Healthcare Professionals in Asia-Pacific Embrace AI for Improved Care Delivery
Healthcare professionals in the Asia-Pacific region are increasingly recognizing the importance of integrating AI technologies into their practices. The adoption of AI is seen as a way to enhance care delivery, streamline operations, and ultimately improve health outcomes in the face of growing demand and workforce shortages.
A recent survey conducted by Philips revealed that a majority of healthcare professionals in the region believe that digital technologies, including AI and predictive analytics, have the potential to reduce hospital admissions and enable earlier interventions to save lives. These professionals are actively involved in the development of technological solutions within their organizations.
Despite the positive outlook on AI adoption, concerns around trust and implementation persist. Professionals worry that existing technologies may not fully meet their needs and raise concerns about potential biases in AI applications that could exacerbate health outcome disparities.
In the United States, a study highlighted significant challenges in the implementation of predictive analytics in healthcare. Business intelligence analyst Rohan Desai identified data integration, quality, model interpretability, and clinical relevance as key obstacles.
Desai’s subsequent article in the Journal of Intelligent Learning Systems and Applications proposed a roadmap for advancing healthcare predictive analytics. The framework emphasizes the use of hybrid machine learning models, such as stacking and boosting techniques, to optimize model performance and interpretability.
As a data modeller at R1 RCM, Desai leverages his expertise in data analysis and machine learning to drive actionable insights. He also volunteers with organizations like the Red Cross and mentors students in innovation challenges.
Interview with Rohan Desai on Healthcare Predictive Analytics
Q. Can you elaborate on the practical application of your proposed framework?
Desai explained that his framework aims to make predictive analytics accessible for everyday decision-making in healthcare, particularly in revenue cycle operations. The approach is cost-effective, leveraging open-source tools and existing data flows to streamline processes without requiring a complete system overhaul.
The framework is designed to integrate seamlessly with standard data formats, enabling easy implementation and minimal disruption to clinicians’ workflows.
Q. What are the major challenges in healthcare analytics deployment in Asia-Pacific?
Desai highlighted disparities in digital maturity between public and private institutions, as well as data quality and access issues as key challenges in the region. The lack of trained personnel and organizational resistance to change further hinder the uptake of analytics technologies.
Q. How can your framework facilitate adoption in low-resource settings?
Desai emphasized the framework’s adaptability to varying data sources and its low dependency on expensive software. By focusing on delivering actionable insights and maintaining a user-friendly interface, the framework can encourage clinical users in low-resource settings to embrace predictive analytics.
Q. Does your framework address data integration and interoperability challenges?
Desai noted that the framework supports common healthcare data formats and is designed to handle data from diverse sources. While full interoperability depends on upstream systems’ standardization, the framework’s flexibility helps bridge data integration gaps.
Q. What are your plans for collaborating with healthcare providers to operationalize the framework?
Desai expressed interest in partnering with research-focused institutions to further test and refine the framework using real-world data. His long-term goal is to develop a versatile toolkit that can be implemented in various healthcare settings to enhance operational efficiency and clinical decision-making.
