Learning and happiness during Covid-19 school closure in urban Malaysia.

COVID-19 school closure has disrupted education systems globally raising concerns over learning time loss. At the same time, social isolation at home has seen a decline in happiness level among young learners. Understanding the link between cognitive effort and emotional wellbeing is important for post-pandemic learning recovery interventions particularly if there is a feedback loop from happiness to learning. In this context, we use primary survey data collected during the first school closure in urban Malaysia to study the complex association between learning loss and student happiness. Machine learning methods are used to accommodate the multi-dimensional and interaction effects between the covariates that influence this association. Empirically, we find that the most important covariates are student gender, social economic status (SES) proxied by the number of books ownership, time spent on play and religious activity. Based on the results, we develop a conceptual framework of learning continuity by formalizing the importance of investment in emotional wellbeing.

Implementation of a Clinical Decision Support System for Children With Minor Blunt Head Trauma Who Are at Nonnegligible Risk for Traumatic Brain Injuries.

STUDY OBJECTIVE: To determine the effect of providing risk estimates of clinically important traumatic brain injuries and management recommendations on emergency department (ED) outcomes for children with isolated intermediate Pediatric Emergency Care Applied Research Network clinically important traumatic brain injury risk factors. METHODS: This was a secondary analysis of a nonrandomized clinical trial with concurrent controls, conducted at 5 pediatric and 8 general EDs between November 2011 and June 2014, enrolling patients younger than 18 years who had minor blunt head trauma. After a baseline period, intervention sites received electronic clinical decision support providing patient-level clinically important traumatic brain injury risk estimates and management recommendations. The following primary outcomes in patients with one intermediate Pediatric Emergency Care Applied Research Network risk factor were compared before and after clinical decision support: proportion of ED computed tomography (CT) scans, adjusted for age, time trend, and site; and prevalence of clinically important traumatic brain injuries. RESULTS: The risk of clinically important traumatic brain injuries was known for 3,859 children with isolated findings (1,711 at intervention sites before clinical decision support, 1,702 at intervention sites after clinical decision support, and 446 at control sites). In this group, pooled CT proportion decreased from 24.2% to 21.6% after clinical decision support (odds ratio 0.86; 95% confidence interval 0.73 to 1.01). Decreases in CT use were noted across intervention EDs, but not in controls. The pooled adjusted odds ratio for CT use after clinical decision support was 0.73 (95% confidence interval 0.60 to 0.88). Among the entire cohort, clinically important traumatic brain injury was diagnosed at the index ED visit for 37 of 37 (100%) patients before clinical decision support and 32 of 33 patients (97.0%) after clinical decision support. CONCLUSION: Providing specific risks of clinically important traumatic brain injury through electronic clinical decision support was associated with a modest and safe decrease in ED CT use for children at nonnegligible risk of clinically important traumatic brain injuries.

Characterizing workflow for pediatric asthma patients in emergency departments using electronic health records.

OBJECTIVE: The purpose of this study was to describe a workflow analysis approach and apply it in emergency departments (EDs) using data extracted from the electronic health record (EHR) system. MATERIALS AND METHODS: We used data that were obtained during 2013 from the ED of a children's hospital and its four satellite EDs. Workflow-related data were extracted for all patient visits with either a primary or secondary diagnosis on discharge of asthma (ICD-9 code=493). For each patient visit, eight different a priori time-stamped events were identified. Data were also collected on mode of arrival, patient demographics, triage score (i.e. acuity level), and primary/secondary diagnosis. Comparison groups were by acuity levels 2 and 3 with 2 being more acute than 3, arrival mode (ambulance versus walk-in), and site. Data were analyzed using a visualization method and Markov Chains. RESULTS: To demonstrate the viability and benefit of the approach, patient care workflows were visually and quantitatively compared. The analysis of the EHR data allowed for exploration of workflow patterns and variation across groups. Results suggest that workflow was different for different arrival modes, settings and acuity levels. DISCUSSION: EHRs can be used to explore workflow with statistical and visual analytics techniques novel to the health care setting. The results generated by the proposed approach could be utilized to help institutions identify workflow issues, plan for varied workflows and ultimately improve efficiency in caring for diverse patient groups. CONCLUSION: EHR data and novel analytic techniques in health care can expand our understanding of workflow in both large and small ED units.

