Part 1. Introduction to the business project

Objective

The objective is to predict hospital capacity need and composition for (area’s in) the Netherlands for the next 25 years. Broadly, a hospital consists of 6 different parts, which have very different usage, building requirements and costs:

  1. Outpatient beds
  2. Inpatient beds
  3. Outpatient operating rooms
  4. Inpatient operating rooms
  5. Consultation rooms
  6. Imaging diagnostics rooms

Business use

A number of stakeholders would use these predictions for their decision-making process. Building a new hospital or rebuilding an outmoded hospital is a very expensive process (~200-500M EUR) with a long lead time (from design to finish ~5 years). This brings along significant uncertainty of the future demand. Hospitals are developed for a usage period of about 20-30 years. Therefore, different parties, directly and indirectly, involved in the building and financing of a hospital benefit from better estimations of the future demand for the hospital:

  1. Hospitals: deciding on (re)building a hospital and the size and composition
  2. Banks: assessing investment proposals
  3. Insurers: assessing financing decision for (part of) the build, pricing of provided care, contract negotiations with hospitals
  4. (Local) governments: assessing financing decision for (part of) the build, assessing whether care is sufficiently ensured for the inhabitants

Part 2. Process

We follow a number of predefined steps in this project, which can be grouped in thre three categories: getting the data ready, doing the calculations, and showing the results. The process is:

  1. Getting the data ready
    1. Collect data from the different sources
    2. Clean the data (for example remove double headers)
    3. Do sanity checks and cross check the data against each other (for example, there should be ~17M people in 2017 in the country)
    4. Visualize and summarize the data
    5. Check how data can be matched and which assumptions are needed (for example for outpatient visits, we don’t have information on age and gender, so we will assume that distribution is equal to that of outpatient admissions)
  2. Perform calculations
    1. Count number of activities in 6 categories (outpatient admissions, inpatient nursing days, outpatient surgeries, inpatient surgeries, outpatient visits, imaging diagnostics) per region, age and gender
    2. Multiply number of activities with the corresponding space requirement in m2 to get required m2 per type of space, region in Netherlands, gender and age
    3. Multiply with demography developments to get required m2 per type of space, region in Netherlands, gender and age for the years 2018-2040.
  3. Visualize results per year and type of space

Part 3. Data

Description

All data is publicly available through www.cbs.nl and www.opendis.nl. We will use three different types of data:

  1. Registered care activities per age and gender for the years 2012-2015. This data is in multiple different files:
    1. hospital admissions (inpatient and outpatient) per age and gender
    2. surgeries (inpatient and outpatient) per age and gender
    3. all activities categorized in 11 categories, not per age and gender. Here fore we will make the assumption that the distribution is equal to that of admissions
  2. Number of square meters needed per building element and single care activity in a year
  3. Forecast of demography of the population (age and gender) for the years 2015-2040.

Gathering and cleaning

We read our data files. The relevant information has to be extracted and put in a common format

The first dataset is the population forecast per year from 2014 to 2040, per region, type of region(city or land) and gender. This is how the first lines of the data looks:

Year Region Type.of.region Gender Age Population_x1000
01 2014 Alkmaar City Mannen Totaal leeftijd 46.6
02 2014 Alkmaar City Mannen 0 tot 5 jaar 2.8
03 2014 Alkmaar City Mannen 5 tot 10 jaar 2.6
04 2014 Alkmaar City Mannen 10 tot 15 jaar 2.7
05 2014 Alkmaar City Mannen 15 tot 20 jaar 2.5

The second dataset provides the surface requirement in m² for the various activities that need to be performed

Dutch.name.activity.cluster Englisch.name.activity.cluster Bruto.space.per.activity Activity.related.space Surcharge Total.normative.space
01 Dagverpleging Day admissions 0.076 0.000 0.012 0.088
02 Verpleegdagen Nursing days 0.120 0.054 0.032 0.206
03 Polikliniek Outpatient visits 0.027 0.024 0.009 0.060
04 Operaties Surgeries 0.154 0.112 0.042 0.308
05 Beeldvormende diagnostiek Imaging diagnostics 0.019 0.000 0.004 0.023

the next dataset provides the number of surgeries performed per gender and age between 1995 and 2010. These data required a first cleaning, as it was incomplete. the missing information is replaced by 0 to obtain a clean table that we can work with

