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Prescription benzodiazepine overdose continues to cause significant morbidity and mortality in the US. Multiple-provider prescribing, due to either fragmented care or “doctor-shopping,” contributes to the problem. To elucidate the effect of provider professional relationships on multiple-provider prescribing of benzodiazepines, using social network analytics. A retrospective analysis of commercial healthcare claims spanning the years 2008 through 2011. Provider patient-sharing networks were modelled using social network analytics. Care team cohesion was measured using care density, defined as the ratio between the total number of patients shared by provider pairs within a patient’s care team and the total number of provider pairs in the care team. Relationships within provider pairs were further quantified using a range of network metrics, including the number and proportion of patients or collaborators shared. The relationship between patient-sharing network metrics and the likelihood of multiple prescribing of benzodiazepines. Patients between the ages of 18 and 64 years who received two or more benzodiazepine prescriptions from multiple providers, with overlapping coverage of more than 14 days. A total of 5659 patients and 1448 provider pairs were included in our study. Among these, 1028 patients (18.2 %) received multiple prescriptions of benzodiazepines, involving 445 provider pairs (30.7 %). Patients whose providers rarely shared patients had a higher risk of being prescribed overlapping benzodiazepines; the median care density was 8.1 for patients who were prescribed overlapping benzodiazepines and 10.1 for those who were not (p

Objective: To analyse patient safety events associated with England’s national program for IT (NPfIT). Methods: Retrospective analysis of all safety events managed by a dedicated IT safety team between September 2005 and November 2011 was undertaken. Events were reviewed against an existing classification for problems associated with IT. The proportion of reported events per problem type, consequences, source of report, resolution within 24 hours, time of day and day of week were examined. Sub-group analyses were undertaken for events involving patient harm and those that occurred on a large scale. Results: Of the 850 events analysed, 68% (n=574) described potentially hazardous circumstances, 24% (n=205) had an observable impact on care delivery, 4% (n=36) were a near miss, and 3% (n=22) were associated with patient harm, including three deaths (0·35%). Eleven events did not have a noticeable consequence (1%) and two were complaints (

The prevalence of asthma has increased alarmingly in the past 2 to 3 decades. Increased antibiotic use in infancy has been suggested to limit exposure to gastrointestinal microbes and to predispose to asthma in later life.To evaluate the association between antibiotic exposure during the first year of life and the development of asthma up to the age of 7 years.A retrospective population-based study of a cohort of children enrolled in a nationwide employer-provided health insurance plan from January 1, 1999, through December 31, 2006, in the United States (n = 62,576). We evaluated the association between antibiotic exposure during the first year of life and subsequent development of 3 asthma phenotypes: transient wheezing (began and resolved before 3 years of age), late-onset asthma (began after 3 years of age), and persistent asthma (began before 3 years of age and persisted through 4-7 years of age).Antibiotic use in the first year of life was associated with the development of transient wheezing (odds ratio [OR], 2.0; 95% confidence interval [CI], 1.9-2.2; P.001) and persistent asthma (OR, 1.6; 95% CI, 1.5-1.7; P.001). A dose-response effect was observed. When 5 or more antibiotic courses were received, the odds of persistent asthma doubled (OR, 1.9; 95% CI, 1.5-2.6; P.001). There is no association between antibiotic use and late-onset asthma.Antibiotic use in the first year life is associated with an increased risk of early-onset childhood asthma that began before 3 years of age. The apparent effect has a clear dose response. Heightened caution about avoiding unnecessary use of antibiotics in infants is warranted.

