NCBI Bookshelf. A service of the National Library of Medicine, National Institutes of Health.

Gliklich RE, Dreyer NA, Leavy MB, et al., editors. 21st Century Patient Registries: Registries for Evaluating Patient Outcomes: A User’s Guide: 3rd Edition, Addendum [Internet]. Rockville (MD): Agency for Healthcare Research and Quality (US); 2018 Mar.

Cover of 21st Century Patient Registries

21st Century Patient Registries: Registries for Evaluating Patient Outcomes: A User’s Guide: 3rd Edition, Addendum [Internet].

Show details

3Digital Health and Patient Registries: Today, Tomorrow, and the Future

, Pharm.D., Ph.D., M.P.H. (Lead); , Ph.D., M.P.H.; , M.P.H.; , B.Sc.; , Ph.D.; , B.S.

Author Information

As use of new technology increases in medical practice and clinical research, registries are uniquely positioned to leverage these innovations to support real-world data collection. In particular, digital health is emerging as an important trend. Digital health is not limited to the use of mobile devices, but rather includes patients, care-takers, and clinicians making use of any digital technology including smartphones, tablets, texting, calling, video conferencing, specialized applications (apps) or monitoring devices to deliver patient care, monitor outcomes, and/or conduct research. Digital health may include the use of devices and apps to conduct real-time monitoring of patient vital signs; to collect digital social and behavioral information; to gather patient-reported data, such as quality of life measures, on a regular basis; to deliver that information to care providers and/or researchers; and to provide care directly through telemedicine apps. While digital health may supplement or supplant other forms of health care in high resource countries, it is becoming particularly important in resource-strapped settings, where digital health options can surmount shortfalls in traditional health care delivery. This chapter explores ways in which registries may be able to take advantage of digital health technologies, with a particular focus on the strengths and limitations of these technologies to collect patient-generated health data, a discussion on how they are currently being used, and where it is anticipated that they will be of value in the future.


The term “digital health” refers to technologies that can receive and transmit electronic data that can be used, directly or indirectly, to monitor or enhance health or coordinate health care services. Digital health includes a wide range of different technologies that can be used in health care including categories such as mobile health (mHealth), health information technology (IT), wearable devices, telehealth and telemedicine, and personalized medicine.1 Other terms such as mHealth, eHealth, social media, and the medically-related Internet of Things2 are sometimes used interchangeably because there are no globally agreed upon definitions. For the purposes of this chapter, we use the term “digital health” to broadly encompass various terms including, but not limited to, mHealth, eHealth, social media, and the Internet of Things.

Major advances have been made in the last decade in low-cost, real-time technologies to assess disease, movement, images, behavior, social interactions, environmental toxins, hormones, and other physiological variables. These advances are due to increased computational sophistication, as well as reductions in the size and power requirements of digital technologies. As shown in Figure 3-1, these technologies provide the potential to advance diagnostics, treatment, public health, and research. Social media’s impact in health care has also significantly grown and while there have been increases in published research using social media, best use cases are not yet clear and policies are still being developed.3 Despite all of this growth in digital health, the collective understanding of how these components, devices, and technologies work has remained fragmented.3

Four interconnected boxes are used to show the four application areas of digital health technologies. These four areas are Diagnostics, Treatment, Public Health, and Research. Within each application area, there are examples of tasks which can be accomplished through digital health technologies. Within the application area of diagnostics, the following tasks are written: point-of-care diagnostics, portable imaging, biomarker sensing, patient-reported symptoms. Within the application area of treatment, the following tasks are written: clinical decision making, chronic disease management, telehealth services, prevention and wellness, disaster support/care. Within the application area of public health, the following tasks are written: enhance service access, dissemination of health information, disease and health surveillance, medication tracking and safety. Within the application area of research, the following tasks are written: remote clinical trials, assessment and screening, treatment adherence, post-market surveillance of devices.

Figure 3-1

Spectrum of application areas for digital health technologies.

This chapter examines the role of digital health technologies and their use in patient registries. It also reviews recent advances with a look towards where the use of these novel technologies may provide value to the future of health care research.

The Rise of Digital Health

While computing and the Internet have been a part of research for decades, the use of digital technologies for health research is much more recent. Table 3-1 below presents the growth of digital health technologies.4

Table 3-1. Expanding use of digital health technologies.

Table 3-1

Expanding use of digital health technologies.

The table above demonstrates significant growth in the use of digital technologies over the past five years and predicts accelerated use of various digital technologies over the next five years.4 However, smart phones and sensors are only one small part of the digital technology field.5 Additional sensors, either worn on the body, implanted in the body, or embedded in the skin are provide access to data on a wide range of biological, physiological, and behavioral variables and expected to grow in use exponentially. Due to rapid adoption of these sensors and mobile devices, it is now feasible for researchers to assess activity, location, images, behaviors, social interactions, environmental toxins, and physiological variables in real time.6

By enabling participants to conduct many of these measurements remotely, the number of assessments within a study can increase without greatly increasing costs. Sensors can identify environmental exposure (e.g., indoor smoke), location (e.g., via Global Positioning System [GPS]), physical activity (e.g., via accelerometry), sleep, social interactions (e.g., via microphones and cameras and use of built in communications such as email and short message service [SMS]), images and visual stimuli (e.g., via smart eyeglasses), and electronic exposure (e.g., via social media data). Assessment of physiology such as blood pressure, heart rate, blood oxygen and respiration can now be done via mobile units with high enough quality that some of these devices have been approved by the U.S. Food and Drug Administration (FDA) for use in hospital settings.7 Others, such as the camera on the smart phone, can be used for less accurate, potentially valuable measurement of heart rate in real time.

The integration of sensors into a “smartwatch” or “activity band” has made sensors convenient and wearable for long durations. These devices have been initially developed to monitor physical activity and some physiological signals (e.g., heart rate), although the quality of their measurements remains unknown. In the near future, commercial device manufacturers (e.g., Apple® and Samsung®) promise to monitor pulse rate, hydration levels, glucose levels, and blood pressure, in addition to physical activity and sleep behavior. The quality of these commercial measures remains to be seen and validation with standardized measures will need to be actively pursued by the research community.5 The challenge, however, is that by the time a study is completed to demonstrate validity, the device may have been updated with new technology and the validation may no longer be current for that device.

The use of the Internet, whether it is accessed on a stationary computer or through smartphones and tablets, also allows for faster and more direct assessment, intervention, and distribution of information. The data that can be collected include not only those collected in conventional surveys, but also data collected by sensors in the device, such as cameras and microphones. Data such as time on the page and number of clicks to get to information are also a part of the digital health environment. Fixed sensors, whether designed for health (such as Bluetooth enabled scales), or developed for other uses (e.g., the Kinect™ gaming sensor or movement sensors) can also be employed as a part of the digital health ecosystem. Although a newer area of research, examples of this work abound in smart homes, which use both stationary and mobile sensors to measure the activities in the home and to make inferences about health and disease.8,9 As more devices in our lives get connected to the internet every day, such as our socks, beds, refrigerators, ovens, televisions, toothbrushes, scales, and thermostats, the possibilities expand for contribution to research. This new industry is attracting the attention of health care, as identified in books by leaders such as Dr. Joseph Kevdar and Dr. Eric Topol.10,11

These data, especially when combined across different technologies, have the potential to yield new insights into factors that lead to disease. They also have the potential to be analyzed and used in real-time to prompt changes in behaviors that can reduce health risks, reduce harmful environmental exposures, or optimize health outcomes. Indeed, this new area of digital health research has the potential to be a transformative force as it is based on the continuous input and assessment process and may scale more cost efficiently than other types of research. It can ensure that important biological, social, behavioral, and environmental data are used to understand the determinants of health and to improve health outcomes. Importantly many of these data can be collected with minimal patient burden (e.g., the fixed sensors embedded in smart homes) or in real-world environments.

