Effect of an Electronic Medication Reconciliation Intervention on Adverse Drug Events: A Cluster Randomized Trial

Prescription of potentially inappropriate medications after an intensive care unit stay for acute respiratory failure

BACKGROUND

Among survivors of critical illness, prescription of potentially inappropriate medications (PIM) at hospital discharge is thought to be an important, modifiable patient safety concern. To date, there are little empirical data evaluating this issue.

RESEARCH QUESTION

The objective of this study was to determine the frequency of PIM prescribed to survivors of acute respiratory failure (ARF) at hospital discharge and explore their association with readmissions or death within 90 days of hospital discharge.

STUDY DESIGN AND METHODS

Prospective multicenter cohort study of ARF survivors admitted to ICUs and discharged home. Prospective of new PIMs with a high-adverse-effect profile ("high impact") at discharge was the primary exposure. Potential inappropriateness was determined by a structured consensus process using Screening Tool of Older Persons' Prescriptions-Screening Tool to Alert to Right Treatment, Beers' criteria, and clinical context of prescriptions by a multidisciplinary team. Covariate balancing propensity score was used for the primary analysis.

RESULTS

Of the 195 Addressing Post Intensive Care Syndrome-01 (APICS-01) patients, 169 (87%) had ≥1 new medications prescribed at discharge, with 154 (91.1%) prescribed with one or more high-impact (HI) medications. Patients were prescribed a median of 5 [3-7] medications, of which 3 [1-4] were HI. Twenty percent of HI medications were potentially inappropriate. Medications with significant central nervous system side-effects were most prescribed potentially inappropriately. Forty-six (30%) patients experienced readmission or death within 90 days of hospital discharge. After adjusting for prespecified covariates, the association between prescription of potentially inappropriate HI medications and the composite primary outcome did not meet the prespecified threshold for statistical significance (risk ratio: 0.54; 0.26-1.13; p = 0.095) or with the constituent endpoints: readmission (risk ratio: 0.57, 0.27-1.11) or death (0.7, 0.05-9.32).

CONCLUSION

At hospital discharge, most ARF survivors are prescribed medications with a high-adverse-effect profile and approximately one-fifth are potentially inappropriate. Although prescription of such medications was not associated with 90-day readmissions and mortality, these results highlight an area for additional investigation.

Discrepancies in Electronic Medical Prescriptions Found in a Hospital Emergency Department: A Prospective Observational Study

The medication in an electronic prescribing system (EPS) does not always match the patient's actual medication. This prospective study analyzes the discrepancies (any inconsistency) between medication prescribed using an EPS and the medication revised by the clinical pharmacist upon admission to the observation area of the emergency department (ED). Adult patients with multimorbidity and/or polypharmacy were included. The pharmacist used multiple sources to obtain the revised medication list, including patient/carer interviews. A total of 1654 discrepancies were identified among 1131 patients. Of these patients, 64.5% had ≥1 discrepancy. The most common types of discrepancy were differences in posology (43.6%), commission (34.7%), and omission (20.9%). Analgesics (11.1%), psycholeptics (10.0%), and diuretics (8.9%) were the most affected. Furthermore, 52.5% of discrepancies affected medication that was high-alert for patients with chronic illnesses and 42.0% of medication involved withdrawal syndromes. Discrepancies increased with the number of drugs (ρ = 0.44, p < 0.01) and there was a difference between non-polypharmacy patients, polypharmacy ones and those with extreme polypharmacy (p < 0.01). Those aged over 75 years had a higher number of prescribed medications and discrepancies occurred more frequently compared with younger patients. The number of discrepancies was larger in women than in men. The EPS medication record requires verification from additional sources, including patient and/or carer interviews.

Rates and risk factors for persistent opioid use after cardiothoracic surgery: A cohort study

BACKGROUND

This study's aim was to estimate potential risk factors for persistent opioid use after cardiothoracic surgery.

METHODS

This study included participants in the McGill University Health Centre clinical trial (2014 to 2016). Provincial medical services, prescription claims, and medical charts data were linked. Persistent opioid use was defined as an initial peri-operative opioid dispensation followed by an opioid dispensation between 91 and 180 days postdischarge. Multivariable Cox Proportional Hazards models were used to assess factors associated with persistent opioid use.

RESULTS

A cohort of 815 patients (mean age: 68.9 [standard deviation = 8.9]) was assembled, of which 8.2% became persistent opioid users. Factors such as higher Charlson Comorbidity Index (adjusted hazard ratio: 3.4, 95% confidence interval: 1.1-10.6), history of diabetes (adjusted hazard ratio: 2.1, 95% confidence interval: 1.3-3.4), substance and alcohol abuse (adjusted hazard ratio: 16.3, 95% confidence interval: 5.3-49.5), and radiotherapy (adjusted hazard ratio: 2.4, 95% confidence interval: 1.5-4.1) were associated with a higher hazard of persistent opioid use. Previous opioid use (adjusted hazard ratio: 1.7, 95% CI: 1.0-2.8), daily peri-operative opioid dose (adjusted hazard ratio: 2.3, 95% confidence interval: 1.5-3.7), having an opioid dispensation 30 days pre-admission (adjusted hazard ratio: 1.7, 95% confidence interval: 1.0-2.8), and pre-admission analgesic use (adjusted hazard ratio: 1.7, 95% confidence interval: 1.0-2.8), were also associated with an increased hazard of persistent use. Being prescribed multimodal analgesia at discharge (adjusted hazard ratio: 0.54, 95% confidence interval: 0.32-0.92) was associated with a 46% decreased hazard of developing persistent opioid use.

CONCLUSION

Multiple patient- and medication-related characteristics were associated with an increased hazard of persistent opioid use.

Electronic health record intervention to increase use of NSAIDs as analgesia for hospitalised patients: a cluster randomised controlled study

BACKGROUND

Prescribing non-opioid pain medications, such as non-steroidal anti-inflammatory (NSAIDs) medications, has been shown to reduce pain and decrease opioid use, but it is unclear how to effectively encourage multimodal pain medication prescribing for hospitalised patients. Therefore, the aim of this study is to evaluate the effect of prechecking non-opioid pain medication orders on clinician prescribing of NSAIDs among hospitalised adults.

