Abstract:Background
Evidence-based digital health tools allow clinicians to keep up with the expanding medical literature and provide safer and more accurate care. Understanding users’ online behavior in low-resource settings can inform programs that encourage the use of such tools. Our program collaborates with digital tool providers, including UpToDate, to facilitate free subscriptions for clinicians serving in low-resource settings globally.
Objective
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“…We estimated the length of specific user sessions as a function of (1) the time between clicks, (2) the content or function clicked on and (3) overall estimates of the amount of time spent reading content, navigating the site and managing user accounts. These methods have been detailed elsewhere 16…”
ObjectivesThis research aimed to understand the barriers and facilitators clinicians face in using a digital clinical decision support tool—UpToDate—around the globe.DesignWe used a mixed-methods cohort study design that enrolled 1681 clinicians (physicians, surgeons or physician assistants) who applied for free access to UpToDate through our established donation programme during a 9-week study enrolment period. Eligibility included working outside of the USA for a limited-resource public or non-profit health facility, serving vulnerable populations, having at least intermittent internet access, completing the application in English; and not being otherwise able to afford the subscription.InterventionAfter consenting to study participation, clinicians received a 1-year subscription to UpToDate. They completed a series of surveys over the year, and we collected clickstream data tracking their use of the tool.Primary and secondary outcome measures(1) The variation in use by demographic; (2) the prevalence of barriers and facilitators of use; and (3) the relationship between barriers, facilitators and use.ResultsOf 1681 study enrollees, 69% were men and 71% were between 25 and 35 years old, with the plurality practicing general medicine and the majority in sub-Saharan Africa or Southeast Asia. Of the 11 barriers we assessed, fitting the tool into the workflow was a statistically significant barrier, making clinicians 50% less likely to use it. Of the 10 facilitators we assessed, a supportive professional context and utility were significant drivers of use.ConclusionsThere are several clear barriers and facilitators to promoting the use of digital clinical decision support tools in practice. We recommend tools like UpToDate be implemented with complementary services. These include generating a supportive professional context, helping clinicians realise the tools’ use and working with health systems to better integrate digital, clinical decision support tools into workflows.
“…We estimated the length of specific user sessions as a function of (1) the time between clicks, (2) the content or function clicked on and (3) overall estimates of the amount of time spent reading content, navigating the site and managing user accounts. These methods have been detailed elsewhere 16…”
ObjectivesThis research aimed to understand the barriers and facilitators clinicians face in using a digital clinical decision support tool—UpToDate—around the globe.DesignWe used a mixed-methods cohort study design that enrolled 1681 clinicians (physicians, surgeons or physician assistants) who applied for free access to UpToDate through our established donation programme during a 9-week study enrolment period. Eligibility included working outside of the USA for a limited-resource public or non-profit health facility, serving vulnerable populations, having at least intermittent internet access, completing the application in English; and not being otherwise able to afford the subscription.InterventionAfter consenting to study participation, clinicians received a 1-year subscription to UpToDate. They completed a series of surveys over the year, and we collected clickstream data tracking their use of the tool.Primary and secondary outcome measures(1) The variation in use by demographic; (2) the prevalence of barriers and facilitators of use; and (3) the relationship between barriers, facilitators and use.ResultsOf 1681 study enrollees, 69% were men and 71% were between 25 and 35 years old, with the plurality practicing general medicine and the majority in sub-Saharan Africa or Southeast Asia. Of the 11 barriers we assessed, fitting the tool into the workflow was a statistically significant barrier, making clinicians 50% less likely to use it. Of the 10 facilitators we assessed, a supportive professional context and utility were significant drivers of use.ConclusionsThere are several clear barriers and facilitators to promoting the use of digital clinical decision support tools in practice. We recommend tools like UpToDate be implemented with complementary services. These include generating a supportive professional context, helping clinicians realise the tools’ use and working with health systems to better integrate digital, clinical decision support tools into workflows.
“…This has become a popular method for analysing customer behaviour. Currently, applications of clickstream datasets have focused primarily on customer profiling [28], customer segmentation [29,30], and the prediction of consumer behaviour [31,32]. However, these studies often fail to account for change over time and customer interest drift, even when recognising that product trends can influence product popularity.…”
Since online shopping has become an important way for consumers to make purchases, consumers have signed up to e-commerce platforms to shop online. However, retailers are beginning to realise the critical role of predicting anonymous consumer purchase intent to improve purchase conversion rates and store profitability. Therefore, this study aims to investigate the prediction of anonymous consumer purchase intent. This research presents a machine learning model (MBT-POP) for predicting customer purchase behaviour based on multi-behavioural trendiness (MBT) and product popularity (POP) using 33,339,730 clicks generated from 445,336 sessions of real e-commerce customers. The results show that the MBT-POP model can effectively predict the purchase behaviour of anonymous customers (F1 = 0.9031), and it achieves the best prediction result with a sliding window of 2 days. Compared to existing studies, the MBT-POP model not only improves the model performance, but also compresses the number of days required for accurate prediction. The present research has argued that product trendiness and popularity can significantly improve the predictive performance of the customer purchase behaviour model and can play an important role in predicting the purchase behaviour of anonymous customers.
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