Utilization of Digital Health in Clinical Research

Smartphones have become an important way for patients to acquire health care information. In fact, in 2013, 38 percent of users in the United States considered their phones to be an “essential” tool for obtaining this information.12 Many stakeholders (clinicians, administrators, professional colleges, academic institutions, ministries of health, pharmaceutical companies, among others) believe that mobile and wearable health technologies can be leveraged to improve health outcomes at lower costs.3,13 These devices are considered strongly influential to the patients who use them.12

Patient-Reported Data

There are many types of mobile health technologies available today. The first discussed in this chapter are patient-reported data which are captured through a mobile, internet-connected device and collected in a clinical study or registry. The term patient-reported data is used in this chapter to encompass information captured directly from the patient, person, citizen, individual, and/or self or other terms that could be used to represent the participant in the study. When the data collected is based on validated measurements of patient outcomes, it is called patient-reported outcomes (PRO).

A device equipped with PRO capabilities typically has software that captures structured queries and specially designed assessments, as well as free text or audio narratives from patients. In addition to this, PRO systems can use Web-based software and telephones. These questionnaires can be in the form of text messages, interactive voice response, or in the use of an app.14 PROs can be synchronized with wearable devices as well. The most notable benefit from these types of tools are the large amount of data that can be collected from patients without requiring many visits to a health care facility.14

PROs are used widely in research and by a variety of stakeholders in health care. The use of PROs extends from a routine office visit to a clinical trial. As an example, The Patient Centered Outcome Research Institute (PCORI) has invested millions in establishing the National Patient-Centered Clinical Research Network, PCORnet, with a goal of working with patients, researchers, clinicians, and health systems leaders to build and run a network that conducts research addressing the real world needs of patients and those who care for them.15 As part of the network, outcomes including patient reported outcomes are collected that are considered clinically meaningful and measured from structured questionnaires as well as from mobile apps and devices.

Many companies have created their own PRO Web-based systems to use within clinical trials and prospective observational studies. Additionally, patients who are willing to use this technology are also more willing to share their information in general.14 Utilizing mobile technology for PRO collection offers the following additional benefits: ease of use, better connectivity, the option to work offline, integrations with other apps and devices, direct connection to an electronic data capturing system, ability to custom-design software according to a researcher’s needs, ease of distribution, and a relatively smaller learning curve.14 In addition, data from PROs and mobile apps are collected in real-time, providing clinicians, and often users, with the ability to analyze results as they come in and respond to issues as soon as they arise.16 Moreover, data collection does not have to have a definite end date and capabilities exist for importing the collected data into an electronic health record system.16

Vital Signs

The capabilities of digital health technologies are constantly expanding. With recent advancements, there are now opportunities to use digital technology to monitor a range of vital signs, including glucometers to track blood sugar and devices to track heart function.17,18 This enables the collection of clinical information outside of the traditional health care settings and thus far has achieved good initial uptake.19 The two major factors that have been driving the adoption of remote monitoring systems are a growing elderly population with associated disabilities and chronic diseases as well as dwindling traditional health care resources.8

There has also been a shift in health care to focus more on a person-centered approach with an emphasis on early detection and prevention of disorders. For the elderly and those with chronic diseases, digital health technologies could make it possible to extend care to the home, enhance chronic disease management, allow for rehabilitation supervision, and curb unnecessary re-hospitalization (saving many resources). Most remote monitoring has been developed with the capability to monitor several different vital signs including electrocardiogram, heart rate, respiratory rate, blood pressure, blood glucose, etc. Some also include the ability to record information from an accelerometer and gyroscope for posture and activity, electrodermal activity sensors to try to assess emotional status, and ambient sensors to record information on the general context of a user (location, temperature, humidity, etc.). All of these data points combined with activity trackers (discussed below) can provide clinicians with the user’s physiological state continuously and in real-time.19

Lastly, digital health technologies as part of a multimodal approach to remotely monitor patients has been shown to improve health and quality of life.19 DeLuca et al, through a randomized controlled trial, found that using digital health properly to monitor patients living in a nursing home and responding accordingly and in a timely manner to any concerning changes, improved the health of the elderly. They also found that the use of these devices in conjunction with psychological counseling improved quality of life, reduced health care service access, hospitalization and all associated costs.20 There are many cloud-based systems that use two or more wearable devices to measure data directly from patients and use a mobile app for recording of PROs.21 The resulting data registry can then be mapped to the patients’ clinical records.21

Activity Tracking

Many digital health technologies are focused on consumer-facing technologies.17 Individuals are using mobile health apps, wristband activity trackers, and athletic sneakers that all collect information from a users’ day-to-day activities.17 The types of activities that these devices track include: sleep patterns, emotions, surrounding conditions, and level and type of physical activity, among others. Most of these technologies are adopted outside of the health care setting, although some hypothesize that this consumer data could be harnessed to improve patient health outcomes and providers’ clinical success. Patients who spend more time on self-care lower costs for everyone within the health care system. Effective self-care allows for prevention of avoidable visits to the emergency department and physician offices, and reduces the chance for lengthy hospitalizations. An unfortunate reality of these devices, however, is that there is a barrier to use them among those who would benefit the most.19 This is often due to their cost, as well as implementation and designs that require too much investment from people who are low in resources (e.g., time, money, technology, or social support).19

There is a shift of focus in this industry to work with researchers and developers to generate user-friendly tools, use data from these devices, and incorporate the information in a patient’s electronic health record to enhance the quality, availability, and utility of patient-generated data.19 There are limited opportunities to collect data from all patients’ observations of daily living, and these social, behavioral, and preventative self-care measurements are often missed in traditional office visits without this kind of technology.17 Wang et al. found that there was an increase in activity among overweight and obese adults that were using a wearable fitness monitor that gave instant feedback on performance through a mobile app with detailed summaries of activity levels.18 This adds support to the belief that there could be far-reaching applications for activity trackers and a new way in which to tackle major public health issues.

To make this kind of remote monitoring a success there will continue to be a need for advancement of more user-friendly devices that provide information when and where people want it, as well as automatic algorithms for online data interpretation, event(s) classification, and identification of invalid data. Advances in user-friendly, meaningful analytics will also be necessary to process the data in real-time and to make meaningful inferences from the data by all stakeholders involved.19


Patient education is incredibly important and the cornerstone of many public health initiatives. Due to the sheer volume of individuals interacting through digital technologies such as social media, such a platform could be a useful tool for patient education. Social media works similarly to traditional educational methods in that specific platforms are more effective in communicating with specific audiences. Facebook™ is considered a suitable channel for education of individuals aged 30 to 50 years; this communication channel is also appropriate for pediatric and elderly patient populations since their caregivers are likely to be in this age group.22 Instagram™ has the youngest users and therefore would be suitable for messaging directly to teenagers and young adults.22

Some have concern that social media is only used by certain age groups; however, the number of US adults using social networking sites has increased from 8 to 72 percent from 2008 to 2013, and the number of U.S. adults aged 50–64 years using social media increased from 7 to 60 percent from 2005–2013.23 The increase in utilization demonstrates that social media can indeed be a useful tool for sharing information with a variety of patient populations and could lower the costs associated with patient education campaigns.

