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Variation in Electronic Health Record Documentation of Social Determinants of Health Across a National Network of Community Health Centers
RESEARCH ARTICLE
Variation in Electronic Health Record Documentation of Social Determinants of Health Across a National Network of Community Health Centers Erika K. Cottrell, PhD, MPP,1,2 Katie Dambrun, MPH,1 Stuart Cowburn, MPH,1 Ned Mossman, MPH,1 Arwen E. Bunce, MA,1 Miguel Marino, PhD,2 Molly Krancari, MPH,1 Rachel Gold, PhD, MPH1,3
Introduction: This paper describes the adoption of an electronic health record-based social determinants of health screening tool in a national network of more than 100 community health centers.
Methods: In 2016, a screening tool with questions on 7 social determinants of health domains was developed and deployed in the electronic health record, with technical instructions on how to use the tool and suggested clinical workflows. To understand adoption patterns, the study team extracted electronic health record data for any patient with a community health center visit between June 2016 and May 2018. Patients were considered “screened” if a response to at least 1 social determinants of health domain was documented in the electronic health record tool.
Results: A total of 31,549 patients (2% of those with a visit in the study period) had a documented social determinants of health screening. The number of screenings increased over time, time; 71 community health centers (67%) conducted at least one screening, but almost 50% took place in only 4 community health centers. Over half (55%) of screenings only included responses for only 1 domain. Screening was most likely to occur during an office visit with an established patient and documented in the electronic health record by a medical assistant.
Conclusions: Screening documentation patterns varied widely across the network of community health centers. Despite the growing national emphasis on the importance of screening for social determinants of health, these findings suggest that simply activating electronic health record tools for social determinants of health screening does not lead to widespread adoption. Potential barriers to screening adoption and implementation should be explored further.
Supplement information: This article is part of a supplement entitled Identifying and Intervening on Social Needs in Clinical Settings: Evidence and Evidence Gaps, which is sponsored by the Agency for Healthcare Research and Quality of the U.S. Department of Health and Human Services, Kaiser Permanente, and the Robert Wood Johnson Foundation. Am J Prev Med 2019;57(6S1):S65−S73. © 2019 American Journal of Preventive Medicine. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
INTRODUCTION
A
ddressing social determinants of health (SDH) —the circumstances in which people live, work, and grow1—is key to mitigating health disparities and moving toward health equity. Research clearly shows that modifiable SDH—such as poverty, lack of
From the 1OCHIN, Inc., Portland, Oregon; 2Department of Family Medicine, Oregon Health and Science University, Portland, Oregon; and 3Kaiser Permanente Center for Health Research, Portland, Oregon Address correspondence to: Erika K. Cottrell, PhD, MPP, OCHIN, Inc., 1881 SW Naito Parkway, Portland OR 97201. E-mail: [email protected]. 0749-3797/$36.00 https://doi.org/10.1016/j.amepre.2019.07.014
© 2019 American Journal of Preventive Medicine. Published by Elsevier Inc. Am J Prev Med 2019;57(6S1):S65−S73 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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social support, and exposure to violence or stress—profoundly impact health.2,3 A growing number of national initiatives call for integrating SDH screening into healthcare delivery and the development of standardized methods for capturing SDH information in electronic health records (EHRs).4−9 Efforts to identify and address SDH are especially pertinent in the community health centers (CHCs) charged with providing care to the nation’s most vulnerable patients. Patients who receive care at CHCs have disproportionately high rates of poverty, unmet social needs, and poorer health outcomes.10−13 Although CHCs have a long history of addressing their patients’ social contexts, until recently, these efforts were not documented systematically in EHRs.14,15 Advocates for SDH screening assert that systematic point-of-care documentation of SDH can support identifying patients’ risk factors, making needed referrals to appropriate social service agencies, increasing shared decision making, and expanding healthcare providers’ ability to tailor services to their patients’ needs.9,16,17 Moreover, capturing standardized SDH data in EHRs has the potential to inform future research on the contextual factors that impact health and the development of evidence-based practices for addressing SDH within healthcare settings.14 Toward this end, the National Academy of Medicine (formerly the Institute of Medicine) convened a panel to identify a standard set of SDH measures for integration into EHRs, to inform Meaningful Use Stage 3 requirements.16 The committee recommended measures covering 11 SDH domains, based on evidence of their association with health, potential clinical utility and ability to be put into action, and the availability of valid measures.17 Around the same time, the National Association of Community Health Centers released the Protocol for Responding to and Assessing Patients’ Assets, Risks, and Experiences (PRAPARE), a standardized social risk assessment tool for use in CHC settings.4 PRAPARE was pilot-tested and templates for integration were developed in collaboration with 5 EHR vendors. Other tools have since been released, including the Centers for Medicare and Medicaid Services Accountable Health Communities Health-Related Social Needs Screening Tool.8 Furthermore, many federal and state initiatives have promoted the integration of SDH into healthcare delivery systems through quality, accreditation, and payment models.18 Although there is some overlap with respect to the SDH domains covered across these tools and initiatives, there are numerous differences, and a consistent set of standardized SDH measures has yet to emerge.19−21 Many healthcare organizations are experimenting with SDH screening and with interventions to address identified risk factors.22−27 Although studies suggest that
systematic EHR-based SDH screening is feasible, there are substantial barriers to adoption, including the need for appropriate training and staffing models, the burden of fitting yet another task into already short primary care visits, and the lack of infrastructure to address identified needs.28−31 With the exception of a handful of peer-reviewed papers describing the rollout of SDH screening tools across a single state32 or within a particular healthcare organization,33 very little is known about how frequently EHR-based SDH screening is taking place or which SDH domains are documented most often. This paper describes patterns of adoption after the rollout of an EHR-based SDH screening tool across a national network of more than 100 primary care CHCs who share a centrally managed instance of Epic EHR. To the authors’ knowledge, this is the earliest large-scale rollout of an EHR-based SDH screening tool, thus presenting a unique opportunity to describe screening patterns across a broad range of healthcare settings.
METHODS Setting OCHIN, Inc. is a nonprofit health center−controlled network that hosts a centrally managed instance of the Epic EHR for >100 CHC organizations (with >500 individual clinic sites) across 20 states, making it the nation’s largest CHC network on a single EHR system. OCHIN serves CHCs providing care for the nation’s most vulnerable patients, most of whom are publicly insured or uninsured. Like most of the CHCs in the U.S., compared with the general population, patients who receive care at OCHIN are disproportionately poor, members of racial and ethnic minorities, and living with multiple chronic conditions.10,34
Tool Development and Implementation OCHIN developed an EHR tool to help clinics document and review SDH screening results. Detailed descriptions of the tool development process, the tools themselves (including the screening questions and screenshots of the user interface), and barriers and facilitators to adoption in 3 pilot CHCs are presented elsewhere.28,35 The tool was developed and adapted through a stakeholder-driven process in collaboration with OCHIN’s Clinical Operations Review Committee, a group of CHC clinicians who collectively review proposed changes to their shared EHR. To meet the diverse needs of OCHIN’s member CHCs including requirements for Meaningful Use Stage 3, the tool included structured fields for questions from PRAPARE, plus any of the National Academy of Medicine−recommended measures not included in PRAPARE (Appendix provides full list of screening questions and responses, available online).4 Beyond the SDH screening questions, the tool included a summary of screening results with positive responses (i.e., identified risk factors) highlighted. To facilitate use in diverse screening workflows, the tool was designed so that multiple team members were able to enter SDH data (e.g., front desk, medical assistants, behavioral health workers, behavioral health staff), and included www.ajpmonline.org
Cottrell et al / Am J Prev Med 2019;57(6S1):S65−S73 the option of sending the questionnaire to patients electronically through the patient portal. The SDH tool was deployed OCHIN-wide in June 2016. Technical instructions on the use of the tool and suggested workflows were made available online. Based on user feedback in the pilot CHCs, screening questions were added in May 2017 asking whether patients would like CHC assistance in addressing identified risk factors.35 Other than this, there were no major changes to the tool between June 2016 and May 2018. Additional modifications to the tool were made after May 2018 to accommodate CHCs taking part in the Centers for Medicare and Medicaid Services Accountable Health Communities Model. However, these changes fall outside of the scope of this analysis.
