"Advancing Healthcare with AI: Insights from Dr. CIP Wadhwa"
My name is Dr. CIP Wadhwa. I’m an internist and geriatrician, and I also completed training in healthcare economics at the Wharton School. I have a strong interest in population health and the use of technology and innovative payment methods to advance healthcare. I am particularly focused on reducing morbidity and preventable mortality.
I’ve had the privilege of working in both academia and regulatory roles, and recently, I’ve become more involved in global health and national models of care. I’m especially excited to collaborate in the Gulf region to advance population health.
One area where conversational AI, like front-end speech recognition combined with ambient clinical documentation, shows promise is in creating new efficiencies for clinicians. This technology helps reduce the time clinicians spend documenting in the electronic health record, allowing them to focus more on patient care rather than on data entry. With ambient clinical documentation, we can record conversations between the patient, physician, and family, and then use generative AI to create an initial draft of the clinical note. The note becomes a byproduct of the encounter, physical exam, and conversation, enabling clinicians to focus on the patient, make eye contact, and pick up on non-verbal cues instead of typing and clicking.
I believe this approach will have a measurable impact on patient experience, as it allows clinicians to concentrate on the patient and the family. This technology captures essential clinical details from conversations needed to document an accurate, comprehensive picture of the patient's health, and enables the physician to take on a review-and-edit role, enhancing productivity, quality of care, and the patient experience.
However, new technology should be approached with caution. While some early adopters may quickly become comfortable with it, a phased rollout with champions within the organization can help address concerns. While technology has promised to simplify healthcare, there have been unintended consequences. Initial experiences with this technology, however, have helped alleviate some worries, and as more people start to use it, we will understand its value more clearly.
AI tools could enhance healthcare in several ways:
Supporting Differential Diagnosis: Although the brain is remarkable, AI-assisted differential diagnosis can offer thoughtful, point-of-care suggestions.
Enabling Earlier Diagnoses: By expanding the differential diagnosis earlier, especially with subtle early symptoms, AI could help bring certain conditions to mind sooner.
Incorporating Social and Genetic Factors: A holistic view of the patient, including social risks, genetics, and life context, could improve diagnosis and care in a structured, unobtrusive manner.
Supporting Public Health: While physicians primarily work on an individual level, these tools can help with the early identification of public health threats, be they infectious or environmental, allowing physicians to wear their public health hats at both micro and macro levels.
Respecting patient privacy and autonomy is essential. Patients should have the right to opt out of AI documentation if they’re uncomfortable with it. It’s also important for patients to be informed if genetic information or biomarkers are used to guide decisions. The days of simply telling patients what to do are over—engaging them in their care enhances both understanding and outcomes.
In the past two years, I’ve spent significant time in the Gulf region, particularly in the UAE, Saudi Arabia, Qatar, and Kuwait, where I’ve had the privilege of working with regulators and health systems. I am impressed by the commitment to population health, thoughtful use of technology, and inclusion of youth in the workforce. The Gulf region is at the forefront of these advancements, with both health systems and regulators adopting a technology-informed, population health approach. It’s an exciting time to be involved in healthcare in the GCC.
One of the challenges with AI is ensuring that its training datasets are free from bias. It’s critical to have explainability and transparency in AI models for both patient and physician trust. Continuous monitoring is essential to avoid unexpected shifts in model performance, commonly known as "model drift." It’s not a “set it and forget it” approach but a continuous process to ensure models remain effective and reliable. Without careful integration, these efforts may face significant challenges.
"Advancing Healthcare with AI: Insights from Dr. CIP Wadhwa"
My name is Dr. CIP Wadhwa. I’m an internist and geriatrician, and I also completed training in healthcare economics at the Wharton School. I have a strong interest in population health and the use of technology and innovative payment methods to advance healthcare. I am particularly focused on reducing morbidity and preventable mortality.
I’ve had the privilege of working in both academia and regulatory roles, and recently, I’ve become more involved in global health and national models of care. I’m especially excited to collaborate in the Gulf region to advance population health.
One area where conversational AI, like front-end speech recognition combined with ambient clinical documentation, shows promise is in creating new efficiencies for clinicians. This technology helps reduce the time clinicians spend documenting in the electronic health record, allowing them to focus more on patient care rather than on data entry. With ambient clinical documentation, we can record conversations between the patient, physician, and family, and then use generative AI to create an initial draft of the clinical note. The note becomes a byproduct of the encounter, physical exam, and conversation, enabling clinicians to focus on the patient, make eye contact, and pick up on non-verbal cues instead of typing and clicking.
I believe this approach will have a measurable impact on patient experience, as it allows clinicians to concentrate on the patient and the family. This technology captures essential clinical details from conversations needed to document an accurate, comprehensive picture of the patient's health, and enables the physician to take on a review-and-edit role, enhancing productivity, quality of care, and the patient experience.
However, new technology should be approached with caution. While some early adopters may quickly become comfortable with it, a phased rollout with champions within the organization can help address concerns. While technology has promised to simplify healthcare, there have been unintended consequences. Initial experiences with this technology, however, have helped alleviate some worries, and as more people start to use it, we will understand its value more clearly.
AI tools could enhance healthcare in several ways:
Respecting patient privacy and autonomy is essential. Patients should have the right to opt out of AI documentation if they’re uncomfortable with it. It’s also important for patients to be informed if genetic information or biomarkers are used to guide decisions. The days of simply telling patients what to do are over—engaging them in their care enhances both understanding and outcomes.
In the past two years, I’ve spent significant time in the Gulf region, particularly in the UAE, Saudi Arabia, Qatar, and Kuwait, where I’ve had the privilege of working with regulators and health systems. I am impressed by the commitment to population health, thoughtful use of technology, and inclusion of youth in the workforce. The Gulf region is at the forefront of these advancements, with both health systems and regulators adopting a technology-informed, population health approach. It’s an exciting time to be involved in healthcare in the GCC.
One of the challenges with AI is ensuring that its training datasets are free from bias. It’s critical to have explainability and transparency in AI models for both patient and physician trust. Continuous monitoring is essential to avoid unexpected shifts in model performance, commonly known as "model drift." It’s not a “set it and forget it” approach but a continuous process to ensure models remain effective and reliable. Without careful integration, these efforts may face significant challenges.