Computerized surveillance for adverse drug events in a pediatric hospital

OUP accepted manuscript

Objectives

(1) Systematically review the literature on computerized audit and feedback (e-A&F) systems in healthcare. (2) Compare features of current systems against e-A&F best practices. (3) Generate hypotheses on how e-A&F systems may impact patient care and outcomes.

Methods

We searched MEDLINE (Ovid), EMBASE (Ovid), and CINAHL (Ebsco) databases to December 31, 2020. Two reviewers independently performed selection, extraction, and quality appraisal (Mixed Methods Appraisal Tool). System features were compared with 18 best practices derived from Clinical Performance Feedback Intervention Theory. We then used realist concepts to generate hypotheses on mechanisms of e-A&F impact. Results are reported in accordance with the PRISMA statement.

Results

Our search yielded 4301 unique articles. We included 88 studies evaluating 65 e-A&F systems, spanning a diverse range of clinical areas, including medical, surgical, general practice, etc. Systems adopted a median of 8 best practices (interquartile range 6–10), with 32 systems providing near real-time feedback data and 20 systems incorporating action planning. High-confidence hypotheses suggested that favorable e-A&F systems prompted specific actions, particularly enabled by timely and role-specific feedback (including patient lists and individual performance data) and embedded action plans, in order to improve system usage, care quality, and patient outcomes.

Conclusions

e-A&F systems continue to be developed for many clinical applications. Yet, several systems still lack basic features recommended by best practice, such as timely feedback and action planning. Systems should focus on actionability, by providing real-time data for feedback that is specific to user roles, with embedded action plans.

Protocol Registration

PROSPERO CRD42016048695.

Incidence and nature of adverse drug events in paediatric intensive care units: A prospective multicentre study

AIMS

The aim of this study was to assess the incidence, nature, preventability and severity of adverse drug events (ADEs) across three paediatric intensive care units (PICUs) in England.

METHODS

A prospective observational cohort study was conducted across three PICUs over a three-month period during 2019. Included patients were aged ≤18 years and stayed in PICU for a minimum of 24 hours. Identification of suspected ADEs was performed by trained PICU pharmacists. A multidisciplinary expert panel assessed causality, preventability and severity of events.

RESULTS

A total of 302 patients were included and 62 ADEs were confirmed (definite/probable causality). One in six patients experienced one or more ADEs. The estimated incidence of ADEs were 20.5 per 100 patients (95% CI 15.3-27.5) and 16.7 per 1000 patient-days (95% CI 9.3-29.9). The majority of ADEs were judged preventable by the expert panel (36/62, 58.1%). ADEs were commonly involved with medicines prescribing (29/62, 46.8%) and caused temporary patient harm (42/62, 67.7%). Medications for the central nervous system (14/62, 22.6%), infections (13/62, 20.9%) and cardiovascular system (12/62, 19.4%) were commonly implicated with ADEs. Multivariable analysis revealed that patients who stayed in PICU for ≥7 days (OR 6.29, 95% CI 2.42-16.32) were more likely to experience an ADE compared to patients with a stay of 1-6 days.

CONCLUSION

ADEs are common in English PICUs and most of them may be preventable. There is a strong association between ADE occurrence and duration of PICU stay, which represents a target for remedial interventions. Exploring contributory factors of preventable ADEs is now necessary to inform preventive policies.

Evaluation of a Broad-Spectrum Partially Automated Adverse Event Surveillance System: A Potential Tool for Patient Safety Improvement in Hospitals With Limited Resources

OBJECTIVE

The aim of the study was to evaluate the sensitivity and resource efficiency of a partially automated adverse event (AE) surveillance system for routine patient safety efforts in hospitals with limited resources.

METHODS

Twenty-eight automated triggers from the hospital information system's clinical and administrative databases identified cases that were then filtered by exclusion criteria per trigger and then reviewed by an interdisciplinary team. The system, developed and implemented using in-house resources, was applied for 45 days of surveillance, for all hospital inpatient admissions (N = 1107). Each trigger was evaluated for its positive predictive value (PPV). Furthermore, the sensitivity of the surveillance system (overall and by AE category) was estimated relative to incidence ranges in the literature.

RESULTS

The surveillance system identified a total of 123 AEs among 283 reviewed medical records, yielding an overall PPV of 52%. The tool showed variable levels of sensitivity across and within AE categories when compared with the literature, with a relatively low overall sensitivity estimated between 21% and 44%. Adverse events were detected in 23 of the 36 AE categories defined by an established harm classification system. Furthermore, none of the detected AEs were voluntarily reported.

