Evaluation of Electronic Health Record–Generated Work Intensity Scores and Nurse Perceptions of Workload Appropriateness

Abstract

Electronic health record–generated work intensity scores represent state-of-the art functionality for dynamic nursing workload estimation in the hospital setting. In contrast to traditional stand-alone patient classification and acuity tools, electronic health record–based tools eliminate the need for dedicated data entry, and scores are automatically updated as new information is entered into patient records. This paper summarizes the method and results of evaluation of electronic health record–generated work intensity scores on six hospital patient care units in a single academic medical center. The correlation between beginning-of-shift work intensity scores and self-reported registered nurse rating of appropriateness of patient assignment was assessed using Spearman rank correlation. A weak negative correlation (−0.09 to −0.23) was observed on all study units, indicating that nurse appropriateness ratings decrease as work intensity scores increase. Electronic health record–generated work intensity scores provide useful information that can augment existing data sources used by charge nurses to create equitable nurse-patient assignments. Additional research is needed to explain observed variation in nurses' appropriateness ratings across similar work intensity point ranges.

A Model for Work Intensity in a Pediatric Training Program

“…The variables most significantly associated with work intensity in the univariate analysis were text messages received, orders entered, and consults ordered ( Our study supports evidence that the EHR can be used to measure trainee workload and that there is significant variation between trainees. 6,9 Unlike Was et al, we were able to detect a an statistically significant correlation between self-perceived work intensity and objective measures such as number of orders entered and number of text messages received. 6,9 These data account for about 25% of the variability in subjective scores of work intensity, which is reasonable given the multitude of factors that may contribute to a trainee's experience.…”

“…6,9 Unlike Was et al, we were able to detect a an statistically significant correlation between self-perceived work intensity and objective measures such as number of orders entered and number of text messages received. 6,9 These data account for about 25% of the variability in subjective scores of work intensity, which is reasonable given the multitude of factors that may contribute to a trainee's experience. Models like this could provide a method for program directors to passively monitor their trainees' workload, in addition to more invasive measures such as surveys and less specific measures like work hours.…”

“…Work hours did not correlate, suggesting that it is the type of work that matters and not just the quantity, similar to findings in the nursing field. 9 As our study focused on specific and measurable variables within our electronic systems, we are conscious that there may be other factors not studied that contribute meaningfully to work intensity. Importantly, these factors likely vary by clinical specialty and level of training, which may make it difficult to generalize across training programs.…”

“…[5][6][7][8] Existing data within electronic health records (EHRs), residency management systems, and other clinical information can be reexamined to understand physician behavior, and ideally to help plan for better resource allocation and utilization. [7][8][9][10] This data can also be used to better identify areas for further instruction of residents, like task prioritization or gaps in clinical exposure. 6,[10][11][12][13][14] One study examining inpatient admissions demonstrated an increase in both patient complexity and the EHR data burden over a 15-year period, suggesting an increase in the workload for trainees caring for these patients.…”

Coincidence Analysis: A Novel Approach to Modeling Nurses' Workplace Experience

“…Case outcomes were drawn from a pool of 683 RN-reported appropriateness ratings in a separately reported study 30 conducted at the same academic medical center. Of 683 previously reported RN ratings, 364 were collected in the ICU study unit.…”