A Quality Improvement Bundle Including Pay for Performance for the Standardization of Order Set Use in Moderate Asthma.

OBJECTIVE: In order to standardize use of our hospital's computerized asthma order set, which was developed based on an asthma clinical practice guideline, for moderately ill children presenting for care of asthma, we developed a quality improvement bundle, including a time-limited pay-for-performance component, for pediatric emergency department and pediatric urgent care faculty members. METHODS: Following baseline measurement, we used a run-in period for education, feedback, and improvement of the asthma order set. Then, faculty members earned 0.1% of salary during each of 10 successive months (evaluation period) in which the asthma order set was used in managing 90% or more of eligible patients. RESULTS: At baseline, the asthma order set was used in managing 60.5% of eligible patients. Order set use rose sharply during the run-in period. During the 10-month evaluation period, use of the asthma order set was significantly above baseline, with a mean of 91.6%; faculty earned pay-for-performance bonuses during 8 of 10 possible months. Following completion of the evaluation period, asthma order set use remained high. CONCLUSIONS: A quality improvement bundle, including a time-limited pay-for-performance component, was associated with a sustained increase in the use of a computerized asthma order set for managing moderately ill asthmatic children.

Improving medication-related clinical decision support.

PURPOSE: Current uses of medication-related clinical decision support (CDS) and recommendations for improving these systems are reviewed. SUMMARY: Using a systematic approach, articles published from 2007 through 2014 were identified in MEDLINE and EMBASE using MeSH terms and keywords relating to the 5 basic medication-related CDS functionalities. A total of 156 full-text articles and 28 conference abstracts were reviewed across each of the 5 areas: drug-drug interaction (DDI) checks (n = 78), drug allergy checks (n = 20), drug dose support (n = 55), drug duplication checks (n = 11), and drug formulary support (n = 20). The success of medication-related CDS depends on users finding the alerts valuable and acting on the information received. Improving alert specificity and sensitivity is important for all domains. Tiering is important for improving the acceptance of DDI alerts. The ability to perform appropriate cross-sensitivity checks is key to producing appropriate drug allergy checks. Drug dosage alerts should be individualized and deliver practical recommendations. How the system is configured to identify certain drug duplications is important to prevent possible patient toxicity. Accurate knowledge databases are needed to produce relevant drug formulary alerts and encourage formulary adherence. Medication-related CDS is still relatively immature in some organizations and has substantial room for improvement. For example, decision support should consider more patient-specific factors, human factors principles should always be considered, and alert specificity must be improved in order to reduce alert fatigue. CONCLUSION: Standardization, integration of patient-specific parameters, and consideration of human factors design principles are central to realizing the potential benefits of medication-related CDS.

Impact of Sustained Use of a Multifaceted Computerized Quality Improvement Intervention for Cardiovascular Disease Management in Australian Primary Health Care.

BACKGROUND: We evaluated a multifaceted, computerized quality improvement intervention for management of cardiovascular disease (CVD) risk in Australian primary health care. After completion of a cluster randomized controlled trial, the intervention was made available to both trial arms. Our objective was to assess intervention outcomes in the post-trial period and any heterogeneity based on original intervention allocation. METHODS AND RESULTS: Data from 41 health services were analyzed. Outcomes were (1) proportion of eligible population with guideline-recommended CVD risk factor measurements; and (2) the proportion at high CVD risk with current prescriptions for guideline-recommended medications. Patient-level analyses were conducted using generalized estimating equations to account for clustering and time effects and tests for heterogeneity were conducted to assess impact of original treatment allocation. Median follow-up for 22 809 patients (mean age, 64.2 years; 42.5% men, 26.5% high CVD risk) was 17.9 months post-trial and 35 months since trial inception. At the end of the post-trial period there was no change in CVD risk factor screening overall when compared with the end of the trial period (64.7% versus 63.5%, P=0.17). For patients at high CVD risk, there were significant improvements in recommended prescriptions at end of the post-trial period when compared with the end of the trial period (65.2% versus 56.0%, P<0.001). There was no heterogeneity of treatment effects on the outcomes based on original randomization allocation. CONCLUSIONS: CVD risk screening improvements were not observed in the post-trial period. Conversely, improvements in prescribing continued, suggesting that changes in provider and patient actions may take time when initiating medications. CLINICAL TRIAL REGISTRATION: URL: http://www.anzctr.org.au. Unique identifier: 12611000478910.

