Utilizing Patient Data and AI to Develop Cost-Efficient Models for Chronic Disease Management in the U.S. Healthcare System

References

[1] Abujaber AA, Nashwan AJ. Ethical framework for artificial intelligence in healthcare research: A path to integrity. World J Methodol. 2024 Sep 20;14(3):94071. doi: 10.5662/wjm.v14.i3.94071. PMID: 39310239; PMCID: PMC11230076.

[2] Alowais SA, Alghamdi SS, Alsuhebany N, Alqahtani T, Alshaya AI, Almohareb SN, Aldairem A, Alrashed M, Bin Saleh K, Badreldin HA, Al Yami MS, Al Harbi S, Albekairy AM. Revolutionizing healthcare: the role of artificial intelligence in clinical practice. BMC Med Educ. 2023 Sep 22;23(1):689. doi: 10.1186/s12909-023-04698-z. PMID: 37740191; PMCID: PMC10517477.

[3] American Health and Drugs Benefits (2022). The Quality of Care and Economic Burden of COPD in the United States: Considerations for Managing Patients and Improving Outcomes. https://www.ahdbonline.com/articles/3223-the-quality-of-care-and-economic-burden-of-copd-in-the-united-states-considerations-for-managing-patients-and-improving-outcomes

[4] American Lung Association (2024). Learn About Asthma. https://www.lung.org/lung-health-diseases/lung-disease-lookup/asthma/learn-about-asthma

[5] Budiarto A, Tsang KCH, Wilson AM, Sheikh A, Shah SA. (2023). Machine Learning-Based Asthma Attack Prediction Models From Routinely Collected Electronic Health Records: Systematic Scoping Review. JMIR AI. 2023 Dec 7;2:e46717. doi: 10.2196/46717. PMID: 38875586; PMCID: PMC11041490.

[6] CDC. 2020 Chronic Disease Indicator. https://www.cdc.gov/cdi/indicator-definitions/chronic-obstructive-pulmonary-disease.html#:~:text=Nearly%2016%20million%20U.S.%20adults,can%20be%20managed%20and%20treated.

[7] Codispoti CD, Greenhawt M, Oppenheimer J. (2022). The Role of Access and Cost-Effectiveness in Managing Asthma: A Systematic Review. J Allergy Clin Immunol Pract. 2022 Aug;10(8):2109-2116. doi: 10.1016/j.jaip.2022.04.025. Epub 2022 May 5. PMID: 35525532; PMCID: PMC9353043.

[8] Ejike Innocent Nwankwo, Ebube Victor Emeihe, Mojeed Dayo Ajegbile, Janet Aderonke Olaboye and Chukwudi Cosmos Maha (2024). Integrating Telemedicine and AI to Improve Healthcare Access in Rural Settings. International Journal of Life Science Research Archive, 2024, 07(01), 059–077. M DOI: https://doi.org/10.53771/ijlsra.2024.7.1.0061

[9] FDA (2024). Artificial Intelligence and Machine Learning in Software as a Medical Device. https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-and-machine-learning-software-medical-device

[10] Garcia-Saiso S, Marti M, Pesce K, Luciani S, Mujica O, Hennis A, D'Agostino M. (2024). Artificial Intelligence as a Potential Catalyst to a More Equitable Cancer Care. JMIR Cancer. 2024 Aug 12;10:e57276. doi: 10.2196/57276. PMID: 39133537; PMCID: PMC11347894.

[11] Gomez Rossi J, Rojas-Perilla N, Krois J, Schwendicke F. 2022. Cost-effectiveness of Artificial Intelligence as a Decision-Support System Applied to the Detection and Grading of Melanoma, Dental Caries, and Diabetic Retinopathy. JAMA Netw Open. 2022 Mar 1;5(3):e220269. doi: 10.1001/jamanetworkopen.2022.0269. PMID: 35289862; PMCID: PMC8924723.

[12] Holahan, Charles & Holahan, Carole & Lim, Sangdon & Powers, Daniel & North, Rebecca. (2021). Living with a Smoker and Physical Inactivity across Eight Years in High-Risk Medical Patients. Behavioral Medicine. 48. 1-14. 10.1080/08964289.2021.1889458.

[13] Hossain, Elias & Rana, Rajib & Higgins, Niall & Soar, Jeffrey & Barua, Prabal Datta & Pisani, Anthony & Turner, Kathryn. (2023). Natural Language Processing in Electronic Health Records in relation to healthcare decision-making: A systematic review. 155. 106649. 10.1016/j.compbiomed.2023.106649.

