Health technologies are reshaping the future of medical practices worldwide. With the cumulative use of medical software platforms, healthcare apps, and wellness wearables, big data becomes essential in research and practice. Hence, to support data management and decision-making, digital health solutions with integrated big data analytics modules are essential tools.
Since analytics techniques can provide insights into patient behaviors and healthcare choices, health analytics is becoming a moving force in healthcare. Application (app) analytics, in particular, are transforming the future of medicine at a rapid pace. Note that the number of mHealth apps is rising, with more than 325,000 health apps being available online. According to data, in the US alone, the mHealth apps market will reach $111.8 billion by 2025.
Due to a large number of mHealth apps, mHealth app analytics are growing in popularity. mHealth app analytics are employed to support the analysis and visualization of metadata collected from various apps and across diverse populations (O’Reilly-Shah, Easton, & Gillespie, 2017). Interestingly, app analytics can be integrated into apps in order to support the release of medical information (ROMI). Analytics tools can provide insights into app usage, location, and patterns. mHealth app analytics can also help users, healthcare professionals, and IT specialists exchange meaningful data to improve healthcare practices across the globe.
Health technologies are disrupting the future of research and routine clinical care. With the increasing use of smartphones and devices, apps that aim to improve health outcomes have the potential to reach a large number of users across the globe (Owen et al., 2015). The wide variety of mHealth apps on the market target different populations and conditions. From healthcare professional finders to managing financial records, users all over the world admit they rely on mHealth solutions and apps, in particular. Nevertheless, to assess the usability and feasibility of mHealth interventions, mHealth app analytics are needed. The capabilities of today’s mHealth app analytics are impressive; mHealth app analytics can provide valuable insights into downloads, user experience, engagement, in-app activity, and efficacy. Analytics can be used to validate quantitative data (e.g., interview-based information and feedback), with both patients and providers being involved in the assessment process. Other benefits include the study of global patterns, real-world settings, and routine clinical care. It’s interesting to mention that mHealth app analytics are vital tools in marketing. For instance, analytics can reveal valuable insights into business marketing and competitor research. Large-scale app analytics can also improve policies regarding a large number of health apps and interventions available on the market. Note that more than 200 health apps are added every day.
It’s not surprising that mHealth app analytics is defined as the key to unlocking the potential of digital health and mHealth, in particular (Nguyen & Poo, 2017). Yet, exploring trends and patterns can be a burdensome task. Quantitative mobile analytics can help experts explore numbers and figures (e.g., the length of a user session). On the other side, qualitative analytics are also fundamental to help specialists understand and improve user experience. Nevertheless, with a wide range of devices and systems, health apps are often prone to in-app crashes; analytics that resolves in-app crashes can determine the success of a medical app. We should note that another vital classification categorizes app analytics into three groups: in-app analytics, app performance analytics, and marketing analytics. In-app analytics are among the most beneficial metrics which can reveal how users interact with the app of interest (e.g., click on the homepage, conversion rates). Experts claim that demographics become a vital factor in the research. A study concluded that although the main users of health apps are younger and more educated patients (Carroll et al., 2017), more and more people are willing to use health apps to improve their lifestyle and well-being. The second category – performance analytics – is also vital. Though performance analytics is similar to in-app analytics, the focus is on the app itself (e.g., crashes, etc.). As explained above, crashes are inevitable, and experts need to resolve them. When it comes to healthcare practices, sponsors agree that a balance between patient well-being and revenue is a must. Thus, marketing analytics, such as in-app purchases, become a leading focus of analysis. Marketing analytics can help companies increase revenue and success.
While healthcare, technologies, and statistics are merging in one, there’s no doubt that medical information is a complex field of analysis. Since mHealth apps must provide high-quality data and up-to-date changes, many apps come with integrated app analytics modules. Big data and multi-dimensional data streams need to be considered; they can help experts deal with the large volume of information mHealth apps provide. The fact that data collection in mHealth apps happens in real-time requires the integration of analytics on streaming and dynamic data processing. At the same time, it’s not only data management that might challenge analysis. With large datasets and diverse populations, data validity and reliability should be tackled to support high-quality data. Most of all, privacy becomes crucial. As personal data is a hot topic worldwide, it’s no surprise that mHealth app analytics differ from standard health analytics. When highly sensitive medical information is transferred wirelessly, ethical regulations and international standards become crucial in mHealth app analytics modules. After all, good clinical practice is essential in medical care.
