Fibromyalgia is a common illness characterized by widespread chronic pain, physical exhaustion, cognitive difficulties, depressed mood, sleep problems, and deteriorated quality of life (QoL).¹ In the general population, the prevalence of fibromyalgia symptoms ranges between 2% and 4%.² The symptoms of fibromyalgia reduce health-related QoL, and pharmacological treatments can improve health outcomes.^1,2

Background Computer-based decision support has been effective in providing alerts for preventive care. Our objective was to determine whether a personalized asthma management computer-based decision support increases the quality of asthma management and reduces the rate of out-of-control episodes. Methods A cluster-randomized trial was conducted in Quebec, Canada among 81 primary care physicians and 4447 of their asthmatic patients. Patients were followed from the first visit for 3–33 months. The physician control group used the Medical Office of the 21st century (MOXXI) system, an integrated electronic health record. A custom-developed asthma decision support system was integrated within MOXXI and was activated for physicians in the intervention group. Results At the first visit, 9.8% (intervention) to 12.9% (control) of patients had out-of-control asthma, which was defined as a patient having had an emergency room visit or hospitalization for respiratory-related problems and/or more than 250 doses of fast-acting β-agonist (FABA) dispensed in the past 3 months. By the end of the trial, there was a significant increase in the ratio of doses of inhaled corticosteroid use to fast-acting β-agonist (0.93 vs. 0.69: difference: 0.27; 95% CI: 0.02–0.51; P = 0.03) in the intervention group. The overall out-of-control asthma rate was 54.7 (control) and 46.2 (intervention) per 100 patients per year (100 PY), a non-significant rate difference of −8.7 (95% CI: −24.7, 7.3; P = 0.29). The intervention’s effect was greater for patients with out-of-control asthma at the beginning of the study, a group who accounted for 44.7% of the 5597 out-of-control asthma events during follow-up, as there was a reduction in the event rate of −28.4 per 100 PY (95% CI: −55.6, −1.2; P = 0.04) compared to patients with in-control asthma at the beginning of the study (−0.08 [95% CI: −10.3, 8.6; P = 0.86]). Discussion This study evaluated the effectiveness of a novel computer-assisted ADS system that facilitates systematic monitoring of asthma control status, follow-up of patients with out of control asthma, and evidence-based, patient-specific treatment recommendations. We found that physicians were more likely to use ADS for out-of-control patients, that in the majority of these patients, they were advised to add an inhaled corticosteroid or a leukotriene inhibitor to the patient s treatment regimen, and the intervention significantly increased the mean ratio of inhaled corticosteroids to FABA during follow-up. It also reduced the rate of out-of-control episodes during follow up among patients whose asthma was out-of-control at the time of study entry. Future research should assess whether coupling patient-specific treatment recommendations, automated follow-up, and home care with comparative feedback on quality and outcomes of care can improve guideline adoption and care outcomes. Conclusions A primary care-personalized asthma management system reduced the rate of out-of-control asthma episodes among patients whose asthma was poorly controlled at the study’s onset. Trial Registration Clinicaltrials.gov Identifier: NCT00170248 http://clinicaltrials.gov/ct2/show/NCT00170248?term=Asthma&spons=McGill+University&state1=NA%3ACA%3AQC&rank=2

Background: Despite restrictive opioid management guidelines, opioid use disorder (OUD) remains a major public health concern. Machine learning (ML) offers a promising avenue for identifying and alerting clinicians about OUD, thus supporting better clinical decision-making regarding treatment. Objective: This study aimed to assess the clinical validity of an ML application designed to identify and alert clinicians of different levels of OUD risk by comparing it to a structured review of medical records by clinicians. Methods: The ML application generated OUD risk alerts on outpatient data for 649,504 patients from 2 medical centers between 2010 and 2013. A random sample of 60 patients was selected from 3 OUD risk level categories (n=180). An OUD risk classification scheme and standardized data extraction tool were developed to evaluate the validity of the alerts. Clinicians independently conducted a systematic and structured review of medical records and reached a consensus on a patient’s OUD risk level, which was then compared to the ML application’s risk assignments. Results: A total of 78,587 patients without cancer with at least 1 opioid prescription were identified as follows: not high risk (n=50,405, 64.1%), high risk (n=16,636, 21.2%), and suspected OUD or OUD (n=11,546, 14.7%). The sample of 180 patients was representative of the total population in terms of age, sex, and race. The interrater reliability between the ML application and clinicians had a weighted kappa coefficient of 0.62 (95% CI 0.53-0.71), indicating good agreement. Combining the high risk and suspected OUD or OUD categories and using the review of medical records as a gold standard, the ML application had a corrected sensitivity of 56.6% (95% CI 48.7%-64.5%) and a corrected specificity of 94.2% (95% CI 90.3%-98.1%). The positive and negative predictive values were 93.3% (95% CI 88.2%-96.3%) and 60.0% (95% CI 50.4%-68.9%), respectively. Key themes for disagreements between the ML application and clinician reviews were identified. Conclusions: A systematic comparison was conducted between an ML application and clinicians for identifying OUD risk. The ML application generated clinically valid and useful alerts about patients’ different OUD risk levels. ML applications hold promise for identifying patients at differing levels of OUD risk and will likely complement traditional rule-based approaches to generating alerts about opioid safety issues.

Onsite and in-person experiential education has been well established to prepare practice-ready healthcare professionals, such as pharmacists. From COVID-19, the integration of remote educational delivery has occurred. As healthcare disciplines adjust to new experiential styles and innovate traditional methods, this paper highlights key areas for remote experiential education that can influence student experiences. Factors that are of importance to continuous quality improvement are described. A survey, utilizing the cloud-based software platform Qualtrics^® headquartered in the United States, was developed to evaluate whether remote rotation delivery was comparable to traditional onsite experiential education, to assist with quality improvement for virtual experiential education, and to ensure the redesigned educational model meets accreditation standards for two schools of pharmacy. Numerous factors including work, time zone, Office of Experiential Education and preceptor responsiveness, and technology, were examined. Chi-Square test, t-test for proportions and odds ratios were utilized to evaluate results. Students with technology concerns throughout a remote rotation had a more than two-fold increase in identifying the virtual experience as worse than most/all other in-person rotations (p = 0.01). Preceptor responsiveness to questions and concerns significantly impact student perceptions of educational quality (p < 0.05). The majority of students perceived remote experiential education is equal to onsite experiences. Since continuous quality improvement is required by pharmacy accreditors and many other healthcare programs offering clinical opportunities, identifying factors is of importance to make future interventions in the remote experiential education delivery. This type of experiential learning became essential with COVID-19 impacting onsite clinical placements, and information can be used across health science disciplines at large.