Projecting Hospital Acute Projecting Hospital Acute Bed Needs for 2010-2015Bed Needs for 2010-2015

Workshop organized by Workshop organized by

US Embassy and the Belgian Health Federal Public ServiceUS Embassy and the Belgian Health Federal Public Service

March 21, 2006March 21, 2006

Prof. Dr. D.Rossi, FUCAM, MonsProf. Dr. D.Rossi, FUCAM, Mons

OverviewOverview

I.I. Objective of the Objective of the studystudy

II.II. Key data sourcesKey data sources

III.III. Data Data analysisanalysis

IV.IV. Projections at horizon 2015 Projections at horizon 2015

V.V. ConclusionsConclusions

I. Objective of the studyI. Objective of the study

• Projecting the number of acute care bed Projecting the number of acute care bed days that will be required in Belgium by days that will be required in Belgium by the year 2015the year 2015

• Describing and Quantifying the potential Describing and Quantifying the potential impact of key factors such as :impact of key factors such as :– Demographic changesDemographic changes– Patterns of hospital use and profiles of illnessPatterns of hospital use and profiles of illness– Trends in lengths of stayTrends in lengths of stay– Shift toward day hospitalization Shift toward day hospitalization

II. Key data sourcesII. Key data sources1.1. Demographic Data (INS)Demographic Data (INS)

Population forecasts Population forecasts • By sexBy sex• By five-year groupingsBy five-year groupings• At national level and by regionAt national level and by region

2.2. Clinical Data (Health Ministry data Warehouse)Clinical Data (Health Ministry data Warehouse) Hospital Discharge Records 2000-2002Hospital Discharge Records 2000-2002

• Clinical data (age, sex, diagnoses, procedures, discharge Clinical data (age, sex, diagnoses, procedures, discharge disposition,…)disposition,…)

• Hospital stay data (los, admission type,…)Hospital stay data (los, admission type,…)• Patient description (anonymized identification number, ZIP Patient description (anonymized identification number, ZIP

code, …)code, …)

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 Groups400 - APR-DRGs

Assignment on additionalinformation

and clinical detailsLevel of data analysis

3.3. Selecting cases to include in data analysisSelecting cases to include in data analysis Data relative to Data relative to inpatient stays verifying the inpatient stays verifying the

following conditions:following conditions:

– Stays admitted in a general and non Stays admitted in a general and non psychiatric hospital (excluding all cases psychiatric hospital (excluding all cases not from an acute care hospital)not from an acute care hospital)

– Stays relative to Belgian residents (All Stays relative to Belgian residents (All people who live in Belgium)people who live in Belgium)

– Stays relative to classical hospitalization Stays relative to classical hospitalization (H type stay) excluding long-stay patients (H type stay) excluding long-stay patients

NoteNoteAll newborn discharges (less than 29 days) were All newborn discharges (less than 29 days) were removed from the analysis providing to be a “without removed from the analysis providing to be a “without problem” stay (Bed Index M)problem” stay (Bed Index M)

III. Data analysisIII. Data analysis

1.1. Demographic analysis of the Belgian population Demographic analysis of the Belgian population and projection on the National Case Mix and projection on the National Case Mix

2.2. Trend analysis Trend analysis

- relative to the lengths of stay by APR-- relative to the lengths of stay by APR-DRG and age DRG and age groupsgroups

- relative to the shift toward day - relative to the shift toward day hospitalization by APR- hospitalization by APR- DRGDRG

1. Demographic Analysis1. Demographic Analysis

Population is agingPopulation is agingPeople over 65 will make up 21% of the overall

population in 2015( 22,8 % in 2020)Taux de variation de la population par classe d'âge

aux horizons 2010-2015 - Année de référence 2005

-20%

-10%

0%

10%

20%

30%

40%

50%

60%

70%

[00-

01[

[01-

05[

[05-

10[

[10-

15[

[15-

20[

[20-

25[

[25-

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[30-

35[

[35-

40[

[40-

45[

[45-

50[

[50-

55[

[55-

60[

[60-

65[

[65-

70[

[70-

75[

[75-

80[

[80-

85[

[85-

90[

[90-

95[

[95-

100[

[100

-->

2010 2015

Variations in unitsVariations in unitsAge groups with positive and

negative growthAscending sorted variations of the population in units and by age

groups for 2010 -2015 - Reference year : 2005

-150000

-100000

-50000

0

50000

100000

150000

200000

[40-

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[35-

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GE100

[95-

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[25-

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[80-

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[20-

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[90-

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[85-

90[

[55-

60[

[50-

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[65-

70[

[60-

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2010 2015

Inpatient stays profile by age and sex Inpatient stays profile by age and sex groupsgroups (2002) (2002)

