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Decision Analysis in SHared decision making for Thromboprophylaxis during Pregnancy (DASH-TOP): a sequential
Brittany Humphries1, Montserrat León-García2,3, Shannon M Bates4
1Department of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, Ontario, Canada.
BMJ Evidence-Based Medicine
|March 1, 2023
Summary
Patients valued a shared decision-making tool for low-molecular-weight heparin (LMWH) during pregnancy, but some found parts of the analysis difficult. The study highlights challenges and opportunities in integrating patient preferences into clinical decisions.
Area of Science:
- Medical Decision Making
- Health Services Research
- Patient-Centered Care
Background:
- Integrating patient preferences with clinical evidence is crucial for informed treatment decisions.
- Personalized decision analysis offers a framework for shared decision-making.
- Antenatal prophylactic low-molecular-weight heparin (LMWH) involves complex choices for pregnant individuals.
Purpose of the Study:
- To explore patient experiences with a personalized decision analysis intervention for prophylactic LMWH in pregnancy.
- To understand how formal methods can integrate patient preferences and clinical evidence.
- To identify challenges and opportunities in using decision analysis for shared decision-making.
Main Methods:
- A mixed-methods explanatory sequential pilot study was conducted.
- Participants (n=15) with prior venous thromboembolism undergoing antenatal LMWH counseling were recruited.
- A shared decision-making intervention included choice exercises, preference elicitation, and personalized decision analysis, followed by questionnaires and interviews.
Main Results:
- Participants appreciated the decision support tool for incorporating personal values and felt better prepared for consultations.
- Reactions to specific components like the standard gamble were mixed, with some participants struggling with understanding.
- Suggestions were made for more informative presentation of decision analysis results.
Conclusions:
- Personalized decision analysis can support shared decision-making by integrating patient preferences and clinical evidence.
- Challenges exist in the usability and presentation of decision analysis tools.
- Further refinement is needed to optimize decision analysis interventions for clinical practice.
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