Impact of Pre-visit Contextual Data Collection on Patient-Physician Communication and Patient Activation: a Randomized Trial

Journal of General Internal Medicine (2021)Cite this article

Abstract

Background

Patient contextual data (PCD) are often missing from electronic health records, limiting the opportunity to incorporate preferences and life circumstances into care. Engaging patients through tools that collect and summarize such data may improve communication and patient activation. However, differential tool adoption by race might widen health care disparities.

Objective

Determine if a digital tool designed to collect and present PCD improves communication and patient activation; secondarily, evaluate if use impacts outcomes by race.

Design, Setting, and Participants

A pragmatic, two-armed, non-blinded, randomized controlled trial conducted during 2019 in a primary care setting.

Intervention

The PCD tool (PatientWisdom) invited patients to identify preferences, values, goals, and barriers to care. Patients were randomized to a standard pre-visit email or facilitated enrollment with dedicated outreach to encourage use of the tool.

Main Outcomes and Measures

Outcomes of interest were post-visit patient communication and patient activation measured by the Communication Assessment Tool (CAT) and Patient Activation Measure (PAM), respectively. Outcomes were evaluated using treatment-on-the-treated (TOT) and intention-to-treat (ITT) principles.

Key Results

A total of 301 patients were enrolled. Facilitated enrollment resulted in a five-fold increase in uptake of the PCD tool. TOT analysis indicated that the PCD tool was associated with notable increases in specific CAT items rated as excellent: “treated me with respect” (+ 13 percentage points; p = 0.04), “showed interest in my ideas” (+ 14 percentage points; p = 0.03), “showed care and concern” (+ 16 percentage points; p = 0.02), and “spent about the right amount of time with me” (+ 11 percentage points; p = 0.05). There were no significant pre/post-visit differences in PAM scores between arms (− 4.41 percentage points; p = 0.58). ITT results were similar. We saw no evidence of the treatment effect varying by race in ITT or TOT analyses.

Conclusions and Relevance

The inclusion of PCD enhanced essential aspects of patient-provider communication but did not affect patient activation. Outcomes did not differ by race.

Trial Registration

Clincaltrials.gov identifier: NCT03766841

This is a preview of subscription content, access via your institution.

We’re sorry, something doesn't seem to be working properly.

Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Figure 1
Figure 2

References

  1. 1.

    Hood CM, Gennuso KP, Swain GR, Catlin BB. County Health Rankings: Relationships Between Determinant Factors and Health Outcomes. Am J Prev Med. 2016;50(2):129–135. doi:https://doi.org/10.1016/j.amepre.2015.08.024

  2. 2.

    Hawkins JM, Mitchell J. The Doctor Never Listens: Older African American Men’s Perceptions of Patient–Provider Communication. Soc Work Res. 2018;42(1):57–63. doi:https://doi.org/10.1093/swr/svx028

    Article  Google Scholar 

  3. 3.

    Mitchell J, Williams E-DG, Perry R, Lobo K. “You Have to Be Part of the Process”: A Qualitative Analysis of Older African American Men’s Primary Care Communication and Participation. Am J Mens Health. 2019;13(4):1557988319861569. doi:https://doi.org/10.1177/1557988319861569

    Article  PubMed  PubMed Central  Google Scholar 

  4. 4.

    Street RL Jr, Makoul G, Arora NK, Epstein RM. How does communication heal? Pathways linking clinician-patient communication to health outcomes. Patient Educ Couns. 2009;74(3):295–301. doi:https://doi.org/10.1016/j.pec.2008.11.015

    Article  PubMed  Google Scholar 

  5. 5.

    Col NF, Solomon AJ, Springmann V, et al. Whose Preferences Matter? A Patient-Centered Approach for Eliciting Treatment Goals. Med Decis Making. 2018;38(1):44–55. doi:https://doi.org/10.1177/0272989X17724434

    Article  PubMed  Google Scholar 

  6. 6.

