Digital Employer Risk Rating Frameworks Supporting Public Health Oriented Social Insurance Compliance Systems

References

[1] Abdulraheem, B. I., Olapipo, A. R., & Amodu, M. O. (2012). Primary health care services in Nigeria: Critical issues and strategies for enhancing the use by the rural communities. Journal of public health and epidemiology, 4(1), 5-13.

[2] Afriyie, D. (2017). Leveraging Predictive People Analytics To Optimize Workforce Mobility, Talent Retention, And Regulatory Compliance In Global Enterprises.

[3] Ahmed, K. (2017). The impact of multichannel engagement tools on the quality of care provided by a health care professional. Revista de Administração de Roraima-RARR, 7(1), 81-98.

[4] Aitken, M., & Gorokhovich, L. (2012). Advancing the responsible use of medicines: applying levers for change. Available at SSRN 2222541.

[5] Aldrighetti, R., Zennaro, I., Finco, S., & Battini, D. (2019). Healthcare supply chain simulation with disruption considerations: A case study from Northern Italy. Global Journal of Flexible Systems Management, 20(Suppl 1), 81-102.

[6] Asi, Y. M., & Williams, C. (2018). The role of digital health in making progress toward Sustainable Development Goal (SDG) 3 in conflict-affected populations. International journal of medical informatics, 114, 114-120.

[7] Assefa, Y., Hill, P. S., Ulikpan, A., & Williams, O. D. (2017). Access to medicines and hepatitis C in Africa: can tiered pricing and voluntary licencing assure universal access, health equity and fairness?. Globalization and health, 13(1), 73.

[8] Atobatele, O. K., Ajayi, O. O., Hungbo, A. Q., & Adeyemi, C. (2019). Leveraging public health informatics to strengthen monitoring and evaluation of global health intervention. IRE Journals, 2(7), 174-193

[9] Atobatele, O. K., Hungbo A. Q., & Adeyemi, C. (2019). Evaluating strategic role of economic research in supporting financial policy decisions and market performance metrics. IRE Journals, 2(10), 442 – 452

[10] Atobatele, O. K., Hungbo, A. Q., & Adeyemi, C. (2019). Digital health technologies and real-time surveillance systems: Transforming public health emergency preparedness through data-driven decision making. IRE Journals, 3(9), 417–421. https://irejournals.com (ISSN: 2456-8880)

[11] Atobatele, O. K., Hungbo, A. Q., & Adeyemi, C. (2019). Digital Health Technologies and Real-Time Surveillance Systems: Transforming Public Health Emergency Preparedness Through Data-Driven Decision Making.

[12] Atobatele, O. K., Hungbo, A. Q., & Adeyemi, C. (2019). Leveraging big data analytics for population health management: A comparative analysis of predictive modeling approaches in chronic disease prevention and healthcare resource optimization. IRE Journals, 3(4), 370–375. https://irejournals.com (ISSN: 2456-8880)

[13] Bam, L., McLaren, Z. M., Coetzee, E., & Von Leipzig, K. H. (2017). Reducing stock-outs of essential tuberculosis medicines: a system dynamics modelling approach to supply chain management. Health Policy and Planning, 32(8), 1127-1134.

[14] Barrett, M., Boyne, J., Brandts, J., Brunner-La Rocca, H. P., De Maesschalck, L., De Wit, K., ... & Zippel-Schultz, B. (2019). Artificial intelligence supported patient self-care in chronic heart failure: a paradigm shift from reactive to predictive, preventive and personalised care. Epma Journal, 10(4), 445-464.

[15] Bennett, C. C., & Hauser, K. (2013). Artificial intelligence framework for simulating clinical decision-making: A Markov decision process approach. Artificial intelligence in medicine, 57(1), 9-19.

[16] Beran, D., Zar, H. J., Perrin, C., Menezes, A. M., & Burney, P. (2015). Burden of asthma and chronic obstructive pulmonary disease and access to essential medicines in low-income and middle-income countries. The Lancet Respiratory Medicine, 3(2), 159-170.

