Allan Zhang
Research Subjects
Allan Zhang has a total of 3 co-authors. Here are the top co-authors by number of shared publications:
- Chun Kit Fu1 shared publication
- Rachel Ai1 shared publication
- Simon Lin
Research Information Solutions and Innovation, The Abigail Wexner Research Institute, Nationwide Children's Hospital, Columbus, Ohio, USA
1 shared publication
Latest Publications
- Open Access
- 13 October 2022
- Published by JMIR Publications Inc. in JMIR Preprints
Differentiating Physicians by Note-taking Practices in the EHR: A Case Study of a Open-source Tool to Cluster Providers into Phenotypes of Electronic Health Record Documentation Behavior (Preprint)
BACKGROUND Background: Documenting clinical notes in Electronic Health Record (EHR) systems is a crucial job for physicians, yet characterizing provider documentation behavior is still difficult because of the lack of applications to do so. OBJECTIVE Objective: This study aims to analyze physician documentation behavior phenotypes at a multi-specialty clinic, as well as develop open-source software to make such profiling more easily available for other clinics to replicate. METHODS Methods: For this observational retrospective study, we analyzed data on physician note-taking behavior from EPIC Signal and applied the k-means clustering algorithm to identify and analyze different phenotypes in the data set. RESULTS Results: We developed an efficient tool and easy-to-follow protocol to profile provider documentation behavior, which identified four physician phenotype clusters within our data. Of these four, two were characterized by physicians with high note-taking efficiency, with one cluster having low documentation length and above-average efficiency, and the other having average documentation length and extremely high efficiency. Of the two remaining clusters, one was characterized by physicians with low note-taking efficiency, and the last consisted of low-volume providers. CONCLUSIONS Conclusions: Cluster analysis is a useful method to characterize different physician note-taking behaviors, which can be used to efficiently identify both physician champions and physicians in need of note-taking training.
- 13 October 2022
- Published by JMIR Publications Inc. in JMIR Preprints
Open AccessDifferentiating Physicians by Note-taking Practices in the EHR: A Case Study of a Open-source Tool to Cluster Providers into Phenotypes of Electronic Health Record Documentation Behavior (Preprint)
BACKGROUND Background: Documenting clinical notes in Electronic Health Record (EHR) systems is a crucial job for physicians, yet characterizing provider documentation behavior is still difficult because of the lack of applications to do so. OBJECTIVE Objective: This study aims to analyze physician documentation behavior phenotypes at a multi-specialty clinic, as well as develop open-source software to make such profiling more easily available for other clinics to replicate. METHODS Methods: For this observational retrospective study, we analyzed data on physician note-taking behavior from EPIC Signal and applied the k-means clustering algorithm to identify and analyze different phenotypes in the data set. RESULTS Results: We developed an efficient tool and easy-to-follow protocol to profile provider documentation behavior, which identified four physician phenotype clusters within our data. Of these four, two were characterized by physicians with high note-taking efficiency, with one cluster having low documentation length and above-average efficiency, and the other having average documentation length and extremely high efficiency. Of the two remaining clusters, one was characterized by physicians with low note-taking efficiency, and the last consisted of low-volume providers. CONCLUSIONS Conclusions: Cluster analysis is a useful method to characterize different physician note-taking behaviors, which can be used to efficiently identify both physician champions and physicians in need of note-taking training.