Fifteen Scientists Short by Year Five: A Supply and Demand Workforce Plan for Medical Laboratory Scientists in a Three-Hospital System
Student Name
American College of Education
HLTH5663: Human Resources in Healthcare Organizations
Module 1 Assignment
Instructor Name
July 3, 2028
Why This Role
The composite health system in this course runs three hospitals, a 310-bed regional center and two community hospitals of about 90 beds each, served by a core laboratory at the regional center and rapid-response laboratories at the two smaller sites. Its human resources committee asked for a workforce plan for the role most likely to limit operations over the next five years. Nursing was the obvious candidate, but nursing already has a system-wide plan. The laboratory director made the case for medical laboratory scientists, the certified professionals who perform and verify complex testing in chemistry, hematology, microbiology and blood banking. The laboratory currently fills 58.5 of 68 budgeted scientist positions and covers the rest with nine traveling scientists at premium rates.
The national picture supports the choice. The American Society for Clinical Pathology's 2022 vacancy survey found higher vacancy rates for laboratory positions in all departments than in 2020 and rising retirement rates across most departments, and its authors concluded that retention deserves as much attention as recruitment (Garcia et al., 2024). An earlier task force of the same society projected that demand for laboratory services would rise sharply as the population aged, and it recommended a broad approach to strengthening the pipeline of candidates for the profession (Bennett et al., 2014). A laboratory cannot hire a nurse to verify a blood bank crossmatch; the shortage of this role has no substitute to absorb it.
Projecting Demand
Demand for scientists is projected from test volume and productivity rather than from current headcount. The laboratories performed about 2.9 million billable tests last year with 68 budgeted positions, about 42,650 tests per position. Volume has grown about 4 percent a year for five years, driven by an aging population and a new outreach contract with area physician practices, and the plan assumes that rate continues. A new automated chemistry and immunoassay line, funded and scheduled to go live at the start of year two, is expected to raise productivity by 10 percent, based on the vendor's estimate discounted by the laboratory director's experience with previous automation.
On those assumptions, demand is 70.7 positions in year one, falls to 66.9 in year two as automation takes effect and then rises again with volume to 75.2 positions by year five. Automation buys the system about one year of relief, not a solution. The projection assumes the current test menu; adding molecular testing in-house, which the system is considering, would add demand not shown here.
Projecting Supply
Supply starts from the 58.5 positions filled today and changes through three flows. Retirements are projected from the age profile, since 22 of 61 scientists are 55 or older, and from a confidential survey of retirement intentions: about three a year. Other turnover, scientists leaving for other employers, travel positions or other careers, has averaged 8 percent a year. Hiring has averaged eight scientists a year: about five new graduates from the regional university's program, which graduates 12 a year and places most of the rest outside the region, and about three experienced hires.
If those flows continue, the filled positions barely move: 58.8 in year one and 59.9 in year five, because hiring only just offsets retirements and turnover. Comparing supply with demand, the gap is 11.9 positions in year one, falls to 7.7 in year two with automation, then grows to 15.3 positions by year five. At current travel rates, covering 15 positions with travelers would cost about $1.1 million a year more than employing scientists, before counting the effect on quality and on staff morale.
The gap is not spread evenly. The two community hospitals' rapid-response laboratories must have a qualified scientist on every shift, around the clock, to run emergency testing and release blood products, and each already depends on travelers for about a third of its night shifts. A single resignation there leaves a shift uncovered unless someone works overtime or a scientist drives in from the regional center. The core laboratory can absorb shortages by delaying non-urgent work or sending some tests to a reference laboratory, at a cost in turnaround time and send-out fees. The plan therefore treats the community hospitals' night coverage as the first place the gap will cause harm.
Testing the Levers
Workforce planning is useful only if it shows which actions would close the gap and by how much. The team modeled three levers together. First, reduce turnover from 8 to 6 percent from year two through the retention measures to be developed later in this course, which keeps about one additional scientist each year. Second, slow retirements from three a year to two through phased retirement, allowing experienced scientists to work reduced schedules and train new staff rather than leaving entirely. Third, raise hiring from eight to ten a year from year three by expanding the university program's clinical rotation seats in the system's laboratories and starting a bridge program that supports the system's own laboratory technicians in completing the scientist degree.
