By Sara Heath
- Relying simply on community-level data rather than conducting an individual social determinants of health screening increases the risk a patient will fall through the crack in social services interventions, according to new data published in JAMA Network Open.
In other words, conducting a social determinants of health screening with individual patients provides more granular information about patient needs than looking at overarching risk factors in a certain geographic region.
These findings come as healthcare organizations increasingly zero in on the social determinants of health as critical influences in patient health outcomes. Organizations recognize that certain factors like food security, housing security, and access to transportation can determine a patient’s ability to achieve and maintain wellness.
To that end, organizations are working to identify which patients experience social determinants of health and best practices to mitigate those risk factors.
“In the absence of standard social risk screening recommendations, some health systems are exploring obtaining social risk information without screening patients directly,” the research team explained. “Community and neighborhood-level data characterizing the ‘conditions in which people are born, grow, live, work and age’ are readily available from public sources, such as the US Census or American Community Survey, and can be geocoded and linked to patients’ addresses.”
READ MORE: Outlining Social Services Options Key to SDOH Screening
“Theoretically, such data could provide an alternative way to identify patients with social risks or to target patients for whom self-reported screening should be prioritized,” they posited.
But that isn’t exactly the case.
In an analysis of over 36,000 patients in 13 US states, the researchers determined that individual social determinants of health screenings were more effective for determining risk than looking at community-level data.
Using census-tract level data, the researchers foremost worked to detect certain social risk factors based on community-level data. The researchers deemed a patient at-risk for social determinants of health if she lived in what the team determined to be an under-resourced neighborhood.
Next, the team looked at individual-level social risk factor data gleaned from patient screenings. Those screenings asked patients about social challenges, like food insecurity, housing insecurity, and financial resource strain. The screenings provided individualized information about the social determinants of health.
READ MORE: Adapting Social Determinants of Health Screening for Remote Care
Analysis of the two data sources did show some overlap—a patient flagging as high-risk in the community-level data set also screened positive for at least one social risk factor in the individualized screens.
But there were also a lot of patients screening positive in the individualized surveys who were not flagged in the community-level assessments. Put simply, many patients were falling through the cracks.
Sixty percent of patients screening for at least one social determinant of health lived in the lowest quartile census tract, or the most disadvantaged neighborhoods. This finding suggests that community-level data can catch 60 percent of patients screening positive for the social determinants of health.
But that means 40 percent of patients would fall through the cracks should a healthcare organization rely solely on community-level data. In fact, patients screening positive for at least one social determinant of health lived in each quartile census tract, from the richest to the poorest neighborhoods.
“Using community-level data as a proxy for patient-level social risk screening or to refine targets for patient-level data collection may heighten the risk of ecologic fallacy, wherein incorrect assumptions are made about an individual based on aggregate-level information about a group,” the researchers said.
READ MORE: Top Considerations for SDOH Screening, Referral Technologies
“Despite the potential utility of community-level data for a variety of purposes, including in cases when universal screening is not feasible, findings within our study population suggest that ecologic fallacy may in fact be an issue when using community-level data to identify patient-level social needs.”
Notably, these findings indicate a need for cost- and time-efficient strategies to screen for social determinants of health. All said, community-level data was 48 percent accurate in flagging patients for social health needs. But conducting individual social determinants of health screenings is not feasible for all healthcare organizations.
The screenings require a strong patient-provider relationship underscored by trust, ample time within the clinician workflow, workable health IT that can transmit this data across the care continuum, and the financial resources to carry out this mission.
“Potential next steps could include identifying a smaller set of risk factors to screen for in clinical settings, or alternatively, developing a valid and reliable single-question screening for social risks,” the researchers posited.
Of course, there is also the question of whether patients even want help with these types of social risk factors. According to this latest study, a sizeable portion of patients said they did not think their healthcare providers needed to address their social needs. More research is necessary to better understand patient perspectives about social services access.
Additionally, researchers may look into whether perceived availability of social services influence whether the patient would like help connecting to those services.
These findings do not make the argument for ignoring community-level social risk factor data. This data can help provide context for patient care management strategies—leniency for diet in a food desert, for example. Additionally, they can help community-level interventions that are not targeted to an individual patient.
“Indeed, other countries have demonstrated the value of using area-based measures of socioeconomic variation to assess community needs, inform research, adjust clinical funding, allocate community resources, and determine policy impact,” the researchers concluded. “More research is needed to understand how patient-level and community-level data can be used in concert to most effectively and efficiently invest limited resources.”
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