Enhancing staffing accuracy with predictive modeling and best practices

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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.

 

David Cowan, senior research scientist, Georgia Institute of Technology, Atlanta, Georgia

 - David Cowan

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

Julie Ritzer Ross is a contributing writer for Medical Imaging Review.