In the current landscape of decentralized and increasingly specialized and fragmented healthcare services, managing the quality of the handoff process is critical in ensuring continuity of patient care. This chapter explores some of the challenges with patient handoff through two case studies. In the first case, inadequate handoff led to a maternal death by postpartum hemorrhage; in the second, communication breakdown resulted in a death by overdose of analgesia. A chain of communication failures, combined with a confluence of human and system errors, cumulatively cascaded into these unfortunate events. Key problems contributing to poor patient handoff included communication failures caused by diversity of expertise and expectations and unavailability of information after transition of care. Communication problems were further exacerbated by workload and resource constraints, and the absence of guidelines and well-defined inter-team lines of responsibility. Strategies to improve handoff should include training individual care providers, as well as tackling the underlying systemic issues. These include standardizing critical clinical processes and inter-team lines of responsibility, instituting induction program to ensure new staff members are familiar with the relevant protocols, providing adequate supervision for junior staff members, better coordination of available resources, and improving information availability through information technology.

Background Ineffective communication of infection control requirements during transitions of care is a potential cause of non-compliance with infection control precautions by healthcare personnel. In this study, interventions to enhance communication during inpatient transfers between wards and radiology were implemented, in the attempt to improve adherence to precautions during transfers. Methods Two interventions were implemented, comprising (i) a pre-transfer checklist used by radiology porters to confirm a patient’s infectious status; (ii) a coloured cue to highlight written infectious status information in the transfer form. The effectiveness of the interventions in promoting adherence to standard precautions by radiology porters when transporting infectious patients was evaluated using a randomised crossover trial at a teaching hospital in Australia. Results 300 transfers were observed over a period of 4 months. Compliance with infection control precautions in the intervention groups was significantly improved relative to the control group (p

Advances in knowledge: Handover and communication errors are prevalent in medical imaging. System-wide changes that facilitate effective communication are required. Conclusion: The handover of patients and clinical information to and from medical imaging is fraught with error, often compromising patient safety and resulting in communication of delayed or wrong diagnoses, unnecessary radiation exposure and a waste of limited resources. Corrective strategies to address safety concerns related to new information technologies, patient transfer and inadequate test result notification policies are relevant to all healthcare settings. Methods: 71 search terms, related to clinical handover and communication, were applied to 3976 incidents in the Radiology Events Register. Detailed classification and thematic analysis of a subset of incidents that involved handover or communication (n5298) were undertaken to identify the most prevalent types of error and to make recommendations about patient safety initiatives in medical imaging. Objective: To determine the type and nature of incidents occurring within medical imaging settings in Australia and identify strategies that could be engaged to reduce the risk of their re-occurrence. Results: Incidents occurred most frequently during patient preparation (34%), when requesting imaging (27%) and when communicating a diagnosis (23%). Frequent problems within each of these stages of the imaging cycle included: inadequate handover of patients (41%) or unsafe or inappropriate transfer of the patient to or from medical imaging (35%); incorrect information on the request form (52%); and delayed communication of a diagnosis (36%) or communication of a wrong diagnosis (36%). Refereed/Peer-reviewed

The objective of this paper is to analyze healthcare information technology (HIT) events associated with patient harm submitted to the US Food and Drug Administration (FDA) Manufacturer and User Facility Device Experience (MAUDE) database. We examined the problems in 46 relevant events submitted to MAUDE from January 2008 to July 2010 to identify natural categories of problems from a clinical perspective. CPOE and PACS were involved in 93% of the events. Adverse events were associated with medications in 41%, clinical processes in 33%, radiation in 15% and surgery in 11%. There were four deaths. Strategies to improve the safety of HIT should focus on designing safe user interfaces, integrated checks of key identifiers and decision support, and engineering safer clinical processes.

