GPs' views on computerized drug interaction alerts: questionnaire survey

An alert tool to promote lung protective ventilation for possible acute respiratory distress syndrome

Objective

Computer-aided decision tools may speed recognition of acute respiratory distress syndrome (ARDS) and promote consistent, timely treatment using lung-protective ventilation (LPV). This study evaluated implementation and service (process) outcomes with deployment and use of a clinical decision support (CDS) synchronous alert tool associated with existing computerized ventilator protocols and targeted patients with possible ARDS not receiving LPV.

Materials and Methods

We performed an explanatory mixed methods study from December 2019 to November 2020 to evaluate CDS alert implementation outcomes across 13 intensive care units (ICU) in an integrated healthcare system with >4000 mechanically ventilated patients annually. We utilized quantitative methods to measure service outcomes including CDS alert tool utilization, accuracy, and implementation effectiveness. Attitudes regarding the appropriateness and acceptability of the CDS tool were assessed via an electronic field survey of physicians and advanced practice providers.

Results

Thirty-eight percent of study encounters had at least one episode of LPV nonadherence. Addition of LPV treatment detection logic prevented an estimated 1812 alert messages (41%) over use of disease detection logic alone. Forty-eight percent of alert recommendations were implemented within 2 h. Alert accuracy was estimated at 63% when compared to gold standard ARDS adjudication, with sensitivity of 85% and positive predictive value of 62%. Fifty-seven percent of survey respondents observed one or more benefits associated with the alert.

Conclusion

Introduction of a CDS alert tool based upon ARDS risk factors and integrated with computerized ventilator protocol instructions increased visibility to gaps in LPV use and promoted increased adherence to LPV.

General practitioner and nurse practitioner attitudes towards electronic reminders in primary care: a qualitative analysis

OBJECTIVES

Reminders in primary care administrative systems aim to help clinicians provide evidence-based care, prescribe safely and save money. However, increased use of reminders can lead to alert fatigue. Our study aimed to assess general practitioners' (GPs) and nurse practitioners' (NPs) views on electronic reminders in primary care.

DESIGN

A qualitative analysis using semistructured interviews.

SETTING AND PARTICIPANTS

Fifteen GPs and NP based in general practices located in North-West London and Yorkshire, England.

METHODS

We collected data on participants' views on: (1) perceptions of the value of information provided; (2) reminder-related behaviours and (3) how to improve reminders. We carried out a thematic analysis.

RESULTS

Participants were familiar with reminders in their clinical systems and felt many were important to support their clinical work. However, participants reported, on average, 70% of reminders were ignored. Four major themes emerged: (1) reaction to a reminder, which was mixed and varied by situation. (2) Factors influencing the decision to act on reminders, often related to experience, consultation styles and interests of participants. Time constraints, alert design, inappropriate presentation and litigation were also factors. (3) Negative consequences of using reminders were increased workload or costs and compromising GP and NPs behaviour. (4) Factors relating to improving users' engagement with reminders were prevention of unnecessary reminders through data linkage across healthcare administrative systems or the development of more intelligent algorithms. Participants felt training was vital to effectively manage reminders.

CONCLUSIONS

GPs and NPs believe reminders are useful in supporting the provision of good quality patient care. Improving GPs and NPs' engagement with reminders centres on further developing their relevance to their clinical practice, which is personalised, considers cognitive workflow and suppresses inappropriate presentation.

Barriers and facilitators to the adoption of electronic clinical decision support systems: a qualitative interview study with UK general practitioners

Background

Well-established electronic data capture in UK general practice means that algorithms, developed on patient data, can be used for automated clinical decision support systems (CDSSs). These can predict patient risk, help with prescribing safety, improve diagnosis and prompt clinicians to record extra data. However, there is persistent evidence of low uptake of CDSSs in the clinic. We interviewed UK General Practitioners (GPs) to understand what features of CDSSs, and the contexts of their use, facilitate or present barriers to their use.

Methods

We interviewed 11 practicing GPs in London and South England using a semi-structured interview schedule and discussed a hypothetical CDSS that could detect early signs of dementia. We applied thematic analysis to the anonymised interview transcripts.

Results

We identified three overarching themes: trust in individual CDSSs; usability of individual CDSSs; and usability of CDSSs in the broader practice context, to which nine subthemes contributed. Trust was affected by CDSS provenance, perceived threat to autonomy and clear management guidance. Usability was influenced by sensitivity to the patient context, CDSS flexibility, ease of control, and non-intrusiveness. CDSSs were more likely to be used by GPs if they did not contribute to alert proliferation and subsequent fatigue, or if GPs were provided with training in their use.

Conclusions

Building on these findings we make a number of recommendations for CDSS developers to consider when bringing a new CDSS into GP patient records systems. These include co-producing CDSS with GPs to improve fit within clinic workflow and wider practice systems, ensuring a high level of accuracy and a clear clinical pathway, and providing CDSS training for practice staff. These recommendations may reduce the proliferation of unhelpful alerts that can result in important decision-support being ignored.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12911-021-01557-z.

Evaluation of context-specific alerts for potassium-increasing drug-drug interactions: A pre-post study

OBJECTIVE

To investigate whether context-specific alerts for potassium-increasing drug-drug interactions (DDIs) in a clinical decision support system reduced the alert burden, increased alert acceptance, and had an effect on the occurrence of hyperkalemia.

MATERIALS AND METHODS

In the pre-intervention period all alerts for potassium-increasing DDIs were level 1 alerts advising absolute contraindication, while in the post-intervention period the same drug combinations could trigger a level 1 (absolute contraindication), a level 2 (monitor potassium values), or a level 3 alert (informative, not shown to physicians) based on the patient's recent laboratory value of potassium. Alert acceptance was defined as non-prescription or non-administration of the interacting drug combination for level 1 alerts and as monitoring of the potassium levels for level 2 alerts.

RESULTS

The alert burden decreased by 92.8%. The relative risk (RR) for alert acceptance based on prescription rates for level 1 alerts and monitoring rates for level 2 alerts was 15.048 (86.5% vs 5.7%; 95% CI 12.037-18.811; P < 0.001). With alert acceptance for level 1 alerts based on actual administration and for level 2 alerts on monitoring rates, the RR was 3.597 (87.6% vs 24.4%; 95% CI 3.192-4.053; P < 0.001). In the generalized linear mixed model the effect of the intervention on the occurrence of hyperkalemia was not significant (OR 1.091, 95% CI 0.172-6.919).

CONCLUSION

The proposed strategy seems effective to get a grip on the delicate balance between over- and under alerting.

Evaluation of community pharmacists' knowledge about drug-drug interaction in Central Saudi Arabia

Introduction

Although all implemented and ongoing initiatives, drug-drug interactions (DDIs) are still a global problem. Most published studies about DDIs in Saudi Arabia are carried out in hospital settings. In addition, assessing the knowledge of drug interactions in Saudi Arabia is limited. The aim of our study is to evaluate the knowledge of potential common drug-drug interactions among community pharmacists particularly in Saudi Arabia.

Methodology

A crosses-sectional study utilizing a self- administered questionnaire was conducted among community pharmacy in Riyadh city Saudi Arabia. DDIs' knowledge was assessed by 26 drug pairs. Community pharmacists were asked to select the DDIs as “contraindication”, “may be used together with monitoring”, “no interaction” and “not sure”.

