2022
DOI: 10.2196/30405
|View full text |Cite
|
Sign up to set email alerts
|

Impact of Electronic Health Records on Information Practices in Mental Health Contexts: Scoping Review

Abstract: Background The adoption of electronic health records (EHRs) and electronic medical records (EMRs) has been slow in the mental health context, partly because of concerns regarding the collection of sensitive information, the standardization of mental health data, and the risk of negatively affecting therapeutic relationships. However, EHRs and EMRs are increasingly viewed as critical to improving information practices such as the documentation, use, and sharing of information and, more broadly, the … Show more

Help me understand this report
View preprint versions

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

0
20
0

Year Published

2022
2022
2024
2024

Publication Types

Select...
9

Relationship

0
9

Authors

Journals

citations
Cited by 27 publications
(20 citation statements)
references
References 143 publications
(413 reference statements)
0
20
0
Order By: Relevance
“…Systematic coding of diagnoses has been shown to improve only in recent years [ 41 ] as EMRs have become more widely used, and more slowly in mental health care than in other medical settings [ 42 ]. This may be due to concerns about the collection of sensitive information, standardization of mental health data, and the risk of negatively impacting therapeutic relationships [ 43 ]. In addition, to maximize the sample, we classified some psychiatric disorders into broad categories that may have within-category differences in the risk associated with particular diagnoses.…”
Section: Discussionmentioning
confidence: 99%
“…Systematic coding of diagnoses has been shown to improve only in recent years [ 41 ] as EMRs have become more widely used, and more slowly in mental health care than in other medical settings [ 42 ]. This may be due to concerns about the collection of sensitive information, standardization of mental health data, and the risk of negatively impacting therapeutic relationships [ 43 ]. In addition, to maximize the sample, we classified some psychiatric disorders into broad categories that may have within-category differences in the risk associated with particular diagnoses.…”
Section: Discussionmentioning
confidence: 99%
“…These worries about the incompatibility of EHR mining and participatory approaches are highlighted in a recent review by Kariotis et al (2022), who note:…”
Section: Speaking For Oneselfmentioning
confidence: 99%
“…Related approaches have used sentiment analysis and topic modeling on discharge notes to predict hospital readmission (McCoy et al, 2015; Rumshisky et al, 2016). More recent work in this area has refined and expanded these approaches for extracting mental health information from various text corpora (see, Le Glaz et al, 2021; Kariotis et al, 2022; Zhang et al, 2022; Zurynski et al, 2021, for relevant reviews).…”
Section: The Ai/ml Applications In Mental Health Researchmentioning
confidence: 99%
“…Despite the difficulties in the development and implementation of some digital technologies, advantages of already implemented technologies could be identified. For example, the use of electronic health record in the mental health care setting led to a significant increase of timely access and availability of patient information for the health professionals [ 11 ]. Furthermore, the implementation of telemental health led to enhanced accessibility of the services – of equivalent therapeutic quality – for immobile patients or patients living in rural regions [ 12 , 13 ].…”
Section: Introductionmentioning
confidence: 99%