Objective: To expand an emerging classification for problems with health information technology (HIT) using reports submitted to the US Food and Drug Administration Manufacturer and User Facility Device Experience (MAUDE) database. Design: HIT events submitted to MAUDE were retrieved using a standardized search strategy. Using an emerging classification with 32 categories of HIT problems, a subset of relevant events were iteratively analyzed to identify new categories. Two coders then independently classified the remaining events into one or more categories. Free-text descriptions were analyzed to identify the consequences of events. Measurements: Descriptive statistics by number of reported problems per category and by consequence; inter-rater reliability analysis using the k statistic for the major categories and consequences. Results: A search of 899 768 reports from January 2008 to July 2010 yielded 1100 reports about HIT. After removing duplicate and unrelated reports, 678 reports describing 436 events remained. The authors identified four new categories to describe problems with software functionality, system configuration, interface with devices, and network configuration; the authors' classification with 32 categories of HIT problems was expanded by the addition of these four categories. Examination of the 436 events revealed 712 problems, 96% were machine related, and 4% were problems at the human-computer interface. Almost half (46%) of the events related to hazardous circumstances. Of the 46 events (11%) associated with patient harm, four deaths were linked to HIT problems (0.9% of 436 events). Conclusions: Only 0.1% of the MAUDE reports searched were related to HIT. Nevertheless, Food and Drug Administration reports did prove to be a useful new source of information about the nature of software problems and their safety implications with potential to inform strategies for safe design and implementation. Refereed/Peer-reviewed

Background/Purpose: In 2009, the Childhood Arthritis and Rheumatology Research Alliance (CARRA) established a longitudinal multi-center, multiple disease U.S. national registry (CARRA Registry) for pediatric rheumatology with the intent of providing a new framework to drive observational clinical research and best practices, evidence-based care. Simultaneously, recognizing that widely variable therapeutic approaches hinder the ability to conduct meaningful comparative effectiveness studies and pragmatic trials in pediatric rheumatic diseases, CARRA investigators convened expert groups to formulate new consensus-based treatment plans (CTPs) in 5 major pediatric rheumatic disease areas. As the CTP approaches are adopted, it is important to establish baseline treatment variability across pediatric rheumatic diseases and clinical sites in the CARRA network. Using longitudinal data from the CARRA Registry, we provide a first description of variability of care across the network. Methods: We examine variations of medication usage across 55 clinical sites in the treatment of 8 rheumatic conditions, including juvenile idiopathic arthritis (JIA), SLE and mixed connective tissue disease (MCTD), juvenile dermatomyositis (JDM), localized scleroderma, systemic sclerosis, juvenile primary fibromyalgia syndrome (JPFS), sarcoidosis, and vasculitis. Management of uveitis in JIA patients was also assessed. Study participants include all CARRA registry subjects enrolled in May 2010 through December 2013. Medications were categorized into 4 major classes: biologics, DMARDs, steroids and NSAIDs. We compare the percentage of patients exposed to each medication class at each; care variations were quantified using dispersion measures of standard deviation and range. A subgroup analysis was conducted to assess care variations among the largest group of subjects with similar characteristics of and low disease activity (JIA subjects with an average active joint count of 0 to 1 averaged over the enrolment period), where treatment were hypothesized to be most similar. Results: 8,869 subjects were included in data analysis. Therapeutic approaches were highly variable for all 8 rheumatic diseases (Table 1, Fig 1). Subgroup analysis for JIA showed persistence of variability (Fig 2). Conclusion: We quantify a substantial degree of therapeutic practice variability across sites, persisting across disease-severity-matched cohorts. Although enrollment bias is a significant limitation, the magnitude of the variability for the largest cohort (JIA) and persistence across multiple diseases and subtypes supports a widespread effect. This baseline quantification and methods developed for assessing variability will support ongoing efforts to monitor new consensus treatment protocol-based standardization efforts across the CARRA network.

