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Journal of the American Medical Informatics Association : JAMIA logoLink to Journal of the American Medical Informatics Association : JAMIA
. 2022 Sep 7;29(12):2110–2116. doi: 10.1093/jamia/ocac154

Physician awareness of social determinants of health documentation capability in the electronic health record

Bradley E Iott 1,2,✉, Matthew S Pantell 3,4, Julia Adler-Milstein 5,6, Laura M Gottlieb 7,8,9
PMCID: PMC9667172  PMID: 36069887

Abstract

Healthcare organizations are increasing social determinants of health (SDH) screening and documentation in the electronic health record (EHR). Physicians may use SDH data for medical decision-making and to provide referrals to social care resources. Physicians must be aware of these data to use them, however, and little is known about physicians’ awareness of EHR-based SDH documentation or documentation capabilities. We therefore leveraged national physician survey data to measure level of awareness and variation by physician, practice, and EHR characteristics to inform practice- and policy-based efforts to drive medical-social care integration. We identify higher levels of social needs documentation awareness among physicians practicing in community health centers, those participating in payment models with social care initiatives, and those aware of other advanced EHR functionalities. Findings indicate that there are opportunities to improve physician education and training around new EHR-based SDH functionalities.

Keywords: social determinants of health, electronic health records, documentation, awareness

INTRODUCTION

US healthcare organizations are increasing efforts to identify and intervene on patients’ social risk factors as part of a comprehensive strategy to improve health outcomes.1–7 As a result, many electronic health record (EHR) platforms now enable structured documentation of both social risk assessment and needs.8–12 Storing social data in the EHR can support efforts to improve care decisions and referrals according to patients’ social circumstances.13 Given the potential benefits, assuring that EHRs have specific fields dedicated to social data has been advanced by key stakeholders, including the National Academy of Medicine,10,11,14 CMS,15,16 and the Office of the National Coordinator on Health Information Technology.17

However, given the breadth of available EHR fields, physicians may not know whether their EHR includes fields to document social determinants of health (SDH),18,19 which are defined as “the conditions in which people are born, grow, live, and age” including financial strain and food insecurity.20 Several factors may influence whether or not an EHR system has SDH documentation capability and those have been explored in prior work.21 However, we could not identify any prior studies examining whether clinicians are aware of whether their EHRs have the capability to document SDH in structured format such as SDH modules. Having the EHR capability to assess and store SDH data in a structured format can allow health systems to understand the burden of social needs on a population level, and is often a precondition to providing EHR-based referral interventions (such as referrals to social care resources within the health system or the community).

Clinician awareness may be driven by multiple factors. For example, those physicians who care for safety net populations, those with robust availability of social services, and those incentivized to support social care may be more likely to be aware of SDH fields. Thus, while universal awareness of SDH EHR fields may be an ideal goal to assess clinic rates of patient social needs and how clinicians are responding to them, awareness of SDH documentation tools in the EHR may vary substantially by clinical setting. Since awareness of SDH EHR fields is a prerequisite for their use, we sought to create the first national-level measures of such awareness using a survey of office-based physicians. We then examined associations between awareness and physician, practice, EHR vendor, and other advanced EHR features to target practice- and policy-based efforts to increase awareness and achieve greater medical-social care integration.

MATERIALS AND METHODS

Data sources

We conducted a secondary data analysis using data from the National Electronic Health Records Survey (NEHRS), an annual nationally representative cross-sectional survey of nonfederally employed, office-based physicians in the United States on issues related to EHR adoption, use, and burden.22 The sampling unit was the physician.22 The 2019 survey was fielded from June 14, 2019 to December 11, 2019 with an unweighted response rate of 41%.22 We used data from the most recently available NEHRS (2019) that includes an assessment of SDH documentation capabilities.

Study sample

The 2019 NEHRS sample includes 1524 physicians. We restricted our sample to physicians who report using an EHR (n = 1372), as prior work showed that by 2017 80.5% of hospitals had adopted EHRs.23 We dropped observations with missing values for included variables, resulting in a final sample of 1134 physicians. We used NEHRS-provided survey weights to produce national estimates.

Measures

Our primary outcome measure was respondents’ awareness of their EHR’s ability to capture SDH data, as measured by the question, “Does the reporting location use a computerized system to: Record social determinants of health (e.g., employment, education)?” This survey question features 3 responses: Yes, No, and Don’t Know. We considered those with “awareness” as those answering either “Yes” or “No.”

