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Personal Health Engagement
Published in Salvatore Volpe, Health Informatics, 2022
Use of the internet for finding health information hasn’t gone away in the years since that Pew Internet study. Google Health Chief Dr. David Feinberg said in 2019 that about 7% of all Google searches are health related. To provide perspective, this equates to about 70,000 searches a minute or a billion health inquiries a day. 60
Value proposition design and business modelling
Published in Lisette van Gemert-Pijnen, Saskia M. Kelders, Hanneke Kip, Robbert Sanderman, eHealth Research, Theory and Development, 2018
Interestingly, decentralization was found to be important for platforms as well (Olleros, 2008). Decentralized within this context refers to multiple parties contributing to the development of the platform rather than one single organization (either being a strong market player or a governmental organization). The success of platforms depends not only on the openness of platforms (Benedict, Schlieter, Burwitz, & Esswein, 2016) but also on the decentralization of the open platforms. Decentralized open platforms with simple core functions push costs, risks, intelligence and initiative to the periphery, enabling rapid evolvement and upscaling (Olleros, 2008). Nonetheless, some very successful platforms are still relatively closed. In the case of Smart Home services, however, most platforms are located in the user’s home and are kept closed for third-party service providers, while only a few cloud-centric, open platforms exist in the market (Nikayin, 2014; Nikayin & De Reuver, 2013). There are some organizations that have developed cloud-based eHealth service platforms, for example, Microsoft HealthVault Portal and Google Health platform, to collect individual health information (e.g. weight and level of activities). A more recent example is the Apple Watch.
Electronic health records for patient-centred healthcare
Published in Wendy Currie, David Finnegan, Integrating Healthcare with Information and Communications Technology, 2018
Recent developments herald a significant convergence in health record systems and may address many of the shortcomings of standalone ePHR identified above. ‘Dossia’ (www.dossia.org), a non-profit consortium of major employers in the US, is offering its employees, family members and dependants a lifelong personally controlled health record. It is based on Indivo (www.indivohealth.org), an open-source ePHR that supports standards-based communication interfaces to connect to current and future health information systems, can import data from networked medical devices and supports a robust patient access and control model (Mandl, et al. 2007). Microsoft Corporation have created HealthVault, a repository for personal health data: it is intended to function as a repository fed from health record systems (such as the New York Presbyterian Hospital EPR) but has a ‘connection centre’ permitting direct upload of data from other sources, including compatible medical devices such as home monitors. Google Health is a more conventional ePHR platform to which anyone (currently only available to US residents) can subscribe without charge. It too offers interfaces to other health record systems such as the iHealthRecord ePHR developed by the iHealth Alliance (www.ihealthrecord.org) and the Epic EPR system in use in Cleveland Clinic.
State health policies and interest in PrEP: evidence from Google Trends
Published in AIDS Care, 2022
Bita Fayaz Farkhad, Mohammadreza Nazari, Man-pui Sally Chan, Dolores Albarracín
We measured interest in PrEP by obtaining monthly, geo-located search data using Google Trends. Google Trends quantifies search patterns from all web queries on the Google search engine, even those made in incognito browser mode (i.e., A browsing mode with cookies and local history deleted when users close the browser; Google LLC, 2020), and the data can be collected through the Google Trends website and Google Health Trends API (Stocking & Matsa, 2017; Zepecki et al., 2020). The Google Trends website provides a scaled result from 0 representing the least popularity to 100 being the most popular based on a topic's search proportion in a given region and time period. The main limitation with this scaled measure is that the data are not on the same scale for all regions, as a result of which the numbers do not show whether a particular area has a higher interest in a specific query compared to another. To resolve this shortcoming, we used the Google Health Trends API, which returns query share defined as:. 2009; Kapitány-Fövény et al., 2019; Morsy et al., 2018; Young et al., 2018; Young & Zhang, 2018).
The application of advanced imaging techniques in glaucoma
Published in Expert Review of Ophthalmology, 2022
Su Ling Young, Nikhil Jain, Andrew J Tatham
Phene and colleagues conducted one of the largest studies in this area in partnership with Google Health [6]. This study developed an algorithm for detecting referrable glaucomatous optic neuropathy from color fundus photographs. The algorithm was trained using a test dataset of 86,618 retinal images and was then validated on three separate datasets to assess accuracy. The datasets which trained the algorithm were analyzed by 43 experienced ‘graders’ comprising of ophthalmologists, senior optometrists, and fellowship trained glaucoma specialists. The algorithm was also designed to try to ascertain which optic nerve head features were most strongly associated with referrable glaucomatous optic neuropathy. On one validation set, the algorithm detected referrable glaucomatous optic neuropathy with an AUC of 0.95, with a sensitivity of 80% and a specificity of 90.2%. On a smaller subset of 411 images from the same validation set, the performance of the algorithm was contrasted with that of 10 graders. The algorithm was found to be significantly more sensitive than 7 out of the 10 graders and more specific than 3 graders with no statistical difference in specificity or sensitivity compared to other graders. On the other two validation sets the algorithm achieved AUCs of 0.855 and 0.881. A vertical cup-to-disc ratio of 0.7 or more, a neuroretinal rim notch, an RNFL defect or baring of circumlinear vessels showed the strongest correlation with glaucomatous optic neuropathy. Conversely, the presence of a disc hemorrhage was not found to be significantly correlated with glaucomatous optic neuropathy. The authors of this study noted several limitations namely that glaucoma diagnosis in clinic is not based on a single photograph in isolation; in real-world practice diagnosis is based on a combination of factors (history, age, race) and repeated testing over time (visual fields, OCT and optic nerve head analysis). Each validation set was comprised of different populations (UK, US and Indian) and therefore the accuracy of the algorithm among any specific ethnicity is difficult to establish. However, this does suggest it could be a more pragmatic tool as many countries have diverse populations and it would be improbable that individual healthcare professionals would use (or have access to) a different algorithm specific to each individual’s ethnicity in the clinic. The use of multimodal imaging and analysis of optic nerve head appearance over time could greatly improve future iterations. That said the study’s primary objective was to identify a reliable algorithm for identifying referable glaucomatous optic neuropathy and their findings would suggest this tool could be a very useful adjunct to healthcare professionals when deciding whether to refer or not.