Objectives To explore the feasibility of using statistical text classification techniques to automatically categorise clinical incident reports. Methods Statistical text classifiers based on Naive Bayes and Support Vector Machine algorithms were trained and tested on incident reports submitted by public hospitals to identify two classes of clinical incidents: inadequate clinical handover and incorrect patient identification. Each classifier was trained on 600 reports (300 positives, 300 negatives), and tested on 372 reports (248 positives, 124 negatives). The results were evaluated using standard measures of accuracy, precision, recall, F-measure and area under curve (AUC) of receiver operating characteristics (ROC). Classifier learning rates were also evaluated, using classifier accuracy against training set size. Results All classifiers performed well in categorising clinical handover and patient identification incidents. Naive Bayes attained the best performance on handover incidents, correctly identifying 86.29% of reporter-classified incidents (precision=0.84, recall=0.90, F-measure=0.87, AUC=0.93) and 91.53% of expert-classified incidents (precision=0.87, recall=0.98, F-measure=0.92, AUC=0.97). For patient identification incidents, the best results were obtained when Support Vector Machine with radial-basis function kernel was used to classify reporter-classified reports (accuracy=97.98%, precision=0.98, recall=0.98, F-measure=0.98, AUC=1.00); and when Naive Bayes was used on expert-classified reports (accuracy=95.97%, precision=0.95, recall=0.98, F-measure=0.96, AUC=0.99). A relatively small training set was found to be adequate, with most classifiers achieving an accuracy above 80% when the training set size was as small as 100 samples. Conclusions This study demonstrates the feasibility of using text classification techniques to automatically categorise clinical incident reports.

Objectives To study the extent and execution of redundant processes during inpatient transfers to Radiology, and their impact on errors during the transfer process; to explore the use of causal and reliability analyses for modelling error detection and redundancy in the transfer process; and to provide guidance on potential system improvements. Methods A prospective observational study at a metropolitan teaching hospital. 101 patient transfers to Radiology were observed over a 6-month period, and errors in patient transfer process were recorded. Fault Tree Analysis was used to model error paths and identify redundant steps. Reliability Analysis was used to quantify system reliability. Results 420 errors were noted, an average of four errors per transfer. No incidents of patient harm were recorded. Inadequate handover was the most common error (43.1%), followed by failure to perform patient identification checks (41.9%), patient inadequately prepared for transfer (7.4%), inadequate infection control precautions (2.9%), inadequate clinical escort (2.1%), inadequate transport vehicle (2.1%) and equipment failure (0.2%). Four redundant steps for communicating patients' infectious status were identified (reliability=0.07, 0.37, 0.26, 0.31). Collectively, these yielded a system reliability of 0.7. The low reliability of each individual step was due to its low rate of execution. Conclusions Analysis of the transfer process revealed a number of redundancies that safeguard against transfer errors. However, they were relatively ineffective in preventing errors, due to the poor compliance rate. Thus, the authors advocate increasing compliance to existing redundant processes as an improvement strategy, before investing resources on new processes.

Objective To analyze patient safety incidents associated with computer use to develop the basis for a classification of problems reported by health professionals. Design Incidents submitted to a voluntary incident reporting database across one Australian state were retrieved and a subset (25%) was analyzed to identify ‘natural categories’ for classification. Two coders independently classified the remaining incidents into one or more categories. Free text descriptions were analyzed to identify contributing factors. Where available medical specialty, time of day and consequences were examined. Measurements Descriptive statistics; inter-rater reliability. Results A search of 42 616 incidents from 2003 to 2005 yielded 123 computer related incidents. After removing duplicate and unrelated incidents, 99 incidents describing 117 problems remained. A classification with 32 types of computer use problems was developed. Problems were grouped into information input (31%), transfer (20%), output (20%) and general technical (24%). Overall, 55% of problems were machine related and 45% were attributed to human–computer interaction. Delays in initiating and completing clinical tasks were a major consequence of machine related problems (70%) whereas rework was a major consequence of human–computer interaction problems (78%). While 38% (n=26) of the incidents were reported to have a noticeable consequence but no harm, 34% (n=23) had no noticeable consequence. Conclusion Only 0.2% of all incidents reported were computer related. Further work is required to expand our classification using incident reports and other sources of information about healthcare IT problems. Evidence based user interface design must focus on the safe entry and retrieval of clinical information and support users in detecting and correcting errors and malfunctions.

