A Comparison of Self-evaluated Survey and Work Sampling Approach for Estimating Patient-care Unit Cost Multiplier in Genetic Nursing Activities

Abstract

To compare patient care multipliers estimated from subjective evaluation against work sampling (WS) techniques in genetic nursing activities. An observational WS technique was conducted from November to December 2019 with nine genetic nurses in a tertiary referral center in Malaysia. The WS activity instrument was devised, validated, and pilot tested. All care- and non-care-related activities were sampled at 10-minute intervals within 8hours of working over 14days, followed by a subjective evaluation of activities survey over the same period. Bonferroni correction was undertaken for multiple testing with a p value of 0.0025. The two techniques produced significant differences in genetic nurses' activities categorization. The WS showed that compared with subjective evaluation, direct care (19.3% vs. 45.0%; p<.001) was estimated to be significantly lower, and indirect care (40.4% vs. 25.6%; p<.001) and unit-related activity (28.5% vs. 16.9%; p<.001) were higher. Both techniques produced a similar proportion of time spent in other non-care activities (12.0%) but differed in genetic meetings and information-gathering activities. While the multipliers for patient face-to-face contact were significantly larger between WS (4.57) and the survey (1.94), the multipliers for patient care time were smaller between WS (1.47) and the survey (1.24), indicating that caution should be taken when multiplying for patient contact time compared to patient care activity to determine the cost of care provision. A considerable proportion of time spent away from the patient needs to be allocated to patient-related care time. Thus, estimating the paid cost solely based on direct time with patients considerably underestimates the cost per hour of nurses' care. It is recommended to employ 'patient-related activity' instead of the 'face-to-face contact' multiplier because the former did not significantly differ from the one estimated using WS.