Surgery Year Gender Age Total_surgeries Total_surgeries_per_10000_inhabitants Inpatient_surgeries Inpatient_surgeries_per_10000_inhabitants Outpatient_surgeries Outpatient_surgeries_per_10000_inhabitants
01 All 1995 Mannen 0 tot 20 jaar 127928 664.9 33696 175.1 94232 489.7
02 All 1995 Mannen 20 tot 45 jaar 120332 386.5 76578 246 43754 140.5
03 All 1995 Mannen 45 tot 65 jaar 108633 608.9 81900 459.1 26733 149.8
04 All 1995 Mannen 65 tot 80 jaar 87010 1284.4 75760 1118.3 11250 166.1
05 All 1995 Mannen 80 jaar of ouder 21813 1511 19055 1319.9 2758 191

The next dataset indexes some information on hospital admissions per gender and age from 1981 and 2012

Year Gender Age Total_admissions_per_10000_inhabitants Outpatient_admissions_per_10000_inhabitants Inpatient_admissions_per_10000_inhabitants Nursingdays_per_10000_inhabitants Average_nursing_days_per_inpatient_admission Average_poulation
01 1981 Mannen 0 jaar 0 0 7419.4 73696.9 9.9 90907
02 1981 Mannen 1 tot 20 jaar 0 0 657.4 5563.1 8.5 2140447
03 1981 Mannen 20 tot 45 jaar 0 0 616.2 6703.7 10.9 2771806
04 1981 Mannen 45 tot 65 jaar 0 0 1238.6 17623.5 14.2 1383986
05 1981 Mannen 65 tot 80 jaar 0 0 2263.6 42526.7 18.8 560848

The next dataset gives the number of details activities per specialisation. Several reference tables are also read to interpret the activities, DOT’s and specialisations

VERSIE DATUM_BESTAND PEILDATUM JAAR BEHANDELEND_SPECIALISME_CD TYPERENDE_DIAGNOSE_CD ZORGPRODUCT_CD ZORGACTIVITEIT_CD ZORGPROFIELKLASSE_CD AANTAL_PAT AANTAL_SUBTRAJECT AANTAL_ZAT
01 1 2017-01-16 2017-01-01 2014 301 101 79699007 39813 4 115 116 123
02 1 2017-01-16 2017-01-01 2014 301 101 79699007 39823 4 62 62 85
03 1 2017-01-16 2017-01-01 2014 301 101 79699013 82042 7 4 4 4
04 1 2017-01-16 2017-01-01 2014 301 102 79699007 39824 4 9 9 12
05 1 2017-01-16 2017-01-01 2014 301 102 79699008 190060 1 13 13 18
VERSIE DATUM_BESTAND PEILDATUM ZORGACTIVITEIT_CD OMSCHRIJVING ZORGPROFIELKLASSE_CD ZORGPROFIELKLASSE_OMS
01 1 2017-01-16 2017-01-01 35135 Niet operatieve ambulante behandeling van haemorrhoïden door middel van scleroseren, bandligatie, infraroodcoagulatie of cryochirurgie. De eerste behandeling. 5 OPERATIEVE VERRICHTINGEN
02 1 2017-01-16 2017-01-01 39239 Selectie allogeen navelstrengbloed bij stamceltransplantatie. 6 OVERIGE THERAPEUTISCHE ACTIVITEITEN
03 1 2017-01-16 2017-01-01 90707 Fusie van image-datasets tbv treatment planning. 6 OVERIGE THERAPEUTISCHE ACTIVITEITEN
04 1 2017-01-16 2017-01-01 39760 Brainstem auditory evoked potentials (BAEP/BER) met auto-akoestische emissie. 4 DIAGNOSTISCHE ACTIVITEITEN
05 1 2017-01-16 2017-01-01 38440 Cervicale discectomie. 5 OPERATIEVE VERRICHTINGEN
VERSIE DATUM_BESTAND PEILDATUM DIAGNOSE_CD SPECIALISME_CD DIAGNOSE_OMSCHRIJVING
01 1 2017-01-16 2017-01-01 51 302 Afwijkingen mondholte
02 1 2017-01-16 2017-01-01 1567 305 Overige enthesopathie elleboog/onderarm
03 1 2017-01-16 2017-01-01 999 362 ICC
04 1 2017-01-16 2017-01-01 109 318 leverbiopt
05 1 2017-01-16 2017-01-01 121 318 videocapsule endoscopie
VERSIE DATUM_BESTAND PEILDATUM ZORGPRODUCT_CD LATIJN_OMS CONSUMENT_OMS DECLARATIE_VERZEKERD_CD DECLARATIE_ONVERZEKERD_CD
01 1 2017-01-16 2017-01-01 149599001 Uitval standaard | Urogenitaal glomeruli/nier/ureter
02 1 2017-01-16 2017-01-01 69499005 Uitval intensieve/ invasieve therapie | Snijdende specialismen | Carpaaltunnelsyndroom | Zenuwstelsel zenuw/-wortel/-plexus
03 1 2017-01-16 2017-01-01 109599015 Uitval intensieve/ invasieve therapie | Ademh pleura
04 1 2017-01-16 2017-01-01 29999015 Uitval niet operatief | Wervelkolom | Nieuwv benigne/onbek ov/nno
05 1 2017-01-16 2017-01-01 990016351 Diagnosen gastroenterologie overig | Klin kort | Met GE activiteiten specifiek | Kindergeneeskunde Maximaal 5 verpleegligdagen (met behandeling of onderzoek door de maag-darm-leverarts) bij Een aandoening van maag / darm / lever 14B801
VERSIE DATUM_BESTAND PEILDATUM SPECIALISME_CD OMSCHRIJVING
01 1 2017-01-16 2017-01-01 303 Medisch specialisten, chirurgie
02 1 2017-01-16 2017-01-01 458 Fysiotherapeuten, manuele therapie, kinderfysiotherapie, oedeemtherapie en geriatrie
03 1 2017-01-16 2017-01-01 9457 Psychologische zorgverleners, kinder- en jeugdpsycholoog NIP, GZ-psychologie, 1e lijn
04 1 2017-01-16 2017-01-01 7695 Leveranciers hulpmiddelen, hulpmiddelen voor communicatie, informatie en signalering
05 1 2017-01-16 2017-01-01 8404 Overige artsen, irisscopie

