Physicians' responses to computerized drug-drug interaction alerts for outpatients

The PrescIT platform: An interoperable Clinical Decision Support System for ePrescription to Prevent Adverse Drug Reactions and Drug-Drug Interactions

INTRODUCTION

Preventable medication errors have been proven to cause significant public health burden, and ePrescription is a key part of the process where medication errors and adverse effects could be prevented. Information systems and "intelligent" computational approaches could provide a valuable tool to prevent such errors with profound impact in clinical practice.

OBJECTIVES

The PrescIT platform is a Clinical Decision Support System (CDSS) that aims to facilitate the prevention of adverse drug reactions (ADRs) and drug-drug interactions (DDIs) in the phase of ePrescription in Greece. The proposed platform could be relatively easily localized for use in other contexts too.

METHODS

The PrescIT platform is based on the use of Knowledge Engineering (ΚΕ) approaches, i.e., the use of Ontologies and Knowledge Graphs (KGs) developed upon openly available data sources. Open standards (i.e., RDF, OWL, SPARQL) are used for the development of the platform enabling the integration with already existing IT systems or for standalone use. The main KG is based on the use of DrugBank, MedDRA, SemMedDB and OpenPVSignal. In addition, the Business Process Management Notation (BPMN) has been used to model long-term therapeutic protocols used during the ePrescription process. Finally, the produced software has been pilot tested in three hospitals by 18 clinical professionals via in-person think-aloud sessions.

RESULTS

The PrescIT platform has been successfully integrated in a transparent fashion in a proprietary Hospital Information System (HIS), and it has also been used as a standalone application. Furthermore, it has been successfully integrated with the Greek National ePrescription system. During the pilot phase, one psychiatric therapeutic protocol was used as a testbed to collect end-users' feedback. Summarizing the feedback from the end-users, they have generally acknowledged the usefulness of such a system while also identifying some challenges in terms of usability and the overall user experience.

CONCLUSIONS

The PrescIT platform has been successfully deployed and piloted in real-world environments to evaluate its ability to support safer medication prescriptions.

Design, development, implementation, and evaluation of a severe drug-drug interaction alert system in the ICU: An analysis of acceptance and override rates

BACKGROUND AND OBJECTIVE

The override rate of Drug-Drug Interaction Alerts (DDIA) in Intensive Care Units (ICUs) is very high. Therefore, this study aimed to design, develop, implement, and evaluate a severe Drug-Drug Alert System (DDIAS) in a system of ICUs and measure the override rate of this system.

METHODS

This is a cross-sectional study that details the design, development, implementation, and evaluation of a DDIAS for severe interactions into a Computerized Provider Order Entry (CPOE) system in the ICUs of Nemazee general teaching hospitals in 2021. The patients exposed to the volume of DDIAS, acceptance and overridden of DDIAS, and usability of DDIAS have been collected. The study was approved by the local Institutional Review Board (IRB) and; the ethics committee of Shiraz University of Medical Science on date: 2019-11-23 (Approval ID: IR.SUMS.REC.1398.1046).

RESULTS

The knowledge base of the DDIAS contains 9,809 severe potential drug-drug interactions (pDDIs). A total of 2672 medications were prescribed in the population study. The volume and acceptance rate for the DDIAS were 81 % and 97.5 %, respectively. The override rate was 2.5 %. The mean System Usability Scale (SUS) score of the DDIAS was 75.

CONCLUSION

This study demonstrates that implementing high-risk DDIAS at the point of prescribing in ICUs improves adherence to alerts. In addition, the usability of the DDIAS was reasonable. Further studies are needed to investigate the establishment of severe DDIAS and measure the prescribers' response to DDIAS on a larger scale.

Congruence rates for pharmacogenomic noninterruptive alerts

Meaningful clinical decision support (CDS) recommendations are vital for implementation of pharmacogenomics (PGx) into routine clinical care. PGx CDS alerts include interruptive and noninterruptive alerts. The objective of this study was to evaluate provider ordering behavior after noninterruptive alerts are displayed. A retrospective manual chart review was conducted from the time of noninterruptive alert implementation to the time of data analysis to determine congruence with CDS recommendations. The congruence rate for noninterruptive alerts was 89.8% across all drug-gene interactions. The drug-gene interaction with the most alerts for analysis included metoclopramide (n = 138). The high rate of medication order congruence after noninterruptive alerts were deployed suggests this modality may be appropriate for PGx CDS as a method for best practice adherence.

