1. Geo Data Institute, University of Southampton 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 Making Good Decisions for: Planning and Managing Health Services & Preventing, Detecting, and Treating Diseases

2. Geo Data Institute, University of Southampton 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

3. Example of Collaboration: Screening for Breast Cancer University of Southampton UK 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 Development and testing of particular models and scenarios Results given to UK Department of Health Decision about national policy made by UK DoH

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

5. Example of Collaboration: 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 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 Modellers Health Professionals 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. Geo Data Institute, University of Southampton 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 3 O.K. 20 large marbles: Estimated and actual volume = 170 cm 3 O.K. 20 small + 20 large marbles: Estimated volume = 191 cm 3 actual volume = 47 + 170 = 217 cm 3 ???? Under-estimate!!! Estimated length of line of 40 marbles = actual length = 83.56cmO.K.

10. Geo Data Institute, University of Southampton 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 + ……..

11. Geo Data Institute, University of Southampton 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 Use of Averages Only

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

14. Geo Data Institute, University of Southampton 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? Example: Critical Care Beds in a UK hospital

15. Geo Data Institute, University of Southampton Total 660 patients in 2002-2003 Patient Classification Analysis: PORT program 414 Level 3 patients246 Level 2 patients 323 Emergency Patients 199 Emergency Patients 91 Elective Patients 47 Elective Patients

16. Geo Data Institute, University of Southampton Lengths of Stay of Classified Patients AllLvl 3 Emrg Lvl 3 Elec Lvl 2 Emrg Lvl 2 Elec No. of patients6603239119947 Mean LOS5.817.074.415.302.05 Minimum LOS0.010.070.210.010.03 Maximum LOS78.6045.9429.9878.6010.92 5% LOS0.290.370.770.240.39 95% LOS23.8527.8015.1521.746.44 Large variability in lengths of stay. Avoid using averages only for planning and managing CCU.

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 Level 2 Level 3 Total DataModelDataModelData Model Emergency Admissions 95% Limits 199198323321522519 500-567 Elective Admissions 95% Limits 47469189138135 128-157 Total Admissions 95% Limits 244246414410660654 636-703 Deferrals 95% Limits ---- ---------56?61 52-82 Transfers 95% Limits ---- ----- ----178156 134-208 Bed Occupancy 95% Limits ---- ---------95% 93-99%

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

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 Level 2 Patients Level 3 Patients All Patients Emergency 308 433741 Elective 56 91138 Total364524888 Case-mix with 50 additional Level 2 patients

22. Geo Data Institute, University of Southampton Changes in 2002-2003 Lengths of Stay 02-03 Mean Values Std.Dev Incrs Mean 1 Std. Dev Incrs Mean 2 Std. Dev Level 2 Emergency 5.3112.066.0013.207.0015.40 Level 2 Elective 1.982.472.504.002.504.00 Level 3 Emergency 7.1415.008.0017.609.0019.80 Level 3 Elective 4.387.245.008.005.008.00

23. Geo Data Institute, University of Southampton Scenarios for Predictions of Effects of Increased Capacities Case -Mix of PatientsLOS Scenario 12002-2003 Scenario 202-03 + 50 Additional Level 22002-2003 Scenario 32002-2003Increase 1 Scenario 402-03 + 50 Additional Level 2Increase 1 Scenario 52002-2003Increase 2 Scenario 602-03 + 50 Additional Level 2Increase 2 Critical Care Unit Capacities: 14 beds and 12 nurses

24. CC U_SIM Predictions and 2003-2004 data Model Data Scn 1Scn 2Scn 3Scn 4Scn 5 Scn 6 03– 04 Lvl 2 Emrg239269224249212231253 Lvl 2 Elec465647564755 Lvl 3 Emrg366363332327305300294 Lvl 3 Elec91 9391929182 Total Adm742779696723656677684 Deferrals29 21.1% 34 23.1% 41 29.3% 48 32.7% 52 37.4% 58 39.7% 49 35.8% Transfers96 13.7% 106 14.4% 140 20.1% 160 21.7% 183 26.1% 208 28.2% 195 26.2% Bed Occ84%85%88%90%91%93%94%

25. Geo Data Institute, University of Southampton 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 Some Southampton Health Services Models

26. Geo Data Institute, University of Southampton 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

27. Geo Data Institute, University of Southampton 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.

28. Treatment Model Stage 1 Stage 2 Stage 3 Stage 4 Treatment Disease free No response Response Treatment Death from Other causes Progressive disease Local Distant Local and distant 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. Geo Data Institute, University of Southampton Particular Diseases Asthma, Breast Cancer, Cataracts, Cervical Cancer, Chlamydial Infection, Colorectal Cancer, Depressive Illness, Diabetes, HIV/AIDS, Trachoma Some Southampton Health Care Models

32. Use of Good Databases in Health Services 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 Purpose built databases Economical Secure Easy to use and modify Mathematical and statistical tools for exploring databases and obtaining inputs for models Models Automatic generation of Graphs and Tables Summary reports Patient level reports Warning signals Links with other databases Links with spread sheets Spread sheets

33. Geo Data Institute, University of Southampton 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