How is AI transforming the healthcare industry? AI adoption improves patient outcomes, reduces costs, and increases operational efficiency. However, to develop a successful AI solution you need to involve the persons who will be working with it i.e. the end users. Involving healthcare practitioners at the initial stages of developing an AI solution helps in faster adoption.
Expert insights
Access to practitioner’s valuable knowledge and experience in handling patient care can make AI tools more efficient. It can be quite beneficial to include radiologists in the process of creating an AI solution for accurate diagnosis. How? The AI model can understand key components of interpreting imaging results. It reduces the possibility of developing clinically irrelevant solutions, thereby preventing expensive adoption errors.
Increasing Trust and Buy-In
There is a concern that AI might reduce the human element of patient care. For example, there is a need for an AI-based chatbot to assist with patient triage i.e. sorting of patients. As the nurses or physicians are closely monitoring the patient’s current health condition, it is essential to involve them and get their insights to develop a sustainable AI solution. Their insights can provide specific responses that reflect the industry best practices, building trust among the users. This directly leads to an increase in AI adoption.
Enhancing Data Quality
High quality data is important for creating a reliable, trustworthy AI solution. Practitioners provide accurate, timely, and relevant data that is used to train AI systems. Let us take the example of using predictive analytics to manage patient populations. In this case, health care providers can identify critical variables such as comorbidities and social determinants. Using these insights, the AI solution will deliver clinically proven results that can be used for improving patient care strategies.
Driving User-Centered Design
Without practitioner engagement, AI solutions can be advanced but could fall short for daily operations. Take the example of creating an AI tool for managing electronic health record (EHRs). The results for usability testing will be more accurate if physicians or administrative personnel are involved in the development process, and the output will be a user-friendly interface. Moreover, it will be easy to integrate the AI tool into the existing workflows without much administrative support. Practitioner engagement will increase the overall efficiency of the AI adoption process.
Would you like to get more insights about seamless adoption of AI in healthcare? Register now for our panel discussion at the AI in Healthcare and Pharma Summit 2024.
