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The aims of the study were to identify publicly available patient safety report databases and to determine whether these databases support safety analyst and data scientist use to identify patterns and trends.An Internet search was conducted to identify publicly available patient safety databases that contained patient safety reports. Each database was analyzed to identify features that enable patient safety analyst and data scientist use of these databases.Seven databases (6 hosted by federal agencies, 1 hosted by a nonprofit organization) containing more than 28.3 million safety reports were identified. Some, but not all, databases contained features to support patient safety analyst use: 57.1% provided the ability to sort/compare/filter data, 42.9% provided data visualization, and 85.7% enabled free-text search. None of the databases provided regular updates or monitoring and only one database suggested solutions to patient safety reports. Analysis of features to support data scientist use showed that only 42.9% provided an application programing interface, most (85.7%) provided batch downloading, all provided documentation about the database, and 71.4% provided a data dictionary. All databases provided open access. Only 28.6% provided a data diagram.Patient safety databases should be improved to support patient safety analyst use by, at a minimum, allowing for data to be sorted/compared/filtered, providing data visualization, and enabling free-text search. Databases should also enable data scientist use by, at a minimum, providing an application programing interface, batch downloading, and a data dictionary.

BACKGROUND Racial and ethnic disparities in healthcare safety have persisted for decades, particularly for patients with language barriers. Previous studies have investigated the frequency and nature of safety events impacting patients with language barriers; others have proposed solutions to fix them. A gap analysis, however, of how we are currently addressing safety issues and why these efforts have not been effective is lacking. METHOD This analysis uses reports from a patient safety event reporting system. Reports contain information regarding no-harm (near miss) events and events where harm may have reached the patient. Reports occurring with patients with a preferred language other than English were extracted and analyzed to determine whether the language barrier contributed to the safety event, the language barrier was mentioned in the resolution, and themes were mentioned for addressing language barriers. RESULTS A subset of 1553 events pertaining to non-English-speaking patients were first categorized as "likely" (3%), "plausibly" (10%), or "unlikely" (87%) related to the patient's language barrier. Second, events related to the patient's language barrier were categorized as directly addressing (19%), indirectly addressing (3%), not mentioning (69%) the language barrier, or containing insufficient information to determine whether the language barrier was addressed (7%). Third, thematic analysis revealed that the most common methods for addressing language barriers included presenting issues to interpreter services and subsequent use of interpreter services. CONCLUSIONS This study found that it is challenging to determine the direct role of certain social determinants of health (e.g., language barriers) in safety events. In many cases, the language barrier was not addressed in the event report. Furthermore, when the language barrier was addressed, solution themes typically involved weaker, less sustainable suggested actions.

Medication information is frequently communicated via free-text computerized provider order entry (CPOE) orders in electronic health records. When such information is transmitted separately from a structured CPOE medication order, there is a significant risk of medication error. Although prior studies have described the frequency of using free-text CPOE orders for communicating medication information, there is a gap in understanding the nature of the medication information contained in the free-text CPOE orders. The aims of this study are to (1) identify the most common medication names communicated in free-text CPOE orders and their risk levels and (2) identify what actions physicians expect that nurses will complete when they place free-text CPOE orders, and (3) describe differences in these patterns across hospitals.This study was a retrospective analysis of a sample of 26,524 free-text CPOE orders from 6 hospitals in the mid-Atlantic U.S. region.Free-text CPOE orders contained in the sample mentioned 193 medication names. Free-text CPOE orders were used frequently to communicate information about naloxone, heparin, flumazenil, and dextrose. Twenty-two percent of the free-text CPOE orders related to discontinuing medication(s), whereas 7% of the free-text CPOE orders relate to giving medication(s). There was high variation across hospitals both in the percentage of free-text CPOE orders mentioning medication information and in the proportion of those that referred to high-risk medications.The prevalence of medication information in free-text CPOE orders may suggest specific communication challenges in respect to urgency, uncertainty, planning, and other aspects of communication and clinical needs. Understanding and addressing communication challenges around commonly mentioned medication names and actions, especially those that are high risk, can help reduce the risk of medication errors.

