Presentation
HE11 - Investigating Technology Implementation in Community Hospitals: A Resilience Engineering Approach
SessionPoster Session 1
DescriptionBackground
Technological innovations hold substantial promise for enhancing clinical practices. Although these advancements can offer unique solutions, their added complexity can also make systems harder to manage, posing challenges to their implementation (Henriqson et al., 2022).
Health systems can be defined as complex adaptive systems (Braithwaite, 2018). As complex systems are unpredictable, adaptative practices and behaviors emerge unexpectedly, allowing the system to maintain itself despite the disturbances caused by internal and external events (Braithwaite et al., 2017). These adaptations enrich the planned workflow and define what is known as work-as-done. This understanding is often more prevalent among frontline workers rather than at the higher levels of an organization (Hollnagel et al., 2006). When promoting changes, it is essential first to recognize the existence of this performance variability and the strategies in place to manage it.
Topic
In this paper, we analyze the challenges in implementing new technologies in a hospital under the lens of Resilience Engineering concepts and methods. We search to understand the usual challenges faced by frontline workers and then verify how the new technology affects it, improving or hindering systems’ capacity to produce the desired results. To this end, we used the Functional Resonance Analysis Method (FRAM) (Hollnagel, 2012). FRAM has proved to be an effective way of understanding, mapping, and exploring complexities in socio-technical systems. We expect the results will support the design of recommendations for integrating technologies.
Application
This study was done with a community hospital that tried adopting new technologies that initially were believed to be beneficial for their processes. Three technologies are investigated in this paper (Technology A, Technology B, and Technology C). Eight staff members participated in this study. Each participant partook in one-on-one semi-structured interview sessions. The pool of participants included physicians, nurses, managers, and technologists. Participants’ workflow descriptions were used to build FRAM models of the processes.
Results and Discussion
In this section, we report some findings of the FRAM analysis by focusing on the discussion of the process in terms of their usual work-as-done and how the technology interacts with their routines. Among the three cases investigated, only Technology C was acquired by the hospital.
Technology A
The FRAM analysis shows that decision-making during this process is driven by many factors. Therefore, in their regular workflow, the main barriers are unclear or unavailable information and the lack of resources to support the patients’ safety and health.
The analysis shows that the new technology did not strongly relate to these barriers and added more steps/functions in the workflow, including functions that displayed significant performance variability. The information provided by the tool may not be available when necessary. In the prospective case where the tool replaces their regular system, the process would suffer delays, impacting the whole unit workflow.
Additionally, pieces of information provided by the technology could be imprecise in some situations. In instances when the physician is absent and staff rely on this information, an incorrect decision could be made, impacting the use of resources and patient’ health.
Technology B
In their regular workflow, the main challenges are related to connectivity and organizational culture. The introduction of the new device did not target any specific issue, but it aimed to provide more information at once, improving physicians’ decision-making process.
The technology had malfunctions leading to delays and poor-quality outputs, requiring repetition of the process. Additionally, design issues made the analyses more difficult, leaving physicians without the right support to inform their decisions in time-sensitive cases and introducing possible variability that was not observed in the modeling of the original workflow.
Technology C
One of the main issues identified in this workflow was associated with logistics, which resulted in more financial costs and risks of material loss and damage. This issue can delay diagnoses and hinder the possibility of a more precise diagnosis for patients. The tool directly addresses this situation, improving the system’s capacity to achieve the desired outcomes.
The new technology eliminates the handling of physical material, allowing online exchange of information, saving time, and preserving the integrity of the original material. The analysis shows that the nature of some functions changed and, consequently, variability was suppressed, increasing the system’s ability to provide better results in less time.
Take-aways
Resilient performance requires knowing what to expect (anticipation), knowing what to look for (attention), and knowing what to do (response). These qualities are influenced by the systems conditions such as the time, knowledge, competence, and resources (Hollnagel et al., 2006). As noticed in our analysis, introducing technologies without adequate planning affects systems’ capacity to deliver care by disrupting workflows, requiring adaptation. This can lead to confusion and inefficiencies, especially if technology does not integrate well with current practices. Staff may face additional challenges in adjusting routines, increasing workload. Poor design or slow performance of technology can hinder efficiency, causing workers to spend more time troubleshooting instead of focusing on patient care. Without proper training, staff may struggle to incorporate new tools effectively.