Séjours versus Population Femmes 2002

0

50000

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300000

350000

400000

450000

<29j

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Classes d'âge

0

10000

20000

30000

40000

50000

60000

70000

80000

90000

Population 02 Séjours

Séjours versus Population Hommes 2002

0

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300000

350000

400000

450000

<29

jour

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>29

jour

s

[1,5

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classes d'âge

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10000

20000

30000

40000

50000

60000

70000

80000

90000

Population Séjours

National Case Mix by MDC and National Case Mix by MDC and Age GroupsAge Groups

Poids relatifs des MDC par classe d'âge au niveau national en 2002

08

05

06

0414

01

03

11

0923

13

07

Autres

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

[00-01[ [01-05[ [05-10[ [10-15[ [15-20[ [20-25[ [25-30[ [30-35[ [35-40[ [40-45[ [45-50[ [50-55[ [55-60[ [60-65[ [65-70[ [70-75[ [75-80[ [80-85[ [85-90[ [90-95[ [95-100[

GE100

Classes d'âge

Po

ids

(%)

42%

55%

Respiratory S.

Circulatory S.

Pregnancy

& Childbirth

Digestive S.

O.R.L.

36%

Muscoskeletal S. 42%

41% Nervous S95%

49%

• Building a forecasting model for LOS by APR-DRG Building a forecasting model for LOS by APR-DRG and age groupand age group

• Selecting DRGs with a statistically significant trend Selecting DRGs with a statistically significant trend (R(R22 = 0,6) = 0,6)

55% of APR-DRGs corresponding to55% of APR-DRGs corresponding to 79% of hospital stays and 79% of hospital stays and 75% of hospitals days have a significant trend in shorter 75% of hospitals days have a significant trend in shorter length of staylength of stay

2. Trend Analysis : 2. Trend Analysis : Length of StayLength of Stay

APRDRG : 302 "Major joint & limb reattach proc of lower extrem exc for trauma"

20,7520,17

19,2218,38

17,6216,64

15,66

y = 22,045e-0,047x

R2 = 0,9914

0

5

10

15

20

25

30

MP96 MP97 MP98 MP99 MP00 MP01 MP02

302 Exponentiel (302)

Horizon 2010 10,89Horizon 2015 8,61

DRG with significant trend

DRG without significant trend

425.089 hospital days

214.055 hospital days

APRDRG : 42 "Degenerative nervous system disorders - MDC 01"

19,2318,58

17,47

19,05 18,64 18,80 18,55

y = -0,0149x + 18,675R2 = 0,0033

0

5

10

15

20

25

30

35

40

MP96 MP97 MP98 MP99 MP00 MP01 MP02

42 Linéaire (42)

3.Shift analysis toward day 3.Shift analysis toward day hospitalizationhospitalization

− Selecting, in the data base, the individual stays equal to Selecting, in the data base, the individual stays equal to 1, 2, and 3 days1, 2, and 3 days

− Picking, in the data base, APR-DRGs with a Picking, in the data base, APR-DRGs with a negative correlation between traditional and negative correlation between traditional and day hospitalization stays.day hospitalization stays.

APR-DRG 73 : LENS PROCEDURES W OR W/0 VITRECTOMY Corrélation= - 0,99

0

10000

20000

30000

40000

50000

60000

70000

1996 1997 1998 1999 2000 2001 2002

H. Traditionelle H. Jour

APR-DRG 320 AUTRES INTERVENTIONS DU SYSTEME MUSCULOSQUELETTIQUE

ET TISSU CONJONCTIF

corrélation + 0,57

0

2000

4000

6000

8000

10000

12000

14000

16000

1996 1997 1998 1999 2000 2001 2002

H.Traditionnelle H.Jour

Example Example to the contrary

− Identifying, for stays ≤ 3 days, APR-Identifying, for stays ≤ 3 days, APR-DRGs with a ratio Day/Traditional > DRGs with a ratio Day/Traditional > 1/3 on the basis of the last 3 years 1/3 on the basis of the last 3 years discharge data (2000-2001-2002)discharge data (2000-2001-2002)