Pharmacogenomic Clinical Decision Support: A Scoping Review

Clinical decision support (CDS) is often cited as an essential part of pharmacogenomics (PGx) implementations. A multitude of strategies are available; however, it is unclear which strategies are effective and which metrics are used to quantify clinical utility. The objective of this scoping review was to aggregate previous studies into a cohesive depiction of the current state of PGx CDS implementations and identify areas for future research on PGx CDS. Articles were included if they (i) described electronic CDS tools for PGx and (ii) reported metrics related to PGx CDS. Twenty of 3,449 articles were included and provided data on PGx CDS metrics from 15 institutions, with 93% of programs located at academic medical centers. The most common tools in CDS implementations were interruptive post-test alerts. Metrics for clinical response and alert response ranged from 12-73% and 21-98%, respectively. Few data were found on changes in metrics over time and measures that drove the evolution of CDS systems. Relatively few data were available regarding support of optimal approaches for PGx CDS. Post-test alerts were the most widely studied approach, and their effectiveness varied greatly. Further research on the usability, effectiveness, and optimization of CDS tools is needed.

Drug Interactions: What Are Important Drug Interactions for the Most Commonly Used Medications in Preventive Cardiology?

Significant drug interactions contribute to hospitalizations, mortality, and health care costs. They often are preventable with a basic understanding of pharmacokinetics and pharmacodynamics. More than quarter of Americans above the age of 40 years take a statin, the most commonly used lipid-lowering therapy in modern times. Because of their pharmacokinetics, statins interact with numerous other drugs and substances, often in a manner that differs from statin to statin. This article provides an overview of important drug interactions for the most commonly used medications in preventive cardiology, with an emphasis on clinically significant interactions involving statins.

Pharmacogenomic Clinical Decision Support: A Review, How-to Guide, and Future Vision

Clinical decision support (CDS) is an essential part of any pharmacogenomics (PGx) implementation. Increasingly, institutions have implemented CDS tools in the clinical setting to bring PGx data into patient care, and several have published their experiences with these implementations. However, barriers remain that limit the ability of some programs to create CDS tools to fit their PGx needs. Therefore, the purpose of this review is to summarize the types, functions, and limitations of PGx CDS currently in practice. Then, we provide an approachable step‐by‐step how‐to guide with a case example to help implementers bring PGx to the front lines of care regardless of their setting. Particular focus is paid to the five “rights” of CDS as a core around designing PGx CDS tools. Finally, we conclude with a discussion of opportunities and areas of growth for PGx CDS.

Adverse Drug Events Caused by Drugs Contraindicated for Coadministration Reported in the Japanese Adverse Drug Event Report Database and Recognized by Reporters

The "INTERACTIONS" section of package inserts aims to provide alert-type warnings in clinical practice; however, these also include many drug-drug interactions that occur rarely. Moreover, considering that drug-drug interaction alert systems were created based on package inserts, repeated alerts can lead to alert fatigue. Although investigations have been conducted to determine prescriptions that induce drug-drug interactions, no studies have focused explicitly on the adverse events induced by drug-drug interactions. We, therefore, sought to investigate the true occurrence of adverse events caused by drug pair contraindications for coadministration in routine clinical practice. Toward this, we created a list of drug combinations that were designated as "contraindications for coadministration" and extracted the cases of adverse drug events from the Japanese Adverse Drug Event Report database that occurred due to combined drug usage. We then calculated the reporters' recognition rate of the drug-drug interactions. Out of the 2121 investigated drug pairs, drug-drug interactions were reported in 43 pairs, 23 of which included an injected drug and many included catecholamines. Warfarin potassium and miconazole (19 reports), azathioprine and febuxostat (11 reports), and warfarin potassium and iguratimod (six reports) were among the 20 most-commonly reported oral medication pairs that were contraindicated for coadministration, for which recognition rates of drug-drug interactions were high. Although these results indicate that only a few drug pair contraindications for coadministration were associated with adverse drug events (43 pairs out of 2121 pairs), it remains necessary to translate these findings into clinical practice.

Medication safety alert fatigue may be reduced via interaction design and clinical role tailoring: a systematic review

OBJECTIVE

Alert fatigue limits the effectiveness of medication safety alerts, a type of computerized clinical decision support (CDS). Researchers have suggested alternative interactive designs, as well as tailoring alerts to clinical roles. As examples, alerts may be tiered to convey risk, and certain alerts may be sent to pharmacists. We aimed to evaluate which variants elicit less alert fatigue.

MATERIALS AND METHODS

We searched for articles published between 2007 and 2017 using the PubMed, Embase, CINAHL, and Cochrane databases. We included articles documenting peer-reviewed empirical research that described the interactive design of a CDS system, to which clinical role it was presented, and how often prescribers accepted the resultant advice. Next, we compared the acceptance rates of conventional CDS-presenting prescribers with interruptive modal dialogs (ie, "pop-ups")-with alternative designs, such as role-tailored alerts.