Data Collection and Statistical Analyses To understand patterns of SDH screening and tool adoption, structured EHR data were extracted from OCHIN’s reporting database among patients who had a primary care or mental or behavioral health visit at any OCHIN CHC between June 2016 and May 2018 (106 CHCs with 581 individual clinic sites were active on OCHIN Epic during this period). All analyses were conducted in September 2018. A patient was considered “screened” if any data were entered into the SDH tools in the EHR. Thus, “screened” does not necessarily mean completion of the entire questionnaire, and patients were counted as “screened” even if information on only 1 SDH domain was documented. Demographic characteristics, including sex, race and ethnicity, language, age, household income, insurance status, homeless status, and migrant status were computed overall and by screening status. Differences between characteristics of patients who were and were not screened were assessed using chi-squared tests and by calculating standard differences. The number of monthly and cumulative SDH screenings between June 2016 and May 2018 was plotted overall and by CHC. Response patterns were examined to assess which SDH domains were documented most frequently, and within specific domains, the percentage of patients who screened positive, negative, or had no response. Finally, among those with positive responses, the percentage who requested help with addressing the identified risk factor was calculated. A complete list of SDH questions and responses, including what was considered a positive response, is included in the Appendix (available online). All analyses were conducted using SAS Enterprise Guide, version 7.15. The Kaiser Permanente Center for Health Research IRB approved this study.
RESULTS A total of 31,549 patients had at least 1 documented SDH screening between June 24, 2016 and May 17, 2018 (Table 1). This represents about 2% of patients who visited an OCHIN CHC during this period. As presented in Table 1, across all categories, there were significant differences between patients who were screened and those who were not screened, suggesting that the patients screened were not representative of all OCHIN patients who had a visit during this period. For example, relative to those who were not screened, patients who were screened were more likely to be female, nonDecember 2019
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Hispanic black, have a household income ≤138% of the federal poverty level, and classified as not homeless (Table 1). A total of 37,015 distinct screenings were documented between June 2016 and May 2018, with some patients screened more than once. The overall number of screenings per month exhibited a gradual upward trend between June 2016 and May 2018, starting with a low of 225 patients screened in July 2016 to a high of 5,160 in April 2018 (Figure 1). However, there was a great deal of variation in the number of screenings documented by CHCs across the network. Although at least 1 SDH screening was documented in 71 of the 106 (67%) CHCs active on OCHIN Epic during this period, most of the SDH documentation took place in relatively few CHCs (Figure 2). Almost 50% (n=17,690) of all screenings took place in only 4 CHCs, with 1 CHC documenting 24% (n=8,917) of all screenings. Conversely, 27 CHCs documented <10 screenings, and 11 CHCs screened only 1 patient during the period of observation (Figure 2). In addition, the pace at which individual CHCs implemented screening documentation varied. For some CHCs, there was a clear time point after which the number of screenings quickly increased, whereas others exhibited a slower, more gradual increase in screenings over time (data not shown). Housing insecurity was the SDH domain documented most frequently (n=21,927; 59% of all screenings), followed by relationship safety (n=21,376; 58% of all screenings), and food insecurity (n=18,090; 49% of all screenings) (Table 2). Responses to all 7 SDH domains were documented in 20% of all screenings (n=7,254). More than half (n=19,323; 55%) of screenings had information documented for only 1 domain. Of those screenings with information documented for only 1 domain, 24% (n=8,940) only included relationship safety (with the majority [n=8,600] of these screenings documented in a single CHC), 18% (n=6,574) only included housing insecurity and 10% (n=3,627) only included food insecurity. Within specific SDH domains, 51% (n=6,257) of patients with documented responses for financial resource strain screened positive, 31% (n=5,657) of those with documented responses for food insecurity screened positive, 14% (n=3,023) screened positive for housing insecurity, 11% (n=2,318) for relationship safety, 67% (n=5,621) for lack of physical exercise, 68% (n=7,841) for social isolation, and 57% (n=6,329) for stress (Table 2). Questions asking whether the patient wanted help with identified risk factors were introduced roughly halfway through this analysis period, so only 87% (n=32,121) of screenings included these questions. When these questions were documented, 7% of those queried indicated they wanted help, 21% declined help,
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Table 1. Patients With Visit to an OCHIN Community Health Center June 24, 2016‒May 17, 2018, by Screening Status
Characteristics Total Sex Female Male No information Race/ethnicity Hispanic Missing Non-Hispanic black Non-Hispanic other Non-Hispanic white Language English No information Other Spanish Age,c y 0‒9 10‒19 20‒29 30‒39 40‒49 50‒59 60‒69 ≥70 No information Mean (SD) Household incomec as % of FPL ≤138% >138% No information Insurance statusc Medicaid Medicare Other public Private Uninsured Homeless statusc Homeless No information Not homeless Migrant/seasonal statusc Migrant/seasonal No information Not migrant/seasonal
All n
Not screened n (%)
Screened n (%)
1,739,812
1,708,263
31,549
p-valuea
Absolute standardized b difference
<0.0001 972,929 766,733 150
954,834 (55.9) 753,280 (44.1) 149 (0.0)
18,095 (57.4) 13,453 (42.6) <10 (0.0)
545,346 100,472 298,095 158,067 637,832
537,388 (31.5) 99,269 (5.8) 288,411 (16.9) 154,744 (9.1) 628,451 (36.8)
7,958 (25.2) 1,203 (3.8) 9,684 (30.7) 3,323 (10.5) 9,381 (29.7)
1,234,791 28,217 123,515 353,289
1,211,524 (70.9) 28,136 (1.6) 120,530 (7.1) 348,073 (20.4)
23,267 (73.7) 81 (0.3) 2,985 (9.5) 5,216 (16.5)
259,146 247,443 282,537 269,013 220,378 225,785 154,131 81,377 <10 34.0 (21.1)
254,860 (14.9) 243,973 (14.3) 278,367 (16.3) 264,622 (15.5) 216,149 (12.7) 220,646 (12.9) 150,355 (8.8) 79,289 (4.6) <10 (0.0) 33.9 (21.1)
4,286 (13.6) 3,470 (11.0) 4,170 (13.2) 4,391 (13.9) 4,229 (13.4) 5,139 (16.3) 3,776 (12.0) 2,088 (6.6) ‒ 37.7 (22.1)
1,078,603 265,644 395,565
1,057,428 (61.9) 261,143 (15.3) 389,692 (22.8)
21,175 (67.1) 4,501 (14.3) 5,873 (18.6)
897,582 156,719 42,906 270,515 372,090
881,721 (51.6) 152,596 (8.9) 41,825 (2.4) 264,558 (15.5) 367,563 (21.5)
15,861 (50.3) 4,123 (13.1) 1,081 (3.4) 5,957 (18.9) 4,527 (14.3)
57,615 745,468 936,729
56,422 (3.3) 742,199 (43.4) 909,642 (53.2)
1,193 (3.8) 3,269 (10.4) 27,087 (85.9)
20,034 791,966 927,812
19,482 (1.1) 786,353 (46.0) 902,428 (52.8)
552 (1.7) 5,613 (17.8) 25,384 (80.5)
0.177 0.029 0.007 <0.0001 0.139 0.093 0.329 0.050 0.150 <0.0001 0.063 0.144 0.087 0.099 <0.0001 0.038 0.099 0.087 0.044 0.022 0.096 0.104 0.086 0.002 0.177 <0.0001 0.109 0.029 0.104 <0.0001 0.027 0.132 0.000 0.090 0.188 <0.0001 0.026 0.804 0.758 <0.0001 0.051 0.636 0.613
p-value from chi-squared test for association between screening status and demographic variable. Boldface indicates statistical significance (p<0.05). Standardized difference = difference in means or proportions divided by SE. c Age, household income, insurance status, homeless status, and migrant/seasonal status all evaluated at the first screening or first encounter in study period. FPL, federal poverty level. a
b
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Figure 1. Number of social determinants of health screenings over time, all OCHIN community health centers June 24, 2016‒May 17, 2018.
and 71% did not answer. Where help was requested (n=2,246 screenings), about 30% of respondents wanted written materials about service agencies, 30% wanted CHC assistance contacting these agencies, 10% wanted both, and 28% did not specify a preference. Finally, SDH screening occurred most frequently during office visits with established patients (n=16,626; 45% of all screenings), followed by preventive medicine visits with established patients (n=7,317; 20% of all screenings), and office visits with new patients (n=3,976; 11% of all screenings). Approximately 6% (n=2,298) of screenings occurred at mental health or behavioral health visits. Only 5 screenings used the patient portal. Most of the screenings (n=22,333; 60%) were entered into the EHR by medical assistants. By contrast, medical providers (physicians, osteopaths, naturopaths, physician assistants, and nurse practitioners) entered data for <2% of screenings (n=628).
