Planning: Canadian RN Shortage Applied

¶ … Planning: Canadian RN Shortage

Applied Needs-Based Planning: Canadian RN Shortage

Murphy and colleagues (2012) investigated possible interventions to eliminate the growing registered nursing (RNs) shortage in Canada's health system. Rather that base their model on a patient to provider ratio, which ignores changes in health needs across demographics, or a demand-based model, which ignores inequalities in health care access, they proposed a more responsive needs-based model. In essence, the health of the population is quantified, specific goals established, and then the number of nurses needed to achieve these population-wide goals estimated. Using this approach also provides considerable latitude in determining the appropriate ratio of doctors to nurses to aids, which will provide the optimum outcome. In other words, Murphy and colleagues view the needs-based model as the best way to determine interventions intended to eliminate staffing inefficiencies in the health care system.

A system dynamics-based simulation model was used to calculate the difference between the requirements for RNs and supply (Murphy et al., 2012). The data fed into the model included the size of the served population, the prevalence of health and illness, risks for future illness, and RN and provider productivity in full time equivalents (FTE). This model was designed to allow policymakers to experiment with the impact of increasing or reducing different variables, rather than to simply make future predictions, thereby determining which policy decisions could have the greatest positive impact on health care delivery efficiency. The validity of the model, according to the authors, depends on the quality of the data used.

Plugging relevant data currently available into the model predicted a shortage of 10,000 FTE RNs for 2007 (Murphy et al., 2012). Based on current health trends among the Canadian population, such as an expanding elderly demographic and continued increases in the prevalence of obesity, diabetes, and heart disease, the gap between supply and need should increase to 60,000 RNs by 2022. This result is based on current trends in RN training, availability of providers, and no change in the patient care roles of RNs. With this baseline prediction in mind, health policy makers were asked to consider changes in the model affecting RN training, retention, and workforce deployment. Increased training enrollment turned out to be a less favorable, although currently popular, intervention because it has a small, delayed effect. A better policy would be to reduce attrition rates in nursing education programs, currently averaging 28%, because the effect would be immediate and long-term. Other interventions predicted to be effective were increasing workforce retention across all ages, lowering absenteeism through improved working conditions, and increasing RN productivity through smarter staffing allocations (increasing the availability of nursing aids). While none of these interventions could eliminate the projected RN gap alone, combining them could.

Research Implications

The needs-based modeling approach used by Murphy and colleagues (2012) to predict current and future nursing shortages, while at the same time providing an experimental tool for testing the efficacy of policy changes, may represent the future direction of human resource planning on a national scale. The comparatively simplistic provider to patient ratios and demand-based estimates cannot accurately predict need; therefore, inefficiencies in the health care system will remain. By comparison, the needs-based model provides at least the possibility of addressing most inefficiencies. This model stands out because it is essentially an iterative model that incorporates 'need' and supply data as it becomes available. Although not perfect, the model is flexible enough that it can be adjusted to include additional parameters and data to minimize the impact of unexpected developments, such as a major weather event or a severe economic downturn. Increasing the predictive accuracy of the model is also straightforward and would depend on investing into data collection efforts. As the authors discuss, the main limitation is the quality of data available; however, patient to provider ratios or demand-based models are still inferior by comparison.

A common approach used for validating predictive models such as the one developed by Murphy and colleagues (2012) is to test it using historical data. While historical data may not have the same quality as today's data does, it might still be informative to see how the model performs when the real-world outcome is already known. It would be particularly interesting to test the model using the data available at the beginning of the current obesity epidemic to see how well it predicts the increase in patient health care needs, especially in light of the ongoing nursing shortage.