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. 2017 Feb 10;2016:734–742.

A Connectivity Framework for Social Information Systems Design in Healthcare

Craig E Kuziemsky 1, Pavel Andreev 1, Morad Benyoucef 1, Tracey O'Sullivan 2, Syam Jamaly 1
PMCID: PMC5333316  PMID: 28269869

Abstract

Social information systems (SISs) will play a key role in healthcare systems’ transformation into collaborative patient-centered systems that support care delivery across the entire continuum of care. SISs enable the development of collaborative networks andfacilitate relationships to integrate people and processes across time and space. However, we believe that a “connectivity” issue, which refers to the scope and extent of system requirements for a SIS, is a significant challenge of SIS design. This paper’s contribution is the development of the Social Information System Connectivity Framework for supporting SIS design in healthcare. The framework has three parts. First, it defines the structure of a SIS as a set of social triads. Second, it identifies six dimensions that represent the behaviour of a SIS. Third, it proposes the Social Information System Connectivity Factor as our approximation of the extent of connectivity and degree of complexity in a SIS.

Introduction

Healthcare systems worldwide are undergoing innovative transformation into networked systems that deliver collaborative patient centered care delivery across the continuum of care1. The design of healthcare systems has not been traditionally focused on facilitation of integrated and collaborative care delivery and, therefore, health information systems (HISs) will play a key role in transformative efforts2, 3. Social information systems (SISs), defined as “information systems based on social technologies and open collaboration” 4, will play a particularly significant role in these transformative efforts because of their ability to facilitate and support social processes of healthcare delivery5, 6 SISs have a different focus than traditional HISs since their purpose is to facilitate collaboration and networking over time as opposed to the automation of specific tasks such as order entry7. The fundamental tenet of SISs are the need to support and nurture the development of social and collaborative competencies over time. Therefore, the scope of SISs is much broader than task based HISs such as decision support or order entry systems as SISs must facilitate provision of a service (e.g. patient participation, community resilience) while also developing the social competencies inherent within these systems. Although we adopt the definition of SIS proposed by4 we believe that SISs in healthcare present more challenges compared to SIS design in general. These challenges are related to the extent of openness, transparency, and open collaboration, the hallmark of SISs4 which are more complex in healthcare compared to other sectors.

Despite SISs being a recent phenomenon they have shown potential for redefining the way in which clinical care or public healthcare can be provided. Examples of SISs can be seen across the healthcare spectrum. Coiera8 has described how social relationships and networks can lead to new approaches for diagnosing and managing illness. At the micro (clinical) level, social networking tools like Dr. Google and PatientsLikeMe® provide information to patients about illness and treatments and enable connectivity for patients’ discussion groups. At a macro (public health) level, activities such as disaster management and community resilience have benefited from social networking applications because they have enabled social interaction and collaboration that is a key aspect of public health activities9, 10. However, despite their benefits, there are also challenges in designing and implementing SISs that must be properly addressed. At a micro level, simply providing patients with access to data in inappropriate contexts may overwhelm patients and provide overload from the information and opinions that they receive through support groups11. Similarly, at a macro level, it has been challenging to develop SISs due to the disparate and multidisciplinary nature of the communities where these systems are used, and the range of user needs within SISs12.

One essential lesson that we have learned from the implementation of HISs is that unintended consequences will arise post implementation. These unintended consequences occur for a number of reasons but a common issue is changes to work practices resulting from technology mediated connectivity, people, and processes13, 14. SISs and the connectivity they enable provide a bigger base for unintended consequences to occur due to the web of connections enabled by SISs. Before we can manage unintended consequences we first need to understand how they originate. However, this is particularly challenging in SISs because of what we refer to as a “connectivity” issue, which refers to the scope and extent of system requirements for a SIS. SISs create connectivity that is unparalleled, and further coupled with the fact that there are different users and patterns of use, SIS connectivity can be a substantial design problem.

We define “connectivity” as the organizations, tasks, people and technology needed to achieve an outcome. Connectivity is a common challenge in HIS design and thus there have been several attempts to understand it in HIS design. Sociotechnical approaches for systems design such as Sittig and Singh’s eight step Sociotechnical Model defines sociotechnical “connectivity” by such dimensions as people, workflows, technologies, and policies associated with designing, implementing, and using HIS15. Participatory design facilitates “connectivity” in design by engaging end users and binding design in user needs16. Behavioral design approaches like Task Technology Fit (TTF) or Cognitive Task Analysis (CTA) use tasks (e.g., decision making needs) to bind system design parameters17, 18. Outside of healthcare, Supply Chain Management (SCM) defines system requirements according to business models that articulate the necessary connectivity to facilitate supply chain activities. For example, SCM has a common reference model called the Supply Chain Operations Reference (SCOR) model with five general processes (plan, source, make, deliver and return)19. The SCOR model has been used to understand supply chain connectivity for IS design and the development of metrics.

