Rodrigo B. Cavalcanti’s research while affiliated with University of Toronto and other places

Background We aimed to examine the impact of sedative prescription patterns in hospitalized older adults on post‐discharge adverse drug events (ADEs), falls, and sleep. Methods We conducted a secondary analysis of the MedSafer randomized controlled trial (RCT; NCT03272607) which included hospitalized adults ≥ 65 years of age who were taking ≥ 5 medications. We identified patients who completed follow‐up at 30 days post‐discharge and provided patient‐reported outcomes for sleep disturbance (PROMIS SD 4a). We grouped patients based on sedative use as follows: nonusers, continued home use (pre‐ and post‐hospitalization), deprescribed home use, and new use at discharge. Using multivariable logistic regression, we compared the odds of patients having experienced ≥ 1 ADE (not necessarily ascribed to sedatives), a fall, or any adverse event within 30 days post‐discharge. We also used ordinal logistic regression and a minimal important difference approach to compare the change in sleep disturbance at 30 days post‐discharge. Results The cohort comprised 3630 patients with a median age of 78. A total of 2810 (77.4%) were categorized as nonusers; 475 (13.1%) continued home use; 293 (8.1%) deprescribed home use; and 52 (1.4%) new users at discharge. Compared to the continued home use group, the deprescribed group was substantially less likely to experience an ADE post‐discharge (adjusted odds ratio [aOR], 0.39 [95% CI, 0.16–0.97]). Correspondingly, new users at discharge had substantially higher odds of falls (aOR, 2.51 [95% CI, 1.13–5.61]). Favorable changes in sleep disturbance were more likely among nonusers (aOR, 1.29 [95% CI, 1.05–1.58]) and deprescribed users (aOR, 1.11 [95% CI, 0.82–1.50]) when compared to continued users. Conclusions In this cohort, patients who had their sedatives deprescribed were 61% less likely than continued users to have a post‐discharge ADE, and new sedative use at discharge was associated with appreciable risk of falls. Hospitalization likely represents a window of opportunity to improve care by promoting sedative deprescription and avoiding new starts.

Postgraduate medical education is an essential societal enterprise that prepares highly skilled physicians for the health workforce. In recent years, PGME systems have been criticized worldwide for problems with variable graduate abilities, concerns about patient safety, and issues with teaching and assessment methods. In response, competency based medical education approaches, with an emphasis on graduate outcomes, have been proposed as the direction for 21st century health profession education. However, there are few published models of large-scale implementation of these approaches. We describe the rationale and design for a national, time-variable competency-based multi-specialty system for postgraduate medical education called Competence by Design. Fourteen innovations were bundled to create this new system, using the Van Melle Core Components of competency based medical education as the basis for the transformation. The successful execution of this transformational training system shows competency based medical education can be implemented at scale. The lessons learned in the early implementation of Competence by Design can inform competency based medical education innovation efforts across professions worldwide.

Transformative changes in health professions education need to incorporate effective faculty development, but few very large-scale faculty development designs have been described. The Royal College of Physicians and Surgeons of Canada’s Competence by Design project was launched to transform the delivery of postgraduate medical education in Canada using a competency-based model. In this paper we outline the goals, principles, and rationale of the Royal College’s national strategy for faculty and resident development initiatives to support the implementation of Competence by Design. We describe the activities and resources for both faculty and trainees that facilitated the redesign of training programs for each specialty and subspecialty at the national level, as well as supporting the implementation of the redesign at the local level. This undertaking was not without its challenges: we thus reflect on those challenges, enablers, and the lessons learned, and discuss a continuous quality improvement approach that was taken to iteratively inform the implementation process moving forward.

A foundational component of digital health involves collecting and leveraging electronic health data to improve health and wellbeing. One of the central technologies for collecting these data are electronic health records (EHRs). In this commentary, the authors explore intersection between digital health and data-driven reflective practice that is described, including an overview of the role of EHRs underpinning technology innovation in healthcare. Subsequently, they argue that EHRs are a rich but under-utilised source of information on the performance of health professionals and healthcare teams that could be harnessed to support reflective practice and behaviour change. EHRs currently act as systems of data collection, not systems of data engagement and reflection by end users such as health professionals and healthcare organisations. Further consideration should be given to supporting reflective practice by health professionals in the design of EHRs and other clinical information systems.

Background: Wearable devices could provide important insights about hospitalized patients that include data collected on variations in heart rate, low activity, and poor sleep. Objective: To determine the accuracy of Fitbit heart rate, sleep, and physical activity in patients hospitalized in general medical ward. Methods: We conducted a prospective study enrolling 50 inpatients, and providing them with a Fitbit Charge. Our main measures were Fitbit heart rate, activity, and sleep as well as nurse-recorded heart rate, nurse assessments of activity, and patient-reported sleep. Results: Comparing of heart rate data, the mean difference was 0.45 beats per minute (Pearson correlation: 0.68, P < 0.001). The correlation between nurses’ recorded activity and Fitbit daily steps was 0.06 (P = 0.52). The association between patient-reported sleep score and Fitbit total sleep duration was 0.19 (P = 0.24). Conclusions: Fitbit heart rate appeared to be correlated well with nurse-recorded heart rate, but Fitbit measurements of activity and sleep did not correlate well with corresponding assessments.

The information systems designed to support clinical care have evolved separately from those that support health professions education. This has resulted in a considerable digital divide between patient care and education, one that poorly serves practitioners and organizations, even as learning becomes ever more important to both. In this perspective, we advocate for the enhancement of existing health information systems so that they intentionally facilitate learning. We describe three well-regarded frameworks for learning that can point toward how health care information systems can best evolve to support learning. The Master Adaptive Learner model suggests ways that the individual practitioner can best organize their activities to ensure continual self-improvement. The PDSA cycle similarly proposes actions for improvement but at a health care organization's workflow level. Senge's Five Disciplines of the Learning Organization, a more general framework from the business literature, serves to further inform how disparate information and knowledge flows can be managed for continual improvement. Our main thesis holds that these types of learning frameworks should inform the design and integration of information systems serving the health professions. An underutilized mediator of educational improvement is the ubiquitous electronic health record. The authors list learning analytic opportunities, including potential modifications of learning management systems and the electronic health record, that would enhance health professions education and support the shared goal of delivering high-quality evidence-based health care.

Wearable devices for hospitalized patients could help improve care. The purpose of this study was to highlight key barriers and facilitators involved in adopting wearable technology in acute care settings using patient and clinician feedback. Hospitalized patients, 18 years or older, were recruited at the General Medicine inpatient units in Toronto, Ontario to wear the Fitbit® Charge 2 or Charge 3. Fifty General Medicine adult inpatients were recruited. Patients and nurses provided feedback on structured questionnaires. Key themes from open-ended questions were analyzed. Primary outcomes of interest included the exploring patient and nurse perceptions of their experiences with wearable devices as well as their feasibility in clinical settings. Overall, both patients (n = 39) and nurses (n = 28) valued the information provided by Fitbits and shared concerns about device functionality and wearable design. Specifically, patients were interested in using wearables to enhance their self-monitoring, while nurses questioned data validity, as well as ease of incorporating wearables into their workflow. We found that patients wanted improved device design and functionality and valued the opportunity to improve their self-efficacy and to work in partnership with the medical team using wearable technology. Nurses wanted more device functionality and validation and easier ways to incorporate them into their workflow. To achieve the potential benefits of using wearable devices for enhanced monitoring, this study identifies challenges that must first be addressed in order for this technology to be widely adopted in clinical settings.

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