See the published version of this preprint at BMC Medical Informatics and Decision Making.
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Research article
Farahnaz Sadoughi
Iran University of Medical Sciences
Corresponding Author
ORCiD: https://orcid.org/0000-0002-7452-0864
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: The clinical decision support systems (CDSSs) for prescription medications is one of the technologies aimed at improving physician practice behavior and patient outcomes by reducing drug prescription errors. This study, thus, was conducted to investigate the effect of various CDSSs on physician practice behavior and patient outcomes.
Methods: This systematic review was conducted by searching in PubMed, EMBASE, Web of Science, Scopus and Cochrane Library from 2005 to 2019. Two researchers independently evaluated the studies. Any discrepancies over the eligibility of the studies between the two researchers were then resolved by consulting a third researcher. Finally, we extracted data from the articles. Then, we conducted a meta-analysis based on medication subgroups and outcome categories; we also presented a narrative form of the findings. Meanwhile, we applied random-effects model to estimate the effects of CDSS on patient outcomes and physician practice performance with 95% confidence interval. Q statistics and I2 was then used to measure heterogeneity.
Results: Based on the inclusion criteria, 46 studies were considered eligible for the analysis in this review. The CDSS for prescription medications had been used for various diseases such as cardiovascular diseases, hypertension, diabetes, gastrointestinal and respiratory diseases, AIDS, appendicitis, kidney disease, malaria, high blood potassium, and mental disease. Meanwhile, other cases such as the concurrent prescription of multiple drugs for patients and its effects on the above-mentioned outcomes were evaluated. The analysis shows that in some cases the application of CDSS provides positive effects on patient outcomes and physician practice behaviors. The effect was statistically significant (std diff in means =0.114, 95% CI: 0.090 to 0.138) as overall. It was also statistically significant for outcome groups such as those showing improved outcomes on physician practice performance and patient outcome or both. No significant difference was observed in comparison between some other cases and conventional methods. We think that this could be due to the disease type, the quantity, and the type of CDSS requirements that influenced the comparison.
Conclusions: Our findings suggest that positive effects of the CDSS are due to factors such as user-friendliness, compliance with clinical guidelines, patient and doctor cooperation, integration of electronic health records, CDSS and pharmaceutical systems, consideration of the views of physicians in assessing the importance of CDSS alerts, and their real-time alerts in the prescription.
Keywords
Computerized Clinical Decision Support Systems, Drug prescription, Systematic review.
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CURRENT STATUS: Published
View the published article at BMC Medical Informatics and Decision Making.
No community comments so far
Editorial decision: Accept
On 16 Dec, 2020
Editor assigned
On 15 Dec, 2020
Submission checks complete
On 15 Dec, 2020
Editor invited
On 15 Dec, 2020
No comments provided
Editorial decision: Minor revision
On 06 Dec, 2020
Review #1 received
Received 04 Dec, 2020
Reviewer #1 agreed
On 01 Dec, 2020
Reviewers invited
Invitations sent on 15 Oct, 2020
Editor assigned
On 22 Sep, 2020
Submission checks complete
On 21 Sep, 2020
Editor invited
On 21 Sep, 2020
Version 2
Posted 28 May, 2020
No comments provided
Editorial decision: Major revision
On 17 Aug, 2020
Review #2 received
Received 12 Aug, 2020
Review #1 received
Received 22 Jun, 2020
Reviewer #2 agreed
On 16 Jun, 2020
Reviewer #1 agreed
On 14 Jun, 2020
Reviewers invited
Invitations sent on 21 May, 2020
Editor assigned
On 19 May, 2020
Submission checks complete
On 18 May, 2020
Editor invited
On 18 May, 2020
No comments provided
Review #2 received
Received 20 Apr, 2020
Editorial decision: Major revision
On 20 Apr, 2020
Review #1 received
Received 17 Apr, 2020
Editor assigned
On 26 Mar, 2020
Reviewers invited
Invitations sent on 26 Mar, 2020
Reviewer #1 agreed
On 26 Mar, 2020
Reviewer #2 agreed
On 26 Mar, 2020
Submission checks complete
On 20 Mar, 2020
Editor invited
On 20 Mar, 2020
First submitted
On 13 Mar, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Medical Informatics
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See the published version of this preprint at BMC Medical Informatics and Decision Making.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research article
Farahnaz Sadoughi
Iran University of Medical Sciences
Corresponding Author
ORCiD: https://orcid.org/0000-0002-7452-0864
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at BMC Medical Informatics and Decision Making.
