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It is currently unclear how the shift towards virtual care during the 2019 novel coronavirus (COVID-19) pandemic may have impacted chronic disease management at a population level. The goals of our study were to provide a description of the levels of use of virtual care services relative to in-person care in patients with chronic disease across Ontario, Canada and to describe levels of healthcare utilization in low versus high virtual care users. We used linked health administrative data to conduct a population-based, repeated cross-sectional study of all ambulatory patient visits in Ontario, Canada (January 1, 2018 to January 16, 2021). Further stratifications were also completed to examine patients with COPD, heart failure, asthma, hypertension, diabetes, mental illness, and angina. Patients were classified as low (max 1 virtual care visit) vs. high virtual care users. A time-series analysis was done using interventional autoregressive integrated moving average (ARIMA) modelling on weekly hospitalizations, outpatient visits, and diagnostic tests. The use of virtual care increased across all chronic disease patient populations. Virtual care constituted at least half of the total care in all conditions. Both low and high virtual care user groups experienced a statistically significant reduction in hospitalizations and laboratory testing at the start of the pandemic. Hospitalization volumes increased again only among the high users, while testing increased in both groups. Outpatient visits among high users remained unaffected by the pandemic but dropped in low users. The decrease of in-person care during the pandemic was accompanied by an increase in virtual care, which ultimately allowed patients with chronic disease to return to the same visit rate as they had before the onset of the pandemic. Virtual care was adopted across various chronic conditions, but the relative adoption of virtual care varied by condition with highest rates seen in mental health.

As the increasing prevalence of type 2 diabetes mellitus has put pressure on health systems to appropriately manage these patients, there have been a growing number of mobile apps designed to improve the self-management of diabetes. One such app, BlueStar, has been shown to significantly reduce hemoglobin A (HbA ) levels in small studies and is the first app in the United States to receive Food and Drug Administration approval as a mobile prescription therapy. However, the impact of the app across real-world population among different clinical sites and health systems remains unclear. The primary objective of this study was to conduct a pragmatic randomized controlled trial of the BlueStar mobile app to determine if app usage leads to improved HbA levels among diverse participants in real-life clinical contexts. We hypothesized that this mobile app would improve self-management and HbA levels compared with controls. The study consisted of a multicenter pragmatic randomized controlled trial. Overall, 110 participants randomized to the immediate treatment group (ITG) received the intervention for 6 months, and 113 participants randomized to the wait-list control (WLC) group received usual care for the first 3 months and then received the intervention for 3 months. The primary outcome was glucose control measured by HbA levels at 3 months. Secondary outcomes assessed intervention impact on patient self-management, experience of care, and self-reported health utilization using validated scales, including the Problem Areas in Diabetes, the Summary of Diabetes Self-Care Activities, and the EuroQol-5D. Intervention usage data were collected directly from the app. The results of an analysis of covariance controlling for baseline HbA levels did not show evidence of intervention impact on HbA levels at 3 months (mean difference [ITG-WLC] -0.42, 95% CI -1.05 to 0.21; P=.19). Similarly, there was no intervention effect on secondary outcomes measuring diabetes self-efficacy, quality of life, and health care utilization behaviors. An exploratory analysis of 57 ITG participants investigating the impact of app usage on HbA levels showed that each additional day of app use corresponded with a 0.016-point decrease in participants' 3-month HbA levels (95% CI -0.03 to -0.003). App usage varied significantly by site, as participants from 1 site logged in to the app a median of 36 days over 14 weeks (interquartile range [IQR] 10.5-124); those at another site used the app significantly less (median 9; IQR 6-51). The results showed no difference between intervention and control arms for the primary clinical outcome of glycemic control measured by HbA levels. Although there was low usage of the app among participants, results indicate contextual factors, particularly site, had a significant impact on overall usage. Future research into the patient and site-specific factors that increase app utilization are needed. Clinicaltrials.gov NCT02813343; https://clinicaltrials.gov/ct2/show/NCT02813343 (Archived by WebCite at https://clinicaltrials.gov/ct2/show/NCT02813343).

