Privacy concerns regarding social risk screening and documentation in the electronic health record: a qualitative study of patients’ perspectives

Bradley E Iott; Shauna Wagner; Jessica Pater; Tammy Toscos; Denise Anthony; Tiffany C Veinot

doi:10.1093/jamiaopen/ooag203

. 2026 Oct 10;9(5):ooag203. doi: 10.1093/jamiaopen/ooag203

Privacy concerns regarding social risk screening and documentation in the electronic health record: a qualitative study of patients’ perspectives

Bradley E Iott ^1,^✉, Shauna Wagner ², Jessica Pater ³, Tammy Toscos ⁴, Denise Anthony ⁵, Tiffany C Veinot ⁶

PMCID: PMC13690484 PMID: 42920455

Abstract

Objective

To characterize patients’ perspectives on privacy on the disclosure, documentation, and visibility of social risk information collected during screening in healthcare settings, and to identify ways to design patient-centered, privacy-protecting screening and documentation systems.

Materials and Methods

Interviews were conducted with 18 patients from a Midwestern healthcare system who had experienced screening. Interviews were analyzed using a multi-cycle coding process.

Results

Participants expressed privacy concerns regarding the disclosure, storage, and visibility of social risk information. Patients judged the relevance of social risk information when choosing to disclose it and when considering how long they would like these data to remain in the electronic health record (EHR). Disclosure and storage risks identified included embarrassment, the potential for these data to be shared with others or used against patients, and the possibility that patients would be unaware of stored social risk data in the EHR. Patients desired trusting relationships with physicians to facilitate disclosure and the ability to edit or delete stored data to ensure privacy.

Discussion

Findings highlight privacy-related concerns about social risk disclosure, as well as patients’ desire for control over these data. Trusting patient-clinician relationships and clear policies governing social risk data use may support disclosure. Patients advocated for screening to occur with trusted healthcare staff and for control over EHR social risk data.

Conclusion

Social risk screening and documentation systems must address patients’ privacy concerns, facilitate safe disclosure, and clarify how data are used. Empowering patients with greater control over social risk data may affirm privacy and improve acceptability.

Keywords: social risk factors, screening, documentation, electronic health records, disclosure

Introduction

Healthcare organizations increasingly screen patients for social risks—often called “health-related social needs” or “social driver/determinants of health” screening—to identify factors that adversely impact patients’ health (eg, housing insecurity or unemployment, which may be correlated with poorer healthcare access and health outcomes).¹^,² The growing literature on social risk screening predominantly focuses on clinicians and screening tools (eg, food insecurity contributing to malnutrition or unsafe housing contributing to physical injury or environmental exposures).³^,⁴ There has been limited investigation of patients’ perspectives on privacy (ie, the access of one actor to another, including to information and how this information may be used⁵^,⁶) in relation to disclosure in social risk screening.^6–12 Patients may view social risk data as sensitive,¹³^,¹⁴ suggesting the need to understand privacy concerns and potential impacts of these data on patient-physician relationships. The introduction of social risk screening into clinical workflows may represent new opportunities for the violation of patient privacy norms by granting clinicians increased access to sensitive information.⁵ Patients may not wish to disclose social or financial issues,¹⁵^,¹⁶ or they may answer questions differently depending on who asks them, and how.¹⁷^,¹⁸ Understanding patients’ views regarding disclosing and storing social risk information is necessary to design information systems that meet patients’ needs and preferences in addition to those of providers.

Prior research has identified patient concerns about disclosing social risks, including fear of stigmatization or discrimination,^19–23 uncertainty about whether disclosed social risks will be addressed, legal repercussions (eg, immigration status, child protective services), and having limited appointment time to explain circumstances.²¹^,^23–27 Patients’ trust in clinicians’ confidentiality and concerns about the security and privacy of health information impact whether they withhold information from them.¹²^,^28–31 Screening programs may create expectations for patients that they will receive help from healthcare organizations when they disclose social risks, rather than from other community organizations, regardless of whether patients want healthcare organizations to assist with these needs.²⁶^,³¹^,³² Understanding patients’ concerns about privacy during social risk screening, including the disclosure, recording/storage, and visibility of disclosed information, is necessary to design acceptable, privacy-protecting screening and information systems.

The increasing documentation of social risk data since 2016,³³ including the use of structured documentation methods (eg, via the Epic social risk wheel³⁴ or standardized terminologies including International Classification of Diseases, Tenth Revision [ ICD-10] Z codes³⁵ and Logical Observation Identifiers Names and Codes [LOINC]³⁶) and the extraction of social risks from free-text notes using natural language processing, may make these data more visible and usable in those electronic health records (EHRs) that support social risk documentation.^37–40 Patients wonder what information is contained in their medical record, who has access to it, and when it is accessed.^41–43 Documented social risks may be visible to wide range of clinicians and could become the basis of stigmatization and bias.⁶^,^21–23^,²⁵^,³¹^,⁴¹^,⁴⁴^,⁴⁵ Critically, those with highly sensitive health or social issues in their records may be less likely than those without such issues to be willing to share their records with other clinicians.⁴⁶ Additionally, the use of EHR social risk data, including for referrals,^47–51 clinical decision support,⁵²^,⁵³ population health surveillance,⁵⁴ and research purposes,⁵⁴ creates a range of potential unintended privacy risks (eg, that social risk information may be shared with unauthorized individuals or used in unintended manners),⁶^,⁴¹^,⁴⁵ and could therefore raise patients’ concerns about how these data are secured.¹²^,^55–57 The Health Insurance Portability and Accountability Act of 1996 (HIPAA) provides privacy protections for individually identifiable protected health information (PHI),⁵⁸^,⁵⁹ but there is concern that the current definition of PHI may not sufficiently cover the wide range of types of social risk data.⁶⁰^,⁶¹ The long-term storage of social risk data in the EHR also means that these data are vulnerable to breaches.⁶²^,⁶³ Furthermore, provider referrals of patients with social risks to community social services agencies⁴⁷ may involve the transfer of social risk data, expanding the number of entities and users with these data, as well as potentially changing the security and privacy protections of those data.⁶⁴ Little is known about patients’ perspectives toward the storage, visibility, and use of social risk information in the EHR, and their views are necessary to design secure and acceptable documentation tools that affirm patient trust. Despite growing recognition of the sensitivity of social risk data captured in clinical settings, most prior research has identified patients’ concerns and potential barriers to disclosure, with little attention to patient-informed solutions for the design of EHR documentation tools. To our knowledge, no prior work has assessed patient recommendations for privacy protection and control of EHR social risk information. This study addresses this gap by exploring patient-informed recommendations for the design of trustworthy, patient-centered social risk documentation tools.

Objective

Using interviews with patients, we characterize patients’ perspectives about disclosing social risk information and the documentation and visibility of these data in the EHR. By understanding patients’ experiences and perceptions regarding the privacy aspects of social risk screening, findings can inform the implementation of patient-centered social risk screening and documentation in healthcare settings in a manner that increases privacy protections.

Methods

Setting and participants

We recruited patients from Parkview Health (Parkview), a nonprofit healthcare system headquartered in Fort Wayne, Indiana. The Parkview system serves over 1.36 million patients in Northeast Indiana and Northwest Ohio and includes over 1200 physicians and advanced practice providers, 300 clinics, and 15 hospitals. The sampling strategy captured patients from settings in which social risk screening was occurring, including primary care, specialty care, and home nursing. To capture patients’ perspectives regarding social risk screening, we purposefully sampled English-speaking adult patients who had participated in social risk screening within the year prior to study enrollment. We identified potential interviewees from patient EHRs purposefully to create a diverse sample in terms of gender and race. We contacted each patient’s physician to obtain permission to contact the patient by phone to invite them to participate. A Parkview researcher conducted recruitment by calling eligible patients, with 2 follow-up calls as needed, while data analysis was ongoing until inductive thematic saturation was reached.⁶⁵^,⁶⁶ Of 75 patients called, 26 agreed to be interviewed (34.7%) and 18 completed an interview (24%). Five patients (6.7%) who agreed to complete an interview did not answer the phone when called for the interview and 3 patients (4%) who had initially agreed changed their minds when called for the interview. Of the 49 patients (65.3%) who were called and did not agree to an interview, 3 (4%) had a language barrier that was not identified in the chart, 2 (2.7%) had disconnected telephones, 1 (1.3%) was too ill to participate, 17 (22.7%) declined, and 26 (34.7%) had unanswered messages left for them to return the call. Interviews were conducted until inductive thematic saturation was reached, as indicated by the confirmation of themes raised in early interviews.⁶⁵^,⁶⁶ Participants were compensated $25.

Data collection

An interview guide was collaboratively developed and pilot tested by the research team, including input from Parkview clinical collaborators (Appendix A). Participants were asked to verbally complete a brief demographic survey at the beginning of the interview. The interview guide was pilot tested with a doctoral student participant and revised to ensure feasibility and clarity. Semi-structured telephone interviews were conducted in August-September 2020 and ranged from 1 to 1.5 hours (average = 73 minutes). No participant had an established relationship with the researchers prior to the study. Telephone interviews facilitated patient participation while minimizing COVID-19 safety risks. S.W., a white cisgender woman and nurse, called interviewees and completed the verbal informed consent process before connecting the first author (B.I., a white male cisgender PhD student with qualitative research experience) and another PhD student (a white cisgender woman) to conduct the interviews. A Parkview researcher remained in each interview to answer questions and offer resources in response to any disclosed social needs. Interviewers explained the purpose of the study prior to beginning the interview. Interviews were audio-recorded, professionally transcribed, and verified before analysis. There were no repeat interviews. Notes were taken during interviews to facilitate discussion. Transcripts were not returned to participants.

Data analysis

A codebook was iteratively developed by all research team members (Appendix B) by collecting individual feedback and discussing patterns as a group over several team meetings until agreement was reached. Our first codebook iteration featured 46 structural codes based on questions from our interview guide. After applying the initial codebook to early interview transcripts, the research team met to discuss the coding process, resolve discrepancies, and refine the codebook. Through this iterative discussion process, we expanded the codebook to capture emergent themes not reflected in the original interview guide-based structural codes. We continued to refine the codebook across several team meetings until consensus was reached, resulting in a final codebook of 50 structural codes, 31 open codes, and 1 evaluation code, which was then applied to all interview transcripts. Inductive qualitative coding was conducted in 2 cycles.⁶⁷ In the first cycle, structural codes based on the interview questions asked, open codes, and in vivo codes were applied independently by B.I. and S.W. using NVivo and then reconciled to determine agreement to ensure the accuracy of our codebook application.⁶⁷^,⁶⁸ Intercoder agreement was calculated between B.I. and S.W. using Cohen’s kappa (kappa = 0.70, indicating substantial agreement). In the second cycle of coding, B.I. used focused coding to develop themes.⁶⁷ This study was approved by the University of Michigan and Parkview Health Institutional Review Boards. Methodological details are provided using the consolidated criteria for reporting qualitative research (COREQ) checklist⁶⁹ in Appendix C.

Results

Eighteen participants were interviewed. Participants’ average age was 52 years (range 24-81, Table 1). Most participants identified as women (61%), while 39% identified as men. The most frequently represented racial groups included non-Hispanic White (39%) and non-Hispanic Black or African American (33%). Participants had varied educational attainments, with 7 having received an associate’s degree or higher (39%).

Table 1.

Characteristics of study participants (n = 18).

	Number	Percentage
Gender
Man	7	39
Woman	11	61
Age
Mean/median	52.4/49.5	–
Range	24-81	–
Race
White (non-Hispanic)	7	38.9
Black or African American (non-Hispanic)	6	33.4
White (Hispanic)	3	16.7
Multi-racial (unspecified)	1	5.5
Asian (unspecified)	1	5.5
Education
High school or less (no diploma)	2	11
High school graduate or General Educational Development (GED)	6	33.4
Trade school	1	5.5
Some college	2	12.2
Associate’s degree	3	16.7
Bachelor’s degree	3	16.7
Doctoral degree	1	5.5

Open in a new tab

Participants articulated perspectives related to the disclosure and storage of social risk information. These included assessments of the relevance of social risk information to healthcare, concerns that social information might negatively affect patient-provider relationship—which participants suggested could be mitigated through trusting relationships—and concerns about documentation and storage, for which participants recommended restricting access and enabling data updating and deletion. Themes are represented via plain text headings and in Table 2, while subthemes are described in text and in Table 2.

Table 2.

Example quotes.