[17] Bizzo, B. C., Almeida, R. R., Michalski, M. H., & Alkasab, T. K. (2019). Artificial intelligence and clinical decision support for radiologists and referring providers. Journal of the American College of Radiology, 16(9), 1351-1356.

[18] Blasimme, A., & Vayena, E. (2019). The ethics of AI in biomedical research, patient care and public health. Patient Care and Public Health (April 9, 2019). Oxford Handbook of Ethics of Artificial Intelligence, Forthcoming.

[19] Bologa, A. R., Bologa, R., & Florea, A. (2013). Big data and specific analysis methods for insurance fraud detection. Database Systems Journal, 4(4).

[20] Boppiniti, S. T. (2019). Revolutionizing healthcare data management: A novel master data architecture for the digital era. Transactions on Latest Trends in IoT, 2(2).

[21] Bradley, B. D., Jung, T., Tandon-Verma, A., Khoury, B., Chan, T. C., & Cheng, Y. L. (2017). Operations research in global health: a scoping review with a focus on the themes of health equity and impact. Health research policy and systems, 15(1), 32.

[22] Brenner, M., Cramer, J., Cohen, S., & Balakrishnan, K. (2018). Leveraging quality improvement and patient safety initiatives to enhance value and patient-centered care in otolaryngology. Current Otorhinolaryngology Reports, 6(3), 231-238.

[23] Browne, A. J., Varcoe, C. M., Wong, S. T., Smye, V. L., Lavoie, J., Littlejohn, D., ... & Lennox, S. (2012). Closing the health equity gap: evidence-based strategies for primary health care organizations. International journal for equity in health, 11(1), 59.

[24] Campbell, B. R., Ingersoll, K. S., Flickinger, T. E., & Dillingham, R. (2019). Bridging the digital health divide: toward equitable global access to mobile health interventions for people living with HIV. Expert review of anti-infective therapy, 17(3), 141-144.

[25] Car, J., Tan, W. S., Huang, Z., Sloot, P., & Franklin, B. D. (2017). eHealth in the future of medications management: personalisation, monitoring and adherence. BMC medicine, 15(1), 73.

[26] Chopra, M., Bhutta, Z., Blanc, D. C., Checchi, F., Gupta, A., Lemango, E. T., ... & Victora, C. G. (2019). Addressing the persistent inequities in immunization coverage. Bulletin of the World Health Organization, 98(2), 146.

[27] Cleaveland, S., Sharp, J., Abela-Ridder, B., Allan, K. J., Buza, J., Crump, J. A., ... & Halliday, J. E. B. (2017). One Health contributions towards more effective and equitable approaches to health in low-and middle-income countries. Philosophical Transactions of the Royal Society B: Biological Sciences, 372(1725), 20160168.

[28] Contreras, I., & Vehi, J. (2018). Artificial intelligence for diabetes management and decision support: literature review. Journal of medical Internet research, 20(5), e10775.

[29] Corral de Zubielqui, G., Jones, J., Seet, P. S., & Lindsay, N. (2015). Knowledge transfer between actors in the innovation system: a study of higher education institutions (HEIS) and SMES. Journal of Business & Industrial Marketing, 30(3/4), 436-458.

[30] Daniel, H., Bornstein, S. S., Kane, G. C., & Health and Public Policy Committee of the American College of Physicians*. (2018). Addressing social determinants to improve patient care and promote health equity: an American College of Physicians position paper. Annals of internal medicine, 168(8), 577-578.

[31] Dankwa-Mullan, I., Rivo, M., Sepulveda, M., Park, Y., Snowdon, J., & Rhee, K. (2019). Transforming diabetes care through artificial intelligence: the future is here. Population health management, 22(3), 229-242.

[32] Davenport, T., & Kalakota, R. (2019). The potential for artificial intelligence in healthcare. Future healthcare journal, 6(2), 94-98.

[33] De Souza, J. A., Hunt, B., Asirwa, F. C., Adebamowo, C., & Lopes, G. (2016). Global health equity: cancer care outcome disparities in high-, middle-, and low-income countries. Journal of Clinical Oncology, 34(1), 6-13.

