There is growing momentum to deploy generative artificial intelligence (AI) to increase administrative efficiency, reduce the burden of clinical documentation, and hyper-personalize the patient care experience.
However, despite the potential benefits of these technologies, many healthcare leaders remain wary of issues such as data accuracy and availability, accountability, and persistent bias. While these concerns may be well-founded, a recent Deloitte report finds that many healthcare executives have potential blind spots that hinder successful technology integration. .
Some organizations focus too much on the importance of data without developing a comprehensive framework that focuses equally on consumers, governance, and workforce.
Three potential blind spots in generative AI to be aware of
The report is based on a survey of 60 healthcare executives, with 70% saying they are very focused on data considerations such as availability, quality, compliance, security and privacy during AI implementation. I understand that. The study also found several potential blind spots (defined as considerations that less than 60% of executives are focused on) that can prevent organizations from successfully integrating generative AI into their workflows. I did. They include:
1 | Implementing effective governance
Implementing a governance model can get buried under other data priorities. Implementing a data-inclusive governance model is critical to ensuring effective use and quality of data, reducing data bias for fair design, and protecting patient privacy, the report says. says. These elements help build consumer and employee trust. Nevertheless, creating a data governance model (60%) and mitigating data bias (45%) ranked lowest among considerations for healthcare managers.
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Establish a strategy with key decision makers that allows your team to test, learn, and build. Although organizations can pursue a variety of decentralized or centralized models, the authors explain that a center of excellence model is the most promising. These models tend to centralize expertise and help ensure that AI applications are developed and deployed according to uniform standards for safety and regulatory compliance. The Center of Excellence fosters interdisciplinary collaboration, optimizes resources, fosters innovation within a risk management framework, ensures ethical considerations in AI deployment, and supports scalability and efficient vendor management. Masu.
2 | Building consumer trust
Surveyed executives said they are placing less emphasis on building consumer trust in generative AI, improving data sharing (50%), and educating consumers about AI and its risks (45%). I am. This comes at a time when many consumers are demanding more transparency about how their data is being used and who is using it. Healthcare organizations that are not aware of this trend may see lower levels of trust and engagement with technology, the authors say.
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Actively engage consumers to understand their most critical pain points and learn what AI solutions they want to use. This can be done through direct consumer feedback and focus groups as you iterate on building, testing, and rolling out new products. This process should be transparent, fair and collaborative.
3 | Meeting investment and workforce needs
This is a low priority issue among respondents. A recent cross-industry study from Deloitte found that early adopters see greater value in using generative AI to upskill and reskill employees, rather than eliminating jobs and cutting costs. It shows what you are thinking. However, the report found that fewer than two-thirds (63%) of respondents focused on upskilling employees, addressing employee concerns (60%), and change management, i.e. changing jobs. As it turns out, many executives may overlook this obligation. Employee composition (57%).
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It is very important to get employee buy-in, as well as consumers. You can start by focusing on your employees' needs and addressing their concerns about technology. Emphasize the employee's AI literacy and the integration of generative AI as an ally for the employee rather than a challenge. This allows workers to focus on aspects of their roles that will help them grow and develop in a changing environment.
