Evaluating the Readiness of Health Information Management Professionals for AI Integration in Healthcare Systems in Bayelsa State, Nigeria

Kurokeyi Ebimene Japheth Teddy; Victor Ayebanengimote

doi:doi:10.11648/j.ajhr.20261405.11

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Evaluating the Readiness of Health Information Management Professionals for AI Integration in Healthcare Systems in Bayelsa State, Nigeria

Kurokeyi Ebimene Japheth Teddy^*

, Victor Ayebanengimote

Published in American Journal of Health Research (Volume 14, Issue 5)

Received: 9 August 2026 Accepted: 20 August 2026 Published: 22 September 2026

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Abstract

Artificial intelligence (AI) is revolutionizing healthcare delivery globally. But existing literature on the extent of AI computer literacy among respectively physicians in Nigeria as an evidence of its level of integration into healthcare is insufficient. This study was designed to evaluate the level of AI awareness and knowledge, digital competence, organizational readiness, professional development, and AI preparedness of healthcare provider (HIM professionals’) towards healthcare integration in Bayelsa State. The study adopted a descriptive cross-sectional survey design. The study population comprised 332 members of AHRIMPN Bayelsa chapter WhattsApp platform. From which 205 turned out to be the respondents and were analyzed with the use of structured 36-item self-administered questionnaire. Data were analyzed by descriptive statistics, Cronbach‘s alpha reliability analysis, Spearman‘s rank correlation, Mann Whitney U test, exploratory factor analysis, relative importance analysis, and multivariable regression at 5% significance level. The results showed moderate AI knowledge and awareness (3.052±0.116), digital competence (2.923±0.100), organizational readiness (3.141±0.124), professional development (3.143± 0.158) and AI readiness for healthcare integration (3.115±0.101). The instrument possessed Cronbach‘s α of 0.760 0.887 while Kaiser–Meyer–Olkin of 0.820 and significant Bartlett‘s Test of 19.155, P value = 0.000 validated the data for factor analysis. There were strong positive relationships between AI knowledge, digital competence and organizational readiness. But multivariable regression analysis revealed none of the observed four explanatory variables individually and significantly predicted AI readiness (p > 0.05). Respondents who had previous AI/digital health training had significantly better reported knowledge, digital competence, organizational readiness and professional development than those who did not have such training. The study revealed moderate AI readiness among healthcare professionals into AI-enabled health systems, though lack of the requisite AI skills and lack of standardized, continuous and obligatory AI capacity building interventions as well as poor investment in digital technology, robustness of organization, and health institution-related variations in knowledge, digital competence, organizational readiness and professional development are impeding AI integration into healthcare. The study concludes that continuous AI capacity development programmes, digital infrastructure investment, organizational autonomy and investment, and well-articulated AI curriculum into practitioner education, healthcare professional professional development programmes and organization-related accreditation are indispensable to successful, affordable and sustainable AI-enabled health systems.

Published in	American Journal of Health Research (Volume 14, Issue 5)
DOI	10.11648/j.ajhr.20261405.11
Page(s)	219-233
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2026. Published by Science Publishing Group

Keywords

Artificial Intelligence, AI Readiness, Digital Competence, Organizational Readiness, Professional Development, Health Information Management Professionals, Healthcare Integration, Nigeria

References

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APA Style

Teddy, K. E. J., Ayebanengimote, V. (2026). Evaluating the Readiness of Health Information Management Professionals for AI Integration in Healthcare Systems in Bayelsa State, Nigeria. American Journal of Health Research, 14(5), 219-233. https://doi.org/10.11648/j.ajhr.20261405.11

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ACS Style

Teddy, K. E. J.; Ayebanengimote, V. Evaluating the Readiness of Health Information Management Professionals for AI Integration in Healthcare Systems in Bayelsa State, Nigeria. Am. J. Health Res. 2026, 14(5), 219-233. doi: 10.11648/j.ajhr.20261405.11

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AMA Style

Teddy KEJ, Ayebanengimote V. Evaluating the Readiness of Health Information Management Professionals for AI Integration in Healthcare Systems in Bayelsa State, Nigeria. Am J Health Res. 2026;14(5):219-233. doi: 10.11648/j.ajhr.20261405.11

