How Communication Failed or Saved the Day : Counterfactual Accounts of Medical Errors

Communication breakdowns among clinicians, patients, and family members can lead to medical errors, yet effective communication may prevent such mistakes. This investigation examined patients\u27 and family members\u27 experiences where they believed communication failures contributed to medical errors or where effective communication prevented a medical error ( close calls ). The study conducted a thematic analysis of open-ended responses to an online survey of patients\u27 and family members\u27 past experiences with medical errors or close calls. Of the 93 respondents, 56 (60%) provided stories of medical errors, and the remaining described close calls. Two predominant themes emerged in medical error stories that were attributed to health care providers-information inadequacy (eg, delayed, inaccurate) and not listening to or being dismissive of a patient\u27s or family member\u27s concerns. In stories of close calls, a patient\u27s or family member\u27s proactive communication (eg, being assertive, persistent) most often saved the day. The findings highlight the importance of encouraging active patient/family involvement in a patient\u27s medical care to prevent errors and of improving systems to provide meaningful information in a timely manner

Use of Electronic Health Record Access and Audit Logs to Identify Physician Actions Following Noninterruptive Alert Opening: Descriptive Study

BACKGROUND: Electronic health record (EHR) access and audit logs record behaviors of providers as they navigate the EHR. These data can be used to better understand provider responses to EHR-based clinical decision support (CDS), shedding light on whether and why CDS is effective. OBJECTIVE: This study aimed to determine the feasibility of using EHR access and audit logs to track primary care physicians\u27 (PCPs\u27) opening of and response to noninterruptive alerts delivered to EHR InBaskets. METHODS: We conducted a descriptive study to assess the use of EHR log data to track provider behavior. We analyzed data recorded following opening of 799 noninterruptive alerts sent to 75 PCPs\u27 InBaskets through a prior randomized controlled trial. Three types of alerts highlighted new medication concerns for older patients\u27 posthospital discharge: information only (n=593), medication recommendations (n=37), and test recommendations (n=169). We sought log data to identify the person opening the alert and the timing and type of PCPs\u27 follow-up EHR actions (immediate vs by the end of the following day). We performed multivariate analyses examining associations between alert type, patient characteristics, provider characteristics, and contextual factors and likelihood of immediate or subsequent PCP action (general, medication-specific, or laboratory-specific actions). We describe challenges and strategies for log data use. RESULTS: We successfully identified the required data in EHR access and audit logs. More than three-quarters of alerts (78.5%, 627/799) were opened by the PCP to whom they were directed, allowing us to assess immediate PCP action; of these, 208 alerts were followed by immediate action. Expanding on our analyses to include alerts opened by staff or covering physicians, we found that an additional 330 of the 799 alerts demonstrated PCP action by the end of the following day. The remaining 261 alerts showed no PCP action. Compared to information-only alerts, the odds ratio (OR) of immediate action was 4.03 (95% CI 1.67-9.72) for medication-recommendation and 2.14 (95% CI 1.38-3.32) for test-recommendation alerts. Compared to information-only alerts, ORs of medication-specific action by end of the following day were significantly greater for medication recommendations (5.59; 95% CI 2.42-12.94) and test recommendations (1.71; 95% CI 1.09-2.68). We found a similar pattern for OR of laboratory-specific action. We encountered 2 main challenges: (1) Capturing a historical snapshot of EHR status (number of InBasket messages at time of alert delivery) required incorporation of data generated many months prior with longitudinal follow-up. (2) Accurately interpreting data elements required iterative work by a physician/data manager team taking action within the EHR and then examining audit logs to identify corresponding documentation. CONCLUSIONS: EHR log data could inform future efforts and provide valuable information during development and refinement of CDS interventions. To address challenges, use of these data should be planned before implementing an EHR-based study.

SUPPORT-AF: Piloting a Multi-Faceted, Electronic Medical Record-Based Intervention to Improve Prescription of Anticoagulation

Background: Only 50% of eligible atrial fibrillation ( AF ) patients receive anticoagulation ( AC ). Feasibility and effectiveness of electronic medical record (EMR)-based interventions to profile and raise provider AC percentage is poorly understood. The SUPPORT-AF (Supporting Use of AC Through Provider Profiling of Oral AC Therapy for AF) study aims to improve rates of adherence to AC guidelines by developing and delivering supportive tools based on the EMR to providers treating patients with AF. Methods and Results: We emailed cardiologists and community-based primary care providers affiliated with our institution reports of their AC percentage relative to peers. We also sent an electronic medical record-based message to these providers the day before an appointment with an atrial fibrillation patient who was eligible but not receiving AC . The electronic medical record message asked the provider to discuss AC with the patient if he or she deemed it appropriate. To assess feasibility, we tracked provider review of our correspondence. We also tracked the change in AC for intervention providers relative to alternate primary care providers not receiving our intervention. We identified 3786, 1054, and 566 patients cared for by 49 cardiology providers, 90 community-based primary care providers, and 88 control providers, respectively. At baseline, the percentage of AC was 71.3%, 63.5%, and 58.3% for these 3 respective groups. Intervention providers reviewed our e-mails and electronic medical record messages 45% and 96% of the time, respectively. For providers responding, patient refusal was the most common reason for patients not being on AC (21%) followed by high bleeding risk (19%). At follow-up 10 weeks later, change in AC was no different for either cardiology or community-based primary care providers relative to controls (0.2% lower and 0.01% higher, respectively). Conclusions: Our intervention profiling AC was feasible, but not sufficient to increase AC in our population