While existing SIS research has developed frameworks for technical architectures and identified challenges to developing SISs4, 20, studies on how to model connectivity as a means of SIS design and evaluation do not exist. SIS design in healthcare is particularly challenging as it requires the connection of people, processes and technology across different workflows and user roles21. Healthcare SIS design requires new approaches that enable us to understand complex connectivity as a precursor to systems design. Coiera proposed Interaction Design Theory (IDT) as a systems design approach that focuses less on individual issues or technologies and instead tries to understand the web of interactions that exist within a system22. IDT allows designers to make predictions about how a group as a whole will interact in complex settings.

However, there is limited work that has looked at SIS design in healthcare to identify specific elements of social connectivity and how they impact systems design. The main contribution of this paper is the Social Information System Connectivity Framework for SIS design in healthcare. The framework has three parts. First, it defines the structure of a SIS as a set of social triads. Second, it identifies six dimensions that represent the behavior of a SIS. Third, it proposes the Social Information System Connectivity Factor as our approximation of the extent of connectivity and degree of complexity in a SIS. The paper has four sections. Section 1 was the introduction. Section 2 is the materials and methods. Section 3 is the description of the Social Information System Connectivity Framework for supporting SIS design in healthcare. We conclude with a discussion of the implications of our work and the next steps from the research presented in this paper.

Materials and Methods

Data Sources

Two data sources informed our study. First, the authors have studied HIS design to support social and collaborative health delivery at clinical and population health levels including studies on perioperative systems, palliative care systems, and community resilience as part of designing disaster management systems12, 23–26. The common thread across all these studies was to understand different types of collaboration, and how it informs HIS design to support the development and maintenance of collaborative and social practices. The case studies involved user engagement methods such as community based participatory research12 and participatory design23. In the context of these methods we spent considerable time with users in order to gain an appreciation of social needs and competencies and how they develop over time.

The second data source was a literature search on SISs, networks, and modelling approaches for SIS design in general and in healthcare. Scopus, IEEE Xplore, and the ACM Digital Library were searched. The literature search provided additional insight on SISs, in particular frameworks for representing and modeling them.

Data Analysis

We used descriptive qualitative content analysis on the literature we retrieved and the integrated findings from our case studies. Our objective was to integrate the empirical data from our studies with the literature on SIS modeling and design to develop a general framework on SIS design in healthcare. To provide a framing for our analysis we drew upon a paper that described how accountable healthcare delivery must be viewed from the perspective of a structure and a set of behaviours27. We adopted that framework as we analyzed our data so our analysis identified structural and behavioral aspects of SISs.

Results

Our results are presented in two sections. First, we describe the connectivity framework for SIS design. Second, we present the three specific components of the framework corresponding to the structure and behavior, and third, we describe operationalization of the framework as the Social Information System Connectivity Factor.

Connectivity Framework for SIS Design

Figure 1 shows our connectivity framework for SIS design in healthcare. The framework addresses the previously described shortcomings in the literature such as the need to represent healthcare delivery as a complex ecosystem that can include multiple actors, settings and information flows that may be subject to different degrees of governance. A first step towards understanding connectivity based SIS design is to decompose the components of the system so they can be modelled. Our framework is labeled with three parts: structure, behavior and operationalization as the Social Information System Connectivity Factor (SISCF). Each part is described in detail below the figure.

Figure 1.

Figure 1.

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Connectivity Framework for SIS Design in Healthcare.

Connectivity Framework – Structure

The structure of the connectivity framework represents a social ecosystem as a number of interrelated social triads with three concepts: person, process and technology. Those three concepts were common in the literature we retrieved on SISs in healthcare as well as in social modelling approaches. Studies on social networks or technologies to support healthcare delivery most often focused on people, processes (e.g., information exchange, communication, care coordination), and technology that is used8, 9,28, 29.