METHODS

This was a cluster randomised controlled trial of adult (≥18 years) hospitalised patients admitted to three hospital sites under one quaternary hospital system in the USA from 2 March 2022 to 3 March 2023. A multimodal pain order panel was embedded in the admission order set, with NSAIDs prechecked in the intervention group. The intervention group could uncheck the NSAID order. The control group had access to the same NSAID order. The primary outcome was an increase in NSAID ordering. Secondary outcomes include NSAID administration, inpatient pain scores and opioid use and prescribing and relevant clinical harms including acute kidney injury, new gastrointestinal bleed and in-hospital death.

RESULTS

Overall, 1049 clinicians were randomised. The study included 6239 patients for a total of 9595 encounters. Both NSAID ordering (36 vs 43%, p<0.001) and administering (30 vs 34%, p=0.001) by the end of the first full hospital day were higher in the intervention (prechecked) group. There was no statistically significant difference in opioid outcomes during the hospitalisation and at discharge. There was a statistically but perhaps not clinically significant difference in pain scores during both the first and last full hospital day.

CONCLUSIONS

This cluster randomised controlled trial showed that prechecking an order for NSAIDs to promote multimodal pain management in the admission order set increased NSAID ordering and administration, although there were no changes to pain scores or opioid use. While prechecking orders is an important way to increase adoption, safety checks should be in place.

Electronic Medication Reconciliation Tools Aimed at Healthcare Professionals to Support Medication Reconciliation: a Systematic Review

The development of health information technology available and accessible to professionals is increasing in the last few years. However, a low number of electronic health tools included some kind of information about medication reconciliation. To identify all the electronic medication reconciliation tools aimed at healthcare professionals and summarize their main features, availability, and clinical impact on patient safety. A systematic review of studies that included a description of an electronic medication reconciliation tool (web-based or mobile app) aimed at healthcare professionals was conducted. The review protocol was registered with PROSPERO: registration number CRD42022366662, and followed PRISMA guidelines. The literature search was performed using four healthcare databases: PubMed, EMBASE, Cochrane Library, and Scopus with no language or publication date restrictions. We identified a total of 1227 articles, of which only 12 met the inclusion criteria.Through these articles,12 electronic tools were detected. Viewing and comparing different medication lists and grouping medications into multiple categories were some of the more recurring features of the tools. With respect to the clinical impact on patient safety, a reduction in adverse drug events or medication discrepancies was detected in up to four tools, but no significant differences in emergency room visits or hospital readmissions were found. 12 e-MedRec tools aimed at health professionals have been developed to date but none was designed as a mobile app. The main features that healthcare professionals requested to be included in e-MedRec tools were interoperability, "user-friendly" information, and integration with the ordering process.

Medication clusters at hospital discharge and risk of adverse drug events at 30 days postdischarge: A population-based cohort study of older adults

AIMS

Certain combinations of medications can be harmful and may lead to serious adverse drug events (ADEs). Identifying potentially problematic medication clusters could help guide prescribing and/or deprescribing decisions in hospital. The aim of this study is to characterize medication prescribing patterns at hospital discharge and determine which medication clusters were associated with an increased risk of ADEs in the 30-day posthospital discharge.

METHODS

All residents of the province of Ontario in Canada aged 66 years or older admitted to hospital between March 2016 and February 2017 were included. Identification of medication clusters prescribed at hospital discharge was conducted using latent class analysis. Cluster identification and categorization were based on medications dispensed up to 30-day posthospitalization. Multivariable logistic regression was used to assess the potential association between membership to a particular medication cluster and ADEs postdischarge, while also evaluating other patient characteristics.

RESULTS

In total, 188 354 patients were included in the study cohort. Median age (interquartile range) was 77 (71-84) years, and patients had a median (IQR) (interquartile range [IQR]) of 9 (6-13) medications dispensed prior to admission. Within the study population, 6 separate clusters of dispensing patterns were identified: cardiovascular (14%), respiratory (26%), complex care needs (12%), cardiovascular and metabolic (15%), infection (10%), and surgical (24%). Overall, 12 680 (7%) patients had an ADE in the 30 days following discharge. After considering other patient characteristics, those belonging to the respiratory cluster had the highest risk of ADEs (adjusted odds ratio: 1.12, 95% confidence interval: 1.08-1.17) compared with all the other clusters, while those in the complex care needs cluster had the lowest risk (adjusted odds ratio: 0.82, 95% confidence interval: 0.77-0.87).

CONCLUSION

This study suggests that ADEs post hospital discharge can be linked with identifiable medication clusters. This information may help clinicians and researchers better understand patient populations that are more or less likely to benefit from peri-hospital discharge interventions aimed at reducing ADEs.

Systematic Review and Meta-analysis of Interventions to Reduce Adverse Drug Reactions in Older Adults: An Update

BACKGROUND

We previously reported that interventions to optimize medication use reduced adverse drug reactions (ADRs) by 21% and serious ADRs by 36% in older adults. With new evidence, we sought to update the systematic review and meta-analysis.

METHOD

We searched OVID, Cochrane Library, ClinicalTrials.gov and Google Scholar from 30 April 2017-30 April 2023. Included studies had to be randomized controlled trials of older adults (mean age ≥65 years) taking medications that examined the outcome of ADRs. Two authors independently reviewed all citations, extracted relevant data, and assessed studies for potential bias. The outcomes were any and serious ADRs. We performed subgroup analyses by intervention type and setting. Random-effects models were used to combine the results from multiple studies and create summary estimates.

RESULTS

Six studies are new to the update, resulting in 19 total studies (15,675 participants). Interventions were pharmacist-led (10 studies), other healthcare professional-led (5 studies), technology based (3 studies), and educational (1 study). The interventions were implemented in various clinical settings, including hospitals, outpatient clinics, long-term care facilities/rehabilitation wards, and community pharmacies. In the pooled analysis, the intervention group participants were 19% less likely to experience an ADR (odds ratio [OR] 0.81, 95% confidence interval [CI] 0.68-0.96) and 32% less likely to experience a serious ADR (OR 0.68, 95% CI 0.48-0.96). We also found that pharmacist-led interventions reduced the risk of any ADR by 35%, compared with 8% for other types of interventions.