CONCLUSIONS

The surveillance system showed variable sensitivity levels across a broad range of AE categories with an acceptable PPV, overcoming certain limitations associated with other harm detection methods. The number of cases captured was substantial, and none had been previously detected or voluntarily reported. For hospitals with limited resources, this methodology provides valuable safety information from which interventions for quality improvement can be formulated.

Assessment of Automating Safety Surveillance From Electronic Health Records: Analysis for the Quality and Safety Review System

BACKGROUND AND OBJECTIVES

In an effort to improve and standardize the collection of adverse event data, the Agency for Healthcare Research and Quality is developing and testing a patient safety surveillance system called the Quality and Safety Review System (QSRS). Its current abstraction from medical records is through manual human coders, taking an average of 75 minutes to complete the review and abstraction tasks for one patient record. With many healthcare systems across the country adopting electronic health record (EHR) technology, there is tremendous potential for more efficient abstraction by automatically populating QSRS. In the absence of real-world testing data and models, which require a substantial investment, we provide a heuristic assessment of the feasibility of automatically populating QSRS questions from EHR data.

METHODS

To provide an assessment of the automation feasibility for QSRS, we first developed a heuristic framework, the Relative Abstraction Complexity Framework, to assess relative complexity of data abstraction questions. This framework assesses the relative complexity of characteristics or features of abstraction questions that should be considered when determining the feasibility of automating QSRS. Questions are assigned a final relative complexity score (RCS) of low, medium, or high by a team of clinicians, human factors, and natural language processing researchers.

RESULTS

One hundred thirty-four QSRS questions were coded using this framework by a team of natural language processing and clinical experts. Fifty-five questions (41%) had high RCS and would be more difficult to automate, such as "Was use of a device associated with an adverse outcome(s)?" Forty-two questions (31%) had medium RCS, such as "Were there any injuries as a result of the fall(s)?" and 37 questions (28%) had low RCS, such as "Did the patient deliver during this stay?" These results suggest that Blood and Hospital Acquired Infections-Clostridium Difficile Infection (HAI-CDI) modules would be relatively easier to automate, whereas Surgery and HAI-Surgical Site Infection would be more difficult to automate.

CONCLUSIONS

Although EHRs contain a wealth of information, abstracting information from these records is still very challenging, particularly for complex questions, such as those concerning patient adverse events. In this work, we developed a heuristic framework, which can be applied to help guide conversations around the feasibility of automating QSRS data abstraction. This framework does not aim to replace testing with real data but complement the process by providing initial guidance and direction to subject matter experts to help prioritize, which abstraction questions to test for feasibility using real data.

Relationship between adverse drug reactions and unlicensed/off-label drug use in hospitalized children (EREMI): A study protocol

INTRODUCTION

To date, few studies have shown a significant association between off-label drug use and adverse drug reactions (ADRs). The main aims of this study is to evaluate the relationship between adverse drug reactions and unlicensed or off-label drugs in hospitalized children and to provide more information on prescribing practice, the amplitude, consequences of unlicensed or off-label drug use in pediatric inpatients.

METHODS

In this multicenter prospective study started from 2013, we use the French summaries of product characteristics in Theriaque (a prescription products guide) as a primary reference source for determining pediatric drug labeling. The detection of ADRs is carried out spontaneously by health professionals and actively by research groups using a trigger tool and patients' electronic health records. The causality between suspected ADRs and medication is evaluated using the Naranjo and the French methods of imputability independently by pharmacovigilance center. All suspected ADRs are submitted for a second evaluation by an independent pharmacovigilance experts.

STRENGTH AND LIMITATIONS OF THIS STUDY

For our best knowledge, EREMI is the first large multicenter prospective and objective study in France with an active ADRs monitoring and independent ADRs validation. This study identifies the risk factors that could be used to adjust preventive actions in children's care, guides future research in the field and increases the awareness of physicians in off-label drug use and in detecting and declaring ADRs. As data are obtained through extraction of information from hospital database and medical records, there is likely to be some under-reporting of items or missing data. In this study the field specialists detect all adverse events, experts in pharmacovigilance centers assess them and finally only the ADRs assessed by the independent committee are confirmed. Although we recruit a high number of patients, this observational study is subject to different confounders.