Theme	Subtheme	Quote
Relevance of social risk information to the clinical team and clinical conditions	Relevant	“if you have high blood pressure and your stressed out at home or things are going wrong at home, [social risk information] will kind of explain it.” (P18, Woman, Asian American) “If I were a person’s doctor and I knew [about a patient’s social risks], I might adjust how I advised them.” (P10, Man, White) “if it was… a legit financial struggle, I would tell [my doctor]. If it was just a couple of rough weeks I probably would just not, but if it was like a legit, ‘I’m struggling,’ yeah.” (P12, Woman, White) “If they’re not eating enough or making enough money to eat… they should know a little bit about that… because it would help… the doctor could point you to the right direction.” (P11, Man, Hispanic)
	Not relevant	“You’ve got to be asking me for a reason. Like, do you have something that I can do to help myself, bring myself up or make some more money or opportunity or you know, a resource? Don’t just ask me just to ask me for your chart.” (P17, Woman, African American) “it’s just a doctor’s office, it’s not a social service office.” (P6, Man, African American) “I didn’t know it was Parkview’s job to help a person find a job. I thought Parkview’s job was more medical.” (P15, Man, White) “those resources are really helpful, but at the end of the day, [the hospital] is a medical facility… I don’t think it’s fair to put on Parkview that they have to help you find a job, learn to read, pay your electrical bill… is it kind that they are willing to navigate that? Absolutely. But is it their obligation? I don’t think it is.” (P12, Woman, White)
Patient-provider/team relationship concerns and the role of trust	Embarrassment	“I always get embarrassed sometimes because I don’t want people to know that I’m having a hard time.” (P2, Woman, White) “it can be embarrassing to say, ‘hey I don’t have any money’.” (P9, Woman, Asian) “[IPV is] a very vulnerable thing [to discuss with a provider].” (P12, Woman, White) “Just because I just finished high school doesn’t mean I don’t know anything outside of that… Because I don’t want them to determine, ‘She’s dumb because she didn’t go to college or she doesn’t have a degree’.” (P17, Woman, African American)
	Potential for these data to be used against patients	“if it’s all over your medical record that you’re below the poverty line… I would hate for a provider to like maybe not offer you a medication that would benefit you—like make a decision based off of that.” (P12, Woman, White) “hopefully they don’t use it against you. Like, ‘well I know this, this, and this are not being well taken care of or [the patient is] not taking care of themselves, I don’t want to see them anymore’.” (P5, Man, Hispanic)
	Patient-physician relationship as a precursor and facilitator to the disclosure of social risks	“my doctor should be my safe haven… to go to. If I can trust them with my health and my life, if I need surgery or things like that, I pretty much already entrust them with the basics.” (P18, Woman, African American) [regarding why this patient feels comfortable disclosing information about housing status with her doctor] “if I trust them with my life, I’ve got to be able to trust them with the rest.” (P18, Woman, African American) “my doctor knows everything about me so, you know, I don’t keep no secrets when it comes to my health. I want to let him know everything because he’s the one gonna give me advice on it.” (P6, Man, African American) “part of it is I’ve known my doctor for a long time, so there’s a relationship there, so I think it would be natural. It would be fine to tell her if it was a real problem.” (P12, Woman, White)
Concerns about documentation and storage: concern that social information might be shared beyond immediate medical team and preferences for access restrictions	Potential for these data to be shared with others without patients’ consent	“when you live in a small town… a lot of the people you see… are people you know… although they can’t share that information, I don’t think I would want them with that knowledge of what’s going on in my household.” (P18, Woman, African American) “if I talk to the doctor about… my personal life, it is between him or her and me [emphasized] That’s confidential… he don’t have to go and broadcast it to other people.” (P3, Woman, African American)
	Access to stored social risk information should be restricted	“[social risk factor data is] not for everybody to see.” (P11, Man, Hispanic) “It don’t really have to be detailed, like ‘me and my boyfriend just broke up, that’s why I’m just so stressed out…’ Nothing like that… just kind of brief, little pointers.” (P17, Woman, African American) “I have no objection to [doctors viewing social risk data], but generally I think it’s a slippery slope with some people, but I’m just one of those people that I don’t really care who knows what about me and I never have so. But I think broadly it’s, the privacy issues that get to be contentious.” (P10, Man, White) “No, I don’t think that even should be in my medical record because that’s personal about houses and financial assistance and stuff. That’s kinda personal… that’s confidential… Only thing that should be in my medical record is about my health… problems.” (P3, Woman, African American)
Concerns about documentation and storage: storage duration concerns and preferences for data updating and deletion	Stored in the EHR indefinitely	“long-term, maybe something… comes up later that would be good that you say ‘well, during that period of my life I was having a really difficult time with this’… to understand why maybe I had developed certain kind of illnesses as a result… you can see a holistic pattern based on what kind of social problems or psychological, emotional or all of those play into what your physical manifestations are.” (P14, Woman, White) “it should be updated… So, like when you take a medication off, it [shows] that you took the medication in the past, but it just doesn’t show that it’s a medication you’re taking at the time. So, once [a housing need is] situated, it will say that they had housing issues prior to and this is the program that helped them, they were successful with it, and this is where they stand now.” (P18, Woman, African American)
	Social risk data should be updated frequently and stored only for as long as the healthcare system is actively helping with a social risk or as long as the data reflect a current risk that is relevant to a patient’s health and healthcare	“[social risk factors] change every day. You never know what the next day is going to bring.” (P6, Man, African American) “if the doctors or hospitals could get you help, they should put that in the computer… whenever it is updated, they should know when you’re back on your feet… if you got help or what kind of help did you get.” (P11, Man, Hispanic) “If it’s got to be in the medical record, I’d say keep it until the situation is resolved. If it was up to them to follow through to make sure that it is resolved and then leave it there for a while and then remove it if everything seems good.” (P8, Woman, White) “when I get out of that situation, I think it should be… taken out of my record [by the clinician].” (P3, Woman, African American)
	Patients may be unaware of what social risk data are stored in the EHR	“I wouldn’t want it to be… [forgotten] about… and then ten years have elapsed and it’s still sitting there.” (P7, Man, African American) “I don’t go to the doctors very often… I know there are some questions that they ask… but I don’t recall what they are.” (P1, Woman, Hispanic)
	Right to erase	“I think it should be stored forever, but I also think that I should have the right to erase it whenever I want.” (P4, Man, White) “if I had the option to update it myself… it should just stay in there. And if I also had the option to delete it and get rid of it, that should be available for me to do too… It’s a matter of, if I got my information in there and I felt like it was just unnecessary because I’m not being offered any help… then why do you need to know my business?” (P17, Woman, African American)
	Social risk data should be automatically removed from the EHR after a defined length of time	“I’d say ten years is a long time and that’s probably about as long as a person usually goes to the same doctor… long enough for the doctor to like look back and say, you know, ‘during that period you were really having a rough time and that’s really why you ended up with all these ulcers’.” (P14, Woman, White) “At least five years…because typically things don’t fall off of your credit [score] unless its five years. I think that’s a decent time.” (P16, Woman, African American) “they should be stored for at least a year… because you never know when they might come up, when they might be needed.” (P13, Male, African American) “I don’t think it should be saved forever, but… I think it should be saved for at least a couple years… because depending on if you switched providers or something and you answered those questions a little different or are still the same, being able to say, ‘here’s some resources that you may not have received before’.” (P1, Woman, Hispanic)

Open in a new tab

Abbreviation: EHR, electronic health record.

Relevance of social risk information to the clinical team and clinical conditions

When considering disclosing social risks, participants considered if this information was relevant to their healthcare context. Many patients felt that social risks may be relevant to the evaluation or treatment of a physical or mental condition (Table 2). If so, then patients thought that being asked about social risks by health providers, and subsequently disclosing that personal information to them, was acceptable. Individuals expressed a desire to choose when to discuss their social risks, suggesting a concern for privacy when disclosing sensitive information. Some preferred to disclose only when they determined their risk was “bad enough” (P15, 79, Male, White), implying a threshold for what should be disclosed to providers and a reluctance to disclose certain social risk information unless necessary (Table 2). Some participants also felt that social risk information documented in the EHR could give clinicians contextual information about patients and their experiences. They thought that clinicians could use such information to understand how it impacted a patient’s health over time, and to help clinicians communicate with patients more effectively.

However, others thought that certain social risk factors (eg, food insecurity) were not relevant to a physician or healthcare organization, and therefore deemed it less acceptable to be asked about within the healthcare context. Social risk factors were considered irrelevant to disclose if they were being collected only for storage in the EHR without offering assistance to patients (Table 2). Additionally, some participants felt that addressing social risks was not the responsibility of their doctor or hospital and thus did not perceive social risk information as relevant to disclose (Table 2).

Patient-provider/team relationship concerns and the role of trust

Participants suggested that the disclosure of social risk information to clinicians and staff may create several risks for patients, including embarrassment and the potential for these data to be used against patients. For the former, some participants felt vulnerable due to the potential for judgment or stigmatization by clinicians when discovering social risk data. For example, participants felt that clinicians may judge patients based on social risk factors, such as having limited educational attainment, while others felt embarrassment and stress when discussing sensitive topics, including financial hardship or interpersonal violence, causing them not to want to disclose this information (Table 2). Participants were also concerned that social risk information may be used against patients, for example, the dropping of a patient from the practice roster due to judgment about their worthiness based on their social circumstances (Table 2). Relatedly, participants cautioned against the potential for biases if social risk data are available in the EHR, such as clinicians not offering expensive but effective medications to patients based on socioeconomic information.

Participants viewed a strong patient-physician relationship, identified by some as a product of time spent interacting with one’s physician and the physician’s compassion, as a precursor and facilitator to the disclosure of sensitive social risks (Table 2). Participants highlighted that primary care physicians and nurses are healthcare professionals with which patients may have the most face time to build rapport and trust, and thus, feel greater comfort and safety when disclosing sensitive social risk information with them. This trust, in conjunction with a feeling that the clinician was trying to understand patients’ circumstances and caring about the patient, facilitated the discussion of social risk factors by showing that clinicians were attentive to patients’ social risk factors and were going to use this information to help.

Participants also valued training for clinicians both regarding how to conduct conversations about social risk factors with sensitivity, as well as how to best address social risks, as components of their expectations of competence in screening.

Concerns about documentation and storage

Concern that social information might be shared beyond immediate medical team and preferences for access restrictions

Participants were concerned about privacy breaches by both clinicians and staff with access to their social risk information and the potential for these data to be shared with others without patients’ consent. Participants worried about overlapping relationships whereby social risk information may be shared inappropriately by a clinician or staff member with another individual known by the patient in the community (Table 2).

Given these concerns, participants suggested that access to stored social risk information should be restricted to certain physicians and staff (Table 2), and some participants wanted to know who had viewed EHR social risk data. Others desired limits to the level of detail about social risk data captured in the EHR. Because of such concerns, 4 participants felt that information about social risk factors should never be documented in the EHR (Table 2).

Storage duration concerns and preferences for data updating and deletion

Most participants felt that if gathered, social risk information should be documented in the EHR, though they varied in how long they thought such information should be stored. Participants in favor of social risk data storage varied in opinion regarding the acceptable duration of storage, ranging from indefinite storage to removal when no longer relevant or after a certain predefined time period.

Some participants thought that social risk data should be stored in the EHR indefinitely. These participants suggested that the value of perpetual documentation of the patient’s story could give doctors the ability to look back at prior data to gain important contextual information about a patient during medical decision making, especially during care transitions (Table 2). According to 1 participant, clinicians could “piggyback” (P18, 34, Woman, African American), or follow up, on previously documented social risks to prompt asking about changes to these social risk factors in a non-judgmental tone and manner to avoid patient discomfort.

Other participants recognized the potential for social risk factors to change more dynamically than medical data over time. These individuals felt that social risk data should be updated frequently and stored only for as long as the healthcare system is actively helping with a social risk or as long as the data reflect a current risk that is relevant to a patient’s health and healthcare. The data should be removed by a clinician when this is no longer the case, such as if the social risk is no longer present or if the patient discontinues healthcare with a given organization or clinician (Table 2).

Participants stressed the importance of preserving the privacy of sensitive social risk data in the EHR. Participants also expressed concern that patients may be unaware of what social risk data are stored in the EHR and for how long these data will be visible to EHR users, or that patients may forget about the storage of these data over time, potentially resulting in the long-term storage of these data without patients’ knowledge (Table 2).

Some participants desired the ability to edit or remove EHR social risk data at their request. These participants emphasized the private nature of social risk information and suggested that the right to erase these data was important if social risk data are to be retained in the EHR, especially indefinitely (Table 2). Participants wanted clinicians to ask about the social risk data stored in the EHR to discuss changes, update stored data, and provide the opportunity to delete data that patients no longer wished to store, similar to how a list of current medications may be confirmed or updated. Participants also suggested that if they manually updated their social risk data in the patient portal, this may helpfully prompt clinicians to use these data to ask patients about how they are doing.