Results

A total of 283 of community pharmacists completed the survey with response rate of 80.9%. Among the 26 drug pairs only 5 of them were identified correctly by most of the participants. To add more 3 out of the 5 pairs had a cutoff of less than 10% between the correct and wrong answer, meaning there still a majority that couldn't identify the correct answer. All the 26 pairs had a statistically significant difference between the correct and incorrect answer.

Conclusion

The results of this study showed that knowledge of community pharmacists about DDIs was inadequate. Community pharmacist should have specific courses in drug interactions to cover the most possible interactions that can be seen in this setting.

Attitude of Physicians Towards Automatic Alerting in Computerized Physician Order Entry Systems

Objectives: To analyze the attitude of physicians towards alerting in CPOE systems in different hospitals in different countries, addressing various organizational and technical settings and the view of physicians not currently using a CPOE.

Methods: A cross-sectional quantitative and qualitative questionnaire survey. We invited 2,600 physicians in eleven hospitals from nine countries to participate. Eight of the hospitals had different CPOE systems in use, and three of the participating hospitals were not using a CPOE system.

Results: 1,018 physicians participated. The general attitude of the physicians towards CPOE alerting is positive and is found to be mostly independent of the country, the specific organizational settings in the hospitals and their personal experience with CPOE systems. Both quantitative and qualitative results show that the majority of the physicians, both CPOE-users and non-users, appreciate the benefits of alerting in CPOE systems on medication safety. However, alerting should be better adapted to the clinical context and make use of more sophisticated ways to present alert information. The vast majority of physicians agree that additional information regarding interactions is useful on demand. Around half of the respondents see possible alert overload as a major problem; in this regard, physicians in hospitals with sophisticated alerting strategies show partly better attitude scores.

Conclusions: Our results indicate that the way alerting information is presented to the physicians may play a role in their general attitude towards alerting, and that hospitals with a sophisticated alerting strategy with less interruptive alerts tend towards more positive attitudes. This aspect needs to be further investigated in future studies.

The Role of Technicians in Managing Computerized Drug–Drug Interaction Alerts in Community Pharmacies and the Relationship to Pharmacist Managers' Attitudes

Background:

Community pharmacies are where identification and prevention of drug–drug interactions (DDIs) typically occur. Technicians have been shown to play some role in the initial screening of DDIs in community pharmacies.

Objectives:

To examine the role of technicians in the management of DDI alerts in community pharmacies and the possible relationship to the attitudes of pharmacy managers toward DDI alerts.

Methods:

A national survey of pharmacist managers in 3,000 community pharmacies was conducted. Data collected included demographics, workload issues, handling of DDIs, and pharmacists' attitudes toward computerized DDI alerts.

Results:

Of questionnaires returned, 736 could be used. Technicians were more often allowed or sometimes allowed to override lower level DDI alerts without prior review by a pharmacist. However, some pharmacies (2.1%) allowed or sometimes allowed technicians to override interactions with the highest potential clinical significance. Stores with the highest use of technology were less likely to allow technicians to override insignificant interactions. Stores that allow technicians to override clinically significant interactions were less confident in their program's ability to provide meaningful alerts and more likely to agree that alerts are a waste of time and that the volume of alerts makes differentiating important from unimportant DDIs more difficult.

Conclusions:

Pharmacy technicians play a limited role in the management of DDI alerts, but could be used to help ensure that a patient's profile of medications is up-to-date and determine whether the patient had already been receiving the combination without notable problem.

Computerized techniques pave the way for drug-drug interaction prediction and interpretation

INTRODUCTION

Health care industry also patients penalized by medical errors that are inevitable but highly preventable. Vast majority of medical errors are related to adverse drug reactions, while drug-drug interactions (DDIs) are the main cause of adverse drug reactions (ADRs). DDIs and ADRs have mainly been reported by haphazard case studies. Experimental in vivo and in vitro researches also reveals DDI pairs. Laboratory and experimental researches are valuable but also expensive and in some cases researchers may suffer from limitations.

METHODS

In the current investigation, the latest published works were studied to analyze the trend and pattern of the DDI modelling and the impacts of machine learning methods. Applications of computerized techniques were also investigated for the prediction and interpretation of DDIs.

RESULTS

Computerized data-mining in pharmaceutical sciences and related databases provide new key transformative paradigms that can revolutionize the treatment of diseases and hence medical care. Given that various aspects of drug discovery and pharmacotherapy are closely related to the clinical and molecular/biological information, the scientifically sound databases (e.g., DDIs, ADRs) can be of importance for the success of pharmacotherapy modalities.

CONCLUSION

A better understanding of DDIs not only provides a robust means for designing more effective medicines but also grantees patient safety.

Impact of electronic-alerting of acute kidney injury: workgroup statements from the 15(th) ADQI Consensus Conference

Purpose of the review

Among hospitalized patients, acute kidney injury is common and associated with significant morbidity and risk for mortality. The use of electronic health records (EHR) for prediction and detection of this important clinical syndrome has grown in the past decade. The steering committee of the 15^th Acute Dialysis Quality Initiative (ADQI) conference dedicated a workgroup with the task of identifying elements that may impact the course of events following Acute Kidney Injury (AKI) e-alert.

Sources of information

Following an extensive, non-systematic literature search, we used a modified Delphi process to reach consensus regarding several aspects of the utilization of AKI e-alerts.

Findings

Topics discussed in this workgroup included progress in evidence base practices, the characteristics of an optimal e-alert, the measures of efficacy and effectiveness, and finally what responses would be considered best practices following AKI e-alerts. Authors concluded that the current evidence for e-alert system efficacy, although growing, remains insufficient. Technology and human-related factors were found to be crucial elements of any future investigation or implementation of such tools. The group also concluded that implementation of such systems should not be done without a vigorous plan to evaluate the efficacy and effectiveness of e-alerts. Efficacy and effectiveness of e-alerts should be measured by context-specific process and patient outcomes. Finally, the group made several suggestions regarding the clinical decision support that should be considered following successful e-alert implementation.

Limitations

This paper reflects the findings of a non-systematic review and expert opinion.

Implications

We recommend implementation of the findings of this workgroup report for use of AKI e-alerts.

New Alert Override Codes for the Drug Utilization Review System Derived from Outpatient Prescription Data from a Tertiary Teaching Hospital in Korea

Objectives

This paper proposes new alert override reason codes that are improvements on existing Drug Utilization Review (DUR) codes based on an analysis of DUR alert override cases in a tertiary medical institution.

Methods

Data were obtained from a tertiary teaching hospital covering the period from April 1, 2012 to January 15, 2013. We analyzed cases in which doctors had used the 11 overlapping prescription codes provided by the Health Insurance Review and Assessment Service (HIRA) or had provided free-text reasons.

Results

We identified 27,955 alert override cases. Among these, 7,772 (27.8%) utilized the HIRA codes, and 20,183 (72.2%) utilized free-text reasons. According to the free-text content analysis, 8,646 cases (42.8%) could be classified using the 11 HIRA codes, and 11,537 (57.2%) could not. In the unclassifiable cases, we identified the need for codes for "prescription relating to operation" and "emergency situations." Two overlapping prescription codes required removal because they were not used. Codes A, C, F, H, I, and J (for drug non-administration cases) explained surrounding situations in too much detail, making differentiation between them difficult. These 6 codes were merged into code J4: "patient was not taking/will not take the medications involved in the DDI." Of the 11 HIRA codes, 6 were merged into a single code, 2 were removed, and 2 were added, yielding 6 alert override codes. We could codify 23,550 (84.2%) alert override cases using these codes.