To ease the data cleaning, we are also loading a technical table which identifies the various age groups and gender, as these are not homogeneous across the raw data

Gender Forecast_age Adm_age Surg_Age Age_gender_ID
01 Mannen Totaal leeftijd 0
02 Mannen 0 tot 5 jaar 0 jaar 0 tot 20 jaar 1
03 Mannen 5 tot 10 jaar 1 tot 20 jaar 0 tot 20 jaar 1
04 Mannen 10 tot 15 jaar 1 tot 20 jaar 0 tot 20 jaar 1
05 Mannen 15 tot 20 jaar 1 tot 20 jaar 0 tot 20 jaar 1

Sanity checks

In the graphs below we show how much the total number of activities from the two sources that we use, deviates from the values that we expected. The expected values are based on open sources that state how many surgeries, nursing days, outpatient admisssions, consultations and imaging diagnostics have taken place in 2014. Due tot he quality of these height level estimates and our data, a deviation up till 20% can be accepted. We see some difference that are bigger, but we can explain these. In OpenDis we see less imaging diagnostics than we would expect. It could be that the reported number contains also imaging diagnostics done by private clinics, which we didn’t include in our analysis. The inpatient surgeries number is too high, which is due to some activities that are classified as surgical, but are no stand-alone surgeries. This may cause the double counting by ~ 35%.

In the CBS data we seem to underestimate the number of Outpatient surgeries and outpatient admissions. This is probably correct, because the CBS data is relatively old (from 2010 and 2012). The same effect explains the difference for inpatient surgeries.

Summary and visualisation

To show how the demography of the population changes, we visualize the composition of the population in 2014 and 2040.

Part 4. Calculations

Preparation of data

As visualized above, the raw data that we are getting from the various sources are not homogeneous. we need to organize and combine them in order to be able to use them together and draw the projections up to 2040.

we start by adding the Age/gender ID to the population forecast table to be able to perform projections later on

The surgeries contain information for two of our six defined usages: inpatient and outpatient operating rooms (surgeries). For each gender or age group, we index the number of surgeries performed per 10,000 inhabitant and the required corresponding surface.

