Social Determinants and Health Equity Activities: Are They Connected with the Adaptation of AI and Telehealth Services in the U.S. Hospitals?
Abstract
1. Introduction
2. Materials and Methods
2.1. Data Source
2.2. Dependent Variables
2.3. Independent Variables
2.4. Statistical Analysis
3. Results
4. Discussion
5. Study Limitations
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Correction Statement
References
- Yu, K.-H.; Beam, A.L.; Kohane, I.S. Artificial intelligence in healthcare. Nat. Biomed. Eng. 2018, 2, 719–731. [Google Scholar] [CrossRef] [PubMed]
- Sharma, A.; Pruthi, M.; Sageena, G. Adoption of telehealth technologies: An approach to improving the healthcare system. Transl. Med. Commun. 2022, 7, 20. [Google Scholar] [CrossRef] [PubMed]
- Bleich, S. Medical errors: Five years after the IOM report. Issue Brief (Commonw Fund) 2005, 830, 1–15. [Google Scholar]
- Moor, J. The Dartmouth College Artificial Intelligence Conference: The Next Fifty Years. AI Mag. 2006, 27, 87–91. [Google Scholar] [CrossRef]
- Mukherjee, J.; Sharma, R.; Dutta, P.; Bhunia, B. Artificial intelligence in healthcare: A mastery. Biotechnol. Genet. Eng. Rev. 2023, 40, 1659–1708. [Google Scholar] [CrossRef] [PubMed]
- Bates, D.W.; Levine, D.; Syrowatka, A.; Kuznetsova, M.; Craig, K.J.T.; Rui, A.; Jackson, G.P.; Rhee, K. The potential of artificial intelligence to improve patient safety: A scoping review. NPJ Digit. Med. 2021, 4, 54. [Google Scholar] [CrossRef] [PubMed]
- Bohr, A.; Memarzadeh, K. The rise of artificial intelligence in healthcare applications. In Artificial Intelligence in Healthcare; Academic Press: Cambridge, MA, USA, 2020; pp. 25–60. [Google Scholar] [CrossRef]
- Kim, P.C.; Tan, L.F.; Kreston, J.; Shariatmadari, H.; Keyoung, E.S.; Shen, J.J.; Wang, B.L. Socioeconomic factors associated with use of telehealth services in outpatient care settings during the COVID-19. BMC Health Serv. Res. 2024, 24, 446. [Google Scholar] [CrossRef] [PubMed]
- Ramphul, K.; Singh Dhaliwal, J.; Sombans, S.; Passi, J.K.; Aggarwal, S.; Kumar, N.; Sakthivel, H.; Ahmed, R.; Verma, R. Trends in admissions for COVID-19 in the United States between April 2020 and December 2021 and cardiovascular events. Arch. Med. Sciences. Atheroscler. Dis. 2024, 9, e60–e65. [Google Scholar] [CrossRef]
- Wosik, J.; Fudim, M.; Cameron, B.; Gellad, Z.F.; Cho, A.; Phinney, D.; Curtis, S.; Roman, M.; Poon, E.G.; Ferranti, J.; et al. Telehealth transformation: COVID-19 and the rise of virtual care. J. Am. Med. Inform. Assoc. JAMIA 2020, 27, 957–962. [Google Scholar] [CrossRef] [PubMed]
- Andino, J.J.; Eyrich, N.W.; Boxer, R.J. Overview of telehealth in the United States since the COVID-19 public health emergency: A narrative review. mHealth 2023, 9, 26. [Google Scholar] [CrossRef] [PubMed]
- Doraiswamy, S.; Jithesh, A.; Mamtani, R.; Abraham, A.; Cheema, S. Telehealth Use in Geriatrics Care during the COVID-19 Pandemic-A Scoping Review and Evidence Synthesis. Int. J. Environ. Res. Public Health 2021, 18, 1755. [Google Scholar] [CrossRef] [PubMed]
- Leonard, C.; Liu, W.; Holstein, A.; Alliance, S.; Nunnery, M.; Rohs, C.; Sloan, M.; Winchester, D.E. Informing Use of Telehealth for Managing Chronic Conditions: Mixed-Methods Evaluation of Telehealth Use to Manage Heart Failure During COVID-19. J. Am. Heart Assoc. 2023, 12, e027362. [Google Scholar] [CrossRef] [PubMed]
- Mesa, A.; Grasdal, M.; Leong, S.; Dean, N.A.; Marwaha, A.; Lee, A.; Berger, M.J.; Bundon, A.; Krassioukov, A.V. Effect of the COVID-19 pandemic on individuals with spinal cord injury: Mental health and use of telehealth. PM & R 2022, 14, 1439–1445. [Google Scholar] [CrossRef]
- Green, B.L.; Murphy, A.; Robinson, E. Accelerating health disparities research with artificial intelligence. Front. Digit. Health 2024, 6, 1330160. [Google Scholar] [CrossRef] [PubMed]