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@article{10.11648/j.ajhr.20261405.11,
  author = {Kurokeyi Ebimene Japheth Teddy and Victor Ayebanengimote},
  title = {Evaluating the Readiness of Health Information Management Professionals for AI Integration in Healthcare Systems in Bayelsa State, Nigeria},
  journal = {American Journal of Health Research},
  volume = {14},
  number = {5},
  pages = {219-233},
  doi = {10.11648/j.ajhr.20261405.11},
  url = {https://doi.org/10.11648/j.ajhr.20261405.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajhr.20261405.11},
  abstract = {Artificial intelligence (AI) is revolutionizing healthcare delivery globally. But existing literature on the extent of AI computer literacy among respectively physicians in Nigeria as an evidence of its level of integration into healthcare is insufficient. This study was designed to evaluate the level of AI awareness and knowledge, digital competence, organizational readiness, professional development, and AI preparedness of healthcare provider (HIM professionals’) towards healthcare integration in Bayelsa State. The study adopted a descriptive cross-sectional survey design. The study population comprised 332 members of AHRIMPN Bayelsa chapter WhattsApp platform. From which 205 turned out to be the respondents and were analyzed with the use of structured 36-item self-administered questionnaire. Data were analyzed by descriptive statistics, Cronbach‘s alpha reliability analysis, Spearman‘s rank correlation, Mann Whitney U test, exploratory factor analysis, relative importance analysis, and multivariable regression at 5% significance level. The results showed moderate AI knowledge and awareness (3.052±0.116), digital competence (2.923±0.100), organizational readiness (3.141±0.124), professional development (3.143± 0.158) and AI readiness for healthcare integration (3.115±0.101). The instrument possessed Cronbach‘s α of 0.760 0.887 while Kaiser–Meyer–Olkin of 0.820 and significant Bartlett‘s Test of 19.155, P value = 0.000 validated the data for factor analysis. There were strong positive relationships between AI knowledge, digital competence and organizational readiness. But multivariable regression analysis revealed none of the observed four explanatory variables individually and significantly predicted AI readiness (p > 0.05). Respondents who had previous AI/digital health training had significantly better reported knowledge, digital competence, organizational readiness and professional development than those who did not have such training. The study revealed moderate AI readiness among healthcare professionals into AI-enabled health systems, though lack of the requisite AI skills and lack of standardized, continuous and obligatory AI capacity building interventions as well as poor investment in digital technology, robustness of organization, and health institution-related variations in knowledge, digital competence, organizational readiness and professional development are impeding AI integration into healthcare. The study concludes that continuous AI capacity development programmes, digital infrastructure investment, organizational autonomy and investment, and well-articulated AI curriculum into practitioner education, healthcare professional professional development programmes and organization-related accreditation are indispensable to successful, affordable and sustainable AI-enabled health systems.},
 year = {2026}
}

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TY  - JOUR
T1  - Evaluating the Readiness of Health Information Management Professionals for AI Integration in Healthcare Systems in Bayelsa State, Nigeria
AU  - Kurokeyi Ebimene Japheth Teddy
AU  - Victor Ayebanengimote
Y1  - 2026/09/22
PY  - 2026
N1  - https://doi.org/10.11648/j.ajhr.20261405.11
DO  - 10.11648/j.ajhr.20261405.11
T2  - American Journal of Health Research
JF  - American Journal of Health Research
JO  - American Journal of Health Research
SP  - 219
EP  - 233
PB  - Science Publishing Group
SN  - 2330-8796
UR  - https://doi.org/10.11648/j.ajhr.20261405.11
AB  - Artificial intelligence (AI) is revolutionizing healthcare delivery globally. But existing literature on the extent of AI computer literacy among respectively physicians in Nigeria as an evidence of its level of integration into healthcare is insufficient. This study was designed to evaluate the level of AI awareness and knowledge, digital competence, organizational readiness, professional development, and AI preparedness of healthcare provider (HIM professionals’) towards healthcare integration in Bayelsa State. The study adopted a descriptive cross-sectional survey design. The study population comprised 332 members of AHRIMPN Bayelsa chapter WhattsApp platform. From which 205 turned out to be the respondents and were analyzed with the use of structured 36-item self-administered questionnaire. Data were analyzed by descriptive statistics, Cronbach‘s alpha reliability analysis, Spearman‘s rank correlation, Mann Whitney U test, exploratory factor analysis, relative importance analysis, and multivariable regression at 5% significance level. The results showed moderate AI knowledge and awareness (3.052±0.116), digital competence (2.923±0.100), organizational readiness (3.141±0.124), professional development (3.143± 0.158) and AI readiness for healthcare integration (3.115±0.101). The instrument possessed Cronbach‘s α of 0.760 0.887 while Kaiser–Meyer–Olkin of 0.820 and significant Bartlett‘s Test of 19.155, P value = 0.000 validated the data for factor analysis. There were strong positive relationships between AI knowledge, digital competence and organizational readiness. But multivariable regression analysis revealed none of the observed four explanatory variables individually and significantly predicted AI readiness (p > 0.05). Respondents who had previous AI/digital health training had significantly better reported knowledge, digital competence, organizational readiness and professional development than those who did not have such training. The study revealed moderate AI readiness among healthcare professionals into AI-enabled health systems, though lack of the requisite AI skills and lack of standardized, continuous and obligatory AI capacity building interventions as well as poor investment in digital technology, robustness of organization, and health institution-related variations in knowledge, digital competence, organizational readiness and professional development are impeding AI integration into healthcare. The study concludes that continuous AI capacity development programmes, digital infrastructure investment, organizational autonomy and investment, and well-articulated AI curriculum into practitioner education, healthcare professional professional development programmes and organization-related accreditation are indispensable to successful, affordable and sustainable AI-enabled health systems.
VL  - 14
IS  - 5
ER  -

