Beyond the override: Using evidence of previous drug tolerance to suppress drug allergy alerts; a retrospective study of opioid alerts

OBJECTIVE

Despite the extensive literature exploring alert fatigue, most studies have focused on describing the phenomenon, but not on fixing it. The authors aimed to identify data useful to avert clinically irrelevant alerts to inform future research on clinical decision support (CDS) design.

METHODS

We conducted a retrospective observational study of opioid drug allergy alert (DAA) overrides for the calendar year of 2019 at a large academic medical center, to identify data elements useful to find irrelevant alerts to be averted.

RESULTS

Overall, 227,815 DAAs were fired in 2019, with an override rate of 91 % (n = 208196). Opioids represented nearly two-thirds of these overrides (n = 129063; 62 %) and were the drug class with the highest override rate (96 %). On average, 29 opioid DAAs were overridden per patient. While most opioid alerts (97.1 %) are fired for a possible match (the drug class of the allergen matches the drug class of the prescribed drug), they are overridden significantly less frequently for definite match (exact match between allergen and prescribed drug) (88 % vs. 95.9 %, p < 0.001). When comparing the triggering drug with previously administered drugs, override rates were equally high for both definite match (95.9 %), no match (95.5 %), and possible match (95.1 %). Likewise, when comparing to home medications, overrides were excessively high for possible match (96.3 %), no match (96 %), and definite match (94.4 %).

CONCLUSION

We estimate that 74.5% of opioid DAAs (46.4% of all DAAs) at our institution could be relatively safely averted, since they either have a definite match for previous inpatient administrations suggesting drug tolerance or are fired as possible match with low risk of cross-sensitivity. Future research should focus on identifying other relevant data elements ideally with automated methods and use of emerging standards to empower CDS systems to suppress false-positive alerts while avoiding safety hazards.

A systematic review of data elements of computerized physician order entry (CPOE): mapping the data to FHIR

OBJECTIVE

Medication errors are the third leading cause of death. There are several methods to prevent prescription errors, one of which is to use a Computerized Physician Order Entry system (CPOE). In a CPOE system, necessary data needs to be collected so that making decisions about prescribing medications and treatment plans could be made. Although many CPOE systems have been developed worldwide, studies have yet to identify the necessary data and data elements of CPOE systems. This study aims to identify data elements of CPOE and standardize these data with Fast Healthcare Interoperability Resources (FHIR) to facilitate data sharing and integration with the electronic health record (EHR) system and reduce data diversity.

METHODS

PubMed, Web of Science, Embase, and Scopus databases for studies up to October 2019 were searched. Two reviewers independently assessed original articles to determine eligibility for inclusion in this review. All articles describing data elements of a COPE system were included. Data elements were obtained from the included articles' text, tables, and figures.Classification of the extracted data elements and mapping them to FHIR was done to facilitate data sharing and integration with the electronic health record (EHR) system and reduce data diversity. The final data elements of CPOE were categorized into five main categories of FHIR (foundation, base, clinical, financial, and specialized) and 146 resources, where possible. One of the researchers did mapping and checked and verified by the second researcher. If a data element could not be mapped to any FHIR resources, this data element was considered an extension to the most relevant resource.

RESULTS

We retrieved 5162 articles through database searches. After the full-text assessment, 21 articles were included. In total, 270 data elements were identified and mapped to the FHIR standard. These elements have been reported in 26 FHIR resources of 146 ones (18%). In total, 71 data elements were considered an extension.

CONCLUSIONS

The results of this study showed that the same data elements were not used in the CPOE systems, and the degree of homogeneity of these systems is limited. The mapping of extracted data with data elements used in the FHIR standard shows the extent to which these systems comply with existing standards. Considering the standards in these systems' design helps developers design more coherent systems that can share data with other systems.

Design, development, implementation, and evaluation of a severe drug-drug interaction alert system in the ICU: An analysis of acceptance and override rates

BACKGROUND AND OBJECTIVE

The override rate of Drug-Drug Interaction Alerts (DDIA) in Intensive Care Units (ICUs) is very high. Therefore, this study aimed to design, develop, implement, and evaluate a severe Drug-Drug Alert System (DDIAS) in a system of ICUs and measure the override rate of this system.

METHODS

This is a cross-sectional study that details the design, development, implementation, and evaluation of a DDIAS for severe interactions into a Computerized Provider Order Entry (CPOE) system in the ICUs of Nemazee general teaching hospitals in 2021. The patients exposed to the volume of DDIAS, acceptance and overridden of DDIAS, and usability of DDIAS have been collected. The study was approved by the local Institutional Review Board (IRB) and; the ethics committee of Shiraz University of Medical Science on date: 2019-11-23 (Approval ID: IR.SUMS.REC.1398.1046).