[34] Desai, A. N., Kraemer, M. U., Bhatia, S., Cori, A., Nouvellet, P., Herringer, M., ... & Lassmann, B. (2019). Real-time epidemic forecasting: challenges and opportunities. Health security, 17(4), 268-275.

[35] Deshpande, P., Rasin, A., Furst, J., Raicu, D., & Antani, S. (2019). Diis: A biomedical data access framework for aiding data driven research supporting fair principles. Data, 4(2), 54.

[36] Devarapu, K., Rahman, K., Kamisetty, A., & Narsina, D. (2019). MLOps-Driven Solutions for Real-Time Monitoring of Obesity and Its Impact on Heart Disease Risk: Enhancing Predictive Accuracy in Healthcare. International Journal of Reciprocal Symmetry and Theoretical Physics, 6, 43-55.

[37] Didi, P. U., Abass, O. S., & Balogun, O. (2019). A predictive analytics framework for optimizing preventive healthcare sales and engagement outcomes. IRE Journals, 2(11), 497–503.

[38] DiMase, D., Collier, Z. A., Heffner, K., & Linkov, I. (2015). Systems engineering framework for cyber physical security and resilience. Environment Systems and Decisions, 35(2), 291-300.

[39] Diraviam, S. P., Sullivan, P. G., Sestito, J. A., Nepps, M. E., Clapp, J. T., & Fleisher, L. A. (2018). Physician engagement in malpractice risk reduction: a UPHS case study. The Joint Commission Journal on Quality and Patient Safety, 44(10), 605-612.

[40] Dzau, V. J., McClellan, M. B., McGinnis, J. M., Burke, S. P., Coye, M. J., Diaz, A., ... & Zerhouni, E. (2017). Vital directions for health and health care: priorities from a National Academy of Medicine initiative. Jama, 317(14), 1461-1470.

[41] Eeckelaert, L., Dhondt, S., Oeij, P., Pot, F. D., Nicolescu, G. I., Webster, J., & Elsler, D. (2012). Review of workplace innovation and its relation with occupational safety and health. Bilbao: European Agency for Safety and Health at Work.

[42] Gatla, T. R. (2019). A cutting-edge research on AI combating climate change: innovations and its impacts. INNOVATIONS, 6(09), 5.

[43] Goel, N. A., Alam, A. A., Eggert, E. M., & Acharya, S. (2017, July). Design and development of a customizable telemedicine platform for improving access to healthcare for underserved populations. In 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 2658-2661). IEEE.

[44] Goundrey-Smith, S. J. (2019). Technologies that transform: digital solutions for optimising medicines use in the NHS. BMJ Health & Care Informatics, 26(1), e100016.

[45] Gragnolati, M., Lindelöw, M., & Couttolenc, B. (2013). Twenty years of health system reform in Brazil: an assessment of the Sistema Único de Saúde. World Bank Publications.

[46] Gronde, T. V. D., Uyl-de Groot, C. A., & Pieters, T. (2017). Addressing the challenge of high-priced prescription drugs in the era of precision medicine: a systematic review of drug life cycles, therapeutic drug markets and regulatory frameworks. PloS one, 12(8), e0182613.

[47] Hale, A., Borys, D., & Adams, M. (2015). Safety regulation: The lessons of workplace safety rule management for managing the regulatory burden. Safety science, 71, 112-122.

[48] Hargreaves, J. R., Boccia, D., Evans, C. A., Adato, M., Petticrew, M., & Porter, J. D. (2011). The social determinants of tuberculosis: from evidence to action. American journal of public health, 101(4), 654-662.

[49] Hearld, L., Alexander, J. A., Wolf, L. J., & Shi, Y. (2019). Dissemination of quality improvement innovations by multisector health care alliances. Journal of Health Organization and Management, 33(4), 511-528.

[50] Henke, N., & Jacques Bughin, L. (2016). The age of analytics: Competing in a data-driven world.

[51] Hill-Briggs, F. (2019). 2018 Health Care & Education Presidential Address: the American Diabetes Association in the era of health care transformation. Diabetes Care, 42(3), 352-358.

