Manuscript Number : GISRRJ236515

A Conceptual Framework for Integrating HMO Data Analytics with Hospital Information Systems for Performance Improvement

Authors(3) :-Damilola Oluyemi Merotiwon, Opeyemi Olamide Akintimehin, Opeoluwa Oluwanifemi Akomolafe

Healthcare delivery systems globally are undergoing a paradigm shift toward data-centric models aimed at enhancing service quality, patient outcomes, and operational efficiency. Central to this evolution is the integration of Health Maintenance Organization (HMO) data analytics with Hospital Information Systems (HIS). This paper proposes a conceptual framework to harmonize these two data-rich environments to enable performance improvement in hospitals. Drawing upon best practices in data warehousing, real-time analytics, and health informatics interoperability standards, the framework aims to enhance decision-making, reduce inefficiencies, and drive proactive care strategies. The research combines qualitative stakeholder mapping, system architecture modeling, and case study synthesis across different health management platforms. Findings indicate that effective integration requires a multi-layered approach that encompasses governance, data standardization, workflow alignment, and analytics maturity. Recommendations for implementation are contextualized for both public and private healthcare ecosystems. The framework establishes a foundational model for future empirical studies and practical deployments.

Authors and Affiliations

Damilola Oluyemi Merotiwon
Independent Researcher, Texas. USA
Opeyemi Olamide Akintimehin
Department of Human Nutrition and Dietetics, University of Ibadan, Nigeria
Opeoluwa Oluwanifemi Akomolafe
Independent Researcher, UK

HMO Data, Hospital Systems, Integration, Data Analytics, Healthcare Performance, Interoperability

  1. C. Chianumba, N. Ikhalea, A. Y. Mustapha, A. Y. Forkuo, and D. Osamika, “Exploring the Role of AI and Machine Learning in Improving Healthcare Diagnostics and Personalized Medicine,” J. Front. Multidiscip. Res., vol. 4, no. 1, pp. 177–182, 2023, doi: 10.54660/.IJFMR.2023.4.1.177-182.
  2. O. KOLAWOLE, A. Y. MUSTAPHA, A. O. MBATA, B. O. TOMOH, A. Y. FORKUO, and M. C. KELVIN-AGWU, “Evaluating the Effectiveness of Community-Based Health Education Programs in Preventing Non-Communicable Diseases,” J. Name Missing, 2023, Online]. Available: https://www.researchgate.net/profile/Adelaide-Forkuo/publication/390193068_Evaluating_the_Effectiveness_of_Community-Based_Health_Education_Programs_in_Preventing_Non-Communicable_Diseases/links/67e3cff2e62c604a0d15c649/Evaluating-the-Effectiveness-of-Community-Based-Health-Education-Programs-in-Preventing-Non-Communicable-Diseases.pdf
  3. R. Morain, N. E. Kass, and C. Grossmann, “What allows a health care system to become a learning health care system: Results from interviews with health system leaders,” Learn. Health Syst., vol. 1, no. 1, p. e10015, Jan. 2017, doi: 10.1002/lrh2.10015.
  4. A. Eckman, C. A. Bennett, J. H. Kaufman, and J. W. Tenner, “Varieties of interoperability in the transformation of the health-care information infrastructure,” IBM Syst. J., vol. 46, no. 1, pp. 19–41, 2007.
  5. C. Kelvin-Agwu, A. Y. Mustapha, A. O. Mbata, B. O. Tomoh, and A. Y. Forkuo, “Development of AI-Assisted Wearable Devices for Early Detection of Respiratory Diseases,” Int. J. Multidiscip. Res. Growth Eval., vol. 4, no. 1, pp. 967–974, 2023, doi: 10.54660/.IJMRGE.2023.4.1.967-974.
  6. Ikhalea, E. C. Chianumba, A. Y. Mustapha, and A. Y. Forkuo, “Developing a Knowledge Graph for Integrating Health Data from Multiple Sources,” 2023, Online]. Available: https://www.researchgate.net/profile/Anfo-Pub-2/publication/392194359_Developing_a_Knowledge_Graph_for_Integrating_Health_Data_from_Multiple_Sources/links/683856708a76251f22ea354d/Developing-a-Knowledge-Graph-for-Integrating-Health-Data-from-Multiple-Sources.pdf
  7. Ben-Assuli, I. Shabtai, and M. Leshno, “Using electronic health record systems to optimize admission decisions: The Creatinine case study,” Health Informatics J., vol. 21, no. 1, pp. 73–88, Mar. 2015, doi: 10.1177/1460458213503646.
  8. Jaleel, T. Mahmood, M. A. Hassan, G. Bano, and S. K. Khurshid, “Towards medical data interoperability through collaboration of healthcare devices,” Ieee Access, vol. 8, pp. 132302–132319, 2020.
  9. Okolo, “Cargo Security Examination and it’s effect on National Security in the United State,” Int. J. Adv. Res. Innov. Ideas Educ., vol. 9, no. 2, pp. 1380–1388, 2023.
