Data analysis in social context

In the previous blog post we talked about the social context of our decision-making processes. We used the example from the healthcare domain to show that decision making these days rarely occurs in isolation and that technical solutions aimed at supporting these processes need to become essentially social. In this post, we will take a step further and talk a bit about designing data analysis solutions to be effective and useful in social and business contexts. These contexts are dynamic and usually more complex that they might seem. They include multiple elements, roles, types of relationships and structures; can be designed and constructed, or grown organically; can exist continuously in background (everybody has multiple ones) or have a short lifespan tied to a specific purpose or situation. Such diverse characteristics can result in completely different functional requirements, what means for data analysis solutions that they need to be very flexible and adaptable.

Data analysis in social context is about sharing, but not only of data and results, but also of efforts, skills, experiences, and - probably the most important here – different points of view. There are some technical elements that are common in all such solutions, including efficient  data exchange that enables natural and smooth interactions, navigation through complex data spaces, and management of relationships (sometimes completely new types). We can also try to identify some higher-level principles that help with building effective and useful solutions for various social contexts:

• Focus is on users as the centers of social contexts. This starts with a personal user experience and need for understanding individual requirements and preferences. But it can quickly get even more difficult, if we have multiple users with incompatible or conflicting goals. There is a need for clarity (do these agents really operate according to my priorities?) and transparency (who can access data or control the process?). In many situations, analysis decision support may include defining contract-based goals and rules of data analysis efforts (e.g. solving a specific problem).
• Data analysis processes are distributed efforts. The scope of data analysis in social context expands from an individual, into groups, communities and eventually societies. This requires effective interactions between multiple participants, both human and agents, across shared data spaces. Here the requirements can be very different and a solution must support various scenarios covering cooperation, negotiations or competition. There can be also the challenges of integrating individual experiences (each with possibly different presentation) into consistent group communication system.
• Data analysis process is usually part of a bigger system. Problems and contexts are unique; types of tasks, best practices, patterns and challenges are more general. A data analysis process can benefit from similar external projects (e.g. for population big picture) and contribute to them (with anonymized data). There are opportunities for sharing competencies, efforts and solutions even externally, in open, research or commercial frameworks. However, integration scenarios require very clear consistent rules and transparency regarding privacy, security or ownership of information.
• Intelligent agents can be essential participants of data analysis. Interactions during analysis or decision making process can take place in networks of human and non-human actors. Intelligent agents can be interactive participants, sharing information with users or performing specific tasks per request. They can also operate in the background, monitoring actions, conversations or external events, and acting when it is needed or useful. In group scenarios, they may take special roles, like optimizing of efforts, balancing the structure, or mediating with odd or even number of agents.

Let’s take a quick look at that last point, as it seems to be the clearest illustration of relationships between technology and social contexts. We will reuse the example from the healthcare domain, introduced in our previous blog post, which shows relationships between a patient’s context (family and friends) and the physician’s context (professional medical network). Figure 1 presents that structure, with the addition of new connections involving intelligent agents, some interactive and others operating in the background. Interactive agents can provide direct assistance and support to patients, their friends and families, along with connections to the medical side, where different types of agents can help with coordination of efforts and collaboration in medical analysis. Background agents can enable various scenarios, like continuous remote monitoring (not only in the scope of physiological metrics), integration with population efforts (connecting physicians working on similar cases) or automatic documentation of decision processes.

Figure 1. An example of a social structure in healthcare combining humans and intelligent agents

Similar scenarios may seem distant, but they are already here, although usually in simpler configurations with a bot or a digital assistant as front-end to a realm of specific services. In the scope of data analysis, including a social context is a natural consequence of focusing on the user’s goals, needs and preferences. In our framework, this focus starts with personalized user experiences based on individual choices and activities. For groups scenarios, it is expanded to also include the user’s role, relationships and characteristics of a social or business context. At this point data analysis is no longer only about sharing, but also about communication and conversations embedded in a shared data space. Intelligent agents can fit in such spaces very naturally and become the key participants. An agent can interact with users, change their behaviors or even become an active driver of interactions between different users and agents. The result is a completely new social structure - technology is not only capable of adopting to a social context, but may shape it or, in some cases, construct it.

Human elements will long remain fundamental in solving real problems and there are great opportunities for solutions facilitating cooperation in complex scenarios. There are situations, where enabling efficient cooperation may actually be more important than selecting the right algorithms and analysis techniques. The data analysis solutions must however be designed for social and business contexts, with clear rules and transparency, always close to users and actively addressing challenges like possible incompatibilities in priorities between individuals or an individual and a group. Including social context in data analysis is becoming however unavoidable, due in part to the increasing popularity of conversation-based interactions. And with the application of intelligent agents, social context is added to all data analysis projects, even those conducted by a single user.

Social context of decision process

Our primary motivations for building data analysis solutions are to help with real problems and to make meaningful impacts. Solving a problem is all about decisions, sometimes a single big one, often a sequence of small steps leading to a preferable outcome. Data analysis software should help make better decisions, based on available data, in a timely manner and using natural experience. Some problems are isolated and solving them requires an individual exploration of vast data spaces – by a single user and with a single set of needs, requirements and preferences. However, in our digital reality, this is rarely the case in practice, as decision making processes usually occur in a social context. That context is based on a social structure of individuals (involved in solving a problem or affected by the solution), but it also includes other components like data sources, available analysis methods and, more and more often, intelligent agents that can actively participate in the decision process.