Aside from traditional social media sources, there are stakeholders in health care that are attempting to build custom mobile technologies for the purpose of patient education. Certain stakeholders have begun to develop digital health technologies intended to educate patients including those who are involved in clinical trials. Their hope is to expand these tools for general use around the globe, creating versions that support multiple languages and countries. Some pharmaceutical companies, for example, have used this type of global educational solution in their clinical trials.21

Mobile Research Systems

Mobile research systems hold great potential to reduce costs and patient burden, increase efficiency, and facilitate recruitment while curbing loss to followup.24 Some experts believe that this technology will increase public awareness of clinical trials and encourage more partnerships between the current stakeholders in health care and crowdsourcing organizations.25 Still, users and researchers alike have expressed concerns about devices and apps, including device failure, user error, data integration, site preparedness, poor quality of data, potential for selection or user bias, and the need to coordinate with several different “help desks” if they were to encounter issues with any of the platforms.21,26 Moreover, these types of devices and apps require the use of a smartphone and an Internet connection to transmit data, which is subject to third-party carrier charges.16 The requirement of keeping up with messaging protocols, updating new versions of hardware/software versions, the need for backward-compatible “hybrid” solutions, evaluating the content validity of questions and answers during transition into a mobile app (especially for informed consent), and how to properly present a survey question are also important considerations.16, 27

Apple’s ResearchKit™ is one example of a platform that allows researchers to build their own data collection apps that will interact with other apps in order to record health information from a multitude of sources. The perceived benefit of these systems solutions is their ability to give researchers access to a global, existing population while providing researchers with secure and Health Insurance Portability and Accountability Act (HIPAA) compliant data-collection options.24 Recently, Google has announced a similar system, called Research Stack.28 In addition, the National Institutes of Health (NIH) has funded Health ePeople, a mobile registry designed to support mHealth research and clinical trials across a diverse population base.29

Examples of Digital Health Uses in Global Patient Registries

Globally, mobile health technologies are being used to perform disease and pharmacovigilance surveillance. Recently, in Cambodia, mobile phone-based, SMS text messages were used to conduct pharmacovigilance on 17 different vaccines.30 Their intention was to provide a timely and efficient pharmacovigilance platform to a developing country that would otherwise not have such a system. There is high unmet need for such a system because of the higher risk for patients in developing countries to be subjected to illegal and counterfeit drugs and vaccines.

Developed nations such as Australia have also used similar technologies with SMS text messages to monitor adverse events following immunization.31 The conclusions drawn from this study were that active surveillance of adverse events following immunizations using SMS has the capacity to complement existing passive reporting systems and has the potential to identify emerging safety signals more rapidly. See Case Example 1 for additional information on how SMS is being used in Australia to monitor adverse events post immunization.

Box Icon

Case Example 1

Active safety vaccine surveillance with mobile Health (mHealth) technology.

In the United States, surveillance systems for communicable diseases such as influenza are tracked through a “Flu Near You” (FNY) mobile health program. FNY prompts users every Monday to report symptoms of influenza-like illness experienced during the previous week. Throughout the 2013–2014 season, 336,933 reports were submitted showing potential to serve as a viable complement to existing outpatient, hospital-based, and laboratory surveillance systems.32 Additional information on FNY is provided in Case Example 2.

Box Icon

Case Example 2

Digital health technology to monitor new cases of influenza .

Many researchers and clinical programs are using mobile technology to provide clinical information to patients, to send out reminders for clinic visits, and also to collect data from patients remotely.13,33 SMS clinic visit reminders have been shown to increase the uptake of prenatal care in resource limited settings such as Zanzibar (Case Example 3).33 Wearable health sensors are also used to collect relevant data from patients. The benefits of wearable health sensors are that they collect both self-reported data and high quality structured data that can be used to evaluate the safety and efficacy of a trial.13 Utilizing automated data collection should generate a more comprehensive database, allowing for more thorough analysis of side effects and other long-term issues that may arise. Given that these data can be collected from the patient while they are at home, these digital technologies should significantly reduce cost over time and reduce patient burden. This technology could also amass a larger pool of data that can be analyzed more thoroughly for signs of side effects and other long-term problems. With the increase in FDA approved health-sensors, the need for technology that can evaluate and that can integrate with these data should be prioritized to ensure that these data are being used to their full potential.13

Box Icon

Case Example 3

Short message service (SMS) text reminders to improve prenatal care uptake.

A unique tool that could be used along with other digital technologies was recently developed to allow patients to record short videos and send them to their medical team via a mobile device.34 This tool has the potential to allow for a more in-depth understanding of patient preferences regarding a drug’s benefits and risks while better informing their health care providers to aid in the decision making process. Since patients use the tool to record free-form videos, patients can provide unrestricted comments to medical teams at the press of a button. The tool allows for the medical team to respond to patient videos as well, potentially enhancing the patient-doctor relationship.34

However, confidentiality of patient data is paramount and must be considered when designing any sort of digital health technology. The level of risk is typically commensurate with how sensitive the information being collected is and how large the data files are.35 Some systems require the collaboration of several outside contract organizations, including access to the data, which introduces additional concerns about security and confidentiality and complicates questions around responsibility of the data.24,26

Advantages of Using Digital Health in Patient Registries

“Fast,” “cost-effective,” “large-scale,” “transparent,” “the ability to have patient-generated internet data in real-time,” and “general usefulness” are all common phrases used to describe the strengths of digital technologies.3639 Much of the data captured from digital technologies are often available in real-time, allowing researchers and health care providers to quickly grasp epidemiological insights such as disease prevalence, as well as impact of medical interventions.40

Passive Monitoring and Social Listening

Few researchers and companies are actively soliciting health care data from digital health technologies (such as adverse event [AE] reports), but a majority are passively monitoring. Many of these entities are using a combination of automated and manual processes to identify individual case safety reports. For example, social listening, the manual or automated collection of patient-generated data that is unsolicited and available publicly or with permission, enables a stakeholder to capture a large amount of patient-generated data.39 Digital health apps and social listening sites have been used to determine where to host a clinical trial or launch a product.41 In spite of the early uptake of social listening by some, many are unaware of social media’s ability to reduce the burden of data collection on all parties, and how to attenuate and mitigate their associated risks.41

Patients first-hand experiences and perspectives provide a valuable data source that can be used to improve the care they receive.42 The widespread use of social media provides registry stakeholders with the ability to listen to a larger population than those typically included in traditional research studies.37,38 The innovative use of this new technology, as well as the rapid uptake, may allow industry, academia, health care providers and others to better understand the patient communities they are serving.21

Social listening can be performed manually or through automated tools that filter and/or classify information acquired from social media, and provide end-users with the resulting data, either in verbatim form or in aggregate. Automated data processes typically employ normalization, text-matching, and natural language processing techniques to collect and filter data.36 Best analytical practices include the use of both automated and manual data collection and processing to clean and curate the data.43