  10. “Blockchain in Supply Chain Transparency: A Conceptual Framework for Real-Time Data Tracking and Reporting Using Blockchain and AI.” Online]. Available: https://scholar.google.com/citations?view_op=view_citation&hl=en&user=BuNCiJUAAAAJ&citation_for_view=BuNCiJUAAAAJ:UeHWp8X0CEIC
  11. Azarm, R. Meehan, and C. Kuziemsky, “Towards a universal patient-centric health record sharing platform,” Health Policy Technol., vol. 12, no. 4, p. 100819, 2023.
  12. C. Okolo, E. A. Etukudoh, O. Ogunwole, G. O. Osho, and J. O. Basiru, “Advances in Cyber-Physical Resilience of Transportation Infrastructure in Emerging Economies and Coastal Regions,” 2023, Online]. Available: https://www.researchgate.net/profile/Olufunmilayo-Ogunwole/publication/391018430_Advances_in_Cyber-Physical_Resilience_of_Transportation_Infrastructure_in_Emerging_Economies_and_Coastal_Regions/links/68083261bd3f1930dd630c43/Advances-in-Cyber-Physical-Resilience-of-Transportation-Infrastructure-in-Emerging-Economies-and-Coastal-Regions.pdf
  13. C. KELVIN-AGWU, A. Y. MUSTAPHA, A. O. MBATA, B. O. TOMOH, A. YEBOAH, and T. O. K. FORKUO, “A Policy Framework for Strengthening Public Health Surveillance Systems in Emerging Economies,” J. Name Missing, 2023, Online]. Available: https://www.researchgate.net/profile/Adelaide-Forkuo/publication/390129171_A_Policy_Framework_for_Strengthening_Public_Health_Surveillance_Systems_in_Emerging_Economies/links/67e19bafe62c604a0d11c129/A-Policy-Framework-for-Strengthening-Public-Health-Surveillance-Systems-in-Emerging-Economies.pdf
  14. Ben-Assuli, I. Shabtai, and M. Leshno, “The influence of EHR components on admission decisions,” Health Technol., vol. 3, no. 1, pp. 29–35, Mar. 2013, doi: 10.1007/s12553-013-0039-6.
  15. Ben-Assuli, I. Shabtai, and M. Leshno, “The impact of EHR and HIE on reducing avoidable admissions: controlling main differential diagnoses,” BMC Med. Inform. Decis. Mak., vol. 13, no. 1, p. 49, Dec. 2013, doi: 10.1186/1472-6947-13-49.
  16. Y. Mustapha, N. Ikhalea, E. C. Chianumba, and A. Y. Forkuo, “A Model for Integrating AI and Big Data to Predict Epidemic Outbreaks,” J. Front. Multidiscip. Res., vol. 4, no. 1, pp. 157–176, 2023, doi: 10.54660/.IJFMR.2023.4.1.157-176.
  17. Komi, “A Conceptual Model for Hybrid Telemedicine Deployment in Faith-Based Health Programs Across Sub-Saharan Africa.” Online]. Available: https://www.researchgate.net/profile/Leesi-Komi/publication/391395676_A_Conceptual_Model_for_Hybrid_Telemedicine_Deployment_in_Faith-Based_Health_Programs_Across_Sub-Saharan_Africa/links/6814ee59bfbe974b23c1f369/A-Conceptual-Model-for-Hybrid-Telemedicine-Deployment-in-Faith-Based-Health-Programs-Across-Sub-Saharan-Africa.pdf
  18. R. Ross et al., “The HMO research network virtual data warehouse: a public data model to support collaboration,” Egems, vol. 2, no. 1, p. 1049, 2014.
  19. M. Judd, E. B. Sloane, and R. J. Silva, “The digital hospital of the 21th century, and information systems management,” in Clinical Engineering Handbook, Elsevier, 2020, pp. 530–542. Online]. Available: https://www.sciencedirect.com/science/article/pii/B978012813467200081X
  20. C. C. A. Y. Forkuo, “A Conceptual Framework for AI in Health Systems: Enhancing Diagnosis and Treatment.” Online]. Available: https://www.researchgate.net/profile/Ernest-Chianumba/publication/390847834_A_Conceptual_Framework_for_AI_in_Health_Systems_Enhancing_Diagnosis_and_Treatment/links/68008737bfbe974b23aaee3a/A-Conceptual-Framework-for-AI-in-Health-Systems-Enhancing-Diagnosis-and-Treatment.pdf
  21. Chaudhry et al., “Systematic Review: Impact of Health Information Technology on Quality, Efficiency, and Costs of Medical Care,” Ann. Intern. Med., vol. 144, no. 10, pp. 742–752, May 2006, doi: 10.7326/0003-4819-144-10-200605160-00125.
  22. Ernest Chinonso Chianumba, Nura Ikhalea, Ashiata Yetunde Mustapha, Adelaide Yeboah Forkuo, “A Conceptual Framework for AI in Health Systems: Enhancing Diagnosis and Treatment.” Online]. Available: https://scholar.google.com/citations?view_op=view_citation&hl=en&user=qAHE9lsAAAAJ&cstart=20&pagesize=80&citation_for_view=qAHE9lsAAAAJ:_FxGoFyzp5QC
  23. C. Chianumba, N. Ikhalea, A. Y. Mustapha, A. Y. Forkuo, and D. Osamika, “Framework for Using Behavioral Science and Public Health Data to Address Healthcare Inequality and Vaccine Hesitancy,” J. Front. Multidiscip. Res., vol. 4, no. 1, pp. 183–187, 2023, doi: 10.54660/.IJFMR.2023.4.1.183-187.