[52] Hiller, J., McMullen, M. S., Chumney, W. M., & Baumer, D. L. (2011). Privacy and security in the implementation of health information technology (electronic health records): US and EU compared. BUJ Sci. & Tech. L., 17, 1.

[53] Hodge, H., Carson, D., Carson, D., Newman, L., & Garrett, J. (2017). Using Internet technologies in rural communities to access services: The views of older people and service providers. Journal of Rural Studies, 54, 469-478.

[54] Holden, K., Akintobi, T., Hopkins, J., Belton, A., McGregor, B., Blanks, S., & Wrenn, G. (2016). Community engaged leadership to advance health equity and build healthier communities. Social Sciences (Basel, Switzerland), 5(1), 2.

[55] Huang, H. C., Singh, B., Morton, D. P., Johnson, G. P., Clements, B., & Meyers, L. A. (2017). Equalizing access to pandemic influenza vaccines through optimal allocation to public health distribution points. PloS one, 12(8), e0182720.

[56] Hungbo, A. Q., & Adeyemi, C. (2019). Community-based training model for practical nurses in maternal and child health clinics. IRE Journals, 2(8), 217-235

[57] Hungbo, A. Q., & Adeyemi, C. (2019). Laboratory safety and diagnostic reliability framework for resource-constrained blood bank operations. IRE Journals, 3(4), 295-318. https://irejournals.com

[58] Index, G. I. (2016). Report. URL: https://www. globalinnovationindex. org/analysis-indicator (дата обращения: 29.09. 2021).–Текст: электронный.

[59] Ismail, A., Karusala, N., & Kumar, N. (2018). Bridging disconnected knowledges for community health. Proceedings of the ACM on Human-Computer Interaction, 2(CSCW), 1-27.

[60] Jacobsen, K. H., Aguirre, A. A., Bailey, C. L., Baranova, A. V., Crooks, A. T., Croitoru, A., ... & Agouris, P. (2016). Lessons from the Ebola outbreak: action items for emerging infectious disease preparedness and response. EcoHealth, 13(1), 200-212.

[61] Khan, M. R. (2019). Application and impact of new technologies in the supply chain management during COVID-19 pandemic: a systematic literature review. Aldrighetti, R., Zennaro, I., Finco, S., Battini, D, 81-102.

[62] Knaul, F. M., González-Pier, E., Gómez-Dantés, O., García-Junco, D., Arreola-Ornelas, H., Barraza-Lloréns, M., ... & Frenk, J. (2012). The quest for universal health coverage: achieving social protection for all in Mexico. The Lancet, 380(9849), 1259-1279.

[63] Kuupiel, D., Bawontuo, V., & Mashamba-Thompson, T. P. (2017). Improving the accessibility and efficiency of point-of-care diagnostics services in low-and middle-income countries: lean and agile supply chain management. Diagnostics, 7(4), 58.

[64] Kwon, S. C., Tandon, S. D., Islam, N., Riley, L., & Trinh-Shevrin, C. (2018). Applying a community-based participatory research framework to patient and family engagement in the development of patient-centered outcomes research and practice. Translational behavioral medicine, 8(5), 683-691.

[65] Larkins, S. L., Preston, R., Matte, M. C., Lindemann, I. C., Samson, R., Tandinco, F. D., ... & on behalf of the Training for Health Equity Network (THEnet). (2013). Measuring social accountability in health professional education: development and international pilot testing of an evaluation framework. Medical Teacher, 35(1), 32-45.

[66] Leath, B. A., Dunn, L. W., Alsobrook, A., & Darden, M. L. (2018). Enhancing rural population health care access and outcomes through the telehealth EcoSystem™ model. Online journal of public health informatics, 10(2), e218.

[67] Lee, B. Y., Connor, D. L., Wateska, A. R., Norman, B. A., Rajgopal, J., Cakouros, B. E., ... & Brown, S. T. (2015). Landscaping the structures of GAVI country vaccine supply chains and testing the effects of radical redesign. Vaccine, 33(36), 4451-4458.

[68] Lee, B. Y., Haidari, L. A., Prosser, W., Connor, D. L., Bechtel, R., Dipuve, A., ... & Brown, S. T. (2016). Re-designing the Mozambique vaccine supply chain to improve access to vaccines. Vaccine, 34(41), 4998-5004.

