Applying Computational Ethnography to Examine Nurses' Workflow Within Electronic Health Records.
Supplemental digital content is available in the text. 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. Objectiv...
| Publicado en: | Nursing Research Vol. 70; no. 2; pp. 132 - 142 |
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| Autores principales: | , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Lippincott Williams & Wilkins
Mar/Apr2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=149225043&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 149225043 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00296562 1HA jtl: Nursing Research issn: 00296562 maglogo: N pubinfo: dt: Mar/Apr2021 vid: 70 iid: 2 pid: 433 pub: Lippincott Williams & Wilkins place: Baltimore, Maryland artinfo: ui: 149225043 149225043 149225043 10.1097/NNR.0000000000000486 149225043 ppf: 132 ppct: 10 formats: tig: atl: Applying Computational Ethnography to Examine Nurses' Workflow Within Electronic Health Records. aug: au: Tolentino, Dante Anthony Subbian, Vignesh Gephart, Sheila M. affil: Dante Anthony Tolentino, PhD, RN-BC, is National Clinician Scholar and Postdoctoral Research Fellow, School of Nursing, Institute for Healthcare Policy and Innovation, University of Michigan Ann Arbor, MI. At the time of manuscript development, he was a doctoral candidate, The University of Arizona College of Nursing. sug: subj: Workflow Evaluation Audit Methods Computing Methodologies Anthropology, Cultural Electronic Health Records Research, Nursing Human Nursing Staff, Hospital Daily Logs Audit Trail Data Mining Data Analysis, Computer Assisted Descriptive Statistics ab: Supplemental digital content is available in the text. 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. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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