Judith W. Dexheimer

Prompting clinicians about preventive care measures: a systematic review of randomized controlled trials

Preventive care measures remain underutilized despite recommendations to increase their use. The objective of this review was to examine the characteristics, types, and effects of paper- and computer-based interventions for preventive care measures. The study provides an update to a previous systematic review. We included randomized controlled trials that implemented a physician reminder and measured the effects on the frequency of providing preventive care. Of the 1,535 articles identified, 28 met inclusion criteria and were combined with the 33 studies from the previous review. The studies involved 264 preventive care interventions, 4,638 clinicians and 144,605 patients. Implementation strategies included combined paper-based with computer generated reminders in 34 studies (56%), paper-based reminders in 19 studies (31%), and fully computerized reminders in 8 studies (13%). The average increase for the three strategies in delivering preventive care measures ranged between 12% and 14%. Cardiac care and smoking cessation reminders were most effective. Computer-generated prompts were the most commonly implemented reminders. Clinician reminders are a successful approach for increasing the rates of delivering preventive care; however, their effectiveness remains modest. Despite increased implementation of electronic health records, randomized controlled trials evaluating computerized reminder systems are infrequent.

Automated clinical trial eligibility prescreening: increasing the efficiency of patient identification for clinical trials in the emergency department

Objectives (1) To develop an automated eligibility screening (ES) approach for clinical trials in an urban tertiary care pediatric emergency department (ED); (2) to assess the effectiveness of natural language processing (NLP), information extraction (IE), and machine learning (ML) techniques on real-world clinical data and trials. Data and methods We collected eligibility criteria for 13 randomly selected, disease-specific clinical trials actively enrolling patients between January 1, 2010 and August 31, 2012. In parallel, we retrospectively selected data fields including demographics, laboratory data, and clinical notes from the electronic health record (EHR) to represent profiles of all 202795 patients visiting the ED during the same period. Leveraging NLP, IE, and ML technologies, the automated ES algorithms identified patients whose profiles matched the trial criteria to reduce the pool of candidates for staff screening. The performance was validated on both a physician-generated gold standard of trial–patient matches and a reference standard of historical trial–patient enrollment decisions, where workload, mean average precision (MAP), and recall were assessed. Results Compared with the case without automation, the workload with automated ES was reduced by 92% on the gold standard set, with a MAP of 62.9%. The automated ES achieved a 450% increase in trial screening efficiency. The findings on the gold standard set were confirmed by large-scale evaluation on the reference set of trial–patient matches. Discussion and conclusion By exploiting the text of trial criteria and the content of EHRs, we demonstrated that NLP-, IE-, and ML-based automated ES could successfully identify patients for clinical trials.

A systematic review of the implementation and impact of asthma protocols

BackgroundAsthma is one of the most common childhood illnesses. Guideline-driven clinical care positively affects patient outcomes for care. There are several asthma guidelines and reminder methods for implementation to help integrate them into clinical workflow. Our goal is to determine the most prevalent method of guideline implementation; establish which methods significantly improved clinical care; and identify the factors most commonly associated with a successful and sustainable implementation.MethodsPUBMED (MEDLINE), OVID CINAHL, ISI Web of Science, and EMBASE.Study Selection: Studies were included if they evaluated an asthma protocol or prompt, evaluated an intervention, a clinical trial of a protocol implementation, and qualitative studies as part of a protocol intervention. Studies were excluded if they had non-human subjects, were studies on efficacy and effectiveness of drugs, did not include an evaluation component, studied an educational intervention only, or were a case report, survey, editorial, letter to the editor.ResultsFrom 14,478 abstracts, we included 101 full-text articles in the analysis. The most frequent study design was pre-post, followed by prospective, population based case series or consecutive case series, and randomized trials. Paper-based reminders were the most frequent with fully computerized, then computer generated, and other modalities. No study reported a decrease in health care practitioner performance or declining patient outcomes. The most common primary outcome measure was compliance with provided or prescribing guidelines, key clinical indicators such as patient outcomes or quality of life, and length of stay.ConclusionsPaper-based implementations are by far the most popular approach to implement a guideline or protocol. The number of publications on asthma protocol reminder systems is increasing. The number of computerized and computer-generated studies is also increasing. Asthma guidelines generally improved patient care and practitioner performance regardless of the implementation method.

