TY  - JOUR
AU  - Barron, Daniel S.
AU  - Baker, Justin T.
AU  - Budde, Kristin S.
AU  - Bzdok, Danilo
AU  - Eickhoff, Simon B.
AU  - Friston, Karl J.
AU  - Fox, Peter T.
AU  - Geha, Paul
AU  - Heisig, Stephen
AU  - Holmes, Avram
AU  - Onnela, Jukka-Pekka
AU  - Powers, Albert
AU  - Silbersweig, David
AU  - Krystal, John H.
TI  - Decision Models and Technology Can Help Psychiatry Develop Biomarkers
JO  - Frontiers in psychiatry
VL  - 12
SN  - 1664-0640
CY  - Lausanne
PB  - Frontiers Research Foundation
M1  - FZJ-2022-01468
SP  - 706655
PY  - 2021
AB  - Why is psychiatry unable to define clinically useful biomarkers? We explore this question from the vantage of data and decision science and consider biomarkers as a form of phenotypic data that resolves a well-defined clinical decision. We introduce a framework that systematizes different forms of phenotypic data and further introduce the concept of decision model to describe the strategies a clinician uses to seek out, combine, and act on clinical data. Though many medical specialties rely on quantitative clinical data and operationalized decision models, we observe that, in psychiatry, clinical data are gathered and used in idiosyncratic decision models that exist solely in the clinician's mind and therefore are outside empirical evaluation. This, we argue, is a fundamental reason why psychiatry is unable to define clinically useful biomarkers: because psychiatry does not currently quantify clinical data, decision models cannot be operationalized and, in the absence of an operationalized decision model, it is impossible to define how a biomarker might be of use. Here, psychiatry might benefit from digital technologies that have recently emerged specifically to quantify clinically relevant facets of human behavior. We propose that digital tools might help psychiatry in two ways: first, by quantifying data already present in the standard clinical interaction and by allowing decision models to be operationalized and evaluated; second, by testing whether new forms of data might have value within an operationalized decision model. We reference successes from other medical specialties to illustrate how quantitative data and operationalized decision models improve patient care.
LB  - PUB:(DE-HGF)16
C6  - pmid:34566711
UR  - <Go to ISI:>//WOS:000698452200001
DO  - DOI:10.3389/fpsyt.2021.706655
UR  - https://juser.fz-juelich.de/record/906465
ER  -