Participants offered several situations in which patients may request the viewing, editing, or deletion of social risk data. First, following a significant reduction in social risk, a record of a past risk was thought by some patients to be no longer necessary to retain in the EHR. Second, participants considered the utility of EHR social risk information to help address social needs and considered removing it if resources were not being offered to address needs or when it was no longer being used (Table 2). Finally, some participants felt that social risk data should be automatically removed from the EHR after a defined length of time, regardless of accuracy or relevance. Participants offered a variety of lengths for which social risk data should be retained, ranging from at least 1 year to 10 years. Many used administrative processes from other aspects of life as reference points for suggested durations of data storage, such as the duration that financial documents should be retained for tax purposes (7 years), or the length of time information was thought to contribute to one’s credit score (5 years).

Discussion

Motivated by new social risk screening requirements, including a Centers for Medicare & Medicaid Services (CMS) measure to screen adult inpatients for social risks, which is due to be rolled back by 2028,⁷⁰ as well as other quality measures from the Committee for Quality Assurance (NCQA) and the Joint Commission,⁷¹^,⁷² healthcare organizations are screening patients for social risk factors and storing elicited data. Prior work demonstrates patients’ concerns about disclosing and the use of EHR social risk information.⁷³^,⁷⁴ Here, we build on this work by exploring patients’ concerns about the disclosure, storage, and visibility of social risk data and offer preliminary patient-informed potential solutions that warrant further investigation in larger, more diverse samples. This exploratory study extends prior work by providing an in-depth qualitative analysis of patients’ privacy concerns specifically related to the disclosure, documentation, and visibility of social risk information in EHRs. We highlight patients’ recommendations for designing documentation systems that enhance privacy and empower patients with greater control over their social risk data—contributions that may inform the future development of patient-centered, privacy-protecting EHR tools. While this exploratory study is limited in scope, its findings take on additional relevance given recent changes to the social, political, and technological landscape of the United States that may both increase the prevalence of social risks and the potential danger of disclosing these risks to clinicians. Examples include the increase in material hardship felt by many Americans due to the COVID pandemic,⁷⁵ changes to state-level abortion laws following Dobbs v. Jackson restricting abortion access for 22 million people who can become pregnant,⁷⁶ anti-LGBTQIA+ legislation reducing access to gender-affirming services,⁷⁷ executive orders forbidding healthcare organizations from participating in diversity, equity, and inclusion activities,⁷⁷ and increased immigration enforcement, including a Supreme Court order allowing detainment on the basis of skin color, accent, or place of work.⁷⁸ Participants’ concerns about the privacy of social risk data predated these changes, suggesting that protecting privacy was, and continues to be, paramount for social risk screening and documentation. In our sample, participants made assessments regarding the relevance of social risk factors to their healthcare when considering disclosing social risks. Privacy norms and perceived trust in clinicians influence decisions about whether to disclose information in relationships generally,⁵^,⁷⁹ which was echoed by patients in this study as they navigated choices to disclose social risks. Some patients were doubtful that social risk information is relevant in the clinical encounter, and prior work has shown that while patients may view screening as important, they may also lack expectations or a desire for the healthcare system to be able to address social risks.⁶^,³⁰^,³¹^,⁸⁰^,⁸¹ This may suggest opportunities for clinicians or intake and screening forms to educate patients about how social risks are relevant to their health and what healthcare systems can do about social risks.^82–84

Participants in our study expressed concern that permanent social risk documentation and their subsequent visibility to future clinicians may constrain patients’ privacy and create fear of embarrassment or worry that these data may be used adversely—such as to make judgments about patients’ worthiness for treatment or to introduce potential bias from healthcare team members—as has been shown in the privacy literature.⁵^,⁷²^,^85–88 Similarly, prior research has shown that patients may be concerned about being labeled as having a stigmatized social risk⁵⁷ or saving face.⁸⁹ Proactive informing of patients that social risk disclosure is voluntary and that non-disclosure will not affect their care or relationships with healthcare providers, as is done with informed consent in research,⁹⁰^,⁹¹ may facilitate patients when deciding to disclose social risks. Furthermore, patient education regarding where social risk data are stored, for how long, who might have access to these data, and how they may be exchanged during referrals may bolster trust and facilitate disclosure.⁸²

Participants expressed that trusting relationships could mitigate some concerns about potential embarrassment, bias, and fear that these data may be used against them during screening. Prior work has suggested that safe environments, characterized by protecting patients’ privacy, refraining from judgment, and being supportive, are vital for maintaining trusting relationships.⁶^,³⁰^,⁸²^,^92–95 Indeed, patients’ trust in the confidentiality of their physician, and concern about security breaches and feeling that one has little impact on how PHI is used, are linked to decisions to withhold medical information.²⁸^,²⁹^,⁹⁶ Thus, leveraging patient-clinician/staff relationships, or providing the option to disclose social risks via a paper, tablet, or patient portal form,¹⁷^,⁹⁷ may increase the perceived safety of social risk information disclosure.⁷⁹ Patients may be less likely to disclose social risks to individuals with whom they do not have a trusting relationship with, as increasingly many members of the healthcare team may be tasked with screening and may not be personally known to patients.⁷⁹^,⁹⁸ As a result, when patients disclose needs, whether to a trusted provider or not, they may wish to make sure there are strong privacy protocols and staff training in place to prevent data breaches of these sensitive data.⁶^,⁴⁴^,⁹⁹

Concerns about privacy violations of social risk data raised by participants in this study suggest the need to ensure that EHR documentation tools are designed to protect social risk data and lessen the risk of unintended patient privacy risks. Patients in our study articulated how social risk documentation creates new ways for these data to be viewed, shared, and used, potentially without patients’ knowledge or consent. The rapid implementation of tools designed to capture, use, and share social risk data³⁷^,^100–102 requires informaticians to consider the unique privacy concerns related to integrating social risk information into the EHR.⁸⁵ One strategy to reduce the risk of harms related to collecting and storing social risk data is to ensure that all clinicians and staff undergo training regarding what constitutes acceptable or unacceptable use of EHR social risk information.¹⁰³ Additionally, healthcare organizations may offer patients the option to be notified when social risk data are accessed, by whom, and for what purpose, as well as displaying all of the social risk information currently documented in the patient portal for awareness.²¹

Given perceived risks of storing social risk information, participants in our study advocated for the right to erase these data, including options to update or remove stored data. This may give patients more control over what is documented in the EHR, especially related to older data that no longer reflect one’s current social circumstances. While this is consistent with recent informatics-driven patient safety efforts showing patients’ desire to modify, delete, or control access to incorrect medical information in the EHR via the patient portal,⁸⁵^,^104–109 as well as the broader privacy efforts focused on the right to be forgotten,^110–112 the ability for patients to edit social risk data in the EHR has not yet been described. Storing social risk information in a manner that allows for modification and deletion at patients’ requests may require the creation of separate databases with distinct assumptions about data provenance and access relative to the rest of the EHR. Alternatively, social risk information could be archived so that additional steps and permissions are necessary for future access. While there have been some efforts to protect the privacy of geocoded EHR social risk data,¹¹³ we have not yet seen specific efforts to preserve patient privacy and the editability of individual-level social risk data in practice. Communicating the ability and processes required to make changes to social risk records will require equitable education to avoid disparities among patients regarding who has agency over the documentation of these data.¹¹⁴

Findings from this exploratory study suggest several potential strategies to mitigate risks to patient privacy when using EHR social risk information that are worthy of further evaluation in broader contexts. To mitigate the risk of biases in the use of social risk information, healthcare organizations could establish policies specifying acceptable and unacceptable uses of social risk information. They could also ensure that efforts to adjust medical decisions based on social risks are done only to aid patients.¹⁰⁰^,¹¹⁴ For example, social risk information may be used to prompt clinicians to connect patients with medication affordability resources,^115–117 but not for decisions about offering or withholding certain treatments. Additionally, clinical decision support tools incorporating social risk information to adjust care could be considered to support, rather than supplant, clinical criteria,¹⁰⁰ and routine audits—potentially through equity-focused dashboards¹¹⁸—of how social risk information is used in care decisions may be used to identify potential patterns of bias. Finally, shared decision-making frameworks could be used to center patients’ preferences and to allow patients to provide feedback about who can access their social risk information and how it is used to adjust care.¹¹⁹^,¹²⁰

Limitations

Several limitations must be considered. First, our sample is from a single Midwestern US healthcare system, and patients from some clinical specialty areas are not represented in our sample. Patient subpopulations may have distinct preferences for the collection and storage of social risk information based on local socioeconomic, cultural, and geographic contexts, as well as access to screening and social care resources, suggesting the future need to elucidate patient preferences in a nationally representative sample. Furthermore, with available data, we are unable to identify each distinct type of social risk screening that our participants have experienced. This information may be important in future studies, as different screening programs may vary in terms of the questions asked, the context of screening, and the type of clinician who conducts the screening. Future work is needed to characterize preferences for screening across a wide range of patient populations and healthcare settings, including which types of healthcare staff should conduct screening, which may impact disclosure of social risks. Finally, phone interviews may have masked participants’ non-verbal cues and may have prevented the participation of those without consistent phone access.

Conclusion

Screening creates opportunities to address social needs, though screening relies on patients’ willingness to disclose private social risk information. Decisions to do so may be dependent on patients’ views of the relevance of these data to their health and perceptions of risks associated with disclosure. Screening-related information systems, workflows, and policies should be designed to prioritize reducing privacy-related harms and to create safe environments for patients to share these data and for clinicians to use them.

Author contributions

Bradley E. Iott (Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Supervision, Validation, Writing—original draft, Writing—review & editing), Shauna Wagner (Data curation, Investigation, Project administration), Jessica Pater (Conceptualization, Data curation, Funding acquisition, Investigation, Project administration, Resources, Supervision, Validation, Writing—review & editing), Tammy Toscos (Conceptualization, Data curation, Funding acquisition, Investigation, Resources, Supervision, Validation, Writing—review & editing), Denise Anthony (Conceptualization, Formal analysis, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Writing—original draft, Writing—review & editing), and Tiffany C. Veinot (Conceptualization, Formal analysis, Funding acquisition, Methodology, Resources, Supervision, Validation, Writing—original draft, Writing—review & editing)

Supplementary Material

ooag203_Supplementary_Data

ooag203_supplementary_data.zip^{(434.6KB, zip)}

Contributor Information

Bradley E Iott, Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI 48109, United States.

Shauna Wagner, Parkview Mirro Center for Research and Innovation, Parkview Health, Fort Wayne, IN 46845, United States.

Jessica Pater, Parkview Mirro Center for Research and Innovation, Parkview Health, Fort Wayne, IN 46845, United States.

Tammy Toscos, Parkview Mirro Center for Research and Innovation, Parkview Health, Fort Wayne, IN 46845, United States.

Denise Anthony, Department of Health Management and Policy, School of Public Health, University of Michigan, Ann Arbor, MI 48109, United States.

Tiffany C Veinot, School of Information, University of Michigan, Ann Arbor, MI 48109, United States.

Supplementary material

Supplementary material is available at JAMIA Open online.

Funding

Funding for this study came from the Parkview Mirro Center for Research and Innovation.

Conflicts of interest

No authors have any conflicts of interest to report.

Data availability

To protect participants’ privacy, data from this study are not able to be shared.