- Saeed, S.A.; Masters, R.M. Disparities in Health Care and the Digital Divide. Curr. Psychiatry Rep. 2021, 23, 61. [Google Scholar] [CrossRef] [PubMed]
- DiMaggio, P.J.; Powell, W.W. The Iron Cage Revisited: Institutional Isomorphism and Collective Rationality in Organizational Fields. Am. Sociol. Rev. 1983, 2, 147–160. [Google Scholar] [CrossRef]
- Adler-Milstein, J.; Kvedar, J.; Bates, D.W. Telehealth Among US Hospitals: Several Factors, Including State Reimbursement And Licensure Policies, Influence Adoption. Health Aff. 2014, 33, 207–215. [Google Scholar] [CrossRef] [PubMed]
- Pham, P.; Zhang, H.; Gao, W.; Zhu, X. Determinants and performance outcomes of artificial intelligence adoption: Evidence from U.S. Hospitals. J. Bus. Res. 2024, 172, 114402. [Google Scholar] [CrossRef]
- Lee, J.Y.; McFadden, K.L.; Lee, M.K.; Gowen, C.R. U.S. hospital culture profiles for better performance in patient safety, patient satisfaction, Six Sigma, and lean implementation. Int. J. Prod. Econ. 2021, 234, 108047. [Google Scholar] [CrossRef]
- Begun, J.W.; Potthoff, S. Moving Upstream in U.S. Hospital Care Toward Investments in Population Health. J. Healthc. Manag. 2017, 62, 343–353. [Google Scholar] [CrossRef] [PubMed]
- Carroll-Scott, A.; Henson, R.M.; Kolker, J.; Purtle, J. The Role Of Nonprofit Hospitals In Identifying And Addressing Health Inequities In Cities. Health Aff. 2017, 36, 1102–1109. [Google Scholar] [CrossRef] [PubMed]
- Lemont, B.; Puro, N.; Franz, B.; Cronin, C.E. Efforts by critical access hospitals to increase health equity through greater engagement with social determinants of health. J. Rural. Health 2023, 39, 728–736. [Google Scholar] [CrossRef] [PubMed]
- American Hospital Association. About the AHA; American Hospital Association: Chicago, IL, USA; Available online: https://www.aha.org/ (accessed on 8 August 2024).
- American Hospital Association. AHA Annual Survey Database. AHA Data. Available online: https://www.ahadata.com/aha-annual-survey-database (accessed on 8 August 2024).
- American Hospital Association. Social Determinants of Health. Available online: https://www.aha.org/system/files/2018-04/value-initiative-icd-10-code-social-determinants-of-health.pdf (accessed on 8 August 2024).
- Cramer, G.R.; Singh, S.R.; Flaherty, S.; Young, G.J. The Progress of US Hospitals in Addressing Community Health Needs. Am. J. Public Health 2017, 107, 255–261. [Google Scholar] [CrossRef] [PubMed]
- Kang, R.; Hasnain-Wynia, R. Hospital commitment to community orientation and its association with quality of care and patient experience. J. Healthc. Manag. 2013, 58, 277–288. [Google Scholar] [CrossRef] [PubMed]
- Venkatesh, K.P.; Raza, M.M.; Diao, J.A.; Kvedar, J.C. Leveraging reimbursement strategies to guide value-based adoption and utilization of medical AI. NPJ Digit. Med. 2022, 5, 112. [Google Scholar] [CrossRef]
- Ford-Eickhoff, K.; Plowman, D.A.; McDaniel, R.R. Hospital boards and hospital strategic focus: The impact of board involvement in strategic decision making. Health Care Manag. Rev. 2011, 36, 145–154. [Google Scholar] [CrossRef] [PubMed]
- Natale-Pereira, A.; Enard, K.R.; Nevarez, L.; Jones, L.A. The role of patient navigators in eliminating health disparities. Cancer 2011, 117 (Suppl. S15), 3541–3550. [Google Scholar] [CrossRef] [PubMed]
- Guarcello, C.; Raupp, E. Pandemic and Innovation in Healthcare: The End-To-End Innovation Adoption Model. BAR Braz. Adm. Rev. 2021, 18, e210009. [Google Scholar] [CrossRef]
- Crossley, M.; Tyler, E.T.; Herbst, J.L. Tax-Exempt Hospitals and Community Health under the Affordable Care Act: Identifying and Addressing Unmet Legal Needs as Social Determinants of Health. Public Health Rep. 2016, 131, 195–199. [Google Scholar] [CrossRef]