Informing the design of clinical decision support services for evaluation of children with minor blunt head trauma in the emergency department: a sociotechnical analysis.

Integration of clinical decision support services (CDSS) into electronic health records (EHRs) may be integral to widespread dissemination and use of clinical prediction rules in the emergency department (ED). However, the best way to design such services to maximize their usefulness in such a complex setting is poorly understood. We conducted a multi-site cross-sectional qualitative study whose aim was to describe the sociotechnical environment in the ED to inform the design of a CDSS intervention to implement the Pediatric Emergency Care Applied Research Network (PECARN) clinical prediction rules for children with minor blunt head trauma. Informed by a sociotechnical model consisting of eight dimensions, we conducted focus groups, individual interviews and workflow observations in 11 EDs, of which 5 were located in academic medical centers and 6 were in community hospitals. A total of 126 ED clinicians, information technology specialists, and administrators participated. We clustered data into 19 categories of sociotechnical factors through a process of thematic analysis and subsequently organized the categories into a sociotechnical matrix consisting of three high-level sociotechnical dimensions (workflow and communication, organizational factors, human factors) and three themes (interdisciplinary assessment processes, clinical practices related to prediction rules, EHR as a decision support tool). Design challenges that emerged from the analysis included the need to use structured data fields to support data capture and re-use while maintaining efficient care processes, supporting interdisciplinary communication, and facilitating family-clinician interaction for decision-making.

Use of Traumatic Brain Injury Prediction Rules With Clinical Decision Support.

OBJECTIVES: We determined whether implementing the Pediatric Emergency Care Applied Research Network (PECARN) traumatic brain injury (TBI) prediction rules and providing risks of clinically important TBIs (ciTBIs) with computerized clinical decision support (CDS) reduces computed tomography (CT) use for children with minor head trauma. METHODS: Nonrandomized trial with concurrent controls at 5 pediatric emergency departments (PEDs) and 8 general EDs (GEDs) between November 2011 and June 2014. Patients were <18 years old with minor blunt head trauma. Intervention sites received CDS with CT recommendations and risks of ciTBI, both for patients at very low risk of ciTBI (no Pediatric Emergency Care Applied Research Network rule factors) and those not at very low risk. The primary outcome was the rate of CT, analyzed by site, controlling for time trend. RESULTS: We analyzed 16 635 intervention and 2394 control patients. Adjusted for time trends, CT rates decreased significantly (P < .05) but modestly (2.3%-3.7%) at 2 of 4 intervention PEDs for children at very low risk. The other 2 PEDs had small (0.8%-1.5%) nonsignificant decreases. CT rates did not decrease consistently at the intervention GEDs, with low baseline CT rates (2.1%-4.0%) in those at very low risk. The control PED had little change in CT use in similar children (from 1.6% to 2.9%); the control GED showed a decrease in the CT rate (from 7.1% to 2.6%). For all children with minor head trauma, intervention sites had small decreases in CT rates (1.7%-6.2%). CONCLUSIONS: The implementation of TBI prediction rules and provision of risks of ciTBIs by using CDS was associated with modest, safe, but variable decreases in CT use. However, some secular trends were also noted.

Clinical Decision Support for a Multicenter Trial of Pediatric Head Trauma: Development, Implementation, and Lessons Learned.