    Bernabeo E, Holmboe ES. Patients, providers, and systems need to acquire a specific set of competencies to achieve truly patient-centered care. Health Aff . 2013;32(2):250–258. doi:https://doi.org/10.1377/hlthaff.2012.1120

    Article  Google Scholar 

  7. 7.

    Britten N, Moore L, Lydahl D, Naldemirci O, Elam M, Wolf A. Elaboration of the Gothenburg model of person-centred care. Health Expect. 2017;20(3):407–418. doi:https://doi.org/10.1111/hex.12468

    Article  PubMed  Google Scholar 

  8. 8.

    Gobat N, Kinnersley P, Gregory JW, Robling M. What is agenda setting in the clinical encounter? Consensus from literature review and expert consultation. Patient Educ Couns. 2015;98(7):822–829. doi:https://doi.org/10.1016/j.pec.2015.03.024

    Article  PubMed  Google Scholar 

  9. 9.

    Cusatis R, Holt JM, Williams J, et al. The Impact of Patient-Generated Contextual Data on Communication in Clinical Practice: A Qualitative Assessment of Patient and Clinician Perspectives. Patient Educ Couns. Published online 2019. doi:https://doi.org/10.1016/j.pec.2019.10.020

  10. 10.

    Holt JM, Cusatis R, Asan O, et al. Incorporating patient-generated contextual data into care: Clinician perspectives using the Consolidated Framework for Implementation Science. Healthcare. Published online August 21, 2019:100369. doi:https://doi.org/10.1016/j.hjdsi.2019.100369

  11. 11.

    Cantor MN, Thorpe L. Integrating Data On Social Determinants Of Health Into Electronic Health Records. Health Aff 2018;37(4):585–590. doi:https://doi.org/10.1377/hlthaff.2017.1252

    Article  Google Scholar 

  12. 12.

    Dzau VJ, Balatbat CA. Health and societal implications of medical and technological advances. Sci Transl Med. 2018;10(463). doi:https://doi.org/10.1126/scitranslmed.aau4778

  13. 13.

    Dzau VJ, McClellan MB, McGinnis JM, et al. Vital Directions for Health and Health Care: Priorities From a National Academy of Medicine Initiative. JAMA. 2017;317(14):1461–1470. doi:https://doi.org/10.1001/jama.2017.1964

    Article  PubMed  Google Scholar 

  14. 14.

    Estiri H, Patel CJ, Murphy SN. Informatics can help providers incorporate context into care. Jamia Open. 2018;1(1):3–6. doi:https://doi.org/10.1093/jamiaopen/ooy025

    Article  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Furukawa MF, King J, Patel V, Hsiao C-J, Adler-Milstein J, Jha AK. Despite substantial progress In EHR adoption, health information exchange and patient engagement remain low in office settings. Health Aff . 2014;33(9):1672–1679. doi:https://doi.org/10.1377/hlthaff.2014.0445

    Article  Google Scholar 

  16. 16.

    Gold R, Cottrell E, Bunce A, et al. Developing Electronic Health Record (EHR) Strategies Related to Health Center Patients’ Social Determinants of Health. J Am Board Fam Med. 2017;30(4):428–447. doi:https://doi.org/10.3122/jabfm.2017.04.170046

    Article  PubMed  PubMed Central  Google Scholar 

  17. 17.

    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. doi:https://doi.org/10.1016/j.ijmedinf.2019.01.012

    Article  PubMed  Google Scholar 

  18. 18.

    Binns-Calvey AE, Malhiot A, Kostovich CT, et al. Validating Domains of Patient Contextual Factors Essential to Preventing Contextual Errors: A Qualitative Study Conducted at Chicago Area Veterans Health Administration Sites. Acad Med. 2017;92(9):1287–1293. doi:https://doi.org/10.1097/ACM.0000000000001659

    Article  PubMed  Google Scholar 

  19. 19.