Covariates included physician and practice characteristics known to be associated with EHR use.24 Physician characteristics included medical specialty (primary care, surgery, or medical care), sex (female, male), and age (under 50 years or 50+ years). Practice characteristics included practice size (1 MD, 2–10 MDs, 11–50 MDs, or 50+ MDs), type (private solo or group practice, or other) and ownership (physician or physician group; insurance company, health plan, or HMO; community health center [CHC]; and medical/academic health center; other hospital; other healthcare corporation; or other). Because participation in public insurance and alternative payment models is likely to influence engagement around SDH initiatives,25,26 we included the following payment model participation variables: whether the physician accepts Medicaid; the proportion of patients insured by Medicaid; whether the physician accepts Medicare; and whether the physician participates in any of the following payment models: patient-centered medical home (PCMH),27,28 Accountable Care Organization (ACO),25,29–31 Pay for Performance (P4P),32,33 Meaningful Use,17,34,35 Merit-based Incentive Payment System (MIPS),36–38 and Advanced Alternative Payment Model (APM).39

We included 2 types of EHR characteristics. The first was vendor to characterize differences in SDH awareness across vendors. Next, to explore associations between SDH awareness and physician’s knowledge of their EHR’s capabilities, as measured by awareness of other available EHR features, we included indicators of awareness of 5 other functions available in NEHRS and chosen to represent an advanced feature set. These advanced features include those which meet Meaningful Use Stage 2 objectives and those which may be found in comprehensive, but not basic, EHR systems.40 These measures—each created from Yes/No/Don’t Know responses as described above—reflect physicians’ awareness of: (1) whether the EHR could document behavioral determinants of health (BDH),11 (2) patient engagement capabilities (whether the EHR had the capacity to create educational resources tailored to the patients’ specific conditions and to exchange secure messages with patients); (3) population management capabilities (whether the EHR had the ability to generate lists of patients with particular health conditions, create reports on clinical care measures for patients with chronic conditions, and create shared care plans that can be made available across the care team); (4) quality measurement capabilities (whether the EHR could send clinical quality measures to public and private insurers); and (5) patient safety capabilities (whether the EHR included the capacity to use computerized provider order entry [CPOE] to order prescriptions, electronically send prescriptions to the pharmacy, provide warnings of drug interactions or contraindications, order lab tests, order radiology tests, and provide reminders for guideline-based interventions or screening tests).

Data analysis

We tabulated sample demographics and then calculated the weighted overall level of awareness of SDH documentation capability in the sample. Next, we examined bivariate associations between the level of awareness and independent variables (physician, practice, and EHR characteristics) using chi-squared tests. We also constructed a multivariable Poisson regression model predicting physician awareness of SDH documentation capability based on all included physician, practice, and EHR characteristics. All analyses were nationally weighted using NEHRS-provided survey weights in Stata 17.0 (StataCorp, College Station, TX).

RESULTS

Sample characteristics

Table 1 presents sample characteristics. Most respondents (49.69%) were primary care physicians, identified as male (68.32%) and were over the age of 50 (74.52%). Most (73.96%) reported working in private practice, in practices with 2–10 physicians (53.33%) and in practices owned by physicians or physician groups (57.43%). While most respondents accepted Medicaid (71.07%) and Medicare (88.76%) and participated in Meaningful Use (52.04%), fewer than half participated in each of the alternative payment programs (PCMH [26.02%], ACO [38.17%], P4P [31.52%], MIPS [30.69%], or APM [9.27%]).

Table 1.

Sample demographics (raw counts, weighted proportions, n = 1134)

Physician, practice, payment, and EHR characteristics # %
Primary care 596 49.69
Surgical care 245 21.87
Medical care 293 28.44
Female 334 31.68
Age 50+ years 782 74.52
Size
 1 MD 202 17.65
 2–10 MDs 574 53.33
 11–50 MDs 214 16.02
 50+ MDs 144 13.01
Private solo or group practice 818 73.96
Ownership
 Physician or physician group 625 57.43
 Insurance company, health plan, or HMO 31 2.91
 Community Health Center 63 4.56
 Medical/Academic health center 162 15.26
 Other hospital 116 8.59
 Other health care corporation 93 6.99
 Other 44 4.28
Accepts Medicaid 884 71.07
Accepts Medicare 997 88.76
Participates in PCMH 340 26.02
Participates in ACO 439 38.17
Participates in P4P 304 31.52
Participates in Meaningful Use 606 52.04
Participates in MIPS 305 30.69
Participates in APM 113 9.27
EHR vendor
 Allscripts 85 6.47
 Amazing charts 18 1.37
 Athenahealth 80 6.28
 Cerner 87 6.4
 eClinicalWorks 107 11.13
 e-MDs 15 0.8
 Epic 278 25.54
 GE/Centricity 34 2.27
 Modernizing medicine 19 1.5
 NextGen 61 6.01
 Practice fusion 27 4.33
 Sage/Vitera/Greenway 50 2.76
 Other 273 25.15
Aware of behavioral determinants of health documentation feature 1112 96.42
Patient engagement capabilities
 Aware of ability to educational resources 1018 89.57
 Aware of ability to exchange messages 1075 93.56
Population management capabilities
 Aware of ability to generate lists 845 75.47
 Aware of ability to create reports 834 71.98
 Aware of ability to create shared care plans 817 71.86
Quality measurement capability
 Aware of ability to send quality measures 699 62.67
Safety capabilities
 Aware of CPOE for prescriptions 1134 100
 Aware of ability to send scripts to pharmacy 1132 99.9
 Aware of having drug interaction warnings 1074 93.76
 Aware of ability to order lab tests 1112 98.24
 Aware of ability to order radiology tests 1098 96.06
 Aware of guideline reminders 989 86.96
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Awareness of SDH documentation capability and associations with EHR vendor