RESULTS

The knowledge base of the DDIAS contains 9,809 severe potential drug-drug interactions (pDDIs). A total of 2672 medications were prescribed in the population study. The volume and acceptance rate for the DDIAS were 81 % and 97.5 %, respectively. The override rate was 2.5 %. The mean System Usability Scale (SUS) score of the DDIAS was 75.

CONCLUSION

This study demonstrates that implementing high-risk DDIAS at the point of prescribing in ICUs improves adherence to alerts. In addition, the usability of the DDIAS was reasonable. Further studies are needed to investigate the establishment of severe DDIAS and measure the prescribers' response to DDIAS on a larger scale.

Drug Allergy Alert System in a Spanish University Hospital: Description and Dynamics of Use

INTRODUCTION

The drug allergy alert system reduces the frequency of adverse drug events, although it is subjected to collateral effects, since 80-90% of alerts are not real, and a large percentage of alerts are overridden (46.2-96.2%). We reviewed how the alert system is used at University Hospital Fundación Alcorcon (HUFA).

METHODS

Data were obtained from the drug allergy alert and the alert overriding notification forms (both in the period 2011-20). We also recorded drug allergy diagnoses at HUFA, drug consumption in primary care in 2016. We calculated the incidence of drug allergy alert activation, temporal trends in use, and correlations between the number of drugs in several datasets.

RESULTS

We collected 15,535 alerts. NSAIDs and penicillins were the drugs with the highest number of drug allergy alerts (36.55% and 26.91%, respectively). A correlation was found between the number of drug alerts and the type of drug allergy in HUFA in 2016. Only 6.83% of the alerts were removed, and, of these, 21.77% were reactivated. Approximately 100 overrides were recorded per year from 2016 (6.8% of 8,434 activated alerts during 2014-2020).

CONCLUSIONS

The number of drug allergy alerts recorded via the drug allergy alert system of HUFA correlates with the distribution of drug allergy diagnoses in the hospital, although many of the alerts could be false positives (as per current published evidence). We detected a very low frequency of removed alerts (6.83%), a relevant frequency of reactivations (one quarter), and a very low frequency of overrides (6.8%).

Barriers to Adoption of Tailored Drug-Drug Interaction Clinical Decision Support

OBJECTIVE

Despite the benefits of the tailored drug-drug interaction (DDI) alerts and the broad dissemination strategy, the uptake of our tailored DDI alert algorithms that are enhanced with patient-specific and context-specific factors has been limited. The goal of the study was to examine barriers and health care system dynamics related to implementing tailored DDI alerts and identify the factors that would drive optimization and improvement of DDI alerts.

METHODS

We employed a qualitative research approach, conducting interviews with a participant interview guide framed based on Proctor's taxonomy of implementation outcomes and informed by the Theoretical Domains Framework. Participants included pharmacists with informatics roles within hospitals, chief medical informatics officers, and associate medical informatics directors/officers. Our data analysis was informed by the technique used in grounded theory analysis, and the reporting of open coding results was based on a modified version of the Safety-Related Electronic Health Record Research Reporting Framework.

RESULTS

Our analysis generated 15 barriers, and we mapped the interconnections of these barriers, which clustered around three entities (i.e., users, organizations, and technical stakeholders). Our findings revealed that misaligned interests regarding DDI alert performance and misaligned expectations regarding DDI alert optimizations among these entities within health care organizations could result in system inertia in implementing tailored DDI alerts.

CONCLUSION

Health care organizations primarily determine the implementation and optimization of DDI alerts, and it is essential to identify and demonstrate value metrics that health care organizations prioritize to enable tailored DDI alert implementation. This could be achieved via a multifaceted approach, such as partnering with health care organizations that have the capacity to adopt tailored DDI alerts and identifying specialists who know users' needs, liaise with organizations and vendors, and facilitate technical stakeholders' work. In the future, researchers can adopt the systematic approach to study tailored DDI implementation problems from other system perspectives (e.g., the vendors' system).

