Matthew T Neame1, Jerry Chacko2, Anna E Surace2, Ian P Sinha1, Daniel B Hawcutt2,3. 1. Global Digital Exemplar Programme, Alder Hey Children's Hospital, Liverpool, United Kingdom. 2. Department of Women's and Children's Health, Institute of Translational Medicine, University of Liverpool, Liverpool, United Kingdom. 3. Alder Hey Clinical Research Facility, National Institute of Health Research Alder Hey Clinical Research Facility, Liverpool, United Kingdom.
Abstract
OBJECTIVE: Health information technology (HIT) interventions include electronic patient records, prescribing, and ordering systems. Clinical pathways are multidisciplinary plans of care that enable the delivery of evidence-based healthcare. Our objective was to systematically review the effects of implementing HIT-supported clinical pathways. MATERIALS AND METHODS: A systematic review protocol was developed including Medline, Embase, and CENTRAL database searches. We recorded data relating to study design, participants, intervention, and outcome characteristics and formally assessed risk of bias. RESULTS: Forty-four studies involving more than 270 000 patients were included. Investigation methodologies included before-after (n = 16, 36.4%), noncomparative (n = 14, 31.8%), interrupted time series (n = 5, 11.4%), retrospective cohort (n = 4, 9.1%), cluster randomized (n = 2, 4.5%), controlled before-after (n = 1, 2.3%), prospective case-control (n = 1, 2.3%), and prospective cohort (n = 1, 2.3%) study designs. Clinical decision support (n = 25, 56.8%), modified electronic documentation (n = 23, 52.3%), and computerized provider order entry (n = 23, 52.3%) were the most frequently utilized HIT interventions. The majority of studies (n = 38, 86.4%) reported benefits associated with HIT-supported pathways. These included reported improvements in objectively measured patient outcomes (n = 15, 34.1%), quality of care (n = 29, 65.9%), and healthcare resource utilization (n = 10, n = 22.7%). DISCUSSION: Although most studies reported improvements in outcomes, the strength of evidence was limited by the study designs that were utilized. CONCLUSIONS: Ongoing evaluations of HIT-supported clinical pathways are justified but would benefit from study designs that report key outcomes (including adverse events) and minimize the risk of bias.
OBJECTIVE: Health information technology (HIT) interventions include electronic patient records, prescribing, and ordering systems. Clinical pathways are multidisciplinary plans of care that enable the delivery of evidence-based healthcare. Our objective was to systematically review the effects of implementing HIT-supported clinical pathways. MATERIALS AND METHODS: A systematic review protocol was developed including Medline, Embase, and CENTRAL database searches. We recorded data relating to study design, participants, intervention, and outcome characteristics and formally assessed risk of bias. RESULTS: Forty-four studies involving more than 270 000 patients were included. Investigation methodologies included before-after (n = 16, 36.4%), noncomparative (n = 14, 31.8%), interrupted time series (n = 5, 11.4%), retrospective cohort (n = 4, 9.1%), cluster randomized (n = 2, 4.5%), controlled before-after (n = 1, 2.3%), prospective case-control (n = 1, 2.3%), and prospective cohort (n = 1, 2.3%) study designs. Clinical decision support (n = 25, 56.8%), modified electronic documentation (n = 23, 52.3%), and computerized provider order entry (n = 23, 52.3%) were the most frequently utilized HIT interventions. The majority of studies (n = 38, 86.4%) reported benefits associated with HIT-supported pathways. These included reported improvements in objectively measured patient outcomes (n = 15, 34.1%), quality of care (n = 29, 65.9%), and healthcare resource utilization (n = 10, n = 22.7%). DISCUSSION: Although most studies reported improvements in outcomes, the strength of evidence was limited by the study designs that were utilized. CONCLUSIONS: Ongoing evaluations of HIT-supported clinical pathways are justified but would benefit from study designs that report key outcomes (including adverse events) and minimize the risk of bias.
Authors: Jonathan S Austrian; Frank Volpicelli; Simon Jones; Mitchell A Bernstein; Jane Padikkala; Ashley Bagheri; Saul Blecker Journal: Appl Clin Inform Date: 2020-02-05 Impact factor: 2.342
Authors: Jerry Chacko; Charlotte King; David Harkness; Shrouk Messahel; Julie Grice; John Roe; Niall Mullen; Ian P Sinha; Daniel B Hawcutt Journal: J Am Coll Emerg Physicians Open Date: 2020-06-02