USE OF A COMPUTER-GUIDED GLUCOSE MANAGEMENT SYSTEM TO IMPROVE GLYCEMIC CONTROL AND ADDRESS NATIONAL QUALITY MEASURES: A 7-YEAR, RETROSPECTIVE OBSERVATIONAL STUDY AT A TERTIARY CARE TEACHING HOSPITAL.

OBJECTIVE: Inpatient hyperglycemia, hypoglycemia, and glucose variability are associated with increased mortality. The use of an electronic glucose management system (eGMS) to guide intravenous (IV) insulin infusion has been found to significantly improve blood glucose (BG) control. This retrospective observational study evaluated the 7-year (January 2009-December 2015) impact of the EndoTool® eGMS in intensive and intermediate units at Vidant Medical Center, a 900-bed tertiary teaching hospital. METHODS: Patients assigned to eGMS had indications for IV insulin infusion, including uncontrolled diabetes, stress hyperglycemia, and/or postoperative BG levels >140 mg/dL. This study evaluated time required to achieve BG control (<180 mg/dL; <140 mg/dL for cardiovascular surgery patients); hypoglycemia incidence (<70 and <40 mg/dL); glucose variability (assessed by SD and coefficient of variation percentage [CV%]); excursions (BG levels >180 mg/dL after control attained); and the impact of eGMS on hospital-acquired condition (HAC)-8 rates. RESULTS: Data were available for all treated patients (492,078 BG readings from 16,850 patients). With eGMS, BG levels were brought to target within 1.5 to 2.3 hours (4.5 to 4.8 hours for cardiovascular patients). Minimal hypoglycemia was observed (BG values <70 mg/dL, 0.93%; <40 mg/dL, 0.03%), and analysis of variance of BG values <70 mg/dL showed significant reductions over time in hypoglycemia frequency, from 1.04% in 2009 to 0.46% in 2015 (P<.0001). The CV% per patient visit was 26.5 (±12.9)%, and 4% of patients experienced glucose excursions (defined as BG levels >180 mg/dL once control was attained). HAC-8 rates were reduced from 0.083 per 1,000 patients (2008) to 0.032 per 1,000 patients (2011). CONCLUSION: The use of eGMS resulted in rapid, effective control of inpatient BG levels, including significantly reduced hypoglycemia rates. ABBREVIATIONS: BG = blood glucose CMS = Centers for Medicare and Medicaid Services CV = coefficient of variation CV% = coefficient of variation percentage eGMS = electronic glucose management system GV = glycemic variability HAC = Hospital-Acquired Condition ICU = intensive care unit IU = intermediate unit IV = intravenous LOS = length of stay VMC = Vidant Medical Center.

Percepción de los médicos de atención primaria sobre la receta electrónica en el Servicio Madrileño de Salud / Perceptions on electronic prescribing by primary care physicians in madrid healthcare service

