A Multifaceted Approach to Safety: The Synergistic Detection of Adverse Drug Events in Adult Inpatients

Current patient safety issues: the role of pharmacovigilance

In the current conditions of the pandemic, the burden on the healthcare system, including the pharmacovigilance system monitoring the safety of pharmacotherapy, has significantly increased in all countries. An integral component in ensuring the safety of pharmacotherapy is the identification and prevention of the development of adverse drug reactions (ADR), which are a serious health problem worldwide. One of the modern problems of healthcare, including pharmacovigilance, was the lack of vaccines and drugs for the treatment and prevention of COVID-19 in the first waves of the pandemic, which led to the use of off-label a large number of drugs (hydroxychloroquine, azithromycin, ivermectin) for the treatment of patients with COVID-19 despite the fact that scientific data their benefits were of poor quality and based on in vitro studies. The accelerated approval of drugs and vaccines to combat the COVID-19 pandemic also highlighted the need for rapid data on the safety of drugs in the post-marketing period. However, despite the fact that pharmacovigilance is developing, it still lags behind the impressive scientific and technological achievements achieved in other areas of medicine. Unfortunately, spontaneous reporting does not assess the true prevalence of ADR well, since reporting indicators can vary significantly depending on the motivation, availability of time, qualifications, fear of punishment and similar factors of the sender. Given these known limitations of the spontaneous messaging method, additional strategies for detecting ADR are often used, including trigger tools, manual viewing of medical records and automated monitoring.

Multi-Ethnic Analysis of Cardiac Pharmacogenetic Markers of Cytochrome P450 and Membrane Transporters Genes in the Russian Population

Aim. To summarize Russian studies using pharmacogenetic testing as applied to cardiology.Material and methods. The authors conducted an online search for articles in December 2018 using the following databases: PubMed, Google Scholar, eLIBRARY. The search was carried out by keywords: "Russia", "Russian", "cardiology" together with the terms associated with the polymorphic marker, including: «P450», «CYP2C19», «CYP2D6», «CYP2B1», «CYP2B6», «CYP2Е1», «CYP2C8», «CYP2C9», «CYP3A4», «CYP3A5», «CYP1A1», «CYP1A2», «CYP4F2», «CYP4F1», «ABCB1», «SLCO1B1», «VKORC1», «GGCX», «SULT1A1», «CULT1», «CES1», «gene», «genes», «pharmacogenetics», «pharmacogenomics», «ethnic group».Results. Generalization of information allowed to identify obscure genes that need to be investigated in pharmacogenetic studies. This information can be used for the development of dosing algorithms and the priority choice of drugs, considering the results of pharmacogenetic testing and planning future research.Conclusion. The results of the literature review indicate the importance of studying the most clinically valid and clinically useful pharmacogenetic markers (CYP2C19, CYP2C9, VKORC1, SLCO1B1) among various ethnic groups in the Russian Federation. With the accumulation of evidence of clinical validity and clinical utility of other pharmacogenetic markers (CES1, CYP2D6*4, etc.), the problem of interethnic differences in the carriage of clinically significant polymorphisms of these genes identified in previous studies in the Russian Federation increasingly requires attention. The most promising for the introduction into the clinical practice in the Russian Federation in the near future are polymorphic markers of the CYP2C19, CYP2C9, VKORC1 and SLCO1B1 genes.

Automated adverse event detection collaborative: electronic adverse event identification, classification, and corrective actions across academic pediatric institutions

BACKGROUND

Historically, the gold standard for detecting medical errors has been the voluntary incident reporting system. Voluntary reporting rates significantly underestimate the number of actual adverse events in any given organization. The electronic health record (EHR) contains clinical and administrative data that may indicate the occurrence of an adverse event and can be used to detect adverse events that may otherwise remain unrecognized. Automated adverse event detection has been shown to be efficient and cost effective in the hospital setting. The Automated Adverse Event Detection Collaborative (AAEDC) is a group of academic pediatric organizations working to identify optimal electronic methods of adverse event detection. The Collaborative seeks to aggregate and analyze data around adverse events as well as identify and share specific intervention strategies to reduce the rate of such events, ultimately to deliver higher quality and safer care. The objective of this study is to describe the process of automated adverse event detection, report early results from the Collaborative, identify commonalities and notable differences between 2 organizations, and suggest future directions for the Collaborative.