  24. R. Almeida, O. Fajarda, A. Pereira, and J. L. Oliveira, “Strategies to Access Patient Clinical Data from Distributed Databases.,” in HEALTHINF, 2019, pp. 466–473. Online]. Available: https://pdfs.semanticscholar.org/1185/84f6ce2846686832681d5140cdd54a9e33f9.pdf
  25. Chukwu, L. Garg, N. Obande-Ogbuinya, and V. K. Chattu, “Standardizing primary health care referral data sets in Nigeria: practitioners’ survey, form reviews, and profiling of Fast Healthcare Interoperability Resources (FHIR),” JMIR Form. Res., vol. 6, no. 7, p. e28510, 2022.
  26. O. Kolawole, A. Y. Mustapha, A. O. Mbata, B. O. Tomoh, A. Y. Forkuo, and M. C. Kelvin-Agwu, “Innovative Strategies for Reducing Antimicrobial Resistance: A Review of Global Policy and Practice,” J. Front. Multidiscip. Res., vol. 4, no. 1, pp. 25–38, 2023, doi: 10.54660/.IJFMR.2023.4.1.25-38.
  27. E. Umberfield, J. Bowie, A. S. Kanter, B. E. Dixon, and E. F. Tallman, “Standardizing health care data across an enterprise,” in Health Information Exchange, Elsevier, 2023, pp. 237–255. Online]. Available: https://www.sciencedirect.com/science/article/pii/B9780323908023000241
  28. C. Kelvin-Agwu, A. Y. Mustapha, A. O. Mbata, B. O. Tomoh, and A. Y. Forkuo, “Modeling a Low-Cost, Portable Hemodialysis Device for Underserved Communities,” J. Front. Multidiscip. Res., vol. 4, no. 1, pp. 39–47, 2023, doi: 10.54660/.IJFMR.2023.4.1.39-47.
  29. Osamika, B. S. Adelusi, M. C. Kelvin-Agwu, A. Y. Mustapha, and N. Ikhalea, “Predictive Analytics for Chronic Respiratory Diseases Using Big Data: Opportunities and Challenges,” Int. J. Multidiscip. Res. Growth Eval., vol. 4, no. 1, pp. 1084–1094, 2023, doi: 10.54660/.IJMRGE.2023.4.1.1084-1094.
  30. Kimberly and M. Rossman, “Semantic Technologies for Data Analysis in Health Care”, Online]. Available: https://www.academia.edu/download/100600695/978-3-319-46547-0_34.pdf
  31. Song, K. Liu, R. Abromitis, and T. L. Schleyer, “Reusing electronic patient data for dental clinical research: a review of current status,” J. Dent., vol. 41, no. 12, pp. 1148–1163, 2013.
  32. Ernest Chinonso Chianumba1, , Adelaide Yeboah Forkuo2, , Ashiata Yetunde Mustapha3, , Damilola Osamika4, , and Leesi Saturday Komi5, “Systematic Review of Maternal Mortality Reduction Strategies Using Technology-Enabled Interventions in Rural Clinics,” Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol..
  33. C. Okolo, E. A. Etukudoh, O. Ogunwole, G. O. Osho, and J. O. Basiru, “Strategic Approaches to Building Digital Workforce Capacity for Cybersecure Transportation Operations and Policy Compliance,” 2023, Online]. Available: https://www.researchgate.net/profile/Olufunmilayo-Ogunwole/publication/391017910_Strategic_Approaches_to_Building_Digital_Workforce_Capacity_for_Cybersecure_Transportation_Operations_and_Policy_Compliance/links/6808355cded433155736a22d/Strategic-Approaches-to-Building-Digital-Workforce-Capacity-for-Cybersecure-Transportation-Operations-and-Policy-Compliance.pdf
  34. T. Rosenbloom, R. J. Carroll, J. L. Warner, M. E. Matheny, and J. C. Denny, “Representing Knowledge Consistently Across Health Systems,” Yearb. Med. Inform., vol. 26, no. 01, pp. 139–147, 2017, doi: 10.15265/IY-2017-018.
  35. Justo et al., “Real-world evidence in healthcare decision making: global trends and case studies from Latin America,” Value Health, vol. 22, no. 6, pp. 739–749, 2019.
  36. M. Oluoha, A. Odeshina, O. Reis, F. Okpeke, V. Attipoe, and O. H. Orieno, “Optimizing Business Decision-Making with Advanced Data Analytics Techniques,” Iconic Res. Eng. J., vol. 6, no. 5, pp. 184–203, Dec. 2022.
  37. Gerber Ayala and Topaz Maxim (Max), “Promoting Meaningful Use of Health Information Technology in Israel: Ministry of Health Vision,” in Studies in Health Technology and Informatics, IOS Press, 2014. doi: 10.3233/978-1-61499-415-2-108.