From the literature review on social modeling we identified social modelling languages such as Social Business Process Model and Notation (SBPMN) that emphasize modelling of social activities such as community generated events and social relationship links30. The social triad aspect of our framework represents the necessary connectivity for SIS design. A social triad can be at the micro level of an individual patient where the extent of social linkages would be at the level of processes and interactions that a patient uses as part of his/her social network for managing his/her own chronic illness. In that example, the connections we would be interested in modelling would be the clinicians (e.g., physician, pharmacist, dietician) that are part of the patient’s social ecosystem as well as the processes (e.g., glucose or diet monitoring, exercise regime) and communication that a patient goes through as part of his/her disease management. If we move to meso level care delivery, we would be linking together two or more social triads such as for the integration of multiple units in a hospital, or multiple clinics that work together as part of providing integrated collaborative community care delivery. The connections we would be interested in modelling at that level would include collaborative care processes (e.g., joint decision making) and integrated data sharing. At the macro level, we would be integrating multiple social triads as part of modelling population health initiatives such as community resilience to support disaster management, or public health initiatives such as managing an influenza outbreak. The connections we would be interested in modelling at the macro level would include collaboration, interoperability and governance across communities and organizations.

Connectivity Framework – Behavior

The behavioral component of our framework defines how the social triads (e.g., structure) work for a particular SIS. From our analysis of the two data sources we identified six behavioral dimensions relevant to SIS design in healthcare: user driven design flexibility, empowerment and responsibility, workflow extensibility, process immaturity, data standards & interoperability, and governance complexity. The behavioral dimensions span a range of considerations from workflow and motivation to use a SIS, to system interoperability and governance issues. Below we discuss the six behavioral dimensions and how they help us understand the degree of complexity as part of SIS modelling and design.

User Driven Design Flexibility

One of the key challenges of SISs is that they are driven by the needs or requirements of users. This introduces two sets of challenges. At a micro level, patient participatory medicine is dependent on the willingness of people to be the active stewards of their own care delivery. Further, SISs to support patient participation can range from basic information retrieval to information exchange and partnership and collaboration on the content31, 32. Those different tasks require very different design solutions. SISs require much more flexibility due to the nature of how people conduct tasks. Similarly, at macro levels it has been shown that the diversity of end users necessitates consideration of the needs of diverse individuals12. While the premise of SISs is connectivity and socialization across human networks, achieving that goal makes user driven design more challenging. To address that issue, the diverse users of SIS and their needs must be identified and used to inform systems design. However, a social community may have users with a range of technological skills and that needs to inform systems design. One of our case studies on SIS design to support community resilience for disaster management highlighted that system design requirements have to start technologically at the lowest common denominator to enable all users to become engaged and comfortable with using the system12.

Empowerment and Responsibility

Unlike HISs designed to support a specific task, SISs are dynamic systems that are intended to develop and maintain relationships over time. That puts the burden of responsibility on system users to maintain activity in the SIS over time to enable it to grow and develop. SISs provide a means of empowering people, but empowerment brings with it certain responsibilities. At a micro level, patients recovering from an illness see something explicit in using a SIS to guide their therapy and disease recovery. However, people may be less motivated to use it for data collection for routine monitoring (e.g., blood pressure measurement, diet). Research has shown that a relatively low percentage of patients adequately document the necessary data for disease management and also that while patients may initially be very keen to collect illness data, the enthusiasm wanes over time9. Macro level public health initiatives have a similar challenge in that community resilience efforts to support disaster management relies on a mixture of private and public and paid and volunteer workers to maintain progress25. A key public health issue is that disaster management or disease surveillance are preparing for events which may never happen and thus keeping parties motivated to continue to use and maintain a SIS can be a challenge12, 33.

Workflow Extensibility

A challenge with socially driven care delivery is the processes that are done may lack rules of engagement for how they are to be conducted. In our case studies we found that workflows for social processes were often poorly defined at both micro and macro levels. At a micro level, patient participatory medicine requires the creation of new workflows to accommodate both patients and clinicians with respect to information exchange and decision making29, 34. As more care delivery moves outside traditional settings such as hospitals and into the community (e.g. initiatives such as aging in place) it adds more people such as informal caregivers to a social triad which adds further workflow complexity. Therefore, workflow modelling must be done in a way that takes into perspective all of these diverse user groups35. Similarly, workflows are often undefined for community level initiatives such as disaster management that can necessitate the need for workflows that span micro (individual) and macro (community) perspectives36, 37.

Process Immaturity

While relationships, social connectivity, and collaboration are the tenets of SISs, a challenge is that many of the social processes that we are trying to implement are still maturing and may be in an evolutionary state. At the micro level, patient centred care and patient participatory medicine are evolving concepts34. Similarly, collaborative team based care delivery at the meso level has been described as existing more in name than in actual implementation38. The rules of engagement for how collaboration needs to occur have to be better understood and defined before we can expect a SIS to implement and foster collaborative care delivery39, 40. One of our macro level case studies described how the common ground needed for social collaboration in a community goes through a development cycle where people need to first develop coordination and communication practices before they can collaborate24.

