Measuring a Safety Culture: Critical Pathway or Academic Activity?

he Institute of Medicine (IOM) identified six core needs in a health care system, the first of which was safety. 1 Furthermore, several IOM committees and others have identified the creation of a “culture of safety ” as the key institutional requirement to achieve safe medical care. 1–3 In this issue of the journal, Modak et al. 4 present an instrument that may help measure the extent to which a patient safety culture exists in an ambulatory setting. While these authors and others have done considerable work on defining and measuring a culture of safety in the hospital setting, 5,6 few have tackled the difficult task of measuring a safety culture in the ambulatory arena within the US health care system. Even in the hospital setting, where there has been more effort, the development of a culture of safety within all US hospitals has been spotty and, for some safety advocates, too slow. 7 There are many potential reasons for the poor progress in developing a culture of safety: confusion about the difference between safety and quality, concerns that increasing safety will further erode profits, or perhaps simply a lack of attention by institutional leaders. Whatever the reasons for the slow pace of transformation across the nation’s 5,000-plus hospitals, it is likely that this transformation will be even more difficult to achieve in the much larger and more diverse ambulatory setting. Thus, it is important to define and measure an ambulatory culture of safety. It is also difficult, perhaps impossible, to change beliefs, attitudes, knowledge, or actions (all components of a “culture”) without some form of feedback. Therefore, a necessary step in creating a culture of safety is to develop tools to measure the components of that culture. For those individuals and institutions that wish to truly improve the safety of the care they deliver, the creation and testing of tools such as the Safety Attitudes Questionnaire-Ambulatory (SAQ-A) version is critical. Beliefs, attitudes, and knowledge do not always lend themselves to clear-cut end points. Thus, we can expect to see more than one safety culture measuremen

Development of a context model to prioritize drug safety alerts in CPOE systems

Background: Computerized physician order entry systems (CPOE) can reduce the number of medication errors and adverse drug events (ADEs) in healthcare institutions. Unfortunately, they tend to produce a large number of partly irrelevant alerts, in turn leading to alert overload and causing alert fatigue. The objective of this work is to identify factors that can be used to prioritize and present alerts depending on the 'context' of a clinical situation. Methods: We used a combination of literature searches and expert interviews to identify and validate the possible context factors. The internal validation of the context factors was performed by calculating the inter-rater agreement of two researcher's classification of 33 relevant articles. Results: We developed a context model containing 20 factors. We grouped these context factors into three categories: characteristics of the patient or case (e. g. clinical status of the patient); characteristics of the organizational unit or user (e. g. professional experience of the user); and alert characteristics (e. g. severity of the effect). The internal validation resulted in nearly perfect agreement (Cohen's Kappa value of 0.97). Conclusion: To our knowledge, this is the first structured attempt to develop a comprehensive context model for prioritizing drug safety alerts in CPOE systems. The outcome of this work can be used to develop future tailored drug safety alerting in CPOE systems

Medication Errors in Patients With Severe Chronic Kidney Disease and Acute Coronary Syndrome: The Impact of Computer-Assisted Decision Support

OBJECTIVE: To evaluate the impact of computerized physician order entry (CPOE) with decision support on the frequency of antithrombotic medication errors in patients with chronic kidney disease (CKD) admitted with acute coronary syndrome (ACS) and to measure what effect it would have on in-hospital bleeding