  38. Oluchukwu Modesta Oluoha, Abisola Odeshina, Oluwatosin Reis, and Friday Okpeke, “(PDF) A Unified Framework for Risk-Based Access Control and Identity Management in Compliance-Critical Environments,” ResearchGate, May 2025, doi: 10.54660/.IJFMR.2022.3.1.23-34.
  39. A. Taylor et al., “Prediction of In‐hospital Mortality in Emergency Department Patients With Sepsis: A Local Big Data–Driven, Machine Learning Approach,” Acad. Emerg. Med., vol. 23, no. 3, pp. 269–278, Mar. 2016, doi: 10.1111/acem.12876.
  40. C. Ogeawuchi, O. E. Akpe, A. A. Abayomi, O. A. Agboola, E. Ogbuefi, and S. Owoade, “Systematic Review of Advanced Data Governance Strategies for Securing Cloud-Based Data Warehouses and Pipelines,” Iconic Res. Eng. J., vol. 6, no. 1, pp. 784–794, Jul. 2022.
  41. Ogbuefi, A. C. Mgbame, O. E. Akpe, A. A. Abayomi, and O. O. Adeyelu, “Affordable Automation: Leveraging Cloud-Based BI Systems for SME Sustainability,” Iconic Res. Eng. J., vol. 5, no. 12, pp. 489–505, Jun. 2022.
  42. Comte et al., “Network and Systems Medicine: Position Paper of the European Collaboration on Science and Technology Action on Open Multiscale Systems Medicine,” Netw. Syst. Med., vol. 3, no. 1, pp. 67–90, Jul. 2020, doi: 10.1089/nsm.2020.0004.
  43. Demirbaga and G. S. Aujla, “MapChain: A blockchain-based verifiable healthcare service management in IoT-based big data ecosystem,” IEEE Trans. Netw. Serv. Manag., vol. 19, no. 4, pp. 3896–3907, 2022.
  44. Ogbuefi, A. C. Mgbame, O. E. Akpe, A. A. Abayomi, and O. O. Adeyelu, “Data Democratization: Making Advanced Analytics Accessible for Micro and Small Enterprises,” Int. J. Manag. Organ. Res., vol. 1, no. 1, pp. 199–212, 2022, doi: 10.54660/IJMOR.2022.1.1.199-212.
  45. A. Balch et al., “Machine learning–enabled clinical information systems using fast healthcare interoperability resources data standards: scoping review,” JMIR Med. Inform., vol. 11, p. e48297, 2023.
  46. C. Mgbame, O. E. Akpe, A. A. Abayomi, E. Ogbuefi, and O. O. Adeyelu, “Building Data-Driven Resilience in Small Businesses: A Framework for Operational Intelligence,” Iconic Res. Eng. J., vol. 5, no. 9, pp. 695–712, Mar. 2022.
  47. C. Mgbame, O. E. Akpe, A. A. Abayomi, E. Ogbuefi, and O. O. Adeyelu, “Developing Low-Cost Dashboards for Business Process Optimization in SMEs,” Int. J. Manag. Organ. Res., vol. 1, no. 1, pp. 214–230, 2022, doi: 10.54660/IJMOR.2022.1.1.214-230.
  48. Joseph Ozigi Basiru, Chinelo Linda Ejiofor, Ekene Cynthia Onukwulu, and Rita Uchenna Attah, “Streamlining procurement processes in engineering and construction companies: A comparative analysis of best practices,” Magna Sci. Adv. Res. Rev., vol. 6, no. 1, pp. 118–135, Oct. 2022, doi: 10.30574/msarr.2022.6.1.0073.
  49. E. Akpe, A. C. Mgbame, E. Ogbuefi, A. A. Abayomi, and O. O. Adeyelu, “The Role of Adaptive BI in Enhancing SME Agility During Economic Disruptions,” Int. J. Manag. Organ. Res., vol. 1, no. 1, pp. 183–198, 2022, doi: 10.54660/IJMOR.2022.1.1.183-198.
  50. Weeks and R. Pardee, “Learning to share health care data: a brief timeline of influential common data models and distributed health data networks in US health care research,” EGEMs, vol. 7, no. 1, p. 4, 2019.
  51. GAMBO, O. OLUWAGBEMI, and P. ACHIMUGU, “Lack of interoperable health information systems in developing countries: an impact analysis,” J. Health Inform. Dev. Ctries., vol. 5, no. 1, 2011, Online]. Available: https://mail.jhidc.org/index.php/jhidc/article/view/60
  52. A. Abayomi, B. C. Ubanadu, A. I. Daraojimba, O. A. Agboola, E. Ogbuefi, and S. Owoade, “A Conceptual Framework for Real-Time Data Analytics and Decision-Making in Cloud-Optimized Business Intelligence Systems,” Iconic Res. Eng. J., vol. 5, no. 9, pp. 713–722, Mar. 2022.