Data Standards & Interoperability

Interoperability across different settings is an essential requirement of HISs41. A key driver of HIS interoperability has been the development of standards for data exchange (e.g., HL7) and terminology (e.g., SNOMED). On one hand, SISs offer the potential for social triad interoperability and data collection beyond traditional healthcare settings and systems, but they also introduce connectivity complexity to traditional interoperability standards. As we use more social media applications (e.g., Facebook) or self-monitoring tools (e.g., Fitbits, Smartphones) as sources of healthcare data, it threatens to erode the extensive work that has been done in developing formal interoperable healthcare data standards. In recognition of this issue, recent research has suggested the need to change the focus from developing formal data standards to developing tools to enable extraction and analysis of social media application and selfmonitoring application data42, 43.

Governance Complexity

Governance complexity refers to the need to consider activities within the larger social structure where they occur, including the relationship across entities (e.g., organizations) that may impact social structures by exerting influence or autonomy24. SISs cross different units both within (i.e., intra-organizational) and across (i.e., inter-organizational) settings. The more social triads that are integrated as part of healthcare delivery, the greater the governance complexity challenges in the form of cross-organizational information sharing and the need to integrate different types of agents, policies, and procedures23, 45. Governance issues can at times be a significant impediment to the development of social relationships, at both micro and macro levels, an example being the inability to share necessary information to support social healthcare processes24, 44.

Connectivity Framework - Operationalization

To operationalize the connectivity framework, we introduce the Social Information System Connectivity Factor (SISCF) – bottom of fig. 1 - as our approximation of the extent of connectivity in a SIS. The SISCF is comprised of two elements, the connectivity complexity (CC), and the connectivity time (CT). The CC helps us approximate and understand the complexity of the structural and behavioral components within a SIS. For example, connectivity is more complex for a meso level SIS that integrates five social triads compared to a SIS integrating two triads because there will be more connections and relationships to consider. As more behavioral dimensions (e.g., governance across multiple settings, different workflows, disparate forms of social data, needs of a variety of end users) are incorporated into a SIS, the degree of complexity also increases. It must also be emphasized that there is no one pattern of alignment for the structural and behavioral dimensions in our framework. Sittig and Singh point out that sociotechnical frameworks in healthcare need to be viewed as complex adaptive systems and that framework dimensions must be studied in a non-linear manner with an emphasis on how different dimensions interact and relate to each other15. An implication of implementing multi-dimensional connectivity is that trade-offs will have to occur in many of the behavioral dimensions. In one of our case studies we showed that providers and administrators can have different needs with respect to how a collaborative HIS is used23. Administrators wanted data to support organizational decision making on workloads and to provide necessary reporting for healthcare accreditation bodies, but collecting that data increased the data entry workload of the front line clinicians. In that situation the workflow and governance behavioral dimensions were in conflict with each other. Similarly, establishing the level of common ground necessary for social processes often means that individuals may have to change how they do things in consideration of social good24. Again, that requires behavioral changes at the individual level as a precursor to achieving social connectivity at a group level.

The CT refers to the length of time that social connectivity has taken place, as some of the above CC dimensions are influenced by temporal properties. A key time related factor is the maturity of processes. Many of the social processes that are done in healthcare such as collaborative healthcare delivery or patient participatory medicine are still maturing and thus will develop over time. Even if a SIS is designed to support social processes such as collaboration and coordination there may be a stepwise progression to using it to its full capacity. One of our case studies showed that even though we designed a palliative care system with alerting and reminder features to support collaborative care delivery they were not used to capacity because the technology was more advanced than the care processes that were using it23. A different study on HIS implementation described how users initially used an EHR system solely as a documentation tool but over time they developed and adapted social coordination strategies that were enabled through the EHR45. Social technologies are multi-purpose tools that can provide a range of social functionality depending on the degree of social processes which are using them. For example, Twitter and Facebook were initially designed for social communication at a personal level but they evolved in use to include clinical and public health activities46, 47. The first step in the development of SISs must be an analysis of the behavioral dimensions of a social system using the above described behavioral dimensions as there is little point in implementing SISs that are more advanced than the people and processes using them. Once the social process baseline has been identified, an SIS can then be designed, which must include tools and features to support social processes in the present while also helping the processes mature and evolve over time.