    Keselman A, Logan R, Smith CA, Leroy G, Zeng-Treitler Q. Developing Informatics Tools and Strategies for Consumer-centered Health Communication. J Am Med Inform Assoc. 2008;15(4):473–483. doi:https://doi.org/10.1197/jamia.M2744

    Article  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Hibbard JH, Stockard J, Mahoney ER, Tusler M. Development of the Patient Activation Measure (PAM): conceptualizing and measuring activation in patients and consumers. Health Serv Res. 2004;39(4 Pt 1):1005–1026. doi:https://doi.org/10.1111/j.1475-6773.2004.00269.x

    Article  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Veinot TC, Mitchell H, Ancker JS. Good intentions are not enough: how informatics interventions can worsen inequality. J Am Med Inform Assoc. 2018;25(8):1080–1088. doi:https://doi.org/10.1093/jamia/ocy052

    Article  PubMed  PubMed Central  Google Scholar 

  22. 22.

    Anderson M, Perrin A, Jiang J, Kumar M. 10% of Americans don’t use the internet. Who are they? Pew Research Center. Published 2019. Accessed October 20, 2020. https://www.pewresearch.org/fact-tank/2019/04/22/some-americans-dont-use-the-internet-who-are-they/

  23. 23.

    Campos-Castillo C, Anthony D. Racial and Ethnic Differences in Self-Reported Telehealth Use during the COVID-19 Pandemic: A Secondary Analysis of a U.S. Survey of Internet Users from Late March. J Am Med Inform Assoc. Published online September 7, 2020. doi:https://doi.org/10.1093/jamia/ocaa221

  24. 24.

    Anthony DL, Campos-Castillo C, Lim PS. Who Isn’t Using Patient Portals And Why? Evidence And Implications From A National Sample Of US Adults. Health Aff. 2018;37(12):1948–1954. doi:https://doi.org/10.1377/hlthaff.2018.05117

    Article  Google Scholar 

  25. 25.

    Din HN, McDaniels-Davidson C, Nodora J, Madanat H. Profiles of a Health Information-Seeking Population and the Current Digital Divide: Cross-Sectional Analysis of the 2015-2016 California Health Interview Survey. J Med Internet Res. 2019;21(5):e11931. doi:https://doi.org/10.2196/11931

    Article  PubMed  PubMed Central  Google Scholar 

  26. 26.

    Goel MS, Brown TL, Williams A, Hasnain-Wynia R, Thompson JA, Baker DW. Disparities in enrollment and use of an electronic patient portal. J Gen Intern Med. 2011;26(10):1112–1116. doi:https://doi.org/10.1007/s11606-011-1728-3

    Article  PubMed  PubMed Central  Google Scholar 

  27. 27.

    Smith SG, O’Conor R, Aitken W, Curtis LM, Wolf MS, Goel MS. Disparities in registration and use of an online patient portal among older adults: findings from the LitCog cohort. J Am Med Inform Assoc. 2015;22(4):888–895. doi:https://doi.org/10.1093/jamia/ocv025

    Article  PubMed  PubMed Central  Google Scholar 

  28. 28.

    Yamin CK, Emani S, Williams DH, et al. The digital divide in adoption and use of a personal health record. Arch Intern Med. 2011;171(6):568–574. doi:https://doi.org/10.1001/archinternmed.2011.34

    Article  PubMed  Google Scholar 

  29. 29.

    Kontos E, Blake KD, Chou WY, Prestin A. Predictors of eHealth usage: insights on the digital divide from the Health Information National Trends Survey 2012. J Med Internet Res. 2014;16(7):e172. doi:https://doi.org/10.2196/jmir.3117

    Article  PubMed  PubMed Central  Google Scholar 

  30. 30.

    Pérez-Stable EJ, Jean-Francois B, Aklin CF. Leveraging Advances in Technology to Promote Health Equity. Med Care. 2019;57 Suppl 6 Suppl 2:S101–S103. doi:https://doi.org/10.1097/MLR.0000000000001112

    Article  PubMed  Google Scholar 

  31. 31.