The COVID-19 pandemic has led to a notable increase in telemedicine adoption. However, the impact of the pandemic on telemedicine use at a population level in rural and remote settings remains unclear. This study aimed to evaluate changes in the rate of telemedicine use among rural populations and identify patient characteristics associated with telemedicine use prior to and during the pandemic. We conducted a repeated cross-sectional study on all monthly and quarterly rural telemedicine visits from January 2012 to June 2020, using administrative data from Ontario, Canada. We compared the changes in telemedicine use among residents of rural and urban regions of Ontario prior to and during the pandemic. Before the pandemic, telemedicine use was steadily low in 2012-2019 for both rural and urban populations but slightly higher overall for rural patients (11 visits per 1000 patients vs 7 visits per 1000 patients in December 2019, P<.001). The rate of telemedicine visits among rural patients significantly increased to 147 visits per 1000 patients in June 2020. A similar but steeper increase (P=.15) was observed among urban patients (220 visits per 1000 urban patients). Telemedicine use increased across all age groups, with the highest rates reported among older adults aged ≥65 years (77 visits per 100 patients in 2020). The proportions of patients with at least 1 telemedicine visit were similar across the adult age groups (n=82,246/290,401, 28.3% for patients aged 18-49 years, n=79,339/290,401, 27.3% for patients aged 50-64 years, and n=80,833/290,401, 27.8% for patients aged 65-79 years), but lower among younger patients <18 years (n=23,699/290,401, 8.2%) and older patients ≥80 years (n=24,284/290,401, 8.4%) in 2020 (P<.001). There were more female users than male users of telemedicine (n=158,643/290,401, 54.6% vs n=131,758/290,401, 45.4%, respectively, in 2020; P<.001). There was a significantly higher proportion of telemedicine users residing in relatively less rural than in more rural regions (n=261,814/290,401, 90.2% vs n=28,587/290,401, 9.8%, respectively, in 2020; P<.001). Telemedicine adoption increased in rural and remote areas during the COVID-19 pandemic, but its use increased in urban and less rural populations. Future studies should investigate the potential barriers to telemedicine use among rural patients and the impact of rural telemedicine on patient health care utilization and outcomes.

Choosing Wisely (CW) campaigns globally have focused attention on the need to reduce low-value care, which can represent up to 30% of the costs of healthcare. Despite early enthusiasm for the CW initiative, few large-scale changes in rates of low-value care have been reported since the launch of these campaigns. Recent commentaries suggest that the focus of the campaign should be on implementation of evidence-based strategies to effectively reduce low-value care. This paper describes the Choosing Wisely De-Implementation Framework (CWDIF), a novel framework that builds on previous work in the field of implementation science and proposes a comprehensive approach to systematically reduce low-value care in both hospital and community settings and advance the science of de-implementation.The CWDIF consists of five phases: Phase 0, identification of potential areas of low-value healthcare; Phase 1, identification of local priorities for implementation of CW recommendations; Phase 2, identification of barriers to implementing CW recommendations and potential interventions to overcome these; Phase 3, rigorous evaluations of CW implementation programmes; Phase 4, spread of effective CW implementation programmes. We provide a worked example of applying the CWDIF to develop and evaluate an implementation programme to reduce unnecessary preoperative testing in healthy patients undergoing low-risk surgeries and to further develop the evidence base to reduce low-value care.

Table 1 Potential workflow changes considering the costs of contact in the pre-COVID and COVID/post-COVID eras Medical condition Medical encounters typically associated with the medical condition Elective laparoscopic cholecystectomy Preoperative consult with surgeon/anaesthesia Preoperative ECG Preoperative laboratory Surgery Postoperative follow-up with surgeon Pre-COVID In person In person In person In person In person COVID/post-COVID Virtual Unnecessary Unnecessary In person Virtual Outpatient assessment of mechanical back pain Visit to primary care Physiotherapy Follow-up primary care Pre-COVID In person In person In person COVID/post-COVID In person/possibly virtual In person/virtual mix Virtual Routine annual follow-up after percutaneous coronary intervention (PCI) Stress test Bloodwork including lipid profile ECG Follow-up visit with cardiologist Pre-COVID In person In person In person In person COVID/post-COVID Unnecessary In person Unnecessary Virtual Challenges to making virtual care the central solution This type of health service redesign would provide needed patient care while substantially reducing the risk of infection as well as lowering patient and system costs. Community pharmacists have been shown to support medication reconciliation and adherence, which can improve chronic disease management.20–24 Retinal screening in people with diabetes can be done using remote technology at an optometrist’s office, with data being analysed off-site by trained ophthalmologists.25 Technology, including wearable devices like portable oxygen saturation monitors and home blood pressure monitors that connect to electronic medical records, may also partially fill in portions of the clinical exam. [...]avoiding a reversion to unnecessary in-person contact will require broader dissemination of payment systems that incentivise providers on factors other than volume of activity. [...]a classic example very relevant to the ‘cost of contact’ from COVID is the RAND Health Insurance Experiment.

