RSS-Feed abonnieren
DOI: 10.1055/s-0043-1768722
The Impact of Clinical Decision Support on Health Disparities and the Digital Divide
Summary
Objectives: This literature review summarizes relevant studies from the last three years (2020-2022) related to clinical decision support (CDS) and CDS impact on health disparities and the digital divide. This survey identifies current trends and synthesizes evidence-based recommendations and considerations for future development and implementation of CDS tools.
Methods: We conducted a search in PubMed for literature published between 2020 and 2022. Our search strategy was constructed as a combination of the MEDLINE®/PubMed® Health Disparities and Minority Health Search Strategy and relevant CDS MeSH terms and phrases. We then extracted relevant data from the studies, including priority population when applicable, domain of influence on the disparity being addressed, and the type of CDS being used. We also made note of when a study discussed the digital divide in some capacity and organized the comments into general themes through group discussion.
Results: Our search yielded 520 studies, with 45 included at the conclusion of screening. The most frequent CDS type in this review was point-of-care alerts/reminders (33.3%). Health Care System was the most frequent domain of influence (71.1%), and Blacks/African Americans were the most frequently included priority population (42.2%). Throughout the literature, we found four general themes related to the technology divide: inaccessibility of technology, access to care, trust of technology, and technology literacy.
This survey revealed the diversity of CDS being used to address health disparities and several barriers which may make CDS less effective or potentially harmful to certain populations. Regular examinations of literature that feature CDS and address health disparities can help to reveal new strategies and patterns for improving healthcare.
Publikationsverlauf
Artikel online veröffentlicht:
06. Juli 2023
© 2023. IMIA and Thieme. This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)
Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany
-
References
- 1 Williams DR, Yan Y, Jackson JS, Anderson NB. Racial Differences in Physical and Mental Health: Socio-economic Status, Stress and Discrimination. J Health Psychol 1997;2(3):335-51. doi: 10.1177/135910539700200305.
- 2 Hill CV, Pérez-Stable EJ, Anderson NA, Bernard MA. The National Institute on Aging Health Disparities Research Framework. Ethn Dis 2015;25(3):245-54. doi: 10.18865/ed.25.3.245.
- 3 Gillispie V, Abrigo R. Racial Disparities in Healthcare. In: Conrad K, editor. Clinical Approaches to Hospital Medicine: Advances, Updates and Controversies. Springer International Publishing; 2022. p. 265-73.
- 4 Saeed SA, Masters RM. Disparities in Health Care and the Digital Divide. Curr Psychiatry Rep 2021;23(9):61. doi: 10.1007/s11920-021-01274-4.
- 5 National Academies of Sciences. The State of Health Disparities in the United States. In: Baciu A, Negussie Y, Geller A, Weinstein JN, editors. Cover of Communities in Action Communities in Action: Pathways to Health Equity. Washington (DC): National Academies Press; 2017.
- 6 Centers for Disease Control and Prevention. Health Disparities and Strategies Reports. [Available from: https://www.cdc.gov/minorityhealth/chdir/index.html].
- 7 Agency for Healthcare Research and Quality. Health Equity: A Key CDS Component. [Available from: https://cds.ahrq.gov/cdsconnect/community/patient_perspective/april2019].
- 8 The Office of the National Coordinator for Health Information Technology. Clinical Decision Support. [Available from: https://www.healthit.gov/topic/safety/clinical-decision-support].
- 9 Gianfrancesco MA, Tamang S, Yazdany J, Schmajuk G. Potential Biases in Machine Learning Algorithms Using Electronic Health Record Data. JAMA Intern Med 2018;178(11):1544-7. doi: 10.1001/jamainternmed.2018.3763.
- 10 Sieck CJ, Sheon A, Ancker JS, Castek J, Callahan B, Siefer A. Digital inclusion as a social determinant of health. NPJ Digit Med 2021;4(1):52. doi: 10.1038/s41746-021-00413-8.