    Holt JM, Cusatis R, Winn A, et al. The Impact of Previsit Contextual Data Collection on Patient-Provider Communication and Patient Activation: Study Protocol for a Randomized Controlled Trial. JMIR Res Protoc. 2020;9(9):e20309. doi:https://doi.org/10.2196/20309

    Article  PubMed  Google Scholar 

  32. 32.

    Makoul G. Essential elements of communication in medical encounters: the Kalamazoo consensus statement. Acad Med. 2001;76(4):390–393. doi:https://doi.org/10.1097/00001888-200104000-00021

    CAS  Article  PubMed  Google Scholar 

  33. 33.

    Sherman DK, Cohen GL. The Psychology of Self-defense: Self-Affirmation Theory. In: Advances in Experimental Social Psychology. Vol 38. Academic Press; 2006:183–242. doi:https://doi.org/10.1016/S0065-2601(06)38004-5

  34. 34.

    Sherman DK, Hartson KA. Reconciling self-protection with self-improvement: Self-affirmation theory. In: Alicke MD, ed. Handbook of Self-Enhancement and Self-Protection (Vol 524). The Guilford Press, 2011:128–151. https://psycnet.apa.org/fulltext/2011-04015-006.pdf.  Accessed 21 Sept 2019.

  35. 35.

    Makoul G, Krupat E, Chang C-H. Measuring patient views of physician communication skills: development and testing of the Communication Assessment Tool. Patient Educ Couns. 2007;67(3):333–342. doi:https://doi.org/10.1016/j.pec.2007.05.005

    Article  PubMed  Google Scholar 

  36. 36.

    Hibbard JH, Mahoney ER, Stockard J, Tusler M. Development and testing of a short form of the patient activation measure. Health Serv Res. 2005;40(6 Pt 1):1918–1930. doi:https://doi.org/10.1111/j.1475-6773.2005.00438.x

    Article  PubMed  PubMed Central  Google Scholar 

  37. 37.

    Fowles JB, Terry P, Xi M, Hibbard J, Bloom CT, Harvey L. Measuring self-management of patients’ and employees’ health: further validation of the Patient Activation Measure (PAM) based on its relation to employee characteristics. Patient Educ Couns. 2009;77(1):116–122. doi:https://doi.org/10.1016/j.pec.2009.02.018

    Article  PubMed  Google Scholar 

  38. 38.

    Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2009;42(2):377–381. doi:https://doi.org/10.1016/j.jbi.2008.08.010

    Article  PubMed  Google Scholar 

  39. 39.

    Greenberg-Worisek AJ, Kurani S, Finney Rutten LJ, Blake KD, Moser RP, Hesse BW. Tracking Healthy People 2020 Internet, Broadband, and Mobile Device Access Goals: An Update Using Data From the Health Information National Trends Survey. J Med Internet Res. 2019;21(6):e13300. doi:https://doi.org/10.2196/13300

    Article  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Dyer N, Sorra JS, Smith SA, Cleary PD, Hays RD. Psychometric properties of the Consumer Assessment of Healthcare Providers and Systems (CAHPS®) Clinician and Group Adult Visit Survey. Med Care. 2012;50 Suppl:S28–S34. doi:https://doi.org/10.1097/MLR.0b013e31826cbc0d

    Article  PubMed  PubMed Central  Google Scholar 

  41. 41.

    Hays RD, Bjorner JB, Revicki DA, Spritzer KL, Cella D. Development of physical and mental health summary scores from the patient-reported outcomes measurement information system (PROMIS) global items. Qual Life Res. 2009;18(7):873–880. doi:https://doi.org/10.1007/s11136-009-9496-9

    Article  PubMed  PubMed Central  Google Scholar 

  42. 42.

    Chew LD, Griffin JM, Partin MR, et al. Validation of screening questions for limited health literacy in a large VA outpatient population. J Gen Intern Med. 2008;23(5):561–566. doi:https://doi.org/10.1007/s11606-008-0520-5

    Article  PubMed  PubMed Central  Google Scholar 

  43. 43.