Role of Artificial Intelligence in Fighting Antimicrobial Resistance in Pediatrics

Artificial intelligence (AI) is a field of science and engineering concerned with the computational understanding of what is commonly called intelligent behavior. AI is extremely useful in many human activities including medicine. The aim of our narrative review is to show the potential role of AI in fighting antimicrobial resistance in pediatric patients. We searched for PubMed articles published from April 2010 to April 2020 containing the keywords “artificial intelligence”, “machine learning”, “antimicrobial resistance”, “antimicrobial stewardship”, “pediatric”, and “children”, and we described the different strategies for the application of AI in these fields. Literature analysis showed that the applications of AI in health care are potentially endless, contributing to a reduction in the development time of new antimicrobial agents, greater diagnostic and therapeutic appropriateness, and, simultaneously, a reduction in costs. Most of the proposed AI solutions for medicine are not intended to replace the doctor’s opinion or expertise, but to provide a useful tool for easing their work. Considering pediatric infectious diseases, AI could play a primary role in fighting antibiotic resistance. In the pediatric field, a greater willingness to invest in this field could help antimicrobial stewardship reach levels of effectiveness that were unthinkable a few years ago.

Prevalence and Nature of Medication Errors and Preventable Adverse Drug Events in Paediatric and Neonatal Intensive Care Settings: A Systematic Review

INTRODUCTION

Children admitted to paediatric and neonatal intensive care units may be at high risk from medication errors and preventable adverse drug events.

OBJECTIVE

The objective of this systematic review was to review empirical studies examining the prevalence and nature of medication errors and preventable adverse drug events in paediatric and neonatal intensive care units.

DATA SOURCES

Seven electronic databases were searched between January 2000 and March 2019.

STUDY SELECTION

Quantitative studies that examined medication errors/preventable adverse drug events using direct observation, medication chart review, or a mixture of methods in children ≤ 18 years of age admitted to paediatric or neonatal intensive care units were included.

DATA EXTRACTION

Data on study design, detection method used, rates and types of medication errors/preventable adverse drug events, and medication classes involved were extracted.

RESULTS

Thirty-five unique studies were identified for inclusion. In paediatric intensive care units, the median rate of medication errors was 14.6 per 100 medication orders (interquartile range 5.7-48.8%, n = 3) and between 6.4 and 9.1 per 1000 patient-days (n = 2). In neonatal intensive care units, medication error rates ranged from 4 to 35.1 per 1000 patient-days (n = 2) and from 5.5 to 77.9 per 100 medication orders (n = 2). In both settings, prescribing and medication administration errors were found to be the most common medication errors, with dosing errors the most frequently reported error subtype. Preventable adverse drug event rates were reported in three paediatric intensive care unit studies as 2.3 per 100 patients (n = 1) and 21-29 per 1000 patient-days (n = 2). In neonatal intensive care units, preventable adverse drug event rates from three studies were 0.86 per 1000 doses (n = 1) and 0.47-14.38 per 1000 patient-days (n = 2). Anti-infective agents were commonly involved with medication errors/preventable adverse drug events in both settings.

CONCLUSIONS

Medication errors occur frequently in critically ill children admitted to paediatric and neonatal intensive care units and may lead to patient harm. Important targets such as dosing errors and anti-infective medications were identified to guide the development of remedial interventions.

Characterization of adverse drug events identified by trigger in Brazilian pediatric inpatients

OBJECTIVE

To describe the frequency and characteristics of adverse drug events in pediatric inpatients in a Brazilian tertiary teaching hospital.

METHODS

A cross-sectional study was conducted by retrospective and manual chart review of 240 pediatric admissions to identify adverse drug events using 17 triggers. When triggers were detected in the chart, reviewers investigated the chart in depth to decide whether an event occurred. Consensus about the occurrence of the event was obtained in meeting with a healthcare team. Events were classified by harm category and drugs were classified according to the Anatomical Therapeutic Chemical Classification. Patients who had suffered were compared to those who had not experienced events using the chi-squared test and the Mann-Whitney U test.

RESULTS

A total of 62 adverse events were found, and 18.8% of the patients had at least one event. Adverse events rates were 25.83 per 100 admissions, 20.27 per 1000 patient-days, 25.94 per 1000 drugs, and 2.12 per 1000 drug doses. All events found were classified as temporary harm, and cardiovascular drugs were most frequently related to events. Groups of patients with and without event were segregated (p<0.05) by the length of stay, number of drugs, and drug doses.

CONCLUSION

The use of triggers demonstrated its utility in a pediatric setting by identifying harm. Adverse events rates were found to be higher than those of previous studies, but the harm rate was lower than other studies. This study enables the measurement of adverse events in order to define strategies to mitigate or reduce harm.

Detecting adverse drug reactions in discharge summaries of electronic medical records using Readpeer

BACKGROUND

Hospital discharge summaries offer a potentially rich resource to enhance pharmacovigilance efforts to evaluate drug safety in real-world clinical practice. However, it is infeasible for experts to read through all discharge summaries to find cases of drug-adverse event (AE) relations.

