IMPORTANCE Despite the broad adoption of electronic health record (EHR) systems across the continuum of care, safety problems persist. OBJECTIVE To measure the safety performance of operational EHRs in hospitals across the country during a 10-year period.
BackgroundElectronic health records (EHR) can improve safety via computerised physician order entry with clinical decision support, designed in part to alert providers and prevent potential adverse drug events at entry and before they reach the patient. However, early evidence suggested performance at preventing adverse drug events was mixed.MethodsWe used data from a national, longitudinal sample of 1527 hospitals in the USA from 2009 to 2016 who took a safety performance assessment test using simulated medication orders to test how well their EHR prevented medication errors with potential for patient harm. We calculated the descriptive statistics on performance on the assessment over time, by years of hospital experience with the test and across hospital characteristics. Finally, we used ordinary least squares regression to identify hospital characteristics associated with higher test performance.ResultsThe average hospital EHR system correctly prevented only 54.0% of potential adverse drug events tested on the 44-order safety performance assessment in 2009; this rose to 61.6% in 2016. Hospitals that took the assessment multiple times performed better in subsequent years than those taking the test the first time, from 55.2% in the first year of test experience to 70.3% in the eighth, suggesting efforts to participate in voluntary self-assessment and improvement may be helpful in improving medication safety performance.ConclusionHospital medication order safety performance has improved over time but is far from perfect. The specifics of EHR medication safety implementation and improvement play a key role in realising the benefits of computerising prescribing, as organisations have substantial latitude in terms of what they implement. Intentional quality improvement efforts appear to be a critical part of high safety performance and may indicate the importance of a culture of safety.
Mobile health applications (“apps”) have rapidly proliferated, yet their ability to improve outcomes for patients remains unclear. A validated tool that addresses apps’ potentially important dimensions has not been available to patients and clinicians. The objective of this study was to develop and preliminarily assess a usable, valid, and open-source rating tool to objectively measure the risks and benefits of health apps. We accomplished this by using a Delphi process, where we constructed an app rating tool called THESIS that could promote informed app selection. We used a systematic process to select chronic disease apps with ≥4 stars and <4-stars and then rated them with THESIS to examine the tool’s interrater reliability and internal consistency. We rated 211 apps, finding they performed fair overall (3.02 out of 5 [95% CI, 2.96–3.09]), but especially poorly for privacy/security (2.21 out of 5 [95% CI, 2.11–2.32]), interoperability (1.75 [95% CI, 1.59–1.91]), and availability in multiple languages (1.43 out of 5 [95% CI, 1.30–1.56]). Ratings using THESIS had fair interrater reliability (κ = 0.3–0.6) and excellent scale reliability (ɑ = 0.85). Correlation with traditional star ratings was low (r = 0.24), suggesting THESIS captures issues beyond general user acceptance. Preliminary testing of THESIS suggests apps that serve patients with chronic disease could perform much better, particularly in privacy/security and interoperability. THESIS warrants further testing and may guide software and policymakers to further improve app performance, so apps can more consistently improve patient outcomes.
Background Uncertainty surrounding COVID-19 regarding rapid progression to acute respiratory distress syndrome and unusual clinical characteristics make discharge from a monitored setting challenging. A clinical risk score to predict 14-day occurrence of hypoxia, ICU admission, and death is unavailable. Objective Derive and validate a risk score to predict suitability for discharge from a monitored setting among an early cohort of patients with COVID-19. Design Model derivation and validation in a retrospective cohort. We built a manual forward stepwise logistic regression model to identify variables associated with suitability for discharge and assigned points to each variable. Event-free patients were included after at least 14 days of follow-up. Participants All adult patients with a COVID-19 diagnosis between March 1, 2020, and April 12, 2020, in 10 hospitals in Massachusetts, USA. Main Measures Fourteen-day composite predicting hypoxia, ICU admission, and death. We calculated a risk score for each patient as a predictor of suitability for discharge evaluated by area under the curve. Key Results Of 2059 patients with COVID-19, 1326 met inclusion. The 1014-patient training cohort had a mean age of 58 years, was 56% female, and 65% had at least one comorbidity. A total of 255 (25%) patients were suitable for discharge. Variables associated with suitability for discharge were age, oxygen saturation, and albumin level, yielding a risk score between 0 and 55. At a cut point of 30, the score had a sensitivity of 83% and specificity of 82%. The respective c-statistic for the derivation and validation cohorts were 0.8939 (95% CI, 0.8687 to 0.9192) and 0.8685 (95% CI, 0.8095 to 0.9275). The score performed similarly for inpatients and emergency department patients. Conclusions A 3-item risk score for patients with COVID-19 consisting of age, oxygen saturation, and an acute phase reactant (albumin) using point of care data predicts suitability for discharge and may optimize scarce resources.
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