When considering the evaluation of social media data, analytical processes should include the following qualities: central, comprehensive management of “topic tags” through a robust taxonomy that includes slang terms; ability to restrict by language and country with the option to “listen” to countries that speak in other languages; manual aggregation and curation of the data; dashboards with filter and comparison options for visualization of analyses; sentiment analyses (refers to the use of natural language processing, text analysis and computational linguistics to identify and extract subjective information in source materials), demographic information; identification of influencers and important topics; historical data for retrospective analyses; and the ability to filter appropriate terms such as personally identifying information (PII) from the data.43 Social media vendors who provide these services should be able to meet software and accountability standards required for any type of research or investigational purpose.43

Certain social media platforms are better for specific uses and/or populations. Each form of social media has demographic characteristics associated with it and, as in all technology, the people who employ a given platform changes over time. Facebook is most useful for specific medical conditions, peer-to-peer support, fundraising, and sharing research amongst researchers and health care providers.3 Twitter™ is most useful for the hashtag (#) feature, which acts as a folder system and allows for the collection of posts referring to one topic or event.3 This is often used for group conversations and could be used to identify conversations amongst specific patient populations.44 Twitter has also had success in facilitating patient-provider conversations.3

Some physicians deliberately decide to connect with their patients on social media; however, most have reflected that they are wary of doing so.45 In spite of this concern, a survey of physicians as well as an analysis of impact of the internet on physician consultations resulted in positive comments outweighing negative comments by 2:1.46 Social-media savvy clinical practices do exist; however, closed social media platforms are most often used to allow patients to be actively involved in their own care coordination, track their clinical progress and have greater access to their physicians.46 The motive for physicians to engage on social media is to provide information to their patient population in hopes that this information will lead to improved patient outcomes.47 Patients also find it helpful to be able to ask their physician general questions about where to find information on a particular topic of concern and could do this through a social media platform.47 Some online patient communities (i.e. PatientsLikeMe, MyHealthTeams, MediGuard, etc.) also provide solutions for patients to communicate and partner together. These communities are often engaged in the health care industry through data-sharing partnerships to improve products, services and care for patients.48

Initial pilot studies on the impact of crowd-sourced research protocol designs indicate that patient participation has increased, and provider burden has been partially alleviated.43 While the data available are limited, the majority of surveyed companies believe that input from social media communities could improve the feedback they receive on clinical trial protocol design feasibility, protocol procedures and scheduling, and case report form design.43 Many companies (including Transparency Life Sciences and Genomera) currently use social media for development, planning and study design through the use of crowdsourcing techniques to engage patients.43

Greater Access to Minority Populations Via Mobile Devices

Another advantage of the increased use of mobile technologies is that it has shown potential for overcoming the digital divide previously identified in the digital health space. The digital divide refers to the chasm between those with regular access and ability to use digital technologies, such as the internet and those lacking such access. Past studies describe the realities of inequities in digital access by race and income and describe fears of the 1990s that the Internet would not scale economically to encompass users of all demographics. These studies indicated minorities, low-income families, and individuals living in rural areas were less likely to have telephone access and household computer access compared with whites living in urban areas and those with household incomes of greater than $75,000 annually. However, past barriers to information access such as costs of laptop and desktop devices and broadband access have been circumvented by the smart phone. In lower income groups, smart phones provide regular access to communication and information with fewer cost and access constraints than traditional broadband services.

Research on population level data of ownership and access to mobile technology show tremendous uptake of smart phone technologies across the country for all Americans, as 91 percent of the adult U.S. population now own a cellular phone with over half having a smart phone as of 2013.49 For example, although African-Americans trail Caucasians by 12 percent when considering broadband access to the internet, both groups now show parallel rates of mobile platform ownership.4954 Ownership of cellphones in Latinos has also increased now to roughly 86 percent.53 Pew research reports also indicate Latinos are more likely than Caucasians to use their mobile devices for accessing the internet.53 New data demonstrating the increased use of smart phones for health specific information underscores the potential for mHealth to capitalize on the narrowing digital divide to reduce health inequities.49,52 Roughly 62 percent of all smartphone owners have used their phone to look up information concerning health conditions.52 Roughly 35 percent of African-Americans and 38 percent of Latinos regularly use mobile platforms to access health related information, compared to 27 percent of Caucasians.50 Minority groups are not only more likely to own mobile phones, and specifically smart phones, but they are also more likely than Caucasians to use their device for health specific information.

Reduced Time and Potential for Increased Retention and Long-Term Followup of Patients

It is estimated that digital health technologies are being used to recruit patients in only 11 percent of all industry clinical trials.55 In spite of the low uptake of use for patient recruitment, several successes have been reported. One study reported being able to recruit enough patients for their entire trial in less than a month, a task that would have taken years to complete through traditional channels.56 While the authors admitted that this particular situation was a “perfect-storm” of circumstances, the usefulness of digital technologies for participant recruitment added to the success.56 Many of the stakeholders facilitating clinical trials are now attempting to use digital health technologies for patient recruitment whether they are taking on the task themselves or partnering with a third party.25,42,57

While digital health technologies have proven useful for patient recruitment, it is not considered a worthwhile tool to screen for eligibility.58 Determining eligibility requires the need to validate data with confidential patient information that is often not readily accessible. Moreover, many digital health technology users are concerned about privacy in public forums and will refrain from discussing the type of medical information that would be needed to determine eligibility.59

Current Limitations and Challenges To Using Digital Health in Patient Registries

Although there are many advantages to digital health approaches, there are also current limitations and challenges to its implementation in registries.

Including Patient Insights on Digital Health Approaches in the Registry Design Process

To effectively implement a digital health approach or device, it is imperative to understand the patients’ perspective on their preferred device or solution. Therefore, engaging with people in the targeted population directly is important to gather data and develop insights that would influence the registry design process.60 This movement towards capturing patient insights in the registry development process is beginning to gain traction, but is not widely used when designing registries that intend to integrate digital health technologies.

The process, costs and timeline in gaining patient feedback, confirming the key research insights and revising the registries’ digital health implementation strategy are additive to current design processes that many researchers employ today. Although they add more work, this patient insight driven approach is critical to enabling the successful integration of digital health technologies into registries (see Figure 3-2). These approaches may also be effective in enhancing recruitment and engagement in non-digital strategies.

A figure of a light bulb with a four-teir process on how to collect patient insights to impact registry design. The first step and top tier is to confirm the patient profile and develop research questions tailored to the approach. Second, recruit targeted patiends through communities, practice networks, or online outreach. Next, collect de-identified data from patients in a rapid period of time in the targeted regions. Lastly, analyse actionable insights of patient needs, believes, and behaviors.

Figure 3-2

Process for collecting patient insights to impact registry design.

In an effort to incorporate digital health approaches, it may be important to evaluate the following questions in the early phases of registry design:

  • How does the user view the problem? What do they see as the problem?
  • Is the user willing to try a digital health approach? What are their interests and comfort with technology?
  • Is the planned digital health approach intuitive to use or require additional education?
  • What aspects of the digital health approach were not understood or caused concerns with the user?
  • Does the digital health approach provide additional value and/or features for the user beyond collecting data?
  • What would motivate and encourage the user to continue engaging with the digital health approach over the course of the study?
  • What aspects of the digital health approach would make the user not want to engage?

Using an iterative design that incorporates user insights and experts in developing user-centered designs as an institutional part of the process before integrating digital health approaches may continue to influence better registry designs and improved data collection.