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Kurokeyi Ebimene Japheth Teddy

Health information Management Department, Bayelsa Medical University, Bayelsa, Nigeria

Contact Email

http://orcid.org/0009-0000-3985-1070
Victor Ayebanengimote

Health Information Management Department, Federal University Otuoke Business School, Bayelsa, Nigeria

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http://orcid.org/0009-0003-5481-5336

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APA Style

Teddy, K. E. J., Ayebanengimote, V. (2026). Evaluating the Readiness of Health Information Management Professionals for AI Integration in Healthcare Systems in Bayelsa State, Nigeria. American Journal of Health Research, 14(5), 219-233. https://doi.org/10.11648/j.ajhr.20261405.11

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ACS Style

Teddy, K. E. J.; Ayebanengimote, V. Evaluating the Readiness of Health Information Management Professionals for AI Integration in Healthcare Systems in Bayelsa State, Nigeria. Am. J. Health Res. 2026, 14(5), 219-233. doi: 10.11648/j.ajhr.20261405.11

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AMA Style

Teddy KEJ, Ayebanengimote V. Evaluating the Readiness of Health Information Management Professionals for AI Integration in Healthcare Systems in Bayelsa State, Nigeria. Am J Health Res. 2026;14(5):219-233. doi: 10.11648/j.ajhr.20261405.11

Copy | Download

@article{10.11648/j.ajhr.20261405.11,
  author = {Kurokeyi Ebimene Japheth Teddy and Victor Ayebanengimote},
  title = {Evaluating the Readiness of Health Information Management Professionals for AI Integration in Healthcare Systems in Bayelsa State, Nigeria},
  journal = {American Journal of Health Research},
  volume = {14},
  number = {5},
  pages = {219-233},
  doi = {10.11648/j.ajhr.20261405.11},
  url = {https://doi.org/10.11648/j.ajhr.20261405.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajhr.20261405.11},
  abstract = {Artificial intelligence (AI) is revolutionizing healthcare delivery globally. But existing literature on the extent of AI computer literacy among respectively physicians in Nigeria as an evidence of its level of integration into healthcare is insufficient. This study was designed to evaluate the level of AI awareness and knowledge, digital competence, organizational readiness, professional development, and AI preparedness of healthcare provider (HIM professionals’) towards healthcare integration in Bayelsa State. The study adopted a descriptive cross-sectional survey design. The study population comprised 332 members of AHRIMPN Bayelsa chapter WhattsApp platform. From which 205 turned out to be the respondents and were analyzed with the use of structured 36-item self-administered questionnaire. Data were analyzed by descriptive statistics, Cronbach‘s alpha reliability analysis, Spearman‘s rank correlation, Mann Whitney U test, exploratory factor analysis, relative importance analysis, and multivariable regression at 5% significance level. The results showed moderate AI knowledge and awareness (3.052±0.116), digital competence (2.923±0.100), organizational readiness (3.141±0.124), professional development (3.143± 0.158) and AI readiness for healthcare integration (3.115±0.101). The instrument possessed Cronbach‘s α of 0.760 0.887 while Kaiser–Meyer–Olkin of 0.820 and significant Bartlett‘s Test of 19.155, P value = 0.000 validated the data for factor analysis. There were strong positive relationships between AI knowledge, digital competence and organizational readiness. But multivariable regression analysis revealed none of the observed four explanatory variables individually and significantly predicted AI readiness (p > 0.05). Respondents who had previous AI/digital health training had significantly better reported knowledge, digital competence, organizational readiness and professional development than those who did not have such training. The study revealed moderate AI readiness among healthcare professionals into AI-enabled health systems, though lack of the requisite AI skills and lack of standardized, continuous and obligatory AI capacity building interventions as well as poor investment in digital technology, robustness of organization, and health institution-related variations in knowledge, digital competence, organizational readiness and professional development are impeding AI integration into healthcare. The study concludes that continuous AI capacity development programmes, digital infrastructure investment, organizational autonomy and investment, and well-articulated AI curriculum into practitioner education, healthcare professional professional development programmes and organization-related accreditation are indispensable to successful, affordable and sustainable AI-enabled health systems.},
 year = {2026}
}