- 11 Shortliffe E, Cimino J, editors. Biomedical Informatics. Computer Applications in Health Care and Biomedicine. London: Springer; 2014.
- 12 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: 10.2196/jmir.3117.
- 13 DiPiro JT, Nesbit TW, Reuland C, Cunningham FE, Schweitzer P, Chisholm-Burns MA, et al. ASHP Foundation Pharmacy Forecast 2023: Strategic Planning Guidance for Pharmacy Departments in Hospitals and Health Systems. Am J Health Syst Pharm 2023;80(2):10-35. doi: 10.1093/ajhp/zxac274.
- 14 Clare CA. Telehealth and the digital divide as a social determinant of health during the COVID-19 pandemic. Netw Model Anal Health Inform Bioinform 2021;10(1):26. doi: 10.1007/s13721-021-00300-y.
- 15 Litchfield I, Shukla D, Greenfield S. Impact of COVID-19 on the digital divide: a rapid review. BMJ Open 2021;11(10):e053440. doi: 10.1136/bmjopen-2021-053440.
- 16 Ramsetty A, Adams C. Impact of the digital divide in the age of COVID-19. J Am Med Inform Assoc 2020;27(7):1147-8. doi: 10.1093/jamia/ocaa078.
- 17 Bright TJ, Wong A, Dhurjati R, Bristow E, Bastian L, Coeytaux RR, et al. Effect of Clinical Decision-Support Systems. Ann Intern Med 2012;157(1):29-43. doi: 10.7326/0003-4819-157-1-201207030-00450.
- 18 Taber P, Radloff C, Del Fiol G, Staes C, Kawamoto K. New Standards for Clinical Decision Support: A Survey of The State of Implementation. Yearb Med Inform 2021;30(1):159-71. doi: 10.1055/s-0041-1726502.
- 19 Jankovic I, Chen JH. Clinical Decision Support and Implications for the Clinician Burnout Crisis. Yearb Med Inform 2020;29(1):145-54. doi: 10.1055/s-0040-1701986.
- 20 National Library of Medicine. MEDLINE®/PubMed® Health Disparities and Minority Health Search Strategy. [Available from: https://www.nlm.nih.gov/services/queries/health_disparities_details.html].
- 21 National Institute on Minority Health and Health Disparities. National Institute on Minority Health and Health Disparities Research Framework. [Available from: https://www.nimhd.nih.gov/docs/research_framework/research-framework-slide.pdf].
- 22 Wright A, Sittig DF, Ash JS, Feblowith J, Meltzer S, McMullen C, et al. Development and evaluation of a comprehensive clinical decision support taxonomy: comparison of front-end tools in commercial and internally developed electronic health record systems. J Am Med Inform Assoc 2011;18(3):232-42. doi: 10.1136/amiajnl-2011-000113.
- 23 National Institute on Aging. Health Disparities Framework [Available from: https://www.nia.nih.gov/research/osp/framework].
- 24 Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann T, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 2021;372:n71. doi: 10.1136/bmj.n71.
- 25 McCoy AB, Wright A, Sittig DF. Cross-vendor evaluation of key user-defined clinical decision support capabilities: a scenario-based assessment of certified electronic health records with guidelines for future development. J Am Med Inform Assoc 2015;22(5):1081-8. doi: 10.1093/jamia/ocv073.
- 26 Dullabh P, Sandberg SF, Heaney-Huls K, Hovey LS, Lobach DF, Boxwala A, et al. Challenges and opportunities for advancing patient-centered clinical decision support: findings from a horizon scan. J Am Med Inform Assoc 2022;29(7):1233-1243. doi: 10.1093/jamia/ocac059.
- 27 Dullabh P, Heaney-Huls K, Lobach DF, Hovey LS, Sandberg SF, Desai PJ, et al. The technical landscape for patient-centered CDS: progress, gaps, and challenges. J Am Med Inform Assoc 2022;29(6):1101-5. doi: 10.1093/jamia/ocac029.
