Reduced Effectiveness of Interruptive Drug-Drug Interaction Alerts after Conversion to a Commercial Electronic Health Record

Development and validation of a clinical decision support system to prevent anticoagulant duplications

BACKGROUND AND OBJECTIVE

Unintended duplicate prescriptions of anticoagulants increase the risk of serious adverse events. Clinical Decision Support Systems (CDSSs) can help prevent such medication errors; however, sophisticated algorithms are needed to avoid alert fatigue. This article describes the steps taken in our hospital to develop a CDSS to prevent anticoagulant duplication (AD).

METHODS

The project was composed of three phases. In phase I, the status quo was established. In phase II, a clinical pharmacist developed an algorithm to detect ADs using daily data exports. In phase III, the algorithm was integrated into the hospital's electronic health record system. Alerts were reviewed by clinical pharmacists before being sent to the prescribing physician. We conducted a retrospective analysis of all three phases to assess the impact of the interventions on the occurrence and duration of ADs. Phase III was analyzed in more detail regarding the acceptance rate, sensitivity, and specificity of the alerts.

RESULTS

We identified 91 ADs in 1581 patients receiving two or more anticoagulants during phase I, 70 ADs in 1692 patients in phase II, and 57 ADs in 1575 patients in phase III. Mean durations of ADs were 1.8, 1.4, and 1.1 calendar days during phases I, II, and III, respectively. In comparison to the baseline in phase I, the relative risk reduction of AD in patients treated with at least two different anticoagulants during phase III was 42% (RR: 0.58, CI: 0.42-0.81). A total of 429 alerts were generated during phase III, many of which were self-limiting, and 186 alerts were sent to the respective prescribing physician. The acceptance rate was high at 97%. We calculated a sensitivity of 87.4% and a specificity of 87.9%.

CONCLUSION

The stepwise development of a CDSS for the detection of AD markedly reduced the frequency and duration of medication errors in our hospital, thereby improving patient safety.

Why do users override alerts? Utilizing large language model to summarize comments and optimize clinical decision support

OBJECTIVES

To evaluate the capability of using generative artificial intelligence (AI) in summarizing alert comments and to determine if the AI-generated summary could be used to improve clinical decision support (CDS) alerts.

MATERIALS AND METHODS

We extracted user comments to alerts generated from September 1, 2022 to September 1, 2023 at Vanderbilt University Medical Center. For a subset of 8 alerts, comment summaries were generated independently by 2 physicians and then separately by GPT-4. We surveyed 5 CDS experts to rate the human-generated and AI-generated summaries on a scale from 1 (strongly disagree) to 5 (strongly agree) for the 4 metrics: clarity, completeness, accuracy, and usefulness.

RESULTS

Five CDS experts participated in the survey. A total of 16 human-generated summaries and 8 AI-generated summaries were assessed. Among the top 8 rated summaries, five were generated by GPT-4. AI-generated summaries demonstrated high levels of clarity, accuracy, and usefulness, similar to the human-generated summaries. Moreover, AI-generated summaries exhibited significantly higher completeness and usefulness compared to the human-generated summaries (AI: 3.4 ± 1.2, human: 2.7 ± 1.2, P = .001).

CONCLUSION

End-user comments provide clinicians' immediate feedback to CDS alerts and can serve as a direct and valuable data resource for improving CDS delivery. Traditionally, these comments may not be considered in the CDS review process due to their unstructured nature, large volume, and the presence of redundant or irrelevant content. Our study demonstrates that GPT-4 is capable of distilling these comments into summaries characterized by high clarity, accuracy, and completeness. AI-generated summaries are equivalent and potentially better than human-generated summaries. These AI-generated summaries could provide CDS experts with a novel means of reviewing user comments to rapidly optimize CDS alerts both online and offline.

Impact of the implementation of the Intelligent Antimicrobial System (iAMS) on clinical outcomes among patients with bacteraemia caused by methicillin-resistant Staphylococcus aureus

OBJECTIVES

This study aimed to investigate the clinical impact of the Intelligent Antimicrobial System (iAMS) on patients with bacteraemia due to methicillin-resistant (MRSA) and methicillin-susceptible Staphylococcus aureus (MSSA).

METHODS

A total of 1008 patients with suspected SA infection were enrolled before and after the implementation of iAMS. Among them, 252 with bacteraemia caused by SA, including 118 in the iAMS and 134 in the non-iAMS groups, were evaluated.

RESULTS

The iAMS group exhibited a 5.2% (from 55.2% to 50.0%; P = 0.96) increase in the 1-year survival rate. For patients with MRSA and MSSA compared to the non-iAMS group, the 1-year survival rate increased by 17.6% (from 70.9% to 53.3%; P = 0.41) and 7.0% (from 52.3% to 45.3%; P = 0.57), respectively, both surpassing the rate of the non-iAMS group. The iAMS intervention resulted in a higher long-term survival rate (from 70.9% to 52.3%; P = 0.984) for MRSA patients than for MSSA patients. MRSA patients experienced a reduced length of hospital stay (from 23.3% to 35.6%; P = 0.038), and the 45-day discharge rate increased by 20.4% (P = 0.064). Furthermore, the intervention resulted in a significant 97.3% relative decrease in near miss medication incidents reported by pharmacists (P = 0.013).