CONCLUSION

Interventions significantly and substantially reduced the risk of ADRs and serious ADRs in older adults. Future research should examine whether effectiveness of interventions vary across health care settings to identify those most likely to benefit. Implementation of successful interventions in health care systems may improve medication safety in older patients.

Comparison of Different Modeling Approaches for Prescription Opioid Use and Its Association With Adverse Events

Previous research linking opioid prescribing to adverse drug events has failed to properly account for the time-varying nature of opioid exposure. This study aimed to explore how the risk of opioid-related emergency department visits, readmissions, or deaths (composite outcome) varies with opioid dose and duration, comparing different novel modeling techniques. A prospective cohort of 1,511 hospitalized patients discharged from 2 McGill-affiliated hospitals in Montreal, 2014-2016, was followed from the first postdischarge opioid dispensation until 1 year after discharge. Marginal structural Cox proportional hazards models and their flexible extensions were used to explore the association between time-varying opioid use and the composite outcome. Weighted cumulative exposure models assessed cumulative effects of past use and explored how its impact depends on the recency of exposure. The patient mean age was 69.6 (standard deviation = 14.9) years; 57.7% were male. In marginal structural model analyses, current opioid use was associated with a 71% increase in the hazard of opioid-related adverse events (adjusted hazard ratio = 1.71, 95% confidence interval: 1.21, 2.43). The weighted cumulative exposure results suggested that the risk cumulates over the previous 50 days of opioid consumption. Flexible modeling techniques helped assess how the risk of opioid-related adverse events may be associated with time-varying opioid exposures while accounting for nonlinear relationships and the recency of past use.

Predictors of frequent emergency department visits among hospitalized cancer patients: a comparative cohort study using integrated clinical and administrative data to improve care delivery

BACKGROUND

Frequent emergency department (FED) visits by cancer patients represent a significant burden to the health system. This study identified determinants of FED in recently hospitalized cancer patients, with a particular focus on opioid use.

METHODS

A prospective cohort discharged from surgical/medical units of the McGill University Health Centre was assembled. The outcome was FED use (≥ 4 ED visits) within one year of discharge. Data retrieved from the universal health insurance system was analyzed using Cox Proportional Hazards (PH) model, adopting the Lunn-McNeil approach for competing risk of death.

RESULTS

Of 1253 patients, 14.5% became FED users. FED use was associated with chemotherapy one-year pre-admission (adjusted hazard ratio (aHR) 2.60, 95% CI: 1.80-3.70), ≥1 ED visit in the previous year (aHR: 1.80, 95% CI 1.20-2.80), ≥15 pre-admission ambulatory visits (aHR 1.54, 95% CI 1.06-2.34), previous opioid and benzodiazepine use (aHR: 1.40, 95% CI: 1.10-1.90 and aHR: 1.70, 95% CI: 1.10-2.40), Charlson Comorbidity Index ≥ 3 (aHR: 2.0, 95% CI: 1.2-3.4), diabetes (aHR: 1.60, 95% CI: 1.10-2.20), heart disease (aHR: 1.50, 95% CI: 1.10-2.20) and lung cancer (aHR: 1.70, 95% CI: 1.10-2.40). Surgery (cardiac (aHR: 0.33, 95% CI: 0.16-0.66), gastrointestinal (aHR: 0.34, 95% CI: 0.14-0.82) and thoracic (aHR: 0.45, 95% CI: 0.30-0.67) led to a decreased risk of FED use.

CONCLUSIONS

Cancer patients with higher co-morbidity, frequent use of the healthcare system, and opioid use were at increased risk of FED use. High-risk patients should be flagged for preventive intervention.

A behavioral intervention to promote use of multimodal pain medication for hospitalized patients: A randomized controlled trial

BACKGROUND

The use of nonsteroidal anti-inflammatory drugs (NSAIDs) can reduce pain and has become a core strategy to decrease opioid use, but there is a lack of data to describe encouraging use when admitting patients using electronic health record systems.

OBJECTIVE

Assess an electronic health record system to increase ordering of NSAIDs for hospitalized adults.

DESIGNS, SETTINGS AND PARTICIPANTS

We performed a cluster randomized controlled trial of clinicians admitting adult patients to a health system over a 9-month period. Clinicians were randomized to use a standard admission order set.

INTERVENTION

Clinicians in the intervention arm were required to actively order or decline NSAIDs; the control arm was shown the same order but without a required response.

MAIN OUTCOME AND MEASURES

The primary outcome was NSAIDs ordered and administered by the first full hospital day. Secondary outcomes included pain scores and opioid prescribing.

RESULTS

A total of 20,085 hospitalizations were included. Among these hospitalizations, patients had a mean age of 58 years, and a Charlson comorbidity score of 2.97, while 50% and 56% were female and White, respectively. Overall, 52% were admitted by a clinician randomized to the intervention arm. NSAIDs were ordered in 2267 (22%) interventions and 2093 (22%) control admissions (p = .10). Similarly, there were no statistical differences in NSAID administration, pain scores, or opioid prescribing. Average pain scores (0-5 scale) were 3.36 in the control group and 3.39 in the intervention group (p = .46). There were no differences in clinical harms.

CONCLUSIONS AND RELEVANCE

Requiring an active decision to order an NSAID at admission had no demonstrable impact on NSAID ordering. Multicomponent interventions, perhaps with stronger decision support, may be necessary to encourage NSAID ordering.

Potentiality of algorithms and artificial intelligence adoption to improve medication management in primary care: a systematic review

OBJECTIVES

The aim of this study is to investigate the effect of artificial intelligence (AI) and/or algorithms on drug management in primary care settings comparing AI and/or algorithms with standard clinical practice. Second, we evaluated what is the most frequently reported type of medication error and the most used AI machine type.