Objetivo. Conocer la opinión de los médicos de atención primaria sobre la receta electrónica. Material y métodos. Estudio descriptivo mediante encuesta enviada a 527 médicos de atención primaria. Periodo: junio de 2014. El cuestionario incluía preguntas cerradas sobre el interés despertado, la satisfacción, las ventajas, las debilidades y las barreras y una pregunta abierta sobre las dificultades, todas ellas referidas a la receta electrónica. La satisfacción se midió en una escala de 1-10 y las ventajas, las debilidades y las barreras se valoraron mediante una escala tipo Likert de 5 ítems. El interés se midió mediante los dos métodos. El cuestionario se envió por correo electrónico para su cumplimentación online a través de la herramienta Google Drive®. Se realizó un análisis estadístico descriptivo. Resultados. Se obtuvo una tasa de respuesta del 47% (248/527). El interés manifestado fue de 8,7 (IC95%; 8,5-8,9) y la satisfacción de 7,9 (IC95%; 7,7-8). El 87,9% (IC95%; 83,8-92) utilizaban receta electrónica siempre que podían. Las ventajas mejor valoradas fueron: un 73,4% (IC95%; 67,8-78,9%) opinaron que facilitaba la revisión del tratamiento y un 59,3% (IC95%; 53,1-65,4%) que disminuía la carga burocrática. Entre las debilidades observadas destacaron las siguientes: el 87,9% (IC95%; 83,8-92%) creía que los médicos de atención especializada también deberían poder utilizar la receta electrónica. En relación con las barreras, el 30,2% (IC 95%; 24,5-36%) manifestaron que incorporar a un paciente al sistema de receta electrónica llevaba demasiado tiempo y el 4% (IC 95%; 1,6-6,5%) opinaba que la herramienta informática era difícil de utilizar. Conclusiones. Los médicos muestran un interés notable en utilizar receta electrónica y una alta satisfacción con el funcionamiento de la herramienta (AU)

Objective. To investigate the opinion of Primary Care physicians regarding electronic prescribing. Methods. Descriptive study by means of a questionnaire sent to 527 primary care physicians. Period: June 2014. The questionnaire included closed questions about interest shown, satisfaction, benefits, weaknesses, and barriers, and one open question about difficulties, all of them referred to electronic prescribing. Satisfaction was measured using 1-10 scale, and benefits, weaknesses, and barriers were evaluated by a 5-ítems Likert scale. Interest was measured using both methods. The questionnaire was sent by e-mail for on line response through Google Drive® tool. A descriptive statistical analysis was performed. Results. The response rate was 47% (248/527). Interest shown was 8.7 (95% CI; 8.5-8.9) and satisfaction was 7.9 (95% CI; 7.8-8). The great majority 87.9% (95% CI; 83.8-92%) of respondents used electronic prescribing where possible. Most reported benefits were: 73.4% (95% CI; 67.8-78.9%) of respondents considered that electronic prescribing facilitated medication review, and 59.3% (95% CI; 53.1-65.4) of them felt that it reduced bureaucratic burden. Among the observed weaknesses, they highlighted the following: 87.9% (95% CI; 83.8-92%) of respondents believed specialist care physicians should also be able to use electronic prescribing. Concerning to barriers: 30.2% (95% CI; 24.5-36%) of respondents think that entering a patient into the electronic prescribing system takes too much time, and 4% (95% CI; 1.6-6.5%) of them perceived the application as difficult to use. Conclusions. Physicians showed a notable interest in using electronic prescribing and high satisfaction with the application performance (AU)

Evaluation of an updated insulin infusion protocol at a large academic medical center.

PURPOSE: The performance of an updated insulin infusion protocol was evaluated at a large, academic medical center. METHODS: A retrospective medical record review was performed after a one-month run-in period for all patients at a large, academic, tertiary care medical center in whom the insulin infusion per protocol was administered from January 1 through February 28, 2014. Data were evaluated to determine the median blood glucose (BG) level, time to achieve BG in the target range, number of BG checks per patient per day, time elapsed between each BG check, and the frequency of hypoglycemia (BG concentration of ≤70 mg/dL). RESULTS: A total of 170 patients were included. The median preinfusion BG was 244 mg/dL (interquartile range [IQR], 204-304 mg/dL), which decreased to a median of 168 mg/dL (IQR, 147.5-199.5 mg/dL) when the protocol was utilized. However, 70 patients (41%) had a median BG concentration of ≥180 mg/dL, and 25 patients' (15%) BG value remained above 180 mg/dL. The median time to achieve the goal BG value was 4.2 hours (95% confidence interval, 3.2-5.1 hours). BG checks were performed a median of every 2.1 hours (IQR, 1.4-2.3 hours). Hypoglycemia was rare, occurring in only 2 (1.2%) patients. CONCLUSION: The median BG with an updated insulin infusion protocol approached the upper limit of the target BG range, and 41% of patients had a median BG above the goal range. Protocol specifications for the frequency of BG monitoring were not commonly followed, but the frequency of hypoglycemia was extremely low.

