Data ambiguity and clinical decision making: A qualitative case study of the use of predictive information technologies in a personalized cancer clinical trial

Abstract

Personalized medicine aims to tailor the treatment to the specific characteristics of the individual patient. In the process, physicians engage with multiple sources of data and information to decide on a personalized treatment. This article draws on a qualitative case study of a clinical trial testing a method for matching treatments for advanced cancer patients. Specialists in the trial used data and information processed by a specifically developed drug-efficacy predictive algorithm and other information artifacts to make personalized clinical decisions. While using high-resolution data in the trial was expected to provide a more accurate basis for action, sociomaterial engagements of oncologists with data and its representation by artifacts paradoxically hindered personalized clinical decisions. I contend that the engagement between human discretion, ambiguous data, and malleable artifacts in this non-standardized trial produced moments of contradiction within entanglement. Sociomaterial approaches should acknowledge such conflicts in further analyses of medical practice transitions.

Data work in healthcare: An Introduction

“…While data infrastructures are built to enable monitoring of populations and early prevention measures, 20 and to monitor the quality of services, 21,22 healthcare workers increasingly learn to maneuver playfully within these environments. 23 Thus, the papers collected here discuss the data work of different groups: doctors struggling to clarify data ambiguity and use predictive algorithms for personalized medicine; 24 coming to terms with data variability-varying data on the same phenomena; 25 clinicians' response to patients' increasing data literacy; 26 nurses' interpretation of patient-generated data as a means of inclusion in their own care; 27,28 and patients generating data about their health. 29,30 Finally, there is work to produce data itself, including skillfully assessing messy charts to create structured datasets, 22 to sanitizing and validating data, 31 and building data integrations between various information systems.…”

“…Further, the number and diversity of individuals and occupational groups involved in healthcare data work is vast and includes clinicians, non-clinical healthcare workers, managers, administrators, patients, caregivers, and external organizations and workers (quality improvement organizations, researchers, IT companies, consultants, and others). For example, Langstrup 30 describes the collection of PROM (Patient-Reported Outcome Measures) data by patients; Hult et al 26 describe the work of nurses adapting to different patient literacies; Chorev 24 chronicles the work of clinicians to make sense of ambiguous data; and Bjørnstad and Ellingsen 32 call attention to the invisible work of creating meaningful integration of information systems. Widespread adoption of digital Information Infrastructures for healthcare increases the capacity to produce, store, and analyze data, 43 and widespread availability of data tools mean that increasing expectations are developing for the types and depth of biomedical and organizational research that can be done using second-order data.…”