The Impact of Nursing Staff on Satisfaction Scores for U.S. Hospitals: A Production Function Approach

Abstract

Hospitals have increasingly relied on nurse assistants to support nurses in the provision of patient care, yet knowledge about their contributions to the patient experience in U.S. hospitals is limited. We address this issue by exploring the impact of nurse assistants and registered nurses on an array of patient satisfaction measures from the Medicare Hospital Consumer Assessment of Healthcare Providers and Systems. Using linked data for 2,807 hospitals from 2008 to 2016, we employ a production function approach to estimate and plot marginal impact curves for both nurse assistants and registered nurses. We find that although registered nurses are more impactful, nurse assistants are the more underdeployed staffing category. We also find that after meeting certain thresholds for minimal hours, nurse assistants have a comparative advantage in improving patient satisfaction scores in the housekeeping and patient support domain. Given their lower labor costs, further employment of nurse assistants may be warranted.

Associations between Nurse Staffing Levels, Patient Experience, and Hospital Rating

“…Nurse staffing was found to be a factor affecting patient experience in general hospitals, and this result was in line with previous studies [ 27 , 28 ]. In previous studies, patient experience scores were related to nurse staffing, because in a work environment with an adequate number of well-trained nurses, nurses are able to provide critical surveillance of their patients, instruct patients on medication and discharge, and provide critical bedside care [ 29 , 30 ].…”

Primary care workforce composition and population, professional, and system outcomes: a retrospective cross-sectional analysis

“…[Table 1 here] We used linear regression to relate practice outcomes to cubic functions of the levels of each staff type. (18,19) We also included interactions between GPs and each of the other three staff types to test for direct substitution (negative interaction coefficient) or direct complementarity (positive interaction coefficient) between staff groups. We standardised all outcome measures using z-scores to aid comparisons.…”

“…In analysing the relationship between workforce composition and outcomes, we used a flexible regression model. This cubic function has been shown to be an acceptable alternative to the most flexible translog specification used in economic studies of production and less susceptible to multicollinearity in applied work because it requires fewer interaction terms (18,19). Although the cubic production function, like many flexible functions, results in correlated variables we are interested in their combined effect rather than the coefficients on individual measures which may be estimated imprecisely due to multicollinearity.…”

Interventions and Hospital Characteristics Associated With Patient Experience: An Update of the Evidence

“…Nursing Care. One study (Delhy et al, 2021) assessed the association between the number of registered nurse (RN) hours per patient day and nursing assistant (NA) hours per patient day as reported in the AHA survey, adjusting for an extensive set of hospital characteristics, state hospital staffing regulations, and state and year fixed effects. The main finding was that more RN and NA hours corresponded to more positive patient care experiences for all eight HCAHPS measures examined.…”

“…In a study that predicted hospital size (coded linearly) from eight HCAHPS measures entered simultaneously, five measures (Communication with Doctors, Staff Responsiveness, Cleanliness, Quietness, Discharge Information) were negatively associated with bed size; two (Communication with Nurses and Overall Rating) were (multivariably) positively associated with hospital size; and one (Communication about Medicines) was (multivariably) unrelated to hospital size (McFarland et al, 2017). Two studies included bed size as a control variable: one generally found that bed size correlated negatively with patient experience measures (Delhy et al, 2021); the second found that smaller hospitals had better scores for Staff Responsiveness but worse for Recommend Hospital than larger hospitals, and that bed size was unrelated to three measures (Care Transition, Discharge Information, and Overall Rating [Hu et al, 2020]).…”