Clinician Awareness of Patient-Reported Health-Related Social Needs: There’s Room for Improvement

Abstract

Introduction:

People experiencing health-related social needs (HRSNs), such as transportation insecurity, are less likely to undergo preventive health screenings. They are more likely to have worse health outcomes overall, including a higher rate of late-stage cancer diagnoses. If primary care clinicians are aware of HRSNs, they can tailor preventive care, including cancer screening approaches. Accordingly, recent guidelines recommend that clinicians “adjust” care based on HRSNs. This study assessed the level of clinician awareness of patient-reported HRSNs and congruence between clinician perception and patient-reported HRSNs.

Methods:

We surveyed patients aged 50 to 85 years and their clinicians in 3 primary care clinics that routinely screen patients for HRSNs. Patients and clinicians reported the presence/absence of 6 HRSNs, including food, transportation, housing and financial insecurity for medications/healthcare, financial insecurity for utilities, and social isolation. Kappa statistics assessed the concordance of reported HRSNs between patients and clinicians.

Results:

Across 237 paired patient-clinician surveys, mean patient age was 65 years, and 62% and 13% of patients were female and Latinx/Hispanic, respectively. Concordance between clinician- and patient-reported HRSNs varied by HRSN, with the lowest agreement for food insecurity (kappa = .08; 95% CI: 0.00, 0.17; P = .01) and highest agreement for transportation insecurity (kappa = .39; 95% CI: 0.18, 0.59; P < .001). The other HRSNs assessed were housing insecurity (kappa = .30; 95% CI: 0.05, 0.55; P < .001), social isolation (kappa = .24; 95% CI: 0.03, 0.45; P < .001), financial insecurity for utilities (kappa = .21; 95% CI: −0.02, 0.45; P < .001), and financial insecurity for healthcare/medications (kappa = .12; 95% CI: −0.02, 0.27; P < .001). In particular, discrepancies were noted in food insecurity prevalence: patient-reported food insecurity was 29% whereas clinician-reported food insecurity was only 3%.

Discussion:

Clinician awareness of patients’ social needs was only modest to fair, and varied by specific HRSN. In order to adjust care for HRSNs, clinics need processes for increased sharing of patient-reported HRSNs screening information with the entire clinical team. Future research should explore options for sharing HRSN data across teams and evaluate whether better HRSN data-sharing impacts outcomes.

Keywords

Introduction

Social risks and resulting health-related social needs (HRSNs),¹ have significant impacts on health outcomes. For example, evidence supports decreased frequency of several types of cancer screening in people experiencing HRSN^2
-6 and increased late-stage diagnoses^7,8 particularly in communities with higher social deprivation indices.^9,10 Diabetes,¹¹ asthma,¹² and mental health conditions¹³ all occur more frequently in people experiencing HRSNs. HRSNs are defined as unmet needs such as lack of adequate nutritious food or reliable transportation resulting from an individual’s social circumstances¹⁴ and are the result of underlying social determinants of health (SDOH), defined as “the conditions in the environments where people are born, live, learn, work, play, worship, and age that affect a wide range of health, functioning and quality of life outcomes”.¹⁵

Based on the influence of SDOH and HRSNs on health equity, many healthcare organizations have begun screening patients for HRSNs and seeking to connect them with needed resources. Early evidence shows acceptability of these processes^16,17 and reduction in HRSNs.¹⁸ Recent preliminary data also suggest promising improvements in health outcomes as a result of screening for HRSNs.^19,20

In addition to promoting screening and referral for HRSNs, current guidelines for HRSN management also recommend that clinicians “adjust” care based on patient-reported health-related social needs.²¹ Adjusting care involves altering clinical care to accommodate identified HRSNs. For example, for people with diabetes, medication or insulin doses can be adjusted in response to unstable food intake that results from food insecurity.²² At this time, recommendations for how to adjust care are fairly general, as evidence supporting specific adjustments to care to improve health outcomes is only now emerging.^23,24 Furthermore, it is unknown how often workflows for screening and referral for HRSN include or share results with clinicians rather than including only care managers and other staff.