CONCLUSIONS

Implementation of iAMS platform improved long-term survival rates, discharge rates, hospitalization days, and medical cost (although no significant differences were observed) among patients with MRSA bacteraemia. Additionally, it demonstrated significant benefits in ensuring drug safety.

Capture of real-time data from electronic health records: scenarios and solutions

Background

The integration of real-time data (RTD) in the electronic health records (EHRs) is transforming the healthcare of tomorrow. In this work, the common scenarios of capturing RTD in the healthcare from EHRs are studied and the approaches and tools to implement real-time solutions are investigated.

Methods

Delivering RTD by representational state transfer (REST) application programming interfaces (APIs) is usually accomplished through a Publish-Subscribe approach. Common technologies and protocols used for implementing subscriptions are REST hooks and WebSockets. Polling is a straightforward mechanism for obtaining updates; nevertheless, it may not be the most efficient or scalable solution. In such cases, other approaches are often preferred. Database triggers and reverse proxies can be useful in RTD scenarios; however, they should be designed carefully to avoid performance bottlenecks and potential issues.

Results

The implementation of subscriptions through REST hooks and WebSocket notifications using a Fast Healthcare Interoperability Resources (FHIR) REST API, as well as the design of a reverse proxy and database triggers is described. Reference implementations of the solutions are provided in a GitHub repository. For the reverse proxy implementation, the Go language (Golang) was used, which is specialized for the development of server-side networking applications. For FHIR servers a python script is provided to create a sample Subscription resource to send RTD when a new Observation resource for specific patient id is created. The sample WebSocket client is written using the "websocket-client" python library. The sample RTD endpoint is created using the "Flask" framework. For database triggers a sample structured query language (SQL) query for Postgres to create a trigger when an INSERT or UPDATE operation is executed on the FHIR resource table is available. Furthermore, a use case clinical example, where the main actors are the healthcare providers (hospitals, physician private practices, general practitioners and medical laboratories), health information networks and the patient are drawn. The RTD flow and exchange is shown in detail and how it could improve healthcare.

Conclusions

Capturing RTD is undoubtedly vital for health professionals and successful digital healthcare. The topic remains unexplored especially in the context of EHRs. In our work for the first time the common scenarios and problems are investigated. Furthermore, solutions and reference implementations are provided which could support and contribute to the development of real-time applications.

Evaluation of Triple Whammy Prescriptions After the Implementation of a Drug Safety Algorithm

BACKGROUND AND OBJECTIVE

The term triple whammy (TW) refers to the concomitant use of non-steroidal anti-inflammatory drugs, diuretics, and angiotensin system inhibitors; this combination significantly increases the risk of acute kidney injury (AKI). To prevent this serious complication, we developed an electronic algorithm that detects TW prescriptions in patients with additional risk factors such as old age and impaired kidney function. The algorithm alerts a clinical pharmacist who then evaluates and forwards the alert to the prescribing physician.

METHODS

We evaluated the performance of this algorithm in a retrospective observational study of clinical data from all adult patients admitted to the Cantonal Hospital of Aarau in Switzerland in 2021. We identified all patients who received a TW prescription, had a TW alert, or developed AKI during TW therapy. Algorithm performance was evaluated by calculating the sensitivity and specificity as a primary endpoint and determining the acceptance rate among clinical pharmacists and physicians as a secondary endpoint.

RESULTS

Among 21,332 hospitalized patients, 290 patients had a TW prescription, of which 12 patients experienced AKI. Overall, 216 patients were detected by the alert algorithm, including 11 of 12 patients with AKI; the algorithm sensitivity is 88.3% with a specificity of 99.7%. Physician acceptance was high (77.7%), but clinical pharmacists were reluctant to forward the alerts to prescribers in some cases.

CONCLUSION

The TW algorithm is highly sensitive and specific in identifying patients with TW therapy at risk for AKI. The algorithm may help to prevent AKI in TW patients in the future.

Transitioning from One Electronic Health Record to Another: A Systematic Review

BACKGROUND

Transitioning to a new electronic health record (EHR) presents different challenges than transitions from paper to electronic records. We synthesized the body of peer-reviewed literature on EHR-to-EHR transitions to evaluate the generalizability of published work and identify knowledge gaps where more evidence is needed.

METHODS

We conducted a broad search in PubMed through July 2022 and collected all publications from two prior reviews. Peer-reviewed publications reporting on data from an EHR-to-EHR transition were included. We extracted data on study design, setting, sample size, EHR systems involved, dates of transition and data collection, outcomes reported, and key findings.

RESULTS

The 40 included publications were grouped into thematic categories for narrative synthesis: clinical care outcomes (n = 15), provider perspectives (n = 11), data migration (n = 8), patient experience (n = 4), and other topics (n = 5). Many studies described single sites that are early adopters of technology with robust research resources, switching from a homegrown system to a commercial system, and emphasized the dynamic effect of transitioning on important clinical care and other outcomes over time.

DISCUSSION

The published literature represents a heterogeneous mix of study designs and outcome measures, and while some of the stronger studies in this review used longitudinal approaches to compare outcomes across more sites, the current literature is primarily descriptive and is not designed to offer recommendations that can guide future EHR transitions. Transitioning from one EHR to another constitutes a major organizational change that requires nearly every person in the organization to change how they do their work. Future research should include human factors as well as diverse methodological approaches such as mixed methods and implementation science.