Prescription Errors with Chemotherapy: Quality Improvement through Standardized Order Templates.

BACKGROUND: Despite the existence of established guidelines advocating the use and value of chemotherapy order templates, chemotherapy orders are still handwritten in many hospitals in Lebanon. This manuscript describes the implementation of standardized chemotherapy order templates (COT) in a Lebanese tertiary teaching hospital through multiple steps. INITIAL ASSESSMENT: An initial assessment was conducted through a retrospective appraisal of completeness of handwritten chemotherapy orders for 100 adult patients to serve as a baseline for the project and identify parameters that might afford improvement. CHOICE OF SOLUTION: Development of over 300 standardized pre-printed COTs based on the National Comprehensive Cancer Network templates and adapted to the practice culture and patient population. IMPLEMENTATION: The COTs were implemented, using Kotter's 8-step model for leading change, by engaging health care providers, and identifying and removing barriers. EVALUATION: Assessment of physicians' compliance with the new practice (122 orders assessed) was completed through two phases and allowed for the identification of areas of improvement. LESSONS LEARNED: Overall, COT implementation showed an average improvement in order completion from 49.5% (handwritten orders) to 77.6% (phase 1-COT) to 87.6% (phase 2-COT) reflecting an increase of 38.1% between baseline and phase 2 and demonstrating that chemotherapy orders completeness was improved by pre-printed COT. As many of the hospitals in Lebanon are moving towards standardized COTs and computerized physician order entry (CPOE) in the next few years, this study provides a prototype for the successful implementation of COT and demonstrates their role in promoting quality improvement of cancer care.

Mutation based treatment recommendations from next generation sequencing data: a comparison of web tools.

Interpretation of complex cancer genome data, generated by tumor target profiling platforms, is key for the success of personalized cancer therapy. How to draw therapeutic conclusions from tumor profiling results is not standardized and may vary among commercial and academically-affiliated recommendation tools. We performed targeted sequencing of 315 genes from 75 metastatic breast cancer biopsies using the FoundationOne assay. Results were run through 4 different web tools including the Drug-Gene Interaction Database (DGidb), My Cancer Genome (MCG), Personalized Cancer Therapy (PCT), and cBioPortal, for drug and clinical trial recommendations. These recommendations were compared amongst each other and to those provided by FoundationOne. The identification of a gene as targetable varied across the different recommendation sources. Only 33% of cases had 4 or more sources recommend the same drug for at least one of the usually several altered genes found in tumor biopsies. These results indicate further development and standardization of broadly applicable software tools that assist in our therapeutic interpretation of genomic data is needed. Existing algorithms for data acquisition, integration and interpretation will likely need to incorporate artificial intelligence tools to improve both content and real-time status.

Development of an algorithm to assess appropriateness of overriding alerts for nonformulary medications in a computerized prescriber-order-entry system.

PURPOSE: An algorithm for assessing the appropriateness of physician overrides of clinical decision support alerts triggered by nonformulary medication (NFM) requests is described. METHODS: Data on a random sample of 5000 NFM alert overrides at Brigham and Women's Hospital over a four-year period (2009-12) were extracted from the hospital's computerized prescriber-order-entry (CPOE) system. Through an iterative process, a scheme for categorizing the reasons given by prescribers for alert overrides was developed. A pharmacist and a physician used the categorization scheme to classify and group alert override reasons, and the resultant data guided the development of an algorithm for assessing alert overrides. RESULTS: In free-text comments written in response to NFM alerts, prescribers provided more than 1150 unique reasons to justify formulary deviation. The compiled reasons were analyzed and grouped into nine categories through the iterative process, with a high degree of interrater agreement (κ = 0.989; 95% confidence interval, 0.985-0.992). An initially developed 30-item "NFM alert override appropriateness algorithm" was simplified to create an 8-question algorithm that was presented to an interdisciplinary team for evaluation, with subsequent refinements for enhanced clinical creditability. The final algorithm can be used by researchers and formulary managers to develop strategies for limiting NFM alert overrides and to avoid the labor-intensive task of creating appropriateness criteria for each NFM. CONCLUSION: A multistep process was used to develop a generalized algorithm for categorizing the appropriateness of reasons given for NFM alert overrides in a CPOE system.