Age_gender_ID Age Gender Number_of_act Type_act Population_x1000 Act_per_1000inhab Space_per_act
01 1 0 tot 20 jaar Mannen 25159 Inpatient_surg 1966.4 12.79445 0.308
02 2 20 tot 45 jaar Mannen 53447 Inpatient_surg 2676.3 19.97048 0.308
03 3 45 tot 65 jaar Mannen 91737 Inpatient_surg 2363.3 38.81733 0.308
04 4 65 tot 80 jaar Mannen 80815 Inpatient_surg 1059.1 76.30535 0.308
05 5 80 jaar of ouder Mannen 23903 Inpatient_surg 258.2 92.57552 0.308
Age_gender_ID Age Gender Number_of_act Type_act Population_x1000 Act_per_1000inhab Space_per_act
01 1 0 tot 20 jaar Mannen 83434 Outpatient_surg 1966.4 42.42982 0.308
02 2 20 tot 45 jaar Mannen 61101 Outpatient_surg 2676.3 22.83040 0.308
03 3 45 tot 65 jaar Mannen 98012 Outpatient_surg 2363.3 41.47252 0.308
04 4 65 tot 80 jaar Mannen 72064 Outpatient_surg 1059.1 68.04268 0.308
05 5 80 jaar of ouder Mannen 22793 Outpatient_surg 258.2 88.27653 0.308

The raw data with the admissions give information on the needed inpatient beds (the nursing days) and outpatient beds (beds used for day admissions) per 10,000 inhabitant. We work the data in order to obtain a table with one column indexing all the different types of activities and their occurence, per age and gender type. The data is extrated for the year 2012 only, because we want to forecast based on the most recent available data.

Age_gender_ID Age Gender Number_of_act Type_act Population_x1000 Act_per_1000inhab Space_per_act
01 1 0 tot 20 jaar Mannen 634152.9 Nursing_days 1966.4 322.4944 0.206
02 2 20 tot 45 jaar Mannen 490935.4 Nursing_days 2676.3 183.4381 0.206
03 3 45 tot 65 jaar Mannen 1327179.1 Nursing_days 2363.3 561.5788 0.206
04 4 65 tot 80 jaar Mannen 1666094.0 Nursing_days 1059.1 1573.1225 0.206
05 5 80 jaar of ouder Mannen 741831.2 Nursing_days 258.2 2873.0876 0.206
Age_gender_ID Age Gender Number_of_act Type_act Population_x1000 Act_per_1000inhab Space_per_act
01 1 0 tot 20 jaar Mannen 127986.30 Outpatient_admissions 1966.4 65.08661 0.088
02 2 20 tot 45 jaar Mannen 154079.70 Outpatient_admissions 2676.3 57.57191 0.088
03 3 45 tot 65 jaar Mannen 341456.42 Outpatient_admissions 2363.3 144.48289 0.088
04 4 65 tot 80 jaar Mannen 304688.03 Outpatient_admissions 1059.1 287.68580 0.088
05 5 80 jaar of ouder Mannen 85805.62 Outpatient_admissions 258.2 332.32230 0.088

The last two activities we want insights on are the consultation and imaging diagnostics. The information is included in the raw data with all healthcare activities that are claimed at the Dutch insurance companies. We will use the most recent year that has been (almost) fully claimed, which is the year 2014.