About 87.85% of respondents were aware of SDH documentation capability availability (Figure 1). Of those with awareness (87.85%), 86.24% of respondents reported having SDH documentation capabilities and 13.76% did not. Levels of SDH documentation capability awareness were similar across different EHR vendors, ranging from 81.38% (GE/Centricity) to 96.71% (Cerner, P = .55).

Figure 1.

Figure 1.

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Respondents’ awareness of having the ability to record SDH in the EHR (weighted, n = 1134).

Associations between awareness of SDH documentation capability and physician and practice characteristics

We observed no significant differences in awareness by specialty area, physician sex, or age (Table 2). Respondents working in CHCs (97.98%) reported the highest level of awareness among ownership categories (P = .011). There were no significant differences in awareness by clinic size or type. Physicians participating in P4P (94.1%, P = .016), MIPS (94.49%, P < .001), and APM (99.57%, P < .001) had higher levels of awareness compared to those not participating in these programs (Table 2).

Table 2.

Associations between awareness of SDH documentation capability and physician, practice, payment, and EHR characteristics (weighted, n = 1134)

Physician, practice, payment, and EHR characteristics Aware Not aware P value
Primary care 91.92 8.08 .062
Surgical care 83.59 16.41
Medical care 83.98 16.02
Female 87.33 12.67 .842
Male 88.08 11.92
Under 50 years 85.7 14.3 .456
50+ years 88.58 11.42
Size
 1 MD 94.16 5.84 .3
 2–10 MDs 87.22 12.78
 11–50 MDs 83.78 16.22
 50+ MDs 86.85 13.15
Clinic type
 Private solo or group practice 88.71 11.29 .363
 Other setting 85.39 14.61
Ownership
 Physician or physician group 88.95 11.05 .011
 Insurance company, health plan, or HMO 66.96 33.04
 Community health center 97.98 2.02
 Medical/Academic health center 79.65 20.35
 Other hospital 93.93 6.07
 Other health care corporation 93.54 6.46
 Other 84.05 15.95
Accepts Medicaid
 Does not accept No 87.22 12.78 .896
 Yes 87.9 12.1
 N/A 91.79 8.21
 Don’t know 83.78 16.22
Accepts Medicare 88.88 11.12 .098
Does not accept Medicare 79.72 20.28
Participates in PCMH 91.5 8.5 .193
Does not participate in PCMH 86.56 13.44
Participates in ACO 91.64 8.36 .077
Does not participate in ACO 85.51 14.49
Participates in P4P 94.1 5.9 .016
Does not participate in P4P 84.97 15.03
Participates in Meaningful Use 89.93 10.07 .207
Does not participate in Meaningful Use 85.58 14.42
Participates in MIPS 94.49 5.51 .013
Does not participate in MIPS 84.9 15.1
Participates in APM 99.57 0.43 <.001
Does not participate in APM 86.65 13.35
EHR vendor .55
 Cerner 96.71 3.29
 NextGen 96.66 3.34
 Sage/Vitera/Greenway 94.94 5.06
 Modernizing medicine 94.23 5.77
 Practice fusion 93.19 6.81
 Allscripts 90.17 9.83
 e-MDs 89.7 10.3
 Other 87.67 12.33
 eClinicalWorks 87.07 12.93
 Amazing charts 83.73 16.27
 Epic 83.33 16.67
 athenahealth 82.99 17.01
 GE/Centricity 81.38 18.62
Aware of behavioral determinants of health feature 91.06 8.94 <.001
Not aware of behavioral determinants of health feature 1.49 98.51
Patient engagement capabilities
 Aware of ability to educational resources 88.87 11.13 .069
 Not aware of ability to educational resources 78.98 21.02
 Aware of ability to exchange messages 89.06 10.94 .006
 Not aware of ability to exchange messages 70.18 29.82
Population management capabilities
 Aware of ability to generate lists 90.96 9.04 .001
 Not aware of ability to generate lists 78.24 21.76
 Aware of ability to create reports 90.85 9.15 .004
 Not aware of ability to create reports 80.11 19.89
 Aware of ability to create shared care plans 89.6 10.4
 Not aware of ability to create shared care plans 83.35 16.65 .087
Quality measurement capability
 Aware of ability to send quality measures 93.13 6.87 <.001
 Not aware of ability to send quality measures 78.93 21.07
Safety capabilities
 Aware of CPOE for prescriptions 87.85 12.15
 Not aware of CPOE for prescriptions 0 0 N/A
 Aware of ability to send scripts to pharmacy 87.83 12.17
 Not aware of ability to send scripts to pharmacy 100 0 .636
 Aware of having drug interaction warnings 88.66 11.34
 Not aware of having drug interaction warnings 75.55 24.45 .061
 Aware of ability to order lab tests 87.93 12.07
 Not aware of ability to order lab tests 83.37 16.63 .552
 Aware of ability to order radiology tests 88.31 11.69
 Not aware of ability to order radiology tests 76.62 23.38 .136
 Aware of guideline reminders 90.17 9.83
 Not aware of guideline reminders 72.24 27.76 <.001
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The bolded P values are significant at the P < .05 level.

