Measuring health-related social needs (HRSNs), often aligned with social determinants of health (SDOH), is a critical consideration in modern healthcare. Recent studies, notably one published in JAMA Network Open, have shed light on the efficacy of various methods employed to gauge these needs. The main takeaway is that no singular measurement method can encompass the entirety of a patient’s social health requirements. This reality underscores the necessity for healthcare organizations to adopt a multifaceted approach to better serve their patient populations.
Understanding Health-Related Social Needs
Health-related social needs cover a broad range of issues that can impact an individual’s health status. These include, but are not limited to, food insecurity, housing instability, transportation barriers, and financial strain. With an increasing acknowledgment of the profound influence these factors have on health outcomes, healthcare providers are progressively integrating routine screenings for HRSNs into their practices.
The Evolving Landscape of HRSN Measurement
The research conducted by the Regenstrief Institute and its collaborators sought to compare various approaches for screening HRSNs. The methods evaluated included:
- Screening Questionnaires: Traditional tools that patients fill out to disclose their social needs.
- Natural Language Processing (NLP): A technology that analyzes clinical notes for relevant insights.
- Rule-Based Computable Phenotypes: Algorithms that categorize patient data into predefined criteria.
- Machine Learning (ML) Classification Models: Advanced computational techniques that learn from data patterns to predict needs.
The study involved over 1,200 adult patients in two primary care clinics in Indiana, conducted from January 2022 to June 2023.
Findings: Performance of Different Methods
Screening Questionnaires
Screening questionnaires were found to be the most effective method among those tested. They demonstrated a high positive predictive value, meaning that flagged individuals could reliably be referred to social services like food assistance or housing programs. However, limitations were noted, especially regarding the detection of housing instability, financial strain, and transportation barriers where true-positive rates were lower than desired.
Natural Language Processing (NLP)
NLP also showed potential, particularly for identifying food insecurity and transportation issues. However, the data available for this analysis is often lacking since patients typically don’t disclose social needs unless prompted by a screening tool. Consequently, this hampers the effectiveness of NLP in real-world applications.
Machine Learning (ML)
While ML models did not perform well overall, they indicated sensitivity to various HRSNs. Researchers noted that ML could complement the high specificity of screening questionnaires in a two-stage screening model that might efficiently identify patients at risk.
Rule-Based Phenotypes
The rule-based approach showed the least effectiveness, demonstrating poor performance even with the introduction of additional features.
Addressing Fairness in HRSN Measurement
An essential finding of the research was the indication of biased performance across various demographic groups. Specifically, disparities in false-negative rates were evident along age and racial lines. This highlights the importance of monitoring and addressing fairness to ensure equitable healthcare access and appropriate referrals to community resources.
The Case for Comprehensive Screening
The emerging consensus from the study advocates for the use of multiple methods to adequately assess HRSNs. While questionnaires may serve as an excellent starting point, integrating additional approaches can help prevent the oversight of individuals with unmet needs.
Healthcare organizations should consider establishing a structured framework for screening that encompasses:
- Standardized Screening Questionnaires: To identify obvious social needs.
- Supplementary Data Analysis through NLP: To uncover additional insights from clinical documentation.
- Utilizing ML for Early Detection: As part of a broader strategy that also includes human oversight to improve patient engagement.
Conclusion
In an era where healthcare providers are increasingly recognizing the pivotal role of HRSNs in shaping health outcomes, it is clear that a one-size-fits-all approach to measurement is inadequate. The findings from recent research indicate that employing a combination of tools is essential for capturing the full spectrum of patient needs, thereby facilitating timely access to resources that can significantly improve health outcomes.
By addressing social determinants of health through comprehensive screening strategies, healthcare systems can ensure that they not only treat medical conditions but also foster holistic well-being for all patients. This integrated approach is essential for driving equity and improving the health of populations across diverse demographics.
Future Directions
Moving forward, healthcare organizations should invest in training staff on the implementation of multi-method screening processes, emphasizing the importance of social health in clinical settings. Continuous monitoring and evaluation of screening effectiveness and fairness should become standard practice, ensuring that all patients are equitably served regardless of their background or circumstances.
By making these concerted efforts, healthcare providers will be better equipped to address and mitigate the impact of health-related social needs, leading to improved outcomes and a more just healthcare environment.
