Geo Data Institute, University of Southampton

1. Making Good Decisions for: Planning and Managing Health Services & Preventing, Detecting, and Treating Diseases A K Shahani, GeoData Institute & School of Mathematics, University of Southampton, UK Paper presented at the 32nd Annual Meeting of the European Working Group on Operational Research Applied to Health Services, Wroclaw, Poland Geo Data Institute, University of Southampton

2. Making Good Decisions for: Planning and Managing Health Services & Preventing, Detecting, and Treating Diseases Main Message Collaborative work + Good databases + Appropriate statistical analysis including classifications + Detailed stochastic mathematical models + Easy to use computer programs for the models + Evaluation of a range of scenarios = High quality information for making good decisions Geo Data Institute, University of Southampton

3. Example ofCollaboration: Screening for Breast Cancer University of SouthamptonUK Department of Health • General research on • inspection of systems & • screening for detection • of disease • Research on growth • and detection of breast • cancer • Information needed for • decisions about a national • policy for screening for breast cancer • Discussions with • Prof Jackson and Dr Shahani • Results given to • UK Department of Health • Development and • testing of • particular models • and scenarios • Decision about national • policy made by UK DoH

4. Example ofCollaboration: Critical Care Capacities University of SouthamptonSouthampton General Hospital • General research on • classification of patients & • flow of patients • Information needed for • decisions about number of intensive care beds • Development and • testing of particular models • and scenarios • Discussions with Dr Shahani • Results given to • Southampton General Hospital • Funding for critical care modelling • work at local, regional and national • levels • Decision made about number • of intensive care beds • Results given to various health authorities • Decisions made

5. Example ofCollaboration: Control of Trachoma University of Southampton • General research on • detection and treatment of diseases • Professor Ward’s • interest in Trachoma • Development of pilot models for • evaluating strategies for control of Trachoma • International Team: Southampton modellers • + USA and UK Trachoma experts • funded by Edna McConnell Clark Foundation • Detailed data analysis and modelling work • Models and scenario analyses delivered to • Edna McConnell Clark Foundation

6. Collaborations: Developments at University of Southampton • Health modelling work developed by the • Operational Research (OR) Group in Mathematics Department • from about 1975. • Options on modelling for Health Services and for the care of people • with particular diseases arranged in MSc OR course. • Collaborative work with various health organisations • Projects for MSc students, PhD students, Research Assistants. • Consulting work. • Collaborative health modelling work is now an important part of • of the work of Southampton University.

7. Necessary Conditions for Successful Collaborations Modellers Health Professionals • Data analysis, • modelling, and computing • expertise • Good Communications • with health professionals • Appreciation of the need • for detailed stochastic models • Good Communications • with modellers • Appropriate data • Collaborative work on developing, testing, validating and • implementing the necessary detailed stochastic models

8. Example of a Poor Model for Number of Beds • Annual Number of Patients to be admitted = 1350 • Average Length of Stay (LOS) = 3.677 Days • Required bed days = 3.677 x 1350 = 4963.95 • With 85% bed occupancy, • Beds Required = 4963.95/ (0.85 x 365) = 16. • 16 beds could be a good estimate. • OR • Typically it would be a substantial under-estimate because variability in LOS is not taken into account. • Decisions based on this sort of model can be described as • Poor practice

9. Dangers of Using Averages Only 20 small marbles. Average diameter = 1.646 cm 20 large marbles. Average diameter = 2.533 cm Average diameter of all 40 marbles = 2.089 cm Estimated volume of 20 small marbles = 20 {/6 (1.646)3} = 47 cm 3 Actual volume of 20 small marbles =47 cm 3O.K. 20 large marbles: Estimated and actual volume = 170cm 3O.K.  20 small + 20 large marbles: Estimated volume = 191cm 3 actualvolume = 47 + 170 = 217 cm 3 ???? Under-estimate!!! Estimated length of line of 40 marbles = actual length = 83.56cmO.K. Geo Data Institute, University of Southampton

10. Variability: Insight Through a Simple Analysis INPUT X SYSTEM OUTPUT Y= f(x) • E(x) =  Deterministic approximation: E(Y) = f() • Expansion of f(x) about  gives • Y = f() + (x-  ) f ´( ) + (x-  )2 f ( ) ´´/2 + …….. • E(Y) = f() + Variance (x) f ´´ ( )/2 + …….. Geo Data Institute, University of Southampton

11. Use of Averages Only • Use of averages only is dangerous when there is • substantial variability and non-linearity. Patient • flows, disease processes, health care, and use of capacities • involve substantial variability and non-linearity. • Seriousness of bottlenecks will be under-estimated • Resources required will be under-estimated • There will be false expectations about service levels that will be provided Geo Data Institute, University of Southampton

12. Nature of the Necessary Models • Sufficiently detailed • Often based on individual patient flows with the help of • classification of the patients • Complexity, variability, uncertainty, and use of resources • are taken properly into account. • Example: Markov models are often not appropriate • Careful testing and validation of the models • Easy to use computer programs for the models

13. Health Services Models Capture Patient Flows and Use of Resources Arrival of Individual patient. Patient type. Care Unit needed Admission rules for Care Units Required capacities available? No Yes Send elsewhere Admit Treat Discharge Evaluate scenarios for organisation of services, patient arrivals, capacities, admissions, etc.

