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.

What Features of Smartphone Medication Applications Are Patients with Chronic Diseases and Caregivers Looking for?

We explored the desired features of medication applications for patients with chronic disease and their caregivers with a questionnaire survey, 50 from patients and 50 from their caregivers. Although the majority of people (75%) are willing to use medication apps, the actual usage rate is quite low (11%). Worrying about privacy of personal information seems to be the main reason of not using applications. The overall score desired for use was 3.29 ± 1.02 (out of 5). Searching medications and diseases and assistance with making doctors' appointments are the most wanted categories. Online shopping for drugs and delivery were the least desired items. The main concerns for people who do not want certain features include: they are not useful, worrying about buying counterfeit drugs and reliability of content. Compared with patients, caregivers seems to be more concerned on nutrition tips for chronic illness, fall detection, and privacy protection (P < 0.05 for all).

Use of computer-assisted drug therapy outside the operating room.

PURPOSE OF REVIEW: The number of procedures performed in the out-of-operating room setting under sedation has increased many fold in recent years. Sedation techniques aim to achieve rapid patient turnover through the use of short-acting drugs with minimal residual side-effects (mainly propofol and opioids). Even for common procedures, the practice of sedation delivery varies widely among providers. Computer-based sedation models have the potential to assist sedation providers and offer a more consistent and safer sedation experience for patients. RECENT FINDINGS: Target-controlled infusions using propofol and other short-acting opioids for sedation have shown promising results in terms of increasing patient safety and allowing for more rapid wake-up times. Target-controlled infusion systems with real-time patient monitoring can titrate drug doses automatically to maintain optimal depth of sedation. The best recent example of this is the propofol-based Sedasys sedation system. Sedasys redefined individualized sedation by the addition of an automated clinical parameter that monitors depth of sedation. However, because of poor adoption and cost issues, it has been recently withdrawn by the manufacturer. SUMMARY: Present automated drug delivery systems can assist in the provision of sedation for out-of-operating room procedures but cannot substitute for anesthesia providers. Use of the available technology has the potential to improve patient outcomes, decrease provider workload, and have a long-term economic impact on anesthesia care delivery outside of the operating room.

Physician perspectives of CYP2C19 and clopidogrel drug-gene interaction active clinical decision support alerts.

OBJECTIVE: To determine if physicians find clinical decision support alerts for pharmacogenomic drug-gene interactions useful and assess their perceptions of usability aspects that impact usefulness. MATERIALS AND METHODS: 52 physicians participated in an online simulation and questionnaire involving a prototype alert for the clopidogrel and CYP2C19 drug-gene interaction. RESULTS: Only 4% of participants stated they would override the alert. 92% agreed that the alerts were useful. 87% found the visual interface appropriate, 91% felt the timing of the alert was appropriate and 75% were unfamiliar with the specific drug-gene interaction. 80% of providers preferred the ability to order the recommended medication within the alert. Qualitative responses suggested that supplementary information is important, but should be provided as external links, and that the utility of pharmacogenomic alerts depends on the broader ecosystem of alerts. PRINCIPAL CONCLUSIONS: Pharmacogenomic alerts would be welcomed by many physicians, can be built with minimalist design principles, and are appropriately placed at the end of the prescribing process. Since many physicians lack familiarity with pharmacogenomics but have limited time, information and educational resources within the alert should be carefully selected and presented in concise ways.

Evaluation of a clinical decision support rule-set for medication adjustments in mHealth-based heart failure management.

Decision-support based medication adjustment in heart failure management. Prospective analysis of clinical decision support in fifteen patients that collected vital parameters and medication intake up to one year within a clinical trial. Correlation of event episodes and medication adjustments with respect to applied rule-sets and medication classes. 713 events were grouped to 195 event episodes. Physicians performed 86 medication adjustments. 30 of them were triggered by event episodes. 35% of all performed medication adjustments occurred between event episodes. 20% of all episodes triggered a medication adjustment. 15% of all episodes triggered the expected medication adjustment. Correlation between episodes and medication adjustment was low. Further analysis needs to be done, to evaluate reasons for low correlation and how the rule-set should be adapted to increase reliability.