An Observational Study to Identify Drug-related Problems (DRP) in Routine Care and An Expert Panel Assessment to Rate Clinical Risk and Preventability by Unit-dose Dispensing Systems (UDDS) with Computerized Physician Order Entry (CPOE) and Clinical Decision-Support Systems (CDSS)

Background and aim: Drug-related problems (DRP) jeopardize patient safety. Unit-dose dispensing systems (UDDS) with computerized-physician-order-entry (CPOE) and clinical-decision-support-systems (CDSS) were reported as a promising concept for preventing DRP. We aimed at identifying and categorizing DRP in peroral drug administration considering their clinical risk and preventability by UDSS/CPOE/CDSS. Investigations: In surgical and internal-medicine departments, we observed routine procedures in peroral drug administration for DRP. An expert panel including pharmaceutical and nursing expertise categorized the identified 18 DRP categories into three levels: DRP that have not yet resulted in medication errors (ME) (Level-I), DRP where ME have occurred but have not yet reached the patient (Level-II), and DRP where ME have occurred and have reached the patient (Level-III). Additionally, the panel categorized DRP according to their clinical risk and whether the implementation of UDSS/CPOE/CDSS can prevent them. Results: In 77 surgical patients, 1,849 peroral drug administration procedures, and in 149 internal-medicine patients, 1,405 procedures were observed. The 18 DRP categories were identified with a frequency of 0.6%-26.7% (Level-I), 0.1%-21.5% (Level-II), and 0.0%-1.0% (Level-III). Of those, four categories were considered of high clinical risk: "Name of the medication is not readable", "Prescribed medication is not prepared for administration", "An incorrect or non-prescribed medication is prepared", and "A medication is prepared for the wrong patient (mix-up)". Twelve DRP categories were categorized as highly preventable by UDSS/CPOE/CDSS. Conclusions:Under routine conditions, we identified a substantial number of DRPs. An expert panel categorized many of those DRPs as clinically highly relevant and highly preventable by UDSS/CPOE/CDSS.

Tackling alert fatigue with a semi-automated clinical decision support system: quantitative evaluation and end-user survey

STUDY AIMS

Clinical decision support systems (CDSS) embedded in hospital electronic health records efficiently reduce medication errors, but there is a risk of low physician adherence due to alert fatigue. At the Cantonal Hospital Aarau, a CDSS is being developed that allows the highly accurate detection and correction of medication errors. The semi-automated CDSS sends its alerts either directly to the physician or to a clinical pharmacist for review first. Our aim was to evaluate the performance of the recently implemented CDSS in terms of acceptance rate and alert burden, as well as physicians' satisfaction with the CDSS.

METHODS

All alerts generated by the clinical decision support systems between January and December 2021 were included in a retrospective quantitative evaluation. A team of clinical pharmacists performed a follow-up to determine whether the recommendation made by the CDSS was implemented by the physician. The acceptance rate was calculated including all alerts for which it was possible to determine an outcome. A web-based survey was conducted amongst physicians to assess their attitude towards the CDSS. The survey questions included overall satisfaction, helpfulness of individual algorithms, and perceived alert burden.

RESULTS

In 2021, a total of 10,556 alerts were generated, of which 619 triggered a direct notification to the physician and 2,231 notifications were send to the physician after evaluation by a clinical pharmacist. The acceptance rates were 89.8% and 68.4%, respectively, which translates as an overall acceptance rate of 72.4%. On average, clinical pharmacists received 17.2 alerts per day, while all of the hospital physicians together received 7.8 notifications per day. In the survey, 94.5% of physicians reported being satisfied or very satisfied with the CDSS. Algorithms addressing potential medication errors concerning anticoagulants received the highest usefulness ratings.

CONCLUSION

The development of this semi-automated clinical decision support system with context-based algorithms resulted in alerts with a high acceptance rate. Involving clinical pharmacists proved a promising approach to limit the alert burden of physicians and thus tackle alert fatigue. The CDSS is well accepted by our physicians.

Impact of automated dispensing cabinets on dispensing errors, interruptions and pillbox preparation time

AIM

This work aimed to evaluate the impact of automated dispensing cabinets on the dispensing error rate, the number of interruptions, and pillbox preparation times.

METHODS

A prospective observational study was conducted across 16 wards in two departments (internal medicine and surgery) of a large teaching hospital. The study compared eight wards using automated dispensing cabinets (ADCs) and eight using a traditional ward stock (TWS) method. A disguised observation technique was used to compare occurrences of dispensing errors and interruptions and pillbox preparation times. The proportion of errors was calculated by dividing the number of doses with one or more errors by the total number of opportunities for error. Wards participating in the 'More time for patients' project-a Lean Management approach-were compared with those not participating. The potential severity of intercepted errors was assessed.