[14] Javad Pool, Saeed Akhlaghpour, Farhad Fatehi, Andrew Burton-Jones, (2024). A systematic analysis of failures in protecting personal health data: A scoping review. International Journal of Information Management, Volume 74, ISSN 0268-4012. https://doi.org/10.1016/j.ijinfomgt.2023.102719.

[15] Jiang L, Gershon AS. (2020). Using Health Administrative Data to Predict Chronic Obstructive Pulmonary Disease Exacerbations. Ann Am Thorac Soc. 2020 Sep;17(9):1056-1057. doi: 10.1513/AnnalsATS.202006-704ED. PMID: 32870058; PMCID: PMC7462322.

[16] Kai Wei, Fang Qian, Yixue Li, Tao Zeng, Tao Huang. (2024). Integrating multi-omics data of childhood asthma using a deep association model, Fundamental Research, Volume 4, Issue 4, ISSN 2667-3258. https://doi.org/10.1016/j.fmre.2024.03.022.

[17] Kong CW, Wilkinson TMA. Predicting and preventing hospital readmission for exacerbations of COPD. ERJ Open Res. 2020 May 11;6(2):00325-2019. doi: 10.1183/23120541.00325-2019. PMID: 32420313; PMCID: PMC7211949.

[18] Lee, JT., Hsieh, CC., Lin, CH. et al. (2021). Prediction of hospitalization using artificial intelligence for urgent patients in the emergency department. Sci Rep 11, 19472 (2021). https://doi.org/10.1038/s41598-021-98961-2

[19] Liu G, Hu J, Yang J, Song J. (2022). Predicting early-onset COPD risk in adults aged 20-50 using electronic health records and machine learning. PeerJ. 2024 Feb 23;12:e16950. doi: 10.7717/peerj.16950. PMID: 38410800; PMCID: PMC10896079.

[20] Marko Tainio, Zorana Jovanovic Andersen, Mark J. Nieuwenhuijsen, Liang Hu, Audrey de Nazelle, Ruopeng An, Leandro M.T. Garcia, Shifalika Goenka, Belen Zapata-Diomedi, Fiona Bull, Thiago Herick de Sá. (2021). Air pollution, physical activity, and health: A mapping review of the evidence, Environment International, Volume 147, ISSN 0160-4120. https://doi.org/10.1016/j.envint.2020.105954.

[21] Martinez-Garcia, Mireya & Hernández-Lemus, Enrique. (2022). Data Integration Challenges for Machine Learning in Precision Medicine. Frontiers in Medicine. 8. 10.3389/fmed.2021.784455.

[22] Meera Tandan, Shane Dunlea, Walter Cullen, Gerard Bury (2024). Teamwork and its impact on chronic disease clinical outcomes in primary care: a systematic review and meta-analysis, Public Health, Volume 229, ISSN 0033-3506. https://doi.org/10.1016/j.puhe.2024.01.019.

[23] Michael H Cho, Brian D Hobbs, Edwin K Silverman. (2022). Genetics of chronic obstructive pulmonary disease: understanding the pathobiology and heterogeneity of a complex disorder. The Lancet Respiratory Medicine, Volume 10, Issue 5, ISSN 2213-2600. https://doi.org/10.1016/S2213-2600(21)00510-5.

[24] Mohamed, Israa & Fouda, Mostafa & Hosny, Khalid. (2022). Machine Learning Algorithms for COPD Patients Readmission Prediction: A Data Analytics Approach. IEEE Access. 10. 1-1. 10.1109/ACCESS.2022.3148600.

[25] Mohanty et al. (2021) explained that machine learning models can precisely forecast hospital readmissions, allowing for targeted interventions to minimize unnecessary costs. They identified previous readmissions, discharge to rehabilitation facilities, length of stay, comorbidities, and frailty indicators as key predictors of 30-day readmissions.

[26] Mohanty SD, Lekan D, McCoy TP, Jenkins M, Manda P. Machine learning for predicting readmission risk among the frail: Explainable AI for healthcare. Patterns (N Y). 2021 Dec 3;3(1):100395. doi: 10.1016/j.patter.2021.100395. PMID: 35079714; PMCID: PMC8767300.