14. Example: Critical Care Beds in a UK hospital What will be effects of increasing capacities from eleven Level 3 beds in 2002-2003 to eleven Level 3 beds and three Level 2 beds in 2003-2004? Geo Data Institute, University of Southampton

15. Patient Classification Analysis: PORT program Total 660 patients in 2002-2003 414 Level 3 patients 246 Level 2 patients 323 Emergency Patients 91 Elective Patients 199 Emergency Patients 47 Elective Patients Geo Data Institute, University of Southampton

16. Lengths of Stay of Classified Patients • Large variability in lengths of stay. Avoid using • averages only for planning and managing CCU. Geo Data Institute, University of Southampton

17. Distributions of Lengths of Stay • Level 3 Emergency Patients

18. Arrival Profiles of Patients • Arrival profiles by month, day, and hour of the classified • were used. Examples shown are monthly and daily arrival • profiles of Level 3 emergency patients

19. Data and Model Results for 2002-2003

20. Data and Model Predictions for 2002-2003 There is a good match between model predictions and 2002-2003 data Geo Data Institute, University of Southampton

21. Scenarios for Effects of Increased Capacities • 2002-2003 case-mix and lengths of stay (LOS) • Additional 50 Level 2 patients and 2002-2003 LOS • Additional 50 Level 2 patients and changed LOS Case-mix with 50 additional Level 2 patients

22. Changes in 2002-2003 Lengths of Stay Geo Data Institute, University of Southampton

23. Scenarios for Predictions of Effects of Increased Capacities • Critical Care Unit Capacities: 14 beds and 12 nurses Geo Data Institute, University of Southampton

24. CCU_SIM Predictions and 2003-2004 data

25. Some Southampton Health ServicesModels Hospital Capacities:Critical Care Units. A&E + MAU. Hospital Care units. Hospital (existing or new) as a whole. Outpatient Clinics:Orthopaedics, Depressive illness, ENT, Eye, Skin. Waiting Lists: Inpatients and Outpatients. Regional Capacities:Cleft lip and Palate, Coronary, Dental. Service Organisation:Maternity Care.Critical Care “Whole System”:Primary Care, Acute Hospital, Post-Acute Care. Forecasts of daily emergency admissions for all hospitals in England. Met Office project Geo Data Institute, University of Southampton

26. Health Care Modelling • Description of community or patient groups. e.g. age, sex, risk groups • Disease history or patient progress • Interventions e.g. screening, vaccination, treatment, socio-economic actions • Resources needed or planned • Costs of resources Geo Data Institute, University of Southampton

27. Treatment of Breast Cancer • Many are treatments available. • Treatment depends on the severity of cancer. • Stage I: Small moveable tumour in breast only. • Stage II:Tumour not advanced but spread to lymph nodes. • Stage III:Locally advanced tumour. May be attached to chest muscles. • Stage IV:Distant metastases. • Mortality rate is a measures of the goodness of treatment. • Mortality rates vary between hospitals and between countries. Geo Data Institute, University of Southampton

28. Treatment Model Stage 1 Progressive disease Local Distant Local and distant Treatment Disease free Stage 2 No response Stage 3 Treatment Stage 4 Treatment Response Death from Other causes Death from Breast cancer

29. Illustrative Results From Treatment Model Survival by cancer stage at diagnosis.

30. Illustrative Results From Treatment Model Survival by age at diagnosis.

31. Some Southampton Health Care Models Particular Diseases Asthma, Breast Cancer, Cataracts, Cervical Cancer, Chlamydial Infection, Colorectal Cancer, Depressive Illness, Diabetes, HIV/AIDS, Trachoma Geo Data Institute, University of Southampton

32. Use of Good Databases in Health Services • Purpose built databases • Economical • Secure • Easy to use and modify • Practical data • Collection Options • Bar coding • Keyboard entry • Scanning forms • Hand held devices • Voice input • Practical data • Collection Options • Bar coding • Keyboard entry • Scanning forms • Hand held devices • Voice input • Automatic generation of • Graphs and Tables • Summary reports • Patient level reports • Warning signals • Links with other databases • Links with spread sheets Mathematical and statistical tools for exploring databases and obtaining inputs for models Models Spread sheets

33. Contact Details Dr Arjan Shahani, Director, Health Data Analysis and Modelling Group, GeoData Institute, University of Southampton, Southampton SO15 7PJ UK A.K.Shahani@soton.ac.uk akshahani@hotmail.com Geo Data Institute, University of Southampton