RESULTS

Our observations recorded 2924 opportunities for error in the preparation of 570 pillboxes by 132 nurses. We measured a significantly lower overall error rate (1.0% vs 5.0%, p=0.0001), significantly fewer interruptions per hour (3.2 vs 5.7, p=0.008), and a significantly faster mean preparation time per drug (32 s vs 40 s, p=0.0017) among ADC wards than among TWS wards, respectively. We observed a significantly lower overall error rate (1.4% vs 4.4%, p=0.0268) and a non-significantly lower number of interruptions per hour (3.8 vs 5.1, p=0.0802) among wards participating in the 'More time for patients' project.

CONCLUSIONS

A high dispensing-error rate was observed among wards using TWS methods. Wards using ADCs connected to computerised physician order entry and installed in a dedicated room had fewer dispensing errors and interruptions and their nurses prepared pillboxes faster. Wards participating in a Lean Management project had lower error rates than wards not using this approach.

Orenstein E, Umontuen U, McNeill D, Kandaswamy S. Reducing Therapeutic Duplication in Inpatient Medication Orders

BACKGROUND

Therapeutic duplication, the presence of multiple agents prescribed for the same indication without clarification for when each should be used, can contribute to serious medical errors. Joint Commission standards require that orders contain clarifying information about when each order should be given. In our system, as needed (PRN) acetaminophen and ibuprofen orders are major contributors to therapeutic duplication.

OBJECTIVE

The objective of this study is to design and evaluate effectiveness of clinical decision support (CDS) to reduce therapeutic duplication with acetaminophen and ibuprofen orders.

METHODS

This study was done in a pediatric health system with three freestanding hospitals. We iteratively designed and implemented two CDS strategies aimed at reducing the therapeutic duplication with these agents: (1) interruptive alert prompting clinicians for clarifying PRN comments at order entry and (2) addition of discrete "first-line" and "second-line" PRN reasons to orders. Therapeutic duplications were measured by manual review of orders for 30-day periods before and after each intervention and 6 months later.

RESULTS

Therapeutic duplications decreased from 1,485 in the 30 days prior to the first alert implementation to 818 in the 30 days after but rose back to 1,208 in the 30 days prior to the second intervention. After discrete reasons were added to the order, therapeutic duplication decreased to 336 in the immediate 30 days and 6 months later remained at 277. Alerts firing rates decreased from 76.0 per 1,000 PRN acetaminophen or ibuprofen orders to 42.9 after the second intervention.

CONCLUSION

Interruptive alerts may reduce therapeutic duplication but are associated with high rates of user frustration and alert fatigue. Leveraging discrete PRN reasons for "first line" and "second line" produced a greater reduction in therapeutic duplication as well as fewer interruptive alerts and less manual entry for providers.

Evaluation of the effectiveness of electronic prescription in reducing medical and medical errors (systematic review study)

INTRODUCTION

The use of electronic systems in prescription is considered as the final solution to overcome the many problems of the paper transcription process, especially with the outbreak of Coronavirus needs more attention than before. But despite the many advantages, its implementation faces many challenges and obstacles. Therefore, the present study was conducted to review the effectiveness of computerized physician order entry systems (CPOE) on relative risk reduction on medication error and adverse drug events (ADE).

METHOD

This study is one of the systematic review studies that was conducted in 2021. In this study, searching for keywords such as E-Electronic Prescription, Patient safety, Medication Errors prescription, Drug Interactions, orginal articles from 2000 to October-2020 in the valid databases such as ISI web of Science PubMed Embase, Scopus and search engines like google was done. The included studies were based on the main objectives of the study and based on the inclusion criteria after several stages of review and quality evaluation. In fact, the main criteria for selecting articles were studies that compared the rate of medication errors with or without assessing the associated harms (real or potential) before and after the implementation of EMS.

RESULTS

Out of 110 selected studies after initial screening, only 16 articles were selected due to their relevance. Among the final studies, there was a significant heterogeneity. Only 6 studies were of good quality. Of the 10 studies prescribing error rates, 9 reported reductions, but variable denominators prevented meta-analysis. Twelve studies provided specific examples of systemic drug errors. 5 cases reported their occurrence slightly. Out of 9 cases that analyzed the effects on drug error rate, 7 cases showed a significant relative reduction between 13 and 99%. Four of the six studies that analyzed the effects on potential ADEs showed a significant relative reduction of between 35 and 98%. Two of the four studies that analyzed the effect of ADEs showed a relative reduction of between 30 and 84%.