No community comments so far
Editorial decision: Accept
On 16 Dec, 2020
Editor assigned
On 15 Dec, 2020
Submission checks complete
On 15 Dec, 2020
Editor invited
On 15 Dec, 2020
No comments provided
Editorial decision: Minor revision
On 06 Dec, 2020
Review #1 received
Received 04 Dec, 2020
Reviewer #1 agreed
On 01 Dec, 2020
Reviewers invited
Invitations sent on 15 Oct, 2020
Editor assigned
On 22 Sep, 2020
Submission checks complete
On 21 Sep, 2020
Editor invited
On 21 Sep, 2020
Version 2
Posted 28 May, 2020
No comments provided
Editorial decision: Major revision
On 17 Aug, 2020
Review #2 received
Received 12 Aug, 2020
Review #1 received
Received 22 Jun, 2020
Reviewer #2 agreed
On 16 Jun, 2020
Reviewer #1 agreed
On 14 Jun, 2020
Reviewers invited
Invitations sent on 21 May, 2020
Editor assigned
On 19 May, 2020
Submission checks complete
On 18 May, 2020
Editor invited
On 18 May, 2020
No comments provided
Review #2 received
Received 20 Apr, 2020
Editorial decision: Major revision
On 20 Apr, 2020
Review #1 received
Received 17 Apr, 2020
Editor assigned
On 26 Mar, 2020
Reviewers invited
Invitations sent on 26 Mar, 2020
Reviewer #1 agreed
On 26 Mar, 2020
Reviewer #2 agreed
On 26 Mar, 2020
Submission checks complete
On 20 Mar, 2020
Editor invited
On 20 Mar, 2020
First submitted
On 13 Mar, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Medical Informatics
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at BMC Medical Informatics and Decision Making.
Background: The clinical decision support systems (CDSSs) for prescription medications is one of the technologies aimed at improving physician practice behavior and patient outcomes by reducing drug prescription errors. This study, thus, was conducted to investigate the effect of various CDSSs on physician practice behavior and patient outcomes.
Methods: This systematic review was conducted by searching in PubMed, EMBASE, Web of Science, Scopus and Cochrane Library from 2005 to 2019. Two researchers independently evaluated the studies. Any discrepancies over the eligibility of the studies between the two researchers were then resolved by consulting a third researcher. Finally, we extracted data from the articles. Then, we conducted a meta-analysis based on medication subgroups and outcome categories; we also presented a narrative form of the findings. Meanwhile, we applied random-effects model to estimate the effects of CDSS on patient outcomes and physician practice performance with 95% confidence interval. Q statistics and I2 was then used to measure heterogeneity.
Results: Based on the inclusion criteria, 46 studies were considered eligible for the analysis in this review. The CDSS for prescription medications had been used for various diseases such as cardiovascular diseases, hypertension, diabetes, gastrointestinal and respiratory diseases, AIDS, appendicitis, kidney disease, malaria, high blood potassium, and mental disease. Meanwhile, other cases such as the concurrent prescription of multiple drugs for patients and its effects on the above-mentioned outcomes were evaluated. The analysis shows that in some cases the application of CDSS provides positive effects on patient outcomes and physician practice behaviors. The effect was statistically significant (std diff in means =0.114, 95% CI: 0.090 to 0.138) as overall. It was also statistically significant for outcome groups such as those showing improved outcomes on physician practice performance and patient outcome or both. No significant difference was observed in comparison between some other cases and conventional methods. We think that this could be due to the disease type, the quantity, and the type of CDSS requirements that influenced the comparison.
Conclusions: Our findings suggest that positive effects of the CDSS are due to factors such as user-friendliness, compliance with clinical guidelines, patient and doctor cooperation, integration of electronic health records, CDSS and pharmaceutical systems, consideration of the views of physicians in assessing the importance of CDSS alerts, and their real-time alerts in the prescription.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
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