  53. “Systematic Review of Mobile Health (mHealth) Applications for Infectious Disease Surveillance in Developing Countries.” Online]. Available: https://scholar.google.com/citations?view_op=view_citation&hl=en&user=pZekPIgAAAAJ&pagesize=80&citation_for_view=pZekPIgAAAAJ:M3NEmzRMIkIC
  54. “Optimizing Smart Contract Development: A Practical Model for Gasless Transactions via Facial Recognition in Blockchain.” Online]. Available: https://scholar.google.com/citations?view_op=view_citation&hl=en&user=BuNCiJUAAAAJ&citation_for_view=BuNCiJUAAAAJ:qjMakFHDy7sC
  55. C. Okolo, E. A. Etukudoh, O. Ogunwole, G. O. Osho, and J. O. Basiru, “Policy-Oriented Framework for Multi-Agency Data Integration Across National Transportation and Infrastructure Systems,” J. Front. Multidiscip. Res., vol. 3, no. 1, pp. 140–149, 2022, doi: 10.54660/.IJFMR.2022.3.1.140-149.
  56. Raghupathi and Kesh, “Interoperable Electronic Health Records Design:Towards a Service-Oriented Architecture,” E-Serv. J., vol. 5, no. 3, p. 39, 2007, doi: 10.2979/esj.2007.5.3.39.
  57. C. Okolo, E. A. Etukudoh, O. Ogunwole, G. O. Osho, and J. O. Basiru, “Advances in Integrated Geographic Information Systems and AI Surveillance for Real-Time Transportation Threat Monitoring,” J. Front. Multidiscip. Res., vol. 6, no. 1, pp. 130–139, 2022, doi: 10.54660/.IJFMR.2022.3.1.130-139.
  58. C. Séror, “Internet infrastructures and health care systems: a qualitative comparative analysis on networks and markets in the British National Health Service and Kaiser Permanente,” J. Med. Internet Res., vol. 4, no. 3, p. e872, 2002.
  59. Y. Mustapha, N. Ikhalea, E. C. Chianumba, and A. Y. Forkuo, “Developing an AI-Powered Predictive Model for Mental Health Disorder Diagnosis Using Electronic Health Records,” Int. J. Multidiscip. Res. Growth Eval., vol. 3, no. 1, pp. 914–931, 2022, doi: 10.54660/.IJMRGE.2022.3.1.914-931.
  60. Tan et al., “Including social and behavioral determinants in predictive models: trends, challenges, and opportunities,” JMIR Med. Inform., vol. 8, no. 9, p. e18084, 2020.
  61. Y. Mustapha, E. C. Chianumba, A. Y. Forkuo, D. Osamika, and L. S. Komi, “Systematic Review of Mobile Health (mHealth) Applications for Infectious Disease Surveillance in Developing Countries,” Methodology, p. 66, 2018.
  62. I. Ogunyemi, D. Meeker, H.-E. Kim, N. Ashish, S. Farzaneh, and A. Boxwala, “Identifying appropriate reference data models for comparative effectiveness research (CER) studies based on data from clinical information systems,” Med. Care, vol. 51, pp. S45–S52, 2013.
  63. S. KOMI, E. C. CHIANUMBA, A. YEBOAH, D. O. FORKUO, and A. Y. MUSTAPHA, “A Conceptual Framework for Training Community Health Workers Through Virtual Public Health Education Modules,” IRE J., vol. 5, no. 11, pp. 332–335, 2022.
  64. R. Mori, M. Mazzeo, G. Mercurio, and R. Verbicaro, “Holistic health: predicting our data future (from inter-operability among systems to co-operability among people),” Int. J. Med. Inf., vol. 82, no. 4, pp. e14–e28, 2013.
  65. S. KOMI, E. C. CHIANUMBA, A. YEBOAH, D. O. FORKUO, and A. Y. MUSTAPHA, “Advances in Community-Led Digital Health Strategies for Expanding Access in Rural and Underserved Populations,” 2021, Online]. Available: https://www.researchgate.net/profile/Leesi-Komi/publication/391486838_Advances_in_Community-Led_Digital_Health_Strategies_for_Expanding_Access_in_Rural_and_Underserved_Populations/links/681a42d5d1054b0207eab997/Advances-in-Community-Led-Digital-Health-Strategies-for-Expanding-Access-in-Rural-and-Underserved-Populations.pdf
  66. S. Komi, E. C. Chianumba, A. Y. Forkuo, D. Osamika, and A. Y. Mustapha, “A conceptual model for delivering telemedicine to internally displaced populations in resource-limited regions,” 2022, Online]. Available: https://www.allmultidisciplinaryjournal.com/uploads/archives/20250513150245_MGE-2025-3-036.1.pdf
  67. A. Mkperedem, P. Ogunlade, C. Igbolekwu, O. Arisukwu, A. O. Afolabi, and R. A. Adedayo, “Healthcare service delivery perception among NHIS-HMO enrollees in Lagos hospitals,” Humanit. Soc. Sci. Commun., vol. 10, no. 1, pp. 1–11, 2023.
  68. Tan and J. Tan, “Healthcare information technologies in an era of healthcare reform: A complex adaptive system perspective,” Health Care Adm. Manag. Organ. Deliv. Syst. Manag. Organ. Deliv. Syst., p. 359, 2010.
  69. S. Komi, E. C. Chianumba, A. Y. Forkuo, D. Osamika, and A. Y. Mustapha, “A Conceptual Framework for Training Community Health Workers Through Virtual Public Health Education Modules,” vol. 5, no. 11, 2022.
