Human beings can never obtain perfection and frankly, we are happier when we do not reach for this impossible goal. In the CE world, many of us live in fear of imperfection, especially when it relates to disclosures and conflicts of interest. Even when we have a good system for collecting and resolving conflicts in our CE, we sometimes miss things or make mistakes.  

Inspired by the work of patient safety professionals, where errors are expected, discussed and problem-solved together, the authors of this article began talking to each other about how we have tried to create a culture of safety, encouraging ourselves to share and problem-solve errors or near-misses in our disclosure process. By acknowledging when our disclosure or conflict of interest process maybe has not worked as intended or an error was made, we can identify weak areas in a system and find ways to address it.

In their most current survey, Harting and Bowser asked survey participants to address an intriguing open-ended question about what they had noticed as poor or unprofessional practices in other needs assessments they had seen.

We have been conducting a multiyear research project aimed at identifying best practices in writing and editing needs assessments (NAs) for continuing education in the health professions, including continuing medical education (CME). Compliance criteria promulgated by the Accreditation Council for Continuing Medical Education (ACCME) require all accredited CME providers to design educational activities to address deficits in knowledge, competence or performance that underlie professional practice gaps.1
 

As Daniel Kraft so eloquently imparted in his keynote speech at the 2019 annual Alliance conference, digital health technologies are already changing healthcare. Digital health is an umbrella term for technologies that include electronic health records (EHRs), social media tools, mobile apps, wearable devices, telemedicine and more. These technologies are designed to generate considerable real-time patient- or provider-generated data, to enable focused care for the individual patient via real-time analytics and feedback, or to guide population-level care based on aggregate patient-reported clinical and outcomes data. Consumer surveys (e.g., Rockville; Pew Research) show high rates of digital health adoption among patients and health consumers (especially for accessing online health information) and growing use of mobile technologies. There existed over 250,000 mHealth apps alone in 2016 (Medium.com, 2017), many with the potential to help patients track symptoms, monitor progress, improve medication adherence, connect with other patients or other potential benefits that could make patient care more personalized while reducing healthcare costs, improving access to care, or streamlining inefficiencies.1 Similarly, many physicians already are using mobile devices to access patient data, communicate with other healthcare providers (HCPs) and access decision support or other business intelligence applications.2 By 2022, market forecasting anticipates that 90 percent of clinicians will use point-of-care mobile devices.3 Accordingly, the United States Food and Drug Administration has launched a Digital Health Innovation Action Plan to address the role of digital health technologies,4 and several randomized clinical trials are ongoing to investigate the feasibility and impact of digital health technologies on disease management (e.g., in diabetes, asthma and mental health) and performance indicator improvement (e.g., hospital readmission).5 

However, the profusion of digital health tools, including EHR, can lead to confusion among providers, information overload, alert fatigue and frustration with the lack of interoperability among systems. For instance, while Kardiaband, a personal electrocardiogram (EKG) device that can be used directly with the Apple Watch, can provide real-time EKG data, physicians are often overwhelmed with longitudinal information about patient heart rhythm and rate that they are unable—and seldom inclined—to act on it.6 At the same time, existing data on the effectiveness of digital health tools to improve patient or population health outcomes varies in credibility and reliability.7 

In their most current survey, Harting and Bowser asked survey participants to address an intriguing open-ended question about what they had noticed as poor or unprofessional practices in other needs assessments they had seen. The correlation between two independent reviewers who each assessed the 67 responses and then categorized them into the predefined categories was very well aligned. The authors do note that the observations given are all secondhand from something seen from the past. Additionally, the qualifications of the writers whose work is in question are unknown.