While existing SIS research has developed frameworks for technical architectures and identified challenges to developing SISs 4 , 20 , studies on how to model connectivity as a means of SIS design and evaluation do not exist.
Reference Modeling : Efficient Information Systems Design Through Reuse of Information Models
SIS design in healthcare is particularly challenging as it requires the connection of people, processes and technology across different workflows and user roles 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 system 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. Second, it identifies six dimensions that represent the behavior of a SIS. The paper has four sections. We conclude with a discussion of the implications of our work and the next steps from the research presented in this paper.
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 systems 12 , 23 — 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 research 12 and participatory design 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. The literature search provided additional insight on SISs, in particular frameworks for representing and modeling them.
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 behaviours We adopted that framework as we analyzed our data so our analysis identified structural and behavioral aspects of SISs.
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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. 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. Each part is described in detail below the figure. 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. 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 links The social triad aspect of our framework represents the necessary connectivity for SIS design.
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In that example, the connections we would be interested in modelling would be the clinicians e. 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. 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.
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The connections we would be interested in modelling at the macro level would include collaboration, interoperability and governance across communities and organizations. The behavioral component of our framework defines how the social triads e. 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. 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 content 31 , 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 individuals 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 system 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. 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 time 9.
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 progress 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 challenge 12 , 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 making 29 , As more care delivery moves outside traditional settings such as hospitals and into the community e. Therefore, workflow modelling must be done in a way that takes into perspective all of these diverse user groups 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 perspectives 36 , 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 concepts Similarly, collaborative team based care delivery at the meso level has been described as existing more in name than in actual implementation 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 delivery 39 , 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 collaborate Interoperability across different settings is an essential requirement of HISs A key driver of HIS interoperability has been the development of standards for data exchange e.
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. 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 data 42 , Governance complexity refers to the need to consider activities within the larger social structure where they occur, including the relationship across entities e.
SISs cross different units both within i. 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 procedures 23 , 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 processes 24 , 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. 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 other An implication of implementing multi-dimensional connectivity is that trade-offs will have to occur in many of the behavioral dimensions.
Reference Modeling. Springer, Heidelberg Google Scholar. Gottschalk, F. Karow, M. In: Proceedings of the Multikonferenz Wirtschaftsinformatik Referenzmodellierung, pp. La Rosa, M.
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