PURPOSE

The objective of this paper is to develop a natural language processing (NLP) framework to detect drug-AE relations from unstructured hospital discharge summaries.

BASIC PROCEDURES

An NLP algorithm was designed using customized dictionaries of drugs, adverse event (AE) terms, and rules based on trigger phrases, negations, fuzzy logic and word distances to recognize drug, AE terms and to detect drug-AE relations. Furthermore, a customized annotation tool was developed to facilitate expert review of discharge summaries from a tertiary hospital in Singapore in 2011.

MAIN FINDINGS

A total of 33 trial sets with 50 to 100 records per set were evaluated (1620 discharge summaries) by our algorithm and reviewed by pharmacovigilance experts. After every 6 trial sets, drug and AE dictionaries were updated, and rules were modified to improve the system. Excellent performance was achieved for drug and AE entity recognition with over 92% precision and recall. On the final 6 sets of discharge summaries (600 records), our algorithm achieved 75% precision and 59% recall for identification of valid drug-AE relations.

PRINCIPAL CONCLUSIONS

Adverse drug reactions are a significant contributor to health care costs and utilization. Our algorithm is not restricted to particular drugs, drug classes or specific medical specialties, which is an important attribute for a national regulatory authority to carry out comprehensive safety monitoring of drug products. Drug and AE dictionaries may be updated periodically to ensure that the tool remains relevant for performing surveillance activities. The development of the algorithm, and the ease of reviewing and correcting the results of the algorithm as part of an iterative machine learning process, is an important step towards use of hospital discharge summaries for an active pharmacovigilance program.

Automated identification of antibiotic overdoses and adverse drug events via analysis of prescribing alerts and medication administration records

Objectives

Electronic trigger detection tools hold promise to reduce Adverse drug event (ADEs) through efficiencies of scale and real-time reporting. We hypothesized that such a tool could automatically detect medication dosing errors as well as manage and evaluate dosing rule modifications.

Materials and Methods

We created an order and alert analysis system that identified antibiotic medication orders and evaluated user response to dosing alerts. Orders associated with overridden alerts were examined for evidence of administration and the delivered dose was compared to pharmacy-derived dosing rules to confirm true overdoses. True overdose cases were reviewed for association with known ADEs.

Results

Of 55 546 orders reviewed, 539 were true overdose orders, which lead to 1965 known overdose administrations. Documentation of loose stools and diarrhea was significantly increased following drug administration in the overdose group. Dosing rule thresholds were altered to reflect clinically accurate dosing. These rule changes decreased overall alert burden and improved the salience of alerts.

Discussion

Electronic algorithm-based detection systems can identify antibiotic overdoses that are clinically relevant and are associated with known ADEs. The system also serves as a platform for evaluating the effects of modifying electronic dosing rules. These modifications lead to decreased alert burden and improvements in response to decision support alerts.

Conclusion

The success of this test case suggests that gains are possible in reducing medication errors and improving patient safety with automated algorithm-based detection systems. Follow-up studies will determine if the positive effects of the system persist and if these changes lead to improved safety outcomes.

Improved drug safety through intensive pharmacovigilance in hospitalized pediatric patients

BACKGROUND

The aim of this study was to detect and analyze Adverse Drug Reactions (ADRs) through Intensive Pharmacovigilance (IPV) in hospitalized pediatric patients to improve drug safety.

METHODS

A prospective 6-month cross-sectional study was performed in the pediatric service of a regional hospital in Mexico in order to assess hospitalized children from 1 day to 18 years old. The inclusion criteria were: both genders, all hospitalization causes, and at least one prescribed medication (indistinct drug group). Notifications were performed through medical visits, phone calls, or spontaneous reports. ADR suspicions were assessed with severity scales: Naranjo algorithm, Schumock & Thornton and Hartwig and Siegel.

RESULTS

From a total of 1083 hospital admissions, 19 ADRs were recorded. The average age of patients in years was 7.2 (±5.9). The causality assessment in this study showed that most of the ADRs were probable (68.4%) and 4 certain (8.2%); causality was mainly attributed to antibiotics (AB) and an antiepileptic drug. We found a relationship of AB with ADRs (p < 0.05) with an increased risk at the third day of prescription (p < 0.05). The average severity was level 2 and 21% were classified as "preventable". Lastly, an increase in hospital stay associated with ADRs (p < 0.05) and with concomitant medications (p < 0.05), was also found. The most severe ADRs were hemolysis and toxic epidermal necrolysis.