Erratum to: J Gen Intern Med DOI: 10.1007/s11606-015-3470-8 The odds ratio for the relationship between median care density and multiple prescribing of benzodiazepine was incorrectly reported in the text as 0.76 (95% CI 0.60 – 0.95; p=0.028). It should have read, “Backward stepwise logistic regression revealed that median care density was negatively associated with the risk of an overlapping benzodiazepine prescription (OR 0.76; 95% CI 0.60 – 0.95; p=0.0178) after adjusting for covariates (Table 3).” In the Discussion, it should have also read the following: “Consistent with these studies, in univariate analysis, we showed a significant relationship between the number of prescribers and overlapping benzodiazepine prescriptions; however, the magnitude of the association was reduced when considered alongside care density in multivariate analysis.”

Epilepsy is a debilitating condition, often with neither a known etiology nor an effective treatment. Autoimmune mechanisms have been increasingly identified.To conduct a population-level study investigating the relationship between epilepsy and several common autoimmune diseases.A retrospective population-based study using claims from a nationwide employer-provided health insurance plan in the United States. Participants were beneficiaries enrolled between 1999 and 2006 (N = 2 518 034).We examined the relationship between epilepsy and 12 autoimmune diseases: type 1 diabetes mellitus, psoriasis, rheumatoid arthritis, Graves disease, Hashimoto thyroiditis, Crohn disease, ulcerative colitis, systemic lupus erythematosus, antiphospholipid syndrome, Sjögren syndrome, myasthenia gravis, and celiac disease.The risk of epilepsy was significantly heightened among patients with autoimmune diseases (odds ratio, 3.8; 95% CI, 3.6-4.0; P .001) and was especially pronounced in children (5.2; 4.1-6.5; P .001). Elevated risk was consistently observed across all 12 autoimmune diseases.Epilepsy and autoimmune disease frequently co-occur; patients with either condition should undergo surveillance for the other. The potential role of autoimmunity must be given due consideration in epilepsy so that we are not overlooking a treatable cause.

Included systematic review conclusions in detail. (PDF 774 kb)

Included systematic review conclusions in detail. (PDF 774 kb)

Age group ranges. Age group ranges identified in the systematic reviews and their classification in the analysis. Shaded regions represent the age groups used in the analysis (orange rectangles) and the set of all age groups identified in the systematic reviews are labelled by their reference number and age range (grey rectangles). (PDF 374 kb)

Associations between systematic review characteristics and conclusions in 22 conclusions of studies that did not specify age group or specified all ages. (PDF 193 kb)

Authorsâ professional roles; (C: clinican; N: non-clinician; G: group; triangles indicate the corresponding author). (PDF 602 kb)

Systematic review conclusion and recommendation statements. (PDF 433 kb)

Associations between systematic review characteristics and conclusions in 10 conclusions of studies that included women aged up to 49Â years. (PDF 193 kb)

Age group ranges. Age group ranges identified in the systematic reviews and their classification in the analysis. Shaded regions represent the age groups used in the analysis (orange rectangles) and the set of all age groups identified in the systematic reviews are labelled by their reference number and age range (grey rectangles). (PDF 374 kb)

Included systematic reviews. References for all included systematic reviews. (PDF 229 kb)

Included systematic reviews. References for all included systematic reviews. (PDF 229 kb)

Associations between systematic review characteristics and conclusions in 5 conclusions of studies that included women aged 70Â years and older. (PDF 193 kb)

Associations between systematic review characteristics and conclusions in 10 conclusions of studies that included women aged up to 49Â years. (PDF 193 kb)

Systematic review conclusion and recommendation statements. (PDF 433 kb)

Associations between systematic review characteristics and conclusions in 22 conclusions of studies that did not specify age group or specified all ages. (PDF 193 kb)

Authorsâ professional roles; (C: clinican; N: non-clinician; G: group; triangles indicate the corresponding author). (PDF 602 kb)

Associations between systematic review characteristics and conclusions in 5 conclusions of studies that included women aged 70Â years and older. (PDF 193 kb)