METHODS

A systematic review of literature was conducted querying PubMed, Cochrane and ISI Web of Science until November 2021. The search strategy and the study selection were conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses and the Population, Intervention, Comparator, Outcome framework. Specifically, the Population chosen was general population of all ages (ie, including paediatric patients) in primary care settings (ie, home setting, ambulatory and nursery homes); the Intervention considered was the analysis AI and/or algorithms (ie, intelligent programs or software) application in primary care for reducing medications errors, the Comparator was the general practice and, lastly, the Outcome was the reduction of preventable medication errors (eg, overprescribing, inappropriate medication, drug interaction, risk of injury, dosing errors or in an increase in adherence to therapy). The methodological quality of included studies was appraised adopting the Quality Assessment of Controlled Intervention Studies of the National Institute of Health for randomised controlled trials.

RESULTS

Studies reported in different ways the effective reduction of medication error. Ten out of 14 included studies, corresponding to 71% of articles, reported a reduction of medication errors, supporting the hypothesis that AI is an important tool for patient safety.

CONCLUSION

This study highlights how a proper application of AI in primary care is possible, since it provides an important tool to support the physician with drug management in non-hospital environments.

Determinants of long-term opioid use in hospitalized patients

BACKGROUND

Long-term opioid use is an increasingly important problem related to the ongoing opioid epidemic. The purpose of this study was to identify patient, hospitalization and system-level determinants of long term opioid therapy (LTOT) among patients recently discharged from hospital.

DESIGN

To be eligible for this study, patient needed to have filled at least one opioid prescription three-months post-discharge. We retrieved data from the provincial health insurance agency to measure medical service and prescription drug use in the year prior to and after hospitalization. A multivariable Cox Proportional Hazards model was utilized to determine factors associated with time to the first LTOT occurrence, defined as time-varying cumulative opioid duration of ≥ 60 days.

RESULTS

Overall, 22.4% of the 1,551 study patients were classified as LTOT, who had a mean age of 66.3 years (SD = 14.3). Having no drug copay status (adjusted hazard ratio (aHR) 1.91, 95% CI: 1.40-2.60), being a LTOT user before the index hospitalization (aHR 6.05, 95% CI: 4.22-8.68) or having history of benzodiazepine use (aHR 1.43, 95% CI: 1.12-1.83) were all associated with an increased likelihood of LTOT. Cardiothoracic surgical patients had a 40% lower LTOT risk (aHR 0.55, 95% CI: 0.31-0.96) as compared to medical patients. Initial opioid dispensation of > 90 milligram morphine equivalents (MME) was also associated with higher likelihood of LTOT (aHR 2.08, 95% CI: 1.17-3.69).

CONCLUSIONS AND RELEVANCE

Several patient-level characteristics associated with an increased risk of ≥ 60 days of cumulative opioid use. The results could be used to help identify patients who are at high-risk of continuing opioids beyond guideline recommendations and inform policies to curb excessive opioid prescribing.

Recommendations for the Design and Delivery of Transitions-Focused Digital Health Interventions: Rapid Review

Background

Older adults experience a high risk of adverse events during hospital-to-home transitions. Implementation barriers have prevented widespread clinical uptake of the various digital health technologies that aim to support hospital-to-home transitions.

Objective

To guide the development of a digital health intervention to support transitions from hospital to home (the Digital Bridge intervention), the specific objectives of this review were to describe the various roles and functions of health care providers supporting hospital-to-home transitions for older adults, allowing future technologies to be more targeted to support their work; describe the types of digital health interventions used to facilitate the transition from hospital to home for older adults and elucidate how these interventions support the roles and functions of providers; describe the lessons learned from the design and implementation of these interventions; and identify opportunities to improve the fit between technology and provider functions within the Digital Bridge intervention and other transition-focused digital health interventions.

Methods

This 2-phase rapid review involved a selective review of providers’ roles and their functions during hospital-to-home transitions (phase 1) and a structured literature review on digital health interventions used to support older adults’ hospital-to-home transitions (phase 2). During the analysis, the technology functions identified in phase 2 were linked to the provider roles and functions identified in phase 1.

Results

In phase 1, various provider roles were identified that facilitated hospital-to-home transitions, including navigation-specific roles and the roles of nurses and physicians. The key transition functions performed by providers were related to the 3 categories of continuity of care (ie, informational, management, and relational continuity). Phase 2, included articles (n=142) that reported digital health interventions targeting various medical conditions or groups. Most digital health interventions supported management continuity (eg, follow-up, assessment, and monitoring of patients’ status after hospital discharge), whereas informational and relational continuity were the least supported. The lessons learned from the interventions were categorized into technology- and research-related challenges and opportunities and informed several recommendations to guide the design of transition-focused digital health interventions.

Conclusions

This review highlights the need for Digital Bridge and other digital health interventions to align the design and delivery of digital health interventions with provider functions, design and test interventions with older adults, and examine multilevel outcomes.

International Registered Report Identifier (IRRID)

RR2-10.1136/bmjopen-2020-045596

The MedSafer Study-Electronic Decision Support for Deprescribing in Hospitalized Older Adults: A Cluster Randomized Clinical Trial

IMPORTANCE

Scalable deprescribing interventions may reduce polypharmacy and the use of potentially inappropriate medications (PIMs); however, few studies have been large enough to evaluate the impact that deprescribing may have on adverse drug events (ADEs).

OBJECTIVE

To evaluate the effect of an electronic deprescribing decision support tool on ADEs after hospital discharge among older adults with polypharmacy.

DESIGN, SETTING, AND PARTICIPANTS

This was a cluster randomized clinical trial of older (≥65 years) hospitalized patients with an expected survival of more than 3 months who were admitted to 1 of 11 acute care hospitals in Canada from August 22, 2017, to January 13, 2020. At admission, participants were taking 5 or more medications per day. Data analyses were performed from January 3, 2021, to September 23, 2021.

INTERVENTIONS

Personalized reports of deprescribing opportunities generated by MedSafer software to address usual home medications and measures of prognosis and frailty. Deprescribing reports provided to the treating team were compared with usual care (medication reconciliation).

MAIN OUTCOMES AND MEASURES

The primary outcome was a reduction of ADEs within the first 30 days postdischarge (including adverse drug withdrawal events) captured through structured telephone surveys and adjudicated blinded to intervention status. Secondary outcomes were the proportion of patients with 1 or more PIMs deprescribed at discharge and the proportion of patients with an adverse drug withdrawal event (ADWE).

