Effect of Electronic Health Record Implementation in Critical Care on Survival and Medication Errors

Han, Jenny E.; Rabinovich, Marina; Abraham, Prasad; Satyanarayana, P; Liao, Tongquan; Udoji, Timothy; Cotsonis, George; Honig, Eric; Martin, Greg S.

doi:10.1016/j.amjms.2016.01.026

Cited by 31 publications

(29 citation statements)

References 15 publications

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“…The electronic prescribing strategies included 24 (63%) standalone clinical decision support systems and 14 (37%) computerized physician order entry systems, of which eight had advanced decision support built within them, 24,51,55,[59][60][61][62][63] three had limited decision support, 40,52,57 two had no decision support, 46,50 and one did not specify. 47 Of the stand-alone decision support systems, nine (38%) evaluated single drug dosing adjustment for insulin (n=8) [28][29][30][31][32][33][34]36 and mycophenolate mofetil (n=1) 27 ; eight (33%) involved surveillance/treatment of infection, 44 including pneumonia management, 56 adherence to guidelines for antibiotic therapy, 54 empiric antibiotic choice, 35,38,43 empiric antibiotics for surgery, 45 or antibiotic adjustment 53 ; and seven (29%) were for post-operative nausea and vomiting, 48,58 rehydration for children, 37 dose adjustment for renally cleared drugs, 42 drug-drug interactions, 41 pain control, 39 and medication reconciliation.…”

Section: Interventionsmentioning

confidence: 99%

“…47 Of the stand-alone decision support systems, nine (38%) evaluated single drug dosing adjustment for insulin (n=8) [28][29][30][31][32][33][34]36 and mycophenolate mofetil (n=1) 27 ; eight (33%) involved surveillance/treatment of infection, 44 including pneumonia management, 56 adherence to guidelines for antibiotic therapy, 54 empiric antibiotic choice, 35,38,43 empiric antibiotics for surgery, 45 or antibiotic adjustment 53 ; and seven (29%) were for post-operative nausea and vomiting, 48,58 rehydration for children, 37 dose adjustment for renally cleared drugs, 42 drug-drug interactions, 41 pain control, 39 and medication reconciliation. 49 The advanced decision support systems within the computerized order entry included tools for detecting drug-drug interactions, 51,59 pediatric weight-based dosing, 24,[60][61][62][63] and specialized chemotherapy ordering. 55 Given that the majority of computerized order entry systems had a decision support system of some form built within them, they were regarded as a single category for analyses.…”

Section: Interventionsmentioning

confidence: 99%

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Effect of Electronic Prescribing Strategies on Medication Error and Harm in Hospital: a Systematic Review and Meta-analysis

et al. 2019

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BACKGROUND: Computerized physician order entry and clinical decision support systems are electronic prescribing strategies that are increasingly used to improve patient safety. Previous reviews show limited effect on patient outcomes. Our objective was to assess the impact of electronic prescribing strategies on medication errors and patient harm in hospitalized patients. METHODS: MEDLINE, EMBASE, CENTRAL, and CINAHL were searched from January 2007 to January 2018. We included prospective studies that compared hospital-based electronic prescribing strategies with control, and reported on medication error or patient harm. Data were abstracted by two reviewers and pooled using random effects model. Study quality was assessed using the Effective Practice and Organisation of Care and evidence quality was assessed using Grading of Recommendations Assessment, Development, and Evaluation. RESULTS: Thirty-eight studies were included; comprised of 11 randomized control trials and 27 non-randomized interventional studies. Electronic prescribing strategies reduced medication errors (RR 0.24 (95% CI 0.13, 0.46), I 2 98%, n=11) and dosing errors (RR 0.17 (95% CI 0.08, 0.38), I 2 96%, n=9), with both risk ratios significantly affected by advancing year of publication. There was a significant effect of electronic prescribing strategies on adverse drug events (ADEs) (RR 0.52 (95% CI 0.40, 0.68), I 2 0%, n=2), but not on preventable ADEs (RR 0.55 (95% CI 0.30, 1.01), I 2 78%, n=3), hypoglycemia (RR 1.03 (95% CI 0.62-1.70), I 2 28%, n=7), length of stay (MD −0.18 (95% −1.42, 1.05), I 2 94%, n=7), or mortality (RR 0.97 (95% CI 0.79, 1.19), I 2 74%, n=9). The quality of evidence was rated very low. DISCUSSION: Electronic prescribing strategies decrease medication errors and adverse drug events, but had no effect on other patient outcomes. Conservative interpretations of these findings are supported by significant heterogeneity and the preponderance of low-quality studies.