Conclusions

These new codes will facilitate the use of the drug–drug interactions alert override in the current DUR system. For further study, an appropriate evaluation should be conducted with prescribing clinicians.

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.

Important drug classes associated with potential drug-drug interactions in critically ill patients: highlights for cardiothoracic intensivists

BACKGROUND

Patients in the intensive care unit (ICU) are more prone to drug-drug interactions (DDIs). The software and charts that indicate all interactions may not be proper for clinical usage. This study aimed to identify the main drug classes associated with clinically significant DDIs in cardiothoracic ICU and categorize DDIs to make cardiothoracic intensivists aware of safe medication usage.

METHODS

This prospective study was conducted over 6 months in a cardiothoracic ICU of a university-affiliated teaching hospital. The presence of potential drug-drug interactions (pDDIs) was assessed by a clinical pharmacologist using Lexi-Interact database. Clinically significant pDDIs were defined according to severity and reliability rating. Interacting drug classes, mechanisms, and recommendations were identified for each interaction.

RESULTS

From 1780 administered drugs, 496 lead to major (D) and contraindicated (X) interactions. Nine drug classes were responsible for D and/or X interactions with excellent (E) and/or good (G) reliability. Anti-infective agents (45.87 %) were the main drug classes that caused clinically significant pDDIs followed by central nervous system drugs (14.67 %). Azole antifungals as the most interacting antimicrobial agents precipitated metabolism inhibition of CYP3A substrates.

CONCLUSIONS

Clinically significant pDDIs as potential patient safety risks were prevalent in critically ill patients. The findings from current study help to improve knowledge and awareness of clinicians in this area and minimize adverse events due to pDDIs.

Alert dwell time: introduction of a measure to evaluate interruptive clinical decision support alerts

Metrics for evaluating interruptive prescribing alerts have many limitations. Additional methods are needed to identify opportunities to improve alerting systems and prevent alert fatigue. In this study, the authors determined whether alert dwell time-the time elapsed from when an interruptive alert is generated to when it is dismissed-could be calculated by using historical alert data from log files. Drug-drug interaction (DDI) alerts from 3 years of electronic health record data were queried. Alert dwell time was calculated for 25,965 alerts, including 777 unique DDIs. The median alert dwell time was 8 s (range, 1-4913 s). Resident physicians had longer median alert dwell times than other prescribers (P < 001). The 10 most frequent DDI alerts (n = 8759 alerts) had shorter median dwell times than alerts that only occurred once (P < 001). This metric can be used in future research to evaluate the effectiveness and efficiency of interruptive prescribing alerts.

Identifying effective computerized strategies to prevent drug-drug interactions in hospital: A user-centered approach

BACKGROUND

Drug-drug interactions (DDIs) are an important and preventable cause of medication errors in hospitals. Recent developments in technology have seen new strategies emerge for preventing DDIs but these computerized strategies are rarely evaluated and are typically implemented with little input from the individuals using them.

AIM

To determine the opinions of both experts and users (prescribers) on computerized strategies available to assist in the identification and prevention of DDIs in hospitals.

METHOD

Eight drug safety experts and 18 prescribers took part in semi-structured interviews. Participants were asked about their confidence in identifying DDIs and their views on potential computerized strategies to prevent DDIs.

RESULTS

No prescribers reported complete confidence in identifying dangerous DDIs, with junior prescribers appearing less confident than senior prescribers. Most prescribers believed that computerized alerts would be the most effective strategy for preventing DDIs, while experts were more critical of alerts.

CONCLUSION

The lack of confidence displayed by prescribers in their ability to identify DDIs suggests that an appropriate strategy would be one that does not rely on individuals seeking out the information themselves. While a large number of problems related to DDI alert implementation have been reported in the literature (e.g. alert overload), prescribers appeared to be receptive to the idea of being alerted. By ensuring users are aware of the limitations of the system and involving them in DDI strategy design we expect greater use and satisfaction with the adopted strategy.

Design and Evaluation of an Electronic Override Mechanism for Medication Alerts to Facilitate Communication Between Prescribers and Pharmacists

Background: Computerized medication alerts can often be bypassed by entering an override rationale, but prescribers’ override reasons are frequently ambiguous to pharmacists who review orders. Objective: To develop and evaluate a new override mechanism for adverse reaction and drug-drug interaction alerts. We hypothesized that the new mechanism would improve usability for prescribers and increase the clinical appropriateness of override reasons. Methods: A counterbalanced, crossover study was conducted with 20 prescribers in a simulated prescribing environment. We modified the override mechanism timing, navigation, and text entry. Instead of free-text entry, the new mechanism presented prescribers with a predefined set of override reasons. We assessed usability (learnability, perceived efficiency, and usability errors) and used a priori criteria to evaluate the clinical appropriateness of override reasons entered. Results: Prescribers rated the new mechanism as more efficient (Wilcoxon signed-rank test, P = 0.032). When first using the new design, 5 prescribers had difficulty finding the new mechanism, and 3 interpreted the navigation to mean that the alert could not be overridden. The number of appropriate override reasons significantly increased with the new mechanism compared with the original mechanism (median change of 3.0; interquartile range = 3.0; P < 0.0001). Conclusions: When prescribers were given a menu-based choice for override reasons, clinical appropriateness of these reasons significantly improved. Further enhancements are necessary, but this study is an important first step toward a more standardized menu of override choices. Findings may be used to improve communication through e-prescribing systems between prescribers and pharmacists.

Evaluation of usage patterns and user perception of the drug-drug interaction database SFINX

PURPOSE

The aim of the present study was to investigate how prescribers and pharmacists use and perceive the drug-drug interaction database SFINX in their clinical work.

METHODS

A questionnaire was developed with questions aimed at the usage of SFINX, and the perceptions of the database. The questionnaire was sent out to all registered users of the web application of SFINX. The anonymous answers from the target users, prescribers and pharmacists were summarized using descriptive statistics. Statistical analysis was performed on age and gender differences for some questions regarding different usage patterns.

RESULTS

The questionnaire was sent to 11,763 registered SFINX users. The response rate was 23%, including 1871 answers from prescribers or pharmacists. SFINX was reported to be used at least weekly or more often by 45% of the prescribers and 51% of the pharmacists. Many prescribers reported using the database during the patient consultation (60%) or directly before or after (56%). Among the prescribers, 74% reported that the information received made them change their action at least sometimes. About 20% of the prescribers and 25% of the pharmacists considered the information as irrelevant sometimes or more often.

CONCLUSION

Most prescribers and pharmacists reported using SFINX in direct association with a patient consultation. Information received by using SFINX makes prescribers and pharmacists change their handling of patients. DDI databases with relevant information about patient handling might improve drug treatment outcome.

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.

Differences of Reasons for Alert Overrides on Contraindicated Co-prescriptions by Admitting Department

OBJECTIVES

To reveal differences in drug-drug interaction (DDI) alerts and the reasons for alert overrides between admitting departments.

METHODS

A retrospective observational study was performed using longitudinal Electronic Health Record (EHR) data and information from an alert and logging system. Adult patients hospitalized in the emergency department (ED) and general ward (GW) during a 46-month period were included. For qualitative analyses, we manually reviewed all reasons for alert overrides, which were recorded as free text in the EHRs.

