Enhancing Patient Experience Continuity Across Care Transitions: An NLP-Driven Approach to Understanding Free-Text Feedback

Dr. Amelia K. Forsyth — 2025-09-01

Background: Patient experience is a cornerstone of quality healthcare, yet continuity of care, particularly during transitions, remains a significant challenge. Traditional feedback mechanisms often lack the depth to capture nuanced patient perspectives on these critical junctures. Natural Language Processing (NLP) offers a scalable solution to analyze vast quantities of unstructured free-text feedback, providing rich insights into patient journeys.

Objective: This study aimed to leverage NLP and machine learning to analyze free-text patient feedback from diverse healthcare settings to identify key themes, sentiments, and specific pain points related to patient experience continuity during care transitions.

Methods: Over 69,000 free-text patient responses collected from various NHS settings (outpatient, inpatient, A&E, and maternity) were analyzed using NLP techniques, including sentiment analysis and trigram analysis. A Support Vector Machine (SVM) model was employed for theme classification, with its performance compared against five other machine learning models.

Results: The SVM model demonstrated superior classification accuracy, achieving 74.5% for outpatient feedback, 72.2% for inpatient, 71.5% for A&E, and 62.7% for maternity feedback. Sentiment analysis revealed that negative feedback predominantly centered on critical transition points, specifically discharge processes, information continuity, and follow-up care. Frequent negative trigrams identified across settings included “seeing different doctor,” “improve discharge process,” and “information aftercare lacking,” underscoring systemic issues in care handovers and communication.

Conclusion: This study demonstrates the viability and efficacy of using NLP and machine learning to process large-scale patient feedback, efficiently uncovering specific areas of dissatisfaction related to care transitions. The insights gained provide actionable intelligence for healthcare providers to design targeted quality improvement interventions, fostering enhanced communication, discharge planning, and inter-departmental coordination to improve patient experience continuity and advance patient-centered care.

Diabetic Retinopathy And Retinal Vascular Diseases

Laura Khachatrain — 2025-09-18

According to WHO, the number of people living with diabetes has increased from 200 to 830 million in the past thirty-five years (since 1990). In 2020, more than a million people became blind due to diabetic retinopathy (DR), and almost 3.28 million suffered from moderate to severe visual impairment (MSI). DR is recognized as one of the most common causes of blindness and visual impairment among the working-age population worldwide. At the same time, early detection and treatment of MVI can reduce the risk of severe vision loss by approximately 90 percent.

The scientific novelty of the article is that it substantiates the need for a combination of anti-VEGF, glucocorticosteroids, laser therapy and systemic treatment, taking into account the pathogenesis features of a particular patient. Particular attention is paid to the development of prolonged forms of anti-VEGF delivery, including implants - a direction that has strategic importance in reducing the frequency of injections. The article emphasizes the relationship between molecular, inflammatory, hemodynamic and neurovascular mechanisms in the development of diabetic retinopathy.

International Journal of Medical Sciences And Clinical Research

Enhancing Patient Experience Continuity Across Care Transitions: An NLP-Driven Approach to Understanding Free-Text Feedback

Diabetic Retinopathy And Retinal Vascular Diseases