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Research article
Economic Impact of Clinical Decision Support Interventions Based on Electronic Health Records
Daniel Lewkowicz
Hasso Plattner Institute
Corresponding Author
ORCiD: https://orcid.org/0000-0002-3167-8741
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published version at BMC Health Services Research.
Background
Unnecessary healthcare utilization, non-adherence to current clinical guidelines or insufficient personalized care are perpetual challenges and remain potential major cost-drivers for healthcare systems around the world. Implementing decision support systems into clinical care is promised to increase quality of care and thereby yield substantial effects onreducing healthcare expenditure. Inthis articleweevaluate the economic impact of clinical decision support (CDS) interventions which are based on electronic health records (EHR).
Methods
We searched for studies published after 2014 using MEDLINE, CENTRAL, WEB OF SCIENCE, EBSCO, and TUFTS CEA registrydatabases that encompass an economic evaluation or consider cost outcome measures of EHR based CDS interventions. Thereupon, we identified best practiceapplication areas and categorized the investigated interventions according to an existing taxonomy of front-endCDS tools.
Results and discussion
Twenty-sevenstudies are investigated in this review. Of those, twenty-twostudies indicate a reduction of healthcare expenditure after implementingan EHRbased CDS system, especiallytowardsprevalent application areas, such as unnecessarylaboratory testing, duplicate order entry, efficient transfusion practice or reduction of antibiotic prescriptions.On the contrary, order facilitators and undiscovered malfunctions revealed to be threats and could lead to new cost drivers in healthcare. While high upfront and maintenance cost of CDS systems are a worldwide implementation barrier, most studies do not consider implementation cost. Finally, four included economic evaluation studies report mixed monetary outcome results and thus highlight the importance for further high quality economic evaluations for these CDS systems.
Conclusion
Current research studies lack to consider comparative cost-outcome metrics as well as detailed cost-components in their analyses. Nonetheless, thepositive economic impact of EHR based CDS interventions especially with regard to reducing waste in healthcare is highly promising.
Keywords
economic evaluation, electronic health record, clinical decision support, behavioral economics
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On 15 Sep, 2020
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Editorial decision: Minor revision
On 19 Aug, 2020
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On 17 Aug, 2020
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On 16 Aug, 2020
Editor invited
On 16 Aug, 2020
No comments provided
Editorial decision: Minor revision
On 08 Aug, 2020
Review #2 received
Received 07 Aug, 2020
Review #1 received
Received 07 Aug, 2020
Reviewer #2 agreed
On 19 Jul, 2020
Reviewer #1 agreed
On 16 Jul, 2020
Editor assigned
On 13 Jul, 2020
Reviewers invited
Invitations sent on 13 Jul, 2020
Submission checks complete
On 12 Jul, 2020
Editor invited
On 12 Jul, 2020
Version 1
Posted 20 May, 2020
No comments provided
Editorial decision: Minor revision
On 26 Jun, 2020
Review #2 received
Received 24 Jun, 2020
Review #1 received
Received 14 Jun, 2020
Reviewer #2 agreed
On 01 Jun, 2020
Reviewer #1 agreed
On 30 May, 2020
Reviewers invited
Invitations sent on 26 May, 2020
Editor assigned
On 19 May, 2020
Submission checks complete
On 18 May, 2020
Editor invited
On 18 May, 2020
First submitted
On 15 May, 2020
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This preprint is under consideration at BMC Health Services Research. A preprint is 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
Economic Impact of Clinical Decision Support Interventions Based on Electronic Health Records
Daniel Lewkowicz
Hasso Plattner Institute
Corresponding Author
ORCiD: https://orcid.org/0000-0002-3167-8741
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
Published
On 15 Sep, 2020
No community comments so far
Submission checks complete
On 26 Aug, 2020
Editorial decision: Accept
On 25 Aug, 2020
No comments provided
Editorial decision: Minor revision
On 19 Aug, 2020
Editor assigned
On 17 Aug, 2020
Submission checks complete
On 16 Aug, 2020
Editor invited
On 16 Aug, 2020
No comments provided
Editorial decision: Minor revision
On 08 Aug, 2020
Review #2 received
Received 07 Aug, 2020
Review #1 received
Received 07 Aug, 2020
Reviewer #2 agreed
On 19 Jul, 2020
Reviewer #1 agreed
On 16 Jul, 2020
Editor assigned
On 13 Jul, 2020
Reviewers invited
Invitations sent on 13 Jul, 2020
Submission checks complete
On 12 Jul, 2020
Editor invited
On 12 Jul, 2020
Version 1
Posted 20 May, 2020
No comments provided
Editorial decision: Minor revision
On 26 Jun, 2020
Review #2 received
Received 24 Jun, 2020
Review #1 received
Received 14 Jun, 2020
Reviewer #2 agreed
On 01 Jun, 2020
Reviewer #1 agreed
On 30 May, 2020
Reviewers invited
Invitations sent on 26 May, 2020
Editor assigned
On 19 May, 2020
Submission checks complete
On 18 May, 2020
Editor invited
On 18 May, 2020
First submitted
On 15 May, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published version at BMC Health Services Research.
Background
Unnecessary healthcare utilization, non-adherence to current clinical guidelines or insufficient personalized care are perpetual challenges and remain potential major cost-drivers for healthcare systems around the world. Implementing decision support systems into clinical care is promised to increase quality of care and thereby yield substantial effects onreducing healthcare expenditure. Inthis articleweevaluate the economic impact of clinical decision support (CDS) interventions which are based on electronic health records (EHR).
Methods
We searched for studies published after 2014 using MEDLINE, CENTRAL, WEB OF SCIENCE, EBSCO, and TUFTS CEA registrydatabases that encompass an economic evaluation or consider cost outcome measures of EHR based CDS interventions. Thereupon, we identified best practiceapplication areas and categorized the investigated interventions according to an existing taxonomy of front-endCDS tools.
Results and discussion
Twenty-sevenstudies are investigated in this review. Of those, twenty-twostudies indicate a reduction of healthcare expenditure after implementingan EHRbased CDS system, especiallytowardsprevalent application areas, such as unnecessarylaboratory testing, duplicate order entry, efficient transfusion practice or reduction of antibiotic prescriptions.On the contrary, order facilitators and undiscovered malfunctions revealed to be threats and could lead to new cost drivers in healthcare. While high upfront and maintenance cost of CDS systems are a worldwide implementation barrier, most studies do not consider implementation cost. Finally, four included economic evaluation studies report mixed monetary outcome results and thus highlight the importance for further high quality economic evaluations for these CDS systems.
Conclusion
Current research studies lack to consider comparative cost-outcome metrics as well as detailed cost-components in their analyses. Nonetheless, thepositive economic impact of EHR based CDS interventions especially with regard to reducing waste in healthcare is highly promising.
Figure 1