Use of mobile devices in the emergency department: A scoping review

Electronic health records are increasingly used in regional health authorities, healthcare systems, hospitals, and clinics throughout North America. The emergency department provides care for urgent and critically ill patients. Over the past several years, emergency departments have become more computerized. Tablet computers and Smartphones are increasingly common in daily use. As part of the computerization trend, we have seen the introduction of handheld computers, tablets, and Smartphones into practice as a way of providing health professionals (e.g. physicians, nurses) with access to patient information and decision support in the emergency department. In this article, we present a scoping review and outline the current state of the research using mobile devices in the emergency departments. Our findings suggest that there is very little research evidence that supports the use of these mobile devices, and more research is needed to better understand and optimize the use of mobile devices. Given the prevalence of handheld devices, it is inevitable that more decision support, charting, and other activities will be performed on these devices. These developments have the potential to improve the quality and timeliness of care but should be thoroughly evaluated.

Feasibility and Potential Benefits of a Web-Based Intervention Delivered Acutely After Mild Traumatic Brain Injury in Adolescents: A Pilot Study.

Background:There is a paucity of evidence-based interventions for mild traumatic brain injury (mTBI). Objective:To evaluate the feasibility and potential benefits of an interactive, Web-based intervention for mTBI. Setting:Emergency department and outpatient settings. Participants:Of the 21 adolescents aged 11 to 18 years with mTBI recruited from November 2013 to June 2014 within 96 hours of injury, 13 completed the program. Design:Prospective, open pilot. Intervention:The Web-based Self-Management Activity-restriction and Relaxation Training (SMART) program incorporates anticipatory guidance and psychoeducation, self-management and pacing of cognitive and physical activities, and cognitive-behavioral principles for early management of mTBI in adolescents. Main Measures:Primary: Daily Post-Concussion Symptom Scale (PCSS). Secondary: Daily self-reported ratings of activities and satisfaction survey. Results:Average time from injury to baseline testing was 14.0 (standard deviation = 16.7) hours. Baseline PCSS was 23.6 (range: 0-46), and daily activity was 1.8 (range: 0-5.75) hours. Repeated-measures, generalized linear mixed-effects model analysis demonstrated a significant decrease of PCSS at a rate of 2.0 points per day that stabilized after about 2 weeks. Daily activities, screen time, and physical activity increased by 0.06 (standard error [SE] = 0.04, P = .09), 0.04 (SE = 0.02, P = .15), and 0.03 (SE = 0.02, P = .05) hours per day, respectively, over the 4-week follow-up. Satisfaction was rated highly by parents and youth. Conclusions:Self-Management Activity-restriction and Relaxation Training is feasible and reported to be helpful and enjoyable by participants. Future research will need to determine the comparative benefits of SMART and ideal target population.

A computerized pneumococcal vaccination reminder system in the adult emergency department

BACKGROUND Pneumococcal vaccination is an effective strategy to prevent invasive pneumococcal disease in the elderly. Emergency department (ED) visits present an underutilized opportunity to increase vaccination rates; however, designing a sustainable vaccination program in an ED is challenging. We examined whether an information technology supported approach would provide a feasible and sustainable method to increase vaccination rates in an adult ED. METHODS During a 1-year period we prospectively evaluated a team-oriented, workflow-embedded reminder system that integrated four different information systems. The computerized triage application screened all patients 65 years and older for pneumococcal vaccine eligibility with information from the electronic patient record. For eligible patients the computerized provider order entry system reminded clinicians to place a vaccination order, which was passed to the order tracking application. Documentation of vaccine administration was then added to the longitudinal electronic patient record. The primary outcome was the vaccine administration rate in the ED. Multivariate logistic regression analysis was used to estimate the odds ratios and their 95% confidence intervals, representing the overall relative risks of ED workload related variables associated with vaccination rate. RESULTS Among 3371 patients 65 years old and older screened at triage 1309 (38.8%) were up-to-date with pneumococcal vaccination and 2062 (61.2%) were eligible for vaccination. Of the eligible patients, 621 (30.1%) consented to receive the vaccination during their ED visit. Physicians received prompts for 428 (68.9%) patients. When prompted, physicians declined to order the vaccine in 192 (30.9%) patients, while 222 (10.8%) of eligible patients actually received the vaccine. The computerized reminder system increased vaccination rate from a baseline of 38.8% to 45.4%. Vaccination during the ED visit was associated younger age (OR: 0.972, CI: 0.953-0.991), Caucasian race (OR: 0.329, CI: 0.241-0.448), and longer ED boarding times (OR: 1.039, CI: 1.013-1.065). CONCLUSION The integrated informatics solution seems to be a feasible and sustainable model to increase vaccination rates in a challenging ED environment.

