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For a healthcare system to run efficiently there is a balance that needs to be found where there is adequate staffing to accommodate the needs of a population. Too much staffing and supply can lead to inefficient, uncompetitive care, while too little staffing can lead to long wait times and equally inefficient care. Striking the balance of supply and demand is crucial to properly staffing a system. As much as we want to think that healthcare acts like normal economics and that the law of supply and demand always holds, it does not. A high supply (of practitioners) can actually lead to higher demand (for services). Prices are not always driven down, and quality is not always improved to assure your services are utilized. This article will start with a non-healthcare example about apples to set the stage, then talk about healthcare at a high level to drive home the concept and then dive into some actual planning metrics for both hospital and professional service planning.
As much as we want to think that healthcare acts like normal economics and that the law of supply and demand always holds, it does not.
To visualize the healthcare system, let’s use a visual of an apple Orchard. Apple orchards are in the business of producing apples that will eventually be sold. Apple farmers want to assure their crops are harvested at the right time and done safely and effectively. To harvest crops the farmers need to use the services of Apple pickers. Apple pickers are crucial to the harvesting of crops, and without them, the farmers are not going to be able to meet deadlines and sell fruit before it is no longer of value. The system is cyclical and to be viable in future years, proper planning needs to be completed and executed.
In the case study of apples here are a few assumptions that we need to think through as it relates to planning (these are illustrative and not actual metrics related to apple harvesting).
- How many apples do I need to deliver? 5000 bushels
- When do I need to deliver the apples? 5 days
- How fast can the apple pickers pick? 10 bushels per hour
- How many hours per day do they work? 10 hours
- How many apple pickers do I need? 5000 / (5 x 10 x 10) = 10
In the end, the math needs to balance the demand for apples to productivity to pick them. That will tell you how many apple pickers are needed to meet the demand for apples needed for delivery. In this case, the 5,000 bushels will require 10 apple pickers over 5 days to complete the task.
Healthcare is more complex than the example about apples, but the concepts are important to creating a system that is properly staffed and set up for success. The first step in translating this example to healthcare is to understand the productivity of providers and demand metrics in the system including:
- Productivity Metrics
- Hospital occupancy rates, available beds, etc.
- Number of office visits a provider can deliver in a day, number of days worked in a week, and number of weeks working in a year
- Number of surgeries a surgeon can perform in a day, number of days surgery is performed per week, and number of weeks working in a year
- Need to understand the demand for services
- Demand for Services in the population
- Required hospital admission rate / 1,000 and average length of stay for a population
- Number of office visits a population needs / 1,000
- Number of surgeries a population needs / 1,000
The metrics above can be obtained by analyzing claims data for a population or there are also public sources of data that can be used as benchmarks / starting points. Productivity information is available from such organizations as AHA, HFMA, AMA, MGMA and other industry and professional sources. Healthcare demand information is most often most easily available from actuarial analysis especially actuarial cost models that show detailed unit cost and utilization metrics. Cost models are a way to review Information on current performance and are often used to compare to ideal or most efficient performance.
From the analysis we have done at Axene Health Partners, we believe that 2/3 of the savings that could be attained are in the inpatient setting.
When looking at healthcare spend, the majority of the claims in healthcare are related to inpatient care. For that reason, properly staffing a hospital is very important to run efficiently and to avoid potentially avoidable costs. From the analysis we have done at Axene Health Partners, we believe that 2/3 of the savings that could be attained are in the inpatient setting.
The first thing to think about when planning for hospital costs is to understand the demand of the population. To keep this sample analysis simple we are only going to focus on the under-65 population (non-Medicare), and for the time being, we will assume that the system is operating similarly to a National PPO product. On average, a National PPO product has statistics similar to the following:
- 70 admits/1,000 members or individuals (i.e., 7% admit rate)
- 4-day average length of stay (ALOS)
- For every 1,000 people we need access to at least 280 days of care during the year
- For 1,000,000 people we need access to 280,000 days of care in a year
To create balance in the hospital setting we need to match the above demand metrics with hospital productivity information. The following are some average productivity numbers for hospitals. These can range dramatically from hospital to hospital, but these are good baselines to consider.
- To provide adequate time to change from one patient to the next, clean up and cleanse the room, hospitals suggest the ideal occupancy is 85%- 90%.
- Higher than this and patients are waiting.
- Lower than this and the hospital is losing revenues.
- For a demand of X beds, this means we need 111% – 118% of X beds
- The demand is also distributed around the various types of stays (e.g., maternity, medical, surgical, etc.
For the 280,000 days of care described earlier and the 85% occupancy assumption, we need access to 329,412 days of care or 903 beds over a 365-day period (329,412 / 365 = 903).
Another factor that needs to be considered when doing hospital planning is the relative efficiency of the hospital. Depending on how the hospital is operating, it can dramatically affect the above calculated bed need. Ideal benchmarks for facilities are much lower and look like the following:
- 45 admits / 1,000
- 3 days average length of stay
- 135 days / 1,000
When factoring these demand metrics into the same one million people and the 85% occupancy target, this demand scenario would require much fewer beds (435 vs. 903). Therefore, this means that a community with more efficient healthcare patterns needs less capacity (or a smaller network). It should also be noted that in more efficient delivery systems, other resources may need to be increased to meet the care needs (e.g., SNF, ambulatory care facilities, sub-acute, virtual care, etc.). As more procedures get moved from the inpatient setting to the outpatient setting, fewer inpatient beds will be required, but overall system staffing might actually increase as more other practitioners may be needed.