[69] Liang, F., Das, V., Kostyuk, N., & Hussain, M. M. (2018). Constructing a data‐driven society: China's social credit system as a state surveillance infrastructure. Policy & Internet, 10(4), 415-453.

[70] Lim, J., Claypool, E., Norman, B. A., & Rajgopal, J. (2016). Coverage models to determine outreach vaccination center locations in low and middle income countries. Operations research for health care, 9, 40-48.

[71] Lönnroth, K., Migliori, G. B., Abubakar, I., D'Ambrosio, L., De Vries, G., Diel, R., ... & Raviglione, M. C. (2015). Towards tuberculosis elimination: an action framework for low-incidence countries. European Respiratory Journal, 45(4), 928-952.

[72] Mackey, T. K., & Nayyar, G. (2017). A review of existing and emerging digital technologies to combat the global trade in fake medicines. Expert opinion on drug safety, 16(5), 587-602.

[73] Main, E. K., Dhurjati, R., Cape, V., Vasher, J., Abreo, A., Chang, S. C., & Gould, J. B. (2018). Improving maternal safety at scale with the mentor model of collaborative improvement. The Joint Commission Journal on Quality and Patient Safety, 44(5), 250-259.

[74] Manyeh, A. K., Ibisomi, L., Baiden, F., Chirwa, T., & Ramaswamy, R. (2019). Using intervention mapping to design and implement quality improvement strategies towards elimination of lymphatic filariasis in Northern Ghana. PLOS Neglected Tropical Diseases, 13(3), e0007267.

[75] Marda, V. (2018). Artificial intelligence policy in India: a framework for engaging the limits of data-driven decision-making. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2133), 20180087.

[76] Mariscal, J., Mayne, G., Aneja, U., & Sorgner, A. (2019). Bridging the gender digital gap. Economics, 13(1), 20190009.

[77] Martinez-Martin, N., Insel, T. R., Dagum, P., Greely, H. T., & Cho, M. K. (2018). Data mining for health: staking out the ethical territory of digital phenotyping. NPJ digital medicine, 1(1), 68.

[78] Mercer, T., Chang, A. C., Fischer, L., Gardner, A., Kerubo, I., Tran, D. N., ... & Pastakia, S. (2019). Mitigating the burden of diabetes in Sub-Saharan Africa through an integrated diagonal health systems approach. Diabetes, metabolic syndrome and obesity: targets and therapy, 2261-2272.

[79] Metcalf, C. J. E., Tatem, A., Bjornstad, O. N., Lessler, J., O'reilly, K., Takahashi, S., ... & Grenfell, B. T. (2015). Transport networks and inequities in vaccination: remoteness shapes measles vaccine coverage and prospects for elimination across Africa. Epidemiology & Infection, 143(7), 1457-1466.

[80] Meyer, J. C., Schellack, N., Stokes, J., Lancaster, R., Zeeman, H., Defty, D., ... & Steel, G. (2017). Ongoing initiatives to improve the quality and efficiency of medicine use within the public healthcare system in South Africa; a preliminary study. Frontiers in pharmacology, 8, 751.

[81] Miah, S. J., Hasan, J., & Gammack, J. G. (2017). On-cloud healthcare clinic: an e-health consultancy approach for remote communities in a developing country. Telematics and Informatics, 34(1), 311-322.

[82] Min, H. (2016). Global business analytics models: Concepts and applications in predictive, healthcare, supply chain, and finance analytics.

[83] Mohammadi, I., Wu, H., Turkcan, A., Toscos, T., & Doebbeling, B. N. (2018). Data analytics and modeling for appointment no-show in community health centers. Journal of primary care & community health, 9, 2150132718811692.

[84] Moore, L. L., Wurzelbacher, S. J., & Shockey, T. M. (2018). Workers' compensation insurer risk control systems: Opportunities for public health collaborations. Journal of safety research, 66, 141-150.