[27] Molfino NA, Turcatel G, Riskin D. Machine Learning Approaches to Predict Asthma Exacerbations: 2024. A Narrative Review. Adv Ther. 2024 Feb;41(2):534-552. doi: 10.1007/s12325-023-02743-3. Epub 2023 Dec 19. PMID: 38110652; PMCID: PMC10838858

[28] N., Nivedhaa. (2024). A COMPREHENSIVE REVIEW OF AI'S DEPENDENCE ON DATA. 10.13140/RG.2.2.27033.63840.

[29] National Library of Medicine. Clinical Data as the Basic Staple of Health Learning: Creating and Protecting a Public Good: Workshop Summary. https://www.ncbi.nlm.nih.gov/books/NBK54293/

[30] Nair M, Svedberg P, Larsson I, Nygren JM. A comprehensive overview of barriers and strategies for AI implementation in healthcare: Mixed-method design. PLoS One. 2024 Aug 9;19(8):e0305949. doi: 10.1371/journal.pone.0305949. PMID: 39121051; PMCID: PMC11315296.

[31] Ono, Gloria & Obi, Evelyn & Chiaghana, Anthonia & Ezegwu, Daniel. (2024). Digital Divide and Access: Addressing Disparities in Artificial Intelligence (AI) Health Information for Nigerian Rural Communities. 10. 31-52.

[32] Ortiz-Barrios, M., Petrillo, A., Arias-Fonseca, S. et al. (2024). An AI-based multiphase framework for improving the mechanical ventilation availability in emergency departments during respiratory disease seasons: a case study. Int J Emerg Med 17, 45 (2024). https://doi.org/10.1186/s12245-024-00626-0

[33] Pal, Subharun. (2024). A Comparative Analysis of Machine Learning Algorithms for Predictive Analytics in Healthcare. 72. 10 - 25.

[34] Rahman A, Hossain MS, Muhammad G, Kundu D, Debnath T, Rahman M, Khan MSI, Tiwari P, Band SS. Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges, and open issues. Cluster Comput. 2022 Aug 17:1-41. doi: 10.1007/s10586-022-03658-4. Epub ahead of print. PMID: 35996680; PMCID: PMC9385101.

[35] Rehburg F, Graefe A, Hübner M, Thun S. 2024. How Interoperability Can Enable Artificial Intelligence in Clinical Applications. Stud Health Technol Inform. 2024 Aug 22;316:596-600. doi: 10.3233/SHTI240485. PMID: 39176813.

[36] Romero-Brufau S, Wyatt KD, Boyum P, Mickelson M, Moore M, Cognetta-Rieke C. (2024) Implementation of Artificial Intelligence-Based Clinical Decision Support to Reduce Hospital Readmissions at a Regional Hospital. Appl Clin Inform. 2020 Aug;11(4):570-577. doi: 10.1055/s-0040-1715827. Epub 2020 Sep 2. PMID: 32877943; PMCID: PMC7467834.

[37] Saeed SA, Masters RM. Disparities in Health Care and the Digital Divide. Curr Psychiatry Rep. 2021 Jul 23;23(9):61. doi: 10.1007/s11920-021-01274-4. PMID: 34297202; PMCID: PMC8300069.

[38] Seh AH, Zarour M, Alenezi M, Sarkar AK, Agrawal A, Kumar R, Khan RA. Healthcare Data Breaches: Insights and Implications. Healthcare (Basel). 2020 May 13;8(2):133. doi: 10.3390/healthcare8020133. PMID: 32414183; PMCID: PMC7349636.

[39] Shen, X., Liu, H. Using machine learning for early detection of chronic obstructive pulmonary disease: a narrative review. Respir Res 25, 336 (2024). https://doi.org/10.1186/s12931-024-02960-6

[40] Singareddy S, Sn VP, Jaramillo AP, Yasir M, Iyer N, Hussein S, Nath TS. (2023). Artificial Intelligence and Its Role in the Management of Chronic Medical Conditions: A Systematic Review. Cureus. 2023 Sep 27;15(9):e46066. doi: 10.7759/cureus.46066. PMID: 37900468; PMCID: PMC10607642.

[41] Suresh Renukappa, Primrose Mudiyi, Subashini Suresh, Wala Abdalla, Chandrashekar Subbara. (2022). Evaluation of challenges for adoption of smart healthcare strategies, Smart Health, Volume 26, ISSN 2352-6483. https://doi.org/10.1016/j.smhl.2022.100330.

[42] Tyler S, Olis M, Aust N, Patel L, Simon L, Triantafyllidis C, Patel V, Lee DW, Ginsberg B, Ahmad H, Jacobs RJ. (2024) Use of Artificial Intelligence in Triage in Hospital Emergency Departments: A Scoping Review. Cureus. 2024 May 8;16(5):e59906. doi: 10.7759/cureus.59906. PMID: 38854295; PMCID: PMC11158416.

