1. Second Issue of 4 Part Series
The Physician-To-Population Ratio Model Is Limiting Access To Healthcare
At Noble Analytics, we believe that people should have access to healthcare based on their
true needs. Unfortunately, most hospital organizations use an ill equipped model based on
the physician-to-population ratio in determining physician needs for their community. This
article, the second in a four part series, will continue to explain the many flaws that occur
when healthcare leaders depend on the physician-to-population ratio to determine
community needs. This common approach assumes that all physicians are created equal
and that every community has the same health needs, regardless of demographics. In fact,
due to frustrations we had over these and other fundamental flaws of the current approach
(that we will continue to detail below), we’ve developed the Noble Community Utilization
Model which matches the level of physician activity in a community to the needs of the
people who live there.
Across the nation, an ongoing debate continues with hospital executives trying to determine
which of two differing courses is best for stocking their facilities with the necessary
physicians. With one option, they can choose to recruit physicians with income guarantees
and with the second, they can hire individual physicians/purchase physician practices to
become employed members of their respective facilities. Both choices have their benefits,
but also carry limitations in terms of costs and other resources. At Noble Analytics, it is
our intent to first offer insight into the opportunities hospitals have at meeting needs with
the recruitment of income guaranteed physicians, before being forced to hire or purchase
physicians and their practices.
Recruiting physicians with an income guarantee is but a small part of the Stark law;
however, it can carry hefty penalties when the analysis is not within the guidelines. To
recruit physicians with an income guarantee, a facility must show a physician need within
the community in order to recruit a new physician and place them inside the designated
geographic service area. Unfortunately, hospital executives have often been limited in
meeting these physician needs due to community needs assessments (CNAs) that fail to
represent the genuine needs that exist. As a result, communities receive inadequate care
due to an inability to retain needed physicians and hospital operations suffer – all because
current methods of determining need rely on flawed physician-to-population ratios or
worse, outdated studies like GEMENAC.
Income guarantees benefit a hospital by allowing it to attract physicians to its community
and by assisting with reimbursement for moving one’s practice into a designated
geographic service area. Many hospitals could not get physicians to relocate any other way,
due to demographics, environment, or available resources limiting the personal goals of
physicians. More importantly, without these physicians, hospitals could not provide the
necessary care to their communities. Furthermore, hospitals and their associative
communities are not the only ones that benefit from this arrangement. Physicians also
benefit because when a hospital is required to show a need for more physicians before it
can offer the guarantee, the physician’s chances of building a successful practice are
increased.
In order to show physician needs, a CNA generally compares the number of physicians in
a community to an established benchmark using physician-to-population ratios. This
NOBLE Analytics & Consulting
P.O. Box 1051 Brentwood, TN 37024 – 1051 | Office: 615-480-0023 | www.nobleanalytics.com

2. Second Issue of 4 Part Series
process requires only four variables; a reliable population estimate, a reasonable
benchmark, the number of physicians in the area, and the location of these physicians.
Population estimates can be obtained from a number of reputable sources and can be
adjusted for differences between the benchmark population and the community population.
These adjustments are important so an individual community will not be at a disadvantage
due to its population being different from the population used in the benchmark. For
example, a community whose residents are significantly younger may have a need for
pediatric specialties which goes unmet because the population used in the benchmark had
a lower rate of children. The physician-to-population ratio and the GEMENAC study from
the early 1980’s are the most commonly used benchmarks to determine physician need.
Many communities across the country are unable to recruit the physicians they need
because of the inherent short comings of the physician to population model or because the
three decade old GEMENAC study has not kept up with changes in healthcare delivery.
The last two variables of the physician-to-population ratio - how many physicians are in
the area and where they are located - have historically been the hardest to measure
accurately. While it is possible to track down and count every individual physician in an
area, it can be a daunting and expensive task, especially in a metropolitan area. However,
even with a count of the raw number of physicians in an area, several other factors can
have a large impact on how much care the community is actually receiving. These elements
can influence how many hours a physician will work and how many patients one will see.
These pivotal factors (unique to each physician) include age, gender, whether the physician
is in private practice or employed, research and education demands, and even personal
motivation.
As a result of these issues, some models do attempt to account for these differences by
using a full time equivalent (FTE) adjustment where each physician in the area is evaluated
based on how much one works. To work, these models apply a coefficient to each physician
showing how close one is to being a full time physician for the community. For example,
a physician who spends approximately half his time teaching counts as a 0.5 FTE physician
instead of being one physician. While measuring a community’s need based on the amount
of healthcare delivered in the area seems on the surface a sound approach, key problems
exist with this FTE adjustment method.
The first problem is the difficulty in measuring a physician’s output based on how much
time one spends in the office. A productive physician in an efficient office might see 20%
or more patients than a typical physician in the area, but FTE adjustments rarely use a
coefficient over one to highlight this productivity.
A second issue arises because the physicians in the area are adjusted, but the physicians
who make up the benchmark are not. For example, let’s say a hospital using a physician-
to-population model has 100 physicians in its area, but counts them as 75 FTEs based on
their perceived production. If this hospital then compares their 75 FTEs to the total number
of physicians in the state, results may show a need of 10 physicians for their area. However,
a key flaw exists as this assumes all the physicians in the state are working full time, even
though the hospital knows its average physician only works 75% of full time. As a result,
if the physicians throughout the state were to mirror the activity of the physicians in the
NOBLE Analytics & Consulting
P.O. Box 1051 Brentwood, TN 37024 – 1051 | Office: 615-480-0023 | www.nobleanalytics.com

3. Second Issue of 4 Part Series
area, the true need for the area will not be met with only 10 additional physicians. This
means the hospital should have said it had 100 average physicians instead of 75 FTE
physicians and goes to show that for an FTE adjustment to have meaning, it needs to apply
not only to the physicians in the hospitals community, but all the physicians that make up
the benchmark.
As for physician location, it is difficult to know exactly where to count physicians because
a physician can, and usually does, practice in more than one place. For example, if a
physician has a practice but also works in two different hospitals and a clinic, all in different
zip codes, then the question of where that physician is located becomes very complex.
As a result, two common approaches are employed in an attempt to address this issue. The
first is to count physicians based on the time they spend in a community. However, this is
a taxing solution because without including the physician’s productivity at each location,
this number is meaningless. Moreover, attempting to include this productivity leads back
to the same problems referenced above with an FTE analysis.
The second solution is to count a physician only at their home office. This approach has
the advantage of being easier and it removes a level of subjective analysis from the hospital.
Unfortunately though, it also unfairly punishes some communities to the benefit of others.
For example, suppose a physician has a home office but spends 90% of time working at a
hospital half a mile away. If this hospital’s GSA does not include the home office, this is
not counted as being in the hospital’s community even though most of the physician’s time
is spent there.
The physician to population ratio is easy, cheap, and fast. Unfortunately, it is not accurate
and denies far too many communities access to the healthcare they need. Evaluating a
community based on the number of people who live there fails to acknowledge that every
community is made up of individuals who have their own preferences on how to consume
healthcare. The recent explosion of available data and advanced analytic techniques allow
for a more precise count of the physicians needed to meet the needs of those individuals.
NOBLE Analytics & Consulting
P.O. Box 1051 Brentwood, TN 37024 – 1051 | Office: 615-480-0023 | www.nobleanalytics.com