Age_gender_ID Age Gender Number_of_act Type_act Population_x1000 Act_per_1000inhab Space_per_act
01 1 0 tot 20 jaar Mannen 2954245 Consultation 1966.4 1502.3622 0.06
02 2 20 tot 45 jaar Mannen 2163475 Consultation 2676.3 808.3828 0.06
03 3 45 tot 65 jaar Mannen 3470426 Consultation 2363.3 1468.4661 0.06
04 4 65 tot 80 jaar Mannen 2551655 Consultation 1059.1 2409.2673 0.06
05 5 80 jaar of ouder Mannen 807058 Consultation 258.2 3125.7088 0.06
Age_gender_ID Age Gender Number_of_act Type_act Population_x1000 Act_per_1000inhab Space_per_act
01 1 0 tot 20 jaar Mannen 1186872 Imaging diagnostics 1966.4 603.5761 0.023
02 2 20 tot 45 jaar Mannen 869179 Imaging diagnostics 2676.3 324.7689 0.023
03 3 45 tot 65 jaar Mannen 1394248 Imaging diagnostics 2363.3 589.9581 0.023
04 4 65 tot 80 jaar Mannen 1025131 Imaging diagnostics 1059.1 967.9265 0.023
05 5 80 jaar of ouder Mannen 324237 Imaging diagnostics 258.2 1255.7591 0.023
We fi nally obtain a t able that we can wo rk with, indexing total a ctivites per 10,000 in habitant, per age, gender, region and t hat we will take as reference for the year 2014, allowing us to then project the future surface needs per activities from 2014 to 2040.
Age_gender_ID Age Gender Number_of_act Type_act Population_x1000 Act_per_1000inhab Space_per_act
01 1 0 tot 20 jaar Mannen 127986.30 Outpatient_admissions 1966.4 65.08661 0.088
02 2 20 tot 45 jaar Mannen 154079.70 Outpatient_admissions 2676.3 57.57191 0.088
03 3 45 tot 65 jaar Mannen 341456.42 Outpatient_admissions 2363.3 144.48289 0.088
04 4 65 tot 80 jaar Mannen 304688.03 Outpatient_admissions 1059.1 287.68580 0.088
05 5 80 jaar of ouder Mannen 85805.62 Outpatient_admissions 258.2 332.32230 0.088
06 6 0 tot 20 jaar Vrouwen 106981.99 Outpatient_admissions 1881.1 56.87204 0.088
07 7 20 tot 45 jaar Vrouwen 310238.55 Outpatient_admissions 2665.1 116.40785 0.088
08 8 45 tot 65 jaar Vrouwen 438250.97 Outpatient_admissions 2347.5 186.68838 0.088
09 9 65 tot 80 jaar Vrouwen 334992.15 Outpatient_admissions 1142.7 293.15844 0.088
10 10 80 jaar of ouder Vrouwen 124808.14 Outpatient_admissions 446.4 279.58814 0.088

Assumptions

We had to make a number of assumptions, because the data is not as complete as hoped for:

  1. Since the surgery data is only available up till 2010, we needed to assume that this is still representative for 2014. The same holds for admissions till 2012.
  2. We assume that the current state of care does not change, only the population size and distribution of age-gender changes. However, in reality we would expect innovation to change the form of care and days we spend in a hospital to change as well.
  3. We made a shortcut by using different years for different types of activities (ranging from 2010 to 2014), but applying the same number of inhabitants (namely of the year 2014) to it. This made calculations much easier and was the available data. The impact of this is probably very small, due to the fact that population does not change to much in 4 years.
  4. For the consultations and imaging diagnostics we had to make an assumption on how the total number of activities is currently distributed over age-gender, because we don’t have this level of detail in the data. We assumed that these activities are the same distributed as outpatient admissions.

Implementation

To build our projection, we are combining the data from the population forecast and the activities table for 2014. We basically perform the following steps: a. Count number of activities in 6 categories (outpatient admissions, inpatient nursing days, outpatient surgeries, inpatient surgeries, outpatient visits, imaging diagnostics) per region, age and gender

  1. Multiply number of activities with the corresponding space requirement in m2 to get required m2 per type of space, region in Netherlands, gender and age

  2. Multiply with demography developments to get required m2 per type of space, region in Netherlands, gender and age for the years 2018-2040.

    Age Gend er Yea r Regi on Acti vity Spa ce_x1000m2
    01 0 tot 20 jaar Mannen 2028 Deventer Outpatient_admissions 0
    02 0 tot 20 jaar Mannen 2028 Deventer Nursing_days 1
    03 0 tot 20 jaar Mannen 2028 Deventer Outpatient_surg 0
    04 0 tot 20 jaar Mannen 2028 Deventer Inpatient_surg 0
    05 0 tot 20 jaar Mannen 2028 Deventer Consultation 1
    06 0 tot 20 jaar Mannen 2028 Deventer Imaging diagnostics 0
    07 0 tot 20 jaar Mannen 2025 ’s-Gravenhage (gemeente) Outpatient_admissions 0
    08 0 tot 20 jaar Mannen 2025 ’s-Gravenhage (gemeente) Nursing_days 4
    09 0 tot 20 jaar Mannen 2025 ’s-Gravenhage (gemeente) Outpatient_surg 1
    10 0 tot 20 jaar Mannen 2025 ’s-Gravenhage (gemeente) Inpatient_surg 0
    11 0 tot 20 jaar Mannen 2025 ’s-Gravenhage (gemeente) Consultation 6
    12 0 tot 20 jaar Mannen 2025 ’s-Gravenhage (gemeente) Imaging diagnostics 1
    13 0 tot 20 jaar Mannen 2032 Dordrecht Outpatient_admissions 0
    14 0 tot 20 jaar Mannen 2032 Dordrecht Nursing_days 1
    15 0 tot 20 jaar Mannen 2032 Dordrecht Outpatient_surg 0
    16 0 tot 20 jaar Mannen 2032 Dordrecht Inpatient_surg 0
    17 0 tot 20 jaar Mannen 2032 Dordrecht Consultation 1
    18 0 tot 20 jaar Mannen 2032 Dordrecht Imaging diagnostics 0
    19 0 tot 20 jaar Mannen 2031 Dordrecht Outpatient_admissions 0
    20 0 tot 20 jaar Mannen 2031 Dordrecht Nursing_days 1