RESULTS

Among 14,780,519 prescriptions, 51,864 had alerts for DDIs (0.35%; 1.32% in the ED and 0.23% in the GW). The alert override rate was higher in the ED (94.0%) than in the GW (57.0%) (p < 0.001). In an analysis of the study population, including ED and GW patients, 'clinically irrelevant alert' (52.0%) was the most common reason for override, followed by 'benefit assessed to be greater than the risk' (31.1%) and 'others' (17.3%). The frequency of alert overrides was highest for anti-inflammatory and anti-rheumatic drugs (89%). In a sub-analysis of the population, 'clinically irrelevant alert' was the most common reason for alert overrides in the ED (69.3%), and 'benefit assessed to be greater than the risk' was the most common reason in the GW (61.4%).

CONCLUSIONS

We confirmed that the DDI alerts and the reasons for alert overrides differed by admitting department. Different strategies may be efficient for each admitting department.

Comparison of two databases to detect potential drug-drug interactions between prescriptions of HIV/AIDS patients in critical care

WHAT IS KNOWN AND OBJECTIVE

Adverse drug events (ADE), common and underestimated in ICU patients, have direct consequences on length of stay, mortality and hospital costs. Critically ill patients with HIV/AIDS are at a high risk of ADE because of their need for multiple drug therapies. ADE can be prevented, especially by the identification of potentially harmful drug-drug interactions (DDIs). Electronic databases are useful tools for the investigation of DDIs to avoid potential ADEs, thereby increasing patient safety. The purpose of this study was to compare the classification and severity rating of potential adverse drug interactions seen in the prescriptions for patients with HIV/AIDS in two databases, one with free access (Drugs.com(™)) and another requiring payment for access (Micromedex(®)).

METHODS

A cross-sectional retrospective study of the prescriptions issued for 40 ICU HIV/AIDS patients on mechanical ventilation, admitted for more than 48 h, in a referral hospital for infectious diseases in Rio de Janeiro, Brazil, was undertaken. One prescription was reviewed each week for each patient from the second day after admission. A list of all drug-drug interactions was generated for each patient using the two drug-drug interactions databases. The weighted kappa index was estimated to assess the agreement between the classifications of DDIs identified by both databases and qualitative assessment made of any discordant classification of recorded drug-drug interactions.

RESULTS AND DISCUSSION

Of the 106 prescriptions analysed, Micromedex(®) and Drugs.com identified 347 and 615 potential DDIs, respectively. A predominance of moderate interactions and pharmacokinetic interactions was observed. The agreement between the databases regarding the severity rating was only 68.3%. The weighted kappa of 0.44 is considered moderate. Better agreement (82.4%) was observed in the classification of mechanism of interaction, with a weighted kappa of 0.61.

WHAT IS NEW AND CONCLUSION

DDIs are common between the prescriptions of patients with HIV/AIDS admitted to the ICU. Although both databases were able to identify the clinically relevant DDIs, we observed a significant discrepancy in the classification of the severity of DDIs in the two bases. The free access database could serve as an alternative to the identification of DDIs in resource-limited settings; however, there is a need for better evidence-based assessments for your use on clinical management of more serious DDIs.

Understanding and preventing drug-drug and drug-gene interactions

Concomitant administration of multiple drugs can lead to unanticipated drug interactions and resultant adverse drug events with their associated costs. A more thorough understanding of the different cytochrome P450 isoenzymes and drug transporters has led to new methods to try to predict and prevent clinically relevant drug interactions. There is also an increased recognition of the need to identify the impact of pharmacogenetic polymorphisms on drug interactions. More stringent regulatory requirements have evolved for industry to classify cytochrome inhibitors and inducers, test the effect of drug interactions in the presence of polymorphic enzymes, and evaluate multiple potentially interacting drugs simultaneously. In clinical practice, drug alert software programs have been developed. This review discusses drug interaction mechanisms and strategies for screening and minimizing exposure to drug interactions. We also provide future perspectives for reducing the risk of clinically significant drug interactions.

The causes of prescribing errors in English general practices: a qualitative study

BACKGROUND

Few detailed studies exist of the underlying causes of prescribing errors in the UK.

AIM

To examine the causes of prescribing and monitoring errors in general practice and provide recommendations for how they may be overcome.

DESIGN AND SETTING

Qualitative interview and focus group study with purposive sampling of English general practices.

METHOD

General practice staff from 15 general practices across three PCTs in England participated in a combination of semi-structured interviews (n = 34) and six focus groups (n = 46). Thematic analysis informed by Reason's Accident Causation Model was used.

RESULTS

Seven categories of high-level error-producing conditions were identified: the prescriber, the patient, the team, the working environment, the task, the computer system, and the primary-secondary care interface. These were broken down to reveal various error-producing conditions: the prescriber's therapeutic training, drug knowledge and experience, knowledge of the patient, perception of risk, and their physical and emotional health; the patient's characteristics and the complexity of the individual clinical case; the importance of feeling comfortable within the practice team was highlighted, as well as the safety implications of GPs signing prescriptions generated by nurses when they had not seen the patient for themselves; the working environment with its extensive workload, time pressures, and interruptions; and computer-related issues associated with mis-selecting drugs from electronic pick-lists and overriding alerts were all highlighted as possible causes of prescribing errors and were often interconnected.

CONCLUSION

Complex underlying causes of prescribing and monitoring errors in general practices were highlighted, several of which are amenable to intervention.

Physician's expectations regarding prescribing clinical decision support systems in a Belgian hospital

OBJECTIVES

Developing and implementing clinical decision support systems (CDSSs) is time-consuming and costly. Therefore, prioritization of the most relevant systems is warranted. The physician's perceived usefulness has been identified as a decisive reason for using CDSSs. The objective of this study was to investigate the physician's perceived usefulness of different types of CDSSs and to identify the user needs and expectations regarding future CDSSs.

METHODS

Cross-sectional single-centre survey among physicians with a clinical assignment in a university hospital. Physicians were questioned about their current experiences with drug prescribing and the perceived usefulness and desired features of future CDSSs.

RESULTS

One hundred and sixty-four physicians completed the survey (52·6%). The majority acknowledged that it is very difficult to take all relevant information into account when prescribing drugs. Drug-drug interaction checking, drug-allergy checking, and dosing guidance were considered as most useful. Automated clinical guidelines and adverse drug event monitoring were considered as least useful. The user-friendliness of the systems, clinical relevance of the alerts, and prevention of alert fatigue were perceived as important aspects for a successful implementation.

CONCLUSIONS

From the physicians' perspective drug-drug interaction checking, drug-allergy checking, and dosing guidance should receive the highest priority for development and implementation. Because the perceived usefulness has been identified as a decisive reason for using CDSSs, it seems feasible to take into account this prioritization when developing and implementing CDSSs. In order to overcome the physicians' perceived disadvantages, attention should go to the development of user-friendly systems that deliver clinical relevant alerts.