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Section: Interventionsmentioning

confidence: 99%

Section: Interventionsmentioning

confidence: 99%

Effect of Electronic Prescribing Strategies on Medication Error and Harm in Hospital: a Systematic Review and Meta-analysis

et al. 2019

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“…The study conducted at the same institution, around the same time, demonstrated that EHR implementation was associated with reduced severity-adjusted mortality and supported the hypothesis that EHR use may improve outcomes in critically ill patients. 34 This study has several strengths. It was conducted over several years therefore we are able to demonstrate the evolution of errors over time related to the implementation and utilization of EHR.…”

mentioning

confidence: 99%

Evaluation of medication errors with implementation of electronic health record technology in the medical intensive care unit

Liao¹,

Rabinovich²,

Abraham³

et al. 2017

OAJCT

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“…The utilization of advanced technologies, including blockchain technology and artificial intelligence both in healthcare and forensic sciences, can help achieve an automated archiving system, building trustworthy, secure, and holistic ecosystems; protecting against data breaches; preventing redundancies and inconsistencies; and assisting in reducing medical errors (Han et al 2016) by optimizing resources and accessing to citizens' medical histories, thus speeding up the forensic human identification process, especially in those caseworks where the nationalities of victims is unknown. It would be inconceivable to keep the healthcare sector and related medical-dental data apart from a forensic use following the need of identifying human remains.…”

Section: Implications Of the Hypothesismentioning

confidence: 99%

Electronic health record and blockchain architecture: forensic chain hypothesis for human identification

Nuzzolese

2020

Egypt J Forensic Sci

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Background Forensic dental identification relies on the collection and comparison of antemortem and postmortem dental data and dental evidence. There are software solutions capable of archiving postmortem (PM) and antemortem (AM) data, with search and comparative tools which are user-centric and do not allow open search options and data mining of all dental related data, except through the use of coded data. This reduces interoperability and raises dental data incompatibility challenges. Blockchain technology could help introduce a trusted, secure, and holistic ecosystem in the electronic health record (EHR) system with a forensic interface accessed by experts in forensic pathology and forensic odontology for the purpose of identifying human remains and retrieve identifying data of compatible missing persons from the health and dental electronic record system, with the further advantage of protecting data breaches, redundancies, inconsistencies, and errors. Blockchain technology, and a forensic chain, can enhance forensic data management and human identification process by managing the missing person lists, create AM data repositories as shared data with the EHRs of living individuals, create PM data repositories of recovered from identifying autopsies unidentified persons, and support the preliminary comparison of compatible biological profiles during the final reconciliation phase. Conclusion In this hypothesis, author investigates possible applications of blockchain technology as a holistic technical and interoperability solution for managing both health/dental data for a medical as forensic human identification use.

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Effect of Electronic Health Record Implementation in Critical Care on Survival and Medication Errors

Cited by 31 publications

References 15 publications

Effect of Electronic Prescribing Strategies on Medication Error and Harm in Hospital: a Systematic Review and Meta-analysis

Effect of Electronic Prescribing Strategies on Medication Error and Harm in Hospital: a Systematic Review and Meta-analysis

Evaluation of medication errors with implementation of electronic health record technology in the medical intensive care unit

Electronic health record and blockchain architecture: forensic chain hypothesis for human identification

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