| 000 -LEADER |
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| fixed length control field |
02565nam a22002177a 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20260717154112.0 |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION |
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| fixed length control field |
ta |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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230424b ||||| |||| 00| 0 eng d |
| 041 ## - LANGUAGE CODE |
|---|
| Language code of text/sound track or separate title |
eng |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
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| Preferred name for the person |
Tolentino, Dante Anthony |
| Relator term |
author. |
| 245 ## - TITLE STATEMENT |
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| Title |
Applying computational ethnography to examine nurses’ workflow within electronic health records. |
| 264 #4 - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
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| Date of publication, distribution, etc |
2021 |
| 520 3# - SUMMARY, ETC. |
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| Summary, etc |
Background <br/><br/>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.<br/><br/>Objective <br/><br/>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.<br/><br/>Methods <br/><br/>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.<br/><br/>Results <br/><br/>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.<br/><br/>Discussion <br/><br/>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. |
| 650 #2 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Nursing |
| General subdivision |
Data processing. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Medical records |
| General subdivision |
Data processing. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Ethnology |
| General subdivision |
Methodology. |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Subbian, Vignesh |
| Relator term |
author. |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Gephart, Sheila M. |
| Relator term |
author. |
| 773 ## - HOST ITEM ENTRY |
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| Title |
Nursing Research |
| Relationship information |
Volume 70, Issue 2 : (March/April 2021), pages 132-141. |
| 942 ## - ADDED ENTRY ELEMENTS |
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| Source of classification or shelving scheme |
|
| Item type |
JOURNAL ARTICLE |