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Physician-To-Population Ratio Model Limiting Access To Healthcare #2


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Physician-To-Population Ratio Model Limiting Access To Healthcare #2

  1. 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 |
  2. 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 |
  3. 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 |