RESULTS

Of 1011 articles returned by the search, we included 39. We found different methods for measuring acceptance rates; these produced incomparable results. The most common type of CDS-in which modals interrupted prescribers-was accepted the least often. Tiering by risk, providing shortcuts for common corrections, requiring a reason to override, and tailoring CDS to match the roles of pharmacists and prescribers were the most common alternatives. Only 1 alternative appeared to increase prescriber acceptance: role tailoring. Possible reasons include the importance of etiquette in delivering advice, the cognitive benefits of delegation, and the difficulties of computing "relevance."

CONCLUSIONS

Alert fatigue may be mitigated by redesigning the interactive behavior of CDS and tailoring CDS to clinical roles. Further research is needed to develop alternative designs, and to standardize measurement methods to enable meta-analyses.

Machine Learning Approach to Reduce Alert Fatigue Using a Disease Medication-Related Clinical Decision Support System: Model Development and Validation

Background

Computerized physician order entry (CPOE) systems are incorporated into clinical decision support systems (CDSSs) to reduce medication errors and improve patient safety. Automatic alerts generated from CDSSs can directly assist physicians in making useful clinical decisions and can help shape prescribing behavior. Multiple studies reported that approximately 90%-96% of alerts are overridden by physicians, which raises questions about the effectiveness of CDSSs. There is intense interest in developing sophisticated methods to combat alert fatigue, but there is no consensus on the optimal approaches so far.

Objective

Our objective was to develop machine learning prediction models to predict physicians’ responses in order to reduce alert fatigue from disease medication–related CDSSs.

Methods

We collected data from a disease medication–related CDSS from a university teaching hospital in Taiwan. We considered prescriptions that triggered alerts in the CDSS between August 2018 and May 2019. Machine learning models, such as artificial neural network (ANN), random forest (RF), naïve Bayes (NB), gradient boosting (GB), and support vector machine (SVM), were used to develop prediction models. The data were randomly split into training (80%) and testing (20%) datasets.

Results

A total of 6453 prescriptions were used in our model. The ANN machine learning prediction model demonstrated excellent discrimination (area under the receiver operating characteristic curve [AUROC] 0.94; accuracy 0.85), whereas the RF, NB, GB, and SVM models had AUROCs of 0.93, 0.91, 0.91, and 0.80, respectively. The sensitivity and specificity of the ANN model were 0.87 and 0.83, respectively.

Conclusions

In this study, ANN showed substantially better performance in predicting individual physician responses to an alert from a disease medication–related CDSS, as compared to the other models. To our knowledge, this is the first study to use machine learning models to predict physician responses to alerts; furthermore, it can help to develop sophisticated CDSSs in real-world clinical settings.

Training Aspects of Laboratory-Based Decision Support

Pathology has a large role to play in the proper development, implementation, and optimization of clinical decision support (CDS). CDS training must be supported by an educational foundation in clinical and pathology informatics. Educational opportunities are currently limited, but expanding, in the pathology residency space with Pathology Informatics Essentials for Residents. The use of an educational version of electronic clinical systems is an important educational tool to support the needed outcomes-driven and exercise-based informatics and CDS training. With the multidisciplinary nature of informatics, it is advantageous to include laboratory professionals in the training exercises as appropriate.

Inhibitory effect of isavuconazole, ketoconazole, and voriconazole on the pharmacokinetics of methadone in vivo and in vitro

The aim of this study was to investigate the possible effect of orally administered isavuconazole, ketoconazole, or voriconazole on the pharmacokinetics of methadone in rats. Twenty Sprague-Dawley (SD) rats were divided randomly into four groups: Group A (control), group B (5 mg/kg isavuconazole), group C (5 mg/kg ketoconazole), and group D (5 mg/kg voriconazole). A single dose of methadone was administrated half an hour later. Methadone in plasma concentrations and its metabolite EDDP in microsomes were determined by ultra-high-performance liquid chromatography-tandem mass spectrometry method (UPLC-MS/MS), and pharmacokinetic parameters were calculated by DAS version 3.0. The C_max of methadone in groups C and D increased to 2.7-fold and 5-fold, respectively. While AUC increased in three groups and group D increased the most. Also, isavuconazole, ketoconazole, and voriconazole showed inhibitory effect on human and rat microsomes. The inhibition ratios of isavuconazole, ketoconazole, and voriconazole in rat liver microsome were 97.87%, 96.74% and 78.9%, respectively (p < 0.01), while in human liver microsome, inhibition ratios were 86.97%, 96.46%, and 53.11%, respectively. And the IC₅₀ for inhibition activity of isavuconazole, ketoconazole, and voriconazole in rat microsomes were 7.76 μM, 8.33 μM, and 4.45 μM, respectively. Our study indicated that taking methadone combine with ketoconazole, isavuconazole, or voriconazole could reduce the metabolism rate of methadone and prolong the pharmacological effects in vivo and in vitro.