From "Local Control" to "Dependency": Transitions to Single-Vendor Integrated Electronic Health Record Systems and Their Implications for the EHR Workforce

BACKGROUND

Healthcare systems that previously used either a single legacy electronic health record (EHR) system or a "best-of-breed" combination of products from multiple vendors are increasingly adopting integrated, single-vendor EHR systems. Though healthcare leaders are beginning to recognize the dramatic collateral consequences of these transitions, their impact on the EHR workforce - internal actors most closely involved in governing and supporting the EHR - is poorly understood.

OBJECTIVE

Identify perceived impacts of adopting single-vendor, integrated EHR systems on the institutional EHR workforce.

DESIGN

In this qualitative study, we conducted semi-structured phone interviews in four healthcare systems in the USA that had adopted an integrated EHR within the previous five years.

PARTICIPANTS

Forty-two staff members of four geographically and organizationally diverse healthcare systems, including 22 individuals with formal informatics roles.

APPROACH

Transcribed interviews were coded and analyzed using qualitative content analysis methods.

KEY RESULTS

Across organizations, participants described a loss of autonomy by the EHR workforce at the individual and institutional level following the adoption of an integrated EHR. We also identified references to transformations in four key professional functions of the EHR workforce: communication, governance, optimization, and education.

CONCLUSIONS

Transitions to integrated EHR systems can have important implications for the autonomy and professional functions of the EHR workforce. These findings may help institutions embarking on similar transitions better anticipate and prepare for these changes through such practices as revising job descriptions, strengthening EHR governance structures, and reinforcing pathways to engage frontline clinicians in supporting the EHR. Findings may also help institutions structure vendor contracts in a way that anticipates and mitigates loss of autonomy.

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.

Tackling alert fatigue with a semi-automated clinical decision support system: quantitative evaluation and end-user survey

STUDY AIMS

Clinical decision support systems (CDSS) embedded in hospital electronic health records efficiently reduce medication errors, but there is a risk of low physician adherence due to alert fatigue. At the Cantonal Hospital Aarau, a CDSS is being developed that allows the highly accurate detection and correction of medication errors. The semi-automated CDSS sends its alerts either directly to the physician or to a clinical pharmacist for review first. Our aim was to evaluate the performance of the recently implemented CDSS in terms of acceptance rate and alert burden, as well as physicians' satisfaction with the CDSS.

METHODS

All alerts generated by the clinical decision support systems between January and December 2021 were included in a retrospective quantitative evaluation. A team of clinical pharmacists performed a follow-up to determine whether the recommendation made by the CDSS was implemented by the physician. The acceptance rate was calculated including all alerts for which it was possible to determine an outcome. A web-based survey was conducted amongst physicians to assess their attitude towards the CDSS. The survey questions included overall satisfaction, helpfulness of individual algorithms, and perceived alert burden.

RESULTS

In 2021, a total of 10,556 alerts were generated, of which 619 triggered a direct notification to the physician and 2,231 notifications were send to the physician after evaluation by a clinical pharmacist. The acceptance rates were 89.8% and 68.4%, respectively, which translates as an overall acceptance rate of 72.4%. On average, clinical pharmacists received 17.2 alerts per day, while all of the hospital physicians together received 7.8 notifications per day. In the survey, 94.5% of physicians reported being satisfied or very satisfied with the CDSS. Algorithms addressing potential medication errors concerning anticoagulants received the highest usefulness ratings.

CONCLUSION

The development of this semi-automated clinical decision support system with context-based algorithms resulted in alerts with a high acceptance rate. Involving clinical pharmacists proved a promising approach to limit the alert burden of physicians and thus tackle alert fatigue. The CDSS is well accepted by our physicians.

SLCO1B1 gene-based clinical decision support reduces statin-associated muscle symptoms risk with simvastatin

Background: SLCO1B1 variants are known to be a strong predictor of statin-associated muscle symptoms (SAMS) risk with simvastatin. Methods: The authors conducted a retrospective chart review on 20,341 patients who had SLCO1B1 genotyping to quantify the uptake of clinical decision support (CDS) for genetic variants known to impact SAMS risk. Results: A total of 182 patients had 417 CDS alerts generated, and 150 of these patients (82.4%) received pharmacotherapy that did not increase risks for SAMS. Providers were more likely to cancel simvastatin orders in response to CDS alerts if genotyping had been done prior to the first simvastatin prescription than after (94.1% vs 28.5%, respectively; p < 0.001). Conclusion: CDS significantly reduces simvastatin prescribing at doses associated with SAMS.

Association between Electronic Health Record Implementations and Hospital-Acquired Conditions in Pediatric Hospitals

BACKGROUND

Implementing an electronic health record (EHR) is one of the most disruptive operational tasks a health system can undergo. Despite anecdotal reports of adverse events around the time of EHR implementations, there is limited corroborating research, particularly in pediatrics. We utilized data from Solutions for Patient Safety (SPS), a network of 145+ children's hospitals that share data and protocols to reduce harm in pediatric care delivery, to study the impact of EHR implementations on patient safety.

OBJECTIVE

Determine if there is an association between the time immediately surrounding an EHR implementation and hospital-acquired conditions (HACs) rates in pediatrics.

