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.

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.

SummaryThe Australian Incident Monitoring Study database was examined for incidents involving inadequate pre-operative patient preparation and/or evaluation. Of 6271 reports, 727 had appropriate keywords, of which 197 (3.1%) were used for subsequent analysis. All surgical categories were represented. In 10% of reports the patient was not reviewed pre-operatively by an anaesthetist, whilst in 23% the anaesthetist involved in the operating theatre had not performed the pre-operative assessment. Death followed in seven cases, major morbidity in 23 cases, admission to a high-dependency unit or intensive care unit in 17 cases, and surgery was cancelled in nine cases. Poor airway assessment, communication problems and inadequate evaluation were the most common contributing factors. Respondents indicated that the incident was preventable in 57% of cases. Proposed corrective strategies include improved communication, quality assurance activities, development of protocols and additional training. A structured assessment of the airway, along with improvements in information exchange, patient assessment, and use of clearly defined patient management plans and pathways would prevent most of the incidents reported. It is essential that all patients undergoing anaesthesia have a pre-operative assessment and management plan. The traditional method of pre-operative preparation involves reviewing the patient the night before surgery, examining the results of investigations ordered by the surgical house officers and ordering premedicant drugs. Evolving anaesthetic practices along with financial constraints have changed this pre-operative process with the advent of dedicated pre-operative clinics staffed by other professionals and an increasing focus on day surgery and day of surgery admission. This has rationalised pre-operative investigations, reduced direct anaesthetist±patient contact prior to surgery and increased reliance on anaesthetists assessing patients for other colleagues.

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Clinical Health Act accelerated the adoption of electronic health records (EHRs) with providers and hospitals, who can claim incentive monies related to meaningful use. Despite the increase in adoption of commercial EHRs in pediatric settings, there has been little support for EHR tools and functionalities that promote pediatric quality improvement and patient safety, and children remain at higher risk than adults for medical errors in inpatient environments. Health information technology (HIT) tailored to the needs of pediatric health care providers can improve care by reducing the likelihood of errors through information assurance and minimizing the harm that results from errors. This technical report outlines pediatric-specific concepts, child health needs and their data elements, and required functionalities in inpatient clinical information systems that may be missing in adult-oriented HIT systems with negative consequences for pediatric inpatient care. It is imperative that inpatient (and outpatient) HIT systems be adapted to improve their ability to properly support safe health care delivery for children.

ObjectiveThe 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 methodsIn 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.ResultsWe 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 conclusionWith 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.

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.

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