The relevance of a social context in decision making is the most visible in healthcare, which is currently going through a digital revolution. With all new data that can be processed and the application of advanced algorithms, healthcare is becoming more data-driven at all stages, including disease prevention, diagnosis and treatment. Different forms of data analysis improve the effectiveness and efficiency of decision processes and become key foundations for the next generations of health care. But with successful automation of specific tasks the importance of human elements only increases. There is obviously a focus on the patient, as health care becomes more personalized, with customization of a process and of the medications (pharmacogenomics). A lot of attention is also given to physicians, due to complexity and non-deterministic nature of this domain and the very high potential cost of an error. But it is still not enough, as success in health care critically depends on partnerships and collaborative relationships.

Social context in health care is built upon the relationship between patients and physicians. These relationships are no longer 1-to-1, nor symmetrical, as social structures on both sides usually include multiple participants. On the patient’s side, this is primarily a social network providing support and influence with dynamics that can get easily complex, especially in scenarios when patients cannot take control over their health (e.g. children or elderly persons). On the physician’s side, there is a virtual team of medical professionals working with the patient; the physician should be the trusted point of contact, but the process can now expand beyond the knowledge and experience of any individual. Social context in health care is unique -  for example we may assume that all participants in the decision process share the same goal, i.e. well-being of the patient. But this means that there are also unique functional requirements for these relationships to work: they must be designed for a long term, simple and convenient on a daily basis, when there are no major problems, but also efficient and natural in case of a serious medical condition or an emergency.

Figure 1 includes a simple social structure built around the traditional relationship between a patient and a physician as its core. This is just a proof of concept, but similar models for real case studies can be created in various ways: defined a priori (e.g. by roles in a team), constructed based on provided information (e.g. key actors), or automatically generated using records of interactions.

Figure 1. An example of a social structure from a context of decision processes in health care

The models for actual social structures and contexts are obviously dynamic and specific to a situation. That, in addition to possible complexity, makes the functional requirements for the quality of the relationships very hard to meet. In order to be successful in domains likes healthcare, technology must be designed and implemented for the social contexts of their applications. This starts with strong generic fundamentals like secure and reliable data processing, natural experiences, or smooth integration with external components. But this is only the beginning if we want to facilitate efficient cooperation (which can be more important than actual data analysis itself), enable building trust and partnership or help with challenges, emotional factors (e.g. fear) and certain behaviors (e.g. avoidance). Such scenarios require a functionality of relationship management, what means that social context must be taken into consideration at each and every stage of creating software - this is no longer another feature, but rather it becomes one of the core fundamentals.

Let’s take a brief look at the seemingly straightforward requirement of keeping participants of a decision process informed. This means, among other things, that the results of data analysis must be useful for the user. However, with a social context, we have multiple users, with individual needs, requirements and preferences. Each of them needs a different type of story -  even the same information should be presented differently to a physician (all the details with analysis decision support) and to a patient (explanations with option of learning more or starting a conversation). One of the features we’re developing in our framework is designed to provide personalized views of shared data space to multiple users and roles of a social structure (for example a company). Personalized user experience is based on individual preferences, but also on analysis of the user’s role, profile (e.g. age for accessibility), the nature of the task/scenario as well as any situational requirements (e.g. pressure due to an emergency). Figure 2 shows possible functional templates of personalized user experience for key users in our example.

Figure 2. Personalized user experiences in a social structure of decision processes in health care

In this post, we used healthcare as the application domain. In this domain, we can see that technology has the potential to improve existing processes and practices but, at the same time, it will change them dramatically. Modern data analysis solutions will not replace physicians, but they will change behavioral patterns of interactions between patients and physicians (and likely beyond). Obviously, healthcare is about people and relationships more than other domains are. But since social context is so essential for our decision processes, we may expect similar changes in other domains affected by democratization of data analysis.  Data analysis is becoming social following the path from data connecting users, through natural interactions and cooperation, to relationships focused on very specific challenges. Social contexts will become even more relevant as we start implementing scenarios involving intelligent software agents that can participate in our decision processes. With that change, we are no longer only adapting to a social context, we are actually trying to shape it.

Healthcare and data analysis

Healthcare is one of three domains we selected for initial applications of our framework for analysis of time oriented data (the other are security and finance). This is undoubtedly the most challenging domain, but also one with the biggest opportunities for delivering meaningful changes and positive impact. Healthcare is currently going through a radical revolution. And due to the nature of this domain, the consequences of the changes will affect everybody. We believe that data analysis is one of the key elements of the next generations of healthcare. But this also works in reverse – requirements and scenarios from this domain are great driving forces for innovation in data analysis.