METHODS

A survey of information technology leaders at pediatric institutions identified EHR implementations occurring between 2012 and 2022. This list was cross-referenced with the SPS database to create an anonymized dataset of 27 sites comprising monthly HAC and care bundle compliance rates in the 7 months preceding and succeeding the transition. Six HACs were analyzed: central-line associated bloodstream infection (CLABSI), catheter-associated urinary tract infection (CAUTI), adverse drug events, surgical site infections (SSIs), pressure injuries (PIs), and falls, in addition to four associated care bundle compliance rates: CLABSI and CAUTI maintenance bundles, SSI bundle, and PI bundle. To determine if there was a statistically significant association with EHR implementation, the observation period was divided into three eras: "before" (months -7 to -3), "during" (months -2 to +2), and "after" go-live (months +3 to +7). Average monthly HAC and bundle compliance rates were calculated across eras. Paired t-tests were performed to compare rates between the eras.

RESULTS

No statistically significant increase in HAC rates or decrease in bundle compliance rates was observed across the EHR implementation eras.

CONCLUSION

This multisite study detected no significant increase in HACs and no decrease in preventive care bundle compliance in the months surrounding an EHR implementation.

Quality and Safety: Learning from the Past and (Re)Imagining the Future

Optimal quality within health care is no longer narrowly focused on preventing harm and has evolved to include the attainment of best outcomes through an understanding of the features attributed to effectively delivering care in complex work environments. The use of the electronic health record has contributed greatly to creating a repository of data that can be leveraged to comprehend the details associated with health care delivery. Medical knowledge alone is no longer sufficient to guarantee safe care or ensure the best outcomes. Clinicians who wish to achieve successful outcomes in the future must partner with their organizations to invest in appropriate infrastructure, anticipate increased accountability, manage an ever-increasing volume of data, and commit to learning how they must both change in order to succeed.

Optimization of Medication Point-Of-Prescribing Alerts at a Multi-Site, Ambulatory Care Organization to Aid Clinical Care and Reduce HealthCare Cost

BACKGROUND

Literature has shown the integration of electronic alerts into patient care has the potential to improve clinicians' workflow by saving time, increasing efficiency, and improving patient safety. However, despite these possible benefits of alerts, studies have shown that alerts are often overridden by clinicians.

OBJECTIVE

The purpose of this study was to optimize the acceptance rates of medication point-of-prescribing alerts within the electronic medical record (EMR) of an ambulatory care organization.

METHODS

The study design evaluated the actions taken by clinicians when they were presented with medication point-of-prescribing alerts. These alerts were created by the clinical pharmacy informatics team to help promote cost-effective and safe prescribing. Alerts determined to be high value alerts were optimized to increase clinicians' likelihood of accepting each alert's recommended alternative. The primary objective was to increase acceptance rates of high value alerts. The exploratory objective was to identify the estimated annualized cost-savings when high value alerts were accepted, and a lower cost alternative prescription resulted.

RESULTS

The acceptance rate of the optimized point-of-prescribing alerts increased to 8.7%, compared to a 3.2% acceptance in the pre-modification period (P <.001). The lower cost alternative prescriptions that resulted from the accepted alerts translated into an estimated annualized cost-savings of over 2 million dollars.

CONCLUSION

The use of point-of-prescribing alerts with optimized information and specific cost comparisons in an ambulatory setting led to an increase in the acceptance rates of the alerts and more cost-conscious prescribing.

'Improving smart medication management': an online expert discussion

Medication safety continues to be a problem inside and outside the hospital, partly because new smart technologies can cause new drug-related challenges to prescribers and patients. Better integrated digital and information technology (IT) systems, improved education on prescribing for prescribers and greater patient-centred care that empowers patients to take control of their medications are all vital to safer and more effective prescribing. In July 2021, a roundtable discussion was held as a spin-off meeting of the International Forum on Quality and Safety in Health Care Europe 2021 to discuss challenges and future direction in smart medication management. This manuscript summarises the discussion focusing on the aspects of digital and IT systems, safe prescribing, improved communication and education, and drug adherence.

In Pursuit of the Quadruple Aim: A Geriatric Prescribing Context's Impact on Clinician Workflows and Alert Fatigue

The impact of a novel Geriatric Prescribing Context (GPC) on hospital clinicians' prescribing workflows is still unknown. A cross-sectional survey was distributed to 346 inpatient pharmacists, physicians, and advance practice providers employed at three pilot site hospitals affected by the GPC to assess awareness and impact to usual workflow. The GPC, a set of medication default doses and frequencies for patients 75 years and older, was unnoticed by 74% of survey respondents (n = 119) with pharmacists more likely to be aware of the context than prescribers. The impact of the GPC on clinicians' workflow differed by setting, with academic respondents reporting no change or decreased time to write or verify orders, and community respondents reporting no change or increased time to write or verify orders. The GPC has smoothly integrated into usual prescribing workflows for both prescribers and pharmacists and both overall reported positive responses to the implementation.

Modulators Influencing Medication Alert Acceptance: An Explorative Review

OBJECTIVES

Clinical decision support systems (CDSSs) use alerts to enhance medication safety and reduce medication error rates. A major challenge of medication alerts is their low acceptance rate, limiting their potential benefit. A structured overview about modulators influencing alert acceptance is lacking. Therefore, we aimed to review and compile qualitative and quantitative modulators of alert acceptance and organize them in a comprehensive model.