Now that we have the detailed projections, we want sum the required surfaces on the Age and Gender to obtain statistics per Region and Activity.

Year Region Activity Space_x1000m2
01 2014 ’s-Gravenhage (gemeente) Consultation 45
02 2015 ’s-Gravenhage (gemeente) Consultation 46
03 2016 ’s-Gravenhage (gemeente) Consultation 45
04 2017 ’s-Gravenhage (gemeente) Consultation 45
05 2018 ’s-Gravenhage (gemeente) Consultation 45
06 2019 ’s-Gravenhage (gemeente) Consultation 45
07 2020 ’s-Gravenhage (gemeente) Consultation 45
08 2021 ’s-Gravenhage (gemeente) Consultation 46
09 2022 ’s-Gravenhage (gemeente) Consultation 46
10 2023 ’s-Gravenhage (gemeente) Consultation 47
11 2024 ’s-Gravenhage (gemeente) Consultation 47
12 2025 ’s-Gravenhage (gemeente) Consultation 48
13 2026 ’s-Gravenhage (gemeente) Consultation 48
14 2027 ’s-Gravenhage (gemeente) Consultation 50
15 2028 ’s-Gravenhage (gemeente) Consultation 50
16 2029 ’s-Gravenhage (gemeente) Consultation 51
17 2030 ’s-Gravenhage (gemeente) Consultation 51
18 2031 ’s-Gravenhage (gemeente) Consultation 51
19 2032 ’s-Gravenhage (gemeente) Consultation 51
20 2033 ’s-Gravenhage (gemeente) Consultation 52

Part 5. Results

In this section the results of the predictive model are shown. For every year between 2014 and 2040, a prediction is made for the needed square meters of capacity. Capacity is divided into the six main categories introduced earlier, which are the main drivers for the type of space needed. E.g. Outpatient Surgeries are performed in operating rooms.

In order to have a look at the step-changes in 5-year intervals, a second chart is added to show the space requirements (m2 needed) for the years 2015, 2020, 2025, 2030, 2035 and 2040.

Lastly, to get an understanding of the required increase or decrease for a certain type of capacity (space in m2), the growth figures are computed; the 2040 projection is compared to base year 2014. Since the intensity of the use of the different capacities is different per age-group and gender, and the composition of the Dutch population is expected to changes from 2014 to 2040, the different capacities have different growth projections. Number of Nursing Days is expected to grow the fastest – a 30% increase between 2014 and 2040 – mainly driven by the ageing population.

Part 6. Usage of process and results

Limitations

There are a number of limitations to the described process for aplication in real-life business problems, mainly caused by availibility of data:

  1. We don’t know the current capacity, its usage or its life expectancy. So we cannot match the required space to the actual space to determine how much extra should be build for the future.
  2. We assume no impact of innovation of care. However, in practice we have seen that nursing days per admission has steadily decreased. The model could be refined to include assumptions on this.
  3. We have no data on specific zip-codes. Including makes it possible to forecast the space requirement for a specific hospital based on its care region.

Usage in different context

We can use the same process for different data sets and purposes:

  1. Different time horizons
  2. Different countries
  3. Refining for specific parts of the country, for example the care region of a hospital
  4. Refining for specializations
  5. Different types of care, such as youth care. Activities are different and space requirements as well, but the process is the same
  6. Alternative ‘weighting’ of activities. By using m2 to weight the 6 different types of care, we get a physical result, but we could also weight the activities with average expenditure to forecast future health expenditure. Another option would be to use the investment cost per m2 of different spaces to come up with the total investment required for a hospital in a specific region.