Healthcare on the eve of a revolution

Healthcare is going through a gale of creative destruction, which will fundamentally change the landscape of the life science industry. It is primarily caused by scientific and technical progress: the revolution in sensors, always connected devices and capabilities to process and store massive amount of data. But it is not only about technologies themselves; it is also about how they have already changed users’ behaviors and expectations. Healthcare cannot be disconnected from the digital world that patients are used to. It is interesting to see that while many research efforts are still driven by the life science industry (e.g. genomics becoming more available), there are also strong initiatives originating from companies traditionally involved in information processing (IBM Watson, Microsoft Health or Apple CareKit). Revolution in healthcare is happening, even if there is significant resistance to change among medical professionals.

In our work we obviously focus on data analysis, which is actually very natural in the case of healthcare, as this domain is all about data. It always has been, long before the first signs of the digital revolution. Even when all doctor-patient interactions were direct and 1-on-1, they were based on insightful observations, and the doctor’s knowledge and experience to process them, to form a diagnosis and propose a therapy. Now we have more data, much more data, that can be available to patients and physicians. The data include genomics, anatomical imaging, physiological metrics, environmental records, patients’ (and physicians’) behaviors, decisions and observations.  There are many functional, technical and business challenges, but these data will eventually flow smoothly in networks of patients, doctors and AI systems. And then we’ll have to focus on different, but already familiar challenges: what to do with the new data that are within our reach and how to use them to help solve old problems? 

Data analysis will change healthcare

It should be clear now that we’re not talking here about data analysis understood as a basic process with some data on input and some results on the output. Data analysis in healthcare must be understood in a much broader scope, not limited only to selected, even if very useful, tasks, like analysis of anatomical images. Technical elements like data processing, extraction of information, creating models, learning from historical data, or providing practical decision support will always remain essential. But since data are expected to flow from multiple sources, and in many directions, data analysis should become one of the foundations, based on which required goal-oriented tasks can be effectively executed. And like any technology designed for digital healthcare, it must be extremely user-centered.

The goals of medicine should be always focused on the well-being of the patient. But in the digital world data are processed automatically, so the scope of data analysis can be much broader, without weakening that focus. What is more important, such expansion will actually increase the effectiveness of individual therapies. There are (at least) 3 levels at which data analysis should be considered in healthcare:

• Individual healthcare will be radically changed by personalized medicine based on individual characteristics and situation. The context of decision making will be vastly expanded by the technology beyond the knowledge and experience of medical professionals that are directly involved. On the other side, patients will become more active participants, also benefiting from decision support mechanisms delivering information in highly usable form.
• Healthcare relationships will become more recognized as essential for healthcare experience. There are concerns about reduced frequency of face to face interactions, but technology actually has the potential to help with establishing data flows and building strong trust-based connections. These should be the foundations for long-term relationships that are simple and convenient in good times, but remain very effective and natural in a case of a medical emergency.
• Population research will be redefined by the availability of detailed data about individual differences and the ability to select subsets of population with a high degree of similarity. Data about individual treatment, after proper processing, will be submitted for population analysis and help with detection of trends and patterns on a local or global level. More importantly however, results from population analysis will also be used in individual diagnosis and treatment.

Healthcare of the future will be very different, though the details still remain unknown. One thing however is certain, it will be based on various types of data automatically collected, shared, processed and analyzed in many ways we are not yet able to foresee.

A pattern case study

Data analysis is not only about big data, as there is also big value hidden in small data and simple methods that can be easily adopted to solve big problems. Figure 1 presents a very basic example of visualization generated using our framework. This simple scenario integrates data streams of different types: physiological metrics of body temperature, and two streams covering medicine intake below the main chart: one with a predefined schedule (MED1), which could be supported by adaptive reminders in a mobile device, and second with a basic record (MED2). The chart also includes analysis artifacts showing target temperature range, notifications when the ranged was reached (for the 1st time, and for continuous 24h), and the expected path of temperature change within selected timespan, assumed to be associated with scheduled treatment.

Figure 1. Tracking temperature and medicine regime with associated analysis artifacts for target value range, expected change, and detected relevant events (SVG)

This example is based on test data, but it is a useful illustration of the analysis & visualization pattern that could be applied in many medical scenarios. This pattern is based on streams with metrics of current state, streams with records of selected actions (including a plan), and artifacts generated from analysis of these data in the context of related models (possibly constructed based on analysis of similar cases). Healthcare is a very practical domain and it is fundamentally focused on a change. It is about learning from the past, understanding current conditions, and planning for the future, with emphasis on available options and the probability of their outcomes. This applies to the context of an individual patient, relationships between patients and physicians as well as populations of different scale. Healthcare is therefore not only about data in general, but about time-oriented data! This makes it a perfect application domain for our framework.

This is our first post about healthcare and it is the beginning of the longer story. We plan several more healthcare-related posts covering topics like practical security and privacy requirements, specific decision support scenarios and design patterns for interactions between patient and physicians. All posts in this series will be tagged as healthcare. In the meantime, we are always interested in healthcare-related research data we could use for template development or experiments with our framework. If you have time-oriented data and you are interested in their analysis or visualization, please don’t hesitate to contact us.

If you want to learn more about challenges and opportunities for modern healthcare, you may want to start with a great book Creative Destruction of Medicine by Eric Topol.