Background: Precision medicine (PM) programs typically use broad consent. This approach requires maintenance of the social license and public trust. The ultimate success of PM programs will thus likely be contingent upon understanding public expectations about data sharing and establishing appropriate governance structures. There is a lack of data on public attitudes towards PM in Asia. Methods: The aim of the research was to measure the priorities and preferences of Singaporeans for sharing health-related data for PM. We used adaptive choice-based conjoint analysis (ACBC) with four attributes: uses, users, data sensitivity and consent. We recruited a representative sample of n = 1000 respondents for an in-person household survey. Results: Of the 1000 respondents, 52% were female and majority were in the age range of 40–59 years (40%), followed by 21–39 years (33%) and 60 years and above (27%). A total of 64% were generally willing to share de-identified health data for IRB-approved research without re-consent for each study. Government agencies and public institutions were the most trusted users of data. The importance of the four attributes on respondents’ willingness to share data were: users (39.5%), uses (28.5%), data sensitivity (19.5%), consent (12.6%). Most respondents found it acceptable for government agencies and hospitals to use de-identified data for health research with broad consent. Our sample was consistent with official government data on the target population with 52% being female and majority in the age range of 40–59 years (40%), followed by 21–39 years (33%) and 60 years and above (27%). Conclusions: While a significant body of prior research focuses on preferences for consent, our conjoint analysis found consent was the least important attribute for sharing data. Our findings suggest the social license for PM data sharing in Singapore currently supports linking health and genomic data, sharing with public institutions for health research and quality improvement; but does not support sharing with private health insurers or for private commercial use.

Precision medicine is an emerging approach to treatment and disease prevention that relies on linkages between very large datasets of health information that is shared amongst researchers and health professionals. While studies suggest broad support for sharing precision medicine data with researchers at publicly funded institutions, there is reluctance to share health information with private industry for research and development. As the private sector is likely to play an important role in generating public benefits from precision medicine initiatives, it is important to understand what the concerns are and how they might be mitigated. This study reports outcomes of a deliberative method of citizen engagement in Singapore that asked whether sharing precision medicine data with private industry would be permissible, and if so, under what circumstances. Findings from this citizens’ jury suggest sharing with industry would be permissible under certain conditions that are set out in nine recommendations. Implications of the recommendations and their underlying assumptions for policy decision makers are discussed. This study aligns with prior international studies which found conditional acceptance for data sharing with private industry, a public benefit requirement, specific reluctance to share with insurance companies and an emphasis on accountability and transparency to demonstrate trustworthiness. However, our results differ from prior studies in that opt-in consent did not dominate the deliberations as jurors were able to set it aside as an assumed prerequisite for participation in a precision medicine programme.

Our study analyzed the economicimpact of a telegeriatrics programme on care of nursing homeresidents, from the healthcare system provider’s perspective. Thisis a retrospective, archival data analysis of multiple data sourcesin 4 nursing homes of Singapore from 2010 to 2015. Individualsadmitted to nursing homes and have undergone telemedicineconsultations (N=859) from 2010 to 2015 were recruited. Weconducted a cost analysis of the programme by reviewing pasthospital admissions’ and specialist outpatient clinic (SOC) visits’billing records, nurse training records, and key performanceindicators’ reports. A significant relationship was observed betweenteleconsultations and SOC visit cost (β1 = -83.366, p-value<0.01) and between teleconsultations and inpatient cost (β1 =-470.971, p-value <0.05). Remote video consultations could reduceunnecessary SOC visits and hospital admissions, and thereforelead to cost savings. Training of nursing home nurses couldtranslate to cost savings as a result of decreased ED transfers.

BACKGROUND: The current screening tools for depression can be tedious to administer, especially in the elderly population with hearing impairment and/or limited proficiency in English language. OBJECTIVES: To look at the feasibility of using emoticon as a screening and assessment tool for depression in the elderly. DESIGN: Cross-sectional study. METHOD: A total of 77 elderly patients completed the study from June 2014 to August 2015 in a general geriatric outpatient clinic of an acute care hospital in Singapore. Patients rated their mood using an emoticon scale, which ranges from 1 (most happy face) to 7 (most sad face). Depression was assessed using the Diagnostic and Statistical Manual of Mental Disorders (4th ed.; DSM-IV) criteria as the gold standard. Sensitivity and specificity for depression were calculated for the cutoff scores from 1 to 7 on the emoticon scale. RESULTS: The sensitivity percentages were low across all cutoff scores. The specificity was more than 90% for the cutoff score of 5 and above on the emoticon scale. However, all the patients who had depression diagnosed using the DSM-IV criteria did not have emoticon scores of 5 and above. CONCLUSIONS: The emoticon scale was easy to use, but its effectiveness in the screening of depression in the elderly needs to be explored further. The inability to use the emoticon scale as a tool may be the lack of measurements in the other domains of the DSM-IV criteria (sleep, energy, appetite, etc.), rather than failure of the emoticon scale to assess mood.