[85] Nascimento, R. C. R. M. D., Álvares, J., Guerra Junior, A. A., Gomes, I. C., Costa, E. A., Leite, S. N., ... & Acurcio, F. D. A. (2017). Availability of essential medicines in primary health care of the Brazilian Unified Health System. Revista de saude publica, 51, 10s.

[86] Novak, M., Costantini, L., Schneider, S., & Beanlands, H. (2013, March). Approaches to self‐management in chronic illness. In Seminars in dialysis (Vol. 26, No. 2, pp. 188-194). Oxford, UK: Blackwell Publishing Ltd.

[87] Olu, O., Muneene, D., Bataringaya, J. E., Nahimana, M. R., Ba, H., Turgeon, Y., ... & Dovlo, D. (2019). How can digital health technologies contribute to sustainable attainment of universal health coverage in Africa? A perspective. Frontiers in public health, 7, 341.

[88] Pacifico Silva, H., Lehoux, P., Miller, F. A., & Denis, J. L. (2018). Introducing responsible innovation in health: a policy-oriented framework. Health research policy and systems, 16(1), 90.

[89] Patrick, A., Adeleke Adeyeni, S., Gbaraba Stephen, V., Pamela, G., & Ezeh Funmi, E. (2019). Community-based strategies for reducing drug misuse: Evidence from pharmacist-led interventions. Iconic Research and Engineering Journals, 2(8), 284–310. Fair East Publishers.

[90] Paul, S., & Venkateswaran, J. (2018). Inventory management strategies for mitigating unfolding epidemics. IISE Transactions on Healthcare Systems Engineering, 8(3), 167-180.

[91] Peckham, T. K., Baker, M. G., Camp, J. E., Kaufman, J. D., & Seixas, N. S. (2017). Creating a future for occupational health. Annals of Work Exposures and Health, 61(1), 3-15.

[92] Perehudoff, S. K., Alexandrov, N. V., & Hogerzeil, H. V. (2019). The right to health as the basis for universal health coverage: A cross-national analysis of national medicines policies of 71 countries. PLoS One, 14(6), e0215577.

[93] Perez, B. H. (2019). Data-driven web-based intelligent decision support system for infection management at point of care. Imperial College London.

[94] Polater, A., & Demirdogen, O. (2018). An investigation of healthcare supply chain management and patient responsiveness: An application on public hospitals. International journal of pharmaceutical and healthcare marketing, 12(3), 325-347.

[95] Portnoy, A., Ozawa, S., Grewal, S., Norman, B. A., Rajgopal, J., Gorham, K. M., ... & Lee, B. Y. (2015). Costs of vaccine programs across 94 low-and middle-income countries. Vaccine, 33, A99-A108.

[96] Pouliakas, K., & Theodossiou, I. (2013). The economics of health and safety at work: an interdiciplinary review of the theory and policy. Journal of Economic Surveys, 27(1), 167-208.

[97] Reddy, S., Fox, J., & Purohit, M. P. (2019). Artificial intelligence-enabled healthcare delivery. Journal of the Royal Society of Medicine, 112(1), 22-28.

[98] Rees, J. (2016). Reforming the workplace: A study of self-regulation in occupational safety. University of Pennsylvania Press.

[99] Reese, C. D. (2018). Occupational health and safety management: a practical approach. CRC press.

[100] Roski, J., Hamilton, B. A., Chapman, W., Heffner, J., Trivedi, R., Del Fiol, G., ... & Pierce, J. (2019). How artificial intelligence is changing health and healthcare. Artificial intelligence in health care: The hope, the hype, the promise, the peril. Washington DC: National Academy of Medicine, 58.

[101] Sardar, P., Abbott, J. D., Kundu, A., Aronow, H. D., Granada, J. F., & Giri, J. (2019). Impact of artificial intelligence on interventional cardiology: from decision-making aid to advanced interventional procedure assistance. Cardiovascular interventions, 12(14), 1293-1303.

[102] Sayed, S., Cherniak, W., Lawler, M., Tan, S. Y., El Sadr, W., Wolf, N., ... & Fleming, K. A. (2018). Improving pathology and laboratory medicine in low-income and middle-income countries: roadmap to solutions. The Lancet, 391(10133), 1939-1952.

