PURPOSE: Computerised decision support systems are designed to support clinicians in making decisions and thereby enhance the quality and safety of care. We aimed to undertake an interpretative review of the empirical evidence on computerised decision support systems, their contexts of use, and summarise evidence on the effectiveness of these tools and insights into how these can be successfully implemented and adopted. METHODS: We systematically searched the empirical literature to identify systematic literature reviews on computerised decision support applications and their impact on the quality and safety of healthcare delivery over a 13-year period (1997-2010). The databases searched included: MEDLINE, EMBASE, The Cochrane Database of Systematic Reviews, Database of Abstracts of Reviews of Effects, The Cochrane Central Register of Controlled Trials, The Cochrane Methodology Register, The Health Technology Assessment Database, and The National Health Service (NHS) Economic Evaluation Database. To be eligible for inclusion, systematic reviews needed to address computerised decision support systems, and at least one of the following: impact on safety; quality; or organisational, implementation or adoption considerations. RESULTS: Our searches yielded 121 systematic reviews relating to eHealth, of which we identified 41 as investigating computerised decision support systems. These indicated that, whilst there was a lack of investigating potential risks, such tools can result in improvements in practitioner performance in the promotion of preventive care and guideline adherence, particularly if specific information is available in real time and systems are effectively integrated into clinical workflows. However, the evidence regarding impact on patient outcomes was less clear-cut with reviews finding either no, inconsistent or modest benefits. CONCLUSIONS: Whilst the potential of clinical decision support systems in improving, in particular, practitioner performance is considerable, such technology may also introduce new risks resulting not only from technical challenges (such as data inaccuracies) but also from disruption of clinical workflows. Moving forward, there is a need for system development, procurement and implementation to be characterised by a user 'pull' and then tailor systems to the needs of users.
PURPOSE: Computerised decision support systems are designed to support clinicians in making decisions and thereby enhance the quality and safety of care. We aimed to undertake an interpretative review of the empirical evidence on computerised decision support systems, their contexts of use, and summarise evidence on the effectiveness of these tools and insights into how these can be successfully implemented and adopted. METHODS: We systematically searched the empirical literature to identify systematic literature reviews on computerised decision support applications and their impact on the quality and safety of healthcare delivery over a 13-year period (1997-2010). The databases searched included: MEDLINE, EMBASE, The Cochrane Database of Systematic Reviews, Database of Abstracts of Reviews of Effects, The Cochrane Central Register of Controlled Trials, The Cochrane Methodology Register, The Health Technology Assessment Database, and The National Health Service (NHS) Economic Evaluation Database. To be eligible for inclusion, systematic reviews needed to address computerised decision support systems, and at least one of the following: impact on safety; quality; or organisational, implementation or adoption considerations. RESULTS: Our searches yielded 121 systematic reviews relating to eHealth, of which we identified 41 as investigating computerised decision support systems. These indicated that, whilst there was a lack of investigating potential risks, such tools can result in improvements in practitioner performance in the promotion of preventive care and guideline adherence, particularly if specific information is available in real time and systems are effectively integrated into clinical workflows. However, the evidence regarding impact on patient outcomes was less clear-cut with reviews finding either no, inconsistent or modest benefits. CONCLUSIONS: Whilst the potential of clinical decision support systems in improving, in particular, practitioner performance is considerable, such technology may also introduce new risks resulting not only from technical challenges (such as data inaccuracies) but also from disruption of clinical workflows. Moving forward, there is a need for system development, procurement and implementation to be characterised by a user 'pull' and then tailor systems to the needs of users.
Authors: Kristy B Arbogast; Allison E Curry; Kristina B Metzger; Ronni S Kessler; Jeneita M Bell; Juliet Haarbauer-Krupa; Mark R Zonfrillo; Matthew J Breiding; Christina L Master Journal: Clin Pediatr (Phila) Date: 2017-05-19 Impact factor: 1.168
Authors: Sheila A Fisher; Annemarie B Docherty; Carolyn Doree; Stephen P Hibbs; Michael F Murphy; Lise J Estcourt Journal: Cochrane Database Syst Rev Date: 2017-02
Authors: Charles A Baillie; Mika Epps; Asaf Hanish; Neil O Fishman; Benjamin French; Craig A Umscheid Journal: Infect Control Hosp Epidemiol Date: 2014-07-25 Impact factor: 3.254
Authors: Nicole Puccinelli-Ortega; Mark Cromo; Kristie L Foley; Mark B Dignan; Ajay Dharod; Anna C Snavely; David P Miller Journal: Appl Clin Inform Date: 2022-01-05 Impact factor: 2.342