Supervised signal detection for adverse drug reactions in medication dispensing data

MOTIVATION

Adverse drug reactions (ADRs) are one of the leading causes of morbidity and mortality and thus should be detected early to reduce consequences on health outcomes. Medication dispensing data are comprehensive sources of information about medicine uses that can be utilized for the signal detection of ADRs. Sequence symmetry analysis (SSA) has been employed in previous studies to detect signals of ADRs from medication dispensing data, but it has a moderate sensitivity and tends to miss some ADR signals. With successful applications in various areas, supervised machine learning (SML) methods are promising in detecting ADR signals. Gold standards of known ADRs and non- ADRs from previous studies create opportunities to take into account additional domain knowledge to improve ADR signal detection with SML.

OBJECTIVE

We assess the utility of SML as a signal detection tool for ADRs in medication dispensing data with the consideration of domain knowledge from DrugBank and MedDRA. We compare the best performing SML method with SSA.

METHODS

We model the ADR signal detection problem as a supervised machine learning problem by linking medication dispensing data with domain knowledge bases. Suspected ADR signals are extracted from the Australian Pharmaceutical Benefit Scheme (PBS) medication dispensing data from 2013 to 2016. We construct predictive features for each signal candidate based on its occurrences in medication dispensing data as well as its pharmacological properties. Pharmaceutical knowledge bases including DrugBank and MedDRA are employed to provide pharmacological features for a signal candidate. Given a gold standard of known ADRs and non-ADRs, SML learns to differentiate between known ADRs and non-ADRs based on their combined predictive features from linked sources, and then predicts whether a new case is a potential ADR signal.

RESULTS

We evaluate the performance of six widely used SML methods with two gold standards of known ADRs and non-ADRs from previous studies. On average, gradient boosting classifier achieves the sensitivity of 77%, specificity of 81%, positive predictive value of 76%, negative predictive value of 82%, area under precision-recall curve of 81%, and area under receiver operating characteristic curve of 82%, most of which are higher than in other SML methods. In particular, gradient boosting classifier has 21% higher sensitivity than and comparable specificity with SSA. Furthermore, gradient boosting classifier detects 10% more unknown potential ADR signals than SSA.

CONCLUSIONS

Our study demonstrates that gradient boosting classifier is a promising supervised signal detection tool for ADRs in medication dispensing data to complement SSA.

An Investigation of Drug-Drug Interaction Alert Overrides at a Pediatric Hospital

OBJECTIVES

Drug-drug interactions (DDIs) can result in patient harm. DDI alerts are intended to help prevent harm; when the majority of alerts presented to providers are being overridden, their value is diminished. Our objective was to evaluate the overall rates of DDI alert overrides and how rates varied by specialty, clinician type, and patient complexity.

METHODS

A retrospective study of DDI alert overrides that occurred during 2012 and 2013 within the inpatient setting described at the medication-, hospital-, provider-, and patient encounter-specific levels was performed at an urban, quaternary-care, pediatric hospital.

RESULTS

There were >41 000 DDI alerts presented to clinicians; ∼90% were overridden. The 5 DDI pairs that were most frequently presented and overridden included the following: potassium chloride-spironolactone, methadone-ondansetron, ketorolac-ibuprofen, cyclosporine-fluconazole, and potassium chloride-enalapril, each with an alert override rate of ≥0.89. Override rates across provider groups ranged between 0.84 and 0.97. In general, patients with high complexity had a higher frequency of alert overrides, but the rates of alert overrides for each DDI pairing did not differ significantly.

CONCLUSIONS

High rates of DDI alert overrides occur across medications, provider groups, and patient encounters. Methods to decrease DDI alerts which are likely to be overridden exist, but it is also clear that more robust and intelligent tools are needed. Characteristics exist at the medication, hospital, provider, and patient levels that can be used to help specialize and enhance information transmission.

Quality of Decision Support in Computerized Provider Order Entry: Systematic Literature Review

Background

Computerized decision support systems have raised a lot of hopes and expectations in the field of order entry. Although there are numerous studies reporting positive impacts, concerns are increasingly high about alert fatigue and effective impacts of these systems. One of the root causes of fatigue alert reported is the low clinical relevance of these alerts.

Objective

The objective of this systematic review was to assess the reported positive predictive value (PPV), as a proxy to clinical relevance, of decision support systems in computerized provider order entry (CPOE).