Communicating to Patients Through Digital Technologies

There are many concerns when it comes to communicating with patients via digital technologies. Patient communication risks may be mitigated by providing non-specific information to patients, rather than recommendations that may mention a specific product/treatment/drug. Engaging patients on social media can provide a public service and comfort to patients and their caregivers.61 However, physicians who use social media to communicate with patients on registries should be aware of potential privacy and confidentiality violations.3

While it is worth noting that the number of these violations committed by physicians is relatively low, Grajaless et al. suggest that physicians abide by four guidelines for risk mitigation during social media interactions: (1) maintain professionalism at all times; (2) be authentic, have fun, and do not be afraid; (3) ask for help; and (4) focus, grab attention, engage, and take action.3 These guidelines provide a basic framework for best practices that can protect physicians, as well as researchers encouraging their medical staff to use social media platforms for patient engagement. Without oversight, there is the risk that patients (as a result of misinformation) could self-diagnose, and/or use a drug or treatment inappropriately.3 By using best practices and risk mitigation strategies for digital health approaches, potential issues with communication to patients on social media can be mitigated.

Longitudinal Nature of Registries: Challenges Due to the Speed of New Technology

As many registries are developed for long-term, multiyear, longitudinal engagement with patients, a challenge to launching these registries with digital health approaches is that the technologies will continue to rapidly update and enhance over time. This challenge requires research teams to add ongoing responsibilities to the project’s scope including, but not limited to—

  • Planning or “future-proofing” proactively to pre-determine what enhancements to a specific digital health approach would require a registry wide upgrade (i.e., if a specific wearable releases a new version with a new data collection endpoint vital to a study endpoint, the actions the study team would need to implement to move forward)
  • Researching and maintaining deep insight on digital health product updates (i.e., manufacture upgrades, automated programming interface (API) revisions, data element additions, new device
  • Educating and training registry stakeholders continuously for new features, improvements or upgrades (i.e., downloading a new version of a mHealth app, etc.)
  • Planning for replacing digital health devices (i.e., wearable bands that may wear down, or be broken or lost during multi-year, hard usage, etc.), and
  • Managing upgraded bring your own device (BYOD) solutions (i.e., the patient upgrades their personal wearable or smartphone)

Changing Digital Health Approaches From an “Additive” Solution to a “Primary” Solution

Many of the digital health innovations today are simply being added to registries. This can increase the complexity and cost of the program, which is the opposite effect of what these solutions were intended to achieve and ultimately makes the uptake of these solutions more challenging. Using these approaches as primary solutions, integrated within broader registry operations, is a challenge. This challenge is overcome by designing registries that employ digital health approaches to replace standard processes (i.e., the way patients are recruited, data are collected, or support is provided to enhance long-term engagement).

To move these solutions from simply additive to “primary” components of registries, it will be important to understand the answers to these questions in the early phases of registry design:

  • If we use this digital health approach, is there another method of data collection that we can remove from the registry design?
  • What data from the digital health approach is considered “validated” and to what extent can it be used for use in research and/or regulatory-based submissions?
  • Can we revise our standard registry schedule of events to decrease interventions or data collection time points with the use of this approach?
  • What data from the digital health approach can be used in supporting a primary or secondary endpoint in the registry?
  • What positive effects (i.e., patient engagement, long-term retention, reduced readmissions, etc.) can this approach provide in addition to data collection?
  • How will the data or impact of the digital health approach be shared with the patient’s physicians, regulatory agencies, or payers?
  • How will this approach interface or connect (i.e., via API) with other systems and applications utilized in the registry?
  • Where best can the training on use of this approach be deployed best to patients, physicians and researchers involved?
  • How do we measure the return on investment and what are the key performance indicators (KPIs) for this approach?
  • Where globally can this specific solution be conducted from a regulatory/ethics committee, data privacy, cultural relevance and digital enablement perspective?

Integration of a Digital Health Approach Within the Study Operations

Establishing a digital health approach early in the registry development process is critical to building an integration strategy that takes into account the real-world use of the approach. Integration can be documented and achieved with inclusion in the following project assets:

  • Project plan—the digital health approach details and pro-active strategy should be included within all relevant projects plans including, but not limited to the project timeline, communication plan, stakeholders training plan, recruitment/retention plan, customer support plan (covering both the patient and the researchers), resource plan, and statistical analysis plan.
  • Architectural plan—this plan should detail how data from the digital health approach will be integrated and interface with other study systems, securely transferred/maintained and validated for specific use in the registry.

A worksheet is provided at the end of the chapter to assist with determining which digital health approach should be used in a registry. It is intended to serve as a guide in the decision making process and is not comprehensive of all study issues that should be evaluated prior to the use of a digital health approach or device within a registry.

A Look at the Future of Digital Health

The convergence of emerging, digital health technologies promises a paradigm shift in the world of health care. The technologies are the enablers, as the intention in many nations is to focus much more on the patient. Jeremy Hunt, Health Secretary in the United Kingdom gave a speech in July 2015, entitled “Making Healthcare More Human-Centred and Not System-Centred” in which he states that “the transition to patient power will dominate health care for the next 25 years.”62

Our bodies generate data 24 hours per day, 7 days per week, and generally those data are only captured by registries in routine health care visits. Although data captured at health care visits and entered into electronic medical records are generally some of the best health care information available today, with the expert provider as the intermediary creating the data entry, there remains a huge gap in understanding what happens to the patient outside of these visits. Patient-generated health information can help fill in these gaps. Some may find it hard to imagine a future where useful data can be captured from outside traditional health care settings. However, many leaders have bold visions of the future, and are making changes to their organizations today, to prepare for that future.

Kathleen Frisbee, co-director of Connected Health at the Veterans Health Administration Office of Information and Analytics, gave a keynote in May 2014 and mentioned, patient-generated data “is going to be the thing that transforms health care. We predict that patient-generated data will be much larger in volume than electronic health care records.”63 Patrick Vallance, Head of GlaxoSmithKline Research and Development, delivered a talk in March 2014 entitled, “Horizon Scanning: Looking Ahead to 2025” and when mentioning sensors in the context of drug safety, cites a future which involves, “Instant feedback in terms of surveillance of medicines post-launch, with various sensing devices/monitors, as well as listening to patients in real-time, much more than we are able to do at the moment.”64

The sources of health data in the future may be different from the sources that exist today. One new player is IBM, who has set up a brand new division, called Watson Health.65 They are positioning themselves not just as a leading health data broker, but seek to provide solutions to researchers that could offer new insights by integrating clinical data with external data such as Twitter.65 If the data that are being generated by patients outside of the health care system are controlled by patients, then it may be that the organizations that patients trust the most are the ones that have access to the most amount of health data. Startups such as DataCoup, have emerged to allow consumers to be paid for sharing their personal data.66 A Self-Generated Health Information Exchange developed at the University of North Carolina at Chapel Hill in partnership with RTI International and Promantus, Inc. further demonstrates this trend towards providing individual control of personal digital health information.67 The continued success of observational research hinges on the ability to access comprehensive, representative and accurate data on populations.

Since data are increasingly viewed as an asset, a potential threat is the availability of individual level data, particularly if some individuals refuse to share their data or will only share it at the right price. In a report from the Institute for the Future examining the outlook for the Information Economy, researchers predict that “Institutions and individuals will engage in a dynamic information economy by buying and selling, donating or trading personal information in exchange for monetary or social gain.” The report envisions a world where a person’s wearable device “could routinely prompt its users to consider sharing their health data with a nonprofit medical research group, or sell it to a pharmaceutical company.”68

As sensor technology improves, we are likely to see sensors embedded in devices above and beyond the activity trackers and smart watches that appear to be cutting edge at present. For example, sensors in flexible bioelectronics, such as smart bandages or smart strips that could be easily affixed and removed from the human body. The NIH has initiated a challenge for a wearable alcohol biosensor that would be able to monitor blood alcohol levels in real time.

