Evaluation of health information technologies with respect to medication safety, workflow, and data usage potential in maternal and newborn care
Creators: name: Devin, Joan
Publisher: name: Royal College of Surgeons in Ireland
Year: 2023
Subjects: classificationCode: 321502
subject: Obstetrics and gynaecology

description: <strong>Introduction: </strong>Health Information Technologies (HIT) are key for the future delivery of healthcare in Ireland. The Maternal and Newborn Clinical Management System (MN- CMS) Electronic Health Record (EHR) has been introduced to four maternity hospitals in Ireland. Though HIT is known to improve and transform care, there are sociotechnical challenges associated with implementation, as well as the potential for new types of technology-generated errors (TGE). <strong>Aims: </strong>To assess the impact of HIT, including MN-CMS, on medication safety and workflow, as well as the data usage potential of HIT in the specialist context of maternal and newborn care. <strong>Methods: </strong>A mixed methods approach with four linked studies: (I) a systematic review and behaviour change technique (BCT) analysis to identify the types of HIT that reduce prescribing errors; (II) a pre-post time and motion study to identify the impact of an EHR on nurses’ and doctors’ task time distribution in a Neonatal Intensive Care Unit (NICU); (III) the design and implementation of a retract-and-reorder (RAR) tool to detect near- miss prescribing errors in an EHR and assess data usage potential of routinely-collected EHR data in the context of safety monitoring and reporting, and; (IV) a retrospective study to identify the epidemiology and clinical characteristics of neonatal COVID-19 in the United States (US) and assess the data usage potential of using routinely-collected EHR data for population health research. Ethics approval was granted for each study where required. Data were analysed using Stata, R, and NVivo. <strong>Results: </strong>The systematic review of 35 studies found decreased rates of prescribing errors with HIT (median OR 0.24, IQR: 0.03-0.57). Effective BCTs included prescriber involvement in system design and monitoring of electronic orders to detect errors. The RAR tool identified a near-miss prescribing error rate of 259/100,000 medication orders in the study hospital, with a positive predictive value (PPE) of 85.0% (95% CI 75% to 91%). Duplicate errors were the most common type of TGE identified. Factors associated with prescribing errors included increased workload, staffing levels, lack of familiarity with task structure in the EHR, and lack of communication with colleagues. MN-CMS was not associated with increased time spent on documentation or medication- elated tasks in the pre-post study, while transcribing tasks were eliminated. COVID-19 was identified in 918 hospitalised neonates in the US (91.1/100,000 [95% CI 85.3 – 97.2]). Most neonates were asymptomatic or developed a mild illness, but some were severely ill enough to receive treatment for COVID-19 using remdesivir and COVID-19 convalescent plasma. Consequently, although rare, SARS-CoV-2 infection may result in severe disease in neonates. <strong>Conclusion: </strong>HIT improves medication safety and error reporting capacity, without adversely impacting workflow. Data generated through HIT use is a source of insight for health services research. The way that healthcare professionals and other users interact with HIT influences the safety and efficiency of these systems. Insight into early HIT adopters in Ireland provides evidence to direct future HIT implementations and optimisation, both in Ireland and internationally.