[103] Schäfer, W., Kroneman, M., Boerma, W., van den Berg, M., Westert, G., Devillé, W., & van Ginneken, E. (2010). The Netherlands: health system review. Health systems in transition, 12(1), v-xxvii.

[104] Schulte, P. A., Guerin, R. J., Schill, A. L., Bhattacharya, A., Cunningham, T. R., Pandalai, S. P., ... & Stephenson, C. M. (2015). Considerations for incorporating “well-being” in public policy for workers and workplaces. American journal of public health, 105(8), e31-e44.

[105] Shrestha, Y. R., Ben-Menahem, S. M., & Von Krogh, G. (2019). Organizational decision-making structures in the age of artificial intelligence. California management review, 61(4), 66-83.

[106] Sim, S. Y., Jit, M., Constenla, D., Peters, D. H., & Hutubessy, R. C. (2019). A scoping review of investment cases for vaccines and immunization programs. Value in health, 22(8), 942-952.

[107] Sqalli, M. T., & Al-Thani, D. (2019, August). AI-supported health coaching model for patients with chronic diseases. In 2019 16th International Symposium on Wireless Communication Systems (ISWCS) (pp. 452-456). IEEE.

[108] Srivastava, S. C., & Shainesh, G. (2015). Bridging the service divide through digitally enabled service innovations. Mis Quarterly, 39(1), 245-268.

[109] Stanfill, M. H., & Marc, D. T. (2019). Health information management: implications of artificial intelligence on healthcare data and information management. Yearbook of medical informatics, 28(01), 056-064.

[110] Stokes, L. B., Rogers, J. W., Hertig, J. B., & Weber, R. J. (2016). Big data: implications for health system pharmacy. Hospital pharmacy, 51(7), 599-603.

[111] Strusani, D., & Houngbonon, G. V. (2019). The role of artificial intelligence in supporting development in emerging markets. International Finance Corporation, Washington, DC.

[112] Tack, C. (2019). Artificial intelligence and machine learning| applications in musculoskeletal physiotherapy. Musculoskeletal Science and Practice, 39, 164-169.

[113] Takala, J., Hämäläinen, P., Saarela, K. L., Yun, L. Y., Manickam, K., Jin, T. W., ... & Lin, G. S. (2014). Global estimates of the burden of injury and illness at work in 2012. Journal of occupational and environmental hygiene, 11(5), 326-337.

[114] Tamraparani, V. (2019). Data-Driven Strategies for Reducing Employee Health Insurance Costs: A Collaborative Approach with Carriers and Brokers. Available at SSRN 5117105.

[115] Tompa, E., Kalcevich, C., Foley, M., McLeod, C., Hogg‐Johnson, S., Cullen, K., ... & Irvin, E. (2016). A systematic literature review of the effectiveness of occupational health and safety regulatory enforcement. American journal of industrial medicine, 59(11), 919-933.

[116] Tresp, V., Overhage, J. M., Bundschus, M., Rabizadeh, S., Fasching, P. A., & Yu, S. (2016). Going digital: a survey on digitalization and large-scale data analytics in healthcare. Proceedings of the IEEE, 104(11), 2180-2206.

[117] Udlis, K. A. (2011). Self‐management in chronic illness: concept and dimensional analysis. Journal of Nursing and Healthcare of Chronic illness, 3(2), 130-139.

[118] Utazi, C. E., Thorley, J., Alegana, V. A., Ferrari, M. J., Takahashi, S., Metcalf, C. J. E., ... & Tatem, A. J. (2019). Mapping vaccination coverage to explore the effects of delivery mechanisms and inform vaccination strategies. Nature communications, 10(1), 1633.

[119] Van Eerd, D., & Saunders, R. (2017). Integrated knowledge transfer and exchange: An organizational approach for stakeholder engagement and communications. Scholarly and Research Communication, 8(1).

[120] Verra, S. E., Benzerga, A., Jiao, B., & Ruggeri, K. (2018). Health promotion at work: A comparison of policy and practice across Europe. Safety and Health at Work, 10(1), 21-29.

