To avoid injuring their patients and themselves, healthcare providers must get in the habit of using safe patient handling and mobility (SPHM) technology. In this supplement, national experts share their perspectives and best practices on topics ranging from dealing with bariatric patients, managing slings, and assessing a patient’s mobility to transforming the culture, building the business case for an SPHM, and developing a successful SPHM program.
Download a PDF of the entire supplement here.
The value of a safe patient handling and mobility (SPHM) program is clear, but the benefits may be difficult for some nurse leaders to quantify. Some investment justifications are available from vendors of SPHM equipment, but even when well done, they don’t give a complete picture of potential benefits—and inevitably are discounted because vendors are in the business of selling equipment. This article describes how to make an independent, unbiased business case for an SPHM program and presents a case study of a decision analysis process used at Stanford University Medical Center.
Elements of a good business case
A business case should:
- describe the proposed program, such as required equipment and training
- quantify program costs and benefits
- show the program’s net benefit (benefits minus costs), expressed either as a net present value or return on investment (ROI).
A good business case considers alternative program designs and includes projections for the results if the proposed program isn’t implemented (such as increased workers’ compensation costs and increased pressure ulcers). Net benefits commonly are measured by subtracting costs with the program in place from costs without the program in place.
Although preparing such projections is feasible for those with a master’s degree in business administration or a similar education, many SPHM program champions have clinical backgrounds. Here are some possible strategies they can use, starting with the easiest but least facility-specific.
Strategy 1: Refer to a published study
The easiest but least facility-specific and least accurate way to prepare an investment justification is to refer to published studies. For example, the risk-management study I undertook for Stanford, published in the April 2011 issue of Journal of Healthcare Risk Management, shows what a facility with all the elements of a successful SPHM program can achieve.
Strategy 2: Complete a simple template
The next most accurate way to prepare an investment justification is to fill out a simple template. Most likely, your employer’s finance department or capital committee has a standard template for proposed expenditures. Most organizations require a cost-benefit projection for 5 years into the future. The cost part is fairly easy, and most people are familiar with preparing budgets for what they propose to spend. Be sure to include estimates for equipment purchases and training time.
As for benefits, the most commonly cited ones for an SPHM program are reductions in workers’ compensation costs and in lost or restricted staff days due to patient handling and mobility injuries. Unless your facility already has identified these costs, you’ll need to crossmatch data from the Occupational Safety and Health Administration Form 300 (listing causes of injuries and whether they led to lost or restricted duty days) against cost data in the workers’ compensation system.
Typically, organizations estimate they’ll save 60% to 80% of workers’ compensation costs related to patient mobilization if they have an SPHM program, and will save zero to 50% of the cost of replacement staff to fill in for out-of-work or restricted-duty staff (depending on the facility’s replacement staff policy). Subtracting each year’s costs from the benefits yields the annual net benefit. If your facility’s template hasn’t built in these costs, someone from the finance department can help convert the year-by-year figures to a net present value or ROI.
Strategy 3: Prepare a decision analysis
Preparing a decision analysis is more difficult than referring to a published study or using a template. But it’s facility-specific and thus provides the most complete and accurate picture. Of course, it must be done by someone skilled in decision analysis. But for large investments, the cost of the analysis is well worth it, because it:
- delivers a highly accurate quantification of costs and benefits, including uncertainties
- shows worst- and best-case scenarios for costs and benefits and describes exactly how these might occur
- identifies how to get more value out of the SPHM program
- specifies which result measures should be tracked to validate that the program is working as it should be, and pinpoints what the values for those measures should be.
Generally, a decision analysis costs much less than 1% of the program cost. What’s more, it produces recommendations for increasing program value, which dwarf the cost of the analysis.
I worked with Stanford on a decision analysis for its SPHM program because it became apparent that the simple-template approach initially used there missed most of the value and wouldn’t justify a program in the new hospital under construction.
Case study: Standford decision analysis
At Stanford, we began by drawing an influence diagram to show all SPHM costs and benefits of interest to leaders. (See Influence diagram.) For each cost or benefit, more detailed work explored exactly how to quantify the results. For example, to estimate the benefits of reduced staff turnover, we needed to know:
- number of nurses mobilizing patients who would be affected by the SPHM program
- average annual staff turnover rate
- average cost to recruit and train a nurse ($60K to $80K, based on a literature search)
- estimate of how much the SPHM program would reduce staff turnover.
We did similar work for each type of cost and benefit. Unlike using a simple template or referring to a published study, the decision-analysis approach enabled us to use a range of numbers to represent uncertainty regarding how significant the future impact might be. For turnover reduction, we used a range of 0% to 20%.
These data were then programmed into a Microsoft Excel spreadsheet. One immediate result was that the total value of an SPHM program (including hard-to-quantify benefits) would amount to more than twice the value of reduced workers’ compensation costs and lost and restricted days alone.
The next step was to set each uncertainty (such as a change in the nurse turnover rate) to the low value in the range, record the total program value, set the uncertainty to the high value in the range, and record the total program value. The difference between the two program values was plotted on a bar chart. When the bars were sorted from highest to lowest impact on program value, the characteristic tornado shape resulted. (See Tornado chart: Key value drivers.)
Stanford leaders were surprised to learn that reduced staff turnover had the greatest potential for getting more value out of the SPHM program, possibly increasing total program value from about $4 million to $6.5 million. As a result, Stanford decided to inform the nursing staff that it was going to put in place the SPHM tools needed to keep them healthy and able to work. Stanford also surveyed staff satisfaction improvements resulting from the SPHM program. Combinations of all the variables produce thousands of scenarios, best shown in a probability distribution. The probability distribution for Stanford showed that the mean program value was more than double the estimate from the template approach. It also showed that in a worst-case scenario, the program would still pay for itself.
An easy way to show the components of program value is to take the overall program value from the base case (all uncertainties set to their middle value) and break these down into components of cost and value. This produces a so-called waterfall chart. (See Components of total SPHM program value.)
Outcome of the decision analysis
Stanford’s decision analysis produced:
- a high degree of confidence that the actual value of the SPHM program and uncertainty in that value had been quantified accurately
- a deeper understanding of how the program would add value and which benefits were most important
- insight into how to get more value from the program
- identification of which value measures would need to be tracked to validate program results.
At Stanford, reductions in workers’ compensation claims were on track (within the 60% to 80% range forecast), but baseline workers’ compensation costs were growing faster than the maximum 19% annual increase forecast. A closer look revealed that a return-to-work program had been discontinued, sending costs skyrocketing. Stanford quickly reinstated that program.
Celona J, Driver J, Hall E. Value-driven ERM: Making ERM an engine for simultaneous value creation and value protection. J Healthc Risk Manag. 2011;30(4):15-33.
Celona JN, McNamee PC. Decision Analysis for the Professional. 4th ed., rev. Menlo Park, CA: SmartOrg, Inc., 2001-2007.
The Facility Guidelines Institute. 2010 Guidelines for Design and Construction of Healthcare Facilities. Dallas, TX: Author; 2010
John Celona is a principal at Decision Analysis Associates, LLC, in San Carlos, California. He is the author of Decision Analysis for the Professional, 4th ed.