Preparing for International Classification of Diseases, 10th Revision, Clinical Modification implementation: strategies for maintaining an efficient workflow.

Abstract The International Classification of Diseases, 10th Revision, is required to be used by the Centers for Medicare and Medicaid Services health care billing data starting in October 2015 in the United States. The International Classification of Diseases, 10th Revision, is an update to the International Classification of Diseases, Ninth Revision, and contains approximately 70,000 codes compared with 14,000 codes. We aimed to discuss how our institution is updating the coding system in a manner that alleviates the possible burden placed on providers including more coding information required and longer load times. We performed a simulation test including testing the diagnosis calculator, timing, and how well the new and old codes mapped. We conducted a gap analysis to ensure that coding could begin in October of 2015 with minimal service interruptions. We will describe strategies and procedures to transition between systems while maintaining efficiency and helping to improve classification.

Usability evaluation of the SMART application for youth with mTBI

OBJECTIVE There is a dearth of evidence-based treatments available to address the significant morbidity associated with mild traumatic brain injury (mTBI). To address this gap, we designed a novel user-friendly, web-based application. We describe the preliminary evaluation of feasibility and usability of the application to promote recovery following mTBI in youth, the Self-Monitoring Activity-Restriction and Relaxation Treatment (SMART). SMART incorporates real-time recommendations for individualized symptom management and activity restriction along with training in cognitive-behavioral coping strategies. METHODS We conducted a usability evaluation to assess and modify the SMART system prior to further study and deployment. Children ages 11-18 years presenting to the emergency department were recruited after symptoms resolved. Usability was assessed using a 60-min think-aloud protocol of teens and parents describing their interaction with the application. Upon completion of the tasks, each participant also completed the system usability scale (SUS). RESULTS We performed tests with 4 parent/child dyads. The average age of the children was 13 years (standard deviation=1.8). The parents were an average of 41.5 years old (standard deviation=6.2). Research revealed that the participants were enthusiastic about the interactive portions of the tool particularly the video based sessions. Parents were concerned about the speed at which their child might move through the program and the children thought that the system required large amounts of reading. Based on user feedback, researchers modified SMART to include an audio file in every module and improved the systems aesthetic properties. The mean SUS score was 85, with high SUS scores (>68) indicating satisfactory usability. CONCLUSION High initial usability and favorable user feedback provide a foundation for further iterative development and testing of the SMART application as a tool for managing recovery from concussion.

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.

Will they participate? Predicting patients’ response to clinical trial invitations in a pediatric emergency department

Abstract Objective (1) To develop an automated algorithm to predict a patient’s response (ie, if the patient agrees or declines) before he/she is approached for a clinical trial invitation; (2) to assess the algorithm performance and the predictors on real-world patient recruitment data for a diverse set of clinical trials in a pediatric emergency department; and (3) to identify directions for future studies in predicting patients’ participation response. Materials and Methods We collected 3345 patients’ response to trial invitations on 18 clinical trials at one center that were actively enrolling patients between January 1, 2010 and December 31, 2012. In parallel, we retrospectively extracted demographic, socioeconomic, and clinical predictors from multiple sources to represent the patients’ profiles. Leveraging machine learning methodology, the automated algorithms predicted participation response for individual patients and identified influential features associated with their decision-making. The performance was validated on the collection of actual patient response, where precision, recall, F-measure, and area under the ROC curve were assessed. Results Compared to the random response predictor that simulated the current practice, the machine learning algorithms achieved significantly better performance (Precision/Recall/F-measure/area under the ROC curve: 70.82%/92.02%/80.04%/72.78% on 10-fold cross validation and 71.52%/92.68%/80.74%/75.74% on the test set). By analyzing the significant features output by the algorithms, the study confirmed several literature findings and identified challenges that could be mitigated to optimize recruitment. Conclusion By exploiting predictive variables from multiple sources, we demonstrated that machine learning algorithms have great potential in improving the effectiveness of the recruitment process by automatically predicting patients’ participation response to trial invitations.