DISCUSSION This paper describes patterns of adoption of an EHRbased SDH screening tool in a national network of more December 2019
than 100 CHCs. Before the release of this tool, SDH information was primarily documented in the EHR as free text or in progress notes. The newly developed SDH tool provided a structured format for systematically documenting SDH information in OCHIN’s EHRs; it was made available to all OCHIN CHCs beginning in June 2016. The number of screenings documented per month increased steadily between June 2016 and May 2018, with 37,015 total screens documented in the EHR during this time. Housing insecurity, relationship safety, and food insecurity were the domains documented most frequently. Within specific SDH screening domains, social isolation had the highest percentage of positive responses (68%), followed by inadequate physical activity (67%), stress (57%), financial resource strain (51%), food insecurity (31%), housing insecurity (14%), and relationship safety (11%). Interestingly, only 7% of those queried indicated that they would like CHC assistance in addressing identified risk factors (21% declined help, and 71% did not answer). Overall, the results illustrate substantial variation in SDH screening patterns in the number of screenings conducted, number of SDH questions asked, types of
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Figure 2. Number of social determinants of health screenings within individual OCHIN community health centers, June 24, 2016‒ May 17, 2018.
patients who were screened, and pace of implementation. SDH screening was performed in a highly selective manner; the questions that CHCs chose to ask and the patients they chose to screen varied broadly. Moreover, only about 2% of patients seen at an OCHIN CHC had at least 1 documented SDH screening and almost half of screenings took place in 4 (of the 106) CHCs. As a result, the identified SDH risk factors likely do not reflect the true distribution in the broader population of CHC patients and should not be interpreted as such.
Because the absence of a single standardized screening tool and the limited evidence base on how to implement SDH screening initiatives, these findings are not surprising and suggest that simply making EHR-based tools available does not automatically lead to widespread adoption. Emerging research points to a myriad of challenges associated with implementing SDH screening in clinical settings, which may help to explain the variation in SDH screening documentation across the CHCs included in this analysis. For example, initiating SDH
Table 2. Response Patterns by SDH Domain for 37,015 Distinct Screenings, June 24, 2016‒May 17, 2018 SDH screening domain Financial resource strain Food insecurity Housing insecurity Inadequate physical activity Relationship safety Social isolation Stress
Positive screen n (%)
Negative screen n (%)
Total documented responses N (% of all screenings) N=37,015
6,257 (50.6) 5,657 (31.3) 3,023 (13.8) 5,621 (67.3) 2,318 (10.8) 7,841 (68.2) 6,329 (57.3)
6,118 (49.4) 12,433 (68.7) 18,904 (86.2) 2,737 (32.7) 19,058 (89.2) 3,652 (31.8) 4,713 (42.7)
12,375 (33.4) 18,090 (48.9) 21,927 (59.2) 8,358 (22.6) 21,376 (57.7) 11,493 (31) 11,042 (29.8)
SDH, social determinants of health.
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screening requires leadership support and staff buy-in and ensuring there are enough staff resources to take on screening tasks.14,36 Once the decision to initiate SDH screening is made, CHCs must address additional barriers to implementing these activities. First, there is no consensus on how to capture or code SDH in EHRs, and no standards on which questions to ask, how often or when to ask these questions, or which patients to screen.37,38 Second, there is little guidance on the optimal workflows for SDH screening.39 CHCs must determine when to do the screening (e.g., at check-in, during the visit, between visits), how to capture the information (e.g., patient self-report via a paper form or tablet, or having staff ask the questions verbally), and who should ensure the data are collected and entered into the EHR.28,40,41 Finally, CHCs may not want to conduct SDH screening until they have a plan for addressing the identified risk factors. There is limited evidence on how to integrate SDH information into clinical decision making and making referrals to local social service agencies involves its own challenges, including creating and maintaining updated lists of local resources and partnerships with local agencies.37,42 As efforts to conduct EHR-based SDH screening expand, potential barriers to implementing SDH screening should be considered. The results of this analysis provide an early look at the documentation patterns employed by CHCs that should be considered as national efforts to increase SDH screening continue to move forward. For example, screenings may be conducted most frequently at medical visits with established patients. Medical assistants, and other members of the care team beyond the medical provider, may be integral components of workflows to conduct SDH screening. In most cases, medical providers were not the ones to physically enter SDH data into the EHR, suggesting that medical assistants may be best positioned to document screening responses as part of the workflow. CHCs may also prefer to only screen for 1 SDH domain at a time. These results also suggest that a national standard for SDH screening might reduce the need for CHCs to figure out SDH screening approaches on their own. By contrast, a national standard might hamper CHCs ability to conduct such screening in a manner that works best for their setting.
Limitations The findings presented here are primarily descriptive and have several limitations largely associated with using EHR data. First, it cannot be determined from the EHR data alone whether questions for a particular SDH domain were not asked or whether the patient refused to answer. Modifications to the tool made in 2018 (after December 2019
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the observation period for this paper) address this limitation by including a “declined” response category. It is also impossible to assess whether SDH data were collected on paper and then entered into the EHR, or whether the patient was queried directly. Qualitative or observational research is needed to better understand this important aspect of SDH screening implementation and to explore alternative modes of data entry, such as tablets or smartphones. The analyses presented in this paper did not include an exploration of CHC characteristics associated with higher and lower screening rates. Although future analyses are planned to address this question, a key limitation of EHR data is the inability to assess the contextual factors that shape patterns of screening. For example, it is possible that some CHCs had screening programs in place before the release of the EHR-based tools, whereas others were starting from scratch. Mixed-methods research is needed to explore the contextual factors that drive CHC decisions with respect to the patients and SDH domains of focus (e.g., state incentive, local initiatives). Finally, it was necessary to select a defined period for these analyses, and the findings presented here do not include screenings documented after the end of May 2018. Preliminary analyses looking at SDH screening through early 2019 show a continued increase in SDH screening documentation. Future analyses will explore these longer-term patterns.
CONCLUSIONS The results of this analysis demonstrate that simply activating EHR tools for SDH screening does not lead to widespread adoption of this practice, despite the growing national emphasis to do so. Furthermore, even when SDH data are documented, patterns of documentation vary widely. This variation in SDH documentation patterns underscores the need for EHR-based SDH screening approaches that are flexible for variations in workflow, staffing models, and screening targets in the absence of evidence-based, standardized screening guidelines.
ACKNOWLEDGMENTS Publication of this article was supported by the Agency for Healthcare Research and Quality (AHRQ), under HHS contract [1R13HS026664], Kaiser Permanente [CRN5374-754415320], and the Robert Wood Johnson Foundation [75922]. The findings and conclusions in this article are those of the authors and do not necessarily represent the official position of any of the sponsors. The authors deeply appreciate the contributions of Mary Middendorf, who created the SDH tool in OCHIN Epic, and Marla Dearing, who managed all Epic-related aspects of the study.
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They also want to thank Nadia Yosuf, who diligently provided necessary project management support for the Approaches to CHC Implementation of SDH Data Collection and Action (ASCEND) study. Lastly, they want to thank OCHIN’s Clinical Operations Review Committee for shepherding the development of these tools and OCHIN’s member CHCs who are committed to providing care for the nation’s most vulnerable patients. This publication was supported by a grant from the National Institute of Diabetes and Digestive and Kidney Diseases, 1R18DK114701−01. RG, EKC, MM, and AB designed the study. RG and EKC directed the project. SC extracted the data and conducted the analyses for the manuscript. EKC, KD, SC, and RG wrote the manuscript with input from all authors. All authors read and approved the final manuscript. Select segments of this manuscript have been previously presented elsewhere. No financial disclosures were reported by the authors of this paper.
SUPPLEMENTAL MATERIAL Supplemental materials associated with this article can be found in the online version at https://doi.org/10.1016/j.amepre.2019. 07.014.
SUPPLEMENT NOTE This article is part of a supplement entitled Identifying and Intervening on Social Needs in Clinical Settings: Evidence and Evidence Gaps, which is sponsored by the Agency for Healthcare Research and Quality of the U.S. Department of Health and Human Services, Kaiser Permanente, and the Robert Wood Johnson Foundation.
REFERENCES 1. WHO. Social determinants of health: about social determinants of health. www.who.int/social_determinants/sdh_definition/en/. Published 2017. Accessed October 3, 2018. 2. McGinnis JM, Williams-Russo P, Knickman JR. The case for more active policy attention to health promotion. Health Aff (Millwood). 2002;21(2):78–93. https://doi.org/10.1377/hlthaff.21.2.78. 3. Galea S, Tracy M, Hoggatt KJ, Dimaggio C, Karpati A. Estimated deaths attributable to social factors in the United States. Am J Public Health. 2011;101(8):1456–1465. https://doi.org/10.2105/ajph.2010.300086. 4. National Association of Community Health Centers. PRAPARE. www.nachc.org/research-and-data/prapare/. Published 2016. Accessed December 3, 2018. 5. Institute of Medicine. Recommended social and behavioral domains and measures for electronic health. http://nationalacademies.org/HMD/ Activities/PublicHealth/SocialDeterminantsEHR.aspxTagedEn. Published 2014. Accessed November 3, 2018. 6. Council On Community Pediatrics. Poverty and child health in the United States. Pediatrics. 2016;137(4):e20160339. https://doi.org/ 10.1542/peds.2016-0339. 7. Centers for Medicare & Medicaid Services. MARCA: delivery system reform, Medicare payment reform. www.cms.gov/Medicare/ Quality-Initiatives-Patient-Assessment-Instruments/Value-BasedPrograms/MACRA-MIPS-and-APMs/MACRA-MIPS-and-APMs. html. Accessed November 16, 2018.