AbstractObjectiveTo test a scalable health system intervention to improve long term adherence to secondary prevention treatments among patients who have had a recent myocardial infarction.DesignThree arm, pragmatic randomised controlled trial with blinded outcome assessment.SettingNine cardiac centres in Ontario, Canada.Participants2632 patients with obstructive coronary artery disease after a myocardial infarction, identified from a centralised cardiac registry.InterventionsParticipants were randomised 1:1:1 to receive usual care, five mail-outs developed through a user centred design process, or mail-outs plus phone calls. The phone calls were delivered first by an interactive automated system to screen for non-adherence to treatment. Trained lay health workers followed up as necessary. Interventions were coordinated centrally but delivered from each patient’s hospital site.Main outcome measuresCo-primary outcomes were completion of cardiac rehabilitation and adherence to recommended medication. Data were collected by blinded assessors through patient report and from administrative health databases at 12 months.Results2632 patients (mean age 66, 71% male) were randomised: 878 to the full intervention (mail plus phone calls), 878 to mail only, and 876 to usual care. Of the respondents, 174 (27%) of 643 in the usual care group, 200 (32%) of 628 in the mail only group, and 196 (37%) of 531 allocated to the full intervention completed cardiac rehabilitation (adjusted odds ratio 1.55, 95% confidence interval 1.18 to 2.03). In the mail plus phone group, 11.7%, 6.0%, 14.4%, 32.9%, and 35.0% reported adherence to 0, 1, 2, 3, and 4 drug classes after one year, respectively, in comparison with 12.5%, 6.8%, 13.6%, 30.2%, and 36.8% in the mail only group, and 12.2%, 8.4%, 13.1%, 30.3%, and 36.1% in the usual care group, respectively (mail only v usual care, odds ratio 0.98, 95% confidence interval 0.81 to 1.19; full intervention v usual care, 0.99, 0.82 to 1.20).ConclusionsScalable interventions delivered by mail plus phone can increase completion of cardiac rehabilitation after myocardial infarction but not adherence to medication. More intensive interventions should be tested to improve adherence to medication and to evaluate the association between attendance at cardiac rehabilitation and adherence to medication.Trial registrationClinicalTrials.gov NCT02382731, registered 9 March 2015 before any patient enrolment.

The increasing use of Web-based solutions for health prevention and promotion presents opportunities to improve self-management and adherence to guideline-based therapy for individuals with type 2 diabetes (T2DM). Despite promising preliminary evidence, many users stop using Web-based solutions due to the burden of data entry, hidden costs, loss of interest, and a lack of comprehensive features. Evaluations tend to focus on effectiveness or impact and fail to evaluate the nuanced variables that may interact to contribute to outcome success (or failure). This study aimed to evaluate a Web-based solution for improving self-management in T2DM to identify key combinations of contextual variables and mechanisms of action that explain for whom the solution worked best and in what circumstances. A qualitative realist evaluation was conducted with one-on-one, semistructured telephonic interviews completed at baseline, and again toward the end of the intervention period (3 months). Topics included participants' experiences of using the Web-based solution, barriers and facilitators of self-management, and barriers and facilitators to effective use. Transcripts were analyzed using thematic analysis strategies, after which the key themes were used to develop statements of the relationships between the key contextual factors, mechanisms of action, and impact on the primary outcome (glycated hemoglobin, HbA ). Twenty-six interviews (14 baseline, 12 follow-up) were completed with 16 participants with T2DM, and the following 3 key groups emerged: the easiest fit, the best fit, and those who failed to activate. Self-efficacy and willingness to engage with the solution facilitated improvement in HbA , whereas competing priorities and psychosocial issues created barriers to engagement. Individuals with high baseline self-efficacy who were motivated, took ownership for their actions, and prioritized diabetes management were early and eager adopters of the app and recorded improvements in HbA over the intervention period. Individuals with moderate baseline self-efficacy and no competing priorities, who identified gaps in understanding of how their actions influence their health, were slow to adopt use but recorded the greatest improvements in HbA . The final group had low baseline self-efficacy and identified a range of psychosocial issues and competing priorities. These participants were uncertain of the benefits of using a Web-based solution to support self-management, ultimately resulting in minimal engagement and no improvement in HbA . Self-efficacy, competing priorities, previous behavior change, and beliefs about Web-based solutions interact to determine engagement and impact on the clinical outcomes. Considering the balance of these patient characteristics is likely to help health care providers identify individuals who are apt to benefit from a Web-based solution to support self-management of T2DM. Web-based solutions could be modified to incorporate the existing screening measures to identify individuals who are at risk of suboptimal adherence to inform the provision of additional support(s) as needed.