To better understand medication non adherence, there are new developments such as pill boxes with sensors that can track when a patient opened the box as well as containers and syringes that illuminate brighter and brighter as a reminder system. Going a step further, Proteus Digital Health has developed an ingestible sensor that can measure medication adherence patterns.69 In 2015, they partnered with Otsuka Pharmaceuticals and submitted a sensor embedded version of the antidepressant, Abilify® for FDA approval.70

Some technology companies anticipate that this is just the beginning. In Japan, Softbank’s Chief Executive Officer Masayoshi Son envisions, “Each individual, on average, will have more than 1,000 devices that are connected to the internet by 2040.”71 His vision includes the chair in our living room being a health care device, capturing and transmitting data about our health. His company has partnered with Aldebaran, and in 2015, launched the world’s first companion robot, Pepper, that reads and responds to human emotions.72 Given Japan’s aging population, one obvious use case for Pepper is to use the robot’s sensors to collect data on elderly patients, perhaps even monitoring and helping dementia patients in their own home.73 In 2016, more of these consumer friendly robots are likely to enter the market, with Jibo from the United States and Buddy from France.74,75

Sensors could become ubiquitous in measuring health data, perhaps even monitoring our health during our daily commutes. Even though Ford Motor Company has halted research into installing heart rate sensors in car seats, could data collected from car journeys be of medical value one day? In Beijing, China a pilot project added sensors to straps in buses that are held on to by commuters during rush hour.76 What about getting a physical as you shop in the supermarket? Project H, a research project in the Netherlands is evaluating a shopping cart that can capture data such as heart rate, one-lead electrocardiogram (ECG), and blood oxygen saturation level (SpO2) from the person pushing the cart around the supermarket.77 In addition to capturing health data more often, it may even be an opportunity to collect data from those who rarely interact with the health care system. Also, with regard to food, what if people wanting to track their calorie intake could do so simply by taking a picture of their meal using their smartphone? One of Google’s research projects, Im2Calories, is working towards making that a reality.78

One of the major drivers behind collecting all of this data is the need to improve health outcomes, to keep patients out of the hospital through remote monitoring, and to reduce costs. Some insurers and employers are also interested in these new sources of data to help them manage risk. Perhaps an airline wants to track sleep data of their pilots to reduce the risk of pilot error in a flight due to fatigue? Insurers who want to calculate risk in real-time may make access to patient generated health data a prerequisite for obtaining health/life insurance, or at least to lower the cost of this insurance.79

Researchers need to be mindful that as more data have been collected, concerns about privacy have also grown. One of the recent examples of when privacy concerns impacted progress, despite good intentions from researchers, is the “” project, intended to improve the health of the UK National Health Service.80 A public backlash in 2013 led to the project being delayed, and has undermined trust.81 In the future, as more patient-generated health data are captured, shared and analyzed, there may be further backlash from consumers. If some of this data collection is made mandatory by employers and insurers, there is a chance that some consumers uncomfortable with 24/7 monitoring could try to falsify data.

The registry of 2025 could look dramatically different from the registry of today. Some researchers are excited about the concept of digital phenotyping, and Dr. Sachin Jain has stated, “We think many diseases will actually have a phenotype that presents through patient use of technology.”82 No discussion about the future is complete without mentioning advancements in genomic medicine. The 100,000 Genomes project in England is the largest national sequencing project of its kind in the world.83 It appears that the Australian government is now considering a similar project.84 South Korea has just launched their largest genome sequencing project with 10,000 patients.85 From a researcher’s perspective, the opportunity to link genome sequence data with electronic patient records and the social and behavioral information from digital health technologies could lead to many new discoveries about the causes of disease. While computable phenotypes using clinical, social, and behavioral data from electronic health records and PROs are possible today, it is the emerging sources of unstructured data outside of health care settings, such as Twitter, along with other patient generated data that are making researchers curious about how we might better characterize diseases in this modern era.86


Digital health is a collection of emerging disciplines and technologies that appear to be evolving and converging at an increasingly rapid pace. Indeed, as digital health technologies continue to demonstrate ways to measure health activities and complement or supplement the traditional approaches to collecting health information, opportunities for registries abound. Opportunities provided by utilizing digital health technologies include improved recruitment and retention, reduced burden on researchers, enhanced uptake of technology solutions by the health care system, and collection of information that is not routinely captured.

However, there are still considerable risks with privacy, quality, and control of data analysis and communication. As new digital technologies are developed, researchers must acquire new skillsets to navigate their use appropriately. New methods will also be needed to appropriately integrate various sources of information and transform unstructured fields to formats that can be used for evaluating the safety and effectiveness of treatments used in the real world.

Investments are needed now to prepare for the decade of new digital technologies and their use within registries. Digital health technology will not transform clinical practice and health care research without being designed with the complex needs of users in mind, as well as the careful assessment and appropriate level of training on the use of these tools by health care professionals. Issues related to interoperability of systems, user engagement, measurement validation, regulatory use in studies, meaningful clinical interpretations, privacy and security, among others, will need to be carefully addressed as these new technologies are utilized in clinical medicine and research.87

Digital Health Approach Worksheet

Download MS Word (73K)