8. Alley DE, Asomugha CN, Conway PH, Sanghavi DM. Accountable health communities—addressing social needs through Medicare and Medicaid. N Engl J Med. 2016;374(1):8–11. https://doi.org/10.1056/ nejmp1512532. 9. Daniel H, Bornstein SS, Kane GC; Health and Public Policy Committee of the American College of Physicians. Addressing social determinants to improve patient care and promote health equity: an American College of Physicians position paper. Ann Intern Med. 2018;168(8):577–578. https://doi.org/10.7326/m17-2441. 10. National Association of Community Health Centers. Community Health Center Chartbook. www.nachc.org/wp-content/uploads/2018/ 06/Chartbook_FINAL_6.20.18.pdf. Published 2018. Accessed March 10, 2019. 11. Bailey SR, O’Malley JP, Gold R, et al. Receipt of diabetes preventive services differs by insurance status at visit. Am J Prev Med. 2015;48 (2):229–233. https://doi.org/10.1016/j.amepre.2014.08.035. 12. Gold R, DeVoe JE, McIntire PJ, et al. Receipt of diabetes preventive care among safety net patients associated with differing levels of insurance coverage. J Am Board Fam Med. 2012;25(1):42–49. https://doi. org/10.3122/jabfm.2012.01.110142. 13. Gold R, DeVoe J, Shah A, Chauvie S. Insurance continuity and receipt of diabetes preventive care in a network of federally qualified health centers. Med Care. 2009;47(4):431. https://doi.org/10.1097/mlr. 0b013e318190ccac. 14. Institute for Alternative Futures. Community Health Centers Leveraging the social determinants of health. Community health centers leveraging the social determinants of health. http://altfutures.org/ pubs/leveragingSDH/IAF-CHCsLeveragingSDH.pdf. Published 2012. Accessed March 13, 2019. 15. Bachrach D, Pfister H, Wallis K, Lipson M. Addressing patients’ social needs: an emerging business case for provider investment. Commonwealth Fund. Published 2014. https://doi.org/10.15868/socialsector.18186. 16. Institute of Medicine, Committee on the Recommended Social Behavioral Domains Measures for Electronic Health. Records. Capturing Social and Behavioral Domains and Measures in Electronic Health Records: phase 2. National Academies Press; 2014. https://doi.org/ 10.17226/18951. 17. Adler NE, Stead WW. Patients in context−EHR capture of social and behavioral determinants of health. N Engl J Med. 2015;372(8):698– 701. https://doi.org/10.1056/nejmp1413945. 18. HHS, Assistant Secretary for Planning and Evaluation Office of Health Policy. Incorporating Social Determinants of Health in Electronic Health Records: A Qualitative Study of Perspectives on Current Practices Among Top Vendors. https://aspe.hhs.gov/system/files/pdf/ 259901/NORCSDH.pdf. Published 2018. Accessed June 4, 2019. 19. Gottlieb L, Tobey R, Cantor J, Hessler D, Adler NE. Integrating social and medical data to improve population health: opportunities and barriers. Health Aff (Millwood). 2016;35(11):2116–2123. https://doi. org/10.1377/hlthaff.2016.0723. 20. Social Interventions Research & Evaluation Network. Social need screening tools comparison table. https://sirenetwork.ucsf.edu/toolsresources/mmi/screening-tools-comparison/adult-nonspecificTagedEn . Published 2019. Accessed June 5, 2019. 21. Gottlieb L, Cottrell EK, Park B, et al. Advancing social prescribing with implementation science. J Am Board Fam Med. 2018;31(3):315– 321. https://doi.org/10.3122/jabfm.2018.03.170249. 22. Gottlieb LM, Wing H, Adler NE. A systematic review of interventions on patients’ social and economic needs. Am J Prev Med. 2017;53 (5):719–729. https://doi.org/10.1016/j.amepre.2017.05.011. 23. Garg A, Toy S, Tripodis Y, Silverstein M, Freeman E. Addressing social determinants of health at well child care visits: a cluster RCT. Pediatrics. 2015;135(2):e296–e304. https://doi.org/10.1542/peds.20142888. 24. Knowles M, Khan S, Palakshappa D, et al. Successes, challenges, and considerations for integrating referral into food insecurity screening
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Cottrell et al / Am J Prev Med 2019;57(6S1):S65−S73
25.
26.
27.
28.
29.
30.
31.
32.
33.
in pediatric settings. J Health Care Poor Underserved. 2018;29(1):181– 191. https://doi.org/10.1353/hpu.2018.0012. Lohr AM, Ingram M, Nu~ nez AV, Reinschmidt KM, Carvajal SC. Community−clinical linkages with community health workers in the United States: a scoping review. Health Promot Pract. 2018;19(3):349– 360. https://doi.org/10.1177/1524839918754868. Pescheny JV, Pappas Y, Randhawa G. Facilitators and barriers of implementing and delivering social prescribing services: a systematic review. BMC Health Serv Res. 2018;18:86. https://doi.org/10.1186/ s12913-018-2893-4. Regenstein M, Trott J, Williamson A, Theiss J. Addressing social determinants of health through medical-legal partnerships. Health Aff (Millwood). 2018;37(3):378–385. https://doi.org/10.1377/hlthaff.2017.1264. Gold R, Bunce A, Cowburn S, et al. Adoption of social determinants of health EHR tools by community health centers. Ann Fam Med. 2018;16(5):399–407. https://doi.org/10.1370/afm.2275. de la Vega PB, Losi S, Martinez LS, et al. Implementing an EHR-based screening and referral system to address social determinants of health in primary care. Med Care. 2019;57:S133–S139. https://doi.org/ 10.1097/mlr.0000000000001029. Tong ST, Liaw WR, Kashiri PL, et al. Clinician experiences with screening for social needs in primary care. J Am Board Fam Med. 2018;31(3):351–363. https://doi.org/10.3122/jabfm.2018.03.170419. Byhoff E, Garg A, Pellicer M, et al. Provider and staff feedback on screening for social and behavioral determinants of health for pediatric patients. J Am Board Fam Med. 2019;32(3):297–306. https://doi. org/10.3122/jabfm.2019.03.180276. Byhoff E, Cohen AJ, Hamati MC, et al. Screening for social determinants of health in Michigan health centers. J Am Board Fam Med. 2017;30(4):418–427. https://doi.org/10.3122/jabfm.2017.04.170079. Friedman NL, Banegas MP. Toward addressing social determinants of health: a health care system strategy. Perm J. 2018;22:18-095. https:// doi.org/10.7812/TPP/18-095.
December 2019
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34. Health Resources & Services Administration Health Center Program. 2017 National Health Center Data. https://bphc.hrsa.gov/uds/datacenter.aspx. Published 2017. Accessed March 9, 2019. 35. Gold R, Cottrell E, Bunce A, et al. Developing electronic health record (EHR) strategies related to health center patients’ social determinants of health. J Am Board Fam Med. 2017;30(4):428–447. https://doi.org/ 10.3122/jabfm.2017.04.170046. 36. Boyce MB, Browne JP, Greenhalgh J. The experiences of professionals with using information from patient-reported outcome measures to improve the quality of healthcare: a systematic review of qualitative research. BMJ Qual Saf. 2014;23(6):508–518. https://doi.org/10.1136/ bmjqs-2013-002524. 37. Cantor MN, Thorpe L. Integrating data on social determinants of health into electronic health records. Health Aff (Millwood). 2018;37 (4):585–590. https://doi.org/10.1377/hlthaff.2017.1252. 38. O’Gurek DT, Henke C. A practical approach to screening for social determinants of health. Fam Pract Manag. 2018;25(3):7–12. 39. Arons A, DeSilvey S, Fichtenberg C, Gottlieb L. Compendium of medical terminology codes for social risk factors. Social interventions research and Evaluation Network. http://sirenetwork.ucsf.edu/toolsresources/mmi/compendium-medical-terminology-codes-social-riskfactors. Published 2018. Accessed July 29, 2019. 40. LaForge K, Gold R, Cottrell E, et al. How 6 organizations developed tools and processes for social determinants of health screening in primary care: an overview. J Ambul Care Manag. 2018;41(1):2–14. https://doi.org/10.1097/jac.0000000000000221. 41. Jensen RE, Rothrock NE, DeWitt EM, et al. The role of technical advances in the adoption and integration of patient-reported outcomes in clinical care. Med Care. 2015;53(2):153–159. https://doi.org/ 10.1097/mlr.0000000000000289. 42. DeMilto L, Nakashian M. Using Social Determinants of Health Data to Improve Health Care and Health: a Learning Report. Princeton, NJ: Robert Wood Johnson Foundation; 2016.