OBJECTIVE Different health information technology (health IT) systems are intended to support medication ordering, reviewing, and administration. We sought to identify the types of medication errors associated with health IT use, whether they reached the patient, where in the medication process those errors occurred, and the specific usability issues contributing to those errors. METHODS Patient safety event reports from more than 595 healthcare facilities entered between January 2013 and September 2018 were analyzed. We computationally identified reports associated with health IT intended to support the medication process, including computerized provider order entry, electronic medication administration record, and barcode medication administration. From these, 2700 reports were manually reviewed to determine the type of medication error, medication process stage, and health IT usability issue. RESULTS Of the 2700 manually reviewed reports, 1508 (55.9%) described a medication error that was associated with health IT use and 750 (49.7%) reached the patient. Improper dose errors were frequent (1214 of 1508, 80.5%) with most errors during ordering (673 of 1508, 44.6%) and reviewing medications (639 of 1508, 42.4%). Most health IT-associated medication error reports described usability issues (n = 1468 of 1508, 97.3%) including data entry, workflow support, and alerting. Data entry usability issues impacted few medication process stages, whereas workflow support and alerting impacted several stages. CONCLUSIONS Health IT usability issues are a prevalent contributing factor to medication errors, many of which reach the patient. Data entry, workflow support, and alerting should be prioritized during usability and safety optimization efforts.

Diagnostic errors are a major source of patient harm, most of which are caused by cognitive errors and biases. Despite research showing the relationship between software systems and cognitive processes, the impact of the electronic health record (EHR) on diagnostic error remains unknown.We conducted a scoping review of the scientific literature to (1) survey the association between aspects of the EHR and diagnostic error, and (2) through a human-systems integration lens, identify the types of EHR issues and their impact on the stages of the diagnostic process.We analyzed 11 research articles for the relationship between EHR use and diagnostic error. These articles highlight specific technical, usability, and workflow issues with the EHR that pose risks for diagnostic error at every stage of the diagnostic process.Although technical problems such as EHR interoperability and data integrity pose critical issues for the diagnostic process, usability and workflow issues such as poor display design, and inability to track test results also hamper clinicians' ability to track, process, and act in the diagnostic process. Current research methods have limited coverage over clinical settings, are not standardized, and rarely include measures of patient harm.The available evidence shows that EHRs pose risks for diagnostic error throughout the diagnostic process, with most issues involving their incompatibility with providers' cognitive processing. A structured and systematic model of collecting and reporting on these errors is needed to understand how the EHR shapes the diagnostic process and improve diagnostic accuracy.

Background and objectives Medical errors are a leading cause of death in the United States. Despite widespread adoption of patient safety reporting systems to address medical errors, making sense of the reports collected in these systems is challenging in practice. Event classification taxonomies used in many reporting systems can be complex and difficult to understand by frontline reporters, leading reporters to classify reports as "miscellaneous" as opposed to assigning a specific event-type category, which may facilitate analysis. Methods To assist patient safety analysts in their analysis of "miscellaneous" reports, we developed an ensemble machine learning natural language processing model to reclassify these reports. We integrated the model into a clinical workflow dashboard, evaluated user feedback, and compared differences in user thresholds for model performance. Results and conclusions Integrating an ensemble model to classify "miscellaneous" event reports with an interactive visualization was helpful to patient safety analysts review "miscellaneous" reports. However, patient safety analysts have different thresholds for model reclassification depending on their role and experience with "miscellaneous" event reports.

OBJECTIVES Despite requirements for electronic health record (EHR) vendor usability testing, usability challenges persist, contributing to patient safety concerns. We sought to identify emergency physicians' perceived EHR usability and safety strengths and shortcomings across major EHR vendor products. METHODS Fifty-five emergency physicians from 4 different hospitals were interviewed. The interviews were qualitatively analyzed, and physician comments were aligned with a usability taxonomy to identify emerging themes by vendor and hospital. RESULTS Of the 194 comments about usability, the 3 most commonly discussed usability topics were Workflow Support (33.5% of comments), Visual Display (20.1%), and Data Entry (14.4%). Electronic health record usability strengths were centered on Visual Display, and the most common shortcoming was the lack of Workflow Support. Fourteen cross-hospital/cross-vendor themes, 6 vendor-specific themes, and 4 hospital-specific themes were identified. CONCLUSIONS Usability shortcomings that spanned across hospitals and vendors may suggest a need for more applied research and improved design to resolve these issues. Shortcomings that are localized to a specific product or hospital may be due to customization and may be addressable by learning from other organizations.

Objective Design criteria specifications (needs, obstacles, and context-of-use considerations) for continuing safe and efficient patient care activities during downtime were identified by using phenomenological analysis. Methods Interview transcripts from medical personnel who had experience with downtime incidents were examined using a phenomenological approach. This process allowed for the identification of design criteria for performing downtime patient care activities. Results A substantial variation in criteria was found from participants in different roles. The differences suggest opportunities to address downtime that may require attention to individual roles. Conclusions Workload distribution and communication are significant issues in patient care during downtime. There may not be an equal work distribution, leading to an increased workload for some personnel during downtime. Phenomenological analysis was completed after participants were interviewed, indicating it is a viable post hoc approach. Some downtime criteria were identified as potential guidelines for the development of better downtime contingency plans.