METHODS

In this retrospective observational study, the implementation and use of an automated adverse event detection system was compared between 2 academic children's hospital participants in the AAEDC, Children's National Medical Center, and Cincinnati Children's Hospital Medical Center. Both organizations use the EHR to identify potential adverse events as designated by specific electronic data triggers. After gathering the electronic data, a clinical investigator at each hospital manually examined the patient record to determine whether an adverse event had occurred, whether the event was preventable, and the level of harm involved.

RESULTS

The Automated Adverse Event Detection Collaborative data from the 2 organizations between July 2006 and October 2010 were analyzed. Adverse event triggers associated with opioid and benzodiazepine toxicity and intravenous infiltration had the greatest positive predictive value (range, 47%- 96%). Triggers associated with hypoglycemia, coagulation disturbances, and renal dysfunction also had good positive predictive values (range, 22%-74%). In combination, the 2 organizations detected 3,264 adverse events, and 1,870 (57.3%) of these were preventable. Of these 3,264 events, clinicians submitted only 492 voluntary incident reports (15.1%).

CONCLUSIONS

This work demonstrates the value of EHR-derived data aggregation and analysis in the detection and understanding of adverse events. Comparison and selection of optimal electronic trigger methods and recognition of adverse event trends within and between organizations are beneficial. Automated detection of adverse events likely contributes to the discovery of opportunities, expeditious implementation of process redesign, and quality improvement.

Hypoglycemia adverse events in PICUs and cardiac ICUs: differentiating preventable and nonpreventable events*

OBJECTIVES

To describe the use of an adverse event detection system to identify, characterize, and categorize preventable versus nonpreventable hypoglycemia AEs in PICUs and cardiac ICUs.

DESIGN

Retrospective observational study.

SETTING

PICU and cardiac ICU of a tertiary pediatric hospital.

SUBJECTS

All hypoglycemia triggers generated over a 3-year period.

INTERVENTIONS

All hypoglycemia triggers generated via an electronic health record-driven surveillance system were investigated to determine if they represented a true adverse event and if that event was preventable or nonpreventable. Clinical and demographic variables were analyzed to identify characteristics of patients who developed a preventable or nonpreventable hypoglycemia adverse event.

MEASUREMENTS AND MAIN RESULTS

There were 197 hypoglycemia adverse events in 90 patients. Thirty percent of the adverse events in the PICU and 36% of the adverse events in the cardiac ICU were characterized as preventable. Of the adverse events, 118 (59.9%) necessitated an intravenous dextrose bolus. No adverse events were associated with reporting of symptoms of hypoglycemia including apnea, altered mental status, or seizures. Events were more likely to be preventable (p < 0.001) if the patient was receiving only parenteral sources of nutrition (intravenous fluids or total parenteral nutrition). Controlling for weekends and holidays, adverse events associated with sole parenteral nutrition source had an increased odds ratio of 9.5 (95% confidence interval: 2.8-31.9) of being preventable. Stratifying by ICU, cardiac ICU events occurring on a weekend or holiday were more likely to be preventable (p = 0.001). Stratifying by unit and controlling for parenteral nutrition source, adverse events in the cardiac ICU occurring on weekends or holidays had an increased odds ratio of 11.6 (95% confidence interval: 2.7-50.2) of being preventable.

CONCLUSIONS

Preventable hypoglycemia adverse events are associated with patients receiving sole parenteral sources of nutrition in both the PICU and cardiac ICU. In the cardiac ICU, there is an association between weekend and holiday time periods and the development of preventable hypoglycemia adverse events.

Comparison of manual versus automated data collection method for an evidence-based nursing practice study

OBJECTIVE

The objective of this study was to investigate and improve the use of automated data collection procedures for nursing research and quality assurance.

METHODS

A descriptive, correlational study analyzed 44 orthopedic surgical patients who were part of an evidence-based practice (EBP) project examining post-operative oxygen therapy at a Midwestern hospital. The automation work attempted to replicate a manually-collected data set from the EBP project.

RESULTS

Automation was successful in replicating data collection for study data elements that were available in the clinical data repository. The automation procedures identified 32 "false negative" patients who met the inclusion criteria described in the EBP project but were not selected during the manual data collection. Automating data collection for certain data elements, such as oxygen saturation, proved challenging because of workflow and practice variations and the reliance on disparate sources for data abstraction. Automation also revealed instances of human error including computational and transcription errors as well as incomplete selection of eligible patients.

CONCLUSION

Automated data collection for analysis of nursing-specific phenomenon is potentially superior to manual data collection methods. Creation of automated reports and analysis may require initial up-front investment with collaboration between clinicians, researchers and information technology specialists who can manage the ambiguities and challenges of research and quality assurance work in healthcare.

