So this talk is going to interdigitate nicely with what Hannah talked about and some of
the others as well.
I have no conflicts to declare.
So again, how many ICU beds are needed?
As we've heard, we don't want too many, we don't want too few.
Now, Hannah has shown this seven-fold difference across developed nations in the per capita
ICU bed base.
And while it's possible that represents a seven-fold difference amongst Western countries
in the rates of critical illness, I don't believe that's likely.
More likely, it represents the inability to really know-- have an effective way to determine--
what's the right number of ICU beds for a given population.
An important issue that several of the other speakers have mentioned is the substantial
temporal fluctuations in demand for ICU beds, and I'm going to talk about that.
So how could we optimize the number of beds?
Well, if we were economists we could try to come up with a metric that we could then optimize
with calculus and then figure out the right number of beds.
But what would that metric be?
There is no consensus and the odds are that there is no way to get a consensus about a
single metric that is the right metric.
If you make widgets, then the metric is how much money you bring in.
But for other things, it's-- for health care, it's not so simple.
But in the real world, administrators and the people responsible have to do something.
They have to decide how many beds to get, to make, to staff, to build-- whatever it
is.
And the evidence-- and I don't have the details here, but I could give you them-- is that
there's published evidence that by and large, jurisdictions use the kind of average, yearly
average, bed information that Hannah alluded to earlier.
But these long term averages, as I'll show you in some detail with real data from Manitoba,
don't account for the short term fluctuations that occur over hours and days.
So our goal in this study, which has been published recently just earlier this year
in Health Services Research, was to compare three different ways of looking at the census
data of how many patients are in the beds to estimate how many beds you need as a starting
point for doing that.
So we looked at all Manitobans, adult Manitobans over seven years, admitted to ICUs in three
geographic areaa-- in Winnipeg, which is the capitol, which contains most of the patients
in-- most of the people in Manitoba where there's 11 ICUs, six hospitals, 82 beds.
And then south rural, which is also reasonably well populated, and then the gigantic remote
north rural area, which has few people, few ICU beds, but a huge amount of land.
Now, what powers this is the ability to identify when patients enter each ICU and when they
leave.
And this is actually available from administrative data, from the hospital abstracts Canada-wide.
So we use those abstracts from Manitoba.
And by putting them all in a row, you can figure out how many patients you had in the
beds in any ICU you want at any time of day or night any day of the year.
So what we did, and this is the key calculation here, is we calculated-- again as Hannah alluded
to nicely as a sort of a foreshadowing of this talk-- what we call the daily peak bed
occupancy, which is the maximum number of patients in a given ICU that day-- at any
time during that day.
The distribution of this represents the fluctuations, the day-to-day fluctuations in census in the
given ICU when you look at it over a year, say.
Now, there's a concept here that again has been alluded to before.
And I call it conglomeration but it could equally be called a regionalization to some
degree.
When you talk about multiple ICUs, used whether within the same hospital or between hospitals,
as being conglomerated, what I mean is they function seamlessly as if they were a single
ICU bed base.
So if there is an empty bed in any of the conglomerated ICUs, the next patient could
go there.
And they're not left waiting.
They might have to be transported between hospitals or from one part of the hospital
to another, but there's not a firm block that keeps them out because there's an empty bed
across town or down the hall, whatever it is.
So that's conglomeration.
Now, we always in this analysis assume conglomeration within a given hospital.
But as Hannah so nicely pointed out, that isn't such a great assumption in many hospitals
where there are silos where never the twain shall meet.
For some analyses, as I'll show you, we consider conglomeration of ICUs across the hospitals
in one of those three geographic regions.
And we always rounded bed estimates up to the next integer because you can't have 2-and-a-half
patients in a bed.
That would be three.
So the first method is the average yearly method.
So it takes the total number of beds, bed days, that we used in that year in that region,
divides by 365, and then uses this fudge factor of 0.8 under the presumption that we don't
want to have more than 80% average occupancy because of some evidence I reference here
that higher occupancy rates than 80% are associated with poor outcomes.
So that's the first estimate-- method one.