DDI-CPI, a server that predicts drug-drug interactions through implementing the chemical-protein interactome

Drug–drug interactions (DDIs) may cause serious side-effects that draw great attention from both academia and industry. Since some DDIs are mediated by unexpected drug–human protein interactions, it is reasonable to analyze the chemical–protein interactome (CPI) profiles of the drugs to predict their DDIs. Here we introduce the DDI-CPI server, which can make real-time DDI predictions based only on molecular structure. When the user submits a molecule, the server will dock user's molecule across 611 human proteins, generating a CPI profile that can be used as a feature vector for the pre-constructed prediction model. It can suggest potential DDIs between the user's molecule and our library of 2515 drug molecules. In cross-validation and independent validation, the server achieved an AUC greater than 0.85. Additionally, by investigating the CPI profiles of predicted DDI, users can explore the PK/PD proteins that might be involved in a particular DDI. A 3D visualization of the drug-protein interaction will be provided as well. The DDI-CPI is freely accessible at http://cpi.bio-x.cn/ddi/.

Development of a computer system to support medication reviews in nursing homes

The frail elderly populations of nursing homes frequently use drugs and suffer from considerable comorbidities. Medication reviews are intended to support evidence based prescribing and optimise therapy. However, literature is still ambiguous regarding the optimal method and the effects of medication reviews. Innovative computerised systems may support the medication reviews in the future. We are developing a clinical decision support system (CDSS) that, independently of the prescribing software, continuously monitors all prescribed drugs while taking into account co-medication, laboratory-data and co-morbidities. The CDSS will be developed in five phases: (1) development of the computerised system, (2) development of the clinical rules, (3) validation of the CDSS, (4) randomised controlled trial, and (5) feasibility for implementation in different nursing homes. The clinical decision support system aims at supporting the traditional medication review.

Evaluation of medication alerts in electronic health records for compliance with human factors principles

INTRODUCTION

Increasing the adoption of electronic health records (EHRs) with integrated clinical decision support (CDS) is a key initiative of the current US healthcare administration. High over-ride rates of CDS alerts strongly limit these potential benefits. As a result, EHR designers aspire to improve alert design to achieve better acceptance rates. In this study, we evaluated drug-drug interaction (DDI) alerts generated in EHRs and compared them for compliance with human factors principles.

METHODS

We utilized a previously validated questionnaire, the I-MeDeSA, to assess compliance with nine human factors principles of DDI alerts generated in 14 EHRs. Two reviewers independently assigned scores evaluating the human factors characteristics of each EHR. Rankings were assigned based on these scores and recommendations for appropriate alert design were derived.

RESULTS

The 14 EHRs evaluated in this study received scores ranging from 8 to 18.33, with a maximum possible score of 26. Cohen's κ (κ=0.86) reflected excellent agreement among reviewers. The six vendor products tied for second and third place rankings, while the top system and bottom five systems were home-grown products. The most common weaknesses included the absence of characteristics such as alert prioritization, clear and concise alert messages indicating interacting drugs, actions for clinical management, and a statement indicating the consequences of over-riding the alert.

CONCLUSIONS

We provided detailed analyses of the human factors principles which were assessed and described our recommendations for effective alert design. Future studies should assess whether adherence to these recommendations can improve alert acceptance.

What, if all alerts were specific - estimating the potential impact on drug interaction alert burden

PURPOSE

Clinical decision support systems (CDSS) may potentially improve prescribing quality, but are subject to poor user acceptance. Reasons for alert overriding have been identified and counterstrategies have been suggested; however, poor alert specificity, a prominent reason of alert overriding, has not been well addressed. This paper aims at structuring modulators that determine alert specificity and estimating their quantitative impact on alert burden.

METHODS

We developed and summarized optimizing strategies to guarantee the specificity of alerts and applied them to a set of 100 critical and frequent drug interaction (DDI) alerts. Hence, DDI alerts were classified as dynamic, i.e. potentially sensitive to prescription-, co-medication-, or patient-related factors that would change alert severity or render the alert inappropriate compared to static, i.e. always applicable alerts not modulated by cofactors.

RESULTS

Within the subset of 100 critical DDI alerts, only 10 alerts were considered as static and for 7 alerts, relevant factors are not generally available in today's patient charts or their consideration would not impact alert severity. The vast majority, i.e. 83 alerts, might require a decrease in alert severity due to factors related to the prescription (N=13), the co-medication (N=11), individual patient data (N=36), or combinations of them (N=23). Patient-related factors consisted mainly of three lab values, i.e. renal function, potassium, and therapeutic drug monitoring results.

CONCLUSION

This paper outlines how promising the refinement of knowledge bases is in order to increase specificity and decrease alert burden and suggests how to structure knowledge bases to refine DDI alerting.

The evidence for the effectiveness of safety alerts in electronic patient medication record systems at the point of pharmacy order entry: a systematic review

BACKGROUND

Electronic Patient Medication Record (ePMR) systems have important safety features embedded to alert users about potential clinical hazards and errors. To date, there is no synthesis of evidence about the effectiveness of these safety features and alerts at the point of pharmacy order entry. This review aims to systematically explore the literature and synthesise published evidence about the effectiveness of safety features and alerts in ePMR systems at the point of pharmacy order entry, in primary and secondary care.

METHODS

We searched MEDLINE, EMBASE, Inspec, International Pharmaceutical Abstracts, PsycINFO, CINHAL (earliest entry to March 2012) and reference lists of articles. Two reviewers examined the titles and abstracts, and used a hierarchical template to identify comparative design studies evaluating the effectiveness of safety features and alerts at the point of pharmacy order entry. The two reviewers independently assessed the quality of the included studies using Cochrane Collaboration's risk of bias tool.

RESULTS

Three randomised trials and two before-after studies met our criteria. Four studies involved integrated care facilities and one was hospital-based. The studies were all from the United States (US). The five studies demonstrated statistically significant reduction in medication errors in patients with renal insufficiency, pregnant women dispensed US Food Drug and Administration (FDA) risk category D (evidence of fetal risk but therapeutic benefits can outweigh the risk) or X (evidence suggests that risk to the fetus outweighs therapeutic benefits) medication, first dispensing of inappropriate medications in patients aged 65 and above, co-dispensing of interacting drugs, and adverse drug events related to hyperkalaemia.

CONCLUSIONS

This systematic review shows that the safety features of ePMR systems are effective in alerting users about potential clinical hazards and errors during pharmacy order entry. There are however, problems such as false alerts and inconsistencies in alert management. More studies are needed from other countries and pharmacy practice settings to assess the effectiveness of electronic safety features and alerts in preventing error and reducing harm to patients.

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

INTRODUCTION

Adverse drug reactions (ADR) increase morbidity and mortality; potential drug-drug interactions (DDI) increase the probability of ADR. Studies have proven that computerized drug-interaction alert systems (DIAS) might reduce medication errors and potential adverse events. However, the relatively high override rates obscure the benefits of alert systems, which result in barriers for availability. It is important to understand the frequency at which physicians override DIAS and the reasons for overriding reminders.

METHOD

All the DDI records of outpatient prescriptions from a tertiary university hospital from 2005 and 2006 detections by the DIAS are included in the study. The DIAS is a JAVA language software that was integrated into the computerized physician order entry system. The alert window is displayed when DDIs occur during order entries, and physicians choose the appropriate action according to the DDI alerts. There are seven response choices are obligated in representing overriding and acceptance: (1) necessary order and override; (2) expected DDI and override; (3) expected DDI with modified dosage and override; (4) no DDI and override; (5) too busy to respond and override; (6) unaware of the DDI and accept; and (7) unexpected DDI and accept. The responses were collected for analysis.