  70. C. Chianumba, N. Ikhalea, A. Y. Mustapha, A. Y. Forkuo, and D. Osamika, “International Journal of Social Science Exceptional Research,” 2022, Online]. Available: https://www.researchgate.net/profile/Ernest-Chianumba/publication/390917280_Developing_a_Predictive_Model_for_Healthcare_Compliance_Risk_Management_and_Fraud_Detection_Using_Data_Analytics/links/680255b3bd3f1930dd5ffd32/Developing-a-Predictive-Model-for-Healthcare-Compliance-Risk-Management-and-Fraud-Detection-Using-Data-Analytics.pdf
  71. C. Chianumba, N. Ikhalea, A. Y. Mustapha, A. Y. Forkuo, and D. Osamika, “Integrating AI, Blockchain, and Big Data to Strengthen Healthcare Data Security, Privacy, and Patient Outcomes,” J. Front. Multidiscip. Res., vol. 3, no. 1, pp. 124–129, 2022, doi: 10.54660/.IJFMR.2022.3.1.124-129.
  72. C. Chianumba, N. Ikhalea, A. Y. Mustapha, A. Y. Forkuo, and D. Osamika, “Developing a Predictive Model for Healthcare Compliance, Risk Management, and Fraud Detection Using Data Analytics,” Int. J. Soc. Sci. Except. Res., vol. 1, no. 1, pp. 232–238, 2022, doi: 10.54660/IJSSER.2022.1.1.232-238.
  73. Colombo, J. Oderkirk, and L. Slawomirski, “Health Information Systems, Electronic Medical Records, and Big Data in Global Healthcare: Progress and Challenges in OECD Countries,” in Handbook of Global Health, R. Haring, I. Kickbusch, D. Ganten, and M. Moeti, Eds., Cham: Springer International Publishing, 2020, pp. 1–31. doi: 10.1007/978-3-030-05325-3_71-1.
  74. J. Holmgren, M. Esdar, J. Hüsers, and J. Coutinho-Almeida, “Health Information Exchange: Understanding the Policy Landscape and Future of Data Interoperability,” Yearb. Med. Inform., vol. 32, no. 01, pp. 184–194, Aug. 2023, doi: 10.1055/s-0043-1768719.
  75. C. Chianumba, N. Ikhalea, A. Y. Mustapha, and A. Y. Forkuo, “A Conceptual Model for Addressing Healthcare Inequality Using AI-Based Decision Support Systems,” 2022, Online]. Available: https://www.researchgate.net/profile/Ernest-Chianumba/publication/390839225_A_Conceptual_Model_for_Addressing_Healthcare_Inequality_Using_AI-Based_Decision_Support_Systems/links/680084b3d1054b0207d4cee1/A-Conceptual-Model-for-Addressing-Healthcare-Inequality-Using-AI-Based-Decision-Support-Systems.pdf
  76. S. Adelusi, D. Osamika, M. C. Kelvin-Agwu, A. Y. Mustapha, and N. Ikhalea, “A Deep Learning Approach to Predicting Diabetes Mellitus Using Electronic Health Records,” J Front Multidiscip Res, vol. 3, no. 1, pp. 47–56, 2022.
  77. R. Miller and C. Tucker, “Health information exchange, system size and information silos,” J. Health Econ., vol. 33, pp. 28–42, 2014.
  78. L. Braunstein, Health Informatics on FHIR: How HL7’s New API is Transforming Healthcare. Cham: Springer International Publishing, 2018. doi: 10.1007/978-3-319-93414-3.
  79. C. Chianumba, N. Ikhalea, A. Y. Mustapha, and A. Y. Forkuo, “Developing a Framework for Using AI in Personalized Medicine to Optimize Treatment Plans,” J. Front. Multidiscip. Res., vol. 3, no. 1, pp. 57–71, 2022, doi: 10.54660/.IJFMR.2022.3.1.57-71.
  80. Adelaide Yeboah Forkuo, Ernest Chinonso Chianumba, Ashiata Yetunde Mustapha, Damilola Osamika, Leesi Saturday Komi, “Advances in digital diagnostics and virtual care platforms for primary healthcare delivery in West Africa.” Online]. Available: https://scholar.google.com/citations?view_op=view_citation&hl=en&user=pZekPIgAAAAJ&pagesize=80&citation_for_view=pZekPIgAAAAJ:maZDTaKrznsC
  81. C. Chianumba, N. Ikhalea, A. Y. Mustapha, A. Y. Forkuo, and D. Osamika, “A Conceptual Framework for Leveraging Big Data and AI in Enhancing Healthcare Delivery and Public Health Policy,” vol. 5, no. 6, 2021.
  82. Kazmierska et al., “From multisource data to clinical decision aids in radiation oncology: the need for a clinical data science community,” Radiother. Oncol., vol. 153, pp. 43–54, 2020.
  83. Frid et al., “Evaluation of OMOP CDM, i2b2 and ICGC ARGO for supporting data harmonization in a breast cancer use case of a multicentric European AI project,” J. Biomed. Inform., vol. 147, p. 104505, 2023.