METHODS

In accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guideline, a literature search in PubMed was started in February 2018 and continued until October 2021. From all included articles, qualitative and quantitative parameters and their impact on alert acceptance were extracted. Related parameters were then grouped into factors, allocated to superordinate determinants, and subsequently further allocated into five categories that were already known to influence alert acceptance.

RESULTS

Out of 539 articles, 60 were included. A total of 391 single parameters were extracted (e.g., patients' comorbidity) and grouped into 75 factors (e.g., comorbidity), and 25 determinants (e.g., complexity) were consequently assigned to the predefined five categories, i.e., CDSS, care provider, patient, setting, and involved drug. More than half of all factors were qualitatively assessed (n = 21) or quantitatively inconclusive (n = 19). Furthermore, 33 quantitative factors clearly influenced alert acceptance (positive correlation: e.g., alert type, patients' comorbidity; negative correlation: e.g., number of alerts per care provider, moment of alert display in the workflow). Two factors (alert frequency, laboratory value) showed contradictory effects, meaning that acceptance was significantly influenced both positively and negatively by these factors, depending on the study. Interventional studies have been performed for only 12 factors while all other factors were evaluated descriptively.

CONCLUSION

This review compiles modulators of alert acceptance distinguished by being studied quantitatively or qualitatively and indicates their effect magnitude whenever possible. Additionally, it describes how further research should be designed to comprehensively quantify the effect of alert modulators.

Investigating Potential Drug-Drug Interactions in Pediatric and Adolescent Patients Receiving Chemotherapy

INTRODUCTION

Pediatric and adolescent oncology patients admitted to receive chemotherapy are at risk for drug-drug interactions (DDI). While adult literature has quoted this risk to be as high as 95% of encounters, the literature in pediatrics is limited. This is a single-center, retrospective chart review of DDI in hospitalized pediatric oncology patients.

METHODS

All patients admitted to Texas Children's Hospital for chemotherapy were included. Medications ordered during the hospitalization were evaluated by Lexicomp® Drug Interactions Tool. Interactions classified as D or X or interactions rated a C including a chemotherapeutic agent were independently reviewed by three clinicians for clinical relevance. Medications associated with central nervous system (CNS) depression or QTc prolongation were counted separately.

RESULTS

Of 100 admissions evaluated, 100% had a flagged interaction. There were a total of 12 X-rated interactions, 8 D-rated interactions, and 12 C-rated interactions with a chemotherapeutic agent found to be clinically relevant. Thirty-three percent of admissions had 4 or more QTc prolonging medications ordered. Twenty-four percent of admissions had 3 or more prescribed CNS depressants. In total 49% of admissions were found to have at least 1 clinically-significant DDI.

CONCLUSIONS

This study exemplifies the risk of drug-drug interactions in children and young adults admitted to the hospital for chemotherapy. We demonstrated a high rate of flagged interactions with about half of admissions found to have a potentially clinically-significant DDI. Concomitant use of multiple QTc prolonging and CNS depressant medications was also prevalent, indicating a need to evaluate monitoring practices.

Using the behavior change wheel to link published deprescribing strategies to identified local primary healthcare needs

BACKGROUND

Polypharmacy is a major global problem. Evidence in primary care shows deprescribing can be beneficial. Behaviour change theories such as the Theoretical Domains Framework (TDF) and the Behaviour Change Wheel (BCW) can help develop successful implementation of deprescribing initiatives.

OBJECTIVES

To link locally identified deprescribing influencers with components of successfully trialed deprescribing strategies, with the aim of informing the development of local deprescribing initiatives.

METHODS

Two background studies were completed. A qualitative study of interviews and focus groups identified influencers of deprescribing from local primary care physicians, nurse practitioners, and pharmacists. Transcripts were coded using the TDF and mapped to the Intervention Functions of the BCW. A scoping review identified studies that investigated primary care deprescribing strategies, which were mapped to the BCW Intervention Functions and the Behaviour Change Techniques (BCTs). For this analysis, six main TDF domains from the qualitative study were linked to the BCTs identified in the scoping review through the Intervention Functions of the BCW.

RESULTS

Within the BCW component Capability, one TDF domain identified in the qualitative study, Memory, Attention and Decision Process, was linked to strategies like academic detailing from the scoping review. For the Opportunity component, two TDF domains, Social Influences and Environmental Context and Resources, were linked to strategies such as pharmacist medication reviews, providing patient information leaflets, and evidence-based deprescribing tools. For the Motivation component, three TDF domains, Social/Professional Role and Identity, Intentions, and Beliefs about Consequences, were linked to strategies such as sending deprescribing information to prescribers, using tools to identify eligible patients, and having patients report adverse events of medications.

CONCLUSIONS

This analysis identified deprescribing strategies that can be used to address influencers related to behaviour change from the perspective of primary care providers, and to assist with future deprescribing initiative development and implementation in the local context.

Canned Comments in the Hospital Laboratory Information System Can Decrease Microbiology Requests

OBJECTIVES

Phone calls to the microbiology laboratory can be to clarify culture results and provide education, but those calls also interrupt laboratory workflow. We characterized calls that the laboratory received and developed targeted comments to educate providers.