Association between awareness of SDH documentation capability and awareness of other advanced EHR capabilities

Respondents who were aware of SDH documentation capabilities were also more likely to be aware of other advanced EHR capabilities. Specifically, we observed a significantly higher level of awareness of SDH documentation among physicians who were also aware of BDH documentation (91.06%, P < .001). Similarly, we observed significantly higher levels of awareness of SDH documentation amongst physicians with awareness of nearly all other advanced EHR capabilities, including patient engagement (89.06%, P = .006), population management (90.85–90.96%, P < .005), quality measurement (93.13%, P < .001), and patient safety features (90.17–100%, P < .001) relative to physicians without awareness of these features (Table 2).

These results persisted in our multivariate regression model (Supplementary Appendix A).

DISCUSSION

In a national survey of nonfederally employed, office-based physicians, almost 9 in 10 were aware of EHR capabilities to document SDH. But levels of awareness varied significantly by EHR and practice characteristics. Our findings can inform the design of promotion and training activities to maximize use of existing, available SDH capacities. Prior work has used the same data (NEHRS) to explore correlations between SDH use and out-of-hours EHR use and described the proportion of physicians using EHRs with SDH documentation capability.21 This work excluded physicians who reported that they did not know if their EHR could record SDH data—which is the key measure in our new analyses. Thus, our approach examines a distinct concept (awareness) and simultaneously strengthens the earlier work, which overestimated capability prevalence by excluding respondents that were not aware of these functionalities. In the current study where we intentionally include these respondents, we find lower national estimates. This could inform efforts to identify potential targets for provider training, including medical and surgical specialties, those not participating in alternative payment models, and those unaware of advanced EHR capabilities. We recommend 2 types of changes to increase providers’ awareness of SDH capabilities: 1) target training initiatives to providers who are less likely to be aware of SDH capabilities based on practice characteristics; and 2) add survey questions about utilization of SDH documentation tools to future NEHRS administrations.

Despite the increasing focus on SDH screening and social care in the US healthcare settings, physicians’ awareness of EHR-based SDH documentation capability varies. Awareness of these capabilities is relevant to direct clinical care. While not all physicians will document social data using the EHR, physicians nonetheless should know that data about patients’ health-relevant social conditions are incorporated into their EHR. Ideally, maximizing awareness of EHR SDH data fields will enable more physicians to both review and use SDH data for medical decision-making.41,42 On a policy level, awareness could have a direct impact on the design and evaluation of interventions incentivizing the use of documentation and referral capabilities to identify and address patients’ social needs more quickly. CMS currently is exploring quality measures reflecting both the prevalence of social risk screening and the prevalence of social risk, and many states have already begun to use or are considering similar quality indicators. Organizational performance on these types of measures is likely to depend on EHR SDH documentation, but lack of awareness about related documentation capabilities introduces some doubt about the reliability of EHR documentation as a source of performance information.43,44 High-quality training may facilitate use of advanced EHR capabilities.45–51 Associations between SDH awareness and awareness of other advanced EHR capabilities reveal that lack of awareness is not unique to SDH functions and training to increase awareness could therefore target a broad set of these high-value functions.