[121] Vogler, S., Paris, V., & Panteli, D. (2018). Ensuring access to medicines: How to redesign pricing, reimbursement and procurement? (p. 30272895). Copenhagen: World Health Organization, Regional Office for Europe.

[122] Wachter, J. K., & Yorio, P. L. (2014). A system of safety management practices and worker engagement for reducing and preventing accidents: An empirical and theoretical investigation. Accident Analysis & Prevention, 68, 117-130.

[123] Wallerstein, N. B., Yen, I. H., & Syme, S. L. (2011). Integration of social epidemiology and community-engaged interventions to improve health equity. American journal of public health, 101(5), 822-830.

[124] Wallerstein, N., Duran, B., Oetzel, J. G., & Minkler, M. (Eds.). (2017). Community-based participatory research for health: Advancing social and health equity. John Wiley & Sons.

[125] Walters, D., Johnstone, R., Frick, K., Quinlan, M., Baril-Gingras, G., & Thébaud-Mony, A. (2011). Regulating workplace risks: a comparative study of inspection regimes in times of change. In Regulating Workplace Risks. Edward Elgar Publishing.

[126] Wang, H., & Rosemberg, N. (2018). Universal health coverage in low-income countries: Tanzania’s efforts to overcome barriers to equitable health service access.

[127] Wirtz, V. J., Hogerzeil, H. V., Gray, A. L., Bigdeli, M., de Joncheere, C. P., Ewen, M. A., ... & Reich, M. R. (2017). Essential medicines for universal health coverage. The Lancet, 389(10067), 403-476.

@article{1713125,
      author = {Sandra C. Anioke, Michael Efetobore Atima},
      title = {Digital Employer Risk Rating Frameworks Supporting Public Health Oriented Social Insurance Compliance Systems},
      journal = {Iconic Research And Engineering Journals},
      year = {2019},
      volume = {3},
      number = {5},
      pages = {411-433},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1713125.pdf},
      abstract = {This study proposes a Digital Employer Risk Rating Framework designed to support public health oriented social insurance compliance systems in complex labor markets. The framework integrates regulatory analytics, organizational risk profiling, and digital reporting infrastructures to systematically assess employer compliance behavior and its implications for workforce health protection. Drawing on principles from occupational health and safety governance, social insurance administration, and data driven risk management, the framework establishes standardized indicators capturing payroll integrity, contribution regularity, workplace health practices, incident reporting quality, and responsiveness to regulatory interventions. Using a conceptual design approach, the study synthesizes evidence from public health policy literature, social security compliance models, and digital governance systems to define a multi dimensional employer risk scoring architecture. Advanced analytics, including rule based scoring, anomaly detection, and longitudinal trend analysis, are incorporated to enable early identification of non compliant or high risk employers. The framework emphasizes interoperability with existing social insurance databases, labor inspection systems, and public health surveillance platforms, ensuring real time information exchange and coordinated regulatory responses. From a public health perspective, the framework positions employer compliance as a determinant of population health outcomes by linking contribution behavior to access to healthcare, injury compensation, and preventive services for workers and their dependents. Risk ratings generated through the framework support targeted inspections, graduated enforcement strategies, and employer engagement programs, thereby optimizing regulatory resources and improving system fairness. Additionally, the framework promotes transparency and accountability by providing auditable digital trails and standardized reporting mechanisms. The study argues that digital employer risk rating frameworks can enhance the effectiveness of social insurance systems by shifting compliance management from reactive enforcement to proactive risk prevention. By aligning employer risk intelligence with public health objectives, the proposed framework contributes to improved coverage, reduced occupational health disparities, and strengthened social protection systems. The findings offer practical guidance for policymakers, regulators, and social insurance administrators seeking to modernize compliance oversight through integrated, data driven, and health centered digital solutions. Future implementation can support equitable labor governance, enhance trust among employers and workers, and reinforce sustainable financing of social insurance and national public health systems across diverse economies.},
      keywords = {Digital risk rating; Employer compliance; Social insurance systems; Public health governance; Regulatory analytics; Occupational health and safety; Digital compliance systems},
      month = {November},
  }