  84. S. KOMI, E. C. CHIANUMBA, A. YEBOAH, D. O. FORKUO, and A. Y. MUSTAPHA, “A Conceptual Framework for Telehealth Integration in Conflict Zones and Post-Disaster Public Health Responses,” 2021, Online]. Available: https://www.researchgate.net/profile/Ernest-Chianumba/publication/391481410_A_Conceptual_Framework_for_Telehealth_Integration_in_Conflict_Zones_and_Post-Disaster_Public_Health_Responses/links/6819ccb7df0e3f544f5224b1/A-Conceptual-Framework-for-Telehealth-Integration-in-Conflict-Zones-and-Post-Disaster-Public-Health-Responses.pdf
  85. Garza, G. Del Fiol, J. Tenenbaum, A. Walden, and M. N. Zozus, “Evaluating common data models for use with a longitudinal community registry,” J. Biomed. Inform., vol. 64, pp. 333–341, 2016.
  86. “A Conceptual Framework for Telehealth Integration in Conflict Zones and Post-Disaster Public Health Responses.” Online]. Available: https://scholar.google.com/citations?view_op=view_citation&hl=en&user=pZekPIgAAAAJ&pagesize=80&citation_for_view=pZekPIgAAAAJ:4JMBOYKVnBMC
  87. L. Richesson et al., “Electronic health records based phenotyping in next-generation clinical trials: a perspective from the NIH Health Care Systems Collaboratory,” J. Am. Med. Inform. Assoc., vol. 20, no. e2, pp. e226–e231, 2013.
  88. LEESI SATURDAY KOMI, ERNEST CHINONSO CHIANUMBA, ADELAIDE YEBOAH, DAMILOLA OSAMIKA FORKUO, ASHIATA YETUNDE MUSTAPHA, “Advances in Community-Led Digital Health Strategies for Expanding Access in Rural and Underserved Populations.” Online]. Available: https://scholar.google.com/citations?view_op=view_citation&hl=en&user=pZekPIgAAAAJ&pagesize=80&citation_for_view=pZekPIgAAAAJ:HDshCWvjkbEC
  89. Everson, W. Barker, and V. Patel, “Electronic health record developer market segmentation contributes to divide in physician interoperable exchange,” J. Am. Med. Inform. Assoc., vol. 29, no. 7, pp. 1200–1207, 2022.
  90. S. KOMI, E. C. CHIANUMBA, A. YEBOAH, D. O. FORKUO, and A. Y. MUSTAPHA, “Advances in Public Health Outreach Through Mobile Clinics and Faith-Based Community Engagement in Africa,” 2021, Online]. Available: https://www.researchgate.net/profile/Ernest-Chianumba/publication/391481430_Advances_in_Public_Health_Outreach_Through_Mobile_Clinics_and_Faith-Based_Community_Engagement_in_Africa/links/6819c990df0e3f544f522459/Advances-in-Public-Health-Outreach-Through-Mobile-Clinics-and-Faith-Based-Community-Engagement-in-Africa.pdf
  91. M. McGinnis, B. Powers, and C. Grossmann, “Digital infrastructure for the learning health system: the foundation for continuous improvement in health and health care: workshop series summary,” 2011, Online]. Available: https://books.google.com/books?hl=en&lr=&id=cHrWk6kEh1EC&oi=fnd&pg=PR1&dq=HMO+data,+hospital+systems,+integration,+data+analytics,+healthcare+performance,+interoperability&ots=jfsEfqQ5kD&sig=mtQmaW8CfCgSHWZgTWQMIaHyUiU
  92. Ashiata Yetunde Mustapha, Ernest Chinonso Chianumba, Adelaide Yeboah Forkuo, Damilola Osamika, Leesi Saturday Komi, “Systematic Review of Digital Maternal Health Education Interventions in Low-Infrastructure Environments.” Online]. Available: https://scholar.google.com/citations?view_op=view_citation&hl=en&user=pZekPIgAAAAJ&pagesize=80&citation_for_view=pZekPIgAAAAJ:JV2RwH3_ST0C
  93. D. Bennett et al., “Data science for child health,” J. Pediatr., vol. 208, p. 12, 2019.
  94. Y. Mustapha, E. C. Chianumba, A. Y. Forkuo, D. Osamika, and L. S. Komi, “Systematic Review of Digital Maternal Health Education Interventions in Low-Infrastructure Environments,” Int. J. Multidiscip. Res. Growth Eval., vol. 2, no. 1, pp. 909–918, 2021, doi: 10.54660/.IJMRGE.2021.2.1.909-918.
  95. Bourret and G. Salzano, “Data for decision making in networked health,” Data Sci. J., vol. 5, pp. 64–78, 2006.
  96. Y. Mustapha, E. C. Chianumba, A. Y. Forkuo, D. Osamika, and L. S. Komi, “Systematic Review of Mobile Health (mHealth) Applications for Infectious Disease Surveillance in Developing Countries,” Methodology, p. 66, 2018.
  97. Toh, R. Platt, J. F. Steiner, and J. S. Brown, “Comparative-Effectiveness Research in Distributed Health Data Networks,” Clin. Pharmacol. Ther., vol. 90, no. 6, pp. 883–887, Dec. 2011, doi: 10.1038/clpt.2011.236.