The impact of electronic medication administration records in a residential aged care home.

PURPOSES: This study aimed to compare between electronic medication administration records and paper-based records in the nursing time spent on various activities in a medication round and the medication administration processes followed by nurses in an Australian residential aged care home. It also aimed to identify the benefits and unintended adverse consequences of using the electronic medication administration records. METHODS: Time-motion observation, taking of field notes, informal conversation and document review were used to collect data in two units of a residential aged care home. Each unit had one nurse administer medication. Seven nurses were observed over 12 morning shifts. Unit 1 used electronic medication administration records and Unit 2 used paper-based records. RESULTS: No significant difference between the two units was found in the nursing time spent on various activities in a medication round, including documentation, verbal communication, medication administration, infection control and transit. Comparison of the medication administration processes between the electronic and paper-based medication administration records identified a procedural problem which violated the organization's documentation requirement. This problem was documenting before providing medication to a resident when using the paper-based records. It was not observed with the electronic medication administration records. Benefits of introducing the electronic medication administration records included improving nurses' compliance with documentation requirements, freedom from the error of signing twice, reducing the possibility of forgetting to medicate a resident, facilitating nurses to record the time of medication administration to a resident and increasing documentation space. Unintended adverse consequences of introducing the electronic medication administration records included inadequate information about residents, late addition of a new resident's medication profile in the records and nurses' forgetting to medicate a resident due to power outage of the portable device. CONCLUSIONS: The electronic medication administration records may not change nursing time spent on various activities in a medication round or substantially alter the medication administration processes, but can generate both benefits and unintended adverse consequences. Future research may investigate whether and how the adverse consequences can be prevented.

Análisis modal de fallos y efectos en las prescripciones farmacológicas informatizadas / Failure mode and effects analysis on computerized drug prescriptions

Objetivo. Determinar y analizar los errores en las prescripciones farmacológicas de pacientes asistidos en un hospital de alta resolución mediante la aplicación de un análisis modal de fallos y efectos (AMFE). Material y métodos. Un grupo multidisciplinar de distintas especialidades médicas y de enfermería analizó historias clínicas, en las que las prescripciones farmacológicas se realizaban en formato de texto libre. Se desarrolló un AMFE en el que el índice de prioridad de riesgos (IPR) se obtuvo a partir de un estudio observacional transversal, mediante una auditoría de historias clínicas realizada en 2 fases: 1) verificación previa a la intervención y 2) evaluación de las acciones de mejora después del primer análisis. El tamaño muestral auditable, calculado con técnica de muestreo estratificado y selección aleatoria de episodios clínicos, fue de 679 historias clínicas. Resultados. Se incluyó a 2.096 pacientes. Los errores de prescripción respecto del total de prescripciones descendieron en la segunda fase un 22,2%. En las variables con IPR mayor («vía de administración no especificada» y «dosificación no especificada») no se observaron descensos significativos en la segunda fase, durante la cual no se detectó «pauta horaria incorrecta», «contraindicación del fármaco por alergia», «paciente incorrecto» ni «duplicidad de prescripción», lo que redundó en la mejora de las prescripciones. Conclusiones. Se han determinado y analizado errores de prescripciones farmacológicas mediante la metodología AMFE, mejorando la seguridad clínica de dichas prescripciones. La herramienta permite monitorizar actualizaciones del sistema de prescripción electrónica. Para evitar dichos errores se requeriría que todos los apartados de una prescripción fueran de registro obligado (AU)