In sum, a key gap to progress in addressing calls to action to adjust care for identified HRSNs is understanding how often an identified HRSN is known to clinicians during an in-person visit. Studies have shown that clinicians are typically not involved in screening workflows,^25,26 often do not review results of HRSNs screening tools²⁷ due to limited time with patients,²⁸ and the information is also not always effectively stored in electronic medical records.^29
-32 These factors could result in low clinician awareness of specific patient HRSNs, and to our knowledge, little exists in the literature exploring this. To address this gap, this study aimed to evaluate the concordance between patient-reported needs and clinician-perceived needs as part of a study examining the relationship between HRSNs and up-to-date status on cancer screening. Understanding whether clinicians are aware of patient-reported needs might help indicate areas for improvement in HRSN screening and referral workflows or clinician-focused interventions around adjusting care based on HRSNs.

Methods

Design

This study was a paired patient and clinician cross-sectional survey collected during primary care clinic visits for chronic or preventive health-related care.

Setting and Participants

Seven primary care clinicians practicing in 3 community-based primary care practices in Western Colorado participated in the study. Clinics had an established workflow for screening and referral for HRSNs prior to the study that included screening every patient at least once per year and screening new patients at their first visit. They had participated in the Accountable Health Communities Model project in western Colorado and used the AHCM screening tool. Each provider completed up to 60 paired surveys with patients aged 50 to 85 years being seen for non-acute primary care visits.

Recruitment

Clinics were recruited by the study lead through an established practice-based research network that she directs. Clinicians were initially contacted with information about the study by email, and then participation was discussed in person or by phone with interested clinicians. All clinicians who responded agreed to participate. Patients were asked to complete the survey by medical assistants after their clinic visit. In an effort to keep the participation burden low, clinicians were not asked to track how many patients declined or their demographics.

Measures

The paired patient and clinician survey was developed by the study team and also contained questions about barriers to and up-to-date status on colon cancer screening. Those results will be reported separately. HRSNs were assessed among patients using 6 questions from the Accountable Health Communities screening tool³³ regarding housing instability, transportation insecurity, food insecurity (2 questions), difficulty paying for utilities, and social isolation; plus, a seventh 2-part question from the PRAPARE screening tool³⁴ about difficulty paying for health care. The clinician answered a dichotomized version of these questions based on what they knew or thought about the patient’s perspective and socioeconomic situation. We also collected basic demographic data. The complete survey tool is included in the Supplemental appendix. This study was approved by the Colorado Multiple Institutional Review Board (approval # 19-1706) on 11/12/2021.

Data Collection

The surveys were completed immediately after a clinic visit. The form was given to patients, explained, and then collected by a medical assistant after the patient completed the survey. The medical assistant also gave the form to the clinician and ensured that the 2 surveys were matched using an identification number with further verification during data cleaning based on completion dates, patient birthdate and sex. Data were double-entered into REDCap by 2 research team members and cleaned by the research team’s quantitative analyst, with discrepancies in data entry resolved by self-adjudication by the data enterers, or further adjudication by senior team members if required to come to consensus.

Analysis

HRSNs were scored dichotomously based on whether they were endorsed or not (including coding missing responses as “not endorsed”). An HRSN measured by 2 items in the patient survey was indicated by a positive response on at least one of the items (ie, “sometimes true” or “often true” for food insecurity; “yes” for difficulty paying for healthcare). Housing instability reflected either lack of a steady place to live or worry about losing it in the future. Social isolation was indicated by “often” or “always” feeling lonely or isolated.

Characteristics of the patient sample (including missing data) were summarized using descriptive statistics. The concordance between patient and clinician endorsement of each HRSN and an overall measure of any of the 6 HRSN(s) was assessed using the unweighted kappa statistic for pairwise comparisons. Also calculated for each HRSN were proportions of specific agreement (indices of positive and negative agreement). These estimate the conditional probability that, given that 1 randomly selected rater (either the patient or clinician) endorses (positive) or does not endorse (negative) the HRSN, that the other rater will also endorse or not endorse it, respectively. All analyses were conducted using SAS software version 9.4 (SAS Institute Inc).

Results

We collected 244 paired patient-clinician surveys from the 3 clinics. The final sample included 237 paired surveys (ranging from 7 to 59 per clinician) after excluding 4 due to ineligibility for colon cancer screening (based on age over 85, decisions to stop screening based on age, or a colon resection related to cancer), and 3 due to missing responses on colon cancer screening up-to-date status (for consistency with other analyses to be reported elsewhere). The mean patient age was 65 years, and 62% and 13% of patients were female and Latinx/Hispanic, respectively (Table 1).

Table 1.

Patient and Clinician Relationship Characteristics.

