

12. T IMELINE

13. C ONCLUSION Grouping the data Identify trends in the patients load Data is somewhat organised Fundamental causes of patient waiting time Low level objectives help reach the higher level goals Challenges Time management Data Grouping Skills to be acquired Statistical tools: MatLab

14. R EFERENCES 1. Ben-Tovim DI, Dougherty M, et al. Patient journeys: the process of clinical redesign 2. Ben-Tovim DI, Bassham JE, Bennett DM, et al. Redesigning care at the Flinders Medical Centre: clinical process redesign using “lean thinking” 3. MacLellan DG, Cregan PC, McCaughan BC, et al. Applying clinical process redesign methods to planned arrivals in New South Wales hospitals

15. Thank you Questions