- Asagbra, O.E. Factors Associated with the Adoption of Health Information Technologies to Increase Patient Engagement in US Hospitals. J. Organ. Psychol. 2019, 19, 24–38. [Google Scholar] [CrossRef]
- Bin Abdul Baten, R. How are US hospitals adopting artificial intelligence? Early evidence from 2022. Health Aff. Sch. 2024, 2, qxae123. [Google Scholar] [CrossRef] [PubMed]
- Bolon, D.S. Comparing Mission Statement Content in For-Profit and Not-For-Profit Hospitals: Does Mission Really Matter? Hosp. Top. 2005, 83, 2–9. [Google Scholar] [CrossRef]
- Gaziel-Yablowitz, M.; Bates, D.W.; Levine, D.M. Telehealth in US hospitals: State-level reimbursement policies no longer influence adoption rates. Int. J. Med. Inform. (Shannon Irel.) 2021, 153, 104540. [Google Scholar] [CrossRef]
- Ali Mohamad, T.; Bastone, A.; Bernhard, F.; Schiavone, F. How artificial intelligence impacts the competitive position of healthcare organizations. J. Organ. Change Manag. 2023, 36, 49–70. [Google Scholar] [CrossRef]
Variables | Percent |
---|---|
Response Variable | |
Use of AI | 24.62% |
Number of AI services, mean (SD) | 0.68 (1.41) |
Use of telehealth | 84.54% |
Number of telehealth services, mean (SD) | 3.06 (2.71) |
All virtual services, mean, (SD), (n = 2893) | 65,553 (304,790) |
Main Independent Variable | |
Community social determinant score, mean (SD) | 13.26 (8.59) |
Health equity score, mean (SD) | 8.66 (8.01) |
Hospital Characteristics | |
Hospital Size | |
6–49 beds | 35.16% |
50–199 beds | 36.81% |
200–399 beds | 17.36% |
400–500 and more beds | 10.66% |
Hospital Type | |
Mental health hospital | 6.55% |
Children’s hospital | 2.27% |
Specialty hospital | 7.57% |
Long-term care hospital | 3.55% |
Short-term acute general hospital | 80.06% |
Hospital Ownership Type | |
Investor-owned | 18.96% |
Not-for-profit | 60.72% |
Public | 20.32% |
Teaching hospital | 39.94% |
Rural hospital | 37.36% |
Hospital Market Type | |
Competitive market | 19.16% |
Mild concentrated market | 16.28% |
Concentrated market | 64.57% |
AI | Telehealth | |||||
---|---|---|---|---|---|---|
Variables | OR | 95% Cl | p-Value | OR | 95% Cl | p-Value |
Main Independent Variables | ||||||
Community Social Determinants | 1.07 | [1.05, 1.09] | <0.0001 | 1.08 | [1.06, 1.09] | <0.0001 |
Health Equity | 1.09 | [1.07, 1.10] | <0.0001 | 1.05 | [1.03, 1.07] | <0.0001 |
Hospital Characteristics | ||||||
Hospital Size | ||||||
≥ 400 beds (reference) | ||||||
6–49 beds | 0.59 | [0.44, 0.79] | 0.0368 | 0.22 | [0.12, 0.41] | <0.0001 |
50–199 beds | 0.59 | [0.45, 0.78] | 0.0144 | 0.35 | [0.20, 0.64] | 0.1214 |
200–399 beds | 0.70 | [0.53, 0.93] | 0.9596 | 0.38 | [0.20, 0.70] | 0.4483 |
Hospital Group | ||||||
General hospital (reference) | ||||||
Mental health hospital | 0.60 | [0.36, 0.98] | 0.1276 | 0.53 | [0.39, 0.72] | 0.6532 |
Children’s hospital | 0.92 | [0.54, 1.57] | 0.6587 | 0.70 | [0.38, 1.30] | 0.1782 |
Specialty hospital | 0.91 | [0.60, 1.37] | 0.5944 | 0.36 | [0.26, 0.48] | 0.0085 |
Long-term care hospital | 0.78 | [0.41, 1.46] | 0.8116 | 0.23 | [0.16, 0.35] | <0.0001 |
Hospital Ownership | ||||||
Not-for-profit (reference) | ||||||
Investor-owned | 0.40 | [0.30, 0.52] | 0.0010 | 0.45 | [0.34, 0.58] | 0.0288 |
Public | 0.43 | [0.31, 0.60] | 0.0281 | 0.34 | [0.26, 0.44] | <0.0001 |
Rural Hospital | 1.01 | [0.83, 1.22] | 0.9580 | 2.52 | [1.95, 3.25] | <0.0001 |
Market Competition | ||||||
Concentrated (reference) | ||||||
Competitive | 1.64 | [1.30, 2.06] | 0.0046 | 0.90 | [0.69, 1.16] | 0.5874 |
Mild concentrated | 1.41 | [1.11, 1.79] | 0.4046 | 0.93 | [0.70, 1.23] | 0.8832 |
Variables | Coefficient | SE | p-Value |
---|---|---|---|
Number of AI Services (n = 4061) | |||