− Attributing a decreasing probability Attributing a decreasing probability

Probability 1, if stay = 1 dayProbability 1, if stay = 1 day

Probability 0.6, if stay = 2 Probability 0.6, if stay = 2 daysdays

Probability 0.4, if stay = 3 Probability 0.4, if stay = 3 daysdays

Simulated Scenarios relative to the shift Simulated Scenarios relative to the shift from traditional toward day hospitalizationfrom 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, Without Stays=1 day, negative correlation, Day Ratio >1/3, prob 100%prob 100%

=2 days, =2 days, negative correlation, Day Ratio negative correlation, Day Ratio >1/3, prob 60%>1/3, prob 60%

= 3 days, = 3 days, negative correlation, Day Ratio >1/3, negative correlation, Day Ratio >1/3, prob 40%prob 40%

Projections integrating demographic evolution Projections integrating demographic evolution and ALOS forecasting models : MODEL 1and ALOS forecasting models : MODEL 1

Projections integrating various scenarios Projections integrating various scenarios relative to the shift toward day hospitalization : relative to the shift toward day hospitalization : MODEL2MODEL2

IV. IV. Projections and Projections and implications of the Results at implications of the Results at

horizons 2010-2015horizons 2010-2015

FIRST MODEL : RESULTS INTEGRATING FIRST MODEL : RESULTS INTEGRATING DEMOGRAPHIC AND LENGTH OF STAY DEMOGRAPHIC AND LENGTH OF STAY

EVOLUTIONSEVOLUTIONS

1,68

5,16

-3,31

1,68

0

2,6

8,54

-4,28

3,9

1,3

-6

-4

-2

0

2

4

6

8

10

Population Inp.Admissions Los Inp.Days Inp.bed need

Var

iati

on

en

%

2010

2015

MODEL 1 : VARIATIONS IN % REFERRING TO 2002

SECOND MODEL : RESULTS INTEGRATING SECOND MODEL : RESULTS INTEGRATING SCENARIOS WITHSCENARIOS WITH SHIFT TOWARD DAY

HOSPITALIZATION

MOD 2 : Scenarios with Shift toward day hospitalization

Inpatient bed need /1000 inh. & 100% Occupancy :Mod 1 : projections with demoraphic changes and LOS evolution

Mod 2 : integrating Impact of shift toward day hospitalization.

3,81 3,813,81

3,22

3,86

3,25

2,8

3

3,2

3,4

3,6

3,8

4

MOD 1 MOD 2

2002 2010 2015

+1,3%

- 16%

- 15%

Impact of shift toward day hospitalization on the Impact of shift toward day hospitalization on the indicator : indicator :

Inpatient bed need / 1000 inh. & 100 % Inpatient bed need / 1000 inh. & 100 % OccupancyOccupancy

Inpatient acute bed needs in number of Inpatient acute bed needs in number of bedsbeds

according simulated scenarios and according simulated scenarios and occupancy rates occupancy rates

at horizons 2010-2015at horizons 2010-2015MOD 2 : Scenarios with Shift toward day hospitalization

Projections for 2010 MOD 1: Demographic

and LOS Evolution

Without Stays : [DS=1;r<0; Ratio?1/3]

Without Stays : [DS=1;r<0;Ratio?1/3] [DS=2;r<0;Ratio?1/3]

Without Stays : [DS=1;r<0;Ratio?1/3] [DS=2;r<0;Ratio?1/3] [DS=3;r<0;Ratio?1/3]

Without Stays : [DS=1;r<0;Ratio;?1/3]

60%[DS=2;r<0;Ratio?1/3] 40%[DS=3;r<0;Ratio?1/3]

Bed Number (Occ.100%) 40165 36269 33585 31598 33867

Bed Number (Occ. 80%) 50206 45336 41981 39498 42334

Bed Number (Occ. 70%) 57378 51813 47979 45140 48382

Projections for 2015MOD 1: Demographic

and LOS Evolution

Without Stays : [DS=1;r<0; Ratio?1/3]

Without Stays : [DS=1;r<0;Ratio?1/3] [DS=2;r<0;Ratio?1/3]

Without Stays : [DS?1;r<0;Ratio?1/3] [DS=2;r<0;Ratio?1/3] [DS=3;r<0;Ratio?1/3]