The next method is the regional level, the region-level daily peak bed occupancy method.
This considered all beds in the region to be conglomerated and calculated accordingly
the regional daily peak occupancy.
So across all the ICUs in that region, we calculated for each day the maximum number
of patients simultaneously at any time occupying the beds in those ICUs in all the hospitals
in that region and then generated the distribution of that for the year.
And so for instance, the 95th percentile of that distribution for that region tells you
that the number of simultaneously occupied ICU beds over the region that was exceeded
5% of the days, which is 18 days a year.
The third method is similar but is the hospital-level daily peak bed occupancy.
So each hospital was independently expected to meet its own ICU demand.
So we calculated the distribution of daily peak that occupancy for each hospital separately
and that's the fluctuation in the hospital ICU census.
We accumulated that-- not conglomerated, but accumulated that for the region.
I'm sorry, let me just come back to that.
The 99th percentile for a given hospital is the number of simultaneously occupied ICU
beds exceeded in that hospital on 1% of days of the year, which is four days a year.
And the regional-- we added those together.
The regional 99th percentile of the daily peak bed occupancy was the sum of the individual
hospital 99 percentiles for each hospital in the region.
So it's a little hard to understand vaguely, but let me give you a simple example.
Two hospitals, each with one ICU-- here's the daily peak bed occupancy in each unit
for a week.
Now, if those two hospitals or two ICUs were conglomerated-- so you can see that at no
time on any day was there more than six people in the two hospitals in the two ICUs together.
So those two hospitals, if they were dealing seamlessly as a single bed base, would need
six ICU beds.
Because there were never more than six.
But if each hospital had to do its own thing separately, independently, the maximum in
each hospital at any day of that week was four.
So that region with the two hospitals, made up of the two hospitals, would need eight
beds.
And that's the difference between methods two and methods three.
So here's method one-- results.
This is the average daily bed occupancy over the whole year.
And I show you on the left in orange the actual number of beds in each region.
And you see that the number of-- that based on the average yearly occupancy, the average
number of bad days per day with that 0.8 fudge factor, that each region would need way fewer
beds than it actually has.
Now, this is the distribution of daily peak bed occupancies with no bed sharing, method
two, and with bed sharing, method three.
And this is just the distribution.
So you don't need to look at all of these numbers, but I do want you to notice the far
right hand column.
The far right hand column is the maximum minus the median divided by the median, which is
some measure of the magnitude of the daily fluctuations compared to the median.
And you see that even in Winnipeg, which is the biggest area, the biggest bed base of
ICU beds in Manitoba, even there the fluctuations are on the 20% to 40% range.
And in the small rural regions, the north rural region which has got few people but
very few ICU beds, you see that the fluctuation in ICU bed census is enormous-- 300% or 200%
depending on whether the beds are shared or not shared across the hospitals in that region.
So let's talk about the daily fluctuations-- this is method three-- with no bed sharing
between hospitals.
And so I've shown you depending on whether you allow or can accept exceeding occupancy
5% of the days, 1% of the days, or no percent of the days, the number of beds you would
need you would need compared on the left to the number beds that they actually had in
those areas.
So what you can see is that without sharing between hospitals, where each hospital in
a region was forced to manage its own demand separately, independently, you always needed
more beds than indicated by the average yearly usage, which is method one, especially-- especially
in regions with a small bed base.
So in the north rural area, which has eight beds but the average daily census would suggest,
over the whole year, would suggest you only need two-- in fact if you only had two, you'd
exceed very frequently-- your fluctuation and demand would outstrip the number of beds
you actually have.
Also, as seems obvious when you think about it conceptually, the fewer days per year it's
considered acceptable to exceed the maximum number of beds that you have, the more beds
you need to avoid that.
So this is method two, which is with the assumption that the bed base in a region is shared amongst
all the ICUs in that region.
And I show you the numbers there.
And method one still underestimates the number of beds needed except in the largest region.
So I'm going to put them together.
Because I want to show you one other thing.