Variation in Electronic Health Record Documentation of Social Determinants of Health Across a National Network of Community Health Centers Erika K. Cottrell, PhD, MPP,1,2 Katie Dambrun, MPH,1 Stuart Cowburn, MPH,1 Ned Mossman, MPH,1 Arwen E. Bunce, MA,1 Miguel Marino, PhD,2 Molly Krancari, MPH,1 Rachel Gold, PhD, MPH1,3
Introduction: This paper describes the adoption of an electronic health record-based social determinants of health screening tool in a national network of more than 100 community health centers.
Methods: In 2016, a screening tool with questions on 7 social determinants of health domains was developed and deployed in the electronic health record, with technical instructions on how to use the tool and suggested clinical workflows. To understand adoption patterns, the study team extracted electronic health record data for any patient with a community health center visit between June 2016 and May 2018. Patients were considered “screened” if a response to at least 1 social determinants of health domain was documented in the electronic health record tool.
Results: A total of 31,549 patients (2% of those with a visit in the study period) had a documented social determinants of health screening. The number of screenings increased over time, time; 71 community health centers (67%) conducted at least one screening, but almost 50% took place in only 4 community health centers. Over half (55%) of screenings only included responses for only 1 domain. Screening was most likely to occur during an office visit with an established patient and documented in the electronic health record by a medical assistant.
Conclusions: Screening documentation patterns varied widely across the network of community health centers. Despite the growing national emphasis on the importance of screening for social determinants of health, these findings suggest that simply activating electronic health record tools for social determinants of health screening does not lead to widespread adoption. Potential barriers to screening adoption and implementation should be explored further.
Supplement information: This article is part of a supplement entitled Identifying and Intervening on Social Needs in Clinical Settings: Evidence and Evidence Gaps, which is sponsored by the Agency for Healthcare Research and Quality of the U.S. Department of Health and Human Services, Kaiser Permanente, and the Robert Wood Johnson Foundation. Am J Prev Med 2019;57(6S1):S65−S73. © 2019 American Journal of Preventive Medicine. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
INTRODUCTION
A
ddressing social determinants of health (SDH) —the circumstances in which people live, work, and grow1—is key to mitigating health disparities and moving toward health equity. Research clearly shows that modifiable SDH—such as poverty, lack of
From the 1OCHIN, Inc., Portland, Oregon; 2Department of Family Medicine, Oregon Health and Science University, Portland, Oregon; and 3Kaiser Permanente Center for Health Research, Portland, Oregon Address correspondence to: Erika K. Cottrell, PhD, MPP, OCHIN, Inc., 1881 SW Naito Parkway, Portland OR 97201. E-mail: [email protected]. 0749-3797/$36.00 https://doi.org/10.1016/j.amepre.2019.07.014
© 2019 American Journal of Preventive Medicine. Published by Elsevier Inc. Am J Prev Med 2019;57(6S1):S65−S73 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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social support, and exposure to violence or stress—profoundly impact health.2,3 A growing number of national initiatives call for integrating SDH screening into healthcare delivery and the development of standardized methods for capturing SDH information in electronic health records (EHRs).4−9 Efforts to identify and address SDH are especially pertinent in the community health centers (CHCs) charged with providing care to the nation’s most vulnerable patients. Patients who receive care at CHCs have disproportionately high rates of poverty, unmet social needs, and poorer health outcomes.10−13 Although CHCs have a long history of addressing their patients’ social contexts, until recently, these efforts were not documented systematically in EHRs.14,15 Advocates for SDH screening assert that systematic point-of-care documentation of SDH can support identifying patients’ risk factors, making needed referrals to appropriate social service agencies, increasing shared decision making, and expanding healthcare providers’ ability to tailor services to their patients’ needs.9,16,17 Moreover, capturing standardized SDH data in EHRs has the potential to inform future research on the contextual factors that impact health and the development of evidence-based practices for addressing SDH within healthcare settings.14 Toward this end, the National Academy of Medicine (formerly the Institute of Medicine) convened a panel to identify a standard set of SDH measures for integration into EHRs, to inform Meaningful Use Stage 3 requirements.16 The committee recommended measures covering 11 SDH domains, based on evidence of their association with health, potential clinical utility and ability to be put into action, and the availability of valid measures.17 Around the same time, the National Association of Community Health Centers released the Protocol for Responding to and Assessing Patients’ Assets, Risks, and Experiences (PRAPARE), a standardized social risk assessment tool for use in CHC settings.4 PRAPARE was pilot-tested and templates for integration were developed in collaboration with 5 EHR vendors. Other tools have since been released, including the Centers for Medicare and Medicaid Services Accountable Health Communities Health-Related Social Needs Screening Tool.8 Furthermore, many federal and state initiatives have promoted the integration of SDH into healthcare delivery systems through quality, accreditation, and payment models.18 Although there is some overlap with respect to the SDH domains covered across these tools and initiatives, there are numerous differences, and a consistent set of standardized SDH measures has yet to emerge.19−21 Many healthcare organizations are experimenting with SDH screening and with interventions to address identified risk factors.22−27 Although studies suggest that
systematic EHR-based SDH screening is feasible, there are substantial barriers to adoption, including the need for appropriate training and staffing models, the burden of fitting yet another task into already short primary care visits, and the lack of infrastructure to address identified needs.28−31 With the exception of a handful of peer-reviewed papers describing the rollout of SDH screening tools across a single state32 or within a particular healthcare organization,33 very little is known about how frequently EHR-based SDH screening is taking place or which SDH domains are documented most often. This paper describes patterns of adoption after the rollout of an EHR-based SDH screening tool across a national network of more than 100 primary care CHCs who share a centrally managed instance of Epic EHR. To the authors’ knowledge, this is the earliest large-scale rollout of an EHR-based SDH screening tool, thus presenting a unique opportunity to describe screening patterns across a broad range of healthcare settings.
METHODS Setting OCHIN, Inc. is a nonprofit health center−controlled network that hosts a centrally managed instance of the Epic EHR for >100 CHC organizations (with >500 individual clinic sites) across 20 states, making it the nation’s largest CHC network on a single EHR system. OCHIN serves CHCs providing care for the nation’s most vulnerable patients, most of whom are publicly insured or uninsured. Like most of the CHCs in the U.S., compared with the general population, patients who receive care at OCHIN are disproportionately poor, members of racial and ethnic minorities, and living with multiple chronic conditions.10,34
Tool Development and Implementation OCHIN developed an EHR tool to help clinics document and review SDH screening results. Detailed descriptions of the tool development process, the tools themselves (including the screening questions and screenshots of the user interface), and barriers and facilitators to adoption in 3 pilot CHCs are presented elsewhere.28,35 The tool was developed and adapted through a stakeholder-driven process in collaboration with OCHIN’s Clinical Operations Review Committee, a group of CHC clinicians who collectively review proposed changes to their shared EHR. To meet the diverse needs of OCHIN’s member CHCs including requirements for Meaningful Use Stage 3, the tool included structured fields for questions from PRAPARE, plus any of the National Academy of Medicine−recommended measures not included in PRAPARE (Appendix provides full list of screening questions and responses, available online).4 Beyond the SDH screening questions, the tool included a summary of screening results with positive responses (i.e., identified risk factors) highlighted. To facilitate use in diverse screening workflows, the tool was designed so that multiple team members were able to enter SDH data (e.g., front desk, medical assistants, behavioral health workers, behavioral health staff), and included www.ajpmonline.org
Cottrell et al / Am J Prev Med 2019;57(6S1):S65−S73 the option of sending the questionnaire to patients electronically through the patient portal. The SDH tool was deployed OCHIN-wide in June 2016. Technical instructions on the use of the tool and suggested workflows were made available online. Based on user feedback in the pilot CHCs, screening questions were added in May 2017 asking whether patients would like CHC assistance in addressing identified risk factors.35 Other than this, there were no major changes to the tool between June 2016 and May 2018. Additional modifications to the tool were made after May 2018 to accommodate CHCs taking part in the Centers for Medicare and Medicaid Services Accountable Health Communities Model. However, these changes fall outside of the scope of this analysis.
Data Collection and Statistical Analyses To understand patterns of SDH screening and tool adoption, structured EHR data were extracted from OCHIN’s reporting database among patients who had a primary care or mental or behavioral health visit at any OCHIN CHC between June 2016 and May 2018 (106 CHCs with 581 individual clinic sites were active on OCHIN Epic during this period). All analyses were conducted in September 2018. A patient was considered “screened” if any data were entered into the SDH tools in the EHR. Thus, “screened” does not necessarily mean completion of the entire questionnaire, and patients were counted as “screened” even if information on only 1 SDH domain was documented. Demographic characteristics, including sex, race and ethnicity, language, age, household income, insurance status, homeless status, and migrant status were computed overall and by screening status. Differences between characteristics of patients who were and were not screened were assessed using chi-squared tests and by calculating standard differences. The number of monthly and cumulative SDH screenings between June 2016 and May 2018 was plotted overall and by CHC. Response patterns were examined to assess which SDH domains were documented most frequently, and within specific domains, the percentage of patients who screened positive, negative, or had no response. Finally, among those with positive responses, the percentage who requested help with addressing the identified risk factor was calculated. A complete list of SDH questions and responses, including what was considered a positive response, is included in the Appendix (available online). All analyses were conducted using SAS Enterprise Guide, version 7.15. The Kaiser Permanente Center for Health Research IRB approved this study.
