Enhancing staffing accuracy with predictive modeling and best practices

In imaging services, “getting it right” is of utmost importance when it comes to selecting the appropriate equipment, administering the correct radiation dose and delivering excellent patient care. Getting it right also is an imperative where staffing is concerned.

Inadequate staffing levels lead to “too little” excellence, according to David Cowan, senior research scientist at the Georgia Institute of Technology in Atlanta. Patients endure longer waits for service, mistakes are made and staff, patients, physicians, and managers are unhappy. Cost savings may be achieved, but only in the short run.


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Similarly, over-staffing results in excessive labor expenditures, and staff morale is often low because idleness sparks complaints about other matters.

Applying a predictive staffing model that takes into account not just the number of procedures performed by a given imaging services provider but myriad other variables is a partial solution to the staffing dilemma, Cowan says. These variables include procedure mix and volume; procedure- and modality-level time standards; individual organizational focus on expense minimization, productivity, and customer service; equipment utilization (particularly of high-volume MR equipment); and OR utilization. Other variables encompass organization size and scope (number of sites), service hours, cadre of equipment, and cadre of “teaching” personnel (medical/surgical/radiology residents and RT students).

Creating the model

The Association for Medical Imaging Management (AHRA), with funding from the AHRA Education Foundation, is developing such a model. Initially evaluated at eight hospitals and currently being tested at 30 sites, the model will yield predicted staffing ranges for each imaging-services section, as well as for the entire imaging-services department. Output will be based on entities’ procedure volume and 20 to 30 individual characteristics, ranging from equipment type and utilization, to department layout and customer service priorities. The structure of the model also will allow users to test various scenarios prior to making staff decisions.

Cowan, who has been a key participant in creating the model, shares an example of how a hospital might leverage it to its full advantage: “Anywhere Hospital” plans to add an emergency department with a CT scanner and a diagnostic room; it expects growth in imaging volume as a result. However, an outpatient imaging center is opening nearby and is expected to reduce the hospital’s image volume by 10 percent. To limit loss of business to its competitor, hospital management decides to place a heightened emphasis on customer service, with patient waiting time to be limited to 20 minutes.

Using the model, Anywhere Hospital’s administrators find that the new additions will increase staffing needs by four CT technicians and two diagnostic technologists. At the same time, they learn, the volume reduction from the advent of the nearby outpatient center will reduce staffing requirements by two CT technologists and four diagnostic technologists. “The upshot is a net increase of two CT technologists and a decrease of two diagnostic technologists—overall, the same total number of FTEs, but with different certifications,” Cowan observes.

Beyond the model

Implementing what Cowan terms “better practices” in conjunction with—and to augment—the use of predictive models also bodes well for imaging services. Exercising flexibility in staffing is paramount, Cowan says, citing as an example the structuring of full-time positions to encompass 32 to 36 hours of work each week. This, he explains, affords hospital radiology departments four to eight hours of additional coverage per employee, without incurring expenditures for overtime.

“It has been a very helpful technique in hospital radiology departments, where overtime can be a real problem,” he observes.

Remaining flexible on the staffing front also involves modality management across multiple imaging sites—i.e., shifting staff among facilities to reflect anticipated workloads—along with effective use of cross-trained staff to cover multiple modalities during less busy shifts.  For instance, Cowan says, a single technologist with general diagnostics and CT training can be assigned to handle the night shift in the emergency department.

Assuming a flexible staffing stance by leveraging outsourced professional resources often has a positive effect on radiology economics, physician compensation and patient care.  Engaging a virtual FTE—or teleradiologist—in lieu of a full time employee is a viable and efficient means of meeting the vicissitudes of staffing requirements.  For example, outsourcing overnight reads or lower value RVU studies allows a practice to maximize the coverage of high-value RVU studies with on-staff physicians.  On-demand access to virtual subspecialist radiologists also allows for better patient care, flexible coverage—with less onerous budget implications.

Moreover, imaging services can benefit when staff are assigned to work in teams where applicable. Cowan offers two examples: In one hospital, an ICU nurse technologist is assigned to assist individual radiology technologists in performing bedside studies using portable equipment. The nurse technologist meets the radiology technologist at each patient’s door, preceding him or her into the room and preparing the patient for the procedure. This improves productivity and paves the way for a high level of customer service. Similarly, in an outpatient imaging center, support staff members assist CT and MRI technologists with handling patients and preparing them for procedures.

Imaging services objectives and other factors should also be taken into consideration before initiating personnel cuts and prior to any expansion. This goes beyond staffing volumes; maximizing the value of personnel to drive client satisfaction and practice profitability is more important. It behooves imaging service providers to evaluate their modalities, including peer comparisons, to assess whether they are missing staffing opportunities.  Looking at how their imaging volumes compare to those of similar hospitals may help them to identify underserved areas in which coverage should be increased, such as women's health and mammography.  Again, shortages and gaps are well handled through flexible staffing arrangements.  That way practices can dial up and dial back their staffing commitments with minimal impact.

“Always look at the big picture,” Cowan says. “Frequently, we see hospitals saying they need to reduce the number of FTEs in the radiology department to conserve costs, but they don’t think about the fact that if they do so, patient wait time will be one hour rather than 20 minutes, which doesn’t meld with a push to better patient service. Similarly, they don’t consider that if they are going to expand their outpatient services to a freestanding center, they will need to adjust the number of FTEs they have in the hospital to accommodate the shift in patients.”

Concludes Cowan, “I don’t think any of us have figured out staffing completely, and as soon as we think we do, a new challenge confronts us. But a combination of better practices, openness to flexible staffing and a solid model is a great start.”