The role of data supported decision-making technology in respiratory care

Millions of people across the world are affected by Chronic Obstructive Pulmonary Disease (COPD). It is one of the most prevalent chronic health conditions in the world. As a life-long condition that effects breathing, it has a huge physical and mental impact on peoples’ lives every single day. COPD is characterised by periods of respiratory exacerbations which, if are not managed swiftly, can result in hospitalisation for emergency care. However, effective self-management and support can help people with COPD to avoid the distress of requiring emergency care, while supporting their quality of life and independence. In addition to the difficulties that COPD introduces to a plethora of people, it also presents a huge challenge for healthcare services around the world. In the UK, COPD generates a high number of hospital admissions annually, with many of these for emergency care. In this highly demanding and time-pressured context, healthcare professionals are required to make timely and evidence-based decisions to effectively care for patients. This is the challenging reality for all healthcare professionals that collaborate in the ongoing management and support involved for COPD care. Data supported decision-making (DSDM) technology holds potential to support the ongoing care of people with COPD, through connecting them and their healthcare professionals with pertinent data that can inform decision-making around care. Examples of such technologies include patient health monitoring apps that share data with healthcare professionals for personalised care planning, and clinical dashboards that interlink data from different sources to support decision-making about patient treatment. However, there is currently limited research working in partnership with people with COPD and respiratory healthcare professionals to truly understand how these technologies might support care in its real-world context. Specifically, there are three key gaps in knowledge which this thesis addresses. First, there is a need to understand how DSDM technologies can be designed to support healthcare professionals to provide COPD care, while considering the challenges of implementing technology into healthcare systems. Furthering this, there is a need to understand how technology could support the self-management of COPD, considering it is progressive and highly debilitating in nature. Finally, there is a need to understand how technology could support the ongoing care collaboration between healthcare professionals and patients through sharing patient-generated data about COPD symptoms. Each of these three areas are important in developing an understanding about how technology could support the real-world context of COPD care. To advance our knowledge in this space, I conducted three novel pieces of research working with people with COPD and healthcare professionals to understand how DSDM technologies could support everyday challenges related to COPD care. First, I worked with 11 healthcare professionals to co-design a DSDM dashboard by exploring their decision-making needs around COPD care. Then I conducted exploratory research involving 171 people with chronic respiratory conditions to understand how technology may support their self-care. Finally, I conducted a small exploratory case study with eight participants to understand the patient experience of self-monitoring their respiratory symptoms and the healthcare professionals’ experience of receiving this data remotely. The thesis concludes with a synthesis of the key novel findings across the three research studies, providing overarching opportunities and nodes of caution when designing and deploying DSDM technologies in this space. This discussion draws attention to the ways that perceptions of data ‘trustworthiness’ affects how DSDM technologies are used for decision-making, the tensions that occur when technology does not align with the local context of care, the need for self-management technology to support the personal and evolving condition journey of COPD, and how we may consider designing patient facing technologies to better accommodate potential reactive self-care pattern

Planning for the things you can’t plan for:lessons learnt from deployments in the home

In this article, we reflect on lessons learned through our own experience of conducting more than 20 technology deployments in participants' homes within the past two years. We shed light on challenges that we have encountered, offering solutions where applicable to enable researchers who are planning to deploy technology in participants' homes.

'The issue with that sort of data?':Clinicians’ accountability concerns around COPD self-monitoring tools

There is an increasing interest in CSCW to understand how technology can be used for the monitoring of chronic conditions, and how collaboration for care planning can occur between clinicians and patients through its use. Many studies in this area have focussed on the patients’experience of using such technology.We report findings from a small-scale study, where a smartphone app for monitoring Chronic Obstructive Pulmonary Disease symptoms was introduced into a community respiratory service for patients’ use. Our findings provide three key insights into the clinicians’experiences in receiving the patient reported data and supporting the patients’ use of the app as part of their service

Exploring Human-Data Interaction in Clinical Decision-making Using Scenarios: Co-design Study

When caring for patients with chronic conditions like Chronic Obstructive Pulmonary Disease (COPD), healthcare professionals (HCPs) rely on multiple data sources to make decisions. Collating and visualizing this data, for example on clinical dashboards, holds potential to support timely and informed decision-making. Most studies about data supported decision-making (DSDM) technologies for healthcare have focused on their technical feasibility or quantitative effectiveness. While these studies are an important contribution to the literature, they do not further our limited understanding of how HCPs engage with these technologies and how they can be designed to support specific contexts of use. To progress our knowledge of this area, we must work with HCPs to explore this space and the real-world complexities of healthcare work and service structures. This research aimed to qualitatively explore how DSDM technologies could support HCPs in their decision-making about COPD care. We created a scenario-based research tool, called Respire, that visualized HCPs’ data needs about their COPD patients and services. We used Respire with HCPs to uncover rich and nuanced findings about human-data interaction in this context, focusing on the real-world challenges that HCPs face when carrying out their work and making decisions. We engaged nine respiratory HCPs from two collaborating healthcare organizations to design Respire. We then used Respire as a tool to investigate human-data interaction in the context of decision-making about COPD care. The study followed a co-design approach that had three stages and spanned two years. The first stage involved five workshops with the HCPs to identify data-interaction scenarios which would support their work. The second stage involved creating Respire, an interactive scenario-based web application that visualized HCPs’ data needs, incorporating feedback from the HCPs. The final stage involved 11 one-to-one sessions with HCPs to use Respire, focusing on how they envisaged it could support their work and decisions about care. We found that: (1) HCPs trust data differently depending on where it came from and who recorded it; (2) sporadic and subjective data generated by patients has value but creates challenges for decision-making; and (3) HCPs require support interpreting and responding to new data and its use cases. Our study uncovers important lessons for the design of DSDM technologies to support healthcare contexts. We show that while DSDM technologies have potential to support patient care and healthcare delivery, important sociotechnical and human data interaction challenges influence how these technologies should be designed and deployed. Exploring these considerations during the design process can ensure DSDM technologies are designed with a holistic view of how decision-making and engagement with data occurs in healthcare contexts

Software Development and CSCW:Standardization and Flexibility in Large-Scale Agile Development

Identifying which agile methods and processes are most effective depends on the goals and aims of an organisation. Agile development promotes an environment of continuous improvement and trust within self-organising teams. Therefore, it is important to allow teams to have the flexibility to customize and tailor their chosen methods. However, in a large-scale agile deployment, there needs to be a degree of process standardization across the organisation; otherwise, different teams will not be able to effectively share knowledge and best practices. This paper addresses this classic CSCW issue of the tensions that arise between process standardization and flexibility in a large-scale agile deployment at the BBC