    Hernán MA, Hernández-Díaz S. Beyond the intention-to-treat in comparative effectiveness research. Clin Trials. 2012;9(1):48–55. doi:https://doi.org/10.1177/1740774511420743

    Article  PubMed  Google Scholar 

  44. 44.

    Hernán MA, Robins JM. Per-Protocol Analyses of Pragmatic Trials. N Engl J Med. 2017;377(14):1391–1398. doi:https://doi.org/10.1056/NEJMsm1605385

    Article  PubMed  Google Scholar 

  45. 45.

    Finkelstein A, Taubman S, Wright B, et al. The Oregon Health Insurance Experiment: Evidence from the First Year. Published online July 2011. doi:https://doi.org/10.3386/w17190

  46. 46.

    Imbens GW, Angrist JD. Identification and Estimation of Local Average Treatment Effects. Econometrica. 1994;62(2):467–475. doi:https://doi.org/10.2307/2951620

    Article  Google Scholar 

  47. 47.

    Jakobsen JC, Gluud C, Wetterslev J, Winkel P. When and how should multiple imputation be used for handling missing data in randomised clinical trials - a practical guide with flowcharts. BMC Med Res Methodol. 2017;17(1):162. doi:https://doi.org/10.1186/s12874-017-0442-1

    Article  PubMed  PubMed Central  Google Scholar 

  48. 48.

    Finkelstein A, Zhou A, Taubman S, Doyle J. Health Care Hotspotting - A Randomized, Controlled Trial. N Engl J Med. 2020;382(2):152–162. doi:https://doi.org/10.1056/NEJMsa1906848

    Article  PubMed  PubMed Central  Google Scholar 

  49. 49.

    Singh Ospina N, Phillips KA, Rodriguez-Gutierrez R, et al. Eliciting the Patient’s Agenda- Secondary Analysis of Recorded Clinical Encounters. J Gen Intern Med. 2019;34(1):36–40. doi:https://doi.org/10.1007/s11606-018-4540-5

    Article  PubMed  Google Scholar 

  50. 50.

    Saba GW, Wong ST, Schillinger D, et al. Shared decision making and the experience of partnership in primary care. Ann Fam Med. 2006;4(1):54–62. doi:https://doi.org/10.1370/afm.393

    Article  PubMed  PubMed Central  Google Scholar 

  51. 51.

    Epstein RM, Street RL Jr. The values and value of patient-centered care. Ann Fam Med. 2011;9(2):100–103. doi:https://doi.org/10.1370/afm.1239

    Article  PubMed  PubMed Central  Google Scholar 

  52. 52.

    Davis K, Schoenbaum SC, Audet A-M. A 2020 vision of patient-centered primary care. J Gen Intern Med. 2005;20(10):953–957. doi:https://doi.org/10.1111/j.1525-1497.2005.0178.x

    Article  PubMed  PubMed Central  Google Scholar 

  53. 53.

    Williams DR, Lawrence JA, Davis BA. Racism and Health: Evidence and Needed Research. Annu Rev Public Health. 2019;40:105–125. doi:https://doi.org/10.1146/annurev-publhealth-040218-043750

    Article  PubMed  PubMed Central  Google Scholar 

  54. 54.

    Wosik J, Fudim M, Cameron B, et al. Telehealth transformation: COVID-19 and the rise of virtual care. J Am Med Inform Assoc. 2020;27(6):957–962. doi:https://doi.org/10.1093/jamia/ocaa067

    Article  PubMed  Google Scholar 

  55. 55.

    Kriegel Gila, Bell Sigall, Delbanco Tom, Walker Jan. Covid-19 as Innovation Accelerator: Cogenerating Telemedicine Visit Notes with Patients. Catalyst non-issue content. 1(3). doi:https://doi.org/10.1056/CAT.20.0154

  56. 56.

    Nathan AG, Marshall IM, Cooper JM, Huang ES. Use of Decision Aids with Minority Patients: a Systematic Review. J Gen Intern Med. 2016;31(6):663–676. doi:https://doi.org/10.1007/s11606-016-3609-2

    Article  PubMed  PubMed Central  Google Scholar 

  57. 57.

