Exploring the Relationship between Shared Decision-Making, Patient-Centered Medicine, and Evidence-Based Medicine

An online evidence-based dictionary of common adverse events of antidepressants: a new tool to empower patients and clinicians in their shared decision-making process

BACKGROUND

Adverse events (AEs) are commonly reported in clinical studies using the Medical Dictionary for Regulatory Activities (MedDRA), an international standard for drug safety monitoring. However, the technical language of MedDRA makes it challenging for patients and clinicians to share understanding and therefore to make shared decisions about medical interventions. In this project, people with lived experience of depression and antidepressant treatment worked with clinicians and researchers to co-design an online dictionary of AEs associated with antidepressants, taking into account its ease of use and applicability to real-world settings.

METHODS

Through a pre-defined literature search, we identified MedDRA-coded AEs from randomised controlled trials of antidepressants used in the treatment of depression. In collaboration with the McPin Foundation, four co-design workshops with a lived experience advisory panel (LEAP) and one independent focus group (FG) were conducted to produce user-friendly translations of AE terms. Guiding principles for translation were co-designed with McPin/LEAP members and defined before the finalisation of Clinical Codes (CCs, or non-technical terms to represent specific AE concepts). FG results were thematically analysed using the Framework Method.

RESULTS

Starting from 522 trials identified by the search, 736 MedDRA-coded AE terms were translated into 187 CCs, which balanced key factors identified as important to the LEAP and FG (namely, breadth, specificity, generalisability, patient-understandability and acceptability). Work with the LEAP showed that a user-friendly language of AEs should aim to mitigate stigma, acknowledge the multiple levels of comprehension in 'lay' language and balance the need for semantic accuracy with user-friendliness. Guided by these principles, an online dictionary of AEs was co-designed and made freely available ( https://thesymptomglossary.com ). The digital tool was perceived by the LEAP and FG as a resource which could feasibly improve antidepressant treatment by facilitating the accurate, meaningful expression of preferences about potential harms through a shared decision-making process.

CONCLUSIONS

This dictionary was developed in English around AEs from antidepressants in depression but it can be adapted to different languages and cultural contexts, and can also become a model for other interventions and disorders (i.e., antipsychotics in schizophrenia). Co-designed digital resources may improve the patient experience by helping to deliver personalised information on potential benefits and harms in an evidence-based, preference-sensitive way.

Assessing patient information needs for new antidiabetic medications to inform shared decision-making: A best-worst scaling experiment in China

BACKGROUND

Shared decision-making (SDM) is a patient-centred approach to improve the quality of care. An essential requirement for the SDM process is to be fully aware of patient information needs.

OBJECTIVES

Our study aimed to assess patient information needs for new antidiabetic medications using the best-worst scaling (BWS) experiment.

METHODS

BWS tasks were developed according to a literature review and the focus group discussion. We used a balanced incomplete block design and blocking techniques to generate choice sets. The final BWS contains 11 attributes, with 6-choice scenarios in each block. The one-to-one, face-to-face BWS survey was conducted among type 2 diabetic patients in Jiangsu Province. Results were analyzed using count-based analysis and modelling approaches. We also conducted a subgroup analysis to observe preference heterogeneity.

RESULTS

Data from 539 patients were available for analysis. The most desired information domain was the comparative effectiveness of new antidiabetic medications. It consists of the incidence of macrovascular complications, the length of extended life years, changes in health-related quality of life, the incidence of microvascular complications, and the control of glycated haemoglobin. Of all the attributes, the incidence of macrovascular complications was the primary concern. Patients' glycemic control and whether they had diabetes complications exerted a significant influence on their information needs.

CONCLUSIONS

Information on health benefits is of critical significance for diabetic patients. Patients have different information needs as their disease progresses. Personalized patient decision aids that integrate patient information needs and provide evidence of new antidiabetic medications are worthy of being established.

PATIENT OR PUBLIC CONTRIBUTION

Before data collection, a pilot survey was carried out among diabetic patients to provide feedback on the acceptability and intelligibility of the attributes.

Accuracy of physician and nurse predictions for 28-day prognosis in ICU: a single center prospective study

The proportion of correctly predicted prognoses and factors associated with prediction accuracy are unknown. The objective of this study was to explore the accuracy of physician and nurse predictions of 28-day mortality in the ICU. This was a prospective observational single-center study. All medical staff in the ICU have access to patient data, can communicate with patients or clients, and can independently predict the prognosis of patients within 24 h of patient admission. The only question of the questionnaire survey was: What is the patient's outcome on day 28 (alive or death)? There were 2155 questionnaires completed by 18 physicians and 1916 submitted by 15 nurses. In the 312 patients included, the 28-day mortality rates were predicted by physicians and nurses. The overall proportion of correct prognosis prediction was 90.1% for physicians and 64.4% for nurses (P = 0.000). There was no significant difference in the overall correct proportion and average correct proportion among physicians with different seniority levels. The overall correct proportion and average correct proportion increased among nurses with seniority. Physicians in the ICU can moderately predict 28-day mortality in critically ill patients. Nurses with a seniority of less than 10 years in ICU cannot accurately predict 28-day mortality in critically ill patients. However, the accuracy of nurses' prediction of patients' 28-day prognosis increased with their seniority in the ICU.

Efficacy of a Decision Support Intervention on Decisional Conflict Related to Hepatocellular Cancer Treatment: A Randomized Controlled Trial

The purpose of this study was to investigate the efficacy of decision support intervention on treatment knowledge, decision self-efficacy, decisional conflict, and decision satisfaction in patients with hepatocellular cancer. The study was a randomized controlled trial. In all, 69 patients with hepatocellular carcinoma (HCC) were recruited and randomly assigned to a decision support group or a control group. Data were collected at baseline, post-test, and follow-up using self-report questionnaires. After controlling for baseline scores, the between-group difference (95% confidence interval [CI]) for treatment-related knowledge in post-test scores was 11.9 (6.1, 17.8). After controlling for baseline scores, the between-group difference (95% CI) for decisional conflict was -7.0 (-12.0, -2.0). There was no statistically significant between-group difference in decision self-efficacy and decision satisfaction. Findings supported the efficacy of decision support intervention to improve treatment knowledge and reduce decisional conflict but had no significant effect on decision self-efficacy and decision satisfaction in patients with HCC.