ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKETING TRANSFORMING PATIENT ENGAGEMENT AND BUSINESS GROWTH

Abstract

Artificial Intelligence (AI) has become an important technology in transforming healthcare marketing by enabling data-driven decision-making, personalised communication, improved patient engagement, and efficient healthcare service promotion. AI-based tools help healthcare organisations understand patient needs, optimise marketing strategies, and enhance customer experiences. However, the implementation of AI in healthcare marketing also presents several challenges, including data privacy concerns, high costs, lack of technical expertise, system integration issues, and resistance to technological changes. This study aims to analyse the benefits and challenges associated with implementing AI in healthcare marketing.

The study adopted a quantitative research design to examine the role of AI in healthcare marketing. Data were collected from a total of 180 respondents using a structured questionnaire. The study was conducted in Chennai City. The respondents were selected using the stratified random sampling technique, where the population was divided into relevant groups to ensure adequate representation from different categories.

The collected data were analysed using statistical tools such as descriptive statistics, ANOVA, and Pearson correlation analysis. ANOVA was used to identify differences in perceived AI benefits based on demographic variables such as age, gender, educational qualification, occupation, and monthly income. Pearson correlation analysis was used to examine the relationship between AI implementation challenges and overall adoption difficulties.

The analysis showed that AI provides significant benefits in healthcare marketing, including improved customer interaction, personalisation, operational efficiency, and better decision-making. The ANOVA results indicated that age, educational qualification, occupation, and monthly income significantly influence respondents’ perceptions of AI benefits, whereas gender does not show a significant influence.

The correlation analysis revealed significant positive relationships between AI implementation challenges and overall adoption difficulties. Factors such as data privacy concerns, implementation costs, system integration problems, lack of AI skills, poor data quality, and resistance from healthcare professionals were identified as major barriers to AI adoption.