Digitizing professional work in healthcare practices

Healthcare organizations, particularly hospitals, have grown into data warehouses. Driven by outside demands for transparency and accountability as well as (especially) hospitals’ desire to better govern care processes and improving outcomes, all kinds of data are collected and processed, like complications after surgery, malnutrition among older persons and nurses’ level of caring competences. Also, data is generated by ‘others’. Patients, for example, are encouraged to write reviews and score their healthcare providers on rating sites, and newspapers provide ranking scores of best performing hospitals. This desire for data collection has sparked the development of a wide range of instruments and technological infrastructures: electronic patient records are used to enable and standardize the registration of patients’ measurements (e.g. pain scores, fall risk, malnutrition) and facilitate the coding of treatments to enable billing, reimbursement and surveillance. Clinical registries are developed to collect (and commensurate) data on patient outcomes. In our research, we study how datafying practices impact and reconfigure professional work and accountability practices.

Even more far-reaching, AI impacts on medical treatment and clinical work. It generates new forms of clinical decision making as it elucidates patterns for disease development and seeks to (at least partly) replace the technological and senses-based diagnostic abilities of human practitioners. How do AI and traditional clinical practices coincide? What new possibilities does it entail? What are the effects for medical and nursing knowledge production, sense-making and accountability? Can AI for example offer a solution to workforce shortages (for instance for high-tech health care provision like ICU treatment), and with what consequences? In this research theme we conduct ethnographic research in hospitals, nursing homes, primary care and at people’s homes.

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