Methods

A systematic search of the scientific literature published between February 2009 and March 2015 on CPOE, clinical decision support systems, and the predictive value associated with alert fatigue was conducted using PubMed database. Inclusion criteria were as follows: English language, full text available (free or pay for access), assessed medication, direct or indirect level of predictive value, sensitivity, or specificity. When possible with the information provided, PPV was calculated or evaluated.

Results

Additive queries on PubMed retrieved 928 candidate papers. Of these, 376 were eligible based on abstract. Finally, 26 studies qualified for a full-text review, and 17 provided enough information for the study objectives. An additional 4 papers were added from the references of the reviewed papers. The results demonstrate massive variations in PPVs ranging from 8% to 83% according to the object of the decision support, with most results between 20% and 40%. The best results were observed when patients’ characteristics, such as comorbidity or laboratory test results, were taken into account. There was also an important variation in sensitivity, ranging from 38% to 91%.

Conclusions

There is increasing reporting of alerts override in CPOE decision support. Several causes are discussed in the literature, the most important one being the clinical relevance of alerts. In this paper, we tried to assess formally the clinical relevance of alerts, using a near-strong proxy, which is the PPV of alerts, or any way to express it such as the rate of true and false positive alerts. In doing this literature review, three inferences were drawn. First, very few papers report direct or enough indirect elements that support the use or the computation of PPV, which is a gold standard for all diagnostic tools in medicine and should be systematically reported for decision support. Second, the PPV varies a lot according to the typology of decision support, so that overall rates are not useful, but must be reported by the type of alert. Finally, in general, the PPVs are below or near 50%, which can be considered as very low.

The use of computer-interpretable clinical guidelines to manage care complexities of patients with multimorbid conditions: A review

Clinical practice guidelines (CPGs) document evidence-based information and recommendations on treatment and management of conditions. CPGs usually focus on management of a single condition; however, in many cases a patient will be at the centre of multiple health conditions (multimorbidity). Multiple CPGs need to be followed in parallel, each managing a separate condition, which often results in instructions that may interact with each other, such as conflicts in medication. Furthermore, the impetus to deliver customised care based on patient-specific information, results in the need to be able to offer guidelines in an integrated manner, identifying and managing their interactions. In recent years, CPGs have been formatted as computer-interpretable guidelines (CIGs). This enables developing CIG-driven clinical decision support systems (CDSSs), which allow the development of IT applications that contribute to the systematic and reliable management of multiple guidelines. This study focuses on understanding the use of CIG-based CDSSs, in order to manage care complexities of patients with multimorbidity. The literature between 2011 and 2017 is reviewed, which covers: (a) the challenges and barriers in the care of multimorbid patients, (b) the role of CIGs in CDSS augmented delivery of care, and (c) the approaches to alleviating care complexities of multimorbid patients. Generating integrated care plans, detecting and resolving adverse interactions between treatments and medications, dealing with temporal constraints in care steps, supporting patient-caregiver shared decision making and maintaining the continuity of care are some of the approaches that are enabled using a CIG-based CDSS.

Pharmacogenomics-Based Point-of-Care Clinical Decision Support Significantly Alters Drug Prescribing

Changes in behavior are necessary to apply genomic discoveries to practice. We prospectively studied medication changes made by providers representing eight different medicine specialty clinics whose patients had submitted to preemptive pharmacogenomic genotyping. An institutional clinical decision support (CDS) system provided pharmacogenomic results using traffic light alerts: green = genomically favorable, yellow = genomic caution, red = high risk. The influence of pharmacogenomic alerts on prescribing behaviors was the primary endpoint. In all, 2,279 outpatient encounters were analyzed. Independent of other potential prescribing mediators, medications with high pharmacogenomic risk were changed significantly more often than prescription drugs lacking pharmacogenomic information (odds ratio (OR) = 26.2 (9.0-75.3), P < 0.0001). Medications with cautionary pharmacogenomic information were also changed more frequently (OR = 2.4 (1.7-3.5), P < 0.0001). No pharmacogenomically high-risk medications were prescribed during the entire study when physicians consulted the CDS tool. Pharmacogenomic information improved prescribing in patterns aimed at reducing patient risk, demonstrating that enhanced prescription decision-making is achievable through clinical integration of genomic medicine.

