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BackgroundAdverse drug events are unintended and harmful effects of medication use. Using existing information and communication technologies (ICTs) to increase information sharing about adverse drug events may improve patient care but can introduce concerns about data privacy. ObjectiveThis study aims to examine the views of patients and their caregivers about data protection when using ICTs to communicate adverse drug event information to improve patient safety. MethodsWe conducted an exploratory qualitative study. A total of 4 focus groups were held among patients who had experienced or were at risk of experiencing an adverse drug event, their family members, and their caregivers. We recruited participants through multiple avenues and iteratively analyzed the data using situational analysis. ResultsOf the 47 participants recruited, 28 attended our focus groups. We identified 3 primary themes. First, participants felt that improved information sharing about adverse drug events within their circle of care would likely improve care. Second, participants were concerned about data handling and inappropriate access but believed that the benefits of information sharing outweighed the risks of privacy breaches. Finally, participants were more concerned about data privacy in the context of stigmatized health conditions. ConclusionsCurrent conditions for maintaining health data privacy are consistent with participants’ preferences, despite the fact that health data are susceptible to breaches and mismanagement. Information sharing that increases patient safety may justify potential privacy risks. Greater attention to patient concerns and the effect of social and contextual concerns in the design and implementation of health information technologies may increase patient confidence in the privacy of their information.

Aims: Medication non‐adherence is a type of adverse drug event that can lead to untreated and exacerbated chronic illness, and that drives healthcare utilization. Research using medication claims data has attempted to identify instances of medication non‐adherence using the proportion of days covered or by examining gaps between medication refills. We sought to validate these measures compared to a gold standard diagnosis of non‐adherence made in hospital. Methods: This was a retrospective analysis of adverse drug events diagnosed during three prospective cohorts in British Columbia between 2008 and 2015 (n = 976). We linked prospectively identified adverse drug events to medication claims data to examine the sensitivity and specificity of typical non‐adherence measures. Results: The sensitivity of the non‐adherence measures ranged from 22.4% to 37.5%, with a proportion of days covered threshold of 95% performing the best; the non‐persistence measures had sensitivities ranging from 10.4% to 58.3%. While a 7‐day gap was most sensitive, it classified 61.2% of the sample as non‐adherent, whereas only 19.6% were diagnosed as such in hospital. Conclusions: The methods used to identify non‐adherence in administrative databases are not accurate when compared to a gold standard diagnosis by healthcare providers. Research that has relied on administrative data to identify non‐adherent patients both underestimates the magnitude of the problem and may label patients as non‐adherent who were in fact adherent. [ABSTRACT FROM AUTHOR]

Introduction: Not all patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection develop symptomatic coronavirus disease 2019 (COVID-19), making it challenging to assess the burden of COVID-19-related hospitalizations and mortality. We aimed to determine the proportion, resource utilization, and outcomes of SARS-CoV-2 positive patients admitted for COVID-19, and assess the impact of using the Center for Disease Control's (CDC) discharge diagnosis-based algorithm and the Massachusetts state department's drug administration-based classification system on identifying admissions for COVID-19. Methods: In this retrospective cohort study, we enrolled consecutive SARS-CoV-2 positive patients admitted to one of five hospitals in British Columbia between December 19, 2021 and May 31,2022. We completed medical record reviews, and classified hospitalizations as being primarily for COVID-19 or with incidental SARS-CoV-2 infection. We applied the CDC algorithm and the Massachusetts classification to estimate the difference in hospital days, intensive care unit (ICU) days and in-hospital mortality and calculated sensitivity and specificity. Results: Of 42,505 Emergency Department patients, 1,651 were admitted and tested positive for SARS-CoV-2, with 858 (52.0%, 95% CI 49.6–54.4) admitted for COVID-19. Patients hospitalized for COVID-19 required ICU admission (14.0% versus 8.2%, p<0.001) and died (12.6% versus 6.4%, p<0.001) more frequently compared with patients with incidental SARS-CoV-2. Compared to case classification by clinicians, the CDC algorithm had a sensitivity of 82.9% (711/858, 95% CI 80.3%, 85.4%) and specificity of 98.1% (778/793, 95% CI 97.2%, 99.1%) for COVID-19-related admissions and underestimated COVID-19 attributable hospital days. The Massachusetts classification had a sensitivity of 60.5% (519/858, 95% CI 57.2%, 63.8%) and specificity of 78.6% (623/793, 95% CI 75.7%, 81.4%) for COVID-19-related admissions, underestimating total number of hospital and ICU bed days while overestimating COVID-19-related intubations, ICU admissions, and deaths. Conclusion: Half of SARS-CoV-2 hospitalizations were for COVID-19 during the Omicron wave. The CDC algorithm was more specific and sensitive than the Massachusetts classification, but underestimated the burden of COVID-19 admissions. Trial registration: Clinicaltrials.gov, NCT04702945. [ABSTRACT FROM AUTHOR]