Using Salesforce CRM and Deep Learning (CNN) Techniques to Improve Patient Journey Mapping and Engagement in Small and Medium Healthcare Organizations
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V6I4P115Keywords:
Convolutional Neural Networks, Salesforce CRM, Patient Journey Optimization, Healthcare Analytics, Lifecycle GovernanceAbstract
Healthcare firms are becoming more and more reliant on predictive analytics and adaptive technology solutions to promote this engagement with the patient to make operations more efficient now and in the future. This paper investigates the process of illustrating merging of Convolutional Neural Network (CNN) as a class of deep neural networks in Salesforce CRM today by fine tuning the implementation of CNN to work on large datasets to deliver personalization and lifecycle governance of the Patient journey with the basic objective to compliment small and medium healthcare organizations across India. Using a mixed-methods design, quantitative data from 450 healthcare facilities are combined with CNN-based predictive modeling to examine points of entry into care, admission forecasts, and predictions of lifecycle transitions. The hypothesis states that the integration of CNN with the CRM systems can lead to significant improvements in patient satisfaction scores, reduce administrative costs and enhanced care coordination as compared to traditional CRM implementations. The result is a 34% increase in patient engagement metrics, 28% decrease in no-shows for appointments, and 41% increase in treatment adherence rates. Statistical analysis indicates significant association of predictive accuracy and optimization of patient outcomes. The CNN model could indicate transitions within patients lifecycle with 92% accuracy, enabling preventive actions. Then it discuss the learning-enabled CRM systems as transformative in resource-limited healthcare environments, focusing on scalability and ethics. Low-cost yet robust adaptive technologies are needed to ensure sustainable healthcare delivery during the pandemic and beyond, as is the need for policy frameworks that support digital transformation to meet healthcare demand in emerging healthcare markets
References
[1] Bates, D. W., Kuperman, G. J., Wang, S., Gandhi, T., Kittler, A., Volk, L., Spurr, C., Khorasani, R., Tanasijevic, M., & Middleton, B. (2003). Ten commandments for effective clinical decision support: Making the practice of evidence-based medicine a reality. Journal of the American Medical Informatics Association, 10(6), 523–530. https://pmc.ncbi.nlm.nih.gov/articles/PMC264429/
[2] S. K. Gunda, "Software Defect Prediction Using Advanced Ensemble Techniques: A Focus on Boosting and Voting Method," 2024 International Conference on Electronic Systems and Intelligent Computing (ICESIC), Chennai, India, 2024, pp. 157-161, https://doi.org/10.1109/ICESIC61777.2024.10846550.
[3] I. Manga, "Scalable Graph Neural Networks for Global Knowledge Representation and Reasoning," 2025 9th International Conference on Inventive Systems and Control (ICISC), Coimbatore, India, 2025, pp. 1399-1404, doi: 10.1109/ICISC65841.2025.11188341.
[4] Chukwunweike, J. N., Praise, A., & Bashirat, B. A. (2024). Harnessing machine learning for cybersecurity: How convolutional neural networks are revolutionizing threat detection and data privacy. https://doi.org/10.55248/gengpi.5.0824.2402
[5] I. Manga, "Federated Learning at Scale: A Privacy-Preserving Framework for Decentralized AI Training," 2025 5th International Conference on Soft Computing for Security Applications (ICSCSA), Salem, India, 2025, pp. 110-115, doi: 10.1109/ICSCSA66339.2025.11170780.
[6] European Commission. (2020). A European strategy for data: Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=celex:52020DC0066
[7] Groves, P., Kayyali, B., Knott, D., & Kuiken, S. V. (2013). The "big data" revolution in healthcare: Accelerating value and innovation. McKinsey & Company. https://www.mckinsey.com/~/media/mckinsey/industries/healthcare%20systems%20and%20services/our%20insights/the%20big%20data%20revolution%20in%20us%20health%20care/the_big_data_revolution_in_healthcare.pdf
[8] S. K. Gunda, "Automatic Software Vulnerabilty Detection Using Code Metrics and Feature Extraction," 2025 2nd International Conference On Multidisciplinary Research and Innovations in Engineering (MRIE), Gurugram, India, 2025, pp. 115-120, https://doi.org/10.1109/MRIE66930.2025.11156601.