References

1. Alderwick H, Gottlieb LM. Meanings and misunderstandings: a social determinants of health lexicon for health care systems. Milbank Q. 2019;97:407-419. 10.1111/1468-0009.12390 [DOI] [PMC free article] [PubMed] [Google Scholar]
2. 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. National Academies Press (US; ); 2015. [PubMed] [Google Scholar]
3. Gottlieb LM, Wing H, Adler NE. A systematic review of interventions on patients’ social and economic needs. Am J Prev Med. 2017;53:719-729. 10.1016/j.amepre.2017.05.011 [DOI] [PubMed] [Google Scholar]
4. Cartier Y, Gottlieb L. The prevalence of social care in US health care settings depends on how and whom you ask. BMC Health Serv Res. 2020;20:481. 10.1186/s12913-020-05338-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
5. Anthony D, Campos-Castillo C, Horne C. Toward a sociology of privacy. Annu Rev Sociol. 2017;43:249-269. 10.1146/annurev-soc-060116-053643 [DOI] [Google Scholar]
6. Byhoff E, De Marchis EH, Hessler D, et al. Part II: a qualitative study of social risk screening acceptability in patients and caregivers. Am J Prev Med. 2019;57:S38-S46. 10.1016/j.amepre.2019.07.016 [DOI] [PMC free article] [PubMed] [Google Scholar]
7. Rudisill AC, Eicken MGA, Gupta D, et al. Patient and care team perspectives on social determinants of health screening in primary care: a qualitative study. JAMA Netw Open. 2023;6:e2345444. 10.1001/jamanetworkopen.2023.45444 [DOI] [PMC free article] [PubMed] [Google Scholar]
8. Kiles TM, Cernasev A, Leibold C, et al. Patient perspectives of discussing social determinants of health with community pharmacists. J Am Pharm Assoc (2003). 2022;62:826-833. 10.1016/j.japh.2022.01.007 [DOI] [PubMed] [Google Scholar]
9. Caicedo NMA, Parry E, Arslan N, et al. Integration of social determinants of health information within the primary care electronic health record: a systematic review of patient perspectives and experiences. BJGP Open. 2024;8:1-12. 10.3399/BJGPO.2023.0155 [DOI] [PMC free article] [PubMed] [Google Scholar]
10. Fichtenberg CM, De Marchis EH, Gottlieb LM. Understanding patients’ interest in healthcare-based social assistance programs. Am J Prev Med. 2022;63:S109-S115. 10.1016/j.amepre.2022.04.026 [DOI] [PubMed] [Google Scholar]
11. Spain AK, Monahan EK, Alvarez K, et al. Latinx family perspectives on social needs screening and referral during well-child visits. MCN Am J Matern Child Nurs. 2021;46:143-149. 10.1097/NMC.0000000000000710 [DOI] [PubMed] [Google Scholar]
12. Albert SM, McCracken P, Bui T, et al. Do patients want clinicians to ask about social needs and include this information in their medical record? BMC Health Serv Res. 2022;22:1275. 10.1186/s12913-022-08652-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
13. Palakshappa D, Doupnik S, Vasan A, et al. Suburban families’ experience with food insecurity screening in primary care practices. Pediatrics. 2017;140:e20170320. 10.1542/peds.2017-0320 [DOI] [PubMed] [Google Scholar]
14. Palakshappa D, Vasan A, Khan S, et al. Clinicians’ perceptions of screening for food insecurity in suburban pediatric practice. Pediatrics. 2017;140:e20170319. 10.1542/peds.2017-0319 [DOI] [PubMed] [Google Scholar]
15. Nguyen OK, Higashi RT, Makam AN, et al. The influence of financial strain on health decision-making. J Gen Intern Med. 2018;33:406-408. 10.1007/s11606-017-4296-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
16. Senteio C, Veinot T. Trying to make things right: adherence work in high-poverty, African American neighborhoods. Qual Health Res. 2014;24:1745-1756. 10.1177/1049732314549027 [DOI] [PubMed] [Google Scholar]
17. Gottlieb L, Hessler D, Long D, et al. A randomized trial on screening for social determinants of health: the iScreen study. Pediatrics. 2014;134:e1611-e1618. 10.1542/peds.2014-1439 [DOI] [PubMed] [Google Scholar]
18. Wallace AS, Luther B, Guo J-W, Wang C-Y, Sisler S, Wong B. Implementing a social determinants screening and referral infrastructure during routine emergency department visits, Utah, 2017–2018. Prev Chronic Dis. 2020;17:E45. 10.5888/pcd17.190339 [DOI] [PMC free article] [PubMed] [Google Scholar]
19. Stablein T, Hall JL, Pervis C, et al. Negotiating stigma in health care: disclosure and the role of electronic health records. Health Sociol Rev. 2015;24:227-241. 10.1080/14461242.2015.1078218 [DOI] [Google Scholar]
20. Nong P, Williamson A, Anthony D, et al. Discrimination, trust, and withholding information from providers: implications for missing data and inequity. SSM Popul Health. 2022;18:101092. 10.1016/j.ssmph.2022.101092 [DOI] [PMC free article] [PubMed] [Google Scholar]
21. Tully KP, Gibson AN, Pearsall MS, et al. Screening and referral for social determinants of health: maternity patient and health care team perspectives. Health Equity. 2022;6:887-897. 10.1089/heq.2022.0020 [DOI] [PMC free article] [PubMed] [Google Scholar]
22. Brown EM, Loomba V, Marchis ED, et al. Patient and patient caregiver perspectives on social screening: a review of the literature. J Am Board Fam Med. 2023;36:66-78. 10.3122/jabfm.2022.220211R1 [DOI] [PubMed] [Google Scholar]
23. Henry T, Hayes M, Eisele CD, et al. Barriers to identifying and addressing health-related social needs in cancer care: patient and patient navigator perspectives. J Cancer Policy. 2024;42:100508. 10.1016/j.jcpo.2024.100508 [DOI] [PMC free article] [PubMed] [Google Scholar]
24. Schleifer D, Diep A, Grisham K. It’s About Trust: Parents’ Perspectives on Pediatricians Screening for Social Needs. United Hospital Fund; 2019. [Google Scholar]
25. Butler E, Morgan A, Morgan AU, et al. Screening for unmet social needs: patient engagement or alienation? NEJM Catal Innov Care Deliv. 2020;1. [Google Scholar]
26. Bouchelle Z, G. Menko S, Yazdani M, et al. Parent perspectives on documentation and sharing of health-related social needs data. Hosp Pediatr. 2024;14:308-316. 10.1542/hpeds.2023-007478 [DOI] [PMC free article] [PubMed] [Google Scholar]
27. LeLaurin JH, Zhao B, Cruz JDL, et al. Recent advances in our understanding of electronic health record-based social needs screening and documentation in pediatrics. Acad Pediatr. 2026;26:103216. 10.1016/j.acap.2025.103216 [DOI] [PubMed] [Google Scholar]
28. Iott BE, Campos-Castillo C, Anthony DL. Trust and privacy: how patient trust in providers is related to privacy behaviors and attitudes. AMIA Annu Symp Proc. 2020. 2019;2019:487-493. 32308842 [PMC free article] [PubMed] [Google Scholar]
29. Agaku IT, Adisa AO, Ayo-Yusuf OA, et al. Concern about security and privacy, and perceived control over collection and use of health information are related to withholding of health information from healthcare providers. J Am Med Inform Assoc. 2014;21:374-378. 10.1136/amiajnl-2013-002079 [DOI] [PMC free article] [PubMed] [Google Scholar]
30. Long KA, Paredes Molina AA, Blakey AO, et al. Perspectives on health-related social needs screening in primary care among Black and Latine patients. JAMA Netw Open. 2025;8:e2529538. 10.1001/jamanetworkopen.2025.29538 [DOI] [PMC free article] [PubMed] [Google Scholar]
31. Olvera RG, Melnyk H, MacEwan SR, et al. Patients’ perspectives about sharing information with providers on social needs during pregnancy: a qualitative study. BMC Health Serv Res. 2025;25:737. 10.1186/s12913-025-12666-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
32. LeLaurin JH, Cruz JDL, Theis RP, et al. Parent perspectives on electronic health record-based social needs screening and documentation: a qualitative study. Acad Pediatr. 2023;23:1446-1453. 10.1016/j.acap.2023.06.011 [DOI] [PMC free article] [PubMed] [Google Scholar]
33. Alley DE, Asomugha CN, Conway PH, et al. Accountable health communities—addressing social needs through Medicare and Medicaid. N Engl J Med. 2016;374:8-11. 10.1056/NEJMp1512532 [DOI] [PubMed] [Google Scholar]
34. Chishtie JA, Tea J, Ester M, et al. Integration of screening and referral tools for social determinants of health and modifiable lifestyle factors in the epic electronic health record system: scoping review. J Med Internet Res. 2025;27:e73615. 10.2196/73615 [DOI] [PMC free article] [PubMed] [Google Scholar]
35. American Hospital Association. ICD-10-CM Coding for Social Determinants of Health. 2022.
36. LOINC. Social determinants of health. Accessed February 15, 2024. https://loinc.org/sdh/
37. Iott BE, Adler-Milstein J, Gottlieb L, et al. Characterizing the relative frequency of clinician engagement with structured social determinants of health data. J Am Med Inform Assoc. 2023;30:503-510. 10.1093/jamia/ocac251 [DOI] [PMC free article] [PubMed] [Google Scholar]
38. Senteio C, Veinot T, Adler-Milstein J, et al. Physicians’ perceptions of the impact of the EHR on the collection and retrieval of psychosocial information in outpatient diabetes care. Int J Med Inform. 2018;113:9-16. 10.1016/j.ijmedinf.2018.02.003 [DOI] [PubMed] [Google Scholar]
39. Patra BG, Sharma MM, Vekaria V, et al. Extracting social determinants of health from electronic health records using natural language processing: a systematic review. J Am Med Inform Assoc. 2021;28:2716-2727. 10.1093/jamia/ocab170 [DOI] [PMC free article] [PubMed] [Google Scholar]
40. Hartzler AL, Xie SJ, Wedgeworth P, et al. ; SDoH Community Champion Advisory Board. Integrating patient voices into the extraction of social determinants of health from clinical notes: ethical considerations and recommendations. J Am Med Inform Assoc. 2023;30:1456-1462. 10.1093/jamia/ocad043 [DOI] [PMC free article] [PubMed] [Google Scholar]
41. Kim KK, Joseph JG, Ohno-Machado L. Comparison of consumers’ views on electronic data sharing for healthcare and research. J Am Med Inform Assoc. 2015;22:821-830. 10.1093/jamia/ocv014 [DOI] [PMC free article] [PubMed] [Google Scholar]
42. Caine K, Kohn S, Lawrence C, et al. Designing a patient-centered user interface for access decisions about EHR data: implications from patient interviews. J Gen Intern Med. 2015;30:S7-S16. 10.1007/s11606-014-3049-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
43. Bakker AR. The need to know the history of the use of digital patient data, in particular the EHR. Int J Med Inform. 2007;76:438-441. 10.1016/j.ijmedinf.2006.09.009 [DOI] [PubMed] [Google Scholar]
44. Millenson M. Big data on social determinants: improved health and unaddressed privacy concerns. NEJM Catal. 2018. [Google Scholar]
45. McVeigh KH, Newton-Dame R, Chan PY, et al. Can electronic health records be used for population health surveillance? Validating population health metrics against established survey data. eGEMs. 2016;4:27. 10.13063/2327-9214.1267 [DOI] [PMC free article] [PubMed] [Google Scholar]
46. Caine K, Hanania R. Patients want granular privacy control over health information in electronic medical records. J Am Med Inform Assoc. 2013;20:7-15. 10.1136/amiajnl-2012-001023 [DOI] [PMC free article] [PubMed] [Google Scholar]
47. Iott B, Eddy C, Casanova C, et al. More than a database: understanding community resource referrals within a socio-technical systems framework. In: Proceedings of the 2020 American Medical Informatics Association Annual Symposium. 2020. [PMC free article] [PubMed]
48. Steeves-Reece AL, Totten AM, Broadwell KD, et al. Social needs resource connections: a systematic review of barriers, facilitators, and evaluation. Am J Prev Med. 2022;62:e303-e315. 10.1016/j.amepre.2021.12.002 [DOI] [PMC free article] [PubMed] [Google Scholar]
49. Mossabir R, Morris R, Kennedy A, et al. A scoping review to understand the effectiveness of linking schemes from healthcare providers to community resources to improve the health and well-being of people with long-term conditions. Health Soc Care Community. 2015;23:467-484. 10.1111/hsc.12176 [DOI] [PubMed] [Google Scholar]
50. Marchis EHD, Torres JM, Benesch T, et al. Interventions addressing food insecurity in health care settings: a systematic review. Ann Fam Med. 2019;17:436-447. 10.1370/afm.2412 [DOI] [PMC free article] [PubMed] [Google Scholar]
51. Escobar ER. Screening and referral care delivery services and unmet health-related social needs: a systematic review. Prev Chronic Dis. 2021;18. 10.5888/pcd18.200569 [DOI] [PMC free article] [PubMed] [Google Scholar]
52. Senteio C, Adler-Milstein J, Richardson C, et al. Psychosocial information use for clinical decisions in diabetes care. J Am Med Inform Assoc. 2019;26:813-824. 10.1093/jamia/ocz053 [DOI] [PMC free article] [PubMed] [Google Scholar]
53. Marchis EHD, Aceves B, Razon N, et al. Adjusting clinical plans based on social context. J Am Board Fam Med. 2024;37:466-478. 10.3122/jabfm.2023.230289R1 [DOI] [PubMed] [Google Scholar]
54. Hatef E, Weiner JP, Kharrazi H. A public health perspective on using electronic health records to address social determinants of health: the potential for a national system of local community health records in the United States. Int J Med Inform. 2019;124:86-89. 10.1016/j.ijmedinf.2019.01.012 [DOI] [PubMed] [Google Scholar]
55. Rutledge SE, Whyte J, Abell N, et al. Measuring stigma among health care and social service providers: the HIV/AIDS provider stigma inventory. AIDS Patient Care STDS. 2011;25:673-682. 10.1089/apc.2011.0008 [DOI] [PubMed] [Google Scholar]
56. Goffman E. Stigma. Notes on the Management of Spoiled Identity. Simon and Shuster. Inc; 1963. [Google Scholar]
57. Link BG, Phelan JC. Conceptualizing stigma. Annu Rev Sociol. 2001;27:363-385. 10.1146/annurev.soc.27.1.363 [DOI] [Google Scholar]
58. Office for Civil Rights (OCR). Summary of the HIPAA privacy rule. HHS.gov. 2008. Accessed March 17, 2021. https://www.hhs.gov/hipaa/for-professionals/privacy/laws-regulations/index.html
59. American Institute of Healthcare Compliance. Identifying Social Determinants of Health, Coding, and HIPAA Compliance. American Institute of Healthcare Compliance; 2019. Accessed April 1, 2021. https://aihc-assn.org/identifying-social-determinants-of-health-coding-and-hipaa-compliance/ [Google Scholar]