Characteristic	Total sample (N = 237)
Age, mean (SD)	65.0 (8.1)
Sex at birth
Female	148 (62.4%)
Male	89 (37.6%)
Race/ethnicity
Hispanic or Latino/a/x	31 (13.1%)
Non-Hispanic White	172 (72.6%)
Other or mixed race	22 (9.3%)
Prefer not to say/missing	12 (5.1%)
Clinician is designated PCP
Yes	212 (89.5%)
No	21 (8.9%)
Missing	4 (1.7%)
Clinician length of care
Today was the first visit	27 (11.4%)
Less than 1 year	25 (10.5%)
1-3 years	178 (75.1%)
More than 3 years	7 (3.0%)

Abbreviations: PCP, primary care provider; SD, standard deviation.

Values represent frequencies and percentages unless otherwise noted.

Among patients, the prevalence of any HRSN(s) was 42% as endorsed by patients, and 18% as endorsed by clinicians. For individual HRSNs, the prevalence ranged from 7% (housing instability and difficulty paying for utilities) to 29% (food insecurity) as endorsed by patients, and from 3% (food insecurity) to 9% (difficulty paying for healthcare) as endorsed by clinicians (Table 2).

Table 2.

Prevalence of and concordance between patient and clinician endorsement of patient HRSNs.

Patient HRSN	Prevalence, n (%)		2 × 2 tables of agreement, n (%)			Specific agreement		Kappa (95% CI)	P-value
	Prevalence, n (%)			Patient-endorsed		Specific agreement
	Patient-endorsed	Clinician-endorsed	Clinician-endorsed	Yes	No	Positive	Negative
Any HRSN(s)	100 (42.2)	43 (18.1)	Yes	30 (12.7)	13 (5.5)	0.42		0.22 (0.11, 0.33)	<.001
Any HRSN(s)	100 (42.2)	43 (18.1)	No	70 (29.5)	124 (52.3)		0.75
Lack of reliable transportation	22 (9.3)	19 (8.0)	Yes	9 (3.8)	10 (4.2)	0.44		0.39 (0.18, 0.59)	<.001
Lack of reliable transportation	22 (9.3)	19 (8.0)	No	13 (5.5)	205 (86.5)		0.95
Lack of steady housing	16 (6.8)	8 (3.4)	Yes	4 (1.7)	4 (1.7)	0.33		0.30 (0.05, 0.55)	<.001
Lack of steady housing	16 (6.8)	8 (3.4)	No	12 (5.1)	217 (91.6)		0.96
Social isolation	17 (7.2)	17 (7.2)	Yes	5 (2.1)	12 (5.1)	0.29		0.24 (0.03, 0.45)	<.001
Social isolation	17 (7.2)	17 (7.2)	No	12 (5.1)	208 (87.8)		0.95
Can’t afford utilities	16 (6.8)	8 (3.4)	Yes	3 (1.3)	5 (2.1)	0.25		0.21 (−0.02, 0.45)	<.001
Can’t afford utilities	16 (6.8)	8 (3.4)	No	13 (5.5)	216 (91.1)		0.96
Can’t afford medications or healthcare	41 (17.3)	21 (8.9)	Yes	7 (3.0)	14 (5.9)	0.23		0.12 (−0.02, 0.27)	.042
Can’t afford medications or healthcare	41 (17.3)	21 (8.9)	No	34 (14.3)	182 (76.8)		0.88
Food insecurity	69 (29.1)	7 (3.0)	Yes	5 (2.1)	2 (0.8)	0.13		0.08 (0.00, 0.17)	.012
Food insecurity	69 (29.1)	7 (3.0)	No	64 (27.0)	166 (70.0)		0.83

Abbreviations: CI, confidence interval; HRSN, health-related social need.

Percentages represent the percentage of 237 patients for whom the patient or clinician endorsed or did not endorse each HRSN.

Prior to recoding as “not endorsed”, 10 patients (4.2%) had 1 self-reported HRSN missing, 5 patients (2.1%) had all 6 self-reported HRSNs missing, 1 patient (0.4%) had 1 missing clinician-reported HRSN missing, and 1 patient (0.4%) had all 6 clinician-reported HRSNs missing.