INTRODUCTION: For children who present to emergency departments (EDs) due to blunt head trauma, ED clinicians must decide who requires computed tomography (CT) scanning to evaluate for traumatic brain injury (TBI). The Pediatric Emergency Care Applied Research Network (PECARN) derived and validated two age-based prediction rules to identify children at very low risk of clinically-important traumatic brain injuries (ciTBIs) who do not typically require CT scans. In this case report, we describe the strategy used to implement the PECARN TBI prediction rules via electronic health record (EHR) clinical decision support (CDS) as the intervention in a multicenter clinical trial. METHODS: Thirteen EDs participated in this trial. The 10 sites receiving the CDS intervention used the Epic(®) EHR. All sites implementing EHR-based CDS built the rules by using the vendor's CDS engine. Based on a sociotechnical analysis, we designed the CDS so that recommendations could be displayed immediately after any provider entered prediction rule data. One central site developed and tested the intervention package to be exported to other sites. The intervention package included a clinical trial alert, an electronic data collection form, the CDS rules and the format for recommendations. RESULTS: The original PECARN head trauma prediction rules were derived from physician documentation while this pragmatic trial led each site to customize their workflows and allow multiple different providers to complete the head trauma assessments. These differences in workflows led to varying completion rates across sites as well as differences in the types of providers completing the electronic data form. Site variation in internal change management processes made it challenging to maintain the same rigor across all sites. This led to downstream effects when data reports were developed. CONCLUSIONS: The process of a centralized build and export of a CDS system in one commercial EHR system successfully supported a multicenter clinical trial.

Use of a remote clinical decision support service for a multicenter trial to implement prediction rules for children with minor blunt head trauma.

OBJECTIVE: To evaluate the architecture, integration requirements, and execution characteristics of a remote clinical decision support (CDS) service used in a multicenter clinical trial. The trial tested the efficacy of implementing brain injury prediction rules for children with minor blunt head trauma. MATERIALS AND METHODS: We integrated the Epic(®) electronic health record (EHR) with the Enterprise Clinical Rules Service (ECRS), a web-based CDS service, at two emergency departments. Patterns of CDS review included either a delayed, near-real-time review, where the physician viewed CDS recommendations generated by the nursing assessment, or a real-time review, where the physician viewed recommendations generated by their own documentation. A backstopping, vendor-based CDS triggered with zero delay when no recommendation was available in the EHR from the web-service. We assessed the execution characteristics of the integrated system and the source of the generated recommendations viewed by physicians. RESULTS: The ECRS mean execution time was 0.74 ±0.72 s. Overall execution time was substantially different at the two sites, with mean total transaction times of 19.67 and 3.99 s. Of 1930 analyzed transactions from the two sites, 60% (310/521) of all physician documentation-initiated recommendations and 99% (1390/1409) of all nurse documentation-initiated recommendations originated from the remote web service. DISCUSSION: The remote CDS system was the source of recommendations in more than half of the real-time cases and virtually all the near-real-time cases. Comparisons are limited by allowable variation in user workflow and resolution of the EHR clock. CONCLUSION: With maturation and adoption of standards for CDS services, remote CDS shows promise to decrease time-to-trial for multicenter evaluations of candidate decision support interventions.

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.

A trigger tool to detect harm in pediatric inpatient settings.

OBJECTIVES: An efficient and reliable process for measuring harm due to medical care is needed to advance pediatric patient safety. Several pediatric studies have assessed the use of trigger tools in varying inpatient environments. Using the Institute for Healthcare Improvement's adult-focused Global Trigger Tool as a model, we developed and pilot tested a trigger tool that would identify the most common causes of harm in pediatric inpatient environments. METHODS: After formal training, 6 academic children's hospitals used this novel pediatric trigger tool to review 100 randomly selected inpatient records per site from patients discharged during the month of February 2012. RESULTS: From the 600 patient charts evaluated, 240 harmful events ("harms") were identified, resulting in a rate of 40 harms per 100 patients admitted and 54.9 harms per 1000 patient days across the 6 hospitals. At least 1 harm was identified in 146 patients (24.3% of patients). Of the 240 total events, 108 (45.0%) were assessed to have been potentially or definitely preventable. The most common patient harms were intravenous catheter infiltrations/burns, respiratory distress, constipation, pain, and surgical complications. CONCLUSIONS: Consistent with earlier rates of all-cause harm in adult hospitals, harm occurs at high rates in hospitalized children. Availability and use of an all-cause harm identification tool will establish the epidemiology of harm and will provide a consistent approach to assessing the effect of interventions on harms in hospitalized children.

