Utilizing Patient Intake Software to Screen Social Determinants of Health (SDOH): The Impact of Private Non-Human Interaction on Disclosure

Purpose: This study explores whether private, non-human screenings for social determinants of health (SDOH) increase the need for assistance compared to traditional clinician-conducted interviews. The objective is to assess whether private patient-directed screenings reduce social barriers, such as fear of judgment and discomfort, thereby improving the accuracy of data collection and enhancing patient care outcomes.

Background: SDOH screening (i.e., income, housing, education, and safety) is crucial to understanding the non-medical factors affecting patient health beyond clinical care. Accurate SDOH data enables healthcare providers to create personalized care plans, promoting health equity and reducing healthcare costs. However, traditional clinician-led screenings can inhibit the full disclosure of sensitive information due to social discomfort. The health belief model is a theoretical model that defines the key factors such as perceived susceptibility, perceived severity, perceived benefits, perceived barriers, cues to action, and self-efficacy to predict whether someone will take preventive or health-promoting actions. This study investigates the removal of human interaction using patient-directed software displays to increase accurate SDOH needs identification thus improving patient outcomes.

Method: A quantitative study involving 100 outpatient participants was conducted. 50 patients used patient-directed intake software, while clinicians interviewed the other 50 patients. Screenings included a safety assessment and other SDOH factors. The study measured safety scores and the frequency of requests for SDOH assistance. Statistical analyses, including p-values and t-values, were employed to determine the significant differences between the groups.

Results: The findings indicated that 56% of patients using the intake software reported a perfect safety score (4), compared to 98% of patients interviewed by clinicians. Additionally, 44% of software users reported a score (>4) indicating at least one safety concern, while only 2% of clinician-interviewed patients did. Furthermore, 30% of software-screened patients requested SDOH assistance, in contrast to 6% of those interviewed by clinicians. These results suggest that patients screened via software provided more candid disclosures.

Conclusions: The study concludes that non-human, private SDOH screenings can significantly improve sensitive data collection, as patients feel more comfortable disclosing personal information without a clinician present. This approach could be crucial for enhancing SDOH screenings for assistance, leading to better identification of social needs and more targeted interventions. Integrating automated screening tools into healthcare settings can promote health equity by ensuring vulnerable populations receive needed care and resources.