Impact of a clinical decision support system for high-alert medications on the prevention of prescription errors.

OBJECTIVE: To evaluate the impact of a high-alert medication clinical decision support system called HARMLESS on point-of-order entry errors in a tertiary hospital. METHOD: HARMLESS was designed to provide three kinds of interventions for five high-alert medications: clinical knowledge support, pop-ups for erroneous orders that block the order or provide a warning, and order recommendations. The impact of this program on prescription order was evaluated by comparing the orders in 6 month periods before and after implementing the program, by analyzing the intervention log data, and by checking for order pattern changes. RESULT: During the entire evaluation period, there were 357,417 orders and 5233 logs. After HARMLESS deployment, orders that omitted dilution fluids and exceeded the maximum dose dropped from 12,878 and 214 cases to 0 and 9 cases, respectively. The latter nine cases were unexpected, but after the responsible programming error was corrected, there were no further such cases. If all blocking interventions were seen as errors that were prevented, this meant that 4137 errors (3584 of which were 'dilution fluid omitted' errors) were prevented over the 6-month post-deployment period. There were some unexpected order pattern changes after deployment and several unexpected errors emerged, including intramuscular or intravenous push orders for potassium chloride (although a case review revealed that the drug was not actually administered via these methods) and an increase in pro re nata (PRN; administer when required) orders for most drugs. CONCLUSION: HARMLESS effectively implemented blocking interventions but was associated with the emergence of unexpected errors. After a program is deployed, it must be monitored and subjected to data analysis to fix bugs and prevent the emergence of new error types.

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.

[Effect of modal computer-based alerts on the prescription of valproic acid and meropenem]. / Impacto de las alertas interactivas modales en la prescripción informatizada de ácido valproico y meropenem.

OBJECTIVE: To analyze the effect of modal computer-based alerts on the concomitant prescription of valproic acid (VPA) and meropenem. MATERIAL AND METHOD: Analytical intervention study conducted in a tertiary hospital for eleven months. Hospitalized patients with a diagnosis of epilepsy and treated with VPA and meropenem in concomitant therapy were included. In the computerized prescription order entry software an automatic non-modal alert was reconverted to a modal one. This was triggered when the physician introduced VPA and meropenem together in the same prescription. To measure the effect of this alert the prescription habits were compared with a previous period in which the alert was not modal. RESULTS: Modal computer-based alert modified the prescription habit by reducing the number of patients with concomitant treatment from 13 to 4 (P=.046). However, it was notable that the number of requests for VPA serum levels decreased, and the average number of concomitant days of treatment rose from 4.7 to 8.75 in those patients in which none of the drugs was suspended. CONCLUSIONS: The implementation of modal computer-based alerts reduces patient exposure to concomitant treatment with meropenem and VPA.

Reduction in chemotherapy order errors with computerized physician order entry.

PURPOSE: To measure the number and type of errors associated with chemotherapy order composition associated with three sequential methods of ordering: handwritten orders, preprinted orders, and computerized physician order entry (CPOE) embedded in the electronic health record. MATERIALS AND METHODS: From 2008 to 2012, a sample of completed chemotherapy orders were reviewed by a pharmacist for the number and type of errors as part of routine performance improvement monitoring. Error frequencies for each of the three distinct methods of composing chemotherapy orders were compared using statistical methods. RESULTS: The rate of problematic order sets-those requiring significant rework for clarification-was reduced from 30.6% with handwritten orders to 12.6% with preprinted orders (preprinted v handwritten, P < .001) to 2.2% with CPOE (preprinted v CPOE, P < .001). The incidence of errors capable of causing harm was reduced from 4.2% with handwritten orders to 1.5% with preprinted orders (preprinted v handwritten, P < .001) to 0.1% with CPOE (CPOE v preprinted, P < .001). CONCLUSION: The number of problem- and error-containing chemotherapy orders was reduced sequentially by preprinted order sets and then by CPOE. CPOE is associated with low error rates, but it did not eliminate all errors, and the technology can introduce novel types of errors not seen with traditional handwritten or preprinted orders. Vigilance even with CPOE is still required to avoid patient harm.