And that is when you compare yellow with green, whether it's 18 days a year, four days a year,
or never that it's acceptable to exceed-- for demand to exceed supply-- yes, demand
to exceed supply.
In any of those pairs of yellow green, you need fewer total beds if there's bed sharing,
regionalization, whatever you want to call it.
I've used the term conglomeration.
And that makes sense, as we talked about.
OK, so first, I think what this data shows is that bed supply decisions based on what
most people appear to use, what most jurisdictions at least that have published their decision
making algorithms have shown that they use this long term average usage, bed supply decisions
based on those kinds of calculations will result in a supply inadequate to deal with
the actual fluctuations in demand-- call it surge capacity-- even including the leeway,
that 0.8 fudge factor.
Using the distribution of daily fluctuations, the daily peak bed occupancy calculation,
to assess the actual magnitude of your own day to day fluctuations is better.
And by the way, if you have data that tells you when patients came into the ICU, date
and time, and when they left, I can give you some SaaS code that can generate the daily
peak bed occupancies.
It's easy.
A key policy issue that won't be decided by us but has got to be decided by the policymakers
somehow or another as a representation of society is how many days per year you can
tolerate having too much demand for your supply.
That's method two versus method three.
So for instance, when you go from 18 days to four days a year-- from 5% to 1% of the
days per year-- that you think it's acceptable for supply to outstrip demand-- for demand
to outstrip supply, sorry-- in our three areas of Manitoba, you need 4% to 40% more beds.
Avoiding it entirely-- if you want resource your region so that you never have too few
beds for the amount of fluctuation in demand that you actually see, the consequence of
that is clear.
You're frequently going to have very low occupancy rates.
So in Winnipeg, the largest of our three regions by far, if we wanted to resource the number
of beds so that we never had, according to the census data, too few beds to satisfy the
demand that we had during this time period of the study, we would have less than 70%
occupancy on 25% of days.
Maybe that's OK, but maybe not.
And that's a societal decision that has to do with some of the issues that were brought
up in the earlier talks, primarily economic issues.
So effective bed sharing, what I call conglomeration or regionalization, reduces the number of
beds needed.
The reason is statistical but easy to understand.
The fluctuations for an entire larger conglomeration is less than the sum of the fluctuations from
the components making up that conglomeration.
And so for instance, we'd need 12% to 36% fewer beds needed in our three regions with
conglomeration.
And the benefits, as I showed you in that complex distribution slide, the benefits are
bigger of conglomeration when your bed base is small than when your bed base is big.
Now, the unavoidable consequence of having a hospital or a region with a small ICU bed
base is that if you need to, because it's remote as was alluded to in an earlier question--
if it's remote and therefore must be able to manage its own surges, its own surges in
demand because it's so far away from every place else, is that average bed occupancy
is going to be low.
You just have to accept that.
In our two rural regions, which are both fairly small, even with regional bed sharing and
allowing demand to exceed supply 5% of the days per year, the average occupancy in both
of those regions of the ICU beds will be under 50%.
So what we've shown is that bed census is a superior starting point for evaluating ICU
beds.
Because I don't want to pretend and don't believe certainly that census numbers tell
you everything you need to know about how many beds you need.
In particular, fill bed counts can underestimate the numbers of beds needed.
Patients triaged away when the units aren't full, as we saw in some of the literature
that was presented in earlier talks, strongly suggested that this happens.
Patients triaged away never get to the ICU but might have gotten there if there were
more beds available.
They aren't counted in consensus numbers, at least not the census numbers that I had
available.
Evidence indicates that outcomes are worse when ICUs are full.
So that's one thing.
On the other hand, filled bed counts may overestimate the number of beds needed.
I work in a place now where the bottleneck is not ICU beds, but ward beds.
We have patients who frequently stay in the ICU a day, two days longer because there ain't
any ward beds to send them to.
So inefficient or unavailability of ward or step down beds increases ICU census.
But those patients don't need the ICU.
But they're just there because there's no place else to send them.
So for both of these reasons, census numbers don't tell the whole story.
So I had a number of collaborators that helped me with this.
And I'll take any questions if we have time to do so.