Applying computational ethnography to examine nurses’ workflow within electronic health records.

Background

Many existing electronic health record (EHR) workflow studies report conflicting results in time spent in the record, documentation demand, and usability and often do not explore the time-based navigation patterns of nurses.

Objective

The aim of this study was to describe computational ethnography as a contemporary and supplemental methodology in EHR workflow analysis and the relevance of this method to nursing research.

Methods

We explore the use of audit logs as a computational ethnographic data source and the utility of data mining techniques, including sequential pattern mining (SPM) and Markov chain analysis (MCA), to analyze nurses’ workflow within the EHRs. SPM extracts frequent patterns in a given transactional database (e.g., audit logs from the record). MCA is a stochastic process that models a sequence of states and allows for calculating the probability of moving from one state to the next. These methods can help uncover nurses’ global navigational patterns (i.e., how nurses navigate within the record) and enable robust workflow analyses.

Results

We demonstrate hypothetical examples from SPM and MCA, such as (a) the most frequent sequential pattern of nurses’ workflow when navigating the EHR using SPM and (b) transition probability from one record screen to the next using MCA. These examples demonstrate new methods to address the inflexibility of current approaches used to examine nursing EHR workflow.

Discussion

Within a clinical context, the use of computational ethnographic data and data mining techniques can inform the optimization of the EHR. Results from these analyses can be used to supplement the data needed in redesigning the EHR, such as organizing and combining features within a screen or predicting future navigation to improve the record that nurses use.