Concordance between clinician and patient endorsement of HRSNs varied by HRSN, with kappa statistics ranging from 0.08 (food insecurity; 95% CI: 0.00, 0.17; P = .01) to 0.39 (transportation insecurity; 95% CI: 0.18, 0.59; P < .001). Positive agreement (ie, the conditional probability that, given a randomly selected patient or clinician endorsed the HRSN, the other rater would also endorse it) ranged from 13% (food insecurity) to 44% (transportation insecurity). Negative agreement (ie, the conditional probability that, given a randomly selected patient or clinician did not endorse the HRSN, the other rater would also not endorse it) ranged from 83% (food insecurity) to 96% (housing instability and difficulty paying for utilities). Kappa statistics for the other HRSNs assessed varied but all were relatively low: 0.30 for housing insecurity (95% CI: 0.00, 0.17; P < .001), 0.24 for social isolation (95% CI: 0.03, 0.45; P < .001), 0.21 for difficulty paying for utilities (95% CI: −0.02, 0.45; P < .001), and 0.12 for difficulty paying for healthcare (95% CI: −0.02, 0.27; P < .001). Overall concordance for any HRSN(s) was also low, with a kappa statistic of 0.22 (95% CI: 0.11, 0.33; P < .001), positive agreement of 42%, and negative agreement of 75% (Table 2).

Discussion

Among responding patient-clinician dyads during a primary care visit, concordance between patient-reported and clinician-perceived social needs was generally low. Clinicians clearly under-appreciated the presence of social needs: the prevalence of reporting any HRSN was 42% among patients but only 18% among clinicians, the conditional probability that the clinician would also endorse the HRSN given a randomly selected patient’s endorsed HRSN (or vice versa) was less than 50% (ie, positive agreement of 42%), and the conditional probability that the clinician would also not endorse an HRSN given a randomly selected patient did not endorse it (or vice versa, ie, negative agreement) was only 75%. While clinician awareness of patient social needs was low across the board, it was lowest for food insecurity and difficulty paying for healthcare, and slightly better for housing and transportation insecurity. The implication of these findings is that clinicians have limited opportunities to adjust care in response to unmet social needs, as clinicians are often unaware of these needs and thus unable to adjust their care. Specifically, clinicians are less likely to be able to adjust care for social needs for which they are more unaware, such as food insecurity.

These findings raise questions about and have implications for the prioritization of screening and referral processes for HRSNs among clinicians. In these practices, clinicians were either not being made aware of social needs or were not retaining that information about their patients—either of these scenarios may indicate a limited ability to consider adjustment to care during the clinic visit based on the HRSNs identified. In contrast, HRSN screening and referral was a clear priority for the practices, as the workflows used for these clinics had been refined over >3 years of screening—simply put, clinician awareness of HRSNs was not a point of emphasis for the HRSN screening and referral workflows used in these busy primary care settings. Other studies have found that clinicians understand the importance of screening for and addressing needs, but face structural barriers to discussing and addressing needs during clinic visits, including limited time and clinical mandates.^28,35 As such, there has always been debate about how clinicians should ideally participate in the screening and referral process,^27,36,37 and general consensus is that these workflows should fall to other team members so clinicians can concentrate on providing medical care.³⁵

In addition, current calls for clinicians to adjust care if patients are experiencing HRSNs are also balanced by concerns that adjusting care may unintentionally lead people experiencing HRSNs to receive substandard care^38,39; for example, due to assumptions made on their ability to follow through after referrals. Taken together with the calls in the literature and guidelines for clinicians to adjust care, our findings raise doubts that current workflows set up clinicians to adjust care well. In particular, as our findings that clinicians are not consistently aware of the HRSNs identified in their practice’s established workflows for screening and referral mean they would be fundamentally unable to adjust the care they provide.

We found that clinicians were most aware of HRSNs that may come up more frequently when discussing treatment options or care plans, such as transportation insecurity and difficulty paying for healthcare. This is in keeping with other evidence that these needs surface preferentially because they interfere with patients’ ability to follow through on recommended medical interventions or referrals,^40,41 or result in missed appointments or late arrivals in primary care settings.⁴¹ However, without specific data-sharing across clinical teams or discussion in the exam room between clinicians and patients, it may be much harder to identify HRSNs such as food insecurity and social isolation that may be less readily apparent. Food insecurity and social isolation also have health impacts, and may make follow through more difficult for clinical recommendations such as eating a healthy diet for the prevention or treatment of chronic disease.