Against this backdrop, our finding that awareness differs based on practice and EHR characteristics suggests the need for different types of interventions to increase the utilization of documentation capabilities based on the extent of social care provided by healthcare organizations. Considering the multiple factors that likely influence physicians’ awareness of SDH EHR fields, some of the variation in SDH EHR awareness by organizational characteristics likely stems from differences in practice motivation or need to provide social care. Physicians with frequent engagement in screening and social care work may be more likely to be aware of SDH capabilities. For instance, physicians in CHCs, which disproportionately serve low-income patients, were more likely to be aware of these capabilities, likely because they have accrued more expertise related to addressing social needs relative to physicians working in other settings. CHCs have also established other implementation facilitators52—including availability of internal champions, external incentives,53 and both dedicated workforce54,55 and workflows56,57—that together can drive awareness of relevant SDH documentation capabilities. Conversely, other practice characteristics (eg, payer-owned clinics and medical centers), however, were associated with lower awareness of SDH documentation capability. Physicians in these organizations with historically less engagement in screening and social care may benefit from targeted educational interventions to increase awareness of SDH capabilities.

Although awareness is a prerequisite to use, it is not the sole or best indicator of use. Moving forward, it will be critical to study the systematic uptake of SDH documentation capabilities58 and associated barriers and facilitators. One strategy to track use would be to add new questions to NEHRS that capture rates of documentation and data review, including the proportion of patients screened and the clinic’s population denominator; workflows for SDH screening and navigation; and information about the workforce involved in SDH-related screening, documentation, and referral functions.59 Together, these data would enable us to identify those characteristics associated with promoting higher levels of use of SDH documentation capabilities.

Limitations

Several key limitations to this dataset should be considered. First, given the binary nature of the survey question assessing SDH documentation capacity, we cannot know whether all respondents interpreted this question in the same way, including what features count as recording SDH, or how one may respond if their EHR supports the documentation of certain social risk factors but not others. Moreover, respondents may have limited understanding of what SDH are and the NEHRS SDH documentation question offers only 2 examples (employment, education) that may not be maximally relevant to practice. Further, because of social desirability bias, respondents may over-report awareness.60 Additionally, NEHRS does not currently measure physicians’ actual use of EHR SDH documentation. Furthermore, the data are limited to physician respondents, though other members of the care team, such as social workers and patient navigators, may be the primary users of SDH documentation capability, we were unable to capture a broader measure of awareness. Finally, some of the statistically significant findings observed in Table 2 may be a result of chance through multiple comparisons.

CONCLUSION

As our healthcare system pursues increased social risk screening and related intervention activities, it is reassuring that almost 90% of physicians are aware of EHR SDH documentation capability. Variation in awareness can be used to inform future efforts to facilitate uptake.

AUTHOR CONTRIBUTIONS

BEI contributed to the conception and design of this study, data collection, data analysis and interpretation, drafting the article, critical revision of the article, and final approval of the version to be published. He agrees to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. MSP contributed to the conception and design of this study, data interpretation, drafting the article, critical revision of the article, and final approval of the version to be published. He agrees to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. JA-M contributed to the conception and design of this study, data interpretation, drafting the article, critical revision of the article, and final approval of the version to be published. She agrees to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. LMG contributed to the conception and design of this study, data interpretation, drafting the article, critical revision of the article, and final approval of the version to be published. She agrees to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Supplementary Material

ocac154_Supplementary_Data
Click here for additional data file. (15.5KB, docx)

ACKNOWLEDGMENTS

We acknowledge the helpful feedback provided by members of the UCSF Social Informatics Research Core and the UCSF Social Interventions Research and Evaluation Network (SIREN) for this study.

CONFLICT OF INTEREST STATEMENT

None declared.

Contributor Information

Bradley E Iott, Center for Clinical Informatics and Improvement Research, University of California, San Francisco (UCSF), San Francisco, California, USA; Social Interventions Research and Evaluation Network, UCSF, San Francisco, California, USA.

Matthew S Pantell, Department of Pediatrics, UCSF, San Francisco, California, USA; Center for Health and Community, UCSF, San Francisco, California, USA.

Julia Adler-Milstein, Center for Clinical Informatics and Improvement Research, University of California, San Francisco (UCSF), San Francisco, California, USA; Department of Medicine, UCSF, San Francisco, California, USA.

Laura M Gottlieb, Social Interventions Research and Evaluation Network, UCSF, San Francisco, California, USA; Center for Health and Community, UCSF, San Francisco, California, USA; Department of Family and Community Medicine, UCSF, San Francisco, California, USA.

SUPPLEMENTARY MATERIAL

Supplementary material is available at Journal of the American Medical Informatics Association online.

Data Availability

The data underlying this article are available from the CDC National Center for Health Statistics, at: https://ftp.cdc.gov/pub/Health_Statistics/NCHS/Dataset_Documentation/NEHRS/.

