Abstract
Nursing is numerically the largest health profession providing direct care, and this, plus the imperative for effective cost control, makes their costs and impact a legitimate study focus and policy target. Production theory techniques can help nurse executives maximize outcome and minimize costs, yet little of such evidence currently exists in mainstream nursing workforce research. The balance of available evidence supports an inverse association between nurse staffing levels and adverse outcomes. However, 'adequate' staffing levels may be perceived as expensive and some providers may try to reduce them. The response, in some US and Australian states, is legislation to force hospitals to implement mandatory minimum patient-to-nurse (P/N) ratios. In this paper, existing data from Aiken et al. [2003. Education levels of hospital nurses and patient mortality. Journal of the American Medical Association 290, 1617-1623] is re-interpreted using production theory, to illustrate the possible relationship between two key workforce variables "Staff Level" and "Staff Mix", and clinical outcome, and show how this informs decision making. Consistent with other studies, this suggests that diminishing returns to each variable exist. This preliminary analysis suggests that increasing the number of graduate RNs in the workforce might be the most cost-effective way to expand the nursing workforce. However, more detailed and rigorous research is needed to estimate speciality specific cost and production functions and compute the optimal solution. This can predict the most cost-effective staff combination for a set outcome, or the set of inputs yielding best outcome for a given budget. With this, nurse executives can systematically maintain service quality or safety in the most economical way.
TY - JOUR
T1 - The production economics of nursing: a discussion paper.
A1 - Newbold,David,
Y1 - 2007/03/26/
PY - 2006/09/06/received
PY - 2007/01/10/revised
PY - 2007/01/16/accepted
PY - 2007/3/24/pubmed
PY - 2008/3/22/medline
PY - 2007/3/24/entrez
SP - 120
EP - 8
JF - International journal of nursing studies
JO - Int J Nurs Stud
VL - 45
IS - 1
N2 - Nursing is numerically the largest health profession providing direct care, and this, plus the imperative for effective cost control, makes their costs and impact a legitimate study focus and policy target. Production theory techniques can help nurse executives maximize outcome and minimize costs, yet little of such evidence currently exists in mainstream nursing workforce research. The balance of available evidence supports an inverse association between nurse staffing levels and adverse outcomes. However, 'adequate' staffing levels may be perceived as expensive and some providers may try to reduce them. The response, in some US and Australian states, is legislation to force hospitals to implement mandatory minimum patient-to-nurse (P/N) ratios. In this paper, existing data from Aiken et al. [2003. Education levels of hospital nurses and patient mortality. Journal of the American Medical Association 290, 1617-1623] is re-interpreted using production theory, to illustrate the possible relationship between two key workforce variables "Staff Level" and "Staff Mix", and clinical outcome, and show how this informs decision making. Consistent with other studies, this suggests that diminishing returns to each variable exist. This preliminary analysis suggests that increasing the number of graduate RNs in the workforce might be the most cost-effective way to expand the nursing workforce. However, more detailed and rigorous research is needed to estimate speciality specific cost and production functions and compute the optimal solution. This can predict the most cost-effective staff combination for a set outcome, or the set of inputs yielding best outcome for a given budget. With this, nurse executives can systematically maintain service quality or safety in the most economical way.
SN - 0020-7489
UR - https://www.unboundmedicine.com/medline/citation/17379228/The_production_economics_of_nursing:_a_discussion_paper_
DB - PRIME
DP - Unbound Medicine
ER -