CONCLUSION

Finally, e-prescribing seems to reduce the risk of medication errors and ADE. However, the studies differed significantly in terms of setting, design, quality and results. More randomized controlled trials (RCTs) are needed to further improve the evidence of health informatics information.

Pseudorandomized Testing of a Discharge Medication Alert to Reduce Free-Text Prescribing

BACKGROUND

Pseudorandomized testing can be applied to perform rigorous yet practical evaluations of clinical decision support tools. We apply this methodology to an interruptive alert aimed at reducing free-text prescriptions. Using free-text instead of structured computerized provider order entry elements can cause medication errors and inequity in care by bypassing medication-based clinical decision support tools and hindering automated translation of prescription instructions.

OBJECTIVE

The objective of this study is to evaluate the effectiveness of an interruptive alert at reducing free-text prescriptions via pseudorandomized testing using native electronic health records (EHR) functionality.

METHODS

Two versions of an EHR alert triggered when a provider attempted to sign a discharge free-text prescription. The visible version displayed an interruptive alert to the user, and a silent version triggered in the background, serving as a control. Providers were assigned to the visible and silent arms based on even/odd EHR provider IDs. The proportion of encounters with a free-text prescription was calculated across the groups. Alert trigger rates were compared in process control charts. Free-text prescriptions were analyzed to identify prescribing patterns.

RESULTS

Over the 28-week study period, 143 providers triggered 695 alerts (345 visible and 350 silent). The proportions of encounters with free-text prescriptions were 83% (266/320) and 90% (273/303) in the intervention and control groups, respectively (p = 0.01). For the active alert, median time to action was 31 seconds. Alert trigger rates between groups were similar over time. Ibuprofen, oxycodone, steroid tapers, and oncology-related prescriptions accounted for most free-text prescriptions. A majority of these prescriptions originated from user preference lists.

CONCLUSION

An interruptive alert was associated with a modest reduction in free-text prescriptions. Furthermore, the majority of these prescriptions could have been reproduced using structured order entry fields. Targeting user preference lists shows promise for future intervention.

Examining medication ordering errors using AHRQ network of patient safety databases

BACKGROUND

Studies examining the effects of computerized order entry (CPOE) on medication ordering errors demonstrate that CPOE does not consistently prevent these errors as intended. We used the Agency for Healthcare Research and Quality (AHRQ) Network of Patient Safety Databases (NPSD) to investigate the frequency and degree of harm of reported events that occurred at the ordering stage, characterized by error type.

MATERIALS AND METHODS

This was a retrospective observational study of safety events reported by healthcare systems in participating patient safety organizations from 6/2010 through 12/2020. All medication and other substance ordering errors reported to NPSD via common format v1.2 between 6/2010 through 12/2020 were analyzed. We aggregated and categorized the frequency of reported medication ordering errors by error type, degree of harm, and demographic characteristics.

RESULTS

A total of 12 830 errors were reported during the study period. Incorrect dose accounted for 3812 errors (29.7%), followed by incorrect medication 2086 (16.3%), and incorrect duration 765 (6.0%). Of 5282 events that reached the patient and had a known level of severity, 12 resulted in death, 4 resulted in severe harm, 45 resulted in moderate harm, 341 resulted in mild harm, and 4880 resulted in no harm.

CONCLUSION

Incorrect dose and incorrect drug orders were the most commonly reported and harmful types of medication ordering errors. Future studies should aim to develop and test interventions focused on CPOE to prevent medication ordering errors, prioritizing wrong-dose and wrong-drug errors.

Reducing prescribing errors: making electronic prescribing work for cystic fibrosis inpatients

Children admitted to our hospital with cystic fibrosis had frequent medication errors due to polypharmacy and addition of specialist and high-risk medications despite an electronic prescribing and medicines administration system in place. We describe a multidisciplinary quality improvement project that combined a computerised order entry system (CPOE) with human factor process changes. Over 12 months, our run chart showed a 43% reduction in prescription errors. For medications prescribable via the CPOE, errors reaching the patient reduced from 50% to 29%. Electronic prescribing can be seen by clinicians as a fixed unalterable system contributing to rather than ameliorating errors. Improving safety requires whole team engagement and working closely with programmers to adapt function and influence human factors.

Reducing Medication Errors in Children's Hospitals

OBJECTIVES

Knowledge of the prevalence and characteristics of medication errors in pediatric and neonatal patients is limited. This study aimed to evaluate the incidence and medication error characteristics in a pediatric hospital over 5 years and to determine whether serial error prevention programs to optimize a computerized physician order entry (CPOE) system reduce error incidence.