Copy | Download

TY  - JOUR
T1  - Evaluating the Readiness of Health Information Management Professionals for AI Integration in Healthcare Systems in Bayelsa State, Nigeria
AU  - Kurokeyi Ebimene Japheth Teddy
AU  - Victor Ayebanengimote
Y1  - 2026/09/22
PY  - 2026
N1  - https://doi.org/10.11648/j.ajhr.20261405.11
DO  - 10.11648/j.ajhr.20261405.11
T2  - American Journal of Health Research
JF  - American Journal of Health Research
JO  - American Journal of Health Research
SP  - 219
EP  - 233
PB  - Science Publishing Group
SN  - 2330-8796
UR  - https://doi.org/10.11648/j.ajhr.20261405.11
AB  - Artificial intelligence (AI) is revolutionizing healthcare delivery globally. But existing literature on the extent of AI computer literacy among respectively physicians in Nigeria as an evidence of its level of integration into healthcare is insufficient. This study was designed to evaluate the level of AI awareness and knowledge, digital competence, organizational readiness, professional development, and AI preparedness of healthcare provider (HIM professionals’) towards healthcare integration in Bayelsa State. The study adopted a descriptive cross-sectional survey design. The study population comprised 332 members of AHRIMPN Bayelsa chapter WhattsApp platform. From which 205 turned out to be the respondents and were analyzed with the use of structured 36-item self-administered questionnaire. Data were analyzed by descriptive statistics, Cronbach‘s alpha reliability analysis, Spearman‘s rank correlation, Mann Whitney U test, exploratory factor analysis, relative importance analysis, and multivariable regression at 5% significance level. The results showed moderate AI knowledge and awareness (3.052±0.116), digital competence (2.923±0.100), organizational readiness (3.141±0.124), professional development (3.143± 0.158) and AI readiness for healthcare integration (3.115±0.101). The instrument possessed Cronbach‘s α of 0.760 0.887 while Kaiser–Meyer–Olkin of 0.820 and significant Bartlett‘s Test of 19.155, P value = 0.000 validated the data for factor analysis. There were strong positive relationships between AI knowledge, digital competence and organizational readiness. But multivariable regression analysis revealed none of the observed four explanatory variables individually and significantly predicted AI readiness (p > 0.05). Respondents who had previous AI/digital health training had significantly better reported knowledge, digital competence, organizational readiness and professional development than those who did not have such training. The study revealed moderate AI readiness among healthcare professionals into AI-enabled health systems, though lack of the requisite AI skills and lack of standardized, continuous and obligatory AI capacity building interventions as well as poor investment in digital technology, robustness of organization, and health institution-related variations in knowledge, digital competence, organizational readiness and professional development are impeding AI integration into healthcare. The study concludes that continuous AI capacity development programmes, digital infrastructure investment, organizational autonomy and investment, and well-articulated AI curriculum into practitioner education, healthcare professional professional development programmes and organization-related accreditation are indispensable to successful, affordable and sustainable AI-enabled health systems.
VL  - 14
IS  - 5
ER  -

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