RESULTS

A total of 11,084 DDI alerts of 1,243,464 outpatient prescriptions were present, 0.89% of all computerized prescriptions. The overall rate for accepting was 8.5%, but most of the alerts were overridden (91.5%). Physicians of family medicine and gynecology-obstetrics were more willing to accept the alerts with acceptance rates of 20.8% and 20.0% respectively (p<0.001). Information regarding the recognition of DDIs indicated that 82.0% of the DDIs were aware by physicians, 15.9% of DDIs were unaware by physicians, and 2.1% of alerts were ignored. The percentage of total alerts declined from 1.12% to 0.79% during 24 months' study period, and total overridden alerts also declined (from 1.04% to 0.73%).

CONCLUSION

We explored the physicians' behavior by analyzing responses to the DDI alerts. Although the override rate is still high, the reasons why physicians may override DDI alerts were well analyzed and most DDI were recognized by physicians. Nonetheless, the trend of total overrides is in decline, which indicates a learning curve effect from exposure to DIAS. By analyzing the computerized responses provided by physicians, efforts should be made to improve the efficiency of the DIAS, and pharmacists, as well as patient safety staffs, can catch physicians' appropriate reasons for overriding DDI alerts, improving patient safety.

Design of decision support interventions for medication prescribing

OBJECTIVE

Describe optimal design attributes of clinical decision support (CDS) interventions for medication prescribing, emphasizing perceptual, cognitive and functional characteristics that improve human-computer interaction (HCI) and patient safety.

METHODS

Findings from published reports on success, failures and lessons learned during implementation of CDS systems were reviewed and interpreted with regard to HCI and software usability principles. We then formulated design recommendations for CDS alerts that would reduce unnecessary workflow interruptions and allow clinicians to make informed decisions quickly, accurately and without extraneous cognitive and interactive effort.

RESULTS

Excessive alerting that tends to distract clinicians rather than provide effective CDS can be reduced by designing only high severity alerts as interruptive dialog boxes and less severe warnings without explicit response requirement, by curating system knowledge bases to suppress warnings with low clinical utility and by integrating contextual patient data into the decision logic. Recommended design principles include parsimonious and consistent use of color and language, minimalist approach to the layout of information and controls, the use of font attributes to convey hierarchy and visual prominence of important data over supporting information, the inclusion of relevant patient data in the context of the alert and allowing clinicians to respond with one or two clicks.

CONCLUSION

Although HCI and usability principles are well established and robust, CDS and EHR system interfaces rarely conform to the best known design conventions and are seldom conceived and designed well enough to be truly versatile and dependable tools. These relatively novel interventions still require careful monitoring, research and analysis of its track record to mature. Clarity and specificity of alert content and optimal perceptual and cognitive attributes, for example, are essential for providing effective decision support to clinicians.

Prevalence and risk of potential cytochrome P450-mediated drug-drug interactions in older hospitalized patients with polypharmacy

BACKGROUND

As rates of polypharmacy rise and medication regimens become more complex, the risk of potential cytochrome P450 (CYP)-mediated drug-drug interactions (DDIs) is a growing clinical concern for older adults.

OBJECTIVE

To determine the prevalence of potential CYP-mediated DDIs in older hospitalized adults with polypharmacy and analyze the relationship between the number of drugs dispensed and the probability of these interactions in this high-risk population.

METHODS

A prospective 16-week cohort study was conducted among consecutive new patients aged 65 years and older with polypharmacy (>5 drugs) admitted to a community hospital. The medication profiles of these patients were analyzed with a new multidrug cytochrome-specific software program. The prevalence of potential CYP-mediated DDIs was determined, with the probability calculated as a function of the number of medications dispensed using multivariate Poisson regression adjusted for age and sex. Comparative performance of the software program and a standard 2-drug alert program for detecting these DDIs was evaluated using the Wilcoxon-Mann-Whitney rank-sum test. Pharmacists' decisions to recommend medication adjustment based on the probability of CYP-mediated DDIs were recorded.

RESULTS

The prevalence of potential CYP-mediated DDIs detected among 275 older adults with polypharmacy was 80%. The probability of at least 1 CYP-mediated DDI was 50% for persons taking 5-9 drugs, 81% with 10-14 drugs, 92% with 15-19 drugs, and 100% with 20 or more drugs. Addition of each medication to a 5-drug regimen conferred a 12% increased risk of a potential CYP-mediated DDI after adjustment for age and sex (OR 1.12; 95% CI 1.09-1.14). The multidrug software identified a median increase of 3 (95% CI 2.5-3.5) potential CYP-mediated DDIs per patient, compared to use of the standard 2-drug alert software. Pharmacists targeted patients for medication adjustment or close clinical monitoring in 23% of cases.

CONCLUSIONS

The prevalence of potential CYP-mediated DDIs is high in geriatric patients with polypharmacy. The risk of DDIs increases as a function of the number of medications dispensed. Pharmacists' decision to intervene for potential CYP-mediated DDIs depends on clinical judgment in addition to the output from drug alert software programs, but may be facilitated by a single multicomponent, multidrug potential CYP-mediated DDI assessment.

Lessons from a successful implementation of a computerized provider order entry system

OBJECTIVES

The electronic health record (EHR) can improve patient safety, care efficiency, cost effectiveness and regulatory compliance. Cincinnati Children's Hospital Medical Center (CCHMC) has successfully implemented an Integrating Clinical Information System (ICIS) that includes Computerized Provider Order Entry (CPOE). This review describes some of the unanticipated challenges and solutions identified during the implementation of ICIS.

METHODS

Data for this paper was derived from user-generated feedback within the ICIS. Feedback reports were reviewed and placed into categories based on root cause of the issue. Recurring issues or problems which led to potential or actual patient injury are included.

RESULTS

NINE DISTINCT CHALLENGES WERE IDENTIFIED: 1) Deterioration in communication; 2) Excessive system alerts to users; 3) Unrecognized discontinuation of medications; 4) Unintended loss of orders; 5) Loss of orders during implementation; 6) Amplification of errors; 7) Unintentional generation of patient care orders by system analysts; 8) Persistence of specific patient care order instructions; 9) Verbal orders entered under the incorrect clinician.

CONCLUSIONS

Unanticipated challenges are expected when implementing EHRs. The implementation plan for any EHR should include methods to identify, evaluate and repair problems quickly. While continued challenges with this complex system are expected, we believe that the EHR will continue to facilitate improved patient care and safety. The lessons learned at CCHMC will permit other institutions to avoid some of these challenges and design robust processes to detect and respond to problems in a timely fashion to ensure implementation success.

Potential drug-drug interactions in prescriptions to patients over 45 years of age in primary care, southern Brazil

BACKGROUND

Few cross-sectional studies involving adults and elderly patients with major DDIs have been conducted in the primary care setting. The study aimed to investigate the prevalence of potential drug-drug interactions (DDIs) in patients treated in primary care.