  98. Musa Adekunle Adewoyin, Enoch Oluwadunmininu Ogunnowo, Joyce Efekpogua Fiemotongha, Thompson Odion Igunma, Adeniyi K Adeleke, “A Conceptual Framework for Dynamic Mechanical Analysis in High-Performance Material Selection.” Accessed: May 31, 2025. Online]. Available: https://scholar.google.com/citations?view_op=view_citation&hl=en&user=6SQ3ZwQAAAAJ&citation_for_view=6SQ3ZwQAAAAJ:eQOLeE2rZwMC
  99. P. Doria-Rose et al., “Collaborating on data, science, and infrastructure: the 20-year journey of the cancer research network,” eGEMs, vol. 7, no. 1, p. 7, 2019.
  100. U. Ojika, W. O. Owobu, O. A. Abieba, O. J. Esan, B. C. Ubamadu, and A. I. Daraojimba, “The Role of AI in Cybersecurity: A Cross-Industry Model for Integrating Machine Learning and Data Analysis for Improved Threat Detection”.
  101. A. Adeogun and M. Faezipour, “Big Data in Healthcare: Acquisition, Management, and Visualization Using System Dynamics,” in 2023 International Conference on Computational Science and Computational Intelligence (CSCI), IEEE, 2023, pp. 611–618. Online]. Available: https://ieeexplore.ieee.org/abstract/document/10590527/
  102. I. Daraojimba, J. C. Ogeawuchi, A. A. Abayomi, O. A. Agboola, and E. Ogbuefi, “Systematic Review of Serverless Architectures and Business Process Optimization,” Iconic Res. Eng. J., vol. 5, no. 4, pp. 284–309, Oct. 2021.
  103. Raghupathi and V. Raghupathi, “Big data analytics in healthcare: promise and potential,” Health Inf. Sci. Syst., vol. 2, no. 1, p. 3, Dec. 2014, doi: 10.1186/2047-2501-2-3.
  104. Sinha, D. W. Garcia, B. Kumar, and P. Banerjee, “Application of Big Data Analytics and Internet of Medical Things (IoMT) in Healthcare with View of Explainable Artificial Intelligence: A Survey,” in Interpretable Cognitive Internet of Things for Healthcare, U. Kose, D. Gupta, A. Khanna, and J. J. P. C. Rodrigues, Eds., in Internet of Things. , Cham: Springer International Publishing, 2023, pp. 129–163. doi: 10.1007/978-3-031-08637-3_8.
  105. T. Penberthy, D. R. Rivera, J. L. Lund, M. A. Bruno, and A. Meyer, “An overview of real‐world data sources for oncology and considerations for research,” CA. Cancer J. Clin., vol. 72, no. 3, pp. 287–300, May 2022, doi: 10.3322/caac.21714.
  106. Enoch Oluwabusayo Alonge 1*, Nsisong Louis Eyo-Udo 2, , Ubamadu Bright Chibunna 3, , Andrew Ifesinachi Daraojimba 4, , Emmanuel Damilare Balogun 5, and , Kolade Olusola Ogunsola, “Enhancing Data Security with Machine Learning: A Study on Fraud Detection Algorithms.” Online]. Available: https://www.multidisciplinaryfrontiers.com/uploads/archives/20250326183447_FMR-2025-1-021.1.pdf
  107. Heart, O. Ben-Assuli, and I. Shabtai, “A review of PHR, EMR and EHR integration: A more personalized healthcare and public health policy,” Health Policy Technol., vol. 6, no. 1, pp. 20–25, 2017.
  108. M. Ndukwe and B. U. Ezeoha, “A hybrid of electronic health record (EHR) system and health maintenance organizations (HMOs): A sure way to improve healthcare system in Nigeria,” Int. J. Comput. Sci. Math. Theory, vol. 4, no. 3, pp. 14–22, 2018.
  109. Datta et al., “A new paradigm for accelerating clinical data science at Stanford Medicine,” Mar. 17, 2020, arXiv: arXiv:2003.10534. doi: 10.48550/arXiv.2003.10534.
  110. Tao et al., “A Survey on Data-driven COVID-19 and Future Pandemic Management,” ACM Comput. Surv., vol. 55, no. 7, pp. 1–36, Jul. 2023, doi: 10.1145/3542818.

Publication Details

Published in : Volume 6 | Issue 5 | September-October 2023
Date of Publication : 2023-09-12
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 183-207
Manuscript Number : GISRRJ236515
Publisher : Technoscience Academy

ISSN : 2582-0095

Cite This Article :

Damilola Oluyemi Merotiwon, Opeyemi Olamide Akintimehin, Opeoluwa Oluwanifemi Akomolafe, "A Conceptual Framework for Integrating HMO Data Analytics with Hospital Information Systems for Performance Improvement", Gyanshauryam, International Scientific Refereed Research Journal (GISRRJ), ISSN : 2582-0095, Volume 6, Issue 5, pp.183-207, September-October.2023
URL : https://gisrrj.com/GISRRJ236515

Article Preview