Cytochrome P450 interactions are common and consequential in Massachusetts hospital discharges

Despite the recognition that drug-drug interactions contribute substantially to preventable health-care costs, the prevalence of such interactions related to the cytochrome P450 system in clinical practice remains poorly characterized. This study drew retrospective hospital discharge cohorts from a large health claims data set and a large health system data set. For every hospital discharge, frequency of co-occurrence of substrates and inducers or inhibitors at cytochrome P450 2D6, 2C19, 3A4 and 1A2 were determined. A total of 124 520 individuals in the state of Massachusetts (health claims cohort) and 77 026 individuals in two large academic medical centers (electronic health record (EHR) cohort) were examined. In the claims cohort, 35 157 (28.2%) exhibited at least one CYP450 drug-drug interaction at hospital discharge, whereas in the EHR cohort, 36 750 (47.7%) had at least one interaction. The most commonly affected CYP450 systems were 2C19 and 2D6, with putative interactions observed in at least 10% of individuals at discharge in each cohort. Odds of hospital readmission within 90 days among those discharged with at least one interaction were 10-16% greater, with mean health-care cost $574/month greater over the subsequent year, after adjusting for age, sex, insurance type, total number of medications prescribed, Charlson comorbidity score and presence or absence of a psychiatric diagnosis. These two distinct clinical data types show that CYP450 drug-drug interactions are prevalent and associated with greater probability of early hospital readmission and greater health-care cost, despite the widespread availability and application of drug-drug interaction checking software.

Decrease in use of contraindicated drugs with automated alerts in children

BACKGROUND

This study evaluated changes in the use of contraindicated drugs in the pediatric population, via automated alerts through the nationwide drug utilization review.

METHODS

We conducted an interrupted time series analysis using the nationwide health insurance database. Study drugs consisted of a total of 72 drugs in 22 classes that were designated as age contraindicated between January 2007 and December 2011. The subjects consisted of the patients in Korea who had been prescribed with any of the study drugs at least once. Changes in the use of age-contraindicated drugs after the regulatory action were estimated as relative and absolute reductions with 95% CI. Regression analysis was carried out based on the monthly number of users prior to the announcement of age-contraindicated drugs on 3 December 2009 to estimate the predicted values, and these were then compared with the observed values after the announcement.

RESULTS

A total of 2 541 888 patients were prescribed age-contraindicated drugs at least once. The percentage of age-contraindicated drug users was 2.10% of the total users (n = 3 309 566) during the period prior to the 2009 regulatory action, but it decreased to 0.30% (n = 542 529) after the action. Overall, there was an 85.71% relative reduction (95% CI: 71.53-102.72) in the percentage of age-contraindicated drug users. The projected monthly number of users of age-contraindicated drugs showed a gradual downward trend.

CONCLUSION

Decreases in contraindicated drugs have accelerated after a regulatory action with automated alerts.

More Medicine, Fewer Clicks: How Informatics Can Actually Help Your Practice

In the information age, we expect data systems to make us more effective and efficient-not to make our lives more difficult! In this article, we discuss how we are using data systems, such as electronic health records (EHRs), to improve care delivery. We illustrate how US Oncology is beginning to use real-world evidence to facilitate trial accrual by automatic identification of eligible patients and how big data and predictive analytics will transform the field of oncology. Some information systems are already being used at the point of care and are already empowering clinicians to improve the care of their patients in real time. Telehealth platforms are being used to bridge gaps that currently exist in expertise, geography, and technical capability. Optimizing virtual collaboration, such as through virtual tumor boards, is empowering communities that are geographically disparate to coordinate care. Informatics methods can provide solutions to the challenging problems of how to manage the vast amounts of data confronting the practicing oncologist, including information about treatment regimens, side effects, and the influence of genomics on the practice of oncology. We also discuss some of the challenges of clinical documentation in the modern era, and review emerging efforts to engage patients as digital donors of their EHR data.

A comparison of five common drug-drug interaction software programs regarding accuracy and comprehensiveness

Objective:

Drug–drug interactions (DDIs) can cause failure in treatment and adverse events. DDIs screening software is an important tool to aid clinicians in the detection and management of DDIs. However, clinicians should be aware of the advantages and limitations of these programs. We compared the ability of five common DDI programs to detect clinically important DDIs.

Methods:

Lexi-Interact, Micromedex Drug Interactions, iFacts, Medscape, and Epocrates were evaluated. The programs' sensitivity, specificity, and positive and negative predictive values were determined to assess their accuracy in detecting DDIs. The accuracy of each program was identified using 360 unknown pair interactions, taken randomly from prescriptions, and forty pairs of clinically important ones. The major reference was a clinical pharmacist alongside the Stockley's Drug Interaction and databases including PubMed, Scopus, and Google Scholar. Comprehensiveness of each program was determined by the number of components in the drug interaction monograph. The aggregate score for accuracy and comprehensiveness was calculated.

Findings:

Scoring 250 out of possible 400 points, Lexi-Interact and Epocrates, provided the most accurate software programs. Micromedex, Medscape, and iFacts ranked third, fourth, and fifth, scoring 236, 202, and 191, respectively. In comprehensiveness test, iFacts showed the highest score, 134 out of possible 134 points, whereas Lexi-Interact rated second, with a score of 120. Scoring 370 and 330 out of possible 534 points, Lexi-Interact and Micromedex, respectively, provided the most competent, complete, and user-friendly applications.