60. Sobhani M. HIPAA isn’t enough: all our data is health data. Digit. Health–Un-Sensored CBC Blog USC Cent. Body Comput. Accessed March 17, 2021. https://cbc.ict.usc.edu/un-sensored/hipaaisnotenough/
61. Williams H. HIPAA challenges: SDoH raise interesting privacy questions. Rec Mag. 2020;32:28. https://www.fortherecordmag.com/archives/JF20p28.shtml [Google Scholar]
62. Executive Office of the President: President’s Council of Advisors on Science and Technology. Report to the President: realizing the full potential of health information technology to improve healthcare for Americans: the path forward. 2010.
63. Anthony DL, Campos-Castillo C. A looming digital divide? Group differences in the perceived importance of electronic health records. Inf Commun Soc. 2015;18:832-846. 10.1080/1369118X.2015.1006657 [DOI] [Google Scholar]
64. Our Commitment to Privacy. Accessed July 25, 2025. https://www.findhelp.org/legal/privacy
65. Saunders B, Sim J, Kingstone T, et al. Saturation in qualitative research: exploring its conceptualization and operationalization. Qual Quant. 2018;52:1893-1907. 10.1007/s11135-017-0574-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
66. Ancker JS, Benda NC, Reddy M, et al. Guidance for publishing qualitative research in informatics. J Am Med Inform Assoc. 2021;28:2743-2748. 10.1093/jamia/ocab195 [DOI] [PMC free article] [PubMed] [Google Scholar]
67. Saldana J. The Coding Manual for Qualitative Researchers. SAGE; 2015. [Google Scholar]
68. NVivo: leading qualitative data analysis software. Lumivero. Accessed January 20, 2026. https://lumivero.com/products/nvivo/
69. Tong A, Sainsbury P, Craig J. Consolidated criteria for reporting qualitative research (COREQ): a 32-item checklist for interviews and focus groups. Int J Qual Health Care. 2007;19:349-357. 10.1093/intqhc/mzm042 [DOI] [PubMed] [Google Scholar]
70. Unraveling reform: key changes to SDoH and health equity in the CMS 2026 proposed rule. 2025. Accessed December 20, 2025. https://www.solventum.com/en-us/home/health-information-technology/resources-education/blog/2025/7/sdoh-and-health-equity-changes-2026/
71. Gottlieb LM, DeSilvey SC, Fichtenberg C, et al. Developing national social care standards. Health Aff Forefr. 10.1377/forefront.20230221.857308 [DOI]
72. FY 2024 hospital inpatient prospective payment system (IPPS) and long-term care hospital prospective payment system (LTCH PPS) final rule–CMS-1785-F and CMS-1788-F fact sheet.
73. McGraw D. Privacy concerns related to inclusion of social and behavioral determinants of health in electronic health records. In: Capturing Social and Behavioral Domains and Measures in Electronic Health Records: Phase 2. National Academies Press (US; ); 2015. [Google Scholar]
74. DesRoches CM, Wachenheim D, Garcia A, et al. Clinician and patient perspectives on the exchange of sensitive social determinants of health information. JAMA Netw Open. 2024;7:e2444376. 10.1001/jamanetworkopen.2024.44376 [DOI] [PMC free article] [PubMed] [Google Scholar]
75. Mayo R, Kliot T, Weinstein R, et al. Social needs screening during the COVID-19 pandemic. Child Care Health Dev. 2022;48:935-941. 10.1111/cch.12942 [DOI] [PubMed] [Google Scholar]
76. Abortion Rights and Access One Year After Dobbs | League of Women Voters. 2023. Accessed February 13, 2026. https://www.lwv.org/blog/abortion-rights-and-access-one-year-after-dobbs
77. Hodge JG, White EN, Piatt JL, et al. Assessing impacts of “anti-equity” legislation on health care and public health services. J Law Med Ethics. 2024;52:172-177. 10.1017/jme.2024.55 [DOI] [PubMed] [Google Scholar]
78. Howe A. Supreme Court allows federal officers to more freely make immigration stops in LA. SCOTUSblog. 2025. Accessed February 13, 2026. https://www.scotusblog.com/2025/09/supreme-court-allows-federal-officers-to-more-freely-make-immigration-stops-in-los-angeles/
79. Potharaju K, Gottlieb LM, Wing HE, et al. Drivers of patient experiences with healthcare-based social care. Health Serv Res. 2026;61:e70020, 9. 10.1111/1475-6773.70020 [DOI] [PMC free article] [PubMed] [Google Scholar]
80. Luther BL, Bybee SG, Elmore CE, et al. From screening to support: exploring patient engagement with health-related social needs services. AJPM Focus. 2025;4:100346. 10.1016/j.focus.2025.100346 [DOI] [PMC free article] [PubMed] [Google Scholar]
81. De Marchis EH, Alderwick H, Gottlieb LM. Do patients want help addressing social risks? J Am Board Fam Med. 2020;33:170-175. 10.3122/jabfm.2020.02.190309 [DOI] [PubMed] [Google Scholar]
82. Schoenthaler A, Hassan I, Fiscella K. The time is now: fostering relationship-centered discussions about patients’ social determinants of health. Patient Educ Couns. 2019;102:810-814. 10.1016/j.pec.2018.10.025 [DOI] [PubMed] [Google Scholar]
83. Byhoff E, Gottlieb LM. When there is value in asking: an argument for social risk screening in clinical practice. Ann Intern Med. 2022;175:1181-1182. 10.7326/M22-0147 [DOI] [PMC free article] [PubMed] [Google Scholar]
84. Brown MC, Paolino AR, Barnes KA, et al. Codesigning online continuing medical education on social health integration and social risk–informed care for primary care providers. Perm J. 2024;28:26-35. 10.7812/TPP/23.113 [DOI] [PMC free article] [PubMed] [Google Scholar]
85. Price WN, Cohen IG. Privacy in the age of medical big data. Nat Med. 2019;25:37-43. 10.1038/s41591-018-0272-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
86. Papoutsi C, Reed JE, Marston C, et al. Patient and public views about the security and privacy of electronic health records (EHRs) in the UK: results from a mixed methods study. BMC Med Inform Decis Mak. 2015;15:86. 10.1186/s12911-015-0202-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
87. Ong R. Factors affecting patient and public perceptions of the adoption of electronic health record sharing: a Hong Kong study. Int J Med Inform. 2023;178:105193. 10.1016/j.ijmedinf.2023.105193 [DOI] [PubMed] [Google Scholar]
88. Entzeridou E, Markopoulou E, Mollaki V. Public and physician’s expectations and ethical concerns about electronic health record: benefits outweigh risks except for information security. Int J Med Inform. 2018;110:98-107. 10.1016/j.ijmedinf.2017.12.004 [DOI] [PubMed] [Google Scholar]
89. Goffman E. On face-work. Psychiatry. 1955;18:213-231. 10.1080/00332747.1955.11023008 [DOI] [PubMed] [Google Scholar]
90. Gupta UC. Informed consent in clinical research: revisiting few concepts and areas. Perspect Clin Res. 2013;4:26-32. 10.4103/2229-3485.106373 [DOI] [PMC free article] [PubMed] [Google Scholar]
91. Falagas ME, Korbila IP, Giannopoulou KP, et al. Informed consent: how much and what do patients understand? Am J Surg. 2009;198:420-435. 10.1016/j.amjsurg.2009.02.010 [DOI] [PubMed] [Google Scholar]
92. Hare AJ, Honig K, Cronholm PF, et al. Patient perspectives on technology-based approaches to social needs screening. Am J Manag Care. 2023;29:e18-e23. 10.37765/ajmc.2023.89309 [DOI] [PubMed] [Google Scholar]
93. Senteio CR, Yoon DB. How primary care physicians elicit sensitive health information from patients: describing access to psychosocial information. Qual Health Res. 2020;30:1338-1348. 10.1177/1049732320911630 [DOI] [PubMed] [Google Scholar]
94. Mechanic D. The functions and limitations of trust in the provision of medical care. J Health Polit Policy Law. 1998;23:661-686. 10.1215/03616878-23-4-661 [DOI] [PubMed] [Google Scholar]
95. Olvera RG, Swoboda CM, Joseph JJ, et al. Individuals’ desire for social needs sharing among healthcare providers: findings from the 2022 health information national trends survey. J Gen Intern Med. 2026;41:374-382. 10.1007/s11606-024-09339-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
96. Haidet P, Paterniti DA. “Building” a history rather than “taking” one: a perspective on information sharing during the medical interview. Arch Intern Med. 2003;163:1134-1140. 10.1001/archinte.163.10.1134 [DOI] [PubMed] [Google Scholar]
97. Gorecki MC, Klein MD, Anyigbo CU, et al. Social needs screening via electronic tablet in pediatric primary care. Pediatrics. 2024;154:e2024065918. 10.1542/peds.2024-065918 [DOI] [PMC free article] [PubMed] [Google Scholar]
98. Friedman S, Caddle S, Motelow JE, et al. Improving screening for social determinants of health in a pediatric resident clinic: a quality improvement initiative. Pediatr Qual Saf. 2021;6:e419. 10.1097/pq9.0000000000000419 [DOI] [PMC free article] [PubMed] [Google Scholar]
99. Gottlieb LM, Tirozzi KJ, Manchanda R, et al. Moving electronic medical records upstream: incorporating social determinants of health. Am J Prev Med. 2015;48:215-218. 10.1016/j.amepre.2014.07.009 [DOI] [PubMed] [Google Scholar]
100. National Academies of Sciences, Engineering, and Medicine. Integrating Social Care into the Delivery of Health Care: Moving Upstream to Improve the Nation’s Health. The National Academies Press; 2019. [PubMed] [Google Scholar]
101. Goldberg ZN, Shah YB, Harness ED, et al. The social determinants of health industry: two years on. Int J Soc Determinants Health Health Serv. 2024;54:344-351. 10.1177/27551938241257041 [DOI] [PubMed] [Google Scholar]
102. Iott BE, Patel V, Richwine C. Physician documentation of social determinants of health: results from two national surveys. J Gen Intern Med. 2025;40:620-628. 10.1007/s11606-024-09184-w [DOI] [PMC free article] [PubMed] [Google Scholar]
103. Garg A, Boynton-Jarrett R, Dworkin PH. Avoiding the unintended consequences of screening for social determinants of health. JAMA. 2016;316:813-814. 10.1001/jama.2016.9282 [DOI] [PubMed] [Google Scholar]
104. Bell SK, Delbanco T, Elmore JG, et al. Frequency and types of patient-reported errors in electronic health record ambulatory care notes. JAMA Netw Open. 2020;3:e205867. 10.1001/jamanetworkopen.2020.5867 [DOI] [PMC free article] [PubMed] [Google Scholar]
105. Bell SK, Gerard M, Fossa A, et al. A patient feedback reporting tool for OpenNotes: implications for patient-clinician safety and quality partnerships. BMJ Qual Saf. 2017;26:312-322. 10.1136/bmjqs-2016-006020 [DOI] [PubMed] [Google Scholar]
106. Bell SK, Mejilla R, Anselmo M, et al. When doctors share visit notes with patients: a study of patient and doctor perceptions of documentation errors, safety opportunities and the patient–doctor relationship. BMJ Qual Saf. 2017;26:262-270. 10.1136/bmjqs-2015-004697 [DOI] [PMC free article] [PubMed] [Google Scholar]
107. DesRoches CM, Bell SK, Dong Z, et al. Patients managing medications and reading their visit notes: a survey of OpenNotes participants. Ann Intern Med. 2019;171:69-71. 10.7326/M18-3197 [DOI] [PubMed] [Google Scholar]
108. Wolff JL, Darer JD, Berger A, et al. Inviting patients and care partners to read doctors’ notes: OpenNotes and shared access to electronic medical records. J Am Med Inform Assoc. 2017;24:e166-e172. 10.1093/jamia/ocw108 [DOI] [PMC free article] [PubMed] [Google Scholar]
109. Walker J, Leveille S, Bell S, et al. OpenNotes after 7 years: patient experiences with ongoing access to their clinicians’ outpatient visit notes. J Med Internet Res. 2019;21:e13876. 10.2196/13876 [DOI] [PMC free article] [PubMed] [Google Scholar]
110. Rosen J. The right to be forgotten. Stanford Law Rev. 2012. Accessed August 18, 2025. https://www.stanfordlawreview.org/online/privacy-paradox-the-right-to-be-forgotten/
111. Newman AL. What the “right to be forgotten” means for privacy in a digital age. Science. 2015;347:507-508. 10.1126/science.aaa4603 [DOI] [PubMed] [Google Scholar]
112. Floridi L, Taddeo M, editors. A Companion to Digital Ethics. Wiley-Blackwell; 2025. [Google Scholar]
113. Abdallah M, Bahroos N, Angyan P, et al. Compliance considerations in the geo-enrichment of an EHR data warehouse with social and environmental determinants of health. J Clin Transl Sci. 2024;8:e95. 10.1017/cts.2024.521 [DOI] [PMC free article] [PubMed] [Google Scholar]
114. Veinot TC, Mitchell H, Ancker JS. Good intentions are not enough: how informatics interventions can worsen inequality. J Am Med Inform Assoc. 2018;25:1080-1088. 10.1093/jamia/ocy052 [DOI] [PMC free article] [PubMed] [Google Scholar]
115. Kiessling KA, Iott BE, Pater JA, et al. Health informatics interventions to minimize out-of-pocket medication costs for patients: what providers want. JAMIA Open. 2022;5:ooac007. 10.1093/jamiaopen/ooac007 [DOI] [PMC free article] [PubMed] [Google Scholar]
116. Richards OK, Iott BE, Toscos TR, et al. “It’s a mess sometimes”: patient perspectives on provider responses to healthcare costs, and how informatics interventions can help support cost-sensitive care decisions. J Am Med Inform Assoc. 2022;29:1029-1039. 10.1093/jamia/ocac010 [DOI] [PMC free article] [PubMed] [Google Scholar]
117. Antonio MG, Swallow J, Richesson R, et al. The administrative burden of medication affordability resources: an environmental scan with implications for health informatics to advance health equity. J Am Med Inform Assoc. 2025;32:1206-1218. 10.1093/jamia/ocaf087 [DOI] [PMC free article] [PubMed] [Google Scholar]
118. Lyles CR, Khoong EC, Stern RJ, et al. ; CHARMED consortium. Championing hypertension remote monitoring for equity and dissemination (CHARMED): a multi-site factorial randomized controlled trial protocol. Contemp Clin Trials. 2025;152:107879. 10.1016/j.cct.2025.107879 [DOI] [PMC free article] [PubMed] [Google Scholar]
119. Peek ME, Gorawara-Bhat R, Quinn MT, et al. Patient trust in physicians and shared decision-making among African-Americans with diabetes. Health Commun. 2013;28:616-623. 10.1080/10410236.2012.710873 [DOI] [PMC free article] [PubMed] [Google Scholar]
120. Sepucha KR, Simmons LH, Barry MJ, et al. Ten years, forty decision aids, and thousands of patient uses: shared decision making at Massachusetts General Hospital. Health Aff (Millwood). 2016;35:630-636. 10.1377/hlthaff.2015.1376 [DOI] [PubMed] [Google Scholar]