Comparison of two kinds of interface, based on guided navigation or usability principles, for improving the adoption of computerized decision support systems: application to the prescription of antibiotics.

CONTEXT: It is important to consider the way in which information is presented by the interfaces of clinical decision support systems, to favor the adoption of these systems by physicians. Interface design can focus on decision processes (guided navigation) or usability principles. OBJECTIVE: The aim of this study was to compare these two approaches in terms of perceived usability, accuracy rate, and confidence in the system. MATERIALS AND METHODS: We displayed clinical practice guidelines for antibiotic treatment via two types of interface, which we compared in a crossover design. General practitioners were asked to provide responses for 10 clinical cases and the System Usability Scale (SUS) for each interface. We assessed SUS scores, the number of correct responses, and the confidence level for each interface. RESULTS: SUS score and percentage confidence were significantly higher for the interface designed according to usability principles (81 vs 51, p=0.00004, and 88.8% vs 80.7%, p=0.004). The percentage of correct responses was similar for the two interfaces. DISCUSSION/CONCLUSION: The interface designed according to usability principles was perceived to be more usable and inspired greater confidence among physicians than the guided navigation interface. Consideration of usability principles in the construction of an interface--in particular 'effective information presentation', 'consistency', 'efficient interactions', 'effective use of language', and 'minimizing cognitive load'--seemed to improve perceived usability and confidence in the system.

A computerized standard protocol order entry for pediatric inpatient acute seizure emergencies reduces time to treatment.

Time to treatment of seizures is critical to efficacy. We performed a quality initiative and evaluated time to treatment of inpatient seizure emergencies with first- and second-line medicines before and after implementation of a computerized, standard treatment protocol. Data from 125 patients revealed that 179 seizure episodes required first-line antiepileptic drugs, and the mean time to treatment was 7.72 minutes. In 87 episodes, patients (49%) received the drugs within 5 minutes. Forty-six episodes required second-line drugs. In 17 (37%), patients received them within 30 minutes (mean 49.48 minutes). After implementation of the protocol, the mean time to treatment with first-line drugs was 3.74 minutes, a reduction of >50% (P < .0001). The mean time to treatment with second-line drugs was 25.05 minutes, a reduction of ∼50% (P < .0001). This effective model for reducing the time to treatment of seizure emergencies may be useful to similar institutions.

Understanding the information dynamics of medication administration in residential aged care facilities (RACFs): a prerequisite for design of effective ICT systems.

Medication information is a critical part of the information required to ensure residents' safety in the highly collaborative care context of RACFs. Studies report poor medication information as a barrier to improve medication management in RACFs. Research exploring medication work practices in aged care settings remains limited. This study aimed to identify contextual and work practice factors contributing to breakdowns in medication information exchange in RACFs in relation to the medication administration process. We employed non-participant observations and semi-structured interviews to explore information practices in three Australian RACFs. Findings identified inefficiencies due to lack of information timeliness, manual stock management, multiple data transcriptions, inadequate design of essential documents such as administration sheets and a reliance on manual auditing procedures. Technological solutions such as electronic medication administration records offer opportunities to overcome some of the identified problems. However these interventions need to be designed to align with the collaborative team based processes they intend to support.

Usage and perceptions of a mobile self-management application for people with type 2 diabetes: qualitative study of a five-month trial.

Despite a growing number of clinical-intervention studies of mobile applications for diabetes self-management, details of participants' engagement with the intervention tools and of usability and feasibility issues are seldom reported. The Few Touch application is a mobile-phone-based self-management system for people with Type 2 diabetes mellitus (T2DM) developed by involving patient-users in design processes from an early phase to a long-term trial. An improved version was tested in a five-month trial by 11 individuals either with T2DM or at high risk of T2DM. Results showed clearer correlations between usage and perceived usefulness among these individuals compared with those involved in the design process. However, feedback on usability issues was mostly consistent between the two trials. This study therefore confirmed: 1) the value of including patient-users not only in design-concept development but also in a long-term trial to identify as many factors critical to usability and usage as possible, and 2) the importance of reflecting their feedback in design iterations to minimize the number of critical factors.