A quality improvement study to improve inpatient problem list use.

BACKGROUND: The problem list is a meaningful use incentivized criterion, and >80% of patients should have 1 problem entered as structured data. OBJECTIVE: The aim of the present study was to use a series of interventions to increase the use of the problem list for inpatients to >80% as measured by at least 1 hospital problem at discharge. METHODS: This study was a quasi-experimental time series quality improvement trial. The primary outcome was 80% of medical and psychiatric inpatients with a problem added to the problem list before discharge. Control charts of percentage (p charts) of medical and psychiatric patients with an inpatient problem list at discharge were constructed with three-σ control limits. Control limits were revised after evidence of improvement. The charts were annotated with interventions, including increasing awareness, focused education, and timely feedback in the form of performance graphs e-mailed to providers. RESULTS: For medical inpatients, use rose from 31% to 97% at its peak in April 2011 and continues to maintain above the goal of 80%. In psychiatry, problem list use rose from 2% initially to an average of 72% after the interventions. CONCLUSIONS: Significant gains were made with inpatient problem list usage by the medical and psychiatric teams. Our goal ascribed by meaningful use for >80% of inpatients to have a problem at discharge was met after initiation of our series of interventions.

Assessing the similarity of surface linguistic features related to epilepsy across pediatric hospitals.

OBJECTIVE: The constant progress in computational linguistic methods provides amazing opportunities for discovering information in clinical text and enables the clinical scientist to explore novel approaches to care. However, these new approaches need evaluation. We describe an automated system to compare descriptions of epilepsy patients at three different organizations: Cincinnati Children's Hospital, the Children's Hospital Colorado, and the Children's Hospital of Philadelphia. To our knowledge, there have been no similar previous studies. MATERIALS AND METHODS: In this work, a support vector machine (SVM)-based natural language processing (NLP) algorithm is trained to classify epilepsy progress notes as belonging to a patient with a specific type of epilepsy from a particular hospital. The same SVM is then used to classify notes from another hospital. Our null hypothesis is that an NLP algorithm cannot be trained using epilepsy-specific notes from one hospital and subsequently used to classify notes from another hospital better than a random baseline classifier. The hypothesis is tested using epilepsy progress notes from the three hospitals. RESULTS: We are able to reject the null hypothesis at the 95% level. It is also found that classification was improved by including notes from a second hospital in the SVM training sample. DISCUSSION AND CONCLUSION: With a reasonably uniform epilepsy vocabulary and an NLP-based algorithm able to use this uniformity to classify epilepsy progress notes across different hospitals, we can pursue automated comparisons of patient conditions, treatments, and diagnoses across different healthcare settings.

Sustaining and spreading the reduction of adverse drug events in a multicenter collaborative.

OBJECTIVES: Adverse drug events (ADEs) occur more frequently in pediatric patients than adults. ADEs frequently cause serious harm to children and increase the cost of care. The purpose of this study was to decrease ADEs by targeting the entire medication-delivery system for all high-risk medications. METHODS: Thirteen freestanding children's hospitals participated in this ADE collaborative. An advisory panel developed a change package of interventions that consisted of standardization of medication-ordering (eg, consensus-based protocols and order sets and high-alert medication protocols), reliable medication-dispensing processes (eg, automated dispensing cabinets and redesign of floor stock procedures), reliable medication-administration processes (eg, safe pump use and reducing interruptions), improvement of patient safety culture (eg, safety-culture changes and reduction of staff intimidation), and clinical decision support (eg, increase ADE detection and redesign care systems). ADE rates were compared from the 3-month baseline period to quarters of the 12-month intervention phase. ADE rates were categorized further as opioid related and other medication related. RESULTS: From baseline to the final quarter, the collaborative resulted in a 42% decrease in total ADEs, a 51% decrease in opioid-related ADEs, and a 41% decrease in other medication ADEs. CONCLUSION: A pediatric collaborative that targeted the medication-delivery system decreased the rate of ADEs at participating institutions.