RESULTS A total of 31,549 patients had at least 1 documented SDH screening between June 24, 2016 and May 17, 2018 (Table 1). This represents about 2% of patients who visited an OCHIN CHC during this period. As presented in Table 1, across all categories, there were significant differences between patients who were screened and those who were not screened, suggesting that the patients screened were not representative of all OCHIN patients who had a visit during this period. For example, relative to those who were not screened, patients who were screened were more likely to be female, nonDecember 2019
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Hispanic black, have a household income ≤138% of the federal poverty level, and classified as not homeless (Table 1). A total of 37,015 distinct screenings were documented between June 2016 and May 2018, with some patients screened more than once. The overall number of screenings per month exhibited a gradual upward trend between June 2016 and May 2018, starting with a low of 225 patients screened in July 2016 to a high of 5,160 in April 2018 (Figure 1). However, there was a great deal of variation in the number of screenings documented by CHCs across the network. Although at least 1 SDH screening was documented in 71 of the 106 (67%) CHCs active on OCHIN Epic during this period, most of the SDH documentation took place in relatively few CHCs (Figure 2). Almost 50% (n=17,690) of all screenings took place in only 4 CHCs, with 1 CHC documenting 24% (n=8,917) of all screenings. Conversely, 27 CHCs documented <10 screenings, and 11 CHCs screened only 1 patient during the period of observation (Figure 2). In addition, the pace at which individual CHCs implemented screening documentation varied. For some CHCs, there was a clear time point after which the number of screenings quickly increased, whereas others exhibited a slower, more gradual increase in screenings over time (data not shown). Housing insecurity was the SDH domain documented most frequently (n=21,927; 59% of all screenings), followed by relationship safety (n=21,376; 58% of all screenings), and food insecurity (n=18,090; 49% of all screenings) (Table 2). Responses to all 7 SDH domains were documented in 20% of all screenings (n=7,254). More than half (n=19,323; 55%) of screenings had information documented for only 1 domain. Of those screenings with information documented for only 1 domain, 24% (n=8,940) only included relationship safety (with the majority [n=8,600] of these screenings documented in a single CHC), 18% (n=6,574) only included housing insecurity and 10% (n=3,627) only included food insecurity. Within specific SDH domains, 51% (n=6,257) of patients with documented responses for financial resource strain screened positive, 31% (n=5,657) of those with documented responses for food insecurity screened positive, 14% (n=3,023) screened positive for housing insecurity, 11% (n=2,318) for relationship safety, 67% (n=5,621) for lack of physical exercise, 68% (n=7,841) for social isolation, and 57% (n=6,329) for stress (Table 2). Questions asking whether the patient wanted help with identified risk factors were introduced roughly halfway through this analysis period, so only 87% (n=32,121) of screenings included these questions. When these questions were documented, 7% of those queried indicated they wanted help, 21% declined help,
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Table 1. Patients With Visit to an OCHIN Community Health Center June 24, 2016‒May 17, 2018, by Screening Status
Characteristics Total Sex Female Male No information Race/ethnicity Hispanic Missing Non-Hispanic black Non-Hispanic other Non-Hispanic white Language English No information Other Spanish Age,c y 0‒9 10‒19 20‒29 30‒39 40‒49 50‒59 60‒69 ≥70 No information Mean (SD) Household incomec as % of FPL ≤138% >138% No information Insurance statusc Medicaid Medicare Other public Private Uninsured Homeless statusc Homeless No information Not homeless Migrant/seasonal statusc Migrant/seasonal No information Not migrant/seasonal
All n
Not screened n (%)
Screened n (%)
1,739,812
1,708,263
31,549
p-valuea
Absolute standardized b difference
<0.0001 972,929 766,733 150
954,834 (55.9) 753,280 (44.1) 149 (0.0)
18,095 (57.4) 13,453 (42.6) <10 (0.0)
545,346 100,472 298,095 158,067 637,832
537,388 (31.5) 99,269 (5.8) 288,411 (16.9) 154,744 (9.1) 628,451 (36.8)
7,958 (25.2) 1,203 (3.8) 9,684 (30.7) 3,323 (10.5) 9,381 (29.7)
1,234,791 28,217 123,515 353,289
1,211,524 (70.9) 28,136 (1.6) 120,530 (7.1) 348,073 (20.4)
23,267 (73.7) 81 (0.3) 2,985 (9.5) 5,216 (16.5)
259,146 247,443 282,537 269,013 220,378 225,785 154,131 81,377 <10 34.0 (21.1)
254,860 (14.9) 243,973 (14.3) 278,367 (16.3) 264,622 (15.5) 216,149 (12.7) 220,646 (12.9) 150,355 (8.8) 79,289 (4.6) <10 (0.0) 33.9 (21.1)
4,286 (13.6) 3,470 (11.0) 4,170 (13.2) 4,391 (13.9) 4,229 (13.4) 5,139 (16.3) 3,776 (12.0) 2,088 (6.6) ‒ 37.7 (22.1)
1,078,603 265,644 395,565
1,057,428 (61.9) 261,143 (15.3) 389,692 (22.8)
21,175 (67.1) 4,501 (14.3) 5,873 (18.6)
897,582 156,719 42,906 270,515 372,090
881,721 (51.6) 152,596 (8.9) 41,825 (2.4) 264,558 (15.5) 367,563 (21.5)
15,861 (50.3) 4,123 (13.1) 1,081 (3.4) 5,957 (18.9) 4,527 (14.3)
57,615 745,468 936,729
56,422 (3.3) 742,199 (43.4) 909,642 (53.2)
1,193 (3.8) 3,269 (10.4) 27,087 (85.9)
20,034 791,966 927,812
19,482 (1.1) 786,353 (46.0) 902,428 (52.8)
552 (1.7) 5,613 (17.8) 25,384 (80.5)
0.177 0.029 0.007 <0.0001 0.139 0.093 0.329 0.050 0.150 <0.0001 0.063 0.144 0.087 0.099 <0.0001 0.038 0.099 0.087 0.044 0.022 0.096 0.104 0.086 0.002 0.177 <0.0001 0.109 0.029 0.104 <0.0001 0.027 0.132 0.000 0.090 0.188 <0.0001 0.026 0.804 0.758 <0.0001 0.051 0.636 0.613
p-value from chi-squared test for association between screening status and demographic variable. Boldface indicates statistical significance (p<0.05). Standardized difference = difference in means or proportions divided by SE. c Age, household income, insurance status, homeless status, and migrant/seasonal status all evaluated at the first screening or first encounter in study period. FPL, federal poverty level. a
b
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Figure 1. Number of social determinants of health screenings over time, all OCHIN community health centers June 24, 2016‒May 17, 2018.
and 71% did not answer. Where help was requested (n=2,246 screenings), about 30% of respondents wanted written materials about service agencies, 30% wanted CHC assistance contacting these agencies, 10% wanted both, and 28% did not specify a preference. Finally, SDH screening occurred most frequently during office visits with established patients (n=16,626; 45% of all screenings), followed by preventive medicine visits with established patients (n=7,317; 20% of all screenings), and office visits with new patients (n=3,976; 11% of all screenings). Approximately 6% (n=2,298) of screenings occurred at mental health or behavioral health visits. Only 5 screenings used the patient portal. Most of the screenings (n=22,333; 60%) were entered into the EHR by medical assistants. By contrast, medical providers (physicians, osteopaths, naturopaths, physician assistants, and nurse practitioners) entered data for <2% of screenings (n=628).