Digital tools have shown great potential to enhance health services’ capacity to achieve the goals of the triple aim (enhance patient experience, improve health outcomes, and control or reduce costs), but their actual impact remains variable. In this commentary, we suggest that shifting from a perspective focused on “implementing” new digital tools in health care settings toward one focused on “service design” will help teams execute more successful digital technology adoption projects. We present value proposition design (VPD) as a service design strategy requiring that stakeholders are brutally honest in determining the value of a new digital tool for their everyday work. Incorporating a perspective focused on how the value proposition of a technology is understood by each team member, and implications for their work routines, will help project teams to better understand how services can be reinvented during technology adoption initiatives. We present the simple heuristic [Tool+Team+Routine] as a reminder of the central considerations that make up a service design initiative, and present an illustrative case scenario of designing the use of a digital care coordination platform in an actual digital technology adoption project. We conclude by outlining two important challenges that need to be addressed to advance service design approaches to technology adoption in health care.

Chronic obstructive pulmonary disease (COPD) is a leading cause of mortality and leads to frequent hospital admissions and emergency department (ED) visits. COPD exacerbations are an important patient outcome, and reducing their frequency would result in significant cost savings. Remote monitoring and self-monitoring could both help patients manage their symptoms and reduce the frequency of exacerbations, but they have different resource implications and have not been directly compared. This study aims to compare the effectiveness of implementing a technology-enabled self-monitoring program versus a technology-enabled remote monitoring program in patients with COPD compared with a standard care group. We conducted a 3-arm randomized controlled trial evaluating the effectiveness of a remote monitoring and a self-monitoring program relative to standard care. Patients with COPD were recruited from outpatient clinics and a pulmonary rehabilitation program. Patients in both interventions used a Bluetooth-enabled device kit to monitor oxygen saturation, blood pressure, temperature, weight, and symptoms, but only patients in the remote monitoring group were monitored by a respiratory therapist. All patients were assessed at baseline and at 3 and 6 months after program initiation. Outcomes included self-management skills, as measured by the Partners in Health (PIH) Scale; patient symptoms measured with the St George's Respiratory Questionnaire (SGRQ); and the Bristol COPD Knowledge Questionnaire (BCKQ). Patients were also asked to self-report on health system use, and data on health use were collected from the hospital. A total of 122 patients participated in the study: 40 in the standard care, 41 in the self-monitoring, and 41 in the remote monitoring groups. Although all 3 groups improved in PIH scores, BCKQ scores, and SGRQ impact scores, there were no significant differences among any of the groups. No effects were observed on the SGRQ activity or symptom scores or on hospitalizations, ED visits, or clinic visits. Despite regular use of the technology, patients with COPD assigned to remote monitoring or self-monitoring did not have any improvement in patient outcomes such as self-management skills, knowledge, or symptoms, or in health care use compared with each other or with a standard care group. This may be owing to low health care use at baseline, the lack of structured educational components in the intervention groups, and the lack of integration of the action plan with the technology. ClinicalTrials.gov NCT03741855; https://clinicaltrials.gov/ct2/show/ NCT03741855.

ImportanceStudies of interventions to reduce low-value care are increasingly common. However, little is known about how the effects of such interventions are measured.ObjectiveTo characterize measures used to assess interventions to reduce low-value care.Evidence ReviewWe searched PubMed and Web of Science to identify studies published between 2010 and 2016 that examined the effects of interventions to reduce low-value care. We also searched ClinicalTrials.gov to identify ongoing studies. We extracted data on characteristics of studies, interventions, and measures. We then developed a framework to classify measures into the following categories: utilization (e.g., number of tests ordered), outcome (e.g., mortality), appropriateness (e.g., overuse of antibiotics), patient-reported (e.g., satisfaction), provider-reported (e.g., satisfaction), patient-provider interaction (e.g., informed decision-making elements), value, and cost. We also determined whether each measure was designed to assess unintended consequences.FindingsA total of 1805 studies were identified, of which 101 published and 16 ongoing studies were included. Of published studies (N = 101), 68% included at least one measure of utilization, 41% of an outcome, 52% of appropriateness, 36% of cost, 8% patient-reported, and 3% provider-reported. Funded studies were more likely to use patient-reported measures (17% vs 0%). Of ongoing studies (registered trials) (N = 16), 69% included at least one measure of utilization, 75% of an outcome, 50% of appropriateness, 19% of cost, 50% patient-reported, 13% provider-reported, and 6% patient-provider interaction. Of published studies, 34% included at least one measure of an unintended consequence as compared to 63% of ongoing studies.Conclusions and RelevanceMost published studies focused on reductions in utilization rather than on clinically meaningful measures (e.g., improvements in appropriateness, patient-reported outcomes) or unintended consequences. Investigators should systematically incorporate more clinically meaningful measures into their study designs, and sponsors should develop standardized guidance for the evaluation of interventions to reduce low-value care.