RESULTS

A total of 5698 participants (median [range] age, 78 [72-85] years; 2858 [50.2%] women; race and ethnicity data were not collected) were enrolled in 3 clusters and were adjudicated for the primary outcome (control, 3204; intervention, 2494). Despite cluster randomization, there were group imbalances, eg, the participants in the intervention arm were older and had more PIMS prescribed at baseline. After hospital discharge, 4989 (87.6%) participants completed an ADE interview. There was no significant difference in ADEs within 30 days of discharge (138 [5.0%] of 2742 control vs 111 [4.9%] of 2247 intervention participants; adjusted risk difference [aRD] -0.8%; 95% CI, -2.9% to 1.3%). Deprescribing increased from 795 (29.8%) of 2667 control to 1249 (55.4%) of 2256 intervention participants [aRD, 22.2%; 95% CI, 16.9% to 27.4%]. There was no difference in ADWEs between groups. Several post hoc sensitivity analyses, including the use of a nonparametric test to address the low cluster number, group imbalances, and potential biases, did not alter study conclusions.

CONCLUSIONS AND RELEVANCE

This cluster randomized clinical trial showed that providing deprescribing clinical decision support during acute hospitalization had no demonstrable impact on ADEs, although the intervention was safe and led to improvements in deprescribing.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT03272607.

Medicines Reconciliation in the Emergency Department: Important Prescribing Discrepancies between the Shared Medication Record and Patients’ Actual Use of Medication

Medication reconciliation is crucial to prevent medication errors. In Denmark, primary and secondary care physicians can prescribe medication in the same electronic prescribing system known as the Shared Medication Record (SMR). However, the SMR is not always updated by physicians, which can lead to discrepancies between the SMR and patients’ actual use of medication. These discrepancies may compromise patient safety upon admission to the emergency department (ED). Here, we investigated (a) the occurrence of discrepancies, (b) factors associated with discrepancies, and (c) the percentage of patients accessible to a clinical pharmacist during pharmacy working hours. The study included all patients age ≥ 18 years who were admitted to the Hvidovre Hospital ED on three consecutive days in June 2020. The clinical pharmacists performed medicines reconciliation to identify prescribing discrepancies. In total, 100 patients (52% male; median age 66.5 years) were included. The patients had a median of 10 [IQR 7–13] medications listed in the SMR and a median of two [IQR 1–3.25] discrepancies. Factors associated with increased rate of prescribing discrepancies were age < 65 years, time since last update of the SMR ≥ 115 days, and patients’ self-dispensing their medications. Eighty-four percent of patients were available for medicines reconciliations during the normal working hours of the clinical pharmacist. In conclusion, we found that discrepancies between the SMR and patients’ actual medication use upon admission to the ED are frequent, and we identified several risk factors associated with the increased rate of discrepancies.

Personal Electronic Records of Medications (PERMs) for medication reconciliation at care transitions: a rapid realist review

Background

Medication reconciliation (MedRec), a process to reduce medication error at care transitions, is labour- and resource-intensive and time-consuming. Use of Personal Electronic Records of Medications (PERMs) in health information systems to support MedRec have proven challenging. Relatively little is known about the design, use or implementation of PERMs at care transitions that impacts on MedRec in the ‘real world’. To respond to this gap in knowledge we undertook a rapid realist review (RRR). The aim was to develop theories to explain how, why, when, where and for whom PERMs are designed, implemented or used in practice at care transitions that impacts on MedRec.

Methodology

We used realist methodology and undertook the RRR between August 2020 and February 2021. We collaborated with experts in the field to identify key themes. Articles were sourced from four databases (Pubmed, Embase, CINAHL Complete and OpenGrey) to contribute to the theory development. Quality assessment, screening and data extraction using NVivo was completed. Contexts, mechanisms and outcomes configurations were identified and synthesised. The experts considered these theories for relevance and practicality and suggested refinements.

Results

Ten provisional theories were identified from 19 articles. Some theories relate to the design (T2 Inclusive design, T3 PERMs complement existing good processes, T7 Interoperability), some relate to the implementation (T5 Tailored training, T9 Positive impact of legislation or governance), some relate to use (T6 Support and on-demand training) and others relate iteratively to all stages of the process (T1 Engage stakeholders, T4 Build trust, T8 Resource investment, T10 Patients as users of PERMs).

Conclusions

This RRR has allowed additional valuable data to be extracted from existing primary research, with minimal resources, that may impact positively on future developments in this area. The theories are interdependent to a greater or lesser extent; several or all of the theories may need to be in play to collectively impact on the design, implementation or use of PERMs for MedRec at care transitions. These theories should now be incorporated into an intervention and evaluated to further test their validity.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12911-021-01659-8.

Effect of electronic medication reconciliation at the time of hospital discharge on inappropriate medication use in the community: an interrupted time-series analysis

BACKGROUND

It is unclear if enhanced electronic medication reconciliation systems can reduce inappropriate medication use and improve patient care. We evaluated trends in potentially inappropriate medication use after hospital discharge before and after adoption of an electronic medication reconciliation system.

METHODS

We conducted an interrupted time-series analysis in 3 tertiary care hospitals in London, Ontario, using linked health care data (2011-2019). We included patients aged 66 years and older who were discharged from hospital. Starting between Apr. 13 and May 21, 2014, physicians were required to complete an electronic medication reconciliation module for each discharged patient. As a process outcome, we evaluated the proportion of patients who continued to receive a benzodiazepine, antipsychotic or gastric acid suppressant as an outpatient when these medications were first started during the hospital stay. The clinical outcome was a return to hospital within 90 days of discharge with a fall or fracture among patients who received a new benzodiazepine or antipsychotic during their hospital stay. We used segmented linear regression for the analysis.

RESULTS

We identified 15 932 patients with a total of 18 405 hospital discharge episodes. Before the implementation of the electronic medication reconciliation system, 16.3% of patients received a prescription for a benzodiazepine, antipsychotic or gastric acid suppressant after their hospital stay. After implementation, there was a significant and immediate 7.0% absolute decline in this proportion (95% confidence interval [CI] 4.5% to 9.5%). Before implementation, 4.1% of discharged patients who newly received a benzodiazepine or antipsychotic returned to hospital with a fracture or fall within 90 days. After implementation, there was a significant and immediate 2.3% absolute decline in this outcome (95% CI 0.3% to 4.3%).