Development of a web-based observational tool for detecting intravenous medication errors with smart infusion pumps.

Computerized smart infusion pumps have been widely implemented to decrease the rate of intravenous (IV) medication errors in hospitals. However, these devices have not always achieved their potential, and important IV errors still persist. Findings from a previous study [1] that assessed the frequency of IV medication errors and the impact of smart infusion pumps identified major issues related to use of smart infusion pumps in a single facility, but generalizability of these results is uncertain. Additionally, lack of standardized methodology for measuring these errors remains an issue. In this study, we developed an observational tool to capture IV medication errors through iterative participatory design with interdisciplinary experts and then tested the tool by using incident cases regarding smart pump errors. We found that the tool could capture all smart infusion pump errors and is ready for testing for use as standard data collection tool in different hospital settings.

Validity of a clinical decision rule-based alert system for drug dose adjustment in patients with renal failure intended to improve pharmacists' analysis of medication orders in hospitals.

OBJECTIVE: The main objective of this study was to assess the diagnostic performances of an alert system integrated into the CPOE/EMR system for renally cleared drug dosing control. The generated alerts were compared with the daily routine practice of pharmacists as part of the analysis of medication orders. MATERIALS AND METHODS: The pharmacists performed their analysis of medication orders as usual and were not aware of the alert system interventions that were not displayed for the purpose of the study neither to the physician nor to the pharmacist but kept with associate recommendations in a log file. A senior pharmacist analyzed the results of medication order analysis with and without the alert system. The unit of analysis was the drug prescription line. The primary study endpoints were the detection of drug dose prescription errors and inter-rater reliability (Kappa coefficient) between the alert system and the pharmacists in the detection of drug dose error. RESULTS: The alert system fired alerts in 8.41% (421/5006) of cases: 5.65% (283/5006) "exceeds max daily dose" alerts and 2.76% (138/5006) "under-dose" alerts. The alert system and the pharmacists showed a relatively poor concordance: 0.106 (CI 95% [0.068-0.144]). According to the senior pharmacist review, the alert system fired more appropriate alerts than pharmacists, and made fewer errors than pharmacists in analyzing drug dose prescriptions: 143 for the alert system and 261 for the pharmacists. Unlike the alert system, most diagnostic errors made by the pharmacists were 'false negatives'. The pharmacists were not able to analyze a significant number (2097; 25.42%) of drug prescription lines because understaffing. CONCLUSION: This study strongly suggests that an alert system would be complementary to the pharmacists' activity and contribute to drug prescription safety.

Computer aided warfarin dosing in the Swedish national quality registry AuriculA - Algorithmic suggestions are performing better than manually changed doses.

INTRODUCTION: Warfarin treatment with a high time in therapeutic range (TTR) is correlated to fewer complications. The TTR in Sweden is generally high but varies partly depending on local expertise and traditions. A dosing algorithm could minimize variations and increase treatment quality. Here we evaluate the performance of a computerized dosing algorithm. MATERIALS AND METHODS: 53.779 warfarin treated patients from 125 centers using the Swedish national quality registry AuriculA. If certain criteria are met, the algorithm gives one of seven possible dose suggestions, which can be unchanged, decreased or increased weekly dose by 5, 10 or 15%. The outcome evaluated by the resulting INR value was compared between dose suggestions arising from the algorithm that were accepted and those that were manually changed. There were no randomization, and outcomes were retrospectively analyzed. RESULTS: Both the algorithm-based and the manually changed doses had worse outcome if only two instead of three previous INR values were available. The algorithm suggestions were superior to manual dosing regarding percent samples within the target range 2-3 (hit-rate) or deviation from INR 2.5 (mean error). Of the seven possible outcomes from the algorithm, six were significantly superior and one equal to the manually changed doses when three previous INR:s were present. CONCLUSIONS: The algorithm-based dosing suggestions show better outcome in most cases. This can make dosing of warfarin easier and more efficient. There are however cases where manual dosing fares better. Here the algorithm will be improved to further enhance its dosing performance in the future.

