Dual processing model of medical decision-making
- Benjamin DjulbegovicAffiliated withCenter for Evidence-based Medicine and Health Outcomes ResearchDepartment of Internal Medicine, Division of Evidence-based Medicine and Health Outcomes Research University of South FloridaDepartments of Hematology and Health Outcomes and Behavior, H. Lee Moffitt Cancer Center & Research InstituteUSF Health Email author
- , Iztok HozoAffiliated withDepartment of Mathematics, Indiana University Northwest
- , Jason BecksteadAffiliated withCollege of Nursing, University of South Florida
- , Athanasios TsalatsanisAffiliated withCenter for Evidence-based Medicine and Health Outcomes ResearchDepartment of Internal Medicine, Division of Evidence-based Medicine and Health Outcomes Research University of South Florida
- , Stephen G PaukerAffiliated withDivision of Clinical Decision Making, Department of Medicine, Tufts Medical Center
Dual processing theory of human cognition postulates that reasoning and decision-making can be described as a function of both an intuitive, experiential, affective system (system I) and/or an analytical, deliberative (system II) processing system. To date no formal descriptive model of medical decision-making based on dual processing theory has been developed. Here we postulate such a model and apply it to a common clinical situation: whether treatment should be administered to the patient who may or may not have a disease.
We developed a mathematical model in which we linked a recently proposed descriptive psychological model of cognition with the threshold model of medical decision-making and show how this approach can be used to better understand decision-making at the bedside and explain the widespread variation in treatments observed in clinical practice.
We show that physician’s beliefs about whether to treat at higher (lower) probability levels compared to the prescriptive therapeutic thresholds obtained via system II processing is moderated by system I and the ratio of benefit and harms as evaluated by both system I and II. Under some conditions, the system I decision maker’s threshold may dramatically drop below the expected utility threshold derived by system II. This can explain the overtreatment often seen in the contemporary practice. The opposite can also occur as in the situations where empirical evidence is considered unreliable, or when cognitive processes of decision-makers are biased through recent experience: the threshold will increase relative to the normative threshold value derived via system II using expected utility threshold. This inclination for the higher diagnostic certainty may, in turn, explain undertreatment that is also documented in the current medical practice.
We have developed the first dual processing model of medical decision-making that has potential to enrich the current medical decision-making field, which is still to the large extent dominated by expected utility theory. The model also provides a platform for reconciling two groups of competing dual processing theories (parallel competitive with default-interventionalist theories).
- Dual processing model of medical decision-making
- Open Access
- Available under Open Access This content is freely available online to anyone, anywhere at any time.
BMC Medical Informatics and Decision Making
- Online Date
- September 2012
- Online ISSN
- BioMed Central
- Additional Links
- Author Affiliations
- 1. Center for Evidence-based Medicine and Health Outcomes Research, Tampa, FL, USA
- 2. Department of Internal Medicine, Division of Evidence-based Medicine and Health Outcomes Research University of South Florida, Tampa, FL, USA
- 3. Departments of Hematology and Health Outcomes and Behavior, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
- 7. USF Health, 12901 Bruce B. Downs Boulevard, MDC27, Tampa, FL, 33612, USA
- 4. Department of Mathematics, Indiana University Northwest, Gary, IN, USA
- 5. College of Nursing, University of South Florida, Tampa, FL, USA
- 6. Division of Clinical Decision Making, Department of Medicine, Tufts Medical Center, Boston, USA