It is not an overstatement to say that appropriate staffing is crucial to the solvency of the clinical lab. Associate wages and benefits account for over half of the typical lab’s direct costs – yet structured staffing studies remain rare, and consequently, benchmarking is difficult.4 Overstaffing can quickly drive costs to unsustainable levels, while understaffing can cripple the lab during periods of higher test volumes, planned / unplanned associate absences, etc.
It is therefore crucial for the lab manager to take a carefully- considered, quantitative approach specific to the institution, rather than a "gut feel" based on previous experience. Having a solid, evidence-based rationale in creating a staffing model keeps staffing decisions objective and will account for normal variations in workflow and associate availability (due to, e.g., vacation, illness, FMLA, etc.).2 But what are the factors to consider when creating a staffing model?
Every industry has studied and practiced resource optimization, including (if only of late) the clinical laboratory. For example, a systems engineer at a major medical center has published a series of articles which discuss in detail how to determine proper staffing in the clinical lab.
In essence, he proposes dividing the activities of the lab into three general categories: direct efforts – time needed to handle samples and perform tests on those samples; indirect efforts – time needed to carry out, e.g., periodic statistics, continuing medical education, stocking, maintenance; and operational time – where associates are attending training sessions, on PTO or FMLA, etc.
He contends that by calculating the handling and processing needs of the testing volume, and comparing that data to the availability of staff that is, on average, available to perform direct and indirect processing efforts, an accurate staffing model can be derived.1,2,3
Nevertheless, these determinations need to take into account factors such as institution size/footprint, the lab’s directory of services, the degree of automation present, access to LIS data, and so on. This is a daunting task for even an experienced lab manager.
Ektelligen® has developed a robust, data-based, proprietary staffing model development tool which can easily allow lab managers to consider all salient factors and develop a staffing model custom-designed for their laboratory. The benefits of such a staffing model include:
Adequate staffing during periods of heavy throughput, staff vacations, etc.
Avoidance of excessive payroll hours
Greater credibility with Administration when an increase of staff is requested
Increase in associate morale – by avoiding both "burnout" and fears of downsizing
Are you ready to create a robust, data-driven staffing model? Let Ektelligen® make it easy for you. We will partner with you to create a staffing model based on your institution, your DOS, your needs. Contact Ektelligen® today, and feel confident about your staffing decisions, today and in the future.
Baisch M, Staffing to workload: operational needs, Mayo Medical Laboratories, 3/30/2017, https://news.mayomedicallaboratories.com, www.medpagetoday.com
Baisch M, Applying the staffing to workload methodology: basic staffing model, Mayo Medical Laboratories, 3/30/2017, www.medpagetoday.com
Baisch M, Staffing to workload: operational needs, Mayo Medical Laboratories, 3/30/2017, www.medpagetoday.com
Jones BA, Darcy T, Souers RJ, Meier FA, Staffing benchmarks for clinical laboratories: a college of american pathologists Q-probes study of laboratory staffing at 98 institutions, Archives of Pathology & Laboratory Medicine: Feb 2012 Vol 136 No 2 pp 140-147