Conclusion:

Lexi-Interact and Micromedex showed the best performances. An increase in sensitivity is possible by the combination of more than one programs and expert pharmacist intervention.

Clinical-pharmacist intervention reduces clinically relevant drug-drug interactions in patients with heart failure: A randomized, double-blind, controlled trial

BACKGROUND

Incidence of drug-drug interactions (DDIs) increases with complexity of treatment and comorbidities, as in heart failure (HF). This randomized, double-blind study evaluated the intervention of the pharmacist on prevalence of clinically relevant DDIs (NCT01855165).

METHODS

Patients admitted with HF were screened for clinically relevant DDIs, and randomized to control or intervention. All attending physicians received standard advice about pharmacological therapy; those in the intervention group also received alerts about clinically relevant DDIs. Primary endpoint was DDI at discharge and secondary were re-hospitalization or death during follow-up.

RESULTS

Of 213 patients, 51 (mean age, 79 ± 6 years; male, 47%) showed 66 clinically relevant DDIs and were randomized. For intervention (n=26) versus control (n=25), the number of patients with and the number of DDIs were significantly lower at discharge: 8 vs. 18 and 10 vs. 31; p=0.003 and 0.0049, respectively. Over a 6 month follow-up period, 11 control and 9 intervention patients were re-hospitalized or died (p>0.2 for all). No significant differences were seen between control and intervention for patients with eGFR <60 mL/min/1.73 m(2) (78%) for re-hospitalization or death (10 vs. 7; p=0.74).

CONCLUSIONS

Pharmacist intervention significantly reduces the number of patients with clinically relevant DDIs, but not clinical endpoints 6 months from discharge.

Physicians' responses to computerized drug interaction alerts with password overrides

Background

Although evidence has suggested that computerized drug-drug interaction alert systems may reduce the occurrence of drug-drug interactions, the numerous reminders and alerts generated by such systems could represent an excessive burden for clinicians, resulting in a high override rate of not only unimportant, but also important alerts.

Methods

We analyzed physicians’ responses to alerts of relative contraindications and contraindications for coadministration in a computerized drug-drug interaction alert system at Hokkaido University Hospital. In this system, the physician must enter a password to override an alert and continue an order. All of the drug-drug interaction alerts generated between December 2011 and November 2012 at Hokkaido University Hospital were included in this study.

Results

The system generated a total of 170 alerts of relative contraindications and contraindication for coadministration; 59 (34.7 %) of the corresponding orders were cancelled after the alert was accepted, and 111 (65.3 %) were overridden. The most frequent contraindication alert was for the combination of 3-hydroxy-3-methylglutaryl–coenzyme A reductase inhibitors and fibrates. No incidents involving drug-drug interactions were reported among patients who were prescribed contraindicated drug pairs after an override.

Conclusions

Although computerized drug-drug interaction alert systems that require password overrides appear useful for promoting medication safety, having to enter passwords to override alerts may represent an excessive burden for the prescribing physician. Therefore, both patient safety and physicians’ workloads should be taken into consideration in future designs of computerized drug-drug interaction alert systems.

Co-Prescribing of Potentially Interacting Drugs during Warfarin Therapy - A Population-Based Register Study

We analysed the occurrence of co-prescribing of potentially interacting drugs during warfarin therapy in the community-dwelling population of Finland. We identified drugs having interaction potential with warfarin using the Swedish Finnish INteraction X-referencing drug-drug interaction database (SFINX) and obtained data on drug purchases from the nationwide Prescription Register. We defined warfarin users as persons purchasing warfarin in 2010 (n = 148,536) and followed them from their first prescription in 2010 until the end of the calendar year. Co-prescribing was defined as at least 1-day overlap between warfarin and interacting drug episodes. In addition, we identified persons who initiated warfarin therapy between 1 January 2007 and 30 September 2010 (n = 110,299) and followed these incident users for a 3-month period since warfarin initiation. Overall, 74.4% of warfarin users were co-prescribed interacting drugs. Co-prescribing covered 46.4% of the total person-years of warfarin exposure. Interacting drugs that should be avoided with warfarin were co-prescribed for 13.4% of warfarin users. The majority of the co-prescriptions were for drugs that are not contraindicated during warfarin therapy but require special consideration. Among incident users, 57.1% purchased potentially interacting drugs during the 3-month period after initiation, while 9.0% purchased interacting drugs that should be avoided with warfarin. To conclude, the occurrence of co-prescribing of potentially interacting drugs was high during warfarin therapy. Our findings highlight the importance of close monitoring of warfarin therapy and the need for further studies on the clinical consequences of co-prescribing of interacting drugs with warfarin.