[9] Gupta, M., Kumar, R., & Bala, M. (2023, June 16). Industry 4.0 challenges and applications in the healthcare industry with emerging technologies. Artificial Intelligence of Health-Enabled Spaces, 102–132.
[10] I. Manga, "Unified Data Engineering for Smart Mobility: Real-Time Integration of Traffic, Public Transport, and Environmental Data," 2025 5th International Conference on Soft Computing for Security Applications (ICSCSA), Salem, India, 2025, pp. 1348-1353, doi: 10.1109/ICSCSA66339.2025.11170800.
[11] Institute of Medicine. (2012). Best care at lower cost: The path to continuously learning health care in America. National Academies Press. https://nap.nationalacademies.org/resource/13444/BestCareReportBrief.pdf
[12] Marr, B. (2016, December 13). Big data in healthcare: Paris hospitals predict admission rates using machine learning. Forbes. https://www.forbes.com/sites/bernardmarr/2016/12/13/big-data-in-healthcare-paris-hospitals-predict-admission-rates-using-machinelearning/
[13] S. K. Gunda, "Enhancing Software Fault Prediction with Machine Learning: A Comparative Study on the PC1 Dataset," 2024 Global Conference on Communications and Information Technologies (GCCIT), BANGALORE, India, 2024, pp. 1-4, https://doi.org/10.1109/GCCIT63234.2024.10862351.
[14] Mell, P., & Grance, T. (2011). The NIST definition of cloud computing (NIST Special Publication 800-145). National Institute of Standards and Technology. https://nvlpubs.nist.gov/nistpubs/legacy/sp/nistspecialpublication800-145.pdf
[15] I. Manga, "Towards Explainable AI: A Framework for Interpretable Deep Learning in High-Stakes Domains," 2025 5th International Conference on Soft Computing for Security Applications (ICSCSA), Salem, India, 2025, pp. 1354-1360, doi: 10.1109/ICSCSA66339.2025.11170778.
[16] Olalekan, K. (2024). Achieving strategic excellence in healthcare projects: Leveraging benefit realization management framework. World Journal of Advanced Research and Reviews, 21(1), 2925–2950. https://doi.org/10.30574/wjarr.2024.21.1.0034
[17] S. K. Gunda, "Comparative Analysis of Machine Learning Models for Software Defect Prediction," 2024 International Conference on Power, Energy, Control and Transmission Systems (ICPECTS), Chennai, India, 2024, pp. 1-6, https://doi.org/10.1109/ICPECTS62210.2024.10780167.
[18] Olukoya, O. (2023). Time series-based quantitative risk models: Enhancing accuracy in forecasting and risk assessment. International Journal of Computer Applications Technology and Research, 12(11), 29–41. https://doi.org/10.7753/IJCATR1211.1006
[19] I. Manga, "AutoML for All: Democratizing Machine Learning Model Building with Minimal Code Interfaces," 2025 3rd International Conference on Sustainable Computing and Data Communication Systems (ICSCDS), Erode, India, 2025, pp. 347-352, doi: 10.1109/ICSCDS65426.2025.11167529.
[20] S. K. Gunda, "Analyzing Machine Learning Techniques for Software Defect Prediction: A Comprehensive Performance Comparison," 2024 Asian Conference on Intelligent Technologies (ACOIT), KOLAR, India, 2024, pp. 1-5, https://doi.org/10.1109/ACOIT62457.2024.10939610.
[21] Raghupathi, W., & Raghupathi, V. (2014). Big data analytics in healthcare: Promise and potential. Health Information Science and Systems, 2(3). https://pmc.ncbi.nlm.nih.gov/articles/PMC4341817/
[22] Sai Krishna Gunda (2024). Device for Continuous Software Testing and Validation (UK Registered Design No. 6400738). Registered with the UK Intellectual Property Office, Class 14-02, granted in November 2024.
[23] Saviano, M., Del Prete, M., Mueller, J., & Caputo, F. (2023, December 18). The challenging meet between human and artificial knowledge: A systems-based view of its influences on firms-customers interaction. Journal of Knowledge Management, 27(11), 101–111.
