ASCP 2024 Vacancy Survey Report Highlights Policy Priorities to Address Laboratory Workforce Shortages Through Credentialing, Advocacy, and Education Expansion
By - January 27, 2026
For nearly four decades, the American Society for Clinical Pathology (ASCP) has conducted its Vacancy Survey to assess the scope and distribution of workforce shortages in medical laboratories across the United States. Each iteration of the survey presents an opportunity to enhance its methodology, ensuring the collection of timely, relevant data while maximizing participation. In the 2024 survey, ASCP introduced new questions to explore emerging factors influencing vacancy rates—most notably, the role of Artificial Intelligence (AI). To provide contextual insights, participants were asked about the current use of AI in their laboratories and its perceived impact on staffing and workforce dynamics.
Results of the 2024 ASCP vacancy survey show that vacancy rates are higher in anatomic pathology, cytogenetics, flow cytometry, histology, LIS and QA/PI, and point-of-care departments compared to the 2022 report (Figure 1). Current vacancy survey data show that trends in vacancy rates are returning close to pre-pandemic levels. Data show that while the current vacancy rates are lower compared to 2022, they are still higher compared to the vacancy rates before COVID-19, which suggests continued challenges in recruitment of laboratory professionals (Table 1).
On average, hiring staff for most departments takes three months to a year, while hiring supervisors takes between three months to over a year. All departments reported that it takes three to six months to fill staff vacancies, except blood bank/transfusion medicine, cytology, flow cytometry, QA/PI, and phlebotomy, where it takes up to seven months to a year. For most of the departments surveyed, the average time to fill a supervisory vacancy takes three to six months except for microbiology and specimen processing which takes seven months to a year; and anatomic pathology, blood bank/transfusion medicine, chemistry/toxicology, immunology and QA/PI which takes more than 12 months.
Qualitative analysis results from this survey show that the most common concerns from respondents were salary, lack of education/training programs, and challenges in staffing impacting affecting recruitment and retention of laboratory professionals. According to one of the respondents, “…the second biggest challenge is retention. The career ladder is limited and does not support the growth of professionals that want to stay and build their career. We have severe compression in our wage/salary bands and on top of that other laboratories in our area are offering 4−5 more per hour with equivalent or better benefits. This affects recruitment and retention.”
Artificial intelligence (AI) and the laboratory workforce
Given the ongoing laboratory professional shortage and retirement rates, artificial intelligence (AI) technologies have been considered a potential support solution for some workflows when appropriately vetted and assessed. Despite its potential benefits, many laboratories are hesitant to incorporate AI into their workplaces. Economically, laboratory implementation of AI technology requires evaluation of business cost-effectiveness against prospective scalability and relevance to clinical impact. Furthermore, biased algorithms could produce incorrect results, especially if used for unique cases or minority populations. Another concern is the lack of regulations on AI in the laboratory, which creates liability and security concerns. Nevertheless, AI’s increasing salience in the laboratory indicates that more research on the laboratory workforce’s experiences and opinions on the matter is necessary to ensure that the field and technology can grow alongside each other.
Overall, only 17.4 percent of respondents reported having incorporated AI tools in their laboratories. The most frequent use is reported in LIS and QA/PI (33.3 percent) and anatomic pathology (30.4 percent), followed by core lab and microbiology/virology/infectious disease, each at 21.7 percent. Moderate adoption levels are seen in blood bank/transfusion medicine, cytogenetics, and hematology/coagulation (each 17.4 percent), while specimen processing, molecular pathology/diagnostics, cytology, and histology report lower rates, ranging from 13.0 percent to 7.2 percent. The most common challenge while implementing AI was adaptation (45.2 percent). Comments about adaptation difficulties included lack of trained staff/IT resources (“Weak IT staff and no support”), resistance to change (“Staff are not excited about changes”), and validation/testing of AI technologies (“The length of time it takes to validate”). The least common challenge was job loss. The most common concern about AI implementation was newness (45.2 percent).
Most laboratories were between somewhat and moderately skeptical (65.2 percent); few were very skeptical (13.6 percent), or not at all skeptical (21.1 percent) about integrating AI into laboratory operations. The most common reasons for skepticism about AI tools in the laboratory were newness (46.0 percent), distrust in AI (37.4 percent), and human involvement (20.3 percent). Other reasons include AI misuse; job loss; governance; and cost. Favorable opinions towards AI increased alongside exposure. Lower skepticism (not at all, somewhat, and moderately enthusiastic) and higher (somewhat, moderately, and very enthusiastic) enthusiasm were found in workplaces that have incorporated AI (somewhat, moderately, and very enthusiastic: 95.6 percent; very skeptical: 4.3 percent) and provided training (somewhat, moderately, and very enthusiastic: 100 percent; very skeptical: 4.5 percent). Overall, skepticism also decreased as proficiency increased, and enthusiasm increased alongside proficiency.
The survey results found that AI enthusiasm was higher in laboratories that implemented AI training and indicated AI proficiency in their staff. AI training could also address employees’ implementation concerns and resistance to change. Therefore, it is recommended that laboratories implement more AI training for their employees. To address distrust in AI performance, laboratories may consider implementing AI in administrative or straightforward tasks. Then, as training advances and employees increase proficiency and trust in AI, laboratories may choose to slowly introduce automation. However, to address job replacement concerns and errors, it should be emphasized that employees have final oversight in any decisions made by AI.
ASCP continues advocating for laboratory workforce issues
ASCP and ASCP BOC have been working to protect federal policies intended to support workforce development, as Congress continues its efforts to cut federal spending. Recently, ASCP contacted congressional leaders on several occasions urging their support for federal workforce development programs. ASCP’s advocacy efforts were successful in getting Congress to enact the Tomorrow’s Workforce Act, which will allow individuals to use their 529 funds for activities related to professional development, such as completing clinical training programs, and obtaining certification (covering fees and related expenses). In addition, ASCP voiced concerns about the impact the budget reconciliation bill could have on workforce development. This legislation would cut student financial aid programs, such as the Grad+ program, and remove federal repayment assistance options. The Public Service Loan Repayment program, which allows forgiveness of certain outstanding federal student loan debt in exchange for a qualifying service obligation, would also be impacted.
