While creating a deliberate CHS environment comes at a cost, the transfer of technology across the disciplines is likely to occur systematically over time, and with it, significant new opportunities and benefits will be created. Indeed, we argue that, as in all synergistic activities, the whole is greater than the sum of its parts, allowing CHS to do more than could be done in isolation by the constituent disciplines. We outline a few CHS opportunities for team learning and synergism as health/social science and computer science merge to address complex problems.
Social Media and Public Health Surveillance
Recognizing the usefulness of online data for public health-related purposes, researchers have become more engaged in using computational modeling to better understand health and health behavior. Indeed, computer science expertise is essential for mining large amounts of online information . Pentland et al.  have noted that users make daily digital transactions through their use of technology. These transactions “leave digital breadcrumbs – tiny records of our daily experiences” that when mined and analyzed can provide insight into health behavior and health outcomes.
As part of the new twenty-first-century Internet or Web 2.0, social media applications have helped to engage, connect, and mobilize individuals as they freely interact in online communities. Associated applications such as Facebook, Twitter, and YouTube provide the mechanism for organizing individuals into online communities where content can be shared. While some authors have expressed concerns about the use of online and social media data in public health , an increasing number of researchers have been quick to point out the novel opportunities offered by these novel data sources to complement, and in some cases, even partially replace, existing practices in health administration, communication, and surveillance, and a number of recent studies have demonstrated the value of online information in understanding public health problems and their determinants (e.g., see [13, 15, 21, 22, 31, 33, 35, 39, 43, 44, 47]).
Specifically, social media applications such as Facebook, Twitter, and YouTube have helped engage, connect, and mobilize individuals as they freely interact and share content in online communities. Recognizing the wealth of information generated by users through their participation with social media, researchers have begun mining this information to gain a better understanding of health outcomes and even health behavior. Several studies have mined YouTube content for information relative to anti-smoking video communities , immunizations , influenza pandemic , quitting smoking , cardiopulmonary resuscitation , kidney stones , and prostate cancer . Similarly, a number of studies have mined Twitter to understand problem drinking , detect flu epidemics [1, 2, 11], classify dental pain messages [8, 27], predict depression , track suicide [6, 32], gain insight into prescription drug abuse [10, 25, 26], and predict heart disease mortality .
Improved Intervention via Data Fusion
Relevant health data is available from a number of different sources, including traditional ones, such as questionnaires (e.g., NHANES, BFSSR), electronic health records (EHRs), and results of randomized control trials (RCTs), as well as less conventional ones, such as social media interactions, wearable devices, smart homes, and Internet of Things (IoT). While health science has generally focused on traditional data sources, much can be gained by fusing data across many sources to improve intervention and outcomes.
As pointed out by Hesse et al. , “research must become rapid if it is to be responsive and relevant to those making treatment and policy decisions now, not 7 to 14 years from now; and more rapid research reduces the risk of producing findings on techniques and procedures that could be dated or obsolete by the time the findings are made available.” Furthermore, they add, “RCTs may be an optimal method for testing the efficacy of a new intervention, but questions such as the effectiveness of the intervention among real patients in real settings, the safety and side effects of the intervention, and the determination of for whom the intervention may be most effective are questions that are better addressed by leveraging health system EHRs and other large data sources.”
Social Network Analysis and Agent-Based Modeling
The synergy between health science and computational methods clearly goes both directions. Working in the context of health-related issues raises interesting technical challenges that, in turn, may lead to valuable contributions in terms of algorithms and computational methods.
For example, most research about health and social media has focused on the content of social media. Yet, perhaps the true value of social media is the underlying structure of the social networks they create. It is theorized in interpersonal health behavior models that individual perceptions and behavior are significantly influenced by that individual’s social network (e.g., family, friends, community) [4, 38]. To leverage a user’s community, or social circle, one needs a method to extract it from social media platforms. One such platform, as mentioned above, is Twitter. There are significant challenges with Twitter in this context: (1) the underlying Twitter network is too large and too dynamic to be known or processed; (2) relations on Twitter, unlike Facebook for example, are directed, i.e., one user may follow another with no enforced nor expected reciprocity; and (3) an individual may belong to several overlapping social circles (e.g., work department, sport club, neighborhood reading group). While there has been work in community mining within computer science (e.g., see  for an excellent survey), very few, if any, have addressed all of these issues. An algorithm was recently designed and implemented to fill that gap . Of significance here is that, had it not been for the fact that work was being done on health-related issues, where relationships are important, and within the context of Twitter, where relations are directed, the authors would probably not have thought of designing such an algorithm.
A similar synergy exists with agent-based modeling, where health-related issues may lead to the design of custom models (e.g., see ), and agent-based models may be leveraged to address complex, systems level issues (e.g., see [5, 50]), or applications where interactions or co-locations play a role, such as drinking behavior (e.g., see ) and disease spread (e.g., see [18, 46]). A recent article provides a brief overview of agent-based modeling, highlights a number of examples of their applications in the context of chronic diseases, and offers some thoughts on future research directions .
One of the other advantages of CHS is that it makes it possible to leverage the strengths of both humans and computers. While some may see this as a threat, we consider it an opportunity, one in which computational techniques complement and enhance human expertise, and where humans are not replaced, but enabled to move up the value chain.
Zamith and Lewis  argue in the context of coding that “an algorithmic approach departs from traditional content analysis in that it can generally be scaled up with ease… Researchers may thus use a larger sample, which is generally more likely to represent the overall population.” On the other hand, they recognize that “while algorithmic approaches yield satisfactory results in surface-level analyses or analyses that focus on structural features, their performance is significantly worse when assessing more complex features of texts.” Thus, in the spirit of CHS, they advocate a hybrid approach, where unique human and computer strengths are leveraged: “The development of computational tools and frameworks that can facilitate the blending of human judgment and algorithmic efficiency strikes us as an area of research that deserves additional attention.” Again, given their interest in coding, they say that “a hybrid approach must be further developed, one that preserves the contextual sensitivity and validity that are central to traditional content analysis and combines it with the large-scale capacity and reliability of computational approaches… truly [blending] the best of both worlds, human and machine alike.”
Other CHS Opportunities
There are, of course, many other application areas and health issues where interdisciplinary CHS research offers unique opportunities. We list a few examples below. Again, these are not intended to be exhaustive, but serve as illustration, based on our knowledge of the current state of research.
The design of novel geo-location algorithms based on content and context to support the study of epidemics.
The design of topic detection and modeling algorithms when text content is sparse (as on Twitter).
The combination of expert-driven topic selection with unsupervised machine learning to analyze health issues.
The analysis of social networks and their impact on health behaviors.
The study of online machine learning techniques and low-energy devices for human activity monitoring (e.g., quantified self).
The integration of survey and data mining technology (e.g., MTurk and Twitter) to improve predictive models of risk behaviors.
In addition, increased use of mobile communication devices linked to the Internet and social media applications has led not only to a digital revolution but also to new health care innovations. mHealth represents a new form of health care delivery and treatment where patients are able to interact with their health care providers through mobile devices—providing additional “bread crumbs” for studying/mining health behaviors and health outcomes .