“Staffing standards” is one issue that oncology social workers mention frequently to researchers. It is possible to use research results to compare staffing levels at your own cancer center with those at similar centers. Often, when oncology social workers have shown administrators that their staffing is below that of other cancer centers, they have been able to hire more social workers. Here is some information to help you calculate a comparison.

Step 1: Determine the Number of Oncology Social Workers in Your Cancer Center. Use your cancer registry to clarify the number of social workers dedicated specifically to oncology. You will need to use a FTE (full-time equivalent) if the total number includes some part-time workers. For example, if you have three full-time workers and one who works four days a week, your number of OSWs is 3.80 FTE.

Step 2: Determine the Number of Cases or the Number of Visits to Your Cancer Center per Year. Compare your center on the basis of your caseload and/or on coverage.
 

The number of “analytic cases” in a given year (caseload) includes all cases diagnosed and/or receiving all or part of first course of therapy at your facility. This is different from new cases. The number of out-patient visits in a given year (coverage) includes but is not limited to scheduled clinic appointments, out-patient procedures, infusion/radiation appointments. What is included in this number will depend on what services your center offers.

Step 3: Calculate Your Ratio(s). 

If you are using caseload (analytic cases) in your calculation, divide the number of analytic cases by the number of oncology social work FTEs. For example, if your cancer center has 4,000 cases per year and you have three full time oncology social workers, your ratio is 4,000 cases ÷ 3.0 FTEs = 1 SW: 1,333 cases. That is, you have one social worker per 1,333 cases. If you are using coverage (visits) in your calculation, divide the number of annual visits by the number of full-time equivalent oncology social workers. For example, if your center has 25,000 visits per year and you have three full time oncology social workers, your ratio is 25,000 visits ÷ 3.0 FTEs = 1 SW: 8,333 visits. That is, you have one social worker per 8,333 visits.

Step 4: Compare the Staffing Ratio(s) at Your Center to Others.

AOSW’s Project to Assure Quality Cancer Care (APAQCC) identified the staffing ratios at 65 Commission on Cancer-Accredited Cancer Centers, including NCI-Designated Cancer Programs, Comprehensive Community Cancer Programs, Community Cancer Programs, Academic Cancer Programs and other program types. Examination of staffing ratios for these different types of programs allows you to compare your program/program type to similar program types. (This data is also available on the AOSW website under Research/Projects/APAQCC.

1.    Table 1. Caseload Staffing Ratios for Commission on Cancer-Accredited Cancer Programs

Type of Cancer Center	Best Case 1 SW FTE: # Analytic Cases	Worst Case 1 SW FTE: # Analytic Cases
NCI-designated cancer programs	1:389	1:2,061
Comprehensive Community cancer programs	1:131	1:3,014
Community cancer programs	1:224	1:1,437
Academic cancer programs	1:270	1:1,380
Other	1:158	1:612

Staffing ratios based on caseloads (“analytic cases”) showed wide variations at all the types of cancer centers. 

2.    Table 2. Coverage Staffing Ratios for Commission on Cancer-Accredited Cancer Programs

Type of Cancer Center	Best Case 1 SW FTE:  # of visits	Worst Case 1 SW FTE:  # of visits
NCI-designated cancer programs	1:3,600	1:25,343
Comprehensive Community cancer programs	1:1,455	1:126,431
Community cancer programs	1:1,535	1:29,714
Academic cancer programs	1:1,572	1:37,961
Other	1:1,162	1:39,673

Staffing ratios based on “coverage” (number of visits) also showed a wide range.

3.    You can now compare the staffing ratio at your cancer center with those found at other similar institutions. Is yours similar to the low end of the range (Best Case) or more like those institutions at the high range (Worst Case)? Do you think your staffing ratio makes sense? For example, if your caseload ratio is higher than competing cancer programs, can administrators legitimately claim high quality cancer care if other cancer programs have better social work staffing ratios and thus are more likely to be addressing patient concerns? Consider also if there are special considerations that would make your staffing needs differ from other similar institutions.

For example, is your population more likely to need social work services because of social, economic or clinical conditions that increase the likelihood of patients utilizing more costly services? In this case, even if your ratio is in the low range, you may be able to make a case to your administration that additional social work staff is needed. If you are comparing on the basis of coverage, consider the staffing ratio in light of the policies of your institution. Are patients screened at every visit or only at the initial visit? Do social workers see all patients, or only those with high distress scores?

Another way to use distress screening data to address staffing ratios is to see how your rate of distress compares with national data. (APAQCC found that about 50 percent of screened patients had scores of 4 and above on the Distress Thermometer.) If your population has higher levels of distress, this could also further your argument for the need for additional staff.

This article focused on how to use research data from the APAQCC project to compare your staffing ratio to that of other centers of similar types. There are many other ways to use research to enhance practice. For example, data on Distress Screening adherence in the article recently published in JNCCN can be used to see how your cancer center compares to others. That is, what percent of patients are not screened? Screened, but not referred to social work? Referred but never seen by social work staff?
 
As our social work research enterprise advances, we will be armed with the data clinicians need to make arguments for oncology social work’s added value in terms of impact on patient outcomes and experiences as well as cost reductions and institutional efficiencies.