Associated Data

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

Supplementary Materials

ooag203_Supplementary_Data

ooag203_supplementary_data.zip^{(434.6KB, zip)}

Data Availability Statement

To protect participants’ privacy, data from this study are not able to be shared.

[ooag203-B1] 1. Alderwick H, Gottlieb LM. Meanings and misunderstandings: a social determinants of health lexicon for health care systems. Milbank Q. 2019;97:407-419. 10.1111/1468-0009.12390 [DOI] [PMC free article] [PubMed] [Google Scholar]

[ooag203-B2] 2. 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. National Academies Press (US; ); 2015. [PubMed] [Google Scholar]

[ooag203-B3] 3. Gottlieb LM, Wing H, Adler NE. A systematic review of interventions on patients’ social and economic needs. Am J Prev Med. 2017;53:719-729. 10.1016/j.amepre.2017.05.011 [DOI] [PubMed] [Google Scholar]

[ooag203-B4] 4. Cartier Y, Gottlieb L. The prevalence of social care in US health care settings depends on how and whom you ask. BMC Health Serv Res. 2020;20:481. 10.1186/s12913-020-05338-8 [DOI] [PMC free article] [PubMed] [Google Scholar]

[ooag203-B5] 5. Anthony D, Campos-Castillo C, Horne C. Toward a sociology of privacy. Annu Rev Sociol. 2017;43:249-269. 10.1146/annurev-soc-060116-053643 [DOI] [Google Scholar]

[ooag203-B6] 6. Byhoff E, De Marchis EH, Hessler D, et al. Part II: a qualitative study of social risk screening acceptability in patients and caregivers. Am J Prev Med. 2019;57:S38-S46. 10.1016/j.amepre.2019.07.016 [DOI] [PMC free article] [PubMed] [Google Scholar]

[ooag203-B7] 7. Rudisill AC, Eicken MGA, Gupta D, et al. Patient and care team perspectives on social determinants of health screening in primary care: a qualitative study. JAMA Netw Open. 2023;6:e2345444. 10.1001/jamanetworkopen.2023.45444 [DOI] [PMC free article] [PubMed] [Google Scholar]

[ooag203-B8] 8. Kiles TM, Cernasev A, Leibold C, et al. Patient perspectives of discussing social determinants of health with community pharmacists. J Am Pharm Assoc (2003). 2022;62:826-833. 10.1016/j.japh.2022.01.007 [DOI] [PubMed] [Google Scholar]

[ooag203-B9] 9. Caicedo NMA, Parry E, Arslan N, et al. Integration of social determinants of health information within the primary care electronic health record: a systematic review of patient perspectives and experiences. BJGP Open. 2024;8:1-12. 10.3399/BJGPO.2023.0155 [DOI] [PMC free article] [PubMed] [Google Scholar]

[ooag203-B10] 10. Fichtenberg CM, De Marchis EH, Gottlieb LM. Understanding patients’ interest in healthcare-based social assistance programs. Am J Prev Med. 2022;63:S109-S115. 10.1016/j.amepre.2022.04.026 [DOI] [PubMed] [Google Scholar]

[ooag203-B11] 11. Spain AK, Monahan EK, Alvarez K, et al. Latinx family perspectives on social needs screening and referral during well-child visits. MCN Am J Matern Child Nurs. 2021;46:143-149. 10.1097/NMC.0000000000000710 [DOI] [PubMed] [Google Scholar]

[ooag203-B12] 12. Albert SM, McCracken P, Bui T, et al. Do patients want clinicians to ask about social needs and include this information in their medical record? BMC Health Serv Res. 2022;22:1275. 10.1186/s12913-022-08652-5 [DOI] [PMC free article] [PubMed] [Google Scholar]

[ooag203-B13] 13. Palakshappa D, Doupnik S, Vasan A, et al. Suburban families’ experience with food insecurity screening in primary care practices. Pediatrics. 2017;140:e20170320. 10.1542/peds.2017-0320 [DOI] [PubMed] [Google Scholar]

[ooag203-B14] 14. Palakshappa D, Vasan A, Khan S, et al. Clinicians’ perceptions of screening for food insecurity in suburban pediatric practice. Pediatrics. 2017;140:e20170319. 10.1542/peds.2017-0319 [DOI] [PubMed] [Google Scholar]

[ooag203-B15] 15. Nguyen OK, Higashi RT, Makam AN, et al. The influence of financial strain on health decision-making. J Gen Intern Med. 2018;33:406-408. 10.1007/s11606-017-4296-3 [DOI] [PMC free article] [PubMed] [Google Scholar]

[ooag203-B16] 16. Senteio C, Veinot T. Trying to make things right: adherence work in high-poverty, African American neighborhoods. Qual Health Res. 2014;24:1745-1756. 10.1177/1049732314549027 [DOI] [PubMed] [Google Scholar]

[ooag203-B17] 17. Gottlieb L, Hessler D, Long D, et al. A randomized trial on screening for social determinants of health: the iScreen study. Pediatrics. 2014;134:e1611-e1618. 10.1542/peds.2014-1439 [DOI] [PubMed] [Google Scholar]

[ooag203-B18] 18. Wallace AS, Luther B, Guo J-W, Wang C-Y, Sisler S, Wong B. Implementing a social determinants screening and referral infrastructure during routine emergency department visits, Utah, 2017–2018. Prev Chronic Dis. 2020;17:E45. 10.5888/pcd17.190339 [DOI] [PMC free article] [PubMed] [Google Scholar]

[ooag203-B19] 19. Stablein T, Hall JL, Pervis C, et al. Negotiating stigma in health care: disclosure and the role of electronic health records. Health Sociol Rev. 2015;24:227-241. 10.1080/14461242.2015.1078218 [DOI] [Google Scholar]

[ooag203-B20] 20. Nong P, Williamson A, Anthony D, et al. Discrimination, trust, and withholding information from providers: implications for missing data and inequity. SSM Popul Health. 2022;18:101092. 10.1016/j.ssmph.2022.101092 [DOI] [PMC free article] [PubMed] [Google Scholar]

[ooag203-B21] 21. Tully KP, Gibson AN, Pearsall MS, et al. Screening and referral for social determinants of health: maternity patient and health care team perspectives. Health Equity. 2022;6:887-897. 10.1089/heq.2022.0020 [DOI] [PMC free article] [PubMed] [Google Scholar]

[ooag203-B22] 22. Brown EM, Loomba V, Marchis ED, et al. Patient and patient caregiver perspectives on social screening: a review of the literature. J Am Board Fam Med. 2023;36:66-78. 10.3122/jabfm.2022.220211R1 [DOI] [PubMed] [Google Scholar]

[ooag203-B23] 23. Henry T, Hayes M, Eisele CD, et al. Barriers to identifying and addressing health-related social needs in cancer care: patient and patient navigator perspectives. J Cancer Policy. 2024;42:100508. 10.1016/j.jcpo.2024.100508 [DOI] [PMC free article] [PubMed] [Google Scholar]

[ooag203-B24] 24. Schleifer D, Diep A, Grisham K. It’s About Trust: Parents’ Perspectives on Pediatricians Screening for Social Needs. United Hospital Fund; 2019. [Google Scholar]

[ooag203-B25] 25. Butler E, Morgan A, Morgan AU, et al. Screening for unmet social needs: patient engagement or alienation? NEJM Catal Innov Care Deliv. 2020;1. [Google Scholar]

[ooag203-B26] 26. Bouchelle Z, G. Menko S, Yazdani M, et al. Parent perspectives on documentation and sharing of health-related social needs data. Hosp Pediatr. 2024;14:308-316. 10.1542/hpeds.2023-007478 [DOI] [PMC free article] [PubMed] [Google Scholar]

[ooag203-B27] 27. LeLaurin JH, Zhao B, Cruz JDL, et al. Recent advances in our understanding of electronic health record-based social needs screening and documentation in pediatrics. Acad Pediatr. 2026;26:103216. 10.1016/j.acap.2025.103216 [DOI] [PubMed] [Google Scholar]

[ooag203-B28] 28. Iott BE, Campos-Castillo C, Anthony DL. Trust and privacy: how patient trust in providers is related to privacy behaviors and attitudes. AMIA Annu Symp Proc. 2020. 2019;2019:487-493. 32308842 [PMC free article] [PubMed] [Google Scholar]

[ooag203-B29] 29. Agaku IT, Adisa AO, Ayo-Yusuf OA, et al. Concern about security and privacy, and perceived control over collection and use of health information are related to withholding of health information from healthcare providers. J Am Med Inform Assoc. 2014;21:374-378. 10.1136/amiajnl-2013-002079 [DOI] [PMC free article] [PubMed] [Google Scholar]

[ooag203-B30] 30. Long KA, Paredes Molina AA, Blakey AO, et al. Perspectives on health-related social needs screening in primary care among Black and Latine patients. JAMA Netw Open. 2025;8:e2529538. 10.1001/jamanetworkopen.2025.29538 [DOI] [PMC free article] [PubMed] [Google Scholar]

[ooag203-B31] 31. Olvera RG, Melnyk H, MacEwan SR, et al. Patients’ perspectives about sharing information with providers on social needs during pregnancy: a qualitative study. BMC Health Serv Res. 2025;25:737. 10.1186/s12913-025-12666-0 [DOI] [PMC free article] [PubMed] [Google Scholar]

[ooag203-B32] 32. LeLaurin JH, Cruz JDL, Theis RP, et al. Parent perspectives on electronic health record-based social needs screening and documentation: a qualitative study. Acad Pediatr. 2023;23:1446-1453. 10.1016/j.acap.2023.06.011 [DOI] [PMC free article] [PubMed] [Google Scholar]

[ooag203-B33] 33. Alley DE, Asomugha CN, Conway PH, et al. Accountable health communities—addressing social needs through Medicare and Medicaid. N Engl J Med. 2016;374:8-11. 10.1056/NEJMp1512532 [DOI] [PubMed] [Google Scholar]

[ooag203-B34] 34. Chishtie JA, Tea J, Ester M, et al. Integration of screening and referral tools for social determinants of health and modifiable lifestyle factors in the epic electronic health record system: scoping review. J Med Internet Res. 2025;27:e73615. 10.2196/73615 [DOI] [PMC free article] [PubMed] [Google Scholar]

[ooag203-B35] 35. American Hospital Association. ICD-10-CM Coding for Social Determinants of Health. 2022.

