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1.
AMIA Annu Symp Proc ; 2018: 498-507, 2018.
Article in English | MEDLINE | ID: mdl-30815090

ABSTRACT

EHRs transform work practices in ways that enhance or impede the quality of care. There is a need for in-depth analysis of EHR workflows, particularly in complex clinical environments. We investigated EHR-basedpre-operative workflows by combining findings from 18 interviews, 7 days of observations, and process mining of EHR interactions from 31 personnel caring for 375 patients at one tertiary referral center. We provided high-definition descriptions of workflows and personnel roles. One third (32.2%) of the time with each patient was spent interacting with the EHR and 4.2% using paper-based artifacts. We also mined personnel social networks validating observed personnel's EHR-interactions. When comparing workflows between two similar pre-operative settings at different hospitals, we found significant differences in physical organization, patient workflow, roles, use of EHR, social networks and time efficiency. This study informs Mayo Clinic's enterprise-wide conversion to a single EHR and will guide before and after workflow comparisons.


Subject(s)
Electronic Health Records/organization & administration , Surgery Department, Hospital/organization & administration , Task Performance and Analysis , Workflow , Humans , Interviews as Topic , Patient Care Team/organization & administration , Social Networking
2.
AMIA Annu Symp Proc ; 2017: 790-799, 2017.
Article in English | MEDLINE | ID: mdl-29854145

ABSTRACT

Information technologies have transformed healthcare delivery and promise to improve efficiency and quality of care. However, in-depth analysis of EHR-mediated workflows is challenging. Our goal was to apply process mining, in combination with observational techniques, to understand EHR-based workflows. We reviewed nearly 76,000 event logs from 15 providers and supporting staff, and 142 patients in a pre-operative setting and we inspected 3 weeks of interviews and video observations. We found that on average 44 minutes were spent per patient interacting with the EHR, 55% of the time of the patient visit was spent by personnel interacting with the EHR and for over 5% of the time personnel used or reviewed paper-based artifacts. We also discovered the handover-of-care network and compared frequency of interactions between personnel. This study suggests that applying process mining in combination with observational techniques has vast potential for informing Mayo Clinic in the forthcoming EHR conversion.


Subject(s)
Data Mining/methods , Electronic Health Records , Preoperative Care/statistics & numerical data , Surgery Department, Hospital/organization & administration , Workflow , Hospital Administration , Humans , Interviews as Topic , Observation , Patient Handoff , Time Factors , Workload/statistics & numerical data
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