[43] Teisberg E, Wallace S, O'Hara S. (2020). Defining and Implementing Value-Based Health Care: A Strategic Framework. Acad Med. doi: 10.1097/ACM.0000000000003122. PMID: 31833857; PMCID: PMC7185050.

[44] Umapathy VR, Rajinikanth B S, Samuel Raj RD, Yadav S, Munavarah SA, Anandapandian PA, Mary AV, Padmavathy K, R A. Perspective of Artificial Intelligence in Disease Diagnosis: A Review of Current and Future Endeavours in the Medical Field. Cureus. 2023 Sep 21;15(9):e45684. doi: 10.7759/cureus.45684. PMID: 37868519; PMCID: PMC10590060.

[45] Verma and Lin (2022) demonstrated that predictive analytics reduced COPD readmissions by employing two methods: prediction-based models (70.35% accuracy) and event-based models (72.73% precision). By analyzing physical activity (PA) data as a proxy for patient health status, the study identified actionable trends and supporting interventions such as lifestyle adjustments or alternative treatments. These metrics not only validate the financial viability of AI-driven models but also highlight their role in improving patient outcomes.

[46] Verma, V. K., & Lin, W. -Y. (2022). Machine Learning-Based 30-Day Hospital Readmission Predictions for COPD Patients Using Physical Activity Data of Daily Living with Accelerometer-Based Device. Biosensors, 12(8), 605. https://doi.org/10.3390/bios12080605

[47] Wang C, Zhang J, Lassi N, Zhang X. Privacy Protection in Using Artificial Intelligence for Healthcare: Chinese Regulation in Comparative Perspective. Healthcare (Basel). 2022 Sep 27;10(10):1878. doi: 10.3390/healthcare10101878. PMID: 36292325; PMCID: PMC9601726.

[48] Wu, Y., Xia, S., Liang, Z. et al. (2024). Artificial intelligence in COPD CT images: identification, staging, and quantitation. Respir Res 25, 319 (2024). https://doi.org/10.1186/s12931-024-02913-z

[49] Yelne S, Chaudhary M, Dod K, Sayyad A, Sharma R. Harnessing the Power of AI: A Comprehensive Review of Its Impact and Challenges in Nursing Science and Healthcare. Cureus. 2023 Nov 22;15(11):e49252. doi: 10.7759/cureus.49252. PMID: 38143615; PMCID: PMC10744168.

@article{1706663,
      author = {Oluwatobi Anthonia Ogunfuye},
      title = {Utilizing Patient Data and AI to Develop Cost-Efficient Models for Chronic Disease Management in the U.S. Healthcare System},
      journal = {Iconic Research And Engineering Journals},
      year = {2024},
      volume = {8},
      number = {6},
      pages = {349-358},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1706663.pdf},
      abstract = {This article explores the transformative potential of data analytics and artificial intelligence (AI) in developing cost-efficient models for managing chronic diseases, with a particular focus on respiratory conditions such as asthma and chronic obstructive pulmonary disease (COPD). It addresses the economic burden these diseases impose on the U.S. healthcare system and highlights the need for innovative, patient-centric solutions. The study investigates the application of patient data, including genetic information, lifestyle factors, and treatment outcomes, combined with advanced AI and machine learning techniques. Key sources and metrics such as emergency department visit reductions, hospitalization rates, and treatment adherence were analyzed to assess the effectiveness of AI-driven approaches. AI-driven models demonstrated significant potential in reducing hospitalizations and lowering overall treatment costs by enabling early interventions and tailored care strategies. Predictive analytics, in particular, was shown to be effective in identifying high-risk patients and mitigating exacerbations, contributing to better resource allocation and operational efficiency. The adoption of AI in chronic disease management could alleviate the financial strain on the U.S. healthcare system, promote equitable access to care, and enhance patient outcomes. However, challenges such as data privacy concerns, implementation costs, and regulatory hurdles must be addressed to realize its full potential. Future advancements in AI precision and real-time data integration hold promise for further revolutionizing respiratory disease management. },
      keywords = {Artificial Intelligence, Chronic Disease Management, Cost-Efficiency, Respiratory Diseases, Predictive Analytics, U.S. Healthcare, Patient Data, Machine Learning, Early Intervention, Economic Burden},
      month = {December},
  }