Patient nutrition acuity as a predictor of the time required to perform medical nutrition therapy.
OBJECTIVE
To determine if patient nutrition acuity accurately predicts the time required to perform medical nutrition therapy (MNT).


METHODS
Data detailing demographic characteristics, patient nutrition acuity, and time spent performing MNT were collected for 12 consecutive days. Random systematic sampling was used to select 25%, or a minimum of 20 patients, from daily admissions to the hospital. Nutrition acuity was categorized using a 27-item patient acuity tool.


METHODS
Analysis included data from 92 acute-care hospitals nationwide; the median census was 271 patients. Of the 7,289 patients in the survey, 3,321 were included in this data analysis. All subjects were assigned an acuity rating and received MNT. Mean age (+/- standard deviation [SD]), was 55 +/- 24 years, and the sample was 48% male and 52% female. Time spent delivering MNT ranged from 5 to 285 minutes (mean +/- SD = 43.3 +/- 34.2 minutes).


METHODS
Stepwise multiple regression analysis (P < .05), with independent variables of age, gender, and 27 acuity descriptors, determined time required to perform MNT.


RESULTS
The number of acuity descriptors assigned to patients ranged from zero (53 patients) to 20 (1 patient); the mean (+/- SD) for all patients was 5.6 +/- 3.1. Gender and 21 of the 27 acuity descriptors were statistically significant in predicting the time required to perform MNT.


CONCLUSIONS
A formula was developed to determine medical nutrition therapy time (MNTT) as minutes per patient sampled. When extrapolated to a facility's patient census, MNTT is the basis for predicting staffing requirements. The MNTT formula is crucial in the present environment of managed care where fiscal accountability challenges staffing rationales.
Simmons
M L
ML
School of Nursing and Health Sciences, University of Phoenix, Ariz., USA. 102020.2621@compuserve.com
Vaughan
L A
LA
D000367:Age Factors; D004045:Dietary Services / Q000379:methods* / Q000706:statistics & numerical data; D005260:Female; D005544:Forecasting; D006801:Humans; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D052756:Nutritional Sciences / Q000193:education*; D010353:Patient Education as Topic / Q000706:statistics & numerical data*; D012044:Regression Analysis; D012737:Sex Factors; D013997:Time Factors