INTERPRETATION

Implementation of an electronic medication reconciliation system in 3 tertiary care hospitals reduced potentially inappropriate medication use and associated adverse events when patients transitioned back to the community. Enhanced electronic medication reconciliation systems may allow other hospitals to improve patient safety.

Supporting medicines management for older people at care transitions - a theory-based analysis of a systematic review of 24 interventions

BACKGROUND

Older patients are at severe risk of harm from medicines following a hospital to home transition. Interventions aiming to support successful care transitions by improving medicines management have been implemented. This study aimed to explore which behavioural constructs have previously been targeted by interventions, which individual behaviour change techniques have been included, and which are yet to be trialled.

METHOD

This study mapped the behaviour change techniques used in 24 randomised controlled trials to the Behaviour Change Technique Taxonomy. Once elicited, techniques were further mapped to the Theoretical Domains Framework to explore which determinants of behaviour change had been targeted, and what gaps, if any existed.

RESULTS

Common behaviour change techniques used were: goals and planning; feedback and monitoring; social support; instruction on behaviour performance; and prompts/cues. These may be valuable when combined in a complex intervention. Interventions mostly mapped to between eight and 10 domains of the Theoretical Domains Framework. Environmental context and resources was an underrepresented domain, which should be considered within future interventions.

CONCLUSION

This study has identified behaviour change techniques that could be valuable when combined within a complex intervention aiming to support post-discharge medicines management for older people. Whilst many interventions mapped to eight or more determinants of behaviour change, as identified within the Theoretical Domains Framework, careful assessment of the barriers to behaviour change should be conducted prior to intervention design to ensure all appropriate domains are targeted.

An old friend turned foe: Metformin-induced diarrhea with resultant symptomatic hypokalemia, hypomagnesemia, hypocalcemia, and hypophosphatemia

Metformin is known for gastrointestinal side effects, but has rarely been associated with diarrhea severe enough to cause symptomatic electrolyte abnormalities and hospitalization. This case illustrates such that occurred despite multiple outpatient encounters for diarrhea. Additionally, this case highlights the importance of a comprehensive medical reconciliation as part of a thorough medical evaluation.

Association of Opioid Consumption Profiles After Hospitalization With Risk of Adverse Health Care Events

IMPORTANCE

Although better pain management has guided policies for opioid use over the past few decades, evidence is limited regarding how patterns of use are associated with the risk of potentially avoidable opioid-related adverse events.

OBJECTIVE

To estimate the risk of harms associated with opioid dose and duration of use, and to ascertain whether the risk is modified by treatment indication and age.

DESIGN, SETTING, AND PARTICIPANTS

This ad hoc cohort study followed up patients who were enrolled in a cluster randomized trial of medication reconciliation between October 1, 2014, and November 30, 2016, 12 months after they were discharged from the McGill University Health Centre in Montreal, Quebec, Canada. To be eligible for this study, patients needed to have filled at least 1 opioid prescription 3 months after discharge. Patients with a history of using methadone or buprenorphine were excluded. Data analyses were performed between February 1, 2019, and February 28, 2020.

EXPOSURES

Time-varying measures of opioid use included current use, daily morphine milligram equivalent (MME) dose, cumulative and continuous use duration, and type of ingredients in prescription opioids used. Hospitalization records, dispensed prescriptions records, and postdischarge interviews were used to evaluate adherence to the opioid prescriptions after discharge.

MAIN OUTCOMES AND MEASURES

Opioid-related emergency department visits, hospital readmissions, or all-cause death. Outcomes were ascertained using provincial medical services claims and hospitalization databases.

RESULTS

Of 3486 participants in the cluster randomized trial (mean [SD] age of 69.6 [14.9] years; 2010 men [57.7%]), 1511 patients were included in this ad hoc cohort study. Among those with at least 1 opioid dispensation, 241 patients (15.9%) experienced an opioid-related emergency department visit, hospital readmission, or death. Results from marginal structural Cox proportional hazards regression models showed more than a 2-fold increase in the risk of opioid-related adverse events associated with a cumulative use duration of more than 90 days (adjusted hazard ratio, 2.56; 95% CI, 1.25-5.27) compared with 1 to 30 days. A 3-fold risk increase was found with a mean daily dose higher than 90 MME (adjusted hazard ratio, 3.51; 95% CI, 1.58-7.82) compared with 90 MME or lower.

CONCLUSIONS AND RELEVANCE

This study found an association between risk of adverse health care events and higher opioid doses and longer treatment duration. This finding can inform policies for limiting opioid duration and dose to attenuate the risk of avoidable morbidity.

Postoperative duration of opioid use and acute healthcare services use in cancer patients hospitalized for thoracic surgery

BACKGROUND

Postoperative pain control is an important cancer care component. However, opioid consumption has resulted in a surge of adverse events, with thoracic surgery patients having the highest rate of persistent use. The effect of opioid duration post-discharge and the risk of increased acute healthcare use in this population remains unclear.

METHODS

A prospective cohort of non-metastatic cancer patients was assembled from an academic health center in Montreal (Canada). Clinical data linked to administrative claims from the universal healthcare program was used to determine the association between time-varying opioid patterns and emergency department (ED) visits/re-admissions/death 3 months following thoracic surgery.

RESULTS

Of the 610 patients, 77% had at least one opioid dispensed post-discharge. Compared to non-opioid users, <15 days of use was associated with a 42% decreased risk of acute healthcare events, adjusted HR 0.58, 95% CI (0.40-0.85); longer durations were not associated with an increased risk. Compared to short-term use (<15 days), use of >30 days was associated with a 72% increased risk of the outcome, aHR: 1.72, 95% CI (1.01-2.93).

CONCLUSION

There was a variation in the risk of acute healthcare use associated with postsurgical opioid use. Findings from this study may be used to inform postoperative prescribing practices.