REFERENCES

  • 1. Muhlestein D, Saunders RS, Richards R, et al. Recent progress in the value journey: growth of ACOs and value-based payment models in 2018 | Health affairs. Heatlh Aff Blog 2018. https://www.healthaffairs.org/do/10.1377/hblog20180810.481968/full/. Accessed November 28, 2021. [Google Scholar]
  • 2. Fichtenberg CM, Alley DE, Mistry KB.. Improving social needs intervention research: key questions for advancing the field. Am J Prev Med 2019; 57 (6 Suppl 1): S47–54. [DOI] [PubMed] [Google Scholar]
  • 3. Gottlieb L, Fichtenberg C, Alderwick H, et al. Social determinants of health: what’s a healthcare system to do? J Healthc Manag 2019; 64 (4): 243–57. [DOI] [PubMed] [Google Scholar]
  • 4. VanLare JM, Blum JD, Conway PH.. Linking performance with payment: implementing the physician value-based payment modifier. Jama 2012; 308 (20): 2089–90. [DOI] [PubMed] [Google Scholar]
  • 5. Meddings J, Reichert H, Smith SN, et al. The impact of disability and social determinants of health on condition-specific readmissions beyond medicare risk adjustments: a cohort study. J Gen Intern Med 2017; 32 (1): 71–80. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6. Ash AS, Mick EO, Ellis RP, et al. Social determinants of health in managed care payment formulas. JAMA Intern Med 2017; 177 (10): 1424–30. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7. Healthcare Intelligence Network. 2019 healthcare benchmarks: social determinants of health. https://hin.3dcartstores.com/2019-Healthcare-Benchmarks-Social-Determinants-of-Health_p_5324.html. Accessed November 20, 2021.
  • 8. Gottlieb LM, Tirozzi KJ, Manchanda R, et al. Moving electronic medical records upstream: incorporating social determinants of health. Am J Prev Med 2015; 48 (2): 215–8. [DOI] [PubMed] [Google Scholar]
  • 9. 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. [DOI] [PubMed] [Google Scholar]
  • 10. Committee on the Recommended Social and Behavioral Domains and Measures for Electronic Health Records; Board on Population Health and Public Health Practice; Institute of Medicine. Capturing Social and Behavioral Domains in Electronic Health Records: Phase 1. Washington, DC: National Academies Press (US; ); 2014. http://www.nationalacademies.org/hmd/Reports/2014/Capturing-Social-and-Behavioral-Domains-in-Electronic-Health-Records-Phase-1.aspx. Accessed July 25, 2018. [PubMed] [Google Scholar]
  • 11. Committee on the Recommended Social and Behavioral Domains and Measures for Electronic Health Records, Board on Population Health and Public Health Practice, Institute of Medicine. Capturing Social and Behavioral Domains and Measures in Electronic Health Records: Phase 2. Washington, DC: National Academies Press (US; ); 2015. http://www.ncbi.nlm.nih.gov/books/NBK268995/. Accessed July 25, 2018. [PubMed] [Google Scholar]
  • 12. Hripcsak G, Forrest CB, Brennan PF, et al. Informatics to support the IOM social and behavioral domains and measures. J Am Med Inform Assoc 2015; 22 (4): 921–4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13. National Academies of Sciences, Engineering, and Medicine. Integrating Social Care into the Delivery of Health Care: Moving Upstream to Improve the Nation’s Health. Washington, DC: The National Academies Press; 2020. [PubMed] [Google Scholar]
  • 14. Adler NE, Cutler DM, Fielding JE, et al. Addressing social determinants of health and health disparities: a vital direction for health and health care. NAM Perspect 2016. doi: 10.31478/201609t. [DOI] [Google Scholar]
  • 15.Centers for Medicare & Medicaid Services. MACRA: delivery system reform, Medicare payment reform. 2018. https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Value-Based-Programs/MACRA-MIPS-and-APMs/MACRA-MIPS-and-APMs.html. Accessed September 17, 2019.
  • 16.Centers for Medicare & Medicaid Services. CMS timeline of important MU dates. Meaningful use. 2021. https://www.cdc.gov/ehrmeaningfuluse/timeline.html. Accessed November 26, 2021.
  • 17. Office of the National Coordinator for Health Information Technology (ONC), Department of Health and Human Services (HHS). 2015 edition Health Information Technology (Health IT) certification criteria, 2015 edition base Electronic Health Record (EHR) definition, and ONC health IT certification program modifications. Final rule. Fed Regist 2015; 80: 62601–759. [PubMed] [Google Scholar]
  • 18. Hier DB, Rothschild A, LeMaistre A, et al. Differing faculty and housestaff acceptance of an electronic health record. Int J Med Inform 2005; 74 (7–8): 657–62. [DOI] [PubMed] [Google Scholar]