METHODOLOGY/PRINCIPAL FINDINGS

A cross-sectional study involving patients aged 45 years or older was conducted at 25 Basic Health Units in the city of Maringá (southern Brazil) from May to December 2010. The data were collected from prescriptions at the pharmacy of the health unit at the time of the delivery of medication to the patient. After delivery, the researcher checked the electronic medical records of the patient. A total of 827 patients were investigated (mean age: 64.1; mean number of medications: 4.4). DDIs were identified in the Micromedex® database. The prevalence of potential DDIs and major DDIs was 63.0% and 12.1%, respectively. In both the univariate and multivariate analyses, the number of drugs prescribed was significantly associated with potential DDIs, with an increasing risk from three to five drugs (OR = 4.74; 95% CI: 2.90-7.73) to six or more drugs (OR = 23.03; 95% CI: 10.42-50.91). Forty drugs accounted for 122 pairs of major DDIs, the most frequent of which involved simvastatin (23.8%), captopril/enalapril (16.4%) and fluoxetine (16.4%).

CONCLUSIONS/SIGNIFICANCE

This is the first large-scale study on primary care carried out in Latin America. Based on the findings, the estimated prevalence of potential DDIs was high, whereas clinically significant DDIs occurred in a smaller proportion. Exposing patients to a greater number of prescription drugs, especially three or more, proved to be a significant predictor of DDIs. Prescribers should be more aware of potential DDIs. Future studies should assess potential DDIs in primary care over a longer period of time.

Physicians' perceptions on the usefulness of contextual information for prioritizing and presenting alerts in Computerized Physician Order Entry systems

BACKGROUND

One possible approach towards avoiding alert overload and alert fatigue in Computerized Physician Order Entry (CPOE) systems is to tailor their drug safety alerts to the context of the clinical situation. Our objective was to identify the perceptions of physicians on the usefulness of clinical context information for prioritizing and presenting drug safety alerts.

METHODS

We performed a questionnaire survey, inquiring CPOE-using physicians from four hospitals in four European countries to estimate the usefulness of 20 possible context factors.

RESULTS

The 223 participants identified the 'severity of the effect' and the 'clinical status of the patient' as the most useful context factors. Further important factors are the 'complexity of the case' and the 'risk factors of the patient'.

CONCLUSIONS

Our findings confirm the results of a prior, comparable survey inquiring CPOE researchers. Further research should focus on implementing these context factors in CPOE systems and on subsequently evaluating their impact.

Identification and weighting of the most critical "real-life" drug-drug interactions with acenocoumarol in a tertiary care hospital

PURPOSE

The objective of this study was to identify the most clinically relevant drug-drug interactions (DDIs) at risk of affecting acenocoumarol safety in our tertiary care university hospital, a 2,000 bed institution.

METHODS

We identified DDIs occurring with acenocoumarol by combining two different sources of information: a 1-year retrospective analysis of acenocoumarol prescriptions and comedications from our Computerized Physician Order Entry (CPOE) system (n = 2,439 hospitalizations) and a retrospective study of clinical pharmacology consultations involving acenocoumarol over the past 14 years (1994-2007) (n = 407). We classified these DDIs using an original risk-analysis method. A criticality index was calculated for each associated drug by multiplying three scores based on mechanism of interaction, involvement in a supratherapeutic international normalized ratio (INR) (≥ 6) and involvement in a severe bleeding.

RESULTS

One hundred and twenty-six DDIs were identified and weighted. Twenty-eight drugs had a criticality index ≥ 20 and were therefore considered at high risk for interacting with acenocoumarol by increasing its effect: 75% of these drugs involved a pharmacokinetic mechanism and 14 % a pharmacodynamic mechanism. An unknown mechanism of interaction was involved in 11 % of drugs.

CONCLUSION

Twenty-eight specific drugs were identified as being at high risk for interacting with acenocoumarol in our hospital using an original risk-analysis method. Most analyzed drugs interact with acenocoumarol via a pharmacokinetic mechanism. Actions such as the implementation of alerts in our CPOE system should be specifically developed for these drugs.

Perceived impact of electronic medical records in physician office practices: a review of survey-based research

BACKGROUND

Physician office practices are increasingly adopting electronic medical records (EMRs). Therefore, the impact of such systems needs to be evaluated to ensure they are helping practices to realize expected benefits. In addition to experimental and observational studies examining objective impacts, the user's subjective view needs to be understood, since ultimate acceptance and use of the system depends on them. Surveys are commonly used to elicit these views.

OBJECTIVE

To determine which areas of EMR implementation in office practices have been addressed in survey-based research studies, to compare the perceived impacts between users and nonusers for the most-addressed areas, and to contribute to the knowledge regarding survey-based research for assessing the impact of health information systems (HIS).

METHODS

We searched databases and systematic review citations for papers published between 2000 and 2012 (May) that evaluated the perceived impact of using an EMR system in an office-based practice, were based on original data, had providers as the primary end user, and reported outcome measures related to the system's positive or negative impact. We identified all the reported metrics related to EMR use and mapped them to the Clinical Adoption Framework to analyze the gap. We then subjected the impact-specific areas with the most reported results to a meta-analysis, which examined overall positive and negative perceived impacts for users and nonusers.

RESULTS

We selected 19 papers for the review. We found that most impact-specific areas corresponded to the micro level of the framework and that appropriateness or effectiveness and efficiency were well addressed through surveys. However, other areas such as access, which includes patient and caregiver participation and their ability to access services, had very few metrics. We selected 7 impact-specific areas for meta-analysis: security and privacy; quality of patient care or clinical outcomes; patient-physician relationship and communication; communication with other providers; accessibility of records and information; business or practice efficiency; and costs or savings. All the results for accessibility of records and information and for communication with providers indicated a positive view. The area with the most mixed results was security and privacy.

CONCLUSIONS

Users sometimes were likelier than nonusers to have a positive view of the selected areas. However, when looking at the two groups separately, we often found more positive views for most of the examined areas regardless of use status. Despite limitations of a small number of papers and their heterogeneity, the results of this review are promising in terms of finding positive perceptions of EMR adoption for users and nonusers. In addition, we identified issues related to survey-based research for HIS evaluation, particularly regarding constructs for evaluation and quality of study design and reporting.

The effect of using the health smart card vs. CPOE reminder system on the prescribing practices of non-obstetric physicians during outpatient visits for pregnant women in Taiwan

INTRODUCTION

There is an evidence that pregnant women have been prescribed a significant number of improper medications that could lead to potential damage for a developing fetus due to discontinuity of care. The safety of pregnant women raises public concern and there is a need to identify ways to prevent potential adverse events to the pregnant woman. This study used a health smart card with a clinical reminder system to keep continuous records of general outpatient visits of pregnant women to protect them from potential adverse events caused by improper prescription.

METHOD

The health smart card, issued to all 23 million citizens in Taiwan, was used to work with a Computerized Physician Order Entry (CPOE) implemented at a 700-bed teaching medical center in Taipei to provide the outpatient information of pregnant women. FDA pregnancy risk classification was used to categorize the risk of pregnant women. The log file, combined with the physicians' and patients' profiles, were statistically examined using the Mantel-Haenszel technique to evaluate the impact of system in changing physician's prescription behavior.

RESULTS

A total of 441 patients ranged in age from 15 to 50 years with 1114 prescriptions involved in FDA pregnancy risk classification C, D, and X during the study period. 144 reminders (13.1%) were accepted by physicians for further assessment and 100 (69.4%) of them were modified. Non-obstetric physicians in non-emergency setting were more intended to accept reminders (27.8%, 4.9 folds than obstetricians). Reminders triggered on patients in second trimester (15.5%) were accepted by all physicians more than third trimester (OR 1.52, p<0.05).

