Objectives With the ongoing practice of datafication of health, ordinary citizens are increasingly able to generate their own health data through new technologies such as wearables and online symptom and health checkers. However, generating data is one thing, interpreting them another. General practitioners (GPs) are likely to be the first to help with interpretations. How do GPs describe their responses to patient-generated data? Policymakers in the European Union are investing heavily in infrastructures to facilitate upload of patient data to the health services and provide GPs access to patient measurements. The ways in which the GPs interpret and use these data can influence the creation or prevention of overdiagnosis significantly. We present the findings from the study in perspective of overdiagnosis.
Method As a part of a recently finished Ph.D. called Datafying Diagnosis: Data Work in General Practice, we conducted in-depth, semi-structured, one-to-one interviews with 23 Danish GPs. We analysed the interviews by drawing on Science and Technology Studies (STS) literature, including a concept of data developed by philosopher of science, Sabina Leonelli. In the perspective of overdiagnosis, we related the GP’s described mundane data work to the policy ambitions with investments in data infrastructures and compared it to the literature of overdiagnosis.
Results The interviewed GPs described being mainly affected by three types of patient-generated data: heart and sleep measurements from wearables and results from online symptom checkers. The GPs showed little interest in using these data as an active component in the diagnostic process. Data were difficult to use: the GPs had to do substantial work before they could ascribe the data clinical relevance. They did not question if data in fact measured what they stated to measure. Instead, the GPs considered how to use the data as support for claims related to their diagnostic considerations. The GPs would use the data in ways that supported action towards something they were more familiar with than data in health: symptoms in diseases. Data featured on par with patient narratives about symptoms in three ways: to represent ‘sensations’; to indicate patients’ ‘concerns’; or to be clinically irrelevant ‘noise’ from physiological measurements.
Conclusions The GPs were ill prepared for handling these new kinds of data and they used them differently than what several actors expected. These data primarily affected the GPs’ clinical decisions when they could serve as agents that could close a case, provide guidance on action, or bring comfort to the patient within the defined time frame of a consultation. In that regard, these data can both create and prevent overdiagnosis depending on the specific GP, condition, and clinical and societal conditions.
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