Juggling digitization and technostress: The case of alert fatigues in the patient care system implementation

Khuntia, Jiban; Tanniru, Mohan; Weiner, Jack

doi:10.1016/j.hlpt.2015.08.005

Cited by 18 publications

(11 citation statements)

References 19 publications

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“…Alarm assessment and evaluation were suggested to be conducted prior to and after adopting an alarming medical device. 55 59 The assessment and evaluation were suggested to be conducted in a clinical environment and to check alarm types, audibility, validity and relevance. 10 17 Factors related to the environment and organisations where the alarms were located such as bed to alarm ratio, purpose and length of alarm conditions, staffing levels, high-risk patient ratio required intense monitoring, unit layout and background noise were also considered important.…”

Section: Resultsmentioning

confidence: 99%

Managing alarm systems for quality and safety in the hospital setting

2018

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ObjectiveTo provide an overview of documented studies and initiatives that demonstrate efforts to manage and improve alarm systems for quality in healthcare by human, organisational and technical factors.MethodsA literature review, a grey literature review, interviews and a review of alarm-related standards (IEC 60601-1-8, IEC 62366-1:2015 and ANSI/Advancement of Medical Instrumentation HE 75:2009/2013) were conducted. Qualitative analysis was conducted to identify common themes of improvement elements in the literature and grey literature reviews, interviews and the review of alarm-related standards.Results21 articles and 7 publications on alarm quality improvement work were included in the literature and grey literature reviews, in which 10 themes of improvement elements were identified. The 10 themes were categorised into human factors (alarm training and education, multidisciplinary teamwork, alarm safety culture), organisational factors (alarm protocols and standard procedures, alarm assessment and evaluation, alarm inventory and prioritisation, and sharing and learning) and technical factors (machine learning, alarm configuration and alarm design). 26 clinicians were interviewed. 9 of the 10 themes were identified from the interview responses. The review of the standards identified 3 of the 10 themes. The study findings are also presented in a step-by-step guide to optimise implementation of the improvement elements for healthcare organisations.ConclusionsImproving alarm safety can be achieved by incorporating human, organisational and technical factors in an integrated approach. There is still a gap between alarm-related standards and how the standards are translated into practice, especially in a clinical environment that uses multiple alarming medical devices from different manufacturers. Standardisation across devices and manufacturers and the use of machine learning in improving alarm safety should be discussed in future collaboration between alarm manufacturers, end users and regulators.

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Section: Resultsmentioning

confidence: 99%

Managing alarm systems for quality and safety in the hospital setting

2018

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“…Data from a patient’s vital signs are analyzed to modify early warning scores that can lead to predicting a cardiac arrest [ 9 ]. Even routinely available data analyzed by providers can lead to decisions on how to alter external actor engagement in supporting clients (e.g., emergency management technicians visiting clients at home, specialists consulting nursing home patients, and staff changes in patient rooms) [ 46 , 68 , 69 ]. In summary, frequent feedback from clients during value-in-use may lead to not only a reconfiguration of digital services, but also broaden the scope of actors used in value-in-use assessment.…”

Section: Designing a Digital Platform To Support Agilitymentioning

confidence: 99%

“…When the speed to respond to a nurse call is altered to improve patient satisfaction, using text alerts and escalation protocols that transfer calls to others when response time is slow, this alters the value fulfillment activities of the nursing staff. If there is a difference in the perceived value-in-use of the patients (e.g., immediate response to a call made to nursing staff) and the perceived value-in-use of the nursing staff (e.g., staff’s perception of the “urgency of the call” depending of the call source, for example surgical or cardiac patients vs. oncology or neurology patients) when the frequency of the value cycle is changed, it can lead to not meeting patient expectations [ 69 , 71 ]. In other words, increasing the value cycle frequency to address the context changes in the clients may have a negative impact, or be misaligned with the actors who are fulfilling the value created.…”

Section: Designing a Digital Platform To Support Agilitymentioning

confidence: 99%

An Agile Digital Platform to Support Population Health—A Case Study of a Digital Platform to Support Patients with Delirium Using IoT, NLP, and AI

Tanniru

Agarwal

Sokan

et al. 2021

IJERPH

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For an organization to be customer centric and service oriented requires that it use each encounter with a customer to create value, leverage advanced technologies to design digital services to fulfill the value, and assess perceived value-in-use to continue to revise the value as customer expectations evolve. The adaptation of value cycles to address the rapid changes in customer expectations requires agile digital platforms with dynamic software ecosystems interacting with multiple actors. For public health agencies focused on population health, these agile digital platforms should provide tailored care to address the distinct needs of select population groups. Using prior research on aging and dynamic software ecosystems, this paper develops a template for the design of an agile digital platform to support value cycle activities among clinical and non-clinical actors, including population groups. It illustrates the design of an agile digital platform to support clients that suffer from delirium, using digital services that leverage Internet of Things, natural language processing, and AI that uses real-time data for learning and care adaption. We conclude the paper with directions for future research.

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“…The primary customers for all these services are patients in a hospital room waiting for care or post-treatment during recovery and prior to discharge. The digitization of each patient service request, if handled by a nurse, can lead to too many alerts and contribute to stress (referred to as technostress [11]). While a robot can help reduce such stress, for the discussion here we will limit ourselves to value propositions that focus on patient support.…”

Section: Servicesmentioning

confidence: 99%

Digital Leadership

Tanniru¹

2018

Management of Information Systems

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Customers are demanding services using evolving technologies, and firms need agility in the way systems are designed and delivered to quickly meet customer expectations. Such agility is in fact an organizational capability where a combination of internal and supplier/partner resources allow firms to quickly create customer value propositions and deliver value through digital services, that is, services using advanced digitization. Leadership that enables such a customer-centric and service-driven culture using technology is referred to as digital leadership. This chapter develops a 10-step methodology not only to show how an innovative value proposition moves from conception to implementation using an agile system and business architecture, but also to lead to the next set of innovations for review. This methodology, developed over four years iteratively using over 100 graduate student projects, is briefly illustrated through two case examples.

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Juggling digitization and technostress: The case of alert fatigues in the patient care system implementation

Cited by 18 publications

References 19 publications

Managing alarm systems for quality and safety in the hospital setting

Managing alarm systems for quality and safety in the hospital setting

An Agile Digital Platform to Support Population Health—A Case Study of a Digital Platform to Support Patients with Delirium Using IoT, NLP, and AI

Digital Leadership

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