Iodinated contrast injection data from a new technology.

PURPOSE: To introduce a new power injection technology that generates data as a digital imaging and communications in medicine (DICOM) image linked to individual patient imaging studies. In addition, to determine the fraction of patients in a subject cohort whose contrast injection data was captured as a DICOM image and to analyze contrast injection properties for those patients. METHODS: Over a 1-month period, authors performed a retrospective evaluation of 242 patients' consecutive contrast-enhanced computed tomography (CT) studies from a single 320-detector CT scanner in an academic radiology department. Authors gathered unique patient and examination identifiers, prescribed and injected contrast and saline volumes, prescribed and injected flow rate, and mean and maximum injection pressures. The literature was reviewed for the initial description of power injectors in radiology. RESULTS: Of the 242 CT studies evaluated, 98% had contrast injection data amended to the radiology images. For all patients, the mean volume of residual contrast was 5 mL. The differences between the prescribed and actual flow rate were small. Three patients reached the maximum pressure of 300 psi. There were no contrast extravasations. Discussion The most clinically relevant finding was that the injector and software system generated a detailed report of contrast administration. In 98% of the cases, this report was incorporated into the patient's permanent medical record and was available to the radiologist via a single DICOM image. CONCLUSION: Contrast injection data can be captured in DICOM format and reliably attached to a clinical contrast-enhanced CT image set for radiologist use.

Prescribers' interactions with medication alerts at the point of prescribing: A multi-method, in situ investigation of the human-computer interaction.

PURPOSE: Few studies have examined prescribers' interactions with medication alerts at the point of prescribing. We conducted an in situ, human factors investigation of outpatient prescribing to uncover factors that influence the prescriber-alert interaction and identify strategies to improve alert design. METHODS: Field observations and interviews were conducted with outpatient prescribers at a major Veterans Affairs Medical Center. Physicians, clinical pharmacists, and nurse practitioners were recruited across five primary care clinics and eight specialty clinics. Prescribers were observed in situ as they ordered medications for patients and resolved alerts. Researchers collected 351 pages of typed notes across 102 hours of observations and interviews. An interdisciplinary team identified emergent themes via inductive qualitative analysis. RESULTS: Altogether, 320 alerts were observed among 30 prescribers and their interactions with 146 patients. Qualitative analysis uncovered 44 emergent themes and 9 overarching factors, which were organized into a framework that describes the prescriber-alert interaction. Prescribers' ability to act on alerts was impeded by the alert interface, which did not adequately support all prescriber types. CONCLUSIONS: This empiric study produced a novel framework for understanding the prescriber-alert interaction. Results revealed key components of the alert interface that influence prescribers and indicate a need for more universal design. Actionable design recommendations are presented and may be used to enhance alert design and patient safety.

Reinforcement learning strategies for clinical trials in nonsmall cell lung cancer.

Typical regimens for advanced metastatic stage IIIB/IV nonsmall cell lung cancer (NSCLC) consist of multiple lines of treatment. We present an adaptive reinforcement learning approach to discover optimal individualized treatment regimens from a specially designed clinical trial (a "clinical reinforcement trial") of an experimental treatment for patients with advanced NSCLC who have not been treated previously with systemic therapy. In addition to the complexity of the problem of selecting optimal compounds for first- and second-line treatments based on prognostic factors, another primary goal is to determine the optimal time to initiate second-line therapy, either immediately or delayed after induction therapy, yielding the longest overall survival time. A reinforcement learning method called Q-learning is utilized, which involves learning an optimal regimen from patient data generated from the clinical reinforcement trial. Approximating the Q-function with time-indexed parameters can be achieved by using a modification of support vector regression that can utilize censored data. Within this framework, a simulation study shows that the procedure can extract optimal regimens for two lines of treatment directly from clinical data without prior knowledge of the treatment effect mechanism. In addition, we demonstrate that the design reliably selects the best initial time for second-line therapy while taking into account the heterogeneity of NSCLC across patients.