Without Stays : [DS=1;r<0;Ratio;?1/3]

60%[DS=2;r<0;Ratio?1/3] 40%[DS=3;r<0;Ratio?1/3]

Bed Number (Occ.) 100%) 41041 36926 34204 32198 34495

Bed Number (Occ. 80%) 51301 46158 42755 40247 43119

Bed Number (Occ. 70%) 58630 52752 48863 45997 49279

Results in Number of beds with 100% at Results in Number of beds with 100% at occupancy rateoccupancy rate

horizons 2010-2015horizons 2010-2015

40165 41041

33867 34495

0

5000

10000

15000

20000

25000

30000

35000

40000

45000

2010 2015

Mod 1

Mod2

Projections for 2010 MOD 1: Demographic

and LOS Evolution

Without Stays : [DS=1;r<0; Ratio?1/3]

Without Stays : [DS=1;r<0;Ratio?1/3] [DS=2;r<0;Ratio?1/3]

Without Stays : [DS=1;r<0;Ratio?1/3] [DS=2;r<0;Ratio?1/3] [DS=3;r<0;Ratio?1/3]

Without Stays : [DS=1;r<0;Ratio;?1/3]

60%[DS=2;r<0;Ratio?1/3] 40%[DS=3;r<0;Ratio?1/3]

Inp.beds/1000 inh.(Occ.100%) 3,81 3,44 3,19 3,00 3,22

Inp.beds/1000 inh.(Occ.80%) 4,77 4,31 3,99 3,75 4,02

Inp.beds/1000 inh.(Occ.70%) 5,45 4,92 4,56 4,29 4,59

Projections for 2015MOD 1: Demographic

and LOS Evolution

Without Stays : [DS=1;r<0; Ratio?1/3]

Without Stays : [DS=1;r<0;Ratio?1/3] [DS=2;r<0;Ratio?1/3]

Without Stays : [DS?1;r<0;Ratio?1/3] [DS=2;r<0;Ratio?1/3] [DS=3;r<0;Ratio?1/3]

Without Stays : [DS=1;r<0;Ratio;?1/3]

60%[DS=2;r<0;Ratio?1/3] 40%[DS=3;r<0;Ratio?1/3]

Inp.beds/1000 inh.(Occ.100%) 3,86 3,48 3,22 3,03 3,25

Inp.beds/1000 inh.(Occ.80%) 4,83 4,34 4,02 3,79 4,06

Inp.beds/1000 inh.(Occ.70%) 5,52 4,96 4,60 4,33 4,64

SYNTHESIS : INPATIENT BEDS / 1000 INH.

2002 : 3,81 beds /1000 inh & 100% occupancy

2015 : 3,25 beds /1000 inh. & 100% occupancy14.5%

MOD 2 : Scenarios with Shift toward day hospitalization

1.1. At the question : Will we have enough ? At the question : Will we have enough ? We give an affirmative answer if the We give an affirmative answer if the trends toward shorter stays and trends toward shorter stays and increased day care do indeed continueincreased day care do indeed continue

2.2. Keep in mind the importance of Keep in mind the importance of continuing to monitor changes in continuing to monitor changes in population changes and hospital usage in population changes and hospital usage in coming yearscoming years

3.3. Current use of hospital use as described Current use of hospital use as described in this report, does not necessarily reflect in this report, does not necessarily reflect “best practice”. Studies have shown that “best practice”. Studies have shown that people who do not need hospital care are people who do not need hospital care are occupied acute care inpatient beds. occupied acute care inpatient beds. Changes in practice and/or policy have Changes in practice and/or policy have the potential to change or reduce the the potential to change or reduce the inappropriate hospital use.inappropriate hospital use.

ConclusionsConclusions

4.4. Study focused exclusively on the Study focused exclusively on the impact on acute inpatient care. impact on acute inpatient care. Projections of other non acute health Projections of other non acute health services should also be analyzedservices should also be analyzed

5.5. Changes in the overall health status of Changes in the overall health status of the population would also likely affect the population would also likely affect the need for inpatient carethe need for inpatient care

6.6. Changes in the make up of the Changes in the make up of the population (including socioeconomic population (including socioeconomic status) may affect the need of hospital status) may affect the need of hospital too.too.

7.7. Projections should integrate patient Projections should integrate patient flows and inter-countries migration.flows and inter-countries migration.