DISCUSSION This paper describes patterns of adoption of an EHRbased SDH screening tool in a national network of more December 2019
than 100 CHCs. Before the release of this tool, SDH information was primarily documented in the EHR as free text or in progress notes. The newly developed SDH tool provided a structured format for systematically documenting SDH information in OCHIN’s EHRs; it was made available to all OCHIN CHCs beginning in June 2016. The number of screenings documented per month increased steadily between June 2016 and May 2018, with 37,015 total screens documented in the EHR during this time. Housing insecurity, relationship safety, and food insecurity were the domains documented most frequently. Within specific SDH screening domains, social isolation had the highest percentage of positive responses (68%), followed by inadequate physical activity (67%), stress (57%), financial resource strain (51%), food insecurity (31%), housing insecurity (14%), and relationship safety (11%). Interestingly, only 7% of those queried indicated that they would like CHC assistance in addressing identified risk factors (21% declined help, and 71% did not answer). Overall, the results illustrate substantial variation in SDH screening patterns in the number of screenings conducted, number of SDH questions asked, types of
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Figure 2. Number of social determinants of health screenings within individual OCHIN community health centers, June 24, 2016‒ May 17, 2018.
patients who were screened, and pace of implementation. SDH screening was performed in a highly selective manner; the questions that CHCs chose to ask and the patients they chose to screen varied broadly. Moreover, only about 2% of patients seen at an OCHIN CHC had at least 1 documented SDH screening and almost half of screenings took place in 4 (of the 106) CHCs. As a result, the identified SDH risk factors likely do not reflect the true distribution in the broader population of CHC patients and should not be interpreted as such.
Because the absence of a single standardized screening tool and the limited evidence base on how to implement SDH screening initiatives, these findings are not surprising and suggest that simply making EHR-based tools available does not automatically lead to widespread adoption. Emerging research points to a myriad of challenges associated with implementing SDH screening in clinical settings, which may help to explain the variation in SDH screening documentation across the CHCs included in this analysis. For example, initiating SDH
Table 2. Response Patterns by SDH Domain for 37,015 Distinct Screenings, June 24, 2016‒May 17, 2018 SDH screening domain Financial resource strain Food insecurity Housing insecurity Inadequate physical activity Relationship safety Social isolation Stress
Positive screen n (%)
Negative screen n (%)
Total documented responses N (% of all screenings) N=37,015
6,257 (50.6) 5,657 (31.3) 3,023 (13.8) 5,621 (67.3) 2,318 (10.8) 7,841 (68.2) 6,329 (57.3)
6,118 (49.4) 12,433 (68.7) 18,904 (86.2) 2,737 (32.7) 19,058 (89.2) 3,652 (31.8) 4,713 (42.7)
12,375 (33.4) 18,090 (48.9) 21,927 (59.2) 8,358 (22.6) 21,376 (57.7) 11,493 (31) 11,042 (29.8)
SDH, social determinants of health.
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screening requires leadership support and staff buy-in and ensuring there are enough staff resources to take on screening tasks.14,36 Once the decision to initiate SDH screening is made, CHCs must address additional barriers to implementing these activities. First, there is no consensus on how to capture or code SDH in EHRs, and no standards on which questions to ask, how often or when to ask these questions, or which patients to screen.37,38 Second, there is little guidance on the optimal workflows for SDH screening.39 CHCs must determine when to do the screening (e.g., at check-in, during the visit, between visits), how to capture the information (e.g., patient self-report via a paper form or tablet, or having staff ask the questions verbally), and who should ensure the data are collected and entered into the EHR.28,40,41 Finally, CHCs may not want to conduct SDH screening until they have a plan for addressing the identified risk factors. There is limited evidence on how to integrate SDH information into clinical decision making and making referrals to local social service agencies involves its own challenges, including creating and maintaining updated lists of local resources and partnerships with local agencies.37,42 As efforts to conduct EHR-based SDH screening expand, potential barriers to implementing SDH screening should be considered. The results of this analysis provide an early look at the documentation patterns employed by CHCs that should be considered as national efforts to increase SDH screening continue to move forward. For example, screenings may be conducted most frequently at medical visits with established patients. Medical assistants, and other members of the care team beyond the medical provider, may be integral components of workflows to conduct SDH screening. In most cases, medical providers were not the ones to physically enter SDH data into the EHR, suggesting that medical assistants may be best positioned to document screening responses as part of the workflow. CHCs may also prefer to only screen for 1 SDH domain at a time. These results also suggest that a national standard for SDH screening might reduce the need for CHCs to figure out SDH screening approaches on their own. By contrast, a national standard might hamper CHCs ability to conduct such screening in a manner that works best for their setting.
Limitations The findings presented here are primarily descriptive and have several limitations largely associated with using EHR data. First, it cannot be determined from the EHR data alone whether questions for a particular SDH domain were not asked or whether the patient refused to answer. Modifications to the tool made in 2018 (after December 2019
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the observation period for this paper) address this limitation by including a “declined” response category. It is also impossible to assess whether SDH data were collected on paper and then entered into the EHR, or whether the patient was queried directly. Qualitative or observational research is needed to better understand this important aspect of SDH screening implementation and to explore alternative modes of data entry, such as tablets or smartphones. The analyses presented in this paper did not include an exploration of CHC characteristics associated with higher and lower screening rates. Although future analyses are planned to address this question, a key limitation of EHR data is the inability to assess the contextual factors that shape patterns of screening. For example, it is possible that some CHCs had screening programs in place before the release of the EHR-based tools, whereas others were starting from scratch. Mixed-methods research is needed to explore the contextual factors that drive CHC decisions with respect to the patients and SDH domains of focus (e.g., state incentive, local initiatives). Finally, it was necessary to select a defined period for these analyses, and the findings presented here do not include screenings documented after the end of May 2018. Preliminary analyses looking at SDH screening through early 2019 show a continued increase in SDH screening documentation. Future analyses will explore these longer-term patterns.
CONCLUSIONS The results of this analysis demonstrate that simply activating EHR tools for SDH screening does not lead to widespread adoption of this practice, despite the growing national emphasis to do so. Furthermore, even when SDH data are documented, patterns of documentation vary widely. This variation in SDH documentation patterns underscores the need for EHR-based SDH screening approaches that are flexible for variations in workflow, staffing models, and screening targets in the absence of evidence-based, standardized screening guidelines.
ACKNOWLEDGMENTS Publication of this article was supported by the Agency for Healthcare Research and Quality (AHRQ), under HHS contract [1R13HS026664], Kaiser Permanente [CRN5374-754415320], and the Robert Wood Johnson Foundation [75922]. The findings and conclusions in this article are those of the authors and do not necessarily represent the official position of any of the sponsors. The authors deeply appreciate the contributions of Mary Middendorf, who created the SDH tool in OCHIN Epic, and Marla Dearing, who managed all Epic-related aspects of the study.
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They also want to thank Nadia Yosuf, who diligently provided necessary project management support for the Approaches to CHC Implementation of SDH Data Collection and Action (ASCEND) study. Lastly, they want to thank OCHIN’s Clinical Operations Review Committee for shepherding the development of these tools and OCHIN’s member CHCs who are committed to providing care for the nation’s most vulnerable patients. This publication was supported by a grant from the National Institute of Diabetes and Digestive and Kidney Diseases, 1R18DK114701−01. RG, EKC, MM, and AB designed the study. RG and EKC directed the project. SC extracted the data and conducted the analyses for the manuscript. EKC, KD, SC, and RG wrote the manuscript with input from all authors. All authors read and approved the final manuscript. Select segments of this manuscript have been previously presented elsewhere. No financial disclosures were reported by the authors of this paper.
SUPPLEMENTAL MATERIAL Supplemental materials associated with this article can be found in the online version at https://doi.org/10.1016/j.amepre.2019. 07.014.
SUPPLEMENT NOTE This article is part of a supplement entitled Identifying and Intervening on Social Needs in Clinical Settings: Evidence and Evidence Gaps, which is sponsored by the Agency for Healthcare Research and Quality of the U.S. Department of Health and Human Services, Kaiser Permanente, and the Robert Wood Johnson Foundation.
REFERENCES 1. WHO. Social determinants of health: about social determinants of health. www.who.int/social_determinants/sdh_definition/en/. Published 2017. Accessed October 3, 2018. 2. McGinnis JM, Williams-Russo P, Knickman JR. The case for more active policy attention to health promotion. Health Aff (Millwood). 2002;21(2):78–93. https://doi.org/10.1377/hlthaff.21.2.78. 3. Galea S, Tracy M, Hoggatt KJ, Dimaggio C, Karpati A. Estimated deaths attributable to social factors in the United States. Am J Public Health. 2011;101(8):1456–1465. https://doi.org/10.2105/ajph.2010.300086. 4. National Association of Community Health Centers. PRAPARE. www.nachc.org/research-and-data/prapare/. Published 2016. Accessed December 3, 2018. 5. Institute of Medicine. Recommended social and behavioral domains and measures for electronic health. http://nationalacademies.org/HMD/ Activities/PublicHealth/SocialDeterminantsEHR.aspxTagedEn. Published 2014. Accessed November 3, 2018. 6. Council On Community Pediatrics. Poverty and child health in the United States. Pediatrics. 2016;137(4):e20160339. https://doi.org/ 10.1542/peds.2016-0339. 7. Centers for Medicare & Medicaid Services. MARCA: delivery system reform, Medicare payment reform. www.cms.gov/Medicare/ Quality-Initiatives-Patient-Assessment-Instruments/Value-BasedPrograms/MACRA-MIPS-and-APMs/MACRA-MIPS-and-APMs. html. Accessed November 16, 2018.