METHODS

Calls were logged and characterized, and we developed comments to address common call subjects. We applied the new comments to cultures and logged calls over the same interval the subsequent year. Data before and after implementation were analyzed.

RESULTS

Call volume decreased from 496 calls to 419 calls after implementation. There was a significant difference in level of training among callers (P < .005), but the nature of the calls did not change. Laboratory response showed an increase in release of previously generated data (eg, suppressed susceptibility results). Comments specifically developed to address intrinsic antibiotic resistance and common susceptibility patterns did not decrease call volume.

CONCLUSIONS

Implementation of comments in the laboratory information system decreased call volume, but targeted comments were less effective than anticipated.

Harnessing the Electronic Health Record to Actively Support Providers with Guideline-Directed Telemetry Use

BACKGROUND

Overuse of cardiac telemetry monitoring (telemetry) can lead to alarm fatigue, discomfort for patients, and unnecessary medical costs. Currently there are evidence-based recommendations describing appropriate telemetry use, but many providers are unaware of these guidelines.

OBJECTIVES

At our multihospital health system, our goal was to support providers in ordering telemetry on acute care in accordance with evidence-based guidelines and discontinuing telemetry when it was no longer medically indicated.

METHODS

We implemented a multipronged electronic health record (EHR) intervention at two academic medical centers, including: (1) an order set requiring providers to choose an indication for telemetry with a recommended duration based on American Heart Association guidelines; (2) an EHR-generated reminder page to the primary provider recommending telemetry discontinuation once the guideline-recommended duration for telemetry is exceeded; and (3) documentation of telemetry interpretation by telemetry technicians in the notes section of the EHR. To determine the impact of the intervention, we compared number of telemetry orders actively discontinued prior to discharge and telemetry duration 1 year pre- to 1 year post-intervention on acute care medicine services. We evaluated sustainability at years 2 and 3.

RESULTS

Implementation of the EHR initiative resulted in a statistically significant increase in active discontinuation of telemetry orders prior to discharge: 15% (63.4-78.7%) at one site and 13% at the other (64.1-77.4%) with greater improvements on resident teams. Fewer acute care medicine telemetry orders were placed on medicine services across the system (1,503-1,305) despite an increase in admissions and the average duration of telemetry decreased at both sites (62 to 47 hours, p < 0.001 and 73 to 60, p < 0.001, respectively). Improvements were sustained 2 and 3 years after intervention.

CONCLUSION

Our study showed that a low-cost, multipart, EHR-based intervention with active provider engagement and no additional education can decrease telemetry usage on acute care medicine services.

Using machine learning to predict severe hypoglycaemia in hospital

AIM

To predict the risk of hypoglycaemia using machine-learning techniques in hospitalized patients.

METHODS

We conducted a retrospective cohort study of patients hospitalized under general internal medicine (GIM) and cardiovascular surgery (CV) at a tertiary care teaching hospital in Toronto, Ontario. Three models were generated using supervised machine learning: least absolute shrinkage and selection operator (LASSO) logistic regression; gradient-boosted trees; and a recurrent neural network. Each model included baseline patient data and time-varying data. Natural-language processing was used to incorporate text data from physician and nursing notes.

RESULTS

We included 8492 GIM admissions and 8044 CV admissions. Hypoglycaemia occurred in 16% of GIM admissions and 13% of CV admissions. The area under the curve for the models in the held-out validation set was approximately 0.80 on the GIM ward and 0.82 on the CV ward. When the threshold for hypoglycaemia was lowered to 2.9 mmol/L (52 mg/dL), similar results were observed. Among the patients at the highest decile of risk, the positive predictive value was approximately 50% and the sensitivity was 99%.

CONCLUSION

Machine-learning approaches can accurately identify patients at high risk of hypoglycaemia in hospital. Future work will involve evaluating whether implementing this model with targeted clinical interventions can improve clinical outcomes.

High-priority drug-drug interaction clinical decision support overrides in a newly implemented commercial computerized provider order-entry system: Override appropriateness and adverse drug events

OBJECTIVE

The study sought to determine frequency and appropriateness of overrides of high-priority drug-drug interaction (DDI) alerts and whether adverse drug events (ADEs) were associated with overrides in a newly implemented electronic health record.

MATERIALS AND METHODS

We conducted a retrospective study of overridden high-priority DDI alerts occurring from April 1, 2016, to March 31, 2017, from inpatient and outpatient settings at an academic health center. We studied highest-severity DDIs that were previously designated as "hard stops" and additional high-priority DDIs identified from clinical experience and literature review. All highest-severity alert overrides (n = 193) plus a stratified random sample of additional overrides (n = 371) were evaluated for override appropriateness, using predetermined criteria. Charts were reviewed to identify ADEs for overrides that resulted in medication administration. A chi-square test was used to compare ADE rate by override appropriateness.

RESULTS

Of 16 011 alerts presented to providers, 15 318 (95.7%) were overridden, including 193 (87.3%) of the highest-severity DDIs and 15 125 (95.8%) of additional DDIs. Override appropriateness was 45.4% overall, 0.5% for highest-severity DDIs and 68.7% for additional DDIs. For alerts that resulted in medication administration (n = 423, 75.0%), 29 ADEs were identified (6.9%, 5.1 per 100 overrides). The rate of ADEs was higher with inappropriate vs appropriate overrides (9.4% vs 4.3%; P = .038).

