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Leveraging big data to guide better nurse staffing strategies
  1. Joanne Spetz
  1. Philip R. Lee Insitute for Health Policy Studies, University of California, San Francisco, CA 94118, USA
  1. Correspondence to Professor Joanne Spetz, Philip R. Lee Insitute for Health Policy Studies, University of California, San Francisco, CA 94118, USA; joanne.spetz{at}

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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 failure to prevent mortality after an in-hospital complication, inpatient mortality for multiple types of patients, hospital-acquired pneumonia, unplanned extubation, respiratory failure and cardiac arrest.1–5 In addition, patients report higher satisfaction when they are cared for in hospitals with higher staffing levels.6 7

To date, most studies have not identified an ‘optimal’ nurse staffing ratio,8 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.

Practical challenges of nurse staffing

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 have measurable effects on the staffing levels resulting from acuity models.9 Second, patient volume and acuity can shift rapidly with changes in the volume of admissions, discharges and transfers between units. Third, staffing plans provide little guidance regarding the optimal mix of permanent staff, variable staff and externally contracted staff.

The paper by Saville and colleagues10 in this issue of BMJ Quality & Safety addresses the latter two issues by …

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