METHODS

We retrospectively reviewed medication errors documented between January 2015 and December 2019.

RESULTS

A total of 2,591,596 prescriptions were checked, and 255 errors were identified. Wrong dose prescriptions constituted the most common errors (56.9%). Medications with the highest rate of errors were antibiotics/antiviral drugs (36.9%). Oral route medications comprised the highest portion (60.8%), followed by intravenous ones (28.6%). The most common stage for medication errors was physician ordering (93.3%). Junior residents were responsible for most errors (45.9%). Most errors occurred in the pediatric ward (53.7%). In total, 221 (86.7%) errors were near misses. Only 4 errors (1.6%) were considered significant and required active monitoring or intervention. Type of error, stage of error, staff composition, and severity level of errors were significantly related to the number of errors in different years. There was a statistically significant decrease in errors per 100,000 prescriptions across different years after optimizing the CPOE system.

CONCLUSIONS

The incidence of medication errors decreased with extensive use of the CPOE system. Continuous application of the CPOE optimization program can effectively reduce medication errors. Further incorporation of pediatric-specific decision-making and support tools and error prevention measures into CPOE systems is needed.

Implementation of a myasthenia gravis drug-disease interaction clinical decision support tool reduces prescribing of high-risk medications

INTRODUCTION/AIMS

High-risk medication exposure is a modifiable risk factor for myasthenic exacerbation and crisis. We evaluated whether real-time electronic clinical decision support (CDS) was effective in reducing the rate of prescribing potentially high-risk medications to avoid or use with caution in patients with myasthenia gravis.

METHODS

An expert panel reviewed the available drug-disease pairings and associated severity levels to activate the alerts for CDS. All unique alerts activated in both inpatient and outpatient contexts were analyzed over a two-year period. Clinical context, alert severity, medication class, and alert action were collected. The primary outcome was alert override rate. Secondary outcomes included the percentage of unique medication exposures avoided and predictors of alert override.

RESULTS

During the analysis period, 2817 unique alerts fired, representing 830 distinct patient-medication exposures for 577 unique patients. The overall alert override rate was 85% (80.3% for inpatient alerts and 95.8% for outpatient alerts). Of unique medication-patient exposures, 19% were avoided because of the alert. Assigned alert severity of "contraindicated" were less likely to be overridden (odds ratio [OR] 0.42, 95% confidence interval [CI] 0.32-0.56), as well as alerts activated during evening staffing (OR 0.69, 95% CI 0.55-0.87).

DISCUSSION

Implementation of a myasthenia gravis drug-disease interaction alert reduced overall patient exposure to potentially harmful medications by approximately 19%. Future optimization includes enhanced provider and pharmacist education. Further refinement of alert logic criteria to optimize medication risk reduction and reduce alert fatigue is warranted to support clinicians in prescribing and reduce electronic health record time burden.

Perspective: leveraging the electronic health record to improve sleep in the hospital

Inpatient sleep loss can worsen health outcomes, including delirium and falls. Sleep disruptions in the hospital often originate from provider-patient interactions ordered electronically through computerized provider order entry. These orders contain clinical decision support systems with default schedules. These defaults are often around-the-clock, may not align with patients' needs, and cause iatrogenic sleep loss. Optimizing clinical decision support in the electronic health record can decrease unnecessary sleep disruptions and influence sleep-friendly decision-making.

CITATION

Kadura S, Siala T, Arora VM. Perspective: Leveraging the electronic health record to improve sleep in the hospital. J Clin Sleep Med. 2023;19(2):421-423.

Distinct components of alert fatigue in physicians' responses to a noninterruptive clinical decision support alert

OBJECTIVE

Clinical decision support (CDS) alerts may improve health care quality but "alert fatigue" can reduce provider responsiveness. We analyzed how the introduction of competing alerts affected provider adherence to a single depression screening alert.

MATERIALS AND METHODS

We analyzed the audit data from all occurrences of a CDS alert at a large academic health system. For patients who screen positive for depression during ambulatory visits, a noninterruptive alert was presented, offering a number of relevant documentation actions. Alert adherence was defined as the selection of any option offered within the alert. We assessed the effect of competing clinical guidance alerts presented during the same encounter and the total of all CDS alerts that the same provider had seen in the prior 90 days, on the probability of depression screen alert adherence, adjusting for physician and patient characteristics.