When assessing hospitals or insurance companies we look at what we call Care Management Effectiveness. This is a metric that shows how far on the spectrum the system is compared to the ideal. A fully inefficient system would be considered unmanaged and have a score of 0%, while an ideally managed system would be at 100%. We live in an imperfect world, and it should be assumed that most systems live closer to the middle of the range with higher performers edging closer to the high end of the range.
This section will explore professional planning in the healthcare system. Professional planning quantifies the number of practitioners who are going to be needed to run the system. This is assessing the number of non-hospital-based FTEs as those would be built into the hospital numbers. These would be practitioners that bill their professional fees separately from the hospital. As an example, we are going to concentrate on primary care coverage.
- Recommend the same starting point for understanding the demand
- For the same population discussed earlier (under age 65 commercial population) some of the key utilization rates are:
- 18 Non-maternity Inpatient surgeries / 1,000 members
- 15 Deliveries / 1,000 members
- 1,500 primary care office visits / 1,000 members
- For the same one million members we would expect
- 18,000 non-maternity surgeries
- 15,000 deliveries (both Normal and C-Section)
- 5 million primary care visits
Recent productivity information for Primary Care physicians shows they work on average:
- 48 weeks/year
- 36 office clinic hours/week
- 4 visits/hour
- 6,912 office visits/year
Assuming the above assumptions on the same 1 million members defined in this report we would suggest that it would require 217 primary care physicians to meet the demand. In this case, we are using a primary care definition that includes family practice, general internal medicine and general pediatrics. When working through the calculation it would say that for this population the target staffing of primary care for this population would be 0.22 MDs / 1,000 population or about 220 total.
What does it mean when we look at other populations that are not commercial- under 65 (healthiest, least demand for services, etc.)? On average when looking at both Medicare and Medicaid it requires higher staffing. For Medicare, the demand is 300%-500% of the commercial population due to high complexity and multiple conditions. For Medicaid, it should be assumed the demand would be 150%-200% of the regular commercial population. This is usually attributed to higher maternity utilization and a different family size mix.
Efficiency matters in the professional staffing modeling as well. As the system becomes more efficient it most often sees increases in primary care visits, shifts to outpatient sites of care, decreased use of emergency room services, increased use of virtual care and increased use of para-professionals (i.e., PAs, NPs, etc.).
For a general population in the US, we often see targeted ideal staffing of:
- 1 – 1.2 MDs per 1,000 population
- 35 – .40 PCPs per 1,000 population
- 1 – 1.1 beds per 1,000 population
Current Staffing in the US Healthcare System
- Today we have:
- 790,000 staffed acute care beds
- 620,000 active MDs in practice (doesn’t include 117,000 DOs)
- 209,000 primary care physicians in practice
- 330 million population
- Current US Supply
- 4 beds / 1,000 people
- 9 MDs / 1,000 people
- 6 PCP / 1,000 people
When looking at the above metrics should we think that we have an oversupply or an undersupply? To make that assessment some further adjustments for efficiency need to be made. There also needs to be an adjustment considered for potentially avoidable care. The ideal Inpatient delivery system is defined as the system with the least potentially avoidable care. In an average system, it could be assumed that around 8 – 10% of the care is potentially avoidable. Studies suggest that as much as 50% of current inpatient utilization is potentially avoidable. When factoring in these adjustments, it can be seen that the national average number comes very close to the ideal metrics laid out earlier in this article. As can be seen below, the 2.4 beds per thousand adjust down very close to the 1.1-1.2 beds at ideal.
0.6 x .65 = .39 adjusted available PCPs / 1,000
Additional Items to Consider When Doing Resource Planning
- All apple pickers are not the same for resource planning.
- Scope of service issues re: NPs, PAs, MD type of training and experience background may change your modeling.
- State Law and Regulation.
- Historical understanding by providers of actuarial roles limits credibility- CFO is a conduit- consider physician/provider help to translate.
- FFS vs. risk-based care background of providers matters – a doc is not a doc is not a doc.
The current system has more than enough supply, although significant maldistribution issues (too many underserved markets). Resource planning is very important and needs to be considered when deciding where and when to add to the healthcare system. More is not always better and, in many cases, less can actually be more effective (aka addition by subtraction). There are also times when more will be needed and spending more on staffing should actually lower the total cost of care. To adequately start this process, it is important to understand your current population, current utilization and projected future usage. Health actuaries are uniquely prepared to help in this manner and we at Axene Health Partners would love to be able to guide you along this type of analysis.
 Building Actuarial Cost Models from Health Care Claims Data for Strategic Decision-Making – Axene Health Partners, LLC (axenehp.com)
About the Author
Joshua W. Axene, FSA, FCA, MAAA, is a Partner and Consulting Actuary at Axene Health Partners, LLC and is based in AHP’s Murrieta, CA office.