CONCLUSION

A health smart card armed with CPOE reminder system and well-defined criteria had the potential to decrease harmful medication prescribed to pregnant patients. The results show better conformance for non-obstetric physicians (26%) and when physicians accepted the alerts they are more likely to went back and review their orders (69%). In sum, reminder criteria of FDA pregnancy risk classification C for obstetricians and reminder based on different trimesters is suggested to be refined to improve system acceptability and to decrease improper prescription.

Notifications received by primary care practitioners in electronic health records: a taxonomy and time analysis

BACKGROUND

Asynchronous electronic health record (EHR)-based alerts used to notify practitioners via an inbox-like format rather than through synchronous computer "pop-up" messages are understudied. Our objective was to create an asynchronous alert taxonomy and measure the impact of different alert types on practitioner workload.

METHODS

We quantified and categorized asynchronous alerts according to the information they conveyed and conducted a time-motion analysis to assess practitioner workload. We reviewed alert information transmitted to all 47 primary care practitioners (PCPs) at a large, tertiary care Veterans Affairs facility over 4 evenly spaced 28-day periods. An interdisciplinary team used content analysis to categorize alerts according to their conveyed information. We then created an alert taxonomy and used it to calculate the mean number of alerts of each type PCPs received each day. We conducted a time-motion study of 26 PCPs while they processed their alerts. We used these data to estimate the uninterrupted time practitioners spend processing alerts each day.

RESULTS

We extracted 295,792 asynchronously generated alerts and created a taxonomy of 33 alert types categorized under 6 major categories: Test Results, Referrals, Note-Based Communication, Order Status, Patient Status Changes, and Incomplete Task Reminders. PCPs received a mean of 56.4 alerts/day containing new information. Based on 749 observed alert processing episodes, practitioners spent an estimated average of 49 minutes/day processing their alerts.

CONCLUSIONS

PCPs receive a large number of EHR-based asynchronous alerts daily and spend significant time processing them. The utility of transmitting large quantities and varieties of alerts to PCPs warrants further investigation.

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.

Development and validation of a survey instrument for assessing prescribers' perception of computerized drug-drug interaction alerts

OBJECTIVE

To develop a theoretically informed and empirically validated survey instrument for assessing prescribers' perception of computerized drug-drug interaction (DDI) alerts.

MATERIALS AND METHODS

The survey is grounded in the unified theory of acceptance and use of technology and an adapted accident causation model. Development of the instrument was also informed by a review of the extant literature on prescribers' attitude toward computerized medication safety alerts and common prescriber-provided reasons for overriding. To refine and validate the survey, we conducted a two-stage empirical validation study consisting of a pretest with a panel of domain experts followed by a field test among all eligible prescribers at our institution.

RESULTS

The resulting survey instrument contains 28 questionnaire items assessing six theoretical dimensions: performance expectancy, effort expectancy, social influence, facilitating conditions, perceived fatigue, and perceived use behavior. Satisfactory results were obtained from the field validation; however, a few potential issues were also identified. We analyzed these issues accordingly and the results led to the final survey instrument as well as usage recommendations.

DISCUSSION

High override rates of computerized medication safety alerts have been a prevalent problem. They are usually caused by, or manifested in, issues of poor end user acceptance. However, standardized research tools for assessing and understanding end users' perception are currently lacking, which inhibits knowledge accumulation and consequently forgoes improvement opportunities. The survey instrument presented in this paper may help fill this methodological gap.

CONCLUSION

We developed and empirically validated a survey instrument that may be useful for future research on DDI alerts and other types of computerized medication safety alerts more generally.

Electronic drug interaction alerts in ambulatory care: the value and acceptance of high-value alerts in US medical practices as assessed by an expert clinical panel

BACKGROUND

Computerized physician order entry systems are known to improve patient safety in acute-care hospitals. However, as clinicians frequently override drug interaction and allergy alerts, their value in ambulatory care remains uncertain.

OBJECTIVE

The purpose of the study was to examine whether ambulatory care clinicians were more likely to accept drug-drug interaction alerts that an expert panel judged to be of high clinical value.

STUDY DESIGN

We convened an expert panel to examine drug-drug interaction alerts generated by 2872 clinicians in Massachusetts, Pennsylvania and New Jersey who used a common electronic prescribing system between 1 January 2006 and 30 September 2006. We selected 120 representative drug interaction alerts from the most commonly encountered class-class interactions.

MEASUREMENTS

The expert panel rated each alert based on the following categories: (i) strength of the scientific evidence; (ii) probability that the interaction would result in an adverse drug event (ADE); (iii) severity of typical and most serious ADEs; (iv) the likelihood that a clinician could act on the information; and (v) the overall value of the alert to the average primary care clinician. We then used multivariate regression techniques to examine the relationship between the alert acceptance rate and the expert panel's mean rating of each category.

RESULTS

The decision of clinicians to accept drug interaction alerts increased (relative to a baseline alert acceptance rate of 8.8%) by 2.7% (95% CI 0.4, 5.1) for interactions that panelists judged would result in an ADE, by 2.3% (95% CI 0.9, 3.7) when primary care providers (PCPs) lacked prior knowledge about the information presented in the alert, and by 3.3% (95% CI 0.9, 5.8) when the PCP could readily act on the information provided in the alert.

CONCLUSION

The value of electronic drug interaction alerts is influenced heavily by clinicians' judgements about the clinical value of the alert. Expert judgement should be taken into account when developing electronic decision support.

The role of computerized decision support in reducing errors in selecting medicines for prescription: narrative review

This narrative review includes a summary of research examining prescribing errors, prescription decision making and the role computerized decision support plays in this decision-making process. A reduction in medication prescribing errors, specifically a reduction in the selection of inappropriate medications, is expected to result from the implementation of an effective computerized decision support system. Previous research has investigated the impact of the implementation of electronic systems on medication errors more broadly. This review examines the specific characteristics of decision support systems that may contribute to fewer knowledge-based mistakes in prescribing, and critically appraises the large volume of information available on the decision-making process of selecting medicines for prescription. The results highlight a need for work investigating what decision strategies are used by doctors with different levels of expertise in the prescribing of medications. The nature of the relationship between decision support and decision performance is not well understood and future research is needed to determine the mechanisms by which computerized decision support influences medication selection.

Development of a context model to prioritize drug safety alerts in CPOE systems

BACKGROUND

Computerized physician order entry systems (CPOE) can reduce the number of medication errors and adverse drug events (ADEs) in healthcare institutions. Unfortunately, they tend to produce a large number of partly irrelevant alerts, in turn leading to alert overload and causing alert fatigue. The objective of this work is to identify factors that can be used to prioritize and present alerts depending on the 'context' of a clinical situation.

METHODS

We used a combination of literature searches and expert interviews to identify and validate the possible context factors. The internal validation of the context factors was performed by calculating the inter-rater agreement of two researcher's classification of 33 relevant articles.

RESULTS

We developed a context model containing 20 factors. We grouped these context factors into three categories: characteristics of the patient or case (e.g. clinical status of the patient); characteristics of the organizational unit or user (e.g. professional experience of the user); and alert characteristics (e.g. severity of the effect). The internal validation resulted in nearly perfect agreement (Cohen's Kappa value of 0.97).

CONCLUSION

To our knowledge, this is the first structured attempt to develop a comprehensive context model for prioritizing drug safety alerts in CPOE systems. The outcome of this work can be used to develop future tailored drug safety alerting in CPOE systems.