- 28 Wiens J, Price WN, Sjoding MW. Diagnosing bias in data-driven algorithms for healthcare. Nat Med 2020;26(1):25-6. doi: 10.1038/s41591-019-0726-6.
- 29 Obermeyer Z, Powers B, Vogeli C, Mullainathan S. Dissecting racial bias in an algorithm used to manage the health of populations. Science 2019;366(6464):447-53. doi: 10.1126/science.aax2342.
- 30 Matheny M, Israni ST, Ahmed M, Whicher D. Artificial intelligence in health care: The hope, the hype, the promise, the peril. Washington, DC: National Academy of Medicine; 2019.
- 31 Solomonides AE, Koski E, Atabaki SM, Weinberg S, McGreevey JD, Kannry JL, et al. Defining AMIA’s artificial intelligence principles. J Am Med Inform Assoc 2022;29(4):585-91. doi: 10.1093/jamia/ocac006.
- 32 Petersen C, Smith J, Freimuth RR, Goodman, KW, Purcell Jackson G, Kannry J, et al. Recommendations for the safe, effective use of adaptive CDS in the US healthcare system: an AMIA position paper. J Am Med Inform Assoc 2021;28(4):677-84. doi: 10.1093/jamia/ocaa319.
- 33 Were MC, Savai S, Mokaya B, Mbugua S, Ribeka N, Cholli P, et al. mUzima Mobile Electronic Health Record (EHR) System: Development and Implementation at Scale. J Med Internet Res 2021;23(12):e26381. doi: 10.2196/26381.
- 34 Pew Research Center. 7% of Americans don’t use the internet. Who are they? [Available from: https://www.pewresearch.org/fact-tank/2021/04/02/7-of-americans-dont-use-the-internet-who-are-they/].
- 35 Lay-Yee R, Milne B, Davis P, Pearson J, McLay J. Determinants and disparities: a simulation approach to the case of child health care. Soc Sci Med 2015;128:202-11. doi: 10.1016/j.socscimed.2015.01.025.
- 36 Beauchamp GA, McGregor AJ, Choo EK, Safdar B, Rayl Greenberg M. Incorporating Sex and Gender into Culturally Competent Simulation in Medical Education. J Womens Health (Larchmt) 2019;28(12):1762-7. doi: 10.1089/jwh.2018.7271.
- 37 Sabatello M. Precision medicine, health disparities, and ethics: the case for disability inclusion. Genetics in Medicine 2018;20(4):397-9. doi: 10.1038/gim.2017.120.
- 38 Dankwa-Mullan I, Bull J, Sy F. Precision medicine and health disparities: advancing the science of individualizing patient care. Am J Public Health 2015;105(S3):S368. doi: 10.2105/AJPH.2015.302755.
- 39 Lau YK, Bhattarai H, Caverly TJ, Hung P-Y, Jimenez-Mendoza E, Patel MR, et al. Lung Cancer Screening Knowledge, Perceptions, and Decision Making Among African Americans in Detroit, Michigan. Am J Prev Med 2021;60(1):e1-e8. doi: 10.1016/j.amepre.2020.07.004.
- 40 Quinn TP, Senadeera M, Jacobs S., Coghlan S, Le V. Trust and medical AI: the challenges we face and the expertise needed to overcome them. J Am Med Inform Assoc 2021;28(4):890-4. doi: 10.1093/jamia/ocaa268.
- 41 Dunn P, Hazzard E. Technology approaches to digital health literacy. Int J Cardiol 2019;293:294-6. doi: 10.1016/j.ijcard.2019.06.039.
- 42 Turchioe MR, Myers A, Isaac S, Baik D, Grossman LV, Ancker JS, et al. A Systematic Review of Patient-Facing Visualizations of Personal Health Data. Appl Clin Inform 2019;10(4):751-70. doi: 10.1055/s-0039-1697592.