Main Independent Variables | |||
Community Social Determinants | 0.022 | 0.003 | <0.0001 |
Health Equity | 0.049 | 0.003 | <0.0001 |
Hospital Characteristics | |||
Hospital Size | 0.117 | 0.028 | <0.0001 |
Hospital Group | |||
Short-term acute general hospital (reference) | |||
Mental hospital | −0.208 | 0.095 | 0.0293 |
Children’s hospital | 0.006 | 0.147 | 0.9684 |
Special hospital | −0.157 | 0.086 | 0.0689 |
Long-term hospital | −0.119 | 0.115 | 0.3020 |
Teaching Hospital | −0.138 | 0.052 | 0.0083 |
Hospital Location | |||
Non-rural hospital (reference) | |||
Rural hospital | 0.038 | 0.046 | 0.4013 |
Hospital Ownership | |||
Non-for-profit (reference) | |||
Investor-owned | −0.198 | 0.064 | 0.0019 |
Public | −0.305 | 0.056 | <0.0001 |
Market Competition | |||
Concentrated (reference) | |||
Competitive | 0.184 | 0.055 | 0.0008 |
Mild concentrated | 0.118 | 0.058 | 0.0421 |
Number of Telehealth Services (n = 4061) | |||
Main Independent Variables | |||
Community Social Determinants | 0.052 | 0.005 | <0.0001 |
Health Equity | 0.075 | 0.006 | <0.0001 |
Hospital Characteristics | |||
Hospital Size | 0.490 | 0.046 | <0.0001 |
Hospital Group | |||
Mental hospital | −0.386 | 0.159 | 0.0151 |
Children’s hospital | −0.636 | 0.245 | 0.0095 |
Special hospital | −0.384 | 0.143 | 0.0073 |
Long-term hospital | −0.485 | 0.192 | 0.0115 |
Teaching Hospital | 0.368 | 0.087 | <0.0001 |
Rural Hospital | 0.736 | 0.076 | <0.0001 |
Hospital Ownership | |||
Investor-owned | −1.231 | 0.106 | <0.0001 |
Public | −0.784 | 0.094 | <0.0001 |
Market Competition | |||
Competitive | 0.131 | 0.091 | 0.1506 |
Mild concentrated | 0.439 | 0.097 | <0.0001 |
Number of All Virtual Services (n = 2893) | |||
Main Independent Variables | |||
Community Social Determinants | 749 | 929 | 0.4202 |
Health Equity | 3125 | 895 | 0.0005 |
Hospital Characteristics | |||
Hospital Size | 59,885 | 7646 | <0.0001 |
Hospital Group | |||
Mental hospital | 2859 | 27,686 | 0.9177 |
Children’s hospital | 47,630 | 39,996 | 0.2338 |
Special hospital | 9361 | 24,132 | 0.6981 |
Long-term hospital | 2067 | 34,056 | 0.9516 |
Teaching Hospital | 16,121 | 14,594 | 0.2694 |
Rural Hospital | 10,095 | 12,448 | 0.4175 |
Hospital Ownership | |||
Investor-owned | −34,373 | 18,473 | 0.0629 |
Public | −13,454 | 15,341 | 0.3805 |
Market Competition | |||
Competitive | 386 | 14,941 | 0.9794 |
Mild concentrated | −5065 | 16,212 | 0.7548 |
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Pinera, P.A.; Kim, P.C.; Pinera, F.A.; Shen, J.J. Social Determinants and Health Equity Activities: Are They Connected with the Adaptation of AI and Telehealth Services in the U.S. Hospitals? Int. J. Environ. Res. Public Health 2025, 22, 294. https://doi.org/10.3390/ijerph22020294
Pinera PA, Kim PC, Pinera FA, Shen JJ. Social Determinants and Health Equity Activities: Are They Connected with the Adaptation of AI and Telehealth Services in the U.S. Hospitals? International Journal of Environmental Research and Public Health. 2025; 22(2):294. https://doi.org/10.3390/ijerph22020294
Chicago/Turabian StylePinera, Pearl A., Pearl C. Kim, Fye A. Pinera, and Jay J. Shen. 2025. "Social Determinants and Health Equity Activities: Are They Connected with the Adaptation of AI and Telehealth Services in the U.S. Hospitals?" International Journal of Environmental Research and Public Health 22, no. 2: 294. https://doi.org/10.3390/ijerph22020294
APA StylePinera, P. A., Kim, P. C., Pinera, F. A., & Shen, J. J. (2025). Social Determinants and Health Equity Activities: Are They Connected with the Adaptation of AI and Telehealth Services in the U.S. Hospitals? International Journal of Environmental Research and Public Health, 22(2), 294. https://doi.org/10.3390/ijerph22020294