CONCLUSIONS

IPV was an effective tool for ADR prevention, detection, and treatment in hospitalized patients. The intensive monitoring approach in pharmacovigilance amplifies ADR detection and this translates into the improvement of drug safety in children.

Development of an Electronic Pediatric All-Cause Harm Measurement Tool Using a Modified Delphi Method

OBJECTIVES

To have impact on reducing harm in pediatric inpatients, an efficient and reliable process for harm detection is needed. This work describes the first step toward the development of a pediatric all-cause harm measurement tool by recognized experts in the field.

METHODS

An international group of leaders in pediatric patient safety and informatics were charged with developing a comprehensive pediatric inpatient all-cause harm measurement tool using a modified Delphi technique. The process was conducted in 5 distinct steps: (1) literature review of triggers (elements from a medical record that assist in identifying patient harm) for inclusion; (2) translation of triggers to likely associated harm, improving the ability for expert prioritization; (3) 2 applications of a modified Delphi selection approach with consensus criteria using severity and frequency of harm as well as detectability of the associated trigger as criteria to rate each trigger and associated harm; (4) developing specific trigger logic and relevant values when applicable; and (5) final vetting of the entire trigger list for pilot testing.

RESULTS

Literature and expert panel review identified 108 triggers and associated harms suitable for consideration (steps 1 and 2). This list was pared to 64 triggers and their associated harms after the first of the 2 independent expert reviews. The second independent expert review led to further refinement of the trigger package, resulting in 46 items for inclusion (step 3). Adding in specific trigger logic expanded the list. Final review and voting resulted in a list of 51 triggers (steps 4 and 5).

CONCLUSIONS

Application of a modified Delphi method on an expert-constructed list of 108 triggers, focusing on severity and frequency of harms as well as detectability of triggers in an electronic medical record, resulted in a final list of 51 pediatric triggers. Pilot testing this list of pediatric triggers to identify all-cause harm for pediatric inpatients is the next step to establish the appropriateness of each trigger for inclusion in a global pediatric safety measurement tool.

Multifaceted approach to reducing occurrence of severe hypoglycemia in a large healthcare system

PURPOSE

Substantial reductions in inpatient episodes of severe hypoglycemia achieved by a large healthcare system through enhanced use of technology and sustained quality-improvement initiatives are described.

SUMMARY

After internal data collection and analysis revealed that severe hypoglycemia accounted for 75% of all systematically monitored adverse drug events in its hospital network, St. Louis-based BJC HealthCare designed and executed a multifaceted approach to reducing hypoglycemia events. Initiated by a pharmacist-led task force, the project entailed (1) automated event detection and creation of dashboards for comparing hypoglycemia rates among at-risk patients at 11 BJC facilities, (2) implementation of evidence-based and internal best practices in use at BJC's top-performing hospitals, (3) development of an online "Hypoglycemic Event Analysis Tool" (HEAT) to support event investigation and collection of data on causative factors, and (4) the assembly of targeted interventions at a "Hypoglycemia Facility Strategy Tracking" (H-FaST) intranet site. As a result of the launch of the HEAT and H-FaST tools and associated provider education activities, the systemwide rate of hypoglycemia events in the specified at-risk patient population declined from 6.45 per 1000 patient-days during a preimplementation baseline period (July-December 2009) to 1.32 per 1000 patient-days during a designated postimplementation period (January-June 2014), an 80% overall reduction in hypoglycemia (p < 0.01); reductions in severe hypoglycemia events ranging from 70% to 100% were observed at all 11 hospitals.

CONCLUSION

A multifaceted, evidence-based, data-driven approach enabled a large healthcare system to markedly reduce the frequency of severe hypoglycemia events.

Unplanned ICU Transfers from Inpatient Units: Examining the Prevalence and Preventability of Adverse Events Associated with ICU Transfer in Pediatrics

Background Adverse events have been associated with unplanned intensive care unit (ICU) transfers in adults. Objective To examine trends in unplanned ICU transfers in pediatrics resulting from adverse events. Design, Setting, Patients Retrospective observational study of pediatric and cardiac ICU transfers from acute care units during a 2-year period in a tertiary care children's hospital. Methods Transfers were identified via electronic health record query and investigated for adverse events. Predefined adverse events included ICU transfers within 12 hours of admission to an acute care unit, readmissions to an ICU within 24 hours, and cardiopulmonary arrest on an acute care unit. Other adverse events examined were not predefined. Adverse events were evaluated for preventability and categorized by type, diagnosis, time of day and weekday versus weekend occurrence, and level of associated patient harm. Results There were 1,008 ICU transfers during the study period; 67% were unplanned. Of the unplanned transfers, 32% were attributed to adverse events, 35% of which were preventable. Unplanned transfers associated with a high rate of preventable adverse events included readmission to an ICU within 24 hours (58%, p = 0.002) and ICU transfer within 12 hours of acute care admission (34%). Conclusions We observed a high rate of preventable adverse events associated with unplanned pediatric ICU transfers, many of which were due to inappropriate triage. Readmission to an ICU within 24 hours of transfer to an acute care unit was significantly associated with preventability.