CONCLUSIONS

The override rate was nearly 90% for even the highest-severity DDI alerts, indicating that stronger suggestions should be made for these alerts, while other alerts should be evaluated for potential suppression.

Transitions from One Electronic Health Record to Another: Challenges, Pitfalls, and Recommendations

OBJECTIVE

We address the challenges of transitioning from one electronic health record (EHR) to another-a near ubiquitous phenomenon in health care. We offer mitigating strategies to reduce unintended consequences, maximize patient safety, and enhance health care delivery.

METHODS

We searched PubMed and other sources to identify articles describing EHR-to-EHR transitions. We combined these references with the authors' extensive experience to construct a conceptual schema and to offer recommendations to facilitate transitions.

RESULTS

Our PubMed query retrieved 1,351 citations: 43 were relevant for full paper review and 18 met the inclusion criterion of focus on EHR-to-EHR transitions. An additional PubMed search yielded 1,014 citations, for which we reviewed 74 full papers and included 5. We supplemented with additional citations for a total of 70 cited. We distinguished 10 domains in the literature that overlap yet present unique and salient opportunities for successful transitions and for problem mitigation.

DISCUSSION

There is scant literature concerning EHR-to-EHR transitions. Identified challenges include financial burdens, personnel resources, patient safety threats from limited access to legacy records, data integrity during migration, cybersecurity, and semantic interoperability. Transition teams must overcome inadequate human infrastructure, technical challenges, security gaps, unrealistic providers' expectations, workflow changes, and insufficient training and support-all factors affecting potential clinician burnout.

CONCLUSION

EHR transitions are remarkably expensive, laborious, personnel devouring, and time consuming. The paucity of references in comparison to the topic's salience reinforces the necessity for this type of review and analysis. Prudent planning may streamline EHR transitions and reduce expenses. Mitigating strategies, such as preservation of legacy data, managing expectations, and hiring short-term specialty consultants can overcome some of the greatest hurdles. A new medical subject headings (MeSH) term for EHR transitions would facilitate further research on this topic.

Clinical Decision Support and Implications for the Clinician Burnout Crisis

OBJECTIVES

This survey aimed to review aspects of clinical decision support (CDS) that contribute to burnout and identify key themes for improving the acceptability of CDS to clinicians, with the goal of decreasing said burnout.

METHODS

We performed a survey of relevant articles from 2018-2019 addressing CDS and aspects of clinician burnout from PubMed and Web of Science™. Themes were manually extracted from publications that met inclusion criteria.

RESULTS

Eighty-nine articles met inclusion criteria, including 12 review articles. Review articles were either prescriptive, describing how CDS should work, or analytic, describing how current CDS tools are deployed. The non-review articles largely demonstrated poor relevance and acceptability of current tools, and few studies showed benefits in terms of efficiency or patient outcomes from implemented CDS. Encouragingly, multiple studies highlighted steps that succeeded in improving both acceptability and relevance of CDS.

CONCLUSIONS

CDS can contribute to clinician frustration and burnout. Using the techniques of improving relevance, soliciting feedback, customization, measurement of outcomes and metrics, and iteration, the effects of CDS on burnout can be ameliorated.

Structured override reasons for drug-drug interaction alerts in electronic health records

OBJECTIVE

The study sought to determine availability and use of structured override reasons for drug-drug interaction (DDI) alerts in electronic health records.

MATERIALS AND METHODS

We collected data on DDI alerts and override reasons from 10 clinical sites across the United States using a variety of electronic health records. We used a multistage iterative card sort method to categorize the override reasons from all sites and identified best practices.

RESULTS

Our methodology established 177 unique override reasons across the 10 sites. The number of coded override reasons at each site ranged from 3 to 100. Many sites offered override reasons not relevant to DDIs. Twelve categories of override reasons were identified. Three categories accounted for 78% of all overrides: "will monitor or take precautions," "not clinically significant," and "benefit outweighs risk."

DISCUSSION

We found wide variability in override reasons between sites and many opportunities to improve alerts. Some override reasons were irrelevant to DDIs. Many override reasons attested to a future action (eg, decreasing a dose or ordering monitoring tests), which requires an additional step after the alert is overridden, unless the alert is made actionable. Some override reasons deferred to another party, although override reasons often are not visible to other users. Many override reasons stated that the alert was inaccurate, suggesting that specificity of alerts could be improved.

CONCLUSIONS

Organizations should improve the options available to providers who choose to override DDI alerts. DDI alerting systems should be actionable and alerts should be tailored to the patient and drug pairs.

Assessing the safety of electronic health records: a national longitudinal study of medication-related decision support

BACKGROUND

Electronic health records (EHR) can improve safety via computerised physician order entry with clinical decision support, designed in part to alert providers and prevent potential adverse drug events at entry and before they reach the patient. However, early evidence suggested performance at preventing adverse drug events was mixed.