    Wisconsin Department of Health Services. Annual Wisconsin Birth and Infant Death Report.; 2019. Accessed July 7, 2020. https://www.dhs.wisconsin.gov/stats/births/index.htm

  58. 58.

    Otado J, Kwagyan J, Edwards D, Ukaegbu A, Rockcliffe F, Osafo N. Culturally Competent Strategies for Recruitment and Retention of African American Populations into Clinical Trials. Clin Transl Sci. 2015;8(5):460–466. doi:https://doi.org/10.1111/cts.12285

    Article  PubMed  PubMed Central  Google Scholar 

  59. 59.

    Krieger N, Williams DR, Moss NE. Measuring social class in US public health research: concepts, methodologies, and guidelines. Annu Rev Public Health. 1997;18:341–378. doi:https://doi.org/10.1146/annurev.publhealth.18.1.341

    CAS  Article  PubMed  Google Scholar 

  60. 60.

    Hays RD, Schalet BD, Spritzer KL, Cella D. Two-item PROMIS® global physical and mental health scales. J Patient Rep Outcomes. 2017;1(1):2. doi:https://doi.org/10.1186/s41687-017-0003-8

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Funding

The project described was supported by the National Center for Advancing Translational Sciences, National Institutes of Health (NIH), Award Number (UL1TR001436), Health Resources and Services Administration (HRSA) Award Number (T32HP10030), and the Advancing a Healthier Wisconsin Endowment Award Number (5520480).

Author information

Affiliations

  1. University of Wisconsin-Milwaukee, College of Nursing, Milwaukee, WI, USA

    Jeana M. Holt Ph.D., DNP, RN, FNP-BC & AkkeNeel Talsma Ph.D., RN, FAAN

  2. Department of Medicine, Medical College of Wisconsin, Milwaukee, WI, USA

    Rachel Cusatis Ph.D., MA, Charles Spanbauer MS, Joni S. Williams MD, MPH, Kathryn E. Flynn Ph.D, Melek Somai MD, MPH, Purushottam Laud Ph.D & Bradley H. Crotty MD, MPH

  3. School of Pharmacy, Medical College of Wisconsin, Milwaukee, WI, USA

    Aaron Winn Ph.D

  4. Stevens Institute of Technology, School of Systems & Enterprises, Hoboken, NJ, USA

    Onur Asan Ph.D.

  5. PatientWisdom, Inc. and Yale School of Medicine, New Haven, CT, USA

    Gregory Makoul Ph.D., MS

Authors
  1. Jeana M. Holt Ph.D., DNP, RN, FNP-BC
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rachel Cusatis Ph.D., MA
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Aaron Winn Ph.D
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Onur Asan Ph.D.
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Charles Spanbauer MS
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Joni S. Williams MD, MPH
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Kathryn E. Flynn Ph.D
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Melek Somai MD, MPH
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. AkkeNeel Talsma Ph.D., RN, FAAN
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Purushottam Laud Ph.D
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Gregory Makoul Ph.D., MS
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Bradley H. Crotty MD, MPH
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jeana M. Holt Ph.D., DNP, RN, FNP-BC.

Ethics declarations

Conflict of Interest

Bradley Crotty reports being an advisor for Buoy Health. Gregory Makoul reports employment with PatientWisdom, Inc. He did not participate in the analysis of trial data.

Disclaimer

The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH, HRSA, or the Advancing a Healthier Wisconsin Endowment.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

ESM 1

(DOCX 17.3 kb)

ESM 2

(DOCX 18.9 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Holt, J.M., Cusatis, R., Winn, A. et al. Impact of Pre-visit Contextual Data Collection on Patient-Physician Communication and Patient Activation: a Randomized Trial. J GEN INTERN MED (2021). https://doi.org/10.1007/s11606-020-06583-7

Download citation

KEY WORDS

  • patient contextual data
  • health information technology
  • patient-provider communication
  • patient participation
  • primary health care
  • randomized controlled trial
  • health care disparities