References for Chapter 3

Bull S, Ezeanochie N. From Foucault to Freire Through Facebook Toward an Integrated Theory of mHealth. Health Educ Behav. September 17, 2015:1–13. [PubMed: 26384499]
Grajaless F III SS, Ho K, Novak-Lauscher H, Eysenbach G. Social Media: A Review and Tutorial of Applications in Medicine and Health Care. J Med Internet Res [Internet]. July 22, 2015; 16(2):[e13 p.]. http://www​ Accessed January 10, 2017.
Topol EJ Steinhubl S, Torkamani A. Digital Medical Tools and Sensors. JAMA. 2015;313(4):353–4. [PMC free article: PMC4751024] [PubMed: 25626031]
Kumar S, Nilsen W. State-of-the-Science in Mobile Health for Diagnostic, Treatment, Public Health, and Health Research. American Association for the Advancement of Science (AAAS) mHealth and Law Workshops2014.
Kumar S, Nilsen W, Abernethy A, et al. Mobile Health Technology Evaluation: The mHealth Evidence Workshop. Amer J Prev Med. 2013;45(2):228–36. [PMC free article: PMC3803146] [PubMed: 23867031]
Riley WT, Serrano K, Nilsen W, Atienza AA. Mobile and Wireless Technologies in Health Behavior and the Potential for Intensively Adaptive Interventions. Current Opinion in Psychology. 2015;5:67–71. [PMC free article: PMC4465113] [PubMed: 26086033]
Skubic M, Alexander G, Popescu M, Rantz M, Keller J. A Smart Home Application to Eldercare: Current Status and Lessons Learned. Technology and Health Care. 2009;17(3):183. [PubMed: 19641257]
Cook DJ, Schmitter-Edgecombe M, Dawadi P. Analyzing Activity Behavior and Movement in a Naturalistic Environment Using Smart Home Techniques. Biomedical and Health Informatics, IEEE Journal of. 2015;19(6):1882–92. [PMC free article: PMC4667369] [PubMed: 26259225]
Kvedar JC, Leider H. The Internet of Healthy Things. Boston MA: Partners Connected Health; 2015.
Topol E. The Patient Will See You Now: The Future of Medicine is in Your Hands: Basic Books; 2015.
U.S. Mobile Health Audience Jumps to 95 Million Adults – New Research Highlights Mobile Opportunities for Pharma Marketers. http:​//manhattanresearch​.com/News-and-Events​/Press-Releases/mobile-health-95-million. Accessed January 10, 2017.
Bigelow B. Qualcomm-Novartis Deal Portend Wave of Clinical Trial Innovation. August 6, 2015. http://www​​/san-diego/2015/01​/15/qualcomm-novartis-deal-portends-wave-of-clinical-trial-innovation/. Accessed January 10, 2017.
Allison M. Reinventing Clinical Trials. Nature Biotechnology [Internet]. August 7, 2015. http://www​​/nbt/journal/v30/n1/pdf/nbt.2083.pdf. Accessed January 10, 2017.
PCORnet: Achievements and Plans on the Road to Transforming Health Research. http://pcornet​.org/2015​/12/pcornet-achievements-and-plans-on-the-road-to-transforming-health-research-2/. Accessed January 10, 2017.
Eastwood B. Can BYOD and Smartphones Move Clinical Trials Forward? CIO [Internet]. July 23, 2015. http://www​​/2384288/vertical-industries​/can-byod-and-smartphones-move-clinical-trials-forward.html. Accessed January 10, 2017.
Sarasohn-Khan M. Why Should Providers Encourage Patient Adoption of Activity Trackers, Wearable Technology and Mobile Health Apps. HIMSS [Internet]. November 11, 2015. http://www​​/ResourceLibrary/ResourceDetail​.aspx?ItemNumber=22391. Accessed January 10, 2017.
Wang JB Cadmus-Bertram L, Natarajan L, White MM, et al. Wearable Sensor/Device (Fitbit One) and SMS Text-Messaging Prompts to Increase Physical Activity in Overweight and Obese Adults: A Randomized Controlled Trial. Telemedicine and e-Health [Internet]. 21(10):[782–92 pp.]. http://online​.liebertpub​​.1089/tmj.2014.0176. Accessed January 10, 2017. [PMC free article: PMC4842945] [PubMed: 26431257]
Zhao F Li M, Tsien JZ. Technology Platforms for Remote Monitoring of Vital Signs in the New Era of Telemedicine. Expert Rev Med Devices. July 2015;12(4):411–29. [PubMed: 26037691]
De Luca R Bramanti A, De Cola MC, Trifiletti A, et al. Tele-health-care in the Elderly Living in Nursing Home: the First Sicilian Multimodal Approach. Aging Clin Exp Res. September 29, 2015;12(42):1–9. [PubMed: 26420423]
EyeforPharma. Patient Focused Medicines Development: a Cross-Industry Initiative Case Study. EyeforPharma. Epub October 1, 2014.
Snipes K. Using Social Media and Digital Media to Increase Patient Recruitment and Retention. Clinical Leader. June 15, 2015.
Jackson W, Park B, Toscani M, Hermes-DeSantis E. Analysis of Social Media Interactions Between Pharmaceutical Companies and Consumers. Sage Journals. December 15, 2014.
Apple Introduces ResearchKit, Giving Medical Researchers the Tools to Revolutionize Medical Studies. https://www​​/pr/library/2015/03​/09Apple-Introduces-ResearchKit-Giving-Medical-Researchers-the-Tools-to-Revolutionize-Medical-Studies.html. Accessed January 10, 2017.
5 Reasons Why Clinical Trials Need Mobie Technology Now. http://www​.mhealthnews​.com/blog/5-reasons-why-clinical-trials-need-mobile-technology-now. Accessed January 10, 2017.
CLINPAL. eClinicalHealth.
Dreyer NA Blackburn S, Mt-Isa S, Richardson JL, Thomas S, Laursen M, Zetstra-van der Woude AP, Jamry-Dziurla A, Hliva V, Bourke A, de Jong-van den Berg, LTW. Direct-to-Patient Research: Piloting a New Approach to Understanding Drug Safety during Pregnancy. JMIR Public Health and Surveillance. 2015;1(2). [PMC free article: PMC4869223] [PubMed: 27227140]
Bringing iPhone-style Medical Research to the Android World. http://bits​.blogs.nytimes​.com/2015/11/12​/bringing-iphone-style-medical-research-to-the-android-world/?_r=0. Accessed January 10, 2017.
UCSF Awarded $9.75 Million to Create Platform Accelerating Mobile Health Research: NIH Award a Response to Health eHeart Study Success. https://www​​/news/2015/10/131911​/ucsf-awarded-975-million-create-platform-accelerating-mobile-health-research. Accessed January 10, 2017.
Baron S, Goutard F, Nguon K, Tarantola A. Use of a Text Message-Based Pharmacovigilance Tool in Cambodia: Pilot Study. J Med Internet Res. 2013;15(4). [PMC free article: PMC3636325] [PubMed: 23591700]
Leeb A, Regan A, Peters I, et al. Using Automated Text Messages to Monitor Adverse Events Following Immunization in General Practice. MJA. 2014;200(7):416–8. [PubMed: 24794676]
Smolinski MS, Crawley A, Baltrusaitis K, et al. Flu Near You: Crowdsourced Symptom Reporting Spanning 2 Influenza Seasons. Am J Public Health. 2015;105(10):2124–30. [PMC free article: PMC4566540] [PubMed: 26270299]
Lund S, Nielsen B, Hemed M, Boas IM, et al. Mobile Phones Improve Prenatal Care Attendance in Zanzibar: A Cluster Randomized Controlled Trial. BMC Pregnancy Childbirth. 2014;14. [PMC free article: PMC3898378] [PubMed: 24438517]
Better Decision Making through Better Patient Insights. http://social​.eyeforpharma​.com/clinical/better-decision-making-through-better-patient-insights. Accessed January 10, 2017.
How do iComply? A Look at Regulatory Topics Surrounding the Use of mHealth Technology in Clinical Trials. http://blog​​/how-do-icomply-a-look-at-regulatory-topics-surrounding-the-use-of-mhealth-technology-in-clinical/. Accessed January 10, 2017.
Global Forum Special Section: Patient Perspective on Social Media. http://www​.diaglobal​.org/en/resources/news#article​=7b3df92f-82b7-443a-a759-ad46b203f0b3. Accessed January 10, 2017.