  • 19. Rao SR, DesRoches CM, Donelan K, et al. Electronic health records in small physician practices: availability, use, and perceived benefits. J Am Med Inform Assoc 2011; 18 (3): 271–5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20. Social Determinants of Health. World health organ. https://www.who.int/health-topics/social-determinants-of-health. Accessed May 24, 2022.
  • 21. Hong Y-R, Turner K, Nguyen OT, et al. Social determinants of health and after-hours electronic health record documentation: a national survey of US physicians. Popul Health Manag 2021. doi: 10.1089/pop.2021.0212. [DOI] [PubMed] [Google Scholar]
  • 22. NEHRS – National Electronic Health Records Survey Homepage. 2021. https://www.cdc.gov/nchs/nehrs/about.htm. Accessed November 20, 2021.
  • 23. Adler-Milstein J, Holmgren AJ, Kralovec P, et al. Electronic health record adoption in US hospitals: the emergence of a digital “advanced use” divide. J Am Med Inform Assoc 2017; 24 (6): 1142–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24. Menachemi N, Powers TL, Brooks RG.. Physician and practice characteristics associated with longitudinal increases in electronic health records adoption. J Healthc Manag 2011; 56 (3): 183–97. [PubMed] [Google Scholar]
  • 25. Garg A, Homer CJ, Dworkin PH.. Addressing social determinants of health: challenges and opportunities in a value-based model. Pediatrics 2019; 143 (4): e20182355. [DOI] [PubMed] [Google Scholar]
  • 26. Alley DE, Asomugha CN, Conway PH, et al. Accountable health communities—addressing social needs through Medicare and Medicaid. N Engl J Med 2016; 374 (1): 8–11. [DOI] [PubMed] [Google Scholar]
  • 27. Moffett ML, Kaufman A, Bazemore A.. Community health workers bring cost savings to patient-centered medical homes. J Community Health 2018; 43 (1): 1–3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28. Garg A, Jack B, Zuckerman B.. Addressing the social determinants of health within the patient-centered medical home: lessons from pediatrics. JAMA 2013; 309 (19): 2001–2. [DOI] [PubMed] [Google Scholar]
  • 29. Browne J, Mccurley JL, Fung V, et al. Addressing social determinants of health identified by systematic screening in a medicaid accountable care organization: a qualitative study. J Prim Care Community Health 2021; 12: 2150132721993651. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30. Fields R. Mission health partners: a community-based social determinants driven accountable care organization. N C Med J 2017; 78 (4): 245–7. [DOI] [PubMed] [Google Scholar]
  • 31. Plescia M, Dulin M.. Accountable care communities. N C Med J 2017; 78 (4): 238–41. [DOI] [PubMed] [Google Scholar]
  • 32. Spencer A, Freda B, McGinnis T, et al. Measuring social determinants of health among Medicaid beneficiaries: early state lessons. 2016. https://www.chcs.org/resource/measuring-social-determinants-health-among-medicaid-beneficiaries-early-state-lessons/. Accessed April 27, 2021.
  • 33. Sills MR, Hall M, Colvin JD, et al. Association of social determinants with children’s hospitals’ preventable readmissions performance. JAMA Pediatr 2016; 170 (4): 350–8. [DOI] [PubMed] [Google Scholar]
  • 34.Social, Psychological, and Behavioral Data | HealthIT.gov. https://www.healthit.gov/test-method/social-psychological-and-behavioral-data. Accessed January 17, 2022.
  • 35.Meaningful Use of Electronic Health Records | CDC. 2021. https://www.cdc.gov/cancer/npcr/meaningful_use.htm. Accessed January 17, 2022.
  • 36. Byrd JN, Chung KC.. Evaluation of the merit-based incentive payment system and surgeons caring for patients at high social risk. JAMA Surg 2021; 156 (11): 1018–24. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37. Khullar D, Schpero WL, Bond AM, et al. Association between patient social risk and physician performance scores in the first year of the merit-based incentive payment system. JAMA 2020; 324 (10): 975–83. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.MIPS and the Social Determinants of Health. 2020. https://www.healthaffairs.org/do/10.1377/he20201013.694857/full/Practice_of_Medicine_2020_slides.pdf. Accessed January 16, 2022.
  • 39.Advanced APMs – QPP. https://qpp.cms.gov/apms/advanced-apms. Accessed January 16, 2022.
  • 40. Adler-Milstein J, DesRoches CM, Furukawa MF, et al. More than half of US hospitals have at least a basic EHR, but stage 2 criteria remain challenging for most. Health Aff (Millwood) 2014; 33 (9): 1664–71. [DOI] [PubMed] [Google Scholar]
  • 41. Bernheim SM, Ross JS, Krumholz HM, et al. Influence of patients’ socioeconomic status on clinical management decisions: a qualitative study. Ann Fam Med 2008; 6 (1): 53–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42. Martin R. Rethinking primary health care ethics: ethics in contemporary primary health care in the United Kingdom. Prim Health Care Res Dev 2004; 5 (4): 317–28. [Google Scholar]