[ooag203-B36] 36. LOINC. Social determinants of health. Accessed February 15, 2024. https://loinc.org/sdh/

[ooag203-B37] 37. Iott BE, Adler-Milstein J, Gottlieb L, et al. Characterizing the relative frequency of clinician engagement with structured social determinants of health data. J Am Med Inform Assoc. 2023;30:503-510. 10.1093/jamia/ocac251 [DOI] [PMC free article] [PubMed] [Google Scholar]

[ooag203-B38] 38. Senteio C, Veinot T, Adler-Milstein J, et al. Physicians’ perceptions of the impact of the EHR on the collection and retrieval of psychosocial information in outpatient diabetes care. Int J Med Inform. 2018;113:9-16. 10.1016/j.ijmedinf.2018.02.003 [DOI] [PubMed] [Google Scholar]

[ooag203-B39] 39. Patra BG, Sharma MM, Vekaria V, et al. Extracting social determinants of health from electronic health records using natural language processing: a systematic review. J Am Med Inform Assoc. 2021;28:2716-2727. 10.1093/jamia/ocab170 [DOI] [PMC free article] [PubMed] [Google Scholar]

[ooag203-B40] 40. Hartzler AL, Xie SJ, Wedgeworth P, et al. ; SDoH Community Champion Advisory Board. Integrating patient voices into the extraction of social determinants of health from clinical notes: ethical considerations and recommendations. J Am Med Inform Assoc. 2023;30:1456-1462. 10.1093/jamia/ocad043 [DOI] [PMC free article] [PubMed] [Google Scholar]

[ooag203-B41] 41. Kim KK, Joseph JG, Ohno-Machado L. Comparison of consumers’ views on electronic data sharing for healthcare and research. J Am Med Inform Assoc. 2015;22:821-830. 10.1093/jamia/ocv014 [DOI] [PMC free article] [PubMed] [Google Scholar]

[ooag203-B42] 42. Caine K, Kohn S, Lawrence C, et al. Designing a patient-centered user interface for access decisions about EHR data: implications from patient interviews. J Gen Intern Med. 2015;30:S7-S16. 10.1007/s11606-014-3049-9 [DOI] [PMC free article] [PubMed] [Google Scholar]

[ooag203-B43] 43. Bakker AR. The need to know the history of the use of digital patient data, in particular the EHR. Int J Med Inform. 2007;76:438-441. 10.1016/j.ijmedinf.2006.09.009 [DOI] [PubMed] [Google Scholar]

[ooag203-B44] 44. Millenson M. Big data on social determinants: improved health and unaddressed privacy concerns. NEJM Catal. 2018. [Google Scholar]

[ooag203-B45] 45. McVeigh KH, Newton-Dame R, Chan PY, et al. Can electronic health records be used for population health surveillance? Validating population health metrics against established survey data. eGEMs. 2016;4:27. 10.13063/2327-9214.1267 [DOI] [PMC free article] [PubMed] [Google Scholar]

[ooag203-B46] 46. Caine K, Hanania R. Patients want granular privacy control over health information in electronic medical records. J Am Med Inform Assoc. 2013;20:7-15. 10.1136/amiajnl-2012-001023 [DOI] [PMC free article] [PubMed] [Google Scholar]

[ooag203-B47] 47. Iott B, Eddy C, Casanova C, et al. More than a database: understanding community resource referrals within a socio-technical systems framework. In: Proceedings of the 2020 American Medical Informatics Association Annual Symposium. 2020. [PMC free article] [PubMed]

[ooag203-B48] 48. Steeves-Reece AL, Totten AM, Broadwell KD, et al. Social needs resource connections: a systematic review of barriers, facilitators, and evaluation. Am J Prev Med. 2022;62:e303-e315. 10.1016/j.amepre.2021.12.002 [DOI] [PMC free article] [PubMed] [Google Scholar]

[ooag203-B49] 49. Mossabir R, Morris R, Kennedy A, et al. A scoping review to understand the effectiveness of linking schemes from healthcare providers to community resources to improve the health and well-being of people with long-term conditions. Health Soc Care Community. 2015;23:467-484. 10.1111/hsc.12176 [DOI] [PubMed] [Google Scholar]

[ooag203-B50] 50. Marchis EHD, Torres JM, Benesch T, et al. Interventions addressing food insecurity in health care settings: a systematic review. Ann Fam Med. 2019;17:436-447. 10.1370/afm.2412 [DOI] [PMC free article] [PubMed] [Google Scholar]

[ooag203-B51] 51. Escobar ER. Screening and referral care delivery services and unmet health-related social needs: a systematic review. Prev Chronic Dis. 2021;18. 10.5888/pcd18.200569 [DOI] [PMC free article] [PubMed] [Google Scholar]

[ooag203-B52] 52. Senteio C, Adler-Milstein J, Richardson C, et al. Psychosocial information use for clinical decisions in diabetes care. J Am Med Inform Assoc. 2019;26:813-824. 10.1093/jamia/ocz053 [DOI] [PMC free article] [PubMed] [Google Scholar]

[ooag203-B53] 53. Marchis EHD, Aceves B, Razon N, et al. Adjusting clinical plans based on social context. J Am Board Fam Med. 2024;37:466-478. 10.3122/jabfm.2023.230289R1 [DOI] [PubMed] [Google Scholar]

[ooag203-B54] 54. Hatef E, Weiner JP, Kharrazi H. A public health perspective on using electronic health records to address social determinants of health: the potential for a national system of local community health records in the United States. Int J Med Inform. 2019;124:86-89. 10.1016/j.ijmedinf.2019.01.012 [DOI] [PubMed] [Google Scholar]

[ooag203-B55] 55. Rutledge SE, Whyte J, Abell N, et al. Measuring stigma among health care and social service providers: the HIV/AIDS provider stigma inventory. AIDS Patient Care STDS. 2011;25:673-682. 10.1089/apc.2011.0008 [DOI] [PubMed] [Google Scholar]

[ooag203-B56] 56. Goffman E. Stigma. Notes on the Management of Spoiled Identity. Simon and Shuster. Inc; 1963. [Google Scholar]

[ooag203-B57] 57. Link BG, Phelan JC. Conceptualizing stigma. Annu Rev Sociol. 2001;27:363-385. 10.1146/annurev.soc.27.1.363 [DOI] [Google Scholar]

[ooag203-B58] 58. Office for Civil Rights (OCR). Summary of the HIPAA privacy rule. HHS.gov. 2008. Accessed March 17, 2021. https://www.hhs.gov/hipaa/for-professionals/privacy/laws-regulations/index.html

[ooag203-B59] 59. American Institute of Healthcare Compliance. Identifying Social Determinants of Health, Coding, and HIPAA Compliance. American Institute of Healthcare Compliance; 2019. Accessed April 1, 2021. https://aihc-assn.org/identifying-social-determinants-of-health-coding-and-hipaa-compliance/ [Google Scholar]

[ooag203-B60] 60. Sobhani M. HIPAA isn’t enough: all our data is health data. Digit. Health–Un-Sensored CBC Blog USC Cent. Body Comput. Accessed March 17, 2021. https://cbc.ict.usc.edu/un-sensored/hipaaisnotenough/

[ooag203-B61] 61. Williams H. HIPAA challenges: SDoH raise interesting privacy questions. Rec Mag. 2020;32:28. https://www.fortherecordmag.com/archives/JF20p28.shtml [Google Scholar]

[ooag203-B62] 62. Executive Office of the President: President’s Council of Advisors on Science and Technology. Report to the President: realizing the full potential of health information technology to improve healthcare for Americans: the path forward. 2010.

[ooag203-B63] 63. Anthony DL, Campos-Castillo C. A looming digital divide? Group differences in the perceived importance of electronic health records. Inf Commun Soc. 2015;18:832-846. 10.1080/1369118X.2015.1006657 [DOI] [Google Scholar]

[ooag203-B64] 64. Our Commitment to Privacy. Accessed July 25, 2025. https://www.findhelp.org/legal/privacy

[ooag203-B65] 65. Saunders B, Sim J, Kingstone T, et al. Saturation in qualitative research: exploring its conceptualization and operationalization. Qual Quant. 2018;52:1893-1907. 10.1007/s11135-017-0574-8 [DOI] [PMC free article] [PubMed] [Google Scholar]

[ooag203-B66] 66. Ancker JS, Benda NC, Reddy M, et al. Guidance for publishing qualitative research in informatics. J Am Med Inform Assoc. 2021;28:2743-2748. 10.1093/jamia/ocab195 [DOI] [PMC free article] [PubMed] [Google Scholar]

[ooag203-B67] 67. Saldana J. The Coding Manual for Qualitative Researchers. SAGE; 2015. [Google Scholar]

[ooag203-B68] 68. NVivo: leading qualitative data analysis software. Lumivero. Accessed January 20, 2026. https://lumivero.com/products/nvivo/

[ooag203-B69] 69. Tong A, Sainsbury P, Craig J. Consolidated criteria for reporting qualitative research (COREQ): a 32-item checklist for interviews and focus groups. Int J Qual Health Care. 2007;19:349-357. 10.1093/intqhc/mzm042 [DOI] [PubMed] [Google Scholar]

[ooag203-B70] 70. Unraveling reform: key changes to SDoH and health equity in the CMS 2026 proposed rule. 2025. Accessed December 20, 2025. https://www.solventum.com/en-us/home/health-information-technology/resources-education/blog/2025/7/sdoh-and-health-equity-changes-2026/

[ooag203-B71] 71. Gottlieb LM, DeSilvey SC, Fichtenberg C, et al. Developing national social care standards. Health Aff Forefr. 10.1377/forefront.20230221.857308 [DOI]

[ooag203-B72] 72. FY 2024 hospital inpatient prospective payment system (IPPS) and long-term care hospital prospective payment system (LTCH PPS) final rule–CMS-1785-F and CMS-1788-F fact sheet.

[ooag203-B73] 73. McGraw D. Privacy concerns related to inclusion of social and behavioral determinants of health in electronic health records. In: Capturing Social and Behavioral Domains and Measures in Electronic Health Records: Phase 2. National Academies Press (US; ); 2015. [Google Scholar]

[ooag203-B74] 74. DesRoches CM, Wachenheim D, Garcia A, et al. Clinician and patient perspectives on the exchange of sensitive social determinants of health information. JAMA Netw Open. 2024;7:e2444376. 10.1001/jamanetworkopen.2024.44376 [DOI] [PMC free article] [PubMed] [Google Scholar]

[ooag203-B75] 75. Mayo R, Kliot T, Weinstein R, et al. Social needs screening during the COVID-19 pandemic. Child Care Health Dev. 2022;48:935-941. 10.1111/cch.12942 [DOI] [PubMed] [Google Scholar]

[ooag203-B76] 76. Abortion Rights and Access One Year After Dobbs | League of Women Voters. 2023. Accessed February 13, 2026. https://www.lwv.org/blog/abortion-rights-and-access-one-year-after-dobbs

[ooag203-B77] 77. Hodge JG, White EN, Piatt JL, et al. Assessing impacts of “anti-equity” legislation on health care and public health services. J Law Med Ethics. 2024;52:172-177. 10.1017/jme.2024.55 [DOI] [PubMed] [Google Scholar]

[ooag203-B78] 78. Howe A. Supreme Court allows federal officers to more freely make immigration stops in LA. SCOTUSblog. 2025. Accessed February 13, 2026. https://www.scotusblog.com/2025/09/supreme-court-allows-federal-officers-to-more-freely-make-immigration-stops-in-los-angeles/

[ooag203-B79] 79. Potharaju K, Gottlieb LM, Wing HE, et al. Drivers of patient experiences with healthcare-based social care. Health Serv Res. 2026;61:e70020, 9. 10.1111/1475-6773.70020 [DOI] [PMC free article] [PubMed] [Google Scholar]

[ooag203-B80] 80. Luther BL, Bybee SG, Elmore CE, et al. From screening to support: exploring patient engagement with health-related social needs services. AJPM Focus. 2025;4:100346. 10.1016/j.focus.2025.100346 [DOI] [PMC free article] [PubMed] [Google Scholar]

