2021
DOI: 10.1186/s13063-021-05259-9
|View full text |Cite
|
Sign up to set email alerts
|

The effect of automated audit and feedback on data completeness in the electronic health record of the general physician: protocol for a cluster randomized controlled trial

Abstract: Background The electronic health record (EHR) of the general physician (GP) is an important tool that can be used to assess and improve the quality of healthcare. However, there are some problems when (re) using the data gathered in the EHR for quality assessments. One problem is the lack of data completeness in the EHR. Audit and feedback (A&F) is a well-known quality intervention that can improve the quality of healthcare. We hypothesize that an automated A&F intervention can be adapt… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1

Citation Types

0
3
0

Year Published

2022
2022
2024
2024

Publication Types

Select...
3
1
1

Relationship

1
4

Authors

Journals

citations
Cited by 6 publications
(3 citation statements)
references
References 36 publications
0
3
0
Order By: Relevance
“…For example, it is not clear how circumstantial factors (such as a lockdown) could influence the quality of registration in the EHR. However, the resulting bias is minimized by accounting for practice differences in the analysis and steps have been taken to improve data completeness [ 42 ]. We collect data from all patients visiting the participating practices, but this does not guarantee that these patients will be a representative sample for the Flemish population.…”
Section: Discussionmentioning
confidence: 99%
“…For example, it is not clear how circumstantial factors (such as a lockdown) could influence the quality of registration in the EHR. However, the resulting bias is minimized by accounting for practice differences in the analysis and steps have been taken to improve data completeness [ 42 ]. We collect data from all patients visiting the participating practices, but this does not guarantee that these patients will be a representative sample for the Flemish population.…”
Section: Discussionmentioning
confidence: 99%
“…Data were collected from 105 practices that are individually responsible for delivering qualitative and complete data. Possible registration errors were minimized by accounting for interpractice differences, and steps have been taken to improve data completeness [ 33 ]. However, there might be a general underregistration of mental health problems in general practice (especially subclinical problems) that we were unable to pick up due to them not being coded.…”
Section: Discussionmentioning
confidence: 99%
“…Furthermore, natural language processing technologies have shown promise in automating data extraction of clinical notes, ranging from radiology reports to discharge summaries 39. In the EHRs of academic health centres, patients are assigned to staff physicians such that data-driven metrics are already being generated to support audit and feedback, physician report card initiatives, and broader quality improvement efforts 17 40 41. However, the transient nature of learners in our health system creates unique challenges when trying to link patient data to resident care, especially in the inpatient setting.…”
Section: Introductionmentioning
confidence: 99%