Physician-To-Population Ratio Model Limiting Access To Healthcare #4

NOBLE Analytics & Consulting
P.O. Box 1051 Brentwood, TN 37024 – 1051 | Office: 615-480-0023 | www.nobleanalytics.com
Fourth 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 final in our 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.
Previously, we showed that the two main components of the common approach - the number of
physicians and the population of a community as compared to a standard benchmark (such as a
physician to population ratio or GMENAC) - present difficulties in maintaining accuracy and
relevancy. Obtaining a realistic count of physicians in any given area proves challenging.
Communities are often underserved because traditional models do not account for differences
in population health, regional environmental factors, or unique problems endured by at risk
communities such as the elderly. So while it may seem like a straight forward approach to get a
population count based on algorithms from the latest census, this alone does not generally lend
an accurate and truly representative picture of a community.
This paper will discuss how the population of a community can change based on the time of
day, the season, or technology. Though these factors will not change the population of a
community as reported by the census bureau, they will change what we are calling the adjusted
population.
Daily Fluctuations
Fast paced and interconnected like never before, we are a mobile society with a non-stagnant
and dynamic populous. Models that cannot account for the multitude of ways that people move
in and out of cities, across the country and even internationally are not going to accurately
represent the demand for healthcare. Even in a different age with less activity, the true accuracy
behind aged census based data was questionable at best. In a modern, complex society, the
effects undoubtedly have a genuine impact on accuracy.
The daily migration of people amongst communities for work or school can significantly alter
the need for physicians. Urban populations swell on weekdays, as suburbanites leave their
bedroom communities, making the daytime population far more than just the resident
population. This pattern is unlikely to go away and communities will continue to see their
population change between daylight and nighttime hours.
Effects of Seasonality
Communities must also deal with the population changes that arise from tourism and people
from the north who migrate south in winter months (snow birds). These groups differ in age,
length of stay in the community, when they visit, and the healthcare needs they are likely to
have while visiting.
Tourists on one hand tend to be younger, with visits lasting a week or less and with the highest
concentration on weekends. Their medical needs are often less severe, such as respiratory
ailments, heat exhaustion, sporting type injuries, headaches and nausea, and minor injuries from

falls that can require visits to urgent care centers and emergency rooms1
. In contrast, snow birds
tend to be older, often stay months at a time and have needs consistent with their age group.
This means, snow birds potentially represent medical populations that require more frequent,
maintenance based care (i.e. asthma, COPD, type 2 diabetes, arthritis, cardiac related issues). In
turn, patients that fall within this category often require a continual, long-term relationship with
physicians in both their permanent and temporary (migration) residences.
As an indication of just how potent such seasonal migrations can be, take for example the snow
bird effect in the Sunshine State. In 2012, more than one million people residing in the northern
states of the US were destined to Florida alone2
. As a result of such occurrences, healthcare
leaders in states that experience seasonal inward migration are pushed to make adjustments to
cope with the additional patients. However as noted, it is not enough to simply know the raw
number of visitors for a community since differing groups have distinct needs.
Additional Migration Considerations
In rural and small urban communities that have limited choices of available physicians, it is not
uncommon for individuals to migrate to larger cities that offer the specialties and physicians
they are seeking in the time frame they need. Additionally, with the advance of technology in
recent years and associated cost and time savings, another type of migration has begun to occur
that does not require anyone to move at all; telemedicine.
Treating patients remotely with the use of phones, cameras, and imaging technology -
telemedicine has an annual growth rate of over 50% according to Forbes3
. As telemedicine
continues to emerge, physicians will be able to see patients from anywhere in the world, making
it nearly impossible to define the communities they serve. For example, it is not unreasonable
to think that a radiologist who rarely sees the patients for the x-rays he examines might decide
to live in Phoenix and have a contract to work for hospitals in Phoenix, Chicago, and Miami. In
such a scenario, a model that looks only at physicians and the population of a community will
have a hard time determining which city to count for this physician.
A model that cannot account for the migration of people or the use of new technology may
dramatically impair the ability of hospitals to recruit the physicians required to meet the needs
of the adjusted population. New data and technology offers a chance to find that right balance
that can meet these challenges.
The Numbers Prove the Point
Throughout this series, we have highlighted the limitations and failings of the physician-to-
population ratio models. While such models have advantages such as being inexpensive, having
an ease of calculation and requiring minimal data, they far too often fail to accurately assess the
healthcare needs in a community. When this occurs, addressing the health challenges in a
1
Top 5 Reasons for Emergency Room visits: http://www.bidmc.org/YourHealth/Health-
Notes/FirstAid/EmergencyCare/Top5ERvisits.aspx
2
Forbes (http://www.forbes.com/sites/robertlaura/2012/10/26/snowbird-report-five-things-to-do-before-heading-south-for-the-
winter/)
3
Top Health Trend For 2014: Telehealth To Grow Over 50%. What Role For Regulation?
(http://www.forbes.com/sites/theapothecary/2013/12/28/top-health-trend-for-2014-telehealth-to-grow-over-50-what-role-for-
regulation/)

