1. Projecting Hospital Acute Bed Needs for 2010-2015 Workshop organized by US Embassy and the Belgian Health Federal Public Service March 21, 2006 Prof. Dr. D.Rossi, FUCAM, Mons

2. Overview I.Objective of the study II.Key data sources III.Data analysis IV.Projections at horizon 2015 V.Conclusions

3. I. Objective of the study Projecting the number of acute care bed days that will be required in Belgium by the year 2015Projecting the number of acute care bed days that will be required in Belgium by the year 2015 Describing and Quantifying the potential impact of key factors such as :Describing and Quantifying the potential impact of key factors such as : –Demographic changes –Patterns of hospital use and profiles of illness –Trends in lengths of stay –Shift toward day hospitalization

4. II. Key data sources 1.Demographic Data (INS)  Population forecasts By sexBy sex By five-year groupingsBy five-year groupings At national level and by regionAt national level and by region 2.Clinical Data (Health Ministry data Warehouse)  Hospital Discharge Records 2000-2002 Clinical data (age, sex, diagnoses, procedures, discharge disposition,…)Clinical data (age, sex, diagnoses, procedures, discharge disposition,…) Hospital stay data (los, admission type,…)Hospital stay data (los, admission type,…) Patient description (anonymized identification number, ZIP code, …)Patient description (anonymized identification number, ZIP code, …)

5. Data Warehouse : Discharge Records 2,790,664 entries (2000) 2,958,314 entries (2002) Major Diagnosis Categories 25-MDC Assignment on demographic and clinical details Diagnosis Related Groups 400 - APR-DRGs Assignment on additional information and clinical details Level of data analysis

6. 3.Selecting cases to include in data analysis  Data relative to inpatient stays verifying the following conditions: –Stays admitted in a general and non psychiatric hospital (excluding all cases not from an acute care hospital) –Stays relative to Belgian residents (All people who live in Belgium) –Stays relative to classical hospitalization (H type stay) excluding long-stay patients Note All newborn discharges (less than 29 days) were removed from the analysis providing to be a “without problem” stay (Bed Index M)

7. III. Data analysis 1.Demographic analysis of the Belgian population and projection on the National Case Mix 2.Trend analysis - relative to the lengths of stay by APR-DRG and age groups - relative to the shift toward day hospitalization by APR- DRG

8. 1. Demographic Analysis  Population is aging People over 65 will make up 21% of the overall population in 2015 ( 22,8 % in 2020)

9.  Variations in units Age groups with positive and negative growth

10.  Inpatient stays profile by age and sex groups (2002)

11.  National Case Mix by MDC and Age Groups 42% 55% Respiratory S. Circulatory S. Pregnancy & Childbirth Digestive S. O.R.L. 36% Muscoskeletal S. 42% 41% Nervous S 95% 49%

12. Building a forecasting model for LOS by APR-DRG and age groupBuilding a forecasting model for LOS by APR-DRG and age group Selecting DRGs with a statistically significant trend (R 2 = 0,6)Selecting DRGs with a statistically significant trend (R 2 = 0,6) 55% of APR-DRGs corresponding to 79% of hospital stays and 75% of hospitals days have a significant trend in shorter length of stay 2. Trend Analysis : Length of Stay DRG with significant trendDRG without significant trend 425.089 hospital days214.055 hospital days

13. 3.Shift analysis toward day hospitalization − Selecting, in the data base, the individual stays equal to 1, 2, and 3 days − Picking, in the data base, APR-DRGs with a negative correlation between traditional and day hospitalization stays. ExampleExample to the contrary

14. −Identifying, for stays ≤ 3 days, APR-DRGs with a ratio Day/Traditional > 1/3 on the basis of the last 3 years discharge data (2000-2001-2002) −Attributing a decreasing probability Probability 1, if stay = 1 day Probability 0.6, if stay = 2 days Probability 0.4, if stay = 3 days

15.  Simulated Scenarios relative to the shift from traditional toward day hospitalization Without Stays=1 day, negative correlation, Day Ratio > 1/3Without Stays=1 day, negative correlation, Day Ratio > 1/3 Without Stays≤ 2 days, negative correlation, Day Ratio > 1/3Without Stays≤ 2 days, negative correlation, Day Ratio > 1/3 Without Stays≤ 3 days, negative correlation, Day Ratio > 1/3Without Stays≤ 3 days, negative correlation, Day Ratio > 1/3 Without Stays=1 day, negative correlation, Day Ratio >1/3, prob 100%Without Stays=1 day, negative correlation, Day Ratio >1/3, prob 100% =2 days, negative correlation, Day Ratio >1/3, prob 60% =2 days, negative correlation, Day Ratio >1/3, prob 60% = 3 days, negative correlation, Day Ratio >1/3, prob 40% = 3 days, negative correlation, Day Ratio >1/3, prob 40%

16.  Projections integrating demographic evolution and ALOS forecasting models : MODEL 1  Projections integrating various scenarios relative to the shift toward day hospitalization : MODEL2 IV. Projections and implications of the Results at horizons 2010-2015

17.  FIRST MODEL : RESULTS INTEGRATING DEMOGRAPHIC AND LENGTH OF STAY EVOLUTIONS

18. MODEL 1 : VARIATIONS IN % REFERRING TO 2002

19.  SECOND MODEL : RESULTS INTEGRATING SCENARIOS WITH  SECOND MODEL : RESULTS INTEGRATING SCENARIOS WITH SHIFT TOWARD DAY HOSPITALIZATION MOD 2 : Scenarios with Shift toward day hospitalization

20. + 1,3% - 16%- 15% Impact of shift toward day hospitalization on the indicator : Inpatient bed need / 1000 inh. & 100 % Occupancy

21.  Inpatient acute bed needs in number of beds according simulated scenarios and occupancy rates at horizons 2010-2015 MOD 2 : Scenarios with Shift toward day hospitalization

22.  Results in Number of beds with 100% at occupancy rate horizons 2010-2015

23. SYNTHESIS : INPATIENT BEDS / 1000 INH. 2002 : 3,81 beds /1000 inh & 100% occupancy 2015 : 3,25 beds /1000 inh. & 100% occupancy 14.5% MOD 2 : Scenarios with Shift toward day hospitalization

24. 1.At the question : Will we have enough ? We give an affirmative answer if the trends toward shorter stays and increased day care do indeed continue 2.Keep in mind the importance of continuing to monitor changes in population changes and hospital usage in coming years 3.Current use of hospital use as described in this report, does not necessarily reflect “best practice”. Studies have shown that people who do not need hospital care are occupied acute care inpatient beds. Changes in practice and/or policy have the potential to change or reduce the inappropriate hospital use. Conclusions

25. 4.Study focused exclusively on the impact on acute inpatient care. Projections of other non acute health services should also be analyzed 5.Changes in the overall health status of the population would also likely affect the need for inpatient care 6.Changes in the make up of the population (including socioeconomic status) may affect the need of hospital too. 7.Projections should integrate patient flows and inter-countries migration.