Objective. To identify and analyze errors in drug prescriptions of patients treated in a «high resolution» hospital by applying a Failure mode and effects analysis (FMEA).Material and methods A multidisciplinary group of medical specialties and nursing analyzed medical records where drug prescriptions were held in free text format. An FMEA was developed in which the risk priority index (RPI) was obtained from a cross-sectional observational study using an audit of the medical records, carried out in 2 phases: 1) Pre-intervention testing, and (2) evaluation of improvement actions after the first analysis. An audit sample size of 679 medical records from a total of 2,096 patients was calculated using stratified sampling and random selection of clinical events. Results. Prescription errors decreased by 22.2% in the second phase. FMEA showed a greater RPI in «unspecified route of administration» and «dosage unspecified», with no significant decreases observed in the second phase, although it did detect, «incorrect dosing time», «contraindication due to drug allergy», «wrong patient» or «duplicate prescription», which resulted in the improvement of prescriptions. Conclusions. Drug prescription errors have been identified and analyzed by FMEA methodology, improving the clinical safety of these prescriptions. This tool allows updates of electronic prescribing to be monitored. To avoid such errors would require the mandatory completion of all sections of a prescription (AU)

Development of a computerised decisions support system for renal risk drugs targeting primary healthcare.

OBJECTIVES: To assess general practitioners (GPs) experience from the implementation and use of a renal computerised decision support system (CDSS) for drug dosing, developed for primary healthcare, integrated into the patient's electronic health record (EHR), and building on estimation of the patient's creatinine clearance (ClCG). DESIGN: Qualitative research design by a questionnaire and a focus group discussion. SETTING AND PARTICIPANTS: Eight GPs at two primary healthcare centres (PHCs). INTERVENTIONS: The GP at PHC 1, and the project group, developed and tested the technical solution of the CDSS. Proof-of-concept was tested by seven GPs at PHC 2. They also participated in a group discussion and answered a questionnaire. A web window in the EHR gave drug and dosage in relation to ClCG. Each advice was according to three principles: If? Why? Because. OUTCOME MEASURES: (1) The GPs' experience of 'easiness to use' and 'perceived usefulness' at PHC 2, based on loggings of use, answers from a questionnaire using a 5-point Likert scale, and answers from a focus group discussion. (2) The number of patients aged 65 years and older with an estimation of ClCG before and after the implementation of the CDSS. RESULTS: The GPs found the CDSS fast, simple and easy to use. They appreciated the automatic presentation of the CICG status on opening the medication list, and the ability to actively look up specific drug recommendations in two steps. The CDSS scored high on the Likert scale. All GPs wanted to continue the use of the CDSS and to recommend it to others. The number of patients with an estimated ClCG increased 1.6-fold. CONCLUSIONS: Acceptance of the simple graphical interface of this push and pull renal CDSS was high among the primary care physicians evaluating this proof of concept. The graphical model should be useful for further development of renal decision support systems.

Factors associated with medication warning acceptance for hospitalized adults.

BACKGROUND: Computerized provider order entry (CPOE) systems can warn clinicians ordering medications about potential allergic or adverse reactions, duplicate therapy, and interactions with other medications. Clinicians frequently override these warnings. Understanding the factors associated with warning acceptance should guide revisions to these systems. OBJECTIVE: Increase understanding of the factors associated with medication warning acceptance. DESIGN: Retrospective study of all single-medication warnings generated in a CPOE system from October 2009 through April 2010. SETTING: Academic medical center. PATIENTS: All adult non-intensive care unit patients hospitalized during the study period. RESULTS: A total of 40,391 medication orders generated a single-medication warning during the 7-month study period. Of these warnings, 47% were duplicate warnings, 47% interaction warnings, 6% allergy warnings, 0.1% adverse reaction warnings, and 9.8% were repeated for the same patient, medication, and provider. Only 4% of warnings were accepted. In multivariate analysis, warning acceptance was positively associated with male patient gender, admission to a service other than internal medicine, caregiver status other than resident, parenteral medications, lower numbers of warnings, and allergy or adverse reaction warning types. Older patient age, longer length of stay, inclusion on the Institute for Safe Medication Practice's List of High Alert Medications, and interaction warning type were all negatively associated with warning acceptance. CONCLUSIONS: Medication warnings are rarely accepted. Acceptance is more likely when the warning is infrequently encountered, and least likely when it is potentially most important. Warning systems should be redesigned to increase their effectiveness for the sickest patients, the least experienced physicians, and the medications with the greatest potential to cause harm.

