Leveraging big data to guide better nurse staffing strategies

nurse and patient at tableImprovements in the creation of cost-effective strategies for hospital workforce deployment can come from linking complex analytical approaches with data on the impact of nurse staffing on the long-term costs of patient care, writes Joanne Spetz, PhD in a recent BMJ Quality and Safety editorial.

Over the past 20 years, a large body of research has documented a relationship between higher nurse-to-patient staffing ratios and better patient outcomes, including shorter hospital stays, lower rates of mortality for multiple types of patients, and higher patient satisfaction. But, most studies have not identified an ‘optimal’ nurse staffing ratio, which creates a challenge for determining appropriate staffing levels. If increasing nurse staffing always produces at least some improvement in the quality of care, how does one determine what staffing level is best? This decision is ultimately an economic one, balancing the benefits of nurse staffing with the other options for which those resources could be used. It is in this context that hospitals develop staffing plans, generally based on historical patterns of patient acuity.

Hospital staffing plans provide the structure necessary for determining hiring and scheduling, but fall short for a number of reasons. First, there are multiple ways in which patient acuity can be measured, which can shift the staffing levels simply due to measurement differences. Second, patient volume and acuity can change rapidly, making planned staffing inadequate in real time. Third, staffing plans provide little guidance regarding the optimal mix of permanent staff, variable staff, and externally-contracted staff.

The application of sophisticated simulation models and other advanced analytical approaches to analysis of nurse staffing has been limited to date, but a study by Saville and colleagues recently used advanced simulation modeling to study this issue. They found hospitals are much less likely to have understaffed shifts if they aim to have higher baseline staffing, primarily because hospitals are more likely to have large unanticipated increases in patient volume and acuity than to have unanticipated decreases. This result is not particularly surprising. What is surprising is that hospitals do not necessarily achieve cost savings by relying on temporary personnel versus setting regular staffing at a higher level. It often is the case that hospitals will have the best cost and patient care results by staffing above the level they might have guessed if simulation modeling had not been done. 

Analytical methods that fully leverage the large datasets compiled through electronic health records, human resources systems and other sources can and should be applied to advance research on the composition of nursing teams to improve quality of care. 

Joanne Spetz, Ph.D., is the Director of the Philip R. Lee Institute for Health Policy Studies and Brenda and Jeffrey L. Kang Presidential Chair in Healthcare Finance. She also is Associate Director for Research at Healthforce Center at UCSF. She holds faculty appointments in the Department of Family and Community Medicine and in the School of Nursing. Her fields of specialty are economics of the health care workforce, organization and quality of health care services, and policies that affect use of and treatment for illicit drugs.

 

Leveraging big data to guide better nurse staffing strategies

 

Spetz J. BMJ Qual Saf. 2021 Jan;30(1):1-3. doi: 10.1136/bmjqs-2020-010970. Epub 2020 Jul 27.