Monitoring the harm associated with use of anticoagulants in pediatric populations through trigger-based automated adverse-event detection

BACKGROUND

The safety profile of anticoagulants, which are being used with increasing frequency in pediatric populations, is not well studied. Automatic triggers built into electronic health record systems (EHR) have been shown to be an effective way to monitor for and identify medication errors. Anticoagulant-associated adverse events were examined through the use of an anticoagulant trigger panel.

METHODS

In a retrospective, five-year (September 2007-September 2012) observational study, four automated triggers were used to detect anticoagulant-related adverse events: activated partial thromboplastin time (aPTT) > 100 seconds in patients on an unfractionated heparin (UFH) infusion, International Normalized Ratio (INR) > 4, anti-factor Xa (anti-FXa) >1.5U/mL for patients on enoxaparin, and the documented use of protamine.

RESULTS

For the 1,664 triggers evaluated, 12 were associated with the aPTT trigger, only 1 of which was preventable. Receiver operator characteristic curve analysis indicated that increasing the aPTT trigger > 140 seconds would optimize sensitivity and specificity. The INR trigger identified four outpatients with adverse events. No adverse events were associated with the anti-FXa trigger. The protamine trigger identified 12 adverse events and was associated with more severe events. Minimal overlap was found with protamine and aPTT triggers.

CONCLUSION

Laboratory- and medication-based triggers can be effective monitoring tools for anticoagulants. For patients receiving a UFH infusion, an aPTT cutoff value of > 140 seconds is more precise. We also found that protamine use as a trigger adds value to a trigger-based anticoagulant monitoring system. Continued improvement in the logic algorithms associated with the EHR-based trigger tool will allow expanded use of this tool in a clinical manner.

Adverse drug events in hospitalized children at Ethiopian University Hospital: a prospective observational study

Background

The nature and magnitude of adverse drug events (ADEs) among hospitalized children in low-income countries is not well described. The aim of this study was thus, to assess the incidence and nature of ADEs in hospitalized children at a teaching hospital in Ethiopia.

Methods

We used prospective observational method to study children that were hospitalized to Jimma University Specialized Hospital between 1 February and 1 May 2011. ADEs were identified using review of treatment charts, interview of patient and care-giver, attendance at ward rounds and/or meetings and voluntary staff reports. Two senior pediatric residents evaluated the severity and preventability of ADEs using preset criteria. Logistic regression analysis was employed to determine predictors of ADEs.

Results

There were 634 admissions with 6182 patient-days of hospital stay. There were 2072 written medication orders accounting for 35,117 medication doses. Fifty eight ADEs were identified with an incidence of 9.2 per 100 admissions, 1.7 per 1000 medication doses and 9.4 per 1000 patient-days. One-third of ADEs were preventable; 47 % of these were due to errors in the administration stage of medication use process. Regarding the severity of ADEs, 91 % caused temporary harms and 9 % resulted in permanent harm/death. Anti-infective drugs were the most common medications associated with ADEs. The occurrence of ADEs increased with age, length of hospital stay, and use of CNS, endocrine and antihistamine medicines.

Conclusion

ADEs are common in hospitalized children in low-income settings; however, one-third deemed preventable. A strategy to prevent the occurrence and consequences of ADEs including education of nurses/physicians is of paramount importance.

Electronic supplementary material

The online version of this article (doi:10.1186/s12887-015-0401-0) contains supplementary material, which is available to authorized users.

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.