Some emerging options for better sharing of HRSNs with clinicians include development of information exchange platforms that share social needs data and are integrated into electronic health records^26,42 or the utilization of high-visibility icons and other advanced features in electronic health records that alert clinicians to patient HRSNs.⁴³ This would likely require financial support as many primary care clinics in low-resource settings lack medical record systems with such advanced features. Another intervention that could increase clinician awareness and understanding of HRSNs is specific training modules or programs that support clinicians in discussing and acting on HRSNs.^44,45 These are areas for further exploration, in particular by supplementing already existing qualitative understanding of perspectives on data sharing that exists in the literature through previous work.^17,46
-48

Strengths and Limitations

Our study has both strengths and limitations. As much of the prior data on HRSNs has come from urban settings, our study fills a gap in that the participating clinics served patients in small metropolitan and rural areas. Another strength is that our study gathered information about real-time understanding of patient HRSNs on the part of real-world clinicians. At the same time, the limitations include that the clinics sampled were a convenience sample from a limited geographic area in Western Colorado, so findings may not be generalizable to other geographic areas. Our respondents may not be fully representative of the clinic population, as the patients responding were a convenience sample of those attending a preventive visit or chronic disease management visit who agreed to respond, thus introducing the potential for non-response bias; we also do not know how many patients were offered participation and did not respond to the survey as practices had insufficient capacity to track non-completers. We also do not know what type of insurance coverage surveyed patients had or whether they were already receiving any support for identified HRSNs. In addition, while these clinics had targeted annual HRSN screening for each patient for the 3 years prior to this study, the patients may not have been screened for HRSN during the visit where this survey was conducted. Furthermore, the sample size was modest due to challenges with recruitment of clinics and clinicians, which may affect the reliability of results. Finally, due to funding constraints we were unable to use a qualitative or mixed-methods approach, which would have shed light on clinician perspectives on their understanding of patient HRSNs as well as barriers and facilitators to integration into the screening process.

Conclusions

Overall, these findings show that even in these clinics with current screening to identify HRSNs, clinicians are not in the loop and appear left to assess needs using a gestalt approach. As all 3 clinics were actively screening for HRSNs, it is clear that in order to adjust care for HRSNs we need better processes to share patient-reported HRSNs screening information with entire clinical teams. This is particularly high-priority in instances where HRSNs may impact health equity directly by limiting patient ability to follow through with care plans, such as the influence of transportation insecurity on patient follow-through with colonoscopy versus stool-based colorectal cancer screening, or the ability to follow dietary recommendations for hypertension or diabetes mellitus among patients experiencing food insecurity. Better sharing of patient-reported HRSNs with clinicians would allow more opportunities for these types of adjustment of care by clinicians, assuming this information could be presented ‘the right way, in the right place, at the right time’. This could also facilitate problem solving between patients and the clinical teams, and may address barriers that prevent people from obtaining cancer screening tests or other needed care.

Supplemental Material

sj-docx-1-jpc-10.1177_21501319241290887 – Supplemental material for Clinician Awareness of Patient-Reported Health-Related Social Needs: There’s Room for Improvement

Supplemental material, sj-docx-1-jpc-10.1177_21501319241290887 for Clinician Awareness of Patient-Reported Health-Related Social Needs: There’s Room for Improvement by Andrea Nederveld, Kaitlyn Booske Bertin, Louise Miriam Dickinson, Shivani Beall, Jordan Nelson, Russell E. Glasgow and Amy G. Huebschmann in Journal of Primary Care & Community Health

Supplemental Material

sj-docx-2-jpc-10.1177_21501319241290887 – Supplemental material for Clinician Awareness of Patient-Reported Health-Related Social Needs: There’s Room for Improvement

Supplemental material, sj-docx-2-jpc-10.1177_21501319241290887 for Clinician Awareness of Patient-Reported Health-Related Social Needs: There’s Room for Improvement by Andrea Nederveld, Kaitlyn Booske Bertin, Louise Miriam Dickinson, Shivani Beall, Jordan Nelson, Russell E. Glasgow and Amy G. Huebschmann in Journal of Primary Care & Community Health

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Research reported in this publication was supported by the National Institutes of Health’s National Cancer Institute (Colorado Implementation Science Center in Cancer Control (Colorado ISC3), University of Colorado School of Medicine Grant # 1 P50 CA244688-01.) The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

ORCID iDs

Andrea Nederveld

Kaitlyn Booske Bertin

Supplemental Material

Supplemental material for this article is available online.

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