The compatibility of reductionistic and complexity approaches in a sociomedical innovation perspective

Datos Bibliográficos

ID21878703
AutoresJenneken Naaldenberg (0000-0003-2675-4516, Radboud University Nijmegen, autor de correspondencia), Noelle Aarts (0000-0001-5134-4004, Radboud University Nijmegen)
Año2020
Volumen5
Número12
Páginase003858
Fecha de publicación2020-12-01
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaBMJ Global Health (JOURNAL)
Identificadores de la revistaISSN: 2059-7908 • E-ISSN: 2059-7908
EditorialBMJ (PUBLISHER • GB)
DOI10.1136/bmjgh-2020-003858
PMID33272945
OpenAlexW3110382977
IdiomaEN
Citas recibidas3
Referencias citadas17

Medical technologies, e-health and personalised medicine are rapidly changing the healthcare landscape. Successful implementation depends on interactions between the technology, the actors and the context. More traditional reductionistic approaches aim to understand isolated factors and linear cause–effect relations and have difficulties in addressing inter-relatedness and interaction. Complexity theory offers a myriad of approaches that focus specifically on behaviour and mechanisms that emerge from interactions between involved actors and the environment. These approaches work from the assumption that change does not take place in isolation and that interaction and inter-relatedness are central concepts to study. However, developments are proceeding fast and along different lines. This can easily lead to confusion about differences and usefulness in clinical and healthcare research and practice. Next to this, reductionistic and complexity approaches have their own merits and much is to be gained from using both approaches complementary. To this end, we propose three lines in complexity research related to health innovation and discuss ways in which complexity approaches and reductionistic approaches can act compatibly and thereby strengthen research designs for developing, implementing and evaluating health innovations

Bioinformatics · Biology · Confusion · Data science · Epistemology · Knowledge management · Management science · Medical research · Reductionism · Computer Science · Engineering · Health Policy Implementation Science · Health Systems, Economic Evaluations, Quality of Life · Health, Environment, Cognitive Aging · Psychology · Artificial Intelligence · Biotechnology