RESULTS

The depression alert fired during 55 649 office visits involving 418 physicians and 40 474 patients over 41 months. After adjustment, physicians who had seen the most alerts in the prior 90 days were much less likely to respond (adjusted OR highest-lowest quartile, 0.38; 95% CI 0.35-0.42; P < .001). Competing alerts in the same visit further reduced the likelihood of adherence only among physicians in the middle two quartiles of alert exposure in the prior 90 days.

CONCLUSIONS

Adherence to a noninterruptive depression alert was strongly associated with the provider's cumulative alert exposure over the past quarter. Health systems should monitor providers' recent alert exposure as a measure of alert fatigue.

Interruptive Electronic Alerts for Choosing Wisely Recommendations: A Cluster Randomized Controlled Trial

OBJECTIVE

To assess the efficacy of interruptive electronic alerts in improving adherence to the American Board of Internal Medicine's Choosing Wisely recommendations to reduce unnecessary laboratory testing.

MATERIALS AND METHODS

We administered 5 cluster randomized controlled trials simultaneously, using electronic medical record alerts regarding prostate-specific antigen (PSA) testing, acute sinusitis treatment, vitamin D testing, carotid artery ultrasound screening, and human papillomavirus testing. For each alert, we assigned 5 outpatient clinics to an interruptive alert and 5 were observed as a control. Primary and secondary outcomes were the number of postalert orders per 100 patients at each clinic and number of triggered alerts divided by orders, respectively. Post hoc analysis evaluated whether physicians experiencing interruptive alerts reduced their alert-triggering behaviors.

RESULTS

Median postalert orders per 100 patients did not differ significantly between treatment and control groups; absolute median differences ranging from 0.04 to 0.40 for PSA testing. Median alerts per 100 orders did not differ significantly between treatment and control groups; absolute median differences ranged from 0.004 to 0.03. In post hoc analysis, providers receiving alerts regarding PSA testing in men were significantly less likely to trigger additional PSA alerts than those in the control sites (Incidence Rate Ratio 0.12, 95% CI [0.03-0.52]).

DISCUSSION

Interruptive point-of-care alerts did not yield detectable changes in the overall rate of undesired orders or the order-to-alert ratio between active and silent sites. Complementary behavioral or educational interventions are likely needed to improve efforts to curb medical overuse.

CONCLUSION

Implementation of interruptive alerts at the time of ordering was not associated with improved adherence to 5 Choosing Wisely guidelines.

TRIAL REGISTRATION

NCT02709772.

Allergic Adverse Drug Events After Alert Overrides in Hospitalized Patients

OBJECTIVES

This study aimed to assess how often overridden drug allergy alerts (ODAAs) lead to allergic adverse drug events (All-ADEs) and to evaluate the frequency with which drug allergy alerts (DAAs) were overridden and the reasons, as well as appropriateness of these overrides.

METHODS

A retrospective observational study of DAA generated between 2014 and 2016 was conducted. The corresponding DAA records were reviewed to determine the frequency of alert overrides. A chart review was performed on a subset of 194 ODAA (the first of every 3 chronologically ordered ODAA) to identify All-ADEs and to evaluate the override reasons and the appropriateness of these overrides.

RESULTS

A total of 2044 DAAs were overridden (override rate of 44.8%). Most were triggered by a nonexact match (93.81%), when ordering nervous system (21.1%) and cardiovascular system (19.6%) drugs and were generated by physicians (72.7%). The main override reason was that the patient was already taking the drug or had previously tolerated the drug. Only 9.28% of ODAAs were inappropriately overridden. Six All-ADEs (3.09%) were identified and were due to anti-infective (1), antineoplastic (1), and iodinated-contrast (4) drug administration. Most All-ADEs were cutaneous and were mild. None was life-threatening or fatal. The All-ADEs rate was higher among inappropriately ODAA (15.79%, P = 0.013).

CONCLUSIONS

Alert overrides are not exempt from clinical consequences, although few are associated with All-ADEs. It is necessary to identify the drugs involved in those reactions and to update allergy lists to generate only specific and important DAA and to avoid the negative consequences of overrides.

Free-Text Computerized Provider Order Entry Orders Used as Workaround for Communicating Medication Information

OBJECTIVES

Medication information is frequently communicated via free-text computerized provider order entry (CPOE) orders in electronic health records. When such information is transmitted separately from a structured CPOE medication order, there is a significant risk of medication error. Although prior studies have described the frequency of using free-text CPOE orders for communicating medication information, there is a gap in understanding the nature of the medication information contained in the free-text CPOE orders. The aims of this study are to (1) identify the most common medication names communicated in free-text CPOE orders and their risk levels and (2) identify what actions physicians expect that nurses will complete when they place free-text CPOE orders, and (3) describe differences in these patterns across hospitals.