O’Connor A, Jackson L, Goldsmith L, Skirton H. Can I get a retweet please? Health research recruitment and the Twittersphere. JAN [Internet]. July 28, 2015. http:​//onlinelibrary​ Accessed January 10, 2017. [PubMed: 23909740]
Rothman M, Gnanaskathy A, Wicks P, Papadopoulos E. Can We Use Social Media to Support Content Validity of Patient-Reported Outcome Instruments in Medical Product Development? Value in Health. 2015;18:1–4. [PubMed: 25595228]
Sarker A, Ginn R, Nikfarjam A, O’Conner K, Smith K, et al. Utilizing Social Media Data for Pharmacovigilance: A Review. Journal of Biomedical Informatics. April 2015;54:202–12. [PMC free article: PMC4408239] [PubMed: 25720841]
Global Forum Special Section: Social Media and Medicine. http://www​.diaglobal​.org/en/resources/news#article​=202b2098-7868-4dfe-8a54-9c2a67e65e7d. Accessed January 10, 2017.
Social Media for Pharma: An Expert’s View. http://www​​/connect/social-media-for-pharma-an-experts-view. Accessed January 10, 2017.
Smart Patients. https://www​ Accessed January 10, 2017.
Tufts Center for the Study of Drug Development. Industry Usage of Social and Digital Media Communities in Clinical Research. Tufts Center for the Study of Drug Development; Contract No.: White Paper.
Hashtag Folksonomy for Cancer Communities on Twitter. https://connection​.asco​.org/commentary/hashtag-folksonomy-cancer-communities-twitter. Accessed January 10, 2017.
Chretien K, Kind T. Social Media as a Tool in Medicine. Circulation.127:1413–21. Epub April 2, 2013. [PubMed: 23547180]
PatientsLikeMe, About Us. https://www​ Accessed January 10, 2017.
Smartphone Ownership 2013. http://www​.pewinternet​.org/2013/06/05/smartphone-ownership-2013/. Accessed January 10, 2017.
African Americans and Technology Use: A Demographic Portrait. http://www​.pewinternet​.org/2014/01/06/african-americans-and-technology-use/. Accessed January 10, 2017.
Smart Phone Use in 2015. http://www​.pewinternet​.org/2015/04/01/us-smartphone-use-in-2015/. Accessed January 10, 2017.
Closing the Digital Divide: Latinos and Technology Adoption. http://www​.pewhispanic​.org/2013/03/07/closing-the-digital-divide-latinos-and-technology-adoption. Accessed January 10, 2017.
Dailey D, Bryne A, Alison P, Karaganis J, Chung J. Broadband Adoption in Low-Income Communities. NY, USA: 2010.
Tufts Center for the Study of Drug Development. Drug Developers Circumspect About Social Media in Clinical Research. Tufts Center for the Study of Drug Development; Contract No.: Impact Report.
Patient Recruitment via Social Media: Lessons Leaned. http://www​.pharmexec​.com/patient-recruitment-social-media-lessons-learned. Accessed January 10, 2017.
Social Media & Cancer Drugs: Conversation, Not Promotion. http://www​​/wisconsin/2014/01​/23/social-media-cancer-drugs-conversation-not-promotion/. Accessed January 10, 2017.
Thompson M. Social Media in Clinical Trials. ASCO Educational Book. 2014:e101–5. [PubMed: 24857086]
Patient-Centered Innovation in Clinical Trials. http://www​.slideshare​.net/ulineumann/whitepaperinnovationclinicaltrials. Accessed January 10, 2017.
Reites J, Downing J, Calderon A. Listening to the Patient: Leveraging Direct-to-Patient Data Collection to Shape Clinical Study Design. J Healthcare Info Manag. 2014 Jul 16, 2014;28(2):52–4. Accessed January 10, 2017.
Using Social Media in Oncology for Education and Patient Engagement. http://www​.cancernetwork​.com/practice-policy​/using-social-media-oncology-education-and-patient-engagement. Accessed January 10, 2017.
Hunt J, editor Making Healthcare More Human-Centred and Not System-Centred 2015; King’s Fund, London: www​ Accessed January 10, 2017.
Patient Data the Next IT Frontier at VA. https://fcw​.com/articles​/2014/05/05/va-apps.aspx?s=FCWmore. Accessed January 10, 2017.
Vallance P, editor Horizon Scanning: Looking Ahead to 2025. FORUM Annual Lecture; 2014; London: The Academy of Medical Sciences.
IBM Positions Itself as Large Broker of Health Data. http://www​​/ibm-positions-itself-as-large-broker-of-health-data-1428961227. Accessed January 10, 2017.
Unlock the Value of Your Personal Data. http://www​ Accessed January 10, 2017.
Caruso TP, Li L, Presnell JM, Capra RG, Kaul A, Marchionini G, Dimitropoulus LL. Self-Generated Health Information Exchange (SGHIx): Protecting and Providing Benefits for Aggregating Personal Health Data. International Workshop on Big Data Discovery and Data Mining IEEE Publication; 2014.
Institute for the Future. The Informing Generation: Tranforming the Future, Today. Institute for the Future; 2015.
Proteus Digital Health Announces FDA Clearance of Ingestible Sensor. http://www​​/press-releases/proteus-digital-health-announces-fda-clearance-of-ingestible-sensor-2/. Accessed January 10, 2017.
Proteus, Otsuka Submit First Commercial Drug with Built-In Sensor to FDA. http:​//mobihealthnews​.com/46680/proteus-otsuka-submit-first-commercial-drug-with-built-in-sensor-to-fda/. Accessed January 10, 2017.
SoftBank CEO: The Average Person Will Have 1,000 Internet-Connected Devices by 2040. https://www​.techinasia​.com/softbank-son-iot-1000-devices-2040/. Accessed January 10, 2017.
Corp S. SoftBank to Launch Sales of ‘Pepper’ — the World’s First Personal Robot That Reads Emotions — on June 20. SoftBank Group. 2015.
Jibo: Meet Jibo, The World’s First Social Robot for the Home. https://www​ Accessed January 10, 2017.
Buddy: The Companion Robot Accessible to Everyone. http://www​ Accessed January 10, 2017.
A Physical During Shopping! https://www​.linkedin​.com/pulse/physical-during-shopping-lucien-engelen. Accessed January 10, 2017.
Google’s A.I. is Training Itself to Count Calories in Food Photos. http://www​​/google-using-ai-count-calories-food-photos. Accessed January 10, 2017.
NO Wearable Device = NO Life Insurance. https://openminds​.swissre​.com/stories/688/. Accessed January 10, 2017.
The Programme - Collecting Information for the Health of the Nation. https://www​.england.nhs​.uk/ourwork/tsd/care-data/. Accessed January 10, 2017.
81. and Access to UK Health Records: Patient Privacy and Public Trust. http://techscience​.org/a/2015081103/. Accessed January 10, 2017.
Harvard Doctors Argue the Digital Phenotype Will Change Healthcare. http:​//mobihealthnews​.com/43327/harvard-doctors-argue-the-digital-phenotype-will-change-healthcare/. Accessed January 10, 2017.
The 100,000 Genomes Project. http://www​.genomicsengland​ Accessed January 10, 2017.
Launch of the “Genome Korea in Ulsan”. http://bme​​/launch-of-the-genome-korea-in-ulsan​/?lang=en. Accessed January 10, 2017.
McIver DJ, Hawkins J, Chunara R, Chatterjee AK, Bhandari A, Fitzgerald TP, Jain SH, Brownstein JS. Characterizing Sleep Issues Using Twitter. J Med Internet Res. 2015;17(6). [PMC free article: PMC4526927] [PubMed: 26054530]
Insititue of Medicine. Digital Infrastructure for the Learning Health System: The Foundation for Continuous Improvement in Health and Health Care: Workshop Series Summary. Washington (DC): National Academies Press: 2011. [PubMed: 22379651]


  • PubReader
  • Print View
  • Cite this Page
  • PDF version of this title (1.3M)

Related information

  • PMC
    PubMed Central citations
  • PubMed
    Links to PubMed

Recent Activity

Your browsing activity is empty.

Activity recording is turned off.

Turn recording back on

See more...