Detection of adverse drug events in e-prescribing and administrative health data: a validation study

BACKGROUND

Administrative health data are increasingly used to detect adverse drug events (ADEs). However, the few studies evaluating diagnostic codes for ADE detection demonstrated low sensitivity, likely due to narrow code sets, physician under-recognition of ADEs, and underreporting in administrative data. The objective of this study was to determine if combining an expanded ICD code set in administrative data with e-prescribing data improves ADE detection.

METHODS

We conducted a prospective cohort study among patients newly prescribed antidepressant or antihypertensive medication in primary care and followed for 2 months. Gold standard ADEs were defined as patient-reported symptoms adjudicated as medication-related by a clinical expert. Potential ADEs in administrative data were defined as physician, ED, or hospital visits during follow-up for known adverse effects of the study medication, as identified by ICD codes. Potential ADEs in e-prescribing data were defined as study drug discontinuations or dose changes made during follow-up for safety or effectiveness reasons.

RESULTS

Of 688 study participants, 445 (64.7%) were female and mean age was 64.2 (SD 13.9). The study drug for 386 (56.1%) patients was an antihypertensive, and for 302 (43.9%) an antidepressant. Using the gold standard definition, 114 (16.6%) patients experienced an ADE, with 40 (10.4%) among antihypertensive users and 74 (24.5%) among antidepressant users. The sensitivity of the expanded ICD code set was 7.0%, of e-prescribing data 9.7%, and of the two combined 14.0%. Specificities were high (86.0-95.0%). The sensitivity of the combined approach increased to 25.8% when analysis was restricted to the 27% of patients who indicated having reported symptoms to a physician.

CONCLUSION

Combining an expanded diagnostic code set with e-prescribing data improves ADE detection. As few patients report symptoms to their physician, higher detection rates may be achieved by collecting patient-reported outcomes via emerging digital technologies such as patient portals and mHealth applications.

The Development and Piloting of the Ambulatory Electronic Health Record Evaluation Tool: Lessons Learned

BACKGROUND

Substantial research has been performed about the impact of computerized physician order entry on medication safety in the inpatient setting; however, relatively little has been done in ambulatory care, where most medications are prescribed.

OBJECTIVE

To outline the development and piloting process of the Ambulatory Electronic Health Record (EHR) Evaluation Tool and to report the quantitative and qualitative results from the pilot.

METHODS

The Ambulatory EHR Evaluation Tool closely mirrors the inpatient version of the tool, which is administered by The Leapfrog Group. The tool was piloted with seven clinics in the United States, each using a different EHR. The tool consists of a medication safety test and a medication reconciliation module. For the medication test, clinics entered test patients and associated test orders into their EHR and recorded any decision support they received. An overall percentage score of unsafe orders detected, and order category scores were provided to clinics. For the medication reconciliation module, clinics demonstrated how their EHR electronically detected discrepancies between two medication lists.

RESULTS

For the medication safety test, the clinics correctly alerted on 54.6% of unsafe medication orders. Clinics scored highest in the drug allergy (100%) and drug-drug interaction (89.3%) categories. Lower scoring categories included drug age (39.3%) and therapeutic duplication (39.3%). None of the clinics alerted for the drug laboratory or drug monitoring orders. In the medication reconciliation module, three (42.8%) clinics had an EHR-based medication reconciliation function; however, only one of those clinics could demonstrate it during the pilot.

CONCLUSION

Clinics struggled in areas of advanced decision support such as drug age, drug laboratory, and drub monitoring. Most clinics did not have an EHR-based medication reconciliation function and this process was dependent on accessing patients' medication lists. Wider use of this tool could improve outpatient medication safety and can inform vendors about areas of improvement.

Incidence and Variables Associated With Inconsistencies in Opioid Prescribing at Hospital Discharge and Its Associated Adverse Drug Outcomes

OBJECTIVES

Opioid-related medication errors (MEs) can have a significant impact on patient health and contribute to opioid misuse. The objective of this study was to estimate the incidence of and variables associated with the receipt of an opioid prescription and opioid-related MEs (omissions, duplications, or dose changes) at hospital discharge. We also determined rates of adverse drug events and risks of emergency department visits, readmissions, or death 30 days and 90 days post discharge associated with MEs.

METHODS

A cohort of hospitalized patients discharged from the McGill University Health Centre between 2014 and 2016 was assembled. The impact of opioid-related MEs was assessed in a propensity score-adjusted logistic regression models. Multivariable logistic regression was used to determine characteristics associated with MEs and discharge opioid prescription.

RESULTS

A total of 1530 (43.9%) of 3486 patients were prescribed opioids, of which 13.4% (n = 205) of patients had at least 1 opioid-related ME. Rates of MEs were higher in handwritten prescriptions compared to the electronic reconciliation discharge prescription group (20.6% vs 1.2%). Computer-based prescriptions were associated with a 69% lower risk of opioid-related MEs (adjusted odds ratio: 0.31, 95% confidence interval: 0.14-0.65) as well as 63% lower risk of receiving an opioid prescription. Opioid-related MEs were associated with a 2.3 times increased risk of healthcare utilization in the 30 days postdischarge period (adjusted odds ratio: 2.32, 95% confidence interval: 1.24-4.32).

CONCLUSIONS

Opioid-related MEs are common in handwritten discharge prescriptions. Our findings highlight the need for computer-based prescribing platforms and careful review of medications during critical periods of care such as hospital transitions.

Medication Use Quality and Safety in Older Adults: 2019 Update

Improving the quality of medication use and medication safety are important priorities for prescribers who care for older adults. The objective of this article was to identify four exemplary articles with this focus in 2019. We selected high-quality studies that moved the field of research forward and were not merely replication studies. The chosen articles cover domains related to aspects of suboptimal prescribing and medication safety. The first study used a nationally representative sample of Medicare beneficiaries to examine the continuation of medications with limited benefit in patients admitted for cancer and non-cancer diagnoses in hospice (domain: potentially inappropriate medications). The second study, a retrospective cohort study of older adults in Ontario, Canada, assessed the association between prescribing oral anticoagulants in an emergency department relative to not prescribing anticoagulants in the emergency department and their persistence at 6 months (domain: underuse of medications). The third study, a cluster randomized trial in Quebec, Canada, evaluated the effect of conducting electronic medication reconciliation on several outcomes including adverse drug events and medication discrepancies (domain: medication safety). Lastly, the fourth study, a retrospective study using national inpatient and outpatient Veteran Health Administration combined with clinical and Medicare Claims data, examined the effects of intensification of antihypertensive medications on older adults' likelihood for hospital re-admission and other important clinical outcomes (domain: medication safety). Collectively, this review succinctly highlights pertinent topics related to promoting safe use of medications and promotes awareness of optimizing older adults' medication regimens.