8. Alley DE, Asomugha CN, Conway PH, Sanghavi DM. Accountable health communities—addressing social needs through Medicare and Medicaid. N Engl J Med. 2016;374(1):8–11. https://doi.org/10.1056/ nejmp1512532. 9. Daniel H, Bornstein SS, Kane GC; Health and Public Policy Committee of the American College of Physicians. Addressing social determinants to improve patient care and promote health equity: an American College of Physicians position paper. Ann Intern Med. 2018;168(8):577–578. https://doi.org/10.7326/m17-2441. 10. National Association of Community Health Centers. Community Health Center Chartbook. www.nachc.org/wp-content/uploads/2018/ 06/Chartbook_FINAL_6.20.18.pdf. Published 2018. Accessed March 10, 2019. 11. Bailey SR, O’Malley JP, Gold R, et al. Receipt of diabetes preventive services differs by insurance status at visit. Am J Prev Med. 2015;48 (2):229–233. https://doi.org/10.1016/j.amepre.2014.08.035. 12. Gold R, DeVoe JE, McIntire PJ, et al. Receipt of diabetes preventive care among safety net patients associated with differing levels of insurance coverage. J Am Board Fam Med. 2012;25(1):42–49. https://doi. org/10.3122/jabfm.2012.01.110142. 13. Gold R, DeVoe J, Shah A, Chauvie S. Insurance continuity and receipt of diabetes preventive care in a network of federally qualified health centers. Med Care. 2009;47(4):431. https://doi.org/10.1097/mlr. 0b013e318190ccac. 14. Institute for Alternative Futures. Community Health Centers Leveraging the social determinants of health. Community health centers leveraging the social determinants of health. http://altfutures.org/ pubs/leveragingSDH/IAF-CHCsLeveragingSDH.pdf. Published 2012. Accessed March 13, 2019. 15. Bachrach D, Pfister H, Wallis K, Lipson M. Addressing patients’ social needs: an emerging business case for provider investment. Commonwealth Fund. Published 2014. https://doi.org/10.15868/socialsector.18186. 16. Institute of Medicine, Committee on the Recommended Social Behavioral Domains Measures for Electronic Health. Records. Capturing Social and Behavioral Domains and Measures in Electronic Health Records: phase 2. National Academies Press; 2014. https://doi.org/ 10.17226/18951. 17. Adler NE, Stead WW. Patients in context−EHR capture of social and behavioral determinants of health. N Engl J Med. 2015;372(8):698– 701. https://doi.org/10.1056/nejmp1413945. 18. HHS, Assistant Secretary for Planning and Evaluation Office of Health Policy. Incorporating Social Determinants of Health in Electronic Health Records: A Qualitative Study of Perspectives on Current Practices Among Top Vendors. https://aspe.hhs.gov/system/files/pdf/ 259901/NORCSDH.pdf. Published 2018. Accessed June 4, 2019. 19. Gottlieb L, Tobey R, Cantor J, Hessler D, Adler NE. Integrating social and medical data to improve population health: opportunities and barriers. Health Aff (Millwood). 2016;35(11):2116–2123. https://doi. org/10.1377/hlthaff.2016.0723. 20. Social Interventions Research & Evaluation Network. Social need screening tools comparison table. https://sirenetwork.ucsf.edu/toolsresources/mmi/screening-tools-comparison/adult-nonspecificTagedEn . Published 2019. Accessed June 5, 2019. 21. Gottlieb L, Cottrell EK, Park B, et al. Advancing social prescribing with implementation science. J Am Board Fam Med. 2018;31(3):315– 321. https://doi.org/10.3122/jabfm.2018.03.170249. 22. Gottlieb LM, Wing H, Adler NE. A systematic review of interventions on patients’ social and economic needs. Am J Prev Med. 2017;53 (5):719–729. https://doi.org/10.1016/j.amepre.2017.05.011. 23. Garg A, Toy S, Tripodis Y, Silverstein M, Freeman E. Addressing social determinants of health at well child care visits: a cluster RCT. Pediatrics. 2015;135(2):e296–e304. https://doi.org/10.1542/peds.20142888. 24. Knowles M, Khan S, Palakshappa D, et al. Successes, challenges, and considerations for integrating referral into food insecurity screening
www.ajpmonline.org
Cottrell et al / Am J Prev Med 2019;57(6S1):S65−S73
25.
26.
27.
28.
29.
30.
31.
32.
33.
in pediatric settings. J Health Care Poor Underserved. 2018;29(1):181– 191. https://doi.org/10.1353/hpu.2018.0012. Lohr AM, Ingram M, Nu~ nez AV, Reinschmidt KM, Carvajal SC. Community−clinical linkages with community health workers in the United States: a scoping review. Health Promot Pract. 2018;19(3):349– 360. https://doi.org/10.1177/1524839918754868. Pescheny JV, Pappas Y, Randhawa G. Facilitators and barriers of implementing and delivering social prescribing services: a systematic review. BMC Health Serv Res. 2018;18:86. https://doi.org/10.1186/ s12913-018-2893-4. Regenstein M, Trott J, Williamson A, Theiss J. Addressing social determinants of health through medical-legal partnerships. Health Aff (Millwood). 2018;37(3):378–385. https://doi.org/10.1377/hlthaff.2017.1264. Gold R, Bunce A, Cowburn S, et al. Adoption of social determinants of health EHR tools by community health centers. Ann Fam Med. 2018;16(5):399–407. https://doi.org/10.1370/afm.2275. de la Vega PB, Losi S, Martinez LS, et al. Implementing an EHR-based screening and referral system to address social determinants of health in primary care. Med Care. 2019;57:S133–S139. https://doi.org/ 10.1097/mlr.0000000000001029. Tong ST, Liaw WR, Kashiri PL, et al. Clinician experiences with screening for social needs in primary care. J Am Board Fam Med. 2018;31(3):351–363. https://doi.org/10.3122/jabfm.2018.03.170419. Byhoff E, Garg A, Pellicer M, et al. Provider and staff feedback on screening for social and behavioral determinants of health for pediatric patients. J Am Board Fam Med. 2019;32(3):297–306. https://doi. org/10.3122/jabfm.2019.03.180276. Byhoff E, Cohen AJ, Hamati MC, et al. Screening for social determinants of health in Michigan health centers. J Am Board Fam Med. 2017;30(4):418–427. https://doi.org/10.3122/jabfm.2017.04.170079. Friedman NL, Banegas MP. Toward addressing social determinants of health: a health care system strategy. Perm J. 2018;22:18-095. https:// doi.org/10.7812/TPP/18-095.
December 2019
S73
34. Health Resources & Services Administration Health Center Program. 2017 National Health Center Data. https://bphc.hrsa.gov/uds/datacenter.aspx. Published 2017. Accessed March 9, 2019. 35. Gold R, Cottrell E, Bunce A, et al. Developing electronic health record (EHR) strategies related to health center patients’ social determinants of health. J Am Board Fam Med. 2017;30(4):428–447. https://doi.org/ 10.3122/jabfm.2017.04.170046. 36. Boyce MB, Browne JP, Greenhalgh J. The experiences of professionals with using information from patient-reported outcome measures to improve the quality of healthcare: a systematic review of qualitative research. BMJ Qual Saf. 2014;23(6):508–518. https://doi.org/10.1136/ bmjqs-2013-002524. 37. Cantor MN, Thorpe L. Integrating data on social determinants of health into electronic health records. Health Aff (Millwood). 2018;37 (4):585–590. https://doi.org/10.1377/hlthaff.2017.1252. 38. O’Gurek DT, Henke C. A practical approach to screening for social determinants of health. Fam Pract Manag. 2018;25(3):7–12. 39. Arons A, DeSilvey S, Fichtenberg C, Gottlieb L. Compendium of medical terminology codes for social risk factors. Social interventions research and Evaluation Network. http://sirenetwork.ucsf.edu/toolsresources/mmi/compendium-medical-terminology-codes-social-riskfactors. Published 2018. Accessed July 29, 2019. 40. LaForge K, Gold R, Cottrell E, et al. How 6 organizations developed tools and processes for social determinants of health screening in primary care: an overview. J Ambul Care Manag. 2018;41(1):2–14. https://doi.org/10.1097/jac.0000000000000221. 41. Jensen RE, Rothrock NE, DeWitt EM, et al. The role of technical advances in the adoption and integration of patient-reported outcomes in clinical care. Med Care. 2015;53(2):153–159. https://doi.org/ 10.1097/mlr.0000000000000289. 42. DeMilto L, Nakashian M. Using Social Determinants of Health Data to Improve Health Care and Health: a Learning Report. Princeton, NJ: Robert Wood Johnson Foundation; 2016.