Comparison of Overridden Medication-related Clinical Decision Support in the Intensive Care Unit between a Commercial System and a Legacy System

Wong, Adrian; Wright, Adam; Seger, Diane L.; Amato, Mary G.; Fiskio, Julie M.; Bates, David W.

doi:10.4338/aci-2017-04-ra-0059

Cited by 19 publications

(16 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Efforts had been continually made to our legacy system to improve available clinical CDS, including increasing its positive predictive value (PPV) to limit potential alert fatigue 18 19. With the transition to the commercial EHR, we found a significant increase in alerts by approximately five-fold, along with a significant increase in over-ride rates of certain alert types 20. Potential reasons for this significant increase were the removal of our tailored CDS as well as the presentation of CDS alerts at the time of order signing, instead of at the time of ordering as in the legacy system.…”

Section: Discussionmentioning

confidence: 91%

Prospective evaluation of medication-related clinical decision support over-rides in the intensive care unit

et al. 2018

Self Cite

View full text Add to dashboard Cite

Approximately four of five identified CDS over-rides were appropriately over-ridden, with the rate varying by alert type. However, inappropriate over-rides were six times as likely to be associated with potential and definite ADEs, compared with appropriate over-rides. Further efforts should be targeted at improving the positive predictive value of CDS such as by suppressing alerts that are appropriately over-ridden.

show abstract

Section: Discussionmentioning

confidence: 91%

Prospective evaluation of medication-related clinical decision support over-rides in the intensive care unit

et al. 2018

Self Cite

View full text Add to dashboard Cite

show abstract

“…Other similar systems described by the scientific literature do not include this facility, and the incorporated commercial bundle of CRs cannot be modified by the user. 30,31 Finally, another major benefit of HIGEA is that it was provided with a large set of CRs that covers a wide range of clinical conditions and provides a specific dosage recommendation for the new prescription. 32 Only 10% of published studies in this area 15 use rules that have been defined according to clinical needs.…”

Section: Discussionmentioning

confidence: 99%

“…It has already been demonstrated that an updated and consistent knowledge base positively affects the efficiency of these systems. 31 The "program of dose adjustment in renal impairment" was prioritized because of the high number of drugs that have to be adjusted in patients with renal impairment and the very scant knowledge of these drugs among health professionals. In fact, it has been demonstrated that the rate of appropriate drug prescribing in kidney impairment is low and that the use of a CDSS could improve patient safety.…”

Section: Discussionmentioning

confidence: 99%

“…We believe that prescreening of alerts by a clinical pharmacist reduce alert fatigue during prescription, and could increase the likelihood of appropriate prescribing of these medications. 10,31,41,42 In fact, a review from van der Sijs et al 16 showed that physicians ignore safety alerts in 49 to 96% of cases. In the future, some alerts might be better presented as online alerts for the physician when immediate action is necessary, that is, those with PPV ¼ 1.…”

Section: Limitationsmentioning

confidence: 99%

See 1 more Smart Citation

Development and Evaluation of a Clinical Decision Support System to Improve Medication Safety

et al. 2019

View full text Add to dashboard Cite

Background Clinical decision support systems (CDSSs) are a good strategy for preventing medication errors and reducing the incidence and severity of adverse drug events (ADEs). However, these systems are not very effective and are subject to multiple limitations that prevent their implementation in clinical practice. Objectives The objective of this study was to evaluate the effectiveness of an advanced CDSS, HIGEA, which generates alerts based on predefined clinical rules to identify patients at risk of an ADE. Methods A multidisciplinary team defined the system and the clinical rules focusing on medication errors commonly encountered in clinical practice. Four intervention programs were defined: (1) dose adjustment in renal impairment; (2) adjustment of anticoagulation/antiplatelet therapy; (3) detection of biochemical/hematologic toxicities; and (4) therapeutic drug monitoring. We performed a 6-month observational prospective study to analyze the effectiveness of these clinical rules by calculating the positive predictive value (PPV). Results The team defined 211 clinical rules. During the study period, HIGEA generated 1,086 alerts (8.9 alerts per working day), which were reviewed by pharmacists. Fifty-one percent (554/1,086) of alerts generated an intervention to prevent a possible ADE; of these, 66% (368/554) required a documented modification to therapy owing to a real prescription error intercepted. The intervention program that induced the highest number of modifications to therapy was the dose adjustment in renal impairment program (PPV = 0.51), followed by the adjustment of anticoagulation/antiplatelet therapy program (PPV = 0.24). The percentage of accepted interventions was similar in surgical units (68%), medical units (67%), and critical care units (63%). Conclusion Our study offers evidence that HIGEA is highly effective in preventing potential ADEs at the prescription stage.

show abstract

“…16 While medication-related alerts in clinical decision support systems often lead to alert fatigues, inappropriate alert overrides may post preventable harm to patients. 17,18 An effective way to prevent errors is to learn from patient safety events, including unsafe conditions, near misses and incidents. Many event-reporting systems have been created and put in use for learning purpose, which enables patient safety specialists to analyze events, identify underlying factors, and generate actionable knowledge to mitigate risks.…”

mentioning

confidence: 99%

Understanding Health Information Technology Induced Medication Safety Events by Two Conceptual Frameworks

Liang

Kang

et al. 2019

Appl Clin Inform

View full text Add to dashboard Cite

Background While health information technology (health IT) is able to prevent medication errors in many ways, it may also potentially introduce new paths to errors. To understand the impact of health IT induced medication errors, this study aims to conduct a retrospective analysis of medication safety reports. Methods From the U.S. Food and Drug Administration (FDA) Manufacturer and User Facility Device Experience database, we identified reports in which health IT is a contributing factor to medication errors. We applied two conceptual frameworks, Sittig and Singh's sociotechnical model and Coiera's information value chain, to examine the identified reports. Results We identified 152 unique reports on health IT induced medication errors as the final report set for review. The majority (65.13%) of the reports involved multiple contributing factors according to the sociotechnical model. Three dimensions, that is, clinical content, human–computer interface, and people, were involved in more reports than the others. The transition of the effects of health IT on medication practice was summarized using information value chain. Health IT related contributing factors may lead to receiving wrong information, missing information, receiving partial information and delayed information, and receiving wrong information and missing information tend to cause the commission errors in decision-making. Conclusion The two frameworks provide an opportunity to understand a comprehensive context of safety event and the impact of health IT induced errors on medication safety. The sociotechnical model helps identify the aspects causing medication safety issues. The information value chain helps uncover the effect of the health IT induced medication errors on health care process and patient outcomes.

show abstract

Comparison of Overridden Medication-related Clinical Decision Support in the Intensive Care Unit between a Commercial System and a Legacy System

Cited by 19 publications

References 32 publications

Prospective evaluation of medication-related clinical decision support over-rides in the intensive care unit

Prospective evaluation of medication-related clinical decision support over-rides in the intensive care unit

Development and Evaluation of a Clinical Decision Support System to Improve Medication Safety

Understanding Health Information Technology Induced Medication Safety Events by Two Conceptual Frameworks

Contact Info

Product

Resources

About