Real-Time Patient Experience Monitoring in Healthcare: A Solution-Oriented Review of Lakehouse-Based Data Engineering Architectures

The patient experience is the key indicator of health care quality and patient-centered care, whereas the traditional assessment instruments rely on the late feedback in the shape of a survey, which is not reflective of the care dynamics in real-time. The rapidly developing digital health systems, telemedicine platforms, and the associated connected patient engagement solutions have introduced the continued flood of experience data that has to be scalably and low-latency analytically processed. The paper presents the solution in the form of a solution-oriented review of the data engineering architectures on the lakeside patient experience monitoring issue in healthcare. The Lakehouse architectures address the issue of fragmentation and latency of legacy systems by uniting the scalability of data lakes with the transactional reliability, governance, and analytical performance of data warehouses. The proposed model enables real-time usage and consumption of heterogeneous signals of patient experience, e.g., electronic health record messages, comments on mobile applications, nurse call systems, and telehealth communication logs. The paper illustrates practical cases of emergency department wait-time analysis, inpatient responsiveness monitoring, and digital front-door experience optimization to indicate how lakehouse-oriented structures can help in the generation of continuous insight and response to proactive service interventions. In addition, the paper determines valuable governance, security, and compliance challenges, and the ways lakehouse platforms enabled scalable and auditable patient experience analytics and compliance-regulatory ones.