Seizure occurrence following nonoptimal anticonvulsant medication management during the transition into the hospital

Previous work has shown that medication errors related to anticonvulsants are common during the transition into the hospital for pediatric patients. The purpose of this work was to evaluate whether children with epilepsy admitted for reasons other than epilepsy experience nonoptimal care in anticonvulsant medication management preceding the occurrence of seizures. Using a retrospective cohort of children with epilepsy admitted for reasons other than epilepsy, we created timelines from data in the medical record for the children who experienced seizures. These timelines included the timing and concentration of anticonvulsant administration and seizure occurrence. Three child neurologists independently identified whether nonoptimal care preceded the occurrence of seizures and potentially contributed to the occurrence of the seizure. Of 120 children, 18 experienced seizures and 12 experienced nonoptimal care in anticonvulsant management preceding seizure occurrence. Nonoptimal care that occurred during the transition into the hospital included missed doses of anticonvulsants, delays in administration during which seizures occurred, and patients inadvertently not receiving their home dosing of medication. Anticonvulsant medication errors are known to occur during the transition into the hospital. Here we present a case series of children who experienced nonoptimal care in anticonvulsant medication management who subsequently experienced seizures. Further work to identify how likely the outcome of seizures is following anticonvulsant medication errors, specifically focusing on timing as well as interventions to change the system issues that lead to these errors, is indicated.

Anticonvulsant medication errors in children with epilepsy during the home-to-hospital transition

Children with epilepsy are at risk of having their anticonvulsant regimens disrupted during the home-to-hospital transition. We sought to estimate the frequency of anticonvulsant medication errors during transition into the hospital in children with epilepsy hospitalized for reasons other than seizures, and to examine factors associated with the occurrence of such errors. We examined the medical records to identify errors related to anticonvulsant administration during the transition into the hospital and we examined potential risk factors for error occurrence. Errors were classified as relating to dosing quantity or missing a dose. Among 120 children, 29 (24%) experienced an anticonvulsant medication error. In a multivariable model, the risk factors of changes in responsibility for anticonvulsant administration and frequency of anticonvulsant administration were strongly associated with increased odds of errors. Anticonvulsant medication errors during the home-to-hospital transition may be unacceptably common in children with epilepsy hospitalized for reasons other than seizures.

Automated detection of adverse events in children

Voluntary reporting has been the standard method for identifying adverse events in hospitals, yet its effectiveness at identifying a comprehensive array of adverse events has always been in question. The electronic health record (EHR) contains clinical data that can be systematically reviewed to identify adverse events and improve adverse event detection. Active use of an automated trigger tool that is embedded in an EHR can identify systematic issues with delivery of high-risk medications and is cost-effective and efficient. Further development of an automated adverse event detection protocol for pediatrics is needed to apply this approach systematically across pediatric institutions.

Improvement in the detection of adverse drug events by the use of electronic health and prescription records: an evaluation of two trigger tools

PURPOSE

To test if two of the adverse event triggers proposed by the Institute of Healthcare Improvement can detect adverse drug events (ADEs) in a UK secondary care setting, using an electronic prescribing and health record system.

METHODS

In order to identify triggers for over-anticoagulation and potential opioid overdose and we undertook a retrospective review of electronic medical and prescription records from 54,244 hospital admissions over a 1-year period, alongside a review of medical incident reports. Once prescription data were linked to triggers and duplicates were removed, case note review eliminated the false positive ADEs. Additionally, we tested the use of an electronic algorithm for the International Normalized Ratio (INR) ≥6 trigger.

RESULTS

The INR ≥6 electronic trigger identified 46 potential ADEs and the naloxone electronic trigger identified 82 ADEs. Based on the available case note review, the INR ≥6 trigger had a positive predictive value (PPV) of 38 % (14/37) and the naloxone trigger had a PPV of 91 % (61/67). The electronic algorithm for the INR ≥6 trigger identified 12 ADEs, thus reducing the need of case note review. This was in comparison with one and two critical incidents reported in the trust medical incident reports system, which respectively related to over-coagulation with warfarin and over-sedation with opioid medication.

CONCLUSIONS

We have integrated automated and manual methods of detecting ADEs using previously defined triggers. Incorporating electronic triggers in already established electronic health records with prescription and laboratory test data can improve the detection of ADEs, and potentially lead to methods to avert them.

Real-time pharmacy surveillance and clinical decision support to reduce adverse drug events in acute kidney injury: a randomized, controlled trial