Discussion

SISs offer great potential for improving healthcare delivery in that they can support the development and ongoing maintenance of relationships to support collaborative healthcare activities. Managing chronic disease at the micro level is dependent on our ability to connect a patient with information, processes, people and technology as part of disease management. Similarity, public health efforts like disaster management and community resilience are dependent on relationship building over time that encompasses a wide variety of user groups. However, SISs introduce a connectivity problem in that the system design considerations, and potential UICs of systems design, are so vast that SIS design becomes a significant challenge. This research helped to address the above issue by proposing the Social Information System Connectivity Framework for SIS design. The framework defines the structure of a SIS as a set of one or more social triads and the behaviour of a SIS according to six behavioural dimensions. The framework is operationalized through the Social Information System Connectivity Factor and the connectivity complexity (CC) and connectivity time (CT) concepts, which help us understand how the degree of connectivity and temporal aspects of social connectivity will impact SIS modelling and design.

SISs present a new perspective on the design and evaluation of health information systems. We do not believe it is possible to eliminate all unintended consequences from SIS implementation due to the complexity of the healthcare domain. Rather our focus should be on identifying and managing unintended consequences. The value of our framework is that it enables us to proactively identify and understand the connectivity complexity of SIS design. We emphasize that we do not provide a prediction of SIS connectivity complexity but rather provide a way of understanding the complexity and temporal considerations associated with it. This research is complementary to existing work that has developed architectures for SIS design or identified challenges in performing SIS design4, 20 as well as to system design approaches like Interaction Design Theory22. While technical aspects of SISs are essential for designing system infrastructures, perhaps a bigger challenge in SIS design are the behavioural dimensions identified in this paper. For example, processes like patient self-disease monitoring (e.g., blood pressure, diet) and community preparation for disaster management are both preparing for events that may not occur for some time (i.e., illness, natural disaster), if at all. Therefore, keeping users motivated to keep a SIS active in the present moment, as well as enabling it to evolve and mature, presents a significant challenge. Similarly, while the value of SISs comes from the social network of users within it, it also introduces connectivity challenges with respect to adaptiveness of workflows to address the dynamic nature of the system, incorporating social data standards, and managing cross organizational interoperability and governance. A significant implication of multi-dimensional connectivity is that SIS design cannot satisfy everyone, and therefore trade-offs will have to be made with respect to the behavioral dimensions of a social system. An example of such a trade-off is in the governance complexity dimension where traditional information sharing agreements that impede cross organizational information sharing will need to be loosened to enable development of social networks. Trade-offs at the individual level as a precursor to achieving common ground at a group level is another example of a trade-off as part of social connectivity.

One significant finding from our work is that the development trajectory of SISs is understated. While social technologies are well developed from a tool perspective, the social processes that use the tools are not nearly as well developed. Therefore, our effort moving forward needs to focus on understanding how to design SIS to provide the necessary connectivity to support present needs but also to enable the ongoing development of social processes. Bottom line is we cannot implement SISs without ensuring that the social processes that will use them have sufficient maturity to benefit from the implementation. Another implication for SIS design from our framework is the need to study how different social networking tools contribute to a SIS. Self-monitoring tools such as Fitbits are fairly bounded in terms of the data they collect (e.g. steps, heartrate) compared to tools such as Twitter or Facebook that are far less bounded in terms of data collected. Thus, there is a need to study connectivity implications for these different data sources. Further, increased sharing of data brings about privacy and security issues, and, while not discussed explicitly in this paper, they are important considerations in SIS design.

Limitations of our research are that we have developed but not used the connectivity framework to model or design SISs. The next stage of our research is to use the framework to design and evaluate SISs at the clinical (micro and meso) and community health (macro) levels.

Conclusion

SISs will play a crucial role in healthcare transformation at all levels (micro, meso and macro). Therefore, there is a need for approaches that help us to understand and model the complex connectivity that is inherent in a SIS. This paper proposed the Social Information System Connectivity Framework for SIS design. This research helped to address the above issue by proposing the Social Information System Connectivity Framework for SIS design. The framework defines the structure of a SIS as a set of one or more social triads and the behavior of a SIS according to six behavioral dimensions. The framework is operationalized through the Social Information System Connectivity Factor and the connectivity complexity (CC) and connectivity time (CT) concepts, which help us understand how the degree of connectivity and temporal aspects of social connectivity will impact SIS modelling and design.

Acknowledgements

We acknowledge funding support from the Natural Sciences and Engineering Research Council of Canada.

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Articles from AMIA Annual Symposium Proceedings are provided here courtesy of American Medical Informatics Association

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