Assessing the International Transferability of a Machine Learning Model for Detecting Medication Error in the General Internal Medicine Clinic: Multicenter Preliminary Validation Study

Background

Although most current medication error prevention systems are rule-based, these systems may result in alert fatigue because of poor accuracy. Previously, we had developed a machine learning (ML) model based on Taiwan’s local databases (TLD) to address this issue. However, the international transferability of this model is unclear.

Objective

This study examines the international transferability of a machine learning model for detecting medication errors and whether the federated learning approach could further improve the accuracy of the model.

Methods

The study cohort included 667,572 outpatient prescriptions from 2 large US academic medical centers. Our ML model was applied to build the original model (O model), the local model (L model), and the hybrid model (H model). The O model was built using the data of 1.34 billion outpatient prescriptions from TLD. A validation set with 8.98% (60,000/667,572) of the prescriptions was first randomly sampled, and the remaining 91.02% (607,572/667,572) of the prescriptions served as the local training set for the L model. With a federated learning approach, the H model used the association values with a higher frequency of co-occurrence among the O and L models. A testing set with 600 prescriptions was classified as substantiated and unsubstantiated by 2 independent physician reviewers and was then used to assess model performance.

Results

The interrater agreement was significant in terms of classifying prescriptions as substantiated and unsubstantiated (κ=0.91; 95% CI 0.88 to 0.95). With thresholds ranging from 0.5 to 1.5, the alert accuracy ranged from 75%-78% for the O model, 76%-78% for the L model, and 79%-85% for the H model.

Conclusions

Our ML model has good international transferability among US hospital data. Using the federated learning approach with local hospital data could further improve the accuracy of the model.

An exploration of Canadian transitional care programs for older adults

Transitional care programs are effective for improving patient outcomes upon discharge from acute care services and reducing the burden of healthcare costs; however, little is known about the types of transitional care programs for older adults across Canada. This exploratory study gathered an in-depth understanding of Canadian transitional care programs and described how each program functions to support older adults and family/friend caregivers. Nine key informants were interviewed about the development of transitional care programs within four Canadian provincial regions including Atlantic, Central, Prairie, and West Coast. Key facilitators and barriers influencing the development and long-term success of transitional care programs included program scope, program structure, continuity of care, funding, and health system infrastructure. Future research is required to identify how a broad range of transitional care programs operate and to disseminate knowledge with health leaders and decision-makers to ensure transitional care programs are embedded as essential health system services.

Successful care transitions for older people: a systematic review and meta-analysis of the effects of interventions that support medication continuity

Background

medication-related problems occur frequently when older patients are discharged from hospital. Interventions to support medication use have been developed; however, their effectiveness in older populations are unknown. This review evaluates interventions that support successful transitions of care through enhanced medication continuity.

Methods

a database search for randomised controlled trials was conducted. Selection criteria included mean participant age of 65 years and older, intervention delivered during hospital stay or following recent discharge and including activities that support medication continuity. Primary outcome of interest was hospital readmission. Secondary outcomes related to the safe use of medication and quality of life. Outcomes were pooled by random-effects meta-analysis where possible.

Results

twenty-four studies (total participants = 17,664) describing activities delivered at multiple time points were included. Interventions that bridged the transition for up to 90 days were more likely to support successful transitions. The meta-analysis, stratified by intervention component, demonstrated that self-management activities (RR 0.81 [0.74, 0.89]), telephone follow-up (RR 0.84 [0.73, 0.97]) and medication reconciliation (RR 0.88 [0.81, 0.96]) were statistically associated with reduced hospital readmissions.

Conclusion

our results suggest that interventions that best support older patients’ medication continuity are those that bridge transitions; these also have the greatest impact on reducing hospital readmission. Interventions that included self-management, telephone follow-up and medication reconciliation activities were most likely to be effective; however, further research needs to identify how to meaningfully engage with patients and caregivers to best support post-discharge medication continuity. Limitations included high subjectivity of intervention coding, study heterogeneity and resource restrictions.

Addressing the epidemic of antibiotic "allergy" over-diagnosis

OBJECTIVE

An epidemic of antibiotic allergy is occurring.

DATA SOURCES

Articles published since 2008.

STUDY SELECTIONS

Articles on antibiotic allergy and stewardship.

RESULTS

A number of overlapping factors contribute. The most important factor is antibiotic overuse. Antibiotics are commonly used in situations in which no antibiotics are indicated. Thirty percent to 50% of ambulatory antibiotic use may be inappropriate. The duration of indicated antibiotic use is often excessive, which leads to more side effects. All antibiotic use can result in adverse reactions, and a fraction of these will be dutifully recorded as an allergy in the electronic health record (EHR). Most EHRs are not well structured to accurately convey information on expected side effects that have occurred, metabolic or other contraindications, dose-related or situational toxicities, personal preferences, clinically significant immunologically mediated hypersensitivity, and other reasons a particular patient may not want or should not be given a specific drug or type of drug in the future. As populations age, their accumulated baggage of reported antibiotic allergies increase. Suspected antibiotic allergy is rarely confirmed with appropriate testing or rechallenge. Patients then receive suboptimal antibiotic therapy and experience more side effects, treatment failures, and serious antibiotic-resistant infections. Reporting an antibiotic allergy in the EHR is nominally done to improve patient safety, but unfortunately, this is often not the actual result.

CONCLUSION

Audit and feedback, to help ensure adherence to Choosing Wisely recommendations and good antibiotic stewardship practices, can help reduce inappropriate antibiotic use. Restructuring EHRs to facilitate correct drug intolerance reporting, along with active antibiotic allergy delabeling programs, can help stem this epidemic.