Using Online Reviews to Drive Person-Centered Care: An HCAHPS-Validated Approach

Area of Science:

• Health Services Research
• Computational Linguistics
• Artificial Intelligence in Healthcare

Background:

• Person-centered care prioritizes individual patient needs and collaborative treatment planning.
• Online patient reviews are an underutilized, rich source of real-time patient experience data.
• Quantifying unstructured online data is crucial for systematic analysis of patient feedback.

Purpose of the Study:

• To demonstrate the utility of online patient reviews for assessing person-centered care.
• To develop and validate a machine learning model for analyzing patient review data.
• To evaluate the alignment of online review data with established healthcare quality metrics.

Main Methods:

• A supervised machine learning model was trained on a large dataset of manually annotated patient reviews.
• Natural language processing (NLP) techniques were employed to quantify unstructured review data.
• The model's performance was evaluated for accuracy in predicting overall scores and relevance to specific patient experience domains.

Main Results:

• The model achieved 99% accuracy in predicting overall patient review scores.
• The model demonstrated 93% to 99% accuracy in identifying patient experience domains like Effective Treatment and Emotional Support.
• Aggregated online patient reviews showed statistically significant alignment with HCAHPS star ratings.

Conclusions:

• Online patient reviews, when analyzed with machine learning, provide a valuable tool for enhancing person-centered care.
• This approach enables objective benchmarking of healthcare systems and evaluation of quality improvement interventions.
• The findings support the integration of online patient feedback into healthcare quality assessment frameworks.