Therefore, some practical recommendations to improve technology adoption include:
Leverage frontline workers’ expertise to identify already existing critical points in the workflow: technologies that explicitly tackle existing problems and support workers’ abilities to manage them – or at least that do not amplify issues and undermine workers’ coping capacities – should be favored, and potential problems created by the new technologies should be anticipated and openly acknowledged. According to Crawford et al. (2014), new projects are frequently managed from the top-down and contributions from the bottom-up can add significant advantages toward expediting system changes. In addition, changes tend to be more accepted when people are involved in the decisions that affect them (Braithwaite, 2018).
Provide ongoing and adequate support for technology use: change management should not be addressed as a discrete event but as a continuous transition. Any transformation impacts the already in-place mechanisms and challenges workers’ knowledge and expectations of the systems. Thus, periods of increased variability are expected, meaning higher turnaround times and rework. Providing the proper support in all moments of the transition, such as training and technical support, may help shorten this period (Henriqson et al., 2022). Health is a complex system, not a simple mechanism. Thus, no procedure or task can be changed in isolation (Hollnagel et al., 2018).
Ensure that technologies minimally meet work requirements: it is important to ensure that the new technologies meet the specific needs and requirements of the activities they are intended to facilitate to guarantee that they serve their intended purpose effectively and provide real improvements in the workflow. Once again, frontline workers should be involved in identifying these requirements to ensure that the technologies are technically compatible with their work situation and, hence, potentially effective in real-world scenarios.
Finally, FRAM emphasizes a systemic view, promoting a deeper understanding of how small changes in one area can affect the entire system. For instance, the analysis shows that providing more training and knowledge could mitigate instances where the use of the new technology led the system to fail to properly accomplish its results despite other variabilities. Results in health and other complex settings emerge from interactions among people, technological and social artifacts, as well as the organizational environment (Sheps & Wears, 2019). Adopting a perspective that acknowledges this dynamic helps avoid simplistic solutions based on cause-effect analysis and supports the design of actionable and effective recommendations to support resilience during change management. It is crucial to approach technology implementation in hospital settings with a targeted strategy that addresses specific existing challenges, unique needs, and complexities.
Technological innovations hold substantial promise for enhancing clinical practices. Although these advancements can offer unique solutions, their added complexity can also make systems harder to manage, posing challenges to their implementation (Henriqson et al., 2022).
Health systems can be defined as complex adaptive systems (Braithwaite, 2018). As complex systems are unpredictable, adaptative practices and behaviors emerge unexpectedly, allowing the system to maintain itself despite the disturbances caused by internal and external events (Braithwaite et al., 2017). These adaptations enrich the planned workflow and define what is known as work-as-done. This understanding is often more prevalent among frontline workers rather than at the higher levels of an organization (Hollnagel et al., 2006). When promoting changes, it is essential first to recognize the existence of this performance variability and the strategies in place to manage it.
Topic
In this paper, we analyze the challenges in implementing new technologies in a hospital under the lens of Resilience Engineering concepts and methods. We search to understand the usual challenges faced by frontline workers and then verify how the new technology affects it, improving or hindering systems’ capacity to produce the desired results. To this end, we used the Functional Resonance Analysis Method (FRAM) (Hollnagel, 2012). FRAM has proved to be an effective way of understanding, mapping, and exploring complexities in socio-technical systems. We expect the results will support the design of recommendations for integrating technologies.
Application
This study was done with a community hospital that tried adopting new technologies that initially were believed to be beneficial for their processes. Three technologies are investigated in this paper (Technology A, Technology B, and Technology C). Eight staff members participated in this study. Each participant partook in one-on-one semi-structured interview sessions. The pool of participants included physicians, nurses, managers, and technologists. Participants’ workflow descriptions were used to build FRAM models of the processes.
Results and Discussion
In this section, we report some findings of the FRAM analysis by focusing on the discussion of the process in terms of their usual work-as-done and how the technology interacts with their routines. Among the three cases investigated, only Technology C was acquired by the hospital.
Technology A
The FRAM analysis shows that decision-making during this process is driven by many factors. Therefore, in their regular workflow, the main barriers are unclear or unavailable information and the lack of resources to support the patients’ safety and health.
The analysis shows that the new technology did not strongly relate to these barriers and added more steps/functions in the workflow, including functions that displayed significant performance variability. The information provided by the tool may not be available when necessary. In the prospective case where the tool replaces their regular system, the process would suffer delays, impacting the whole unit workflow.