METHODS

This study was a retrospective analysis of a sample of 26,524 free-text CPOE orders from 6 hospitals in the mid-Atlantic U.S. region.

RESULTS

Free-text CPOE orders contained in the sample mentioned 193 medication names. Free-text CPOE orders were used frequently to communicate information about naloxone, heparin, flumazenil, and dextrose. Twenty-two percent of the free-text CPOE orders related to discontinuing medication(s), whereas 7% of the free-text CPOE orders relate to giving medication(s). There was high variation across hospitals both in the percentage of free-text CPOE orders mentioning medication information and in the proportion of those that referred to high-risk medications.

CONCLUSIONS

The prevalence of medication information in free-text CPOE orders may suggest specific communication challenges in respect to urgency, uncertainty, planning, and other aspects of communication and clinical needs. Understanding and addressing communication challenges around commonly mentioned medication names and actions, especially those that are high risk, can help reduce the risk of medication errors.

Overriding Drug-Drug Interaction Alerts in Clinical Decision Support Systems: A Scoping Review

Ineffective computerized alerts for potential Drug-Drug Interactions (DDI) is a longstanding informatics issue. Prescribing clinicians often ignore or override such alerts due to lack of context and clinical relevance, among various other reasons. In this study, we reveiwed published data on the rate of DDI alert overrides and medications involved in the overrides. We identified 34 eligible studies from sites across Asia, Europe, the United States, and the United Kingdom. The override rate of DDI alerts ranged from 55% to 98%, with more than half of the studies reporting the most common drug pairs or medications involved in acceptance or overriding of alerts. The high prevalance of alert overrides highlights the need for decision support systems that take user, drug, and institutional factors into consideration, as well as actionable metrics to better characterize harm associated with overrides.

Can CPOE Based on Electronic Order Sets Cause Unintended Consequences (Expensive and Unnecessary Tests) at the Emergency Department?

Computerized Provider Order Entry (CPOE) systems may cause unintended consequences. This study aimed to describe the on-going system for CPOE order sets, and to explore an economic evaluation at the Emergency Department. First, we developed a costs dashboard which showed us the significant and excessive use of medical tests per consultation. We identified the top 10 most widely used and most expensive tests. Additionally we noticed that the labs seemed to continually increase. Then, we found that 27% of the consultations have at least one item of laboratory practice between January and February 2020, and this represents more than 80% of the consultation costs. Health care spending has reached epic proportions globally. We think that it is time to rethink effective strategies. Maybe it is time to deactivate/remove electronic order sets (EOSs) and the functionality to develop and create their own "private" order sets, in order to eliminate waste and inefficiencies.

Reducing Pediatric Emergency Department Prescription Errors

BACKGROUND

Prescription errors are a significant cause of iatrogenic harm in the health care system. Pediatric emergency department (ED) patients are particularly vulnerable to error. We sought to decrease prescription errors in an academic pediatric ED by 20% over a 24-month period by implementing identified national best practice guidelines.

METHODS

From 2017 to 2019, a multidisciplinary, fellow-driven quality improvement (QI) project was conducted using the Model for Improvement. Four key drivers were identified including simplifying the electronic order entry into prescription folders, improving knowledge of dosing by indication, increasing error feedback to prescribers, and creating awareness of common prescription pitfalls. Four interventions were subsequently implemented. Outcome measures included prescription errors per 1000 prescriptions written for all medications and top 10 error-prone antibiotics. Process measures included provider awareness and use of prescription folders; the balancing measure was provider satisfaction. Differences in outcome measures were assessed by statistical process control methodology. Process and balancing measures were analyzed using 1-way analysis of variance and χ2 testing.

RESULTS

Before our interventions, 8.6 errors per 1000 prescriptions written were identified, with 62% of errors from the top 10 most error-prone antibiotics. After interventions, error rate per 1000 prescriptions decreased from 8.6 to 4.5 overall and from 20.1 to 8.8 for top 10 error-prone antibiotics. Provider awareness of prescription folders was significantly increased.

CONCLUSION

QI efforts to implement previously defined best practices, including simplifying and standardizing computerized provider order entry (CPOE), significantly reduced prescription errors. Synergistic effect of educational and technological efforts likely contributed to the measured improvement.