Towards the creation of a flexible classification scheme for voluntarily reported transfusion and laboratory safety events

BACKGROUND

Transfusion and clinical laboratory services are high-volume activities involving complicated workflows across both ambulatory and inpatient environments. As a result, there are many opportunities for safety lapses, leading to patient harm and increased costs. Organizational techniques such as voluntary safety event reporting are commonly used to identify and prioritize risk areas across care settings. Creation of functional, standardized safety data structures that facilitate effective exploratory examination is therefore essential to drive quality improvement interventions. Unfortunately, voluntarily reported adverse event data can often be unstructured or ambiguously defined.

RESULTS

To address this problem, we sought to create a "best-of-breed" patient safety classification for data contained in the Duke University Health System Safety Reporting System (SRS). Our approach was to implement the internationally recognized World Health Organization International Classification for Patient Safety Framework, supplemented with additional data points relevant to our organization. Data selection and integration into the hierarchical framework is discussed, as well as placement of the classification into the SRS. We evaluated the impact of the new SRS classification on system usage through comparisons of monthly average report rates and completion times before and after implementation. Monthly average inpatient transfusion reports decreased from 102.1 ± 14.3 to 91.6 ± 11.2, with the proportion of transfusion reports in our system remaining consistent before and after implementation. Monthly average transfusion report rates in the outpatient and homecare environments were not significantly different. Significant increases in clinical lab report rates were present across inpatient and outpatient environments, with the proportion of lab reports increasing after implementation. Report completion times increased modestly but not significantly from a practical standpoint.

CONCLUSIONS

A common safety vocabulary can facilitate integration of information from disparate systems and processes to permit meaningful measurement and interpretation of data to improve safety within and across organizations. Formation of a "best-of-breed" classification for voluntary reporting necessitates an internal examination of localized data needs and workflow in order to design a product that enables comprehensive data capture. A team of clinical, safety, and information technology experts is necessary to integrate the data structures into the reporting system. We have found that a "best-of-breed" patient safety classification provides a solid, extensible model for adverse event analysis, healthcare leader communication, and intervention identification.

Tailoring "best-of-breed" safety classification for patient fall voluntary reporting

OBJECTIVE

Voluntary safety event reporting often produces poorly defined data points, which complicate data analyses across health care settings. Such data should be restructured into a standard patient safety language translatable within and outside health care organizations. We designed and implemented a "best-of-breed" patient safety classification for data created by the Duke University Health System Safety Reporting System.

METHODS

We report our approach for patient fall classification. Our strategy was to deploy the International Classification for Patient Safety Framework of the World Health Organization augmented with additional data points of interest, thereby allowing for data translatability while maintaining local practices. System interface redesign using the "best-of-breed" fall classification was mindful of workflows and known reporting barriers. Custom aggregate reports were also developed.

RESULTS

We estimated the impact of the redesigned portal on Safety Reporting System usage before and after classification through comparisons of fall report volume and report completion time. When normalized as falls per day, the rate of falls only changed slightly, indicating that the enhancement had little effect on reporting desire. Report completion time increased modestly but not significantly from a practical standpoint. The presence of structured data eliminated substantial hours dedicated to manual data management and enabled evaluation of quality improvement interventions within and outside our organization.

CONCLUSIONS

Creation and implementation of a "best-of-breed" patient safety classification for voluntary reporting requires multidisciplinary collaboration between clinical experts, frontline clinicians, and functional and technical analysts. Formal usability evaluations of reporting systems are needed to ensure design facilitates effective data collection.

Recognizing hypoglycemia in children through automated adverse-event detection

BACKGROUND

Automated adverse-event detection using triggers derived from the electronic health record (EHR) is an effective method of identifying adverse events, including hypoglycemia. However, the true occurrence of adverse events related to hypoglycemia in pediatric inpatients and the harm that results remain largely unknown.

OBJECTIVE

We describe the use of an automated adverse-event detection system to detect and categorize hypoglycemia-related adverse events in pediatric inpatients.

METHODS

A retrospective observational study of all hypoglycemia triggers generated by an EHR-driven surveillance system was conducted at a large urban children's hospital during a 1-year period. All hypoglycemia triggers were investigated to determine if they represented a true adverse event and if that event followed or deviated from the local standard of care. Clinical and demographic variables were analyzed to identify subpopulations at risk for hypoglycemia.