  • 43.Centers for Medicare & Medicaid Services. List of measures under consideration for December 1, 2021. https://www.qualityforum.org/WorkArea/linkit.aspx?LinkIdentifier=id&ItemID=96464. Accessed March 23, 2022.
  • 44. National Committee for Quality Assurance. Health equity and social determinants of health in HEDIS: data for measurement. 2021. https://www.ncqa.org/wp-content/uploads/2021/06/20210622_NCQA_Health_Equity_Social_Determinants_of_Health_in_HEDIS.pdf. Accessed March 23, 2022.
  • 45. Goetz Goldberg D, Kuzel AJ, Feng LB, et al. EHRs in primary care practices: benefits, challenges, and successful strategies. Am J Manag Care 2012; 18 (2): e48–54. [PubMed] [Google Scholar]
  • 46. Randhawa GK, Shachak A, Courtney KL, et al. Evaluating a post-implementation electronic medical record training intervention for diabetes management in primary care. BMJ Health Care Inform 2019; 26 (1): e100086. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47. Watt R. Does customized in-practice support improve EMR meaningful use in primary care? Evidence from a retrospective mixed methods evaluation. Published Online First: 2014. https://dspace.library.uvic.ca/handle/1828/5698. Accessed March 26, 2022.
  • 48. Trudel M-C, Marsan J, Paré G, et al. Ceiling effect in EMR system assimilation: a multiple case study in primary care family practices. BMC Med Inform Decis Mak 2017; 17 (1): 46. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49. Price M, Singer A, Kim J.. Adopting electronic medical records: are they just electronic paper records? Can Fam Physician 2013; 59 (7): e322–9. [PMC free article] [PubMed] [Google Scholar]
  • 50. Otto P, Nevo D.. Electronic health records: A simulation model to measure the adoption rate from policy interventions. J Enterp Inf Manag 2013; 26 (1/2): 165–82. [Google Scholar]
  • 51. Rahal RM, Mercer J, Kuziemsky C, et al. Factors affecting the mature use of electronic medical records by primary care physicians: a systematic review. BMC Med Inform Decis Mak 2021; 21 (1): 67. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52. Drake C, Eisenson H.. Assessing and addressing social needs in primary care. NEJM Catal. Published Online First: 6 November, 2019. https://catalyst.nejm.org/doi/full/10.1056/CAT.19.0693. Accessed December 9, 2021. [Google Scholar]
  • 53. Gruß I, Bunce A, Davis J, et al. Initiating and implementing social determinants of health data collection in community health centers. Popul Health Manag 2021; 24 (1): 52–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 54. Olson D, Oldfield B, Morales Navarro S.. Standardizing social determinants of health assessments | Health affairs blog. Health Aff Blog. https://www-healthaffairs-org.proxy.lib.umich.edu/do/10.1377/hblog20190311.823116/full/. Accessed April 27, 2021. [Google Scholar]
  • 55. Schickedanz A, Hamity C, Rogers A, et al. Clinician experiences and attitudes regarding screening for social determinants of health in a large integrated health system. Med Care 2019; 57 (Suppl 6) (Suppl 2): S197–201. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 56. 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. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 57. Cottrell EK, Dambrun K, Cowburn S, et al. Variation in electronic health record documentation of social determinants of health across a national network of community health centers. Am J Prev Med 2019; 57 (6 Suppl 1): S65–73. [DOI] [PubMed] [Google Scholar]
  • 58. Pantell MS, Adler-Milstein J, Wang MD, et al. A call for social informatics. J Am Med Inform Assoc 2020; 27 (11): 1798–801. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 59. Cartier Y, Fichtenberg C, Gottlieb LM.. Implementing community resource referral technology: facilitators and barriers described by early adopters. Health Aff (Millwood) 2020; 39 (4): 662–9. [DOI] [PubMed] [Google Scholar]
  • 60. Callegaro M. Social desirability. In: Lavrakas P, ed. Encyclopedia of Survey Research Methods. Thousand Oaks, CA: Sage Publications, Inc.; 2008. [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

ocac154_Supplementary_Data
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Data Availability Statement

The data underlying this article are available from the CDC National Center for Health Statistics, at: https://ftp.cdc.gov/pub/Health_Statistics/NCHS/Dataset_Documentation/NEHRS/.

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