A 2014 medical informatics perspective on clinical decision support systems: do we hit the ceiling of effectiveness?

OBJECTIVE

To summarize recent research and propose a selection of best papers published in 2013 in the field of computer-based decision support in health care.

METHOD

Two literature reviews were performed by the two section editors from bibliographic databases with a focus on clinical decision support systems (CDSSs) and computer provider order entry in order to select a list of candidate best papers to be peer-reviewed by external reviewers.

RESULTS

The full review process highlighted three papers, illustrating current trends in the domain of clinical decision support. The first trend is the development of theoretical approaches for CDSSs, and is exemplified by a paper proposing the integration of family histories and pedigrees in a CDSS. The second trend is illustrated by well-designed CDSSs, showing good theoretical performances and acceptance, while failing to show a clinical impact. An example is given with a paper reporting on scorecards aiming to reduce adverse drug events. The third trend is represented by research works that try to understand the limits of CDSS use, for instance by analyzing interactions between general practitioners, patients, and a CDSS.

CONCLUSIONS

CDSSs can achieve good theoretical results in terms of sensibility and specificity, as well as a good acceptance, but evaluations often fail to demonstrate a clinical impact. Future research is needed to better understand the causes of this observation and imagine new effective solutions for CDSS implementation.

We work with them? Healthcare workers interpretation of organizational relations mined from electronic health records

OBJECTIVE

Models of healthcare organizations (HCOs) are often defined up front by a select few administrative officials and managers. However, given the size and complexity of modern healthcare systems, this practice does not scale easily. The goal of this work is to investigate the extent to which organizational relationships can be automatically learned from utilization patterns of electronic health record (EHR) systems.

METHOD

We designed an online survey to solicit the perspectives of employees of a large academic medical center. We surveyed employees from two administrative areas: (1) Coding & Charge Entry and (2) Medical Information Services and two clinical areas: (3) Anesthesiology and (4) Psychiatry. To test our hypotheses we selected two administrative units that have work-related responsibilities with electronic records; however, for the clinical areas we selected two disciplines with very different patient responsibilities and whose accesses and people who accessed were similar. We provided each group of employees with questions regarding the chance of interaction between areas in the medical center in the form of association rules (e.g., Given someone from Coding & Charge Entry accessed a patient's record, what is the chance that someone from Medical Information Services access the same record?). We compared the respondent predictions with the rules learned from actual EHR utilization using linear-mixed effects regression models.

RESULTS

The findings from our survey confirm that medical center employees can distinguish between association rules of high and non-high likelihood when their own area is involved. Moreover, they can make such distinctions between for any HCO area in this survey. It was further observed that, with respect to highly likely interactions, respondents from certain areas were significantly better than other respondents at making such distinctions and certain areas' associations were more distinguishable than others.

CONCLUSIONS

These results illustrate that EHR utilization patterns may be consistent with the expectations of HCO employees. Our findings show that certain areas in the HCO are easier than others for employees to assess, which suggests that automated learning strategies may yield more accurate models of healthcare organizations than those based on the perspectives of a select few individuals.

Potential drug-drug interactions in pediatric outpatient prescriptions for newborns and infants

OBJECTIVES

To surveyed the quantities, types, and related information of potential drug-drug interactions (DDIs) and estimate the off-label use percentage of pediatric outpatient prescriptions for newborns and infants from the National Health Insurance Research Database (NHIRD) of Taiwan.

BACKGROUND

Adverse drug reactions (ADR) may cause morbidity and mortality, potential drug-drug interactions (DDI) increase the probability of ADR. Research on ADR and DDI in infants is of particular urgency and importance but the related profiles in these individuals are not well known.

METHODS

All prescriptions written by physicians in 2000 were analyzed to identify potential DDIs among drugs appearing on the same prescription sheet.

RESULTS

Of a total of 150.6 million prescription sheets, with 669.5 million prescriptions registered in the NHIRD of Taiwan, six million (3.99%) prescription sheets were for 2.1 million infants with 19.4 million (2.85%) prescriptions. There were 672,020 potential DDIs in this category, accounting for 3.53% per prescription; an estimated one DDI in every three patients. The interactions between aspirin and aluminum/magnesium hydroxide were most common (4.42%). Of the most significant drug-drug interactions, the interaction of digoxin with furosemide ranked first (20.14%), followed by the interactions of cisapride with furosemide and erythromycin (6.02% and 4.85%, respectively). The interactions of acetaminophen and anti-cholinergic agents comprised most types of drug-drug interactions (6.62%).

CONCLUSION

Although the prevalence rates of DDIs are low, life-threatening interactions may develop. Physicians must be reminded of the potential DDIs when prescribing medications for newborns and infants.