With all three levers, the gap narrows from 11.9 positions in year one to 5.6 in year two, 3.9 in year three and 1.7 in year five. No single lever closes it. Lower turnover by itself would still leave about 11 positions open in year five, and the larger pipeline by itself about 10. The combination works because the levers act on different flows: retention and phased retirement reduce losses, while the pipeline increases gains. The fastest way to add scientists in year five is to keep the ones hired in year two.
What the Projection Cannot See
The model is simple by design, and its limits should be stated. It assumes volume grows steadily, but a new outreach contract or the loss of one could move demand by several positions in a year. It treats scientists as interchangeable, while in practice blood bank and microbiology require specialized experience, and a shortage of three experienced microbiologists would be more damaging than a shortage of three generalists. It assumes turnover responds to retention efforts as planned, which is uncertain. The American Society for Clinical Pathology's survey of laboratory professionals found that job stress and burnout were high even where job satisfaction was high, with workload and understaffing as the main contributing factors (Garcia et al., 2020). That finding points to a risk the model does not capture: if the gap persists, the staff who remain may leave faster, widening it further.
The plan therefore recommends that the projection be updated every six months with actual turnover, hires and volume, and that the laboratory track its gap by department, so that the specialty areas where one resignation matters most are visible.
Conclusion and Questions for the Next Modules
If nothing changes, the system will be about 15 medical laboratory scientists short in five years, a gap that automation delays by only a year and that travelers could cover only at a premium of about $1.1 million a year. Closing it requires three levers at once: a larger pipeline, fewer departures and slower retirements. Each raises a question for the rest of this course. How should the system recruit and select candidates whose eligibility depends on certification and, in some states, licensure? What pay and differentials will compete with travel positions? How can performance be managed fairly in a short-staffed laboratory? And what will actually reduce turnover among the scientists already here?
References
Bennett, A., Garcia, E., Schulze, M., Bailey, M., Doyle, K., Finn, W., Glenn, D., Holladay, E. B., Jacobs, J., Kroft, S., Patterson, S., Petersen, J., Tanabe, P., & Zaleski, S. (2014). Building a laboratory workforce to meet the future: ASCP Task Force on the Laboratory Professionals Workforce. American Journal of Clinical Pathology, 141(2), 154-167. https://doi.org/10.1309/AJCPIV2OG8TEGHHZ
Garcia, E., Kundu, I., Kelly, M., & Soles, R. (2024). The American Society for Clinical Pathology 2022 vacancy survey of medical laboratories in the United States. American Journal of Clinical Pathology, 161(3), 289-304. https://doi.org/10.1093/ajcp/aqad149
Garcia, E., Kundu, I., Kelly, M., Soles, R., Mulder, L., & Talmon, G. A. (2020). The American Society for Clinical Pathology's job satisfaction, well-being, and burnout survey of laboratory professionals. American Journal of Clinical Pathology, 153(4), 470-486. https://doi.org/10.1093/ajcp/aqaa008
How this HLTH 5663 Module 1 example is structured
HLTH 5663 Module 1 often begins with workforce planning against a projected shortage in a specific role; your classroom's instructions decide the role and the planning horizon. This example explains why the role was chosen, then builds demand and supply projections separately with every assumption stated, so the gap can be traced to its causes. A scenario section tests the effect of specific levers, and a section on risks names what the projection cannot see. The conclusion sets out the questions later modules on recruitment, pay and retention must answer.
HLTH5663 Module 1 questions, answered
What does HLTH5663 Module 1 usually ask for?
HLTH5663 Module 1 often asks students to develop a workforce plan for a specific health care role facing a projected shortage, typically comparing future demand with future supply and proposing strategies. Your classroom's instructions decide the role, the setting and the planning horizon.
How do I project workforce demand?
Base demand on the work to be done rather than current headcount: volume, expected growth and productivity per position. Adjust for known changes such as automation or new services, and state each assumption so it can be tested.
How do I project workforce supply?
Start with current filled positions and model the flows: retirements, other turnover and hiring, each based on local data. Projecting each flow separately shows which lever, such as retention or recruitment, would change the result most.
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