Technologies have become an integrated part of global practices. mHealth apps, in particular, are increasing in popularity. As explained above, the number of users and apps on the market is increasing at a rapid pace. Nevertheless, to assess user resentment, data quality, and relevance in routine clinical care, experts must consider a wide variety of mHealth app analytics. Both quantitative and qualitative analytics have the potential to improve user engagement and app performance. When it comes to in-app behavior, factors, such as location, gender, age, and operating system, need to be assessed. Screen views per visit, retention time, and average visit time are also crucial metrics. When it comes to new users, the number of users and the time of use can provide valuable information. Note that A/B testing is a powerful tool to assess user experience and resentment. To be more precise, A/B testing is defined as multivariant testing, in which more than two versions of the same app are checked to encourage in-app behavior (e.g., quicker navigation). mHealth app performance analytics concerning API latency, speed, crashes, and data transactions should be implemented in practice as well. mHealth apps must analyze a wide range of indicators to improve user experience and effectiveness. In fact, customer satisfaction metrics are essential. App ratings, feedback, and touch heatmaps should be analyzed in detail. Last but not least, some of the vital marketing analytics cover opens, downloads, registration, purchases, and installs. They can improve competitor research and benchmark.
In the maze of numbers and charts, one of the main purposes of mHealth app analytics is to reveal the benefits of a given app in real-world settings. It’s interesting to mention that a recent study (Owen et al., 2015) assessed quantitative and qualitative data sources to examine a powerful app for managing posttraumatic stress disorders (PTSD). Note that the app was able to measure self-reports. It’s not a secret that subjective measures have numerous benefits in research and practice as they reveal hidden patterns and symptoms known only to the patients themselves. The research team collected data between 2011 and 2014 from 153,834 downloads, 156 user reviews (on both the App Store and Google Play Store), as well as star ratings. Note that qualitative methods, such as genuine reviews, can help experts assess user experience, usage, reach, and reception in order to improve their products. At the same time, quantitative methods and population-level app engagement data can show how users interact with the app. Analytics regarding the number of sessions, rolling retention, return rate, and stream data were also employed. The results showed that mHealth apps regarding mental health could be beneficial in real-world settings.
When it comes to real-world settings, apps, and digital solutions can support not only patients but healthcare providers. Interestingly, concierge doctors are more likely to prescribe mHealth apps to boost patient well-being. That said, more and more providers worldwide are adopting mHealth apps in practice. App analytics can help experts deliver high-quality data, assess global practices, and support educational programs. We should mention that a recent study (O’Reilly-Shah, Easton, & Gillespie, 2017) assessed the use of a free anesthesia calculator app, designed primarily for pediatric populations. 31,173 providers across 206 countries were assessed. The research team employed app analytics, in-app surveys, back-end analytics, and feedback. Note that the custom analytics and survey administration modules were integrated into the app. The study findings indicated that users from low-income countries were more likely to engage with the app. In addition, they rated its significance higher (p<0.001). With a wide range of insights, mHealth app analytics can reveal vital crowdsourced information about healthcare provider behavior and improve routine practices across the globe.
With the increasing use of health technologies, mHealth solutions are becoming more and more beneficial in research and practice. Since the vast majority of people worldwide own a smartphone with a data plan, mHealth apps have become an integrated part of people’s everyday lives. As stated earlier, stats show that there are more than 325,000 apps on the market. Apps can help users manage symptoms, lose weight, and quit smoking (Robbins et al., 2017). In addition, mHealth apps are beneficial in populations with chronic conditions, such as diabetes and mental problems. Tech solutions can also improve medication adherence and health outcomes.
Still, mHealth apps and analytics go hand in hand. mHealth app analytics can be used to improve app performance, marketing, and user engagement. Capabilities, such as GPS settings and push-up notifications, can support both patients and providers. Consequently, app analytics can help experts capture and visualize data from different sources, operating systems, and servers. Note that in healthcare practices, data mining and predictive modeling are essential factors in analytics, with centralized dashboards being powerful tools in data analysis and visualization. In the end, understanding user engagement, patient satisfaction, and app performance can boost business success and benchmark.
To sum up, from electronic health records to mHealth apps, technologies and analytics are the future of medicine. In fact, more and more consumers state that health technologies are beneficial and reliable. mHealth app analytics can help patients manage symptoms, track drug compliance, and improve health outcomes across chronic and acute conditions. Interestingly, the increasing role of mHealth apps in healthcare mirror the powerful patient-center approach in digital health. By having access to information about location, doctor tools, library information, and ER waiting times, patients become active participants in decision-making. Thus, mHealth apps support today’s medicine which is defined as personalized, predictive, participatory, and preventive care; with mHealth app analytics being the key to success and patient well-being.
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