OBJECTIVES: Clinical decision support (CDS), such as computerized alerts, improves prescribing in the setting of acute kidney injury (AKI), but considerable opportunity remains to improve patient safety. The authors sought to determine whether pharmacy surveillance of AKI patients could detect and prevent medication errors that are not corrected by automated interventions. METHODS: The authors conducted a randomized clinical trial among 396 patients admitted to an academic, tertiary care hospital between June 1, 2010 and August 31, 2010 with an acute 0.5 mg/dl change in serum creatinine over 48 hours and a nephrotoxic or renally cleared medication order. Patients randomly assigned to the intervention group received surveillance from a clinical pharmacist using a web-based surveillance tool to monitor drug prescribing and kidney function trends. CDS alerting and standard pharmacy services were active in both study arms. Outcome measures included blinded adjudication of potential adverse drug events (pADEs), adverse drug events (ADEs) and time to provider modification or discontinuation of targeted nephrotoxic or renally cleared medications. RESULTS: Potential ADEs or ADEs occurred for 104 (8.0%) of control and 99 (7.1%) of intervention patient-medication pairs (p=0.4). Additionally, the time to provider modification or discontinuation of targeted nephrotoxic or renally cleared medications did not differ between control and intervention patients (33.4 hrs vs. 30.3 hrs, p=0.3). CONCLUSIONS: Pharmacy surveillance had no incremental benefit over previously implemented CDS alerts.

Drug side effect extraction from clinical narratives of psychiatry and psychology patients

OBJECTIVE

To extract physician-asserted drug side effects from electronic medical record clinical narratives.

MATERIALS AND METHODS

Pattern matching rules were manually developed through examining keywords and expression patterns of side effects to discover an individual side effect and causative drug relationship. A combination of machine learning (C4.5) using side effect keyword features and pattern matching rules was used to extract sentences that contain side effect and causative drug pairs, enabling the system to discover most side effect occurrences. Our system was implemented as a module within the clinical Text Analysis and Knowledge Extraction System.

RESULTS

The system was tested in the domain of psychiatry and psychology. The rule-based system extracting side effects and causative drugs produced an F score of 0.80 (0.55 excluding allergy section). The hybrid system identifying side effect sentences had an F score of 0.75 (0.56 excluding allergy section) but covered more side effect and causative drug pairs than individual side effect extraction.

DISCUSSION

The rule-based system was able to identify most side effects expressed by clear indication words. More sophisticated semantic processing is required to handle complex side effect descriptions in the narrative. We demonstrated that our system can be trained to identify sentences with complex side effect descriptions that can be submitted to a human expert for further abstraction.

CONCLUSION

Our system was able to extract most physician-asserted drug side effects. It can be used in either an automated mode for side effect extraction or semi-automated mode to identify side effect sentences that can significantly simplify abstraction by a human expert.

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.

Applying ontology techniques to develop a medication history search and alert system in department of nuclear medicine

Nowadays, patients usually take more than three drugs for diseases such as hypertension, diabetes, and dyslipidemia. Hence, nuclear medicine physicians should be very careful about the medication history of each patient and ensure that their medication will not cause false positive or false negative imaging results, because either condition will interfere with adequate treatment of the patient and result in a wrong diagnosis. The aim of the present paper is to develop an ontology-based medication search and alert system for scintiphotography of Chang Gung Memorial hospital at Kaohsiung. Composed of four sub-systems, including Medication History Collect Agent (MHCA), Medication History Search System (MHSS), Patient Medication Consultation System (PMCS), and Patient Medication Alert System (PMAS), this medication search and alert system for scintiphotography is expected to support decision making of nuclear medicine examination, improve accuracy of image reading, and offer detailed data for further research. The ultimate goal of this system is to ensure patient safety.

Tailoring adverse drug event surveillance to the paediatric inpatient

Introduction

Although paediatric patients have an increased risk for adverse drug events, few detection methodologies target this population. To utilise computerised adverse event surveillance, specialised trigger rules are required to accommodate the unique needs of children. The aim was to develop new, tailored rules sustainable for review and robust enough to support aggregate event rate monitoring.

Methods

The authors utilised a voluntary staff incident-reporting system, lab values and physician insight to design trigger rules. During Phase 1, problem areas were identified by reviewing 5 years of paediatric voluntary incident reports. Based on these findings, historical lab electrolyte values were analysed to devise critical value thresholds. This evidence informed Phase 2 rule development. For 3 months, surveillance alerts were evaluated for occurrence of adverse drug events.

Results

In Phase 1, replacement preparations and total parenteral nutrition comprised the majority (36.6%) of adverse drug events in 353 paediatric patients. During Phase 2, nine new trigger rules produced 225 alerts in 103 paediatric inpatients. Of these, 14 adverse drug events were found by the paediatric hypoglycaemia rule, but all other electrolyte trigger rules were ineffective. Compared with the adult-focused hypoglycaemia rule, the new, tailored version increased the paediatric event detection rate from 0.43 to 1.51 events per 1000 patient days.

Conclusions

Relying solely on absolute lab values to detect electrolyte-related adverse drug events did not meet our goals. Use of compound rule logic improved detection of hypoglycaemia. More success may be found in designing real-time rules that leverage lab trends and additional clinical information.