METHODS

We used data from a national, longitudinal sample of 1527 hospitals in the USA from 2009 to 2016 who took a safety performance assessment test using simulated medication orders to test how well their EHR prevented medication errors with potential for patient harm. We calculated the descriptive statistics on performance on the assessment over time, by years of hospital experience with the test and across hospital characteristics. Finally, we used ordinary least squares regression to identify hospital characteristics associated with higher test performance.

RESULTS

The average hospital EHR system correctly prevented only 54.0% of potential adverse drug events tested on the 44-order safety performance assessment in 2009; this rose to 61.6% in 2016. Hospitals that took the assessment multiple times performed better in subsequent years than those taking the test the first time, from 55.2% in the first year of test experience to 70.3% in the eighth, suggesting efforts to participate in voluntary self-assessment and improvement may be helpful in improving medication safety performance.

CONCLUSION

Hospital medication order safety performance has improved over time but is far from perfect. The specifics of EHR medication safety implementation and improvement play a key role in realising the benefits of computerising prescribing, as organisations have substantial latitude in terms of what they implement. Intentional quality improvement efforts appear to be a critical part of high safety performance and may indicate the importance of a culture of safety.

Reducing two-unit red cell transfusions on the oncology ward: a choosing wisely initiative

Background/context

Despite Choosing Wisely recommendations for single unit red blood cell transfusion orders, ~50% of orders on the oncology ward at London Health Sciences Centre (LHSC) were for two units. The oncology ward at LHSC is a 60 bed tertiary care unit. In mid 2016, LHSC was 18 months into its implementation of computerised provider order entry (CPOE).

Aim/objectives

By December 2017, increase the proportion of one-unit red cell transfusion orders on the oncology ward from 50% to 80%

Measures

Outcome: % one-unit red cell transfusion orders (aggregated monthly).

Improvement/innovation/change ideas

Our initial theory was that unawareness of the guidelines (established in 2014) and subscription to the obsolete doctrine of two-unit transfusions were the primary behavioural drivers. Initial change ideas included an educational/awareness blitz including rounds presentations, memos and posters. Failure led us to revisit our hypothesis and carry out a real-time audit, where our team was notified on each two-unit transfusion. This revealed the true root cause: the overwhelming majority of two-unit transfusions could be traced back to standing orders that were entered on an admission order set. After provider engagement, we proceeded to remove all admission order sets containing two-unit transfusions.

Impact/lessons learned/results

After order set removal, our one-unit transfusion rate rose to 86% and was sustained for 17 months. We learnt two primary lessons. First that CPOE and poor order set design combined to perpetuate poor ordering practices. Second that revisiting our hypothesis and engaging in thoughtful root cause analysis that included direct observation ultimately led to an effective, sustainable solution.

Discussion/spread

Our study underscores the importance of executing root cause analysis on a microsystem level. We would expect the factors driving poor performance to be completely different on a service such as general internal medicine. Our study also highlights the potential pitfalls of CPOE and the importance of regular order set review to ensure adherence to current evidence.

Managing Immuno-Oncology Toxicity: Top 10 Innovative Institutional Solutions

Expanded use of immuno-oncology (IO) therapy to treat cancer has led to an increased frequency of novel toxicities known as immune-related adverse events (irAEs). Delayed recognition of IO toxicity can be life-threatening or even fatal. To address this issue, intervention is possible at three levels: patients, medical providers, and institutions. Patients and the medical community need institutional safeguards in place to promote swift recognition, assessment, and treatment of IO toxicity. Patients receiving IO therapy must be educated to identify the drugs they have received and to recognize potential IO toxicity, and they must know how to report symptoms. Medical providers must be able to reliably identify that patients have received IO therapy as well as recognize rare or subtle symptoms of IO toxicity. Institutions can establish guidelines and order sets to standardize the treatment of patients receiving IO therapy with irAEs, including the complex management of steroid-refractory irAEs. Additional interventions at an institutional level include identification of IO toxicity champions (subspecialists with expertise in IO toxicity), creating immunotherapy-specific tumor boards and lecture series to educate clinicians and staff, and establishing research programs to evaluate IO toxicity. IO therapy and toxicity experiences must be published and shared with both oncology and nononcology providers in the local, national, and international medical community. These efforts aim to improve patient-related outcomes, increase provider education and awareness, and build institutional safety standards for our oncology patients.

Smashing the strict hierarchy: three cases of clinical decision support malfunctions involving carvedilol

Clinical vocabularies allow for standard representation of clinical concepts, and can also contain knowledge structures, such as hierarchy, that facilitate the creation of maintainable and accurate clinical decision support (CDS). A key architectural feature of clinical hierarchies is how they handle parent-child relationships - specifically whether hierarchies are strict hierarchies (allowing a single parent per concept) or polyhierarchies (allowing multiple parents per concept). These structures handle subsumption relationships (ie, ancestor and descendant relationships) differently. In this paper, we describe three real-world malfunctions of clinical decision support related to incorrect assumptions about subsumption checking for β-blocker, specifically carvedilol, a non-selective β-blocker that also has α-blocker activity. We recommend that 1) CDS implementers should learn about the limitations of terminologies, hierarchies, and classification, 2) CDS implementers should thoroughly test CDS, with a focus on special or unusual cases, 3) CDS implementers should monitor feedback from users, and 4) electronic health record (EHR) and clinical content developers should offer and support polyhierarchical clinical terminologies, especially for medications.