## 4 Discussion  
In this study, we successfully developed a dominant window detector through the Golang programming language, which was utilized to retrieve the alert dwell times within the APOE system of an academic medical center in Taiwan. Few studies to date have reported alert dwell times [21; 22; 23]. We showed that alert dwell times can be used to assess which categories of administrative and clinical alerts are most time-consuming, and/or are being ignored. By looking for potentially resolvable problems of the APOE alert system in this way, the clinical workflow can ultimately be improved.  
Most APOE systems use alerts to prevent clinical errors and to optimize the safety and quality of clinical decisions [29], such as improving physician compliance with guidelines [30] and reducing unintentional duplicate orders [31]. Previous studies have shown that it takes physicians 8 to 10 s to completely read alerts. However, in our study, the most common alert dwell time was around one second, suggesting that our physicians had become victims of alert fatigue, or that they had learned to reflexively disregard messages of low or no clinical importance [32]. One indication of alert fatigue is when clinicians spend less time per alert, sometimes going as far as not reading the alert at all [33]. The most frequent alerts in our APOE alert system were administrative, highlighting the need to optimize them preferentially.  
Intuitively, the most frequent alerts have the advantage of clinician familiarity. Our study showed that more frequent alerts have lower alert dwell times, which may be suggestive of the phenomenon of alert fatigue existing in relation to our hospital's current APOE system. Previous studies have indicated that alert relevance (i.e., accuracy of automation) had no impact on alert dwell time [21]. Interestingly, we found that there was also no association between alert message length and alert dwell time. We hypothesize that this was because physicians did not read the alert message completely no matter how long the content. Since physicians frequently received clinically irrelevant alerts, they probably learned to override them expeditiously to optimize clinical workflow.  
Our results also showed that the alert dwell times among different professional groups remained consistent. One possible contributor to this observation is that clinical processes affecting different professional groups in the hospital are analogous to one another. Another potential explanation is that the different professional groups receive alerts with a similar distribution of clinical importance. By identifying alerts with low dwell times owing to negligible clinical importance, dwell times can provide additional support for changing or even removing them [34; 35].  
Numerous studies of physician behavior have employed a wide array of analytical methods, including questionnaires, interviews, and observations [36; 37; 38]. The eventual goal of these studies is to understand the features of alerts that contribute to alert fatigue, and thus help to optimize the overall clinical workflow. The dominant window detector that we developed for this study not only retrieves the alert dwell times, but also analyzes the time spent by clinicians in different program windows during the consultation. This opens up the opportunity to analyze various elements of the clinical workflow, such as the time spent on different programs, and the identification of the most time-consuming software. Furthermore, by visualizing how computers are being used during various digitized clinical processes, any bottlenecks in the computerized elements of clinical workflow potentially become easier to discover and improve. By using a dominant window detector like ours in studies of clinician behavior on APOE systems, the medical prescribing process may be ameliorated by decreasing alert fatigue, with potentially beneficial consequences for patient safety [39; 40; 41].