Additionally, pieces of information provided by the technology could be imprecise in some situations. In instances when the physician is absent and staff rely on this information, an incorrect decision could be made, impacting the use of resources and patient’ health.
Technology B
In their regular workflow, the main challenges are related to connectivity and organizational culture. The introduction of the new device did not target any specific issue, but it aimed to provide more information at once, improving physicians’ decision-making process.
The technology had malfunctions leading to delays and poor-quality outputs, requiring repetition of the process. Additionally, design issues made the analyses more difficult, leaving physicians without the right support to inform their decisions in time-sensitive cases and introducing possible variability that was not observed in the modeling of the original workflow.
Technology C
One of the main issues identified in this workflow was associated with logistics, which resulted in more financial costs and risks of material loss and damage. This issue can delay diagnoses and hinder the possibility of a more precise diagnosis for patients. The tool directly addresses this situation, improving the system’s capacity to achieve the desired outcomes.
The new technology eliminates the handling of physical material, allowing online exchange of information, saving time, and preserving the integrity of the original material. The analysis shows that the nature of some functions changed and, consequently, variability was suppressed, increasing the system’s ability to provide better results in less time.
Take-aways
Resilient performance requires knowing what to expect (anticipation), knowing what to look for (attention), and knowing what to do (response). These qualities are influenced by the systems conditions such as the time, knowledge, competence, and resources (Hollnagel et al., 2006). As noticed in our analysis, introducing technologies without adequate planning affects systems’ capacity to deliver care by disrupting workflows, requiring adaptation. This can lead to confusion and inefficiencies, especially if technology does not integrate well with current practices. Staff may face additional challenges in adjusting routines, increasing workload. Poor design or slow performance of technology can hinder efficiency, causing workers to spend more time troubleshooting instead of focusing on patient care. Without proper training, staff may struggle to incorporate new tools effectively.
Therefore, some practical recommendations to improve technology adoption include:
Leverage frontline workers’ expertise to identify already existing critical points in the workflow: technologies that explicitly tackle existing problems and support workers’ abilities to manage them – or at least that do not amplify issues and undermine workers’ coping capacities – should be favored, and potential problems created by the new technologies should be anticipated and openly acknowledged. According to Crawford et al. (2014), new projects are frequently managed from the top-down and contributions from the bottom-up can add significant advantages toward expediting system changes. In addition, changes tend to be more accepted when people are involved in the decisions that affect them (Braithwaite, 2018).
Provide ongoing and adequate support for technology use: change management should not be addressed as a discrete event but as a continuous transition. Any transformation impacts the already in-place mechanisms and challenges workers’ knowledge and expectations of the systems. Thus, periods of increased variability are expected, meaning higher turnaround times and rework. Providing the proper support in all moments of the transition, such as training and technical support, may help shorten this period (Henriqson et al., 2022). Health is a complex system, not a simple mechanism. Thus, no procedure or task can be changed in isolation (Hollnagel et al., 2018).
Ensure that technologies minimally meet work requirements: it is important to ensure that the new technologies meet the specific needs and requirements of the activities they are intended to facilitate to guarantee that they serve their intended purpose effectively and provide real improvements in the workflow. Once again, frontline workers should be involved in identifying these requirements to ensure that the technologies are technically compatible with their work situation and, hence, potentially effective in real-world scenarios.
Finally, FRAM emphasizes a systemic view, promoting a deeper understanding of how small changes in one area can affect the entire system. For instance, the analysis shows that providing more training and knowledge could mitigate instances where the use of the new technology led the system to fail to properly accomplish its results despite other variabilities. Results in health and other complex settings emerge from interactions among people, technological and social artifacts, as well as the organizational environment (Sheps & Wears, 2019). Adopting a perspective that acknowledges this dynamic helps avoid simplistic solutions based on cause-effect analysis and supports the design of actionable and effective recommendations to support resilience during change management. It is crucial to approach technology implementation in hospital settings with a targeted strategy that addresses specific existing challenges, unique needs, and complexities.
Event Type
Poster Presentation
TimeMonday, March 314:45pm - 6:15pm EDT
LocationFrontenac Foyer
Digital Health (DH)
Simulation and Education (SE)
Hospital Environments (HE)
Medical and Drug Delivery Devices (MDD)
Patient Safety and Research Initiatives (PS)