Objectives Interruptions and distractions have been shown to be a frequent occurrence across health care and have been linked to negative outcomes that create potential patient safety risks. Although observational studies have catalogued interruption frequency and source, the impact of an interruption is difficult to observe. We analyzed patient safety event (PSE) reports related to interruptions to identify clinical processes reported to be frequently interrupted and the reported outcomes of those interruptions. Methods We retrospectively analyzed PSE reports entered by frontline staff between January 2013 and January 2016. Of 79,428 total PSEs entered, 220 reports were identified using keyword matching and subsequent manual review as being directly related to a clinical interruption. Categories were developed to identify the cause of the interruption, task being interrupted, and the result of the interruption. Percentages were calculated. Results Nurses were most often reported to be interrupted in the PSEs (50%). General distractions (43.2%) or high workload (18.6%) were most commonly noted to interrupt the individual's work. The interrupted activity was most often a medication task (50.9%), frequently in the administration phase (24.1%), or the ordering phase (16.8%). The most common medication error was wrong dose administration (14.4% of total medication-related errors). Laboratory processes were reported to be disturbed by interruptions in 22.7% of reports, and this frequently resulted in mislabeling of specimens (75% of laboratory-related errors). Conclusions This retrospective review of PSE reports involving interruptions of clinical activities reveals that interruptions affect a variety of aspects of patient care and can help to guide future work on interruption management.

Objectives The increase in patient safety reporting systems has led to the challenge of effectively analyzing these data to identify and mitigate safety hazards. Patient safety analysts, who manage reports, may be ill-equipped to make sense of report data. We sought to understand the cognitive needs of patient safety analysts as they work to leverage patient safety reports to mitigate risk and improve patient care. Methods Semistructured interviews were conducted with 21 analysts, from 11 hospitals across 3 healthcare systems. Data were parsed into utterances and coded to extract major themes. Results From 21 interviews, 516 unique utterances were identified and categorized into the following 4 stages of data analysis: input (15.1% of utterances), transformation (14.1%), extrapolation (30%), and output (14%). Input utterances centered on the source (35.9% of inputs) and preprocessing of data. Transformation utterances centered on recategorizing patient safety events (57.5% of transformations) or integrating external data sources (42.5% of transformations). The focus of interviews was on extrapolation and trending data (56.1% of extrapolations); alarmingly, 16.1% of trend utterances explicitly mentioned a reliance on memory. The output was either a report (56.9% of outputs) or an action (43.1% of outputs). Conclusions Major gaps in the analysis of patient safety report data were identified. Despite software to support reporting, many reports come from other sources. Transforming data are burdensome because of recategorization of events and integration with other data sources, processes that can be automated. Surprisingly, trend identification was mostly based on patient analyst memory, highlighting a need for new tools that better support analysts.

Background and objectives In an effort to improve and standardize the collection of adverse event data, the Agency for Healthcare Research and Quality is developing and testing a patient safety surveillance system called the Quality and Safety Review System (QSRS). Its current abstraction from medical records is through manual human coders, taking an average of 75 minutes to complete the review and abstraction tasks for one patient record. With many healthcare systems across the country adopting electronic health record (EHR) technology, there is tremendous potential for more efficient abstraction by automatically populating QSRS. In the absence of real-world testing data and models, which require a substantial investment, we provide a heuristic assessment of the feasibility of automatically populating QSRS questions from EHR data. Methods To provide an assessment of the automation feasibility for QSRS, we first developed a heuristic framework, the Relative Abstraction Complexity Framework, to assess relative complexity of data abstraction questions. This framework assesses the relative complexity of characteristics or features of abstraction questions that should be considered when determining the feasibility of automating QSRS. Questions are assigned a final relative complexity score (RCS) of low, medium, or high by a team of clinicians, human factors, and natural language processing researchers. Results One hundred thirty-four QSRS questions were coded using this framework by a team of natural language processing and clinical experts. Fifty-five questions (41%) had high RCS and would be more difficult to automate, such as "Was use of a device associated with an adverse outcome(s)?" Forty-two questions (31%) had medium RCS, such as "Were there any injuries as a result of the fall(s)?' and 37 questions (28%) had low RCS, such as "Did the patient deliver during this stay?' These results suggest that Blood and Hospital Acquired Infections-Clostridium Difficile Infection (HAI-CDI) modules would be relatively easier to automate, whereas Surgery and HAI-Surgical Site Infection would be more difficult to automate. Conclusions Although EHRs contain a wealth of information, abstracting information from these records is still very challenging, particularly for complex questions, such as those concerning patient adverse events. In this work, we developed a heuristic framework, which can be applied to help guide conversations around the feasibility of automating QSRS data abstraction. This framework does not aim to replace testing with real data but complement the process by providing initial guidance and direction to subject matter experts to help prioritize, which abstraction questions to test for feasibility using real data.