AWS Cloud Architecture for Scalable Healthcare Contact Centers

Authors

  • Suresh Padala Independent Researcher, USA. Author

DOI:

https://doi.org/10.63282/3117-5481/AIJCST-V1I2P103

Keywords:

Healthcare Cloud Contact Center Architecture, HIPAA-Compliant Cloud Infrastructure, Scalable Healthcare Telecommunications, Healthcare Disaster Recovery Cloud Computing's Conversational AI Patient Engagement

Abstract

Many healthcare organizations are using contact center infrastructure technology to address patient needs, including administration of visits, clinical triage, insurance eligibility and response to emergencies. Customary, on-premise PBX offerings are not designed for the high volume of calls that a healthcare environment generates, the capital expenditure required, or variations in demand. This article explores our managed cloud services for scalable, resilient, and HIPAA-compliant patient communication platforms for cloud-native contact centers, which include the architecture for a cloud-native contact center that utilizes cloud-based telephony and routing, a cloud-native backend orchestration platform, and conversational AI for automated patient engagement and clinical NLP for structured information extraction. Security architecture features include encryption, role-based access control, role-based network isolation and auditing for compliance. Resilience engineering features include deploying in multiple availability zones, failover to multiple regions, and replication of data to guarantee that a system is always available to users. The article also mentions that cloud-native architectures enable healthcare organizations to elastically scale to demand, remain compliant with regulations with integrated security frameworks, and transform their operations by moving away from maintaining infrastructure to improving patient experience and clinical workflows.

References

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Published

2019-03-10

Issue

Section

Articles

How to Cite

[1]
S. Padala, “AWS Cloud Architecture for Scalable Healthcare Contact Centers”, AIJCST, vol. 1, no. 2, pp. 21–26, Mar. 2019, doi: 10.63282/3117-5481/AIJCST-V1I2P103.

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