Characterization of the Context of Drug Concepts in Research Protocols: An Empiric Study to Guide Ontology Development.

We examined a large body of research study documents (protocols) to identify mentions of drug concepts and established base concepts and roles needed to characterize the semantics of these instances. We found these concepts in three general situations: background knowledge about the drug, study procedures involving the drug, and other roles of the drug in the study. We identified 18 more specific contexts (e.g., adverse event information, administration and dosing of the drug, and interactions between the study drug and other drugs). The ontology was validated against a test set of protocol documents from NIH and ClinicalTrial.gov. The goal is to support the automated extraction of drug information from protocol documents to support functions such as study retrieval, determination of subject eligibility, generation of order sets, and creation of logic for decision support alerts and reminders. Further work is needed to formally extend existing ontologies of clinical research.

Towards evidence-based CDSSs implementing the medical reasoning contained in CPGs: application to antibiotic prescription.

UNLABELLED: Clinical practice guidelines (CPGs) are documents giving recommendations based on expert reasoning, weighing up the pros and cons of treatments on the basis of the available evidence. We propose a new approach to the construction of clinical decision support systems (CDSS), making use of the evidence-based medical reasoning used by experts in CPGs. In this study, we determined whether this approach could retrieve the recommendations for antibiotic prescription for empirical treatment in primary care. METHODS: We manually extracted, from CPGs, all the properties of antibiotics underlying recommendations for their prescription or non-prescription. We then used these properties to establish an algorithm in the form of a sequence of conditions, leading to a list of recommended antibiotics. The optimal sequence was determined by studying, for each sequence, the degree of similarity between the list of antibiotics recommended in CPGs and the list obtained with the algorithm. RESULTS: 12 antibiotic properties were used in the form of conditions in an algorithm. For 95% of clinical situations, 10 sequences retrieved the recommended treatment. DISCUSSION: This algorithm could be used in a CDSS for antibiotic treatment and would be useful for experts drawing up CPGs.

Core drug-drug interaction alerts for inclusion in pediatric electronic health records with computerized prescriber order entry.

OBJECTIVE: The study aims to develop a core set of pediatric drug-drug interaction (DDI) pairs for which electronic alerts should be presented to prescribers during the ordering process. METHODS: A clinical decision support working group composed of Children's Hospital Association (CHA) members was developed. CHA Pharmacists and Chief Medical Information Officers participated. RESULTS: Consensus was reached on a core set of 19 DDI pairs that should be presented to pediatric prescribers during the order process. CONCLUSIONS: We have provided a core list of 19 high value drug pairs for electronic drug-drug interaction alerts to be recommended for inclusion as high value alerts in prescriber order entry software used with a pediatric patient population. We believe this list represents the most important pediatric drug interactions for practical implementation within computerized prescriber order entry systems.

Design of the asthma treat smart system in a pediatric institution.

Asthma is one of the most common chronic pediatric conditions. Providing evidence-based, guideline-appropriate care for asthma is complex. A computerized system may help providers with guideline compliance. The AsthmaTreatSmart application is a stand-alone web-based system developed in Pulmonary medicine with a multidisciplinary team based on national asthma guidelines. The application collects history and symptom information to assign a severity categorization. Medication reconciliation of asthma medications is performed with input from the patient. Severity and medication recommendation are provided for the provider. A personalized action plan and medication summary including medications, etc. is created. The system is successfully used in 6 outpatient clinics and current work is focused on integration with the electronic health record.