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1.
BMJ Open ; 14(3): e080610, 2024 Mar 12.
Article in English | MEDLINE | ID: mdl-38479736

ABSTRACT

OBJECTIVE: To identify barriers to hospital participation in controlled cluster trials of clinical decision support (CDS) and potential strategies for addressing barriers. DESIGN: Qualitative descriptive design comprising semistructured interviews. SETTING: Five hospitals in New South Wales and one hospital in Queensland, Australia. PARTICIPANTS: Senior hospital staff, including department directors, chief information officers and those working in health informatics teams. RESULTS: 20 senior hospital staff took part. Barriers to hospital-level recruitment primarily related to perceptions of risk associated with not implementing CDS as a control site. Perceived risks included reductions in patient safety, reputational risk and increased likelihood that benefits would not be achieved following electronic medical record (EMR) implementation without CDS alerts in place. Senior staff recommended clear communication of trial information to all relevant stakeholders as a key strategy for boosting hospital-level participation in trials. CONCLUSION: Hospital participation in controlled cluster trials of CDS is hindered by perceptions that adopting an EMR without CDS is risky for both patients and organisations. The improvements in safety expected to follow CDS implementation makes it challenging and counterintuitive for hospitals to implement EMR without incorporating CDS alerts for the purposes of a research trial. To counteract these barriers, clear communication regarding the evidence base and rationale for a controlled trial is needed.


Subject(s)
Decision Support Systems, Clinical , Humans , Australia , Hospitals , Qualitative Research , Queensland
2.
BMJ Open ; 9(8): e026034, 2019 08 18.
Article in English | MEDLINE | ID: mdl-31427312

ABSTRACT

INTRODUCTION: Drug-drug interaction (DDI) alerts in hospital electronic medication management (EMM) systems are generated at the point of prescribing to warn doctors about potential interactions in their patients' medication orders. This project aims to determine the impact of DDI alerts on DDI rates and on patient harm in the inpatient setting. It also aims to identify barriers and facilitators to optimal use of alerts, quantify the alert burden posed to prescribers with implementation of DDI alerts and to develop algorithms to improve the specificity of DDI alerting systems. METHODS AND ANALYSIS: A controlled pre-post design will be used. Study sites include six major referral hospitals in two Australian states, New South Wales and Queensland. Three hospitals will act as control sites and will implement an EMM system without DDI alerts, and three as intervention sites with DDI alerts. The medical records of 280 patients admitted in the 6 months prior to and 6 months following implementation of the EMM system at each site (total 3360 patients) will be retrospectively reviewed by study pharmacists to identify potential DDIs, clinically relevant DDIs and associated patient harm. To identify barriers and facilitators to optimal use of alerts, 10-15 doctors working at each intervention hospital will take part in observations and interviews. Non-identifiable DDI alert data will be extracted from EMM systems 6-12 months after system implementation in order to quantify alert burden on prescribers. Finally, data collected from chart review and EMM systems will be linked with clinically relevant DDIs to inform the development of algorithms to trigger only clinically relevant DDI alerts in EMM systems. ETHICS AND DISSEMINATION: This research was approved by the Hunter New England Human Research Ethics Committee (18/02/21/4.07). Study results will be published in peer-reviewed journals and presented at local and international conferences and workshops.


Subject(s)
Decision Support Systems, Clinical , Electronic Health Records/statistics & numerical data , Medical Order Entry Systems/statistics & numerical data , Medication Systems, Hospital/standards , Reminder Systems/supply & distribution , Data Collection , Drug Interactions , Follow-Up Studies , Humans , New South Wales , Queensland , Retrospective Studies
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