[ooag203-B81] 81. De Marchis EH, Alderwick H, Gottlieb LM. Do patients want help addressing social risks? J Am Board Fam Med. 2020;33:170-175. 10.3122/jabfm.2020.02.190309 [DOI] [PubMed] [Google Scholar]

[ooag203-B82] 82. Schoenthaler A, Hassan I, Fiscella K. The time is now: fostering relationship-centered discussions about patients’ social determinants of health. Patient Educ Couns. 2019;102:810-814. 10.1016/j.pec.2018.10.025 [DOI] [PubMed] [Google Scholar]

[ooag203-B83] 83. Byhoff E, Gottlieb LM. When there is value in asking: an argument for social risk screening in clinical practice. Ann Intern Med. 2022;175:1181-1182. 10.7326/M22-0147 [DOI] [PMC free article] [PubMed] [Google Scholar]

[ooag203-B84] 84. Brown MC, Paolino AR, Barnes KA, et al. Codesigning online continuing medical education on social health integration and social risk–informed care for primary care providers. Perm J. 2024;28:26-35. 10.7812/TPP/23.113 [DOI] [PMC free article] [PubMed] [Google Scholar]

[ooag203-B85] 85. Price WN, Cohen IG. Privacy in the age of medical big data. Nat Med. 2019;25:37-43. 10.1038/s41591-018-0272-7 [DOI] [PMC free article] [PubMed] [Google Scholar]

[ooag203-B86] 86. Papoutsi C, Reed JE, Marston C, et al. Patient and public views about the security and privacy of electronic health records (EHRs) in the UK: results from a mixed methods study. BMC Med Inform Decis Mak. 2015;15:86. 10.1186/s12911-015-0202-2 [DOI] [PMC free article] [PubMed] [Google Scholar]

[ooag203-B87] 87. Ong R. Factors affecting patient and public perceptions of the adoption of electronic health record sharing: a Hong Kong study. Int J Med Inform. 2023;178:105193. 10.1016/j.ijmedinf.2023.105193 [DOI] [PubMed] [Google Scholar]

[ooag203-B88] 88. Entzeridou E, Markopoulou E, Mollaki V. Public and physician’s expectations and ethical concerns about electronic health record: benefits outweigh risks except for information security. Int J Med Inform. 2018;110:98-107. 10.1016/j.ijmedinf.2017.12.004 [DOI] [PubMed] [Google Scholar]

[ooag203-B89] 89. Goffman E. On face-work. Psychiatry. 1955;18:213-231. 10.1080/00332747.1955.11023008 [DOI] [PubMed] [Google Scholar]

[ooag203-B90] 90. Gupta UC. Informed consent in clinical research: revisiting few concepts and areas. Perspect Clin Res. 2013;4:26-32. 10.4103/2229-3485.106373 [DOI] [PMC free article] [PubMed] [Google Scholar]

[ooag203-B91] 91. Falagas ME, Korbila IP, Giannopoulou KP, et al. Informed consent: how much and what do patients understand? Am J Surg. 2009;198:420-435. 10.1016/j.amjsurg.2009.02.010 [DOI] [PubMed] [Google Scholar]

[ooag203-B92] 92. Hare AJ, Honig K, Cronholm PF, et al. Patient perspectives on technology-based approaches to social needs screening. Am J Manag Care. 2023;29:e18-e23. 10.37765/ajmc.2023.89309 [DOI] [PubMed] [Google Scholar]

[ooag203-B93] 93. Senteio CR, Yoon DB. How primary care physicians elicit sensitive health information from patients: describing access to psychosocial information. Qual Health Res. 2020;30:1338-1348. 10.1177/1049732320911630 [DOI] [PubMed] [Google Scholar]

[ooag203-B94] 94. Mechanic D. The functions and limitations of trust in the provision of medical care. J Health Polit Policy Law. 1998;23:661-686. 10.1215/03616878-23-4-661 [DOI] [PubMed] [Google Scholar]

[ooag203-B95] 95. Olvera RG, Swoboda CM, Joseph JJ, et al. Individuals’ desire for social needs sharing among healthcare providers: findings from the 2022 health information national trends survey. J Gen Intern Med. 2026;41:374-382. 10.1007/s11606-024-09339-9 [DOI] [PMC free article] [PubMed] [Google Scholar]

[ooag203-B96] 96. Haidet P, Paterniti DA. “Building” a history rather than “taking” one: a perspective on information sharing during the medical interview. Arch Intern Med. 2003;163:1134-1140. 10.1001/archinte.163.10.1134 [DOI] [PubMed] [Google Scholar]

[ooag203-B97] 97. Gorecki MC, Klein MD, Anyigbo CU, et al. Social needs screening via electronic tablet in pediatric primary care. Pediatrics. 2024;154:e2024065918. 10.1542/peds.2024-065918 [DOI] [PMC free article] [PubMed] [Google Scholar]

[ooag203-B98] 98. Friedman S, Caddle S, Motelow JE, et al. Improving screening for social determinants of health in a pediatric resident clinic: a quality improvement initiative. Pediatr Qual Saf. 2021;6:e419. 10.1097/pq9.0000000000000419 [DOI] [PMC free article] [PubMed] [Google Scholar]

[ooag203-B99] 99. Gottlieb LM, Tirozzi KJ, Manchanda R, et al. Moving electronic medical records upstream: incorporating social determinants of health. Am J Prev Med. 2015;48:215-218. 10.1016/j.amepre.2014.07.009 [DOI] [PubMed] [Google Scholar]

[ooag203-B100] 100. National Academies of Sciences, Engineering, and Medicine. Integrating Social Care into the Delivery of Health Care: Moving Upstream to Improve the Nation’s Health. The National Academies Press; 2019. [PubMed] [Google Scholar]

[ooag203-B101] 101. Goldberg ZN, Shah YB, Harness ED, et al. The social determinants of health industry: two years on. Int J Soc Determinants Health Health Serv. 2024;54:344-351. 10.1177/27551938241257041 [DOI] [PubMed] [Google Scholar]

[ooag203-B102] 102. Iott BE, Patel V, Richwine C. Physician documentation of social determinants of health: results from two national surveys. J Gen Intern Med. 2025;40:620-628. 10.1007/s11606-024-09184-w [DOI] [PMC free article] [PubMed] [Google Scholar]

[ooag203-B103] 103. Garg A, Boynton-Jarrett R, Dworkin PH. Avoiding the unintended consequences of screening for social determinants of health. JAMA. 2016;316:813-814. 10.1001/jama.2016.9282 [DOI] [PubMed] [Google Scholar]

[ooag203-B104] 104. Bell SK, Delbanco T, Elmore JG, et al. Frequency and types of patient-reported errors in electronic health record ambulatory care notes. JAMA Netw Open. 2020;3:e205867. 10.1001/jamanetworkopen.2020.5867 [DOI] [PMC free article] [PubMed] [Google Scholar]

[ooag203-B105] 105. Bell SK, Gerard M, Fossa A, et al. A patient feedback reporting tool for OpenNotes: implications for patient-clinician safety and quality partnerships. BMJ Qual Saf. 2017;26:312-322. 10.1136/bmjqs-2016-006020 [DOI] [PubMed] [Google Scholar]

[ooag203-B106] 106. Bell SK, Mejilla R, Anselmo M, et al. When doctors share visit notes with patients: a study of patient and doctor perceptions of documentation errors, safety opportunities and the patient–doctor relationship. BMJ Qual Saf. 2017;26:262-270. 10.1136/bmjqs-2015-004697 [DOI] [PMC free article] [PubMed] [Google Scholar]

[ooag203-B107] 107. DesRoches CM, Bell SK, Dong Z, et al. Patients managing medications and reading their visit notes: a survey of OpenNotes participants. Ann Intern Med. 2019;171:69-71. 10.7326/M18-3197 [DOI] [PubMed] [Google Scholar]

[ooag203-B108] 108. Wolff JL, Darer JD, Berger A, et al. Inviting patients and care partners to read doctors’ notes: OpenNotes and shared access to electronic medical records. J Am Med Inform Assoc. 2017;24:e166-e172. 10.1093/jamia/ocw108 [DOI] [PMC free article] [PubMed] [Google Scholar]

[ooag203-B109] 109. Walker J, Leveille S, Bell S, et al. OpenNotes after 7 years: patient experiences with ongoing access to their clinicians’ outpatient visit notes. J Med Internet Res. 2019;21:e13876. 10.2196/13876 [DOI] [PMC free article] [PubMed] [Google Scholar]

[ooag203-B110] 110. Rosen J. The right to be forgotten. Stanford Law Rev. 2012. Accessed August 18, 2025. https://www.stanfordlawreview.org/online/privacy-paradox-the-right-to-be-forgotten/

[ooag203-B111] 111. Newman AL. What the “right to be forgotten” means for privacy in a digital age. Science. 2015;347:507-508. 10.1126/science.aaa4603 [DOI] [PubMed] [Google Scholar]

[ooag203-B112] 112. Floridi L, Taddeo M, editors. A Companion to Digital Ethics. Wiley-Blackwell; 2025. [Google Scholar]

[ooag203-B113] 113. Abdallah M, Bahroos N, Angyan P, et al. Compliance considerations in the geo-enrichment of an EHR data warehouse with social and environmental determinants of health. J Clin Transl Sci. 2024;8:e95. 10.1017/cts.2024.521 [DOI] [PMC free article] [PubMed] [Google Scholar]

[ooag203-B114] 114. Veinot TC, Mitchell H, Ancker JS. Good intentions are not enough: how informatics interventions can worsen inequality. J Am Med Inform Assoc. 2018;25:1080-1088. 10.1093/jamia/ocy052 [DOI] [PMC free article] [PubMed] [Google Scholar]

[ooag203-B115] 115. Kiessling KA, Iott BE, Pater JA, et al. Health informatics interventions to minimize out-of-pocket medication costs for patients: what providers want. JAMIA Open. 2022;5:ooac007. 10.1093/jamiaopen/ooac007 [DOI] [PMC free article] [PubMed] [Google Scholar]

[ooag203-B116] 116. Richards OK, Iott BE, Toscos TR, et al. “It’s a mess sometimes”: patient perspectives on provider responses to healthcare costs, and how informatics interventions can help support cost-sensitive care decisions. J Am Med Inform Assoc. 2022;29:1029-1039. 10.1093/jamia/ocac010 [DOI] [PMC free article] [PubMed] [Google Scholar]

[ooag203-B117] 117. Antonio MG, Swallow J, Richesson R, et al. The administrative burden of medication affordability resources: an environmental scan with implications for health informatics to advance health equity. J Am Med Inform Assoc. 2025;32:1206-1218. 10.1093/jamia/ocaf087 [DOI] [PMC free article] [PubMed] [Google Scholar]

[ooag203-B118] 118. Lyles CR, Khoong EC, Stern RJ, et al. ; CHARMED consortium. Championing hypertension remote monitoring for equity and dissemination (CHARMED): a multi-site factorial randomized controlled trial protocol. Contemp Clin Trials. 2025;152:107879. 10.1016/j.cct.2025.107879 [DOI] [PMC free article] [PubMed] [Google Scholar]

[ooag203-B119] 119. Peek ME, Gorawara-Bhat R, Quinn MT, et al. Patient trust in physicians and shared decision-making among African-Americans with diabetes. Health Commun. 2013;28:616-623. 10.1080/10410236.2012.710873 [DOI] [PMC free article] [PubMed] [Google Scholar]

[ooag203-B120] 120. Sepucha KR, Simmons LH, Barry MJ, et al. Ten years, forty decision aids, and thousands of patient uses: shared decision making at Massachusetts General Hospital. Health Aff (Millwood). 2016;35:630-636. 10.1377/hlthaff.2015.1376 [DOI] [PubMed] [Google Scholar]

PERMALINK

Privacy concerns regarding social risk screening and documentation in the electronic health record: a qualitative study of patients’ perspectives

Bradley E Iott, PhD

Shauna Wagner, BSN

Jessica Pater, PhD

Tammy Toscos, PhD

Denise Anthony, PhD

Tiffany C Veinot, PhD

Roles

Abstract

Objective

Materials and Methods

Results

Discussion

Conclusion

Introduction

Objective

Methods

Setting and participants

Data collection

Data analysis

Results

Table 1.

Table 2.

Relevance of social risk information to the clinical team and clinical conditions

Patient-provider/team relationship concerns and the role of trust

Concerns about documentation and storage

Concern that social information might be shared beyond immediate medical team and preferences for access restrictions

Storage duration concerns and preferences for data updating and deletion

Discussion

Limitations

Conclusion

Author contributions

Supplementary Material

Contributor Information

Supplementary material

Funding

Conflicts of interest

Data availability

References

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

Similar articles

Cited by other articles

Links to NCBI Databases