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ObjectiveThis study validates the previously tested Screening for Poverty And Related social determinants to improve Knowledge of and access to resources (‘SPARK Tool’) against comparison questions from well-established national surveys (Post Survey Questionnaire (PSQ)) to inform the development of a standardised tool to collect patients’ demographic and social needs data in healthcare.DesignCross-sectional study.SettingPan-Canadian study of participants from four Canadian provinces (SK, MB, ON and NL).Participants192 participants were interviewed concurrently, completing both the SPARK tool and PSQ survey.Main outcomesSurvey topics included demographics: language, immigration, race, disability, sex, gender identity, sexual orientation; and social needs: education, income, medication access, transportation, housing, social support and employment status. Concurrent validity was performed to assess agreement and correlation between SPARK and comparison questions at an individual level as well as within domain clusters. We report on Cohen’s kappa measure of inter-rater reliability, Pearson correlation coefficient and Cramer’s V to assess overall capture of needs in the SPARK and PSQ as well as within each domain. Agreement between the surveys was described using correct (true positive and true negative) and incorrect (false positive and false negative) classification.ResultsThere was a moderate correlation between SPARK and PSQ (0.44, p<0.0001). SPARK correctly classified 71.4% of participants with or without a social need. There was strong agreement with most demographic questions. SPARK correctly classified 74.3% of participants as having financial insecurity. Clustering financial security questions had fewer false negatives (6.4%, n=11/171 vs 9.9%, n=17/171) and more false positives (19.3%, n=33/171 vs 11.1%, n=19/171) when compared with the question ‘difficulty making ends meet’. When looking specifically at participants with high UCLA loneliness scores (>60), SPARK correctly classified 90.5% (n=176/191).ConclusionsSPARK provides a brief 15 min screening tool for primary care clinics to capture social and access needs. SPARK was able to correctly classify most participants within each domain. Related ongoing research is needed to further validate SPARK in a large representative sample and explore primary care implementation strategies to support integration.

Data on the social determinants of health could be used to improve care, support quality improvement initiatives, and track progress toward health equity. However, this data collection is not widespread. Artificial intelligence (AI), specifically natural language processing and machine learning, could be used to derive social determinants of health data from electronic medical records. This could reduce the time and resources required to obtain social determinants of health data. This study aimed to understand perspectives of a diverse sample of Canadians on the use of AI to derive social determinants of health information from electronic medical record data, including benefits and concerns. Using a qualitative description approach, in-depth interviews were conducted with 195 participants purposefully recruited from Ontario, Newfoundland and Labrador, Manitoba, and Saskatchewan. Transcripts were analyzed using an inductive and deductive content analysis. A total of 4 themes were identified. First, AI was described as the inevitable future, facilitating more efficient, accessible social determinants of health information and use in primary care. Second, participants expressed concerns about potential health care harms and a distrust in AI and public systems. Third, some participants indicated that AI could lead to a loss of the human touch in health care, emphasizing a preference for strong relationships with providers and individualized care. Fourth, participants described the critical importance of consent and the need for strong safeguards to protect patient data and trust. These findings provide important considerations for the use of AI in health care, and particularly when health care administrators and decision makers seek to derive social determinants of health data.

Background Healthcare organizations are increasingly exploring ways to address the social determinants of health. Accurate data on social determinants is essential to identify opportunities for action to improve health outcomes, to identify patterns of inequity, and to help evaluate the impact of interventions. The objective of this study was to refine a standardized tool for the collection of social determinants data through cognitive testing. Methods An initial set of questions on social determinants for use in healthcare settings was developed by a collaboration of hospitals and a local public health organization in Toronto, Canada during 2011–2012. Subsequent research on how patients interpreted the questions, and how they performed in primary care and other settings led to revisions. We administered these questions and conducted in-depth cognitive interviews with all the participants, who were from Saskatchewan, Manitoba, Ontario, and Newfoundland and Labrador. Cognitive interviewing was used, with participants invited to verbalize thoughts and feelings as they read the questions. Interview notes were grouped thematically, and high frequency themes were addressed. Results Three hundred and seventy-five individuals responded to the study advertisements and 195 ultimately participated in the study. Although all interviews were conducted in English, participants were diverse. For many, the value of this information being collected in typical healthcare settings was unclear, and hence, we included descriptors for each question. In general, the questions were understood, but participants highlighted a number of ways the questions could be changed to be even clearer and more inclusive. For example, more response options were added to the question of sexual orientation and the “making ends meet” question was completely reworded in light of challenges to understand the informal phrasing cited by English as a Second Language (ESL) users of the tool. Conclusion In this work we have refined an initial set of 16 sociodemographic and social needs questions into a simple yet comprehensive 18-question tool. The changes were largely related to wording, rather than content. These questions require validation against accepted, standardized tools. Further work is required to enable community data governance, and to ensure implementation of the tool as well as the use of its data is successful in a range of organizations.