RESULTS

Of the 1254 hypoglycemia triggers produced, 198 were adverse events (positive predictive value: 15.8%). No hypoglycemic adverse events were identified via the hospital's voluntary incident-reporting system. The majority of hypoglycemia-related adverse events occurred in the NICU (n = 123 of 198 [62.1%]). A total of 154 (77.8%) of the 198 adverse events hospital-wide and 102 (83%) of the 123 adverse events in the NICU occurred in patients who were receiving insulin therapy.

CONCLUSIONS

Hypoglycemia is common in hospitalized children, particularly neonates and those who receive insulin. An EHR-driven automated adverse-event detection system was effective in identifying hypoglycemia in this population. Automated adverse-event detection holds great promise in augmenting the safety program of organizations who have adopted the EHR.

Using a computerized provider order entry system to meet the unique prescribing needs of children: description of an advanced dosing model

Background

It is well known that the information requirements necessary to safely treat children with therapeutic medications cannot be met with the same approaches used in adults. Over a 1-year period, Duke University Hospital engaged in the challenging task of enhancing an established computerized provider order entry (CPOE) system to address the unique medication dosing needs of pediatric patients.

Methods

An advanced dosing model (ADM) was designed to interact with our existing CPOE application to provide decision support enabling complex pediatric dose calculations based on chronological age, gestational age, weight, care area in the hospital, indication, and level of renal impairment. Given that weight is a critical component of medication dosing that may change over time, alerting logic was added to guard against erroneous entry or outdated weight information.

Results

Pediatric CPOE was deployed in a staggered fashion across 6 care areas over a 14-month period. Safeguards to prevent miskeyed values became important in allowing providers the flexibility to override the ADM logic if desired. Methods to guard against over- and under-dosing were added. The modular nature of our model allows us to easily add new dosing scenarios for specialized populations as the pediatric population and formulary change over time.

Conclusions

The medical needs of pediatric patients vary greatly from those of adults, and the information systems that support those needs require tailored approaches to design and implementation. When a single CPOE system is used for both adults and pediatrics, safeguards such as redirection and suppression must be used to protect children from inappropriate adult medication dosing content. Unlike other pediatric dosing systems, our model provides active dosing assistance and dosing process management, not just static dosing advice.

Culture counts--sustainable inpatient computerized surveillance across Duke University Health System

PURPOSE

The authors report on the managerial and logistical details of deploying a computerized adverse drug event surveillance system that was at first a grant-funded research project and ultimately was changed to a sustained safety-monitoring application serving 3 different hospitals.

METHODS

Surveillance was deployed in 3 phases to 2 community-based hospitals and an academic medical center. A logic-based rules engine surveyed electronic records for laboratory, medication, and demographic information indicative of safety concerns. Potential adverse events triggered manual chart review by pharmacists to verify patient harm.

RESULTS

During Phase 1, the research team created trigger rules for each hospital. In Phase 2, the trigger review was transitioned to hospital personnel and rule sets were reshaped for specific hospital needs. In Phase 3, surveillance was integrated into daily work flows and organizational balanced scorecards where it was accepted as a quantitative measure of medication safety performance.

DISCUSSION AND CONCLUSION

Computerized surveillance helps detect potentially harmful events regardless of hospital size. Active leadership, change-tolerant culture, and hospital pharmacy practice models significantly impact successful adoption. Entrenched cultural issues impeded sustainability at the academic center but not at the 2 community hospitals. Tailoring surveillance to the needs of different inpatient settings is crucial to developing a sustainable model.

Developing a patient safety surveillance system to identify adverse events in the intensive care unit

Aggregation of adverse drug event data has evolved in the last decade. Several approaches are available to augment the standard voluntary incident reporting system. Most of these methods are applicable to nonmedication adverse events as well. To identify appropriately system trends as well as process failures, intensive care units should participate in various collection methods. Several different methods are available for robust adverse drug event data collection, such as target chart review, nontargeted chart review, and direct observation. As the various methods usually capture different types of events, employing more than one technique will improve the assessment of intensive care unit care. Some of these surveillance methods offer real-time or near real-time identification of adverse drug events and potentially afford the practitioner time for intervention. Continued development of adverse drug event detection will allow for further quality improvement efforts and preventive strategies to be utilized.

Recent Publications on Medications and Pharmacy

Hospital Pharmacy presents this feature to keep pharmacists abreast of new publications in the medical/pharmacy literature. Articles of interest regarding a broad scope of topics are abstracted monthly.