TY - JOUR

T1 - Exploring Human-Data Interaction in Clinical Decision-making Using Scenarios: Co-design Study

AU - Tendedez, Helena

AU - Ferrario, Maria Angela

AU - McNaney, Roisin

AU - Gradinar, Adrian

PY - 2022/5/6

Y1 - 2022/5/6

N2 - 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.

AB - 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.

KW - Human Data Interaction

KW - Chronic obstructive pulmonary disease

KW - Healthcare

KW - data supported decision-making

U2 - 10.2196/32456

DO - 10.2196/32456

M3 - Journal article

VL - 9

JO - JMIR Human Factors

JF - JMIR Human Factors

SN - 2292-9495

IS - 2

M1 - e32456

ER -