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      Segmenting Clinicians’ Usage Patterns of a Digital Health Tool in Resource-Limited Settings: Clickstream Data Analysis and Survey Study

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          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

          We aimed to define segments of clinicians based on their usage patterns of UpToDate, describe the demographics of those segments, and relate the segments to self-reported professional climate measures.

          Methods

          We collected 12 months of clickstream data (a record of users’ clicks within the tool) as well as repeated surveys. We calculated the total number of sessions, time spent online, type of activity (navigating, reading, or account management), calendar period of use, percentage of days active online, and minutes of use per active day. We defined behavioral segments based on the distributions of these statistics and related them to survey data.

          Results

          We enrolled 1681 clinicians from 75 countries over a 9-week period. We based the following five behavioral segments on the length and intensity of use: short-term, light users (420/1681, 25%); short-term, heavy users (252/1681, 15%); long-term, heavy users (403/1681, 24%); long-term, light users (370/1681, 22%); and never-users (252/1681, 15%). Users spent a median of 5 hours using the tool over the year. On days when users logged on, they spent a median of 4.4 minutes online and an average of 71% of their time reading medical content as opposed to navigating or managing their account. Over half (773/1432, 54%) of the users actively used the tool for 48 weeks or more during the 52-week study period. The distribution of segments varied by age, with lighter and less use among those aged 35 years or older compared to that among younger users. The speciality of medicine had the heaviest use, and emergency medicine had the lightest use. Segments varied strongly by geographic region. As for professional climate, most respondents (1429/1681, 85%) reported that clinicians in their area would view the use of a online tool positively, and compared to those who reported other views, these respondents were less likely to be never-users (286/1681, 17% vs 387/1681, 23%) and more likely to be long-term users (655/1681, 39% vs 370/1681, 22%).

          Conclusions

          We believe that these behavioral segments can help inform the implementation of digital health tools, identify users who may need assistance, tailor training and messaging for users, and support research on digital health efforts. Methods for combining clickstream data with demographic and survey data have the potential to inform global health implementation. Our forthcoming analysis will use these methods to better elucidate what drives digital health tool use.

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          Most cited references18

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          Use of UpToDate and outcomes in US hospitals.

          Computerized clinical knowledge mana-gement systems hold enormous potential for improving quality and efficiency. However, their impact on clinical practice is not well known. To examine the impact of UpToDate on outcomes of care. Retrospective study. National sample of US inpatient hospitals. Fee-for-service Medicare beneficiaries. Adoption of UpToDate in US hospitals. Risk-adjusted lengths of stay, mortality rates, and quality performance. We found that patients admitted to hospitals using UpToDate had shorter lengths of stay than patients admitted to non-UpToDate hospitals overall (5.6 days vs 5.7 days; P < 0.001) and among 6 prespecified conditions (range, -0.1 to -0.3 days; P < 0.001 for each). Further, patients admitted to UpToDate hospitals had lower risk-adjusted mortality rate for 3 of the 6 conditions (range, -0.1% to -0.6% mortality reduction; P < 0.05). Finally, hospitals with UpToDate had better quality performance for every condition on the Hospital Quality Alliance metrics. In subgroup analyses, we found that it was the smaller hospitals and the non-teaching hospitals where the benefits of the UpToDate seemed most pronounced, compared to the larger, teaching institutions where the benefits of UpToDate seemed small or nonexistent. We found a very small but consistent association between use of UpToDate and reduced length of stay, lower risk-adjusted mortality rates, and better quality performance, at least in the smaller, non-teaching institutions. These findings may suggest that computerized tools such as UpToDate could be helpful in improving care. Copyright © 2011 Society of Hospital Medicine.
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            Click Here for Internet Insight: Advances in Clickstream Data Analysis in Marketing

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              • Abstract: not found
              • Article: not found

              Analysis of navigation behaviour in web sites integrating multiple information systems

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                Author and article information

                Contributors
                Journal
                JMIR Form Res
                JMIR Form Res
                JFR
                JMIR Formative Research
                JMIR Publications (Toronto, Canada )
                2561-326X
                May 2022
                9 May 2022
                : 6
                : 5
                Affiliations
                [1 ] Better Evidence Ariadne Labs Boston, MA United States
                [2 ] Department of Health Policy and Management Harvard TH Chan School of Public Health Boston, MA United States
                [3 ] Division of Global Health Equity Brigham and Women's Hospital Boston, MA United States
                Author notes
                Corresponding Author: Rebecca Weintraub rweintraub@ 123456ariadnelabs.org
                Article
                v6i5e30320
                10.2196/30320
                9127647
                35532985
                5f39acd3-0c55-48b1-ba7a-568316c65003
                ©Kate Miller, Julie Rosenberg, Olivia Pickard, Rebecca Hawrusik, Ami Karlage, Rebecca Weintraub. Originally published in JMIR Formative Research (https://formative.jmir.org), 09.05.2022.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Formative Research, is properly cited. The complete bibliographic information, a link to the original publication on https://formative.jmir.org, as well as this copyright and license information must be included.

                Categories
                Original Paper
                Original Paper

                informatics,clinical decision support tools,low-income settings,provider behavior,digital health,behavioral segments,clinicians,clickstream data,web usage mining

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