community becomes exceedingly difficult since Stark laws only allow hospitals to offer
incentives like income guarantees when they can show a need for physicians in the community.
There are significant differences in comparing the needs of the communities because commonly
used models look at physician and population supply, while our model looks at physician
activity to determine the health of communities. Given this key difference and in an attempt to
quantify the magnitude of the errors, Noble Analytics looked at three metropolitan areas and
compared the results of commonly used physician-to-population ratio and GMENAC models to
the model we developed.
The ZIP codes selected for our studies are intended to capture most of the population for these
cities and do not represent the needs of any specific institution. The physician-to-population
ratio models determined the ratio of physicians to the population of the areas studied and then
compared these results with a ratio of physicians to the population of the relative states and the
GMENAC physician needs model. The results are as follows:
Metropolitan Study Area #1
Specialty
CardiovascularDisease
Dermatology
Endocrinology/Diabetes/Metabolism
Gastroenterology
Neurology
Obstetrics/Gynecology
Oncology
OrthopedicSurgery
Pediatrics
PulmonaryDisease
State Physician Count 697 268 136 356 349 1,116 359 707 1,320 247
Metropolitan Study Area Physician Count 380 176 94 208 195 616 199 349 773 138
PTPR Model's Physician Surplus 43.9 46.8 28.4 36.3 26.7 77.8 25.9 8.1 136.5 18.9
GMENAC Study's Physician Surplus 224.6 35.1 55.1 76.9 83.3 135.1 19.3 47.9 151.3 65.1
Noble Analytics' Physician Deficits -40.8 -13.8 -10.9 -22.1 -22.0 -8.9 -18.4 -8.9 -34.7 -10.8
State Population Count: 10,072,230
Metropolitan Study Area Population Count: 4,857,127
PTPR – Physician-To-Population Ratio
Positive (+) Physician Needs numbers in blue show the inability to recruit physicians due to surplus of physicians
Negative (-) Physician Needs numbers in red show the ability to recruit physicians due to deficits

Specialty Family/GeneralPractice
InternalMedicine
NeurologicalSurgery
Neurology
Oncology
OrthopedicSurgery
Pediatrics
PulmonaryDisease
Urology
State Physician Count 4,978 5,360 294 991 1,880 884 1,474 2,720 590 629
Specialty
CardiovascularDisease
Endocrinology/Diabetes/Metabolism
Gastroenterology
NeurologicalSurgery
Neurology
OrthopedicSurgery
Pediatrics
Radiology
Urology
State Physician Count 548 102 265 110 288 697 561 992 592 204

Many of the specialties chosen in the comparison graph were highly sought after, yet
unavailable for recruitment in the three chosen metropolitan cities due to surpluses. However,
the chart of specialty comparison among the most commonly used physician-to-population ratio
and GMENAC models and the Noble Analytics’ model shows a drastic difference in meeting
the needs of the communities.
In each of these communities, the physician-to-population ratio models show large surpluses of
physicians to the point that in some cases, the supply of physicians is ~180% of the projected
need. In contrast, our Noble Analytics’ model generally showed a modest deficit of physicians.
So which is right? We believe that physicians are as in tune to market forces as any other
professional. Therefore, we find it unreasonable to assume that in each of these cities and for
each of these specialties, physicians are choosing to practice when there is little demand for
their services. As such, a more compelling case can be made that the residents of these cities are
suffering from the effects of models that count physicians and people, but do not consider how
those physicians practice or how the people consume health care. Examining how people
actually use healthcare leads to conclusions which are more consistent with physicians being
rational agents.
The Consequences of Inadequate Physician Needs Models
Models that underestimate the true need for physicians and suggest communities are over
supplied when there is often a shortage, leave hospitals with two options. The first is the buyout
option. This choice has been used heavily during the last few years and typically entails a
process of either forcing hospitals to hire physicians or to buy practices and employ those
physicians who work there in order to compete in markets that may inaccurately show surpluses
of physicians. “Employing physicians” is highly expensive and adds unnecessary costs to an
already costly system, yet health systems across the country are continuing to expand the
number of physicians they employ. Physician employment increased 3.8% between 2013 and
2014, according to Modern Healthcare's annual Systems Survey, which this year included
responses from about 80 health systems across the country.”4
The second option left for hospitals unwilling or able to choose the buyout option, is to do
without physicians that their communities need. As a result, added strains to the current delivery
infrastructure and negative impacts in healthcare availability can result. Case and point, both
the “buy-out” and “do nothing” options can bring great harm to members of communities in
terms of costs, quality and/or care accessibility. Fortunately though, these issues can be
corrected by using a different method of determining the healthcare needs of a community.
With the advent and advancement of big data and the way technology is continually evolving
our healthcare delivery systems, it is time that we move beyond the old and limited models of
physician-to-population ratio and GMENAC models to address the inherent shortcomings
we’ve discussed throughout this series and meet the true healthcare needs of our communities.
4
No hangover: Doc buying binge rolls on as systems learn from past deals
(http://www.modernhealthcare.com/article/20150620/MAGAZINE/306209979?utm_source=modernhealthcare&utm_medium=e
mail&utm_content=externalURL&utm_campaign=am)

Physician-To-Population Ratio Model Limiting Access To Healthcare #4

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