CXOToday has engaged in an exclusive interview with Subhro Mallik, EVP and Global Head of Life Sciences, Infosys
- How do AI-powered platforms improve the quality of interaction between patients and healthcare providers?
AI-powered platforms enhance patient-provider interactions by offering early-stage identification, personalized insights, targeted therapies, and real-time data sharing. These platforms enable healthcare providers to effortlessly assess and connect preventive and diagnostic information, access comprehensive patient histories, monitor ongoing treatments, and adjust care plans based on real-time data. This leads to proactive interventions, more informed decision-making, improved therapy adherence, and reduced adverse events.
2.Can you explain how Infosys leverages AI to accelerate drug discovery and shorten the R&D lifecycle in pharmaceuticals?
Infosys leverages AI in multiple ways to accelerate drug discovery and shorten the R&D lifecycle. Infosys Topaz, an AI-first set of services, solutions and platforms using generative AI technologies, includes solutions that can process large datasets from research and clinical functions to identify potential drug candidates faster, to optimize clinical trial designs for improved efficiencies, to analyze trial data and generate meaningful insights, to detect potential safety issues early during the trial process, and in general either speed up the process or make it more efficient. Infosys also leverages Gen AI based solutions, especially for Medical Writing, to speed up and optimize development of various documents including Translations, Informed Consent Documents, Layperson Summaries, Safety Narratives, CSR Narratives, Biostatistical Analysis, and Regulatory Dossier. This reduces the time and cost associated with R&D, enabling quicker market entry for new therapies.
3.In what ways does predictive analytics transform the delivery of personalized treatment plans?
Predictive analytics transforms personalize treatment by analyzing vast amounts of patient data, including diagnostics, food intake, exercise, sleep patterns, and anything else that may influence the condition of a patient to forecast health outcomes and tailor interventions. It helps connect disparate datasets to detect patterns, generate meaningful insights, identify the most effective therapies, predict potential complications, and adjust treatment plans in real-time. This approach ensures that patients receive care specifically tailored to their unique health profiles, leading to better outcomes and higher patient satisfaction. By leveraging historical data and machine learning algorithms, predictive analytics enables proactive healthcare management, reducing hospital readmissions and improving overall patient care. Infosys’ Digital Health Platform is one such platform that incorporates these features and can be leveraged for multiple therapeutic areas.
4.Could you elaborate on the key challenges that AI platforms are helping solve in healthcare delivery?
AI platforms address several key challenges in healthcare delivery, including limited patient-physician interaction time, misinformation, and the need for improved therapy efficacy. They enhance patient education, provide real-time monitoring, and deliver personalized insights. By integrating data from various sources, AI platforms help reduce adverse events, improve treatment adherence, and streamline clinical workflows, ultimately leading to better patient outcomes, more efficient healthcare delivery and information about their treatments.
5. What safeguards has Infosys implemented to ensure patient data privacy and security while leveraging AI analytics?
Infosys ensures patient data privacy and security by adhering to regulatory standards such as GDPR and HIPAA. They implement robust security measures, including data encryption, access controls, and regular audits. Infosys Topaz employs advanced AI algorithms to detect and prevent data breaches, ensuring that patient information remains secure. Additionally, Infosys has a dedicated global privacy and security team to oversee compliance and safeguard patient data.
6. What role does machine learning play in generating actionable insights for both patients and providers?
Machine learning plays a crucial role in generating actionable insights by processing and analyzing large datasets from various sources. It identifies patterns and trends in patient data, providing personalized recommendations and predictive analytics. These insights help both patients and providers make informed decisions about treatment and care, leading to improved health outcomes and more efficient healthcare delivery. Machine learning also supports early diagnosis and intervention, enhances disease management, and facilitates the development of personalized treatment plans, ultimately improving the quality of care.
7. What metrics are being used to evaluate the effectiveness of AI in streamlining healthcare processes?
Metrics used to evaluate AI effectiveness include patient outcomes, therapy adherence rates, reduction in adverse events, and overall healthcare costs. Infosys Topaz enhances these evaluations by providing comprehensive data insights and predictive analytics. The efficiency of data processing, accuracy of predictive models, and user satisfaction are also key indicators. These metrics help assess the value and impact of AI-driven healthcare solutions, ensuring they contribute to improved patient care and operational efficiency.
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