The development and implementation of Artificial Intelligence in healthcare is complex and costly, so health organizations need to make smart decisions and develop strategic plans that enable them to bring real value to their organizations. Below are some considerations for the successful development, deployment, and integration of AI in healthcare.
As decision makers, it is essential to consider both short-term and long-term goals as you develop an AI strategy for your organization. In the immediate future, you need to build a use case by identifying the most pressing problems your organization is facing and determining how those problems can be solved using AI methods and technologies. Cost savings available.
In the long term, you need to envision the future of your organization, considering how your organization can evolve and how existing and emerging AI technologies can be extended to transform your organization. Effectively, build a hospital for tomorrow. Many medical organizations are now focusing on ML on hospital EHR data. As 5G super-fast connectivity becomes available, there will be a convergence of AI technologies, sensors, voice chat, virtual/augmented reality, and other interactive media. Real-time monitoring, diagnosis and treatment optimization based on historical and current data of both individuals and populations will become possible. This will enable the development of a smart, integrated and connected nationwide digital health ecosystem that will not only support medical decision making and clinical research but also improve patient education, engagement and home care. So healthcare leaders need to design their AI strategies and infrastructure capabilities with a vision of both the present and the future.
Technology alone will not change healthcare; it needs people who derive value from AI and those who make an impact on the Healthcare Management Forum in your organization. Senior leaders can make a difference in their AI projects by providing the necessary funding, talent, and resources. In addition, it is important to build a pool of people with the diverse expertise needed to develop AI, integrate technology, migrate data, and integrate health services. Equally important is developing a corporate culture that engages the entire organization in AI innovation. Healthcare organizations need to be prepared to work with partners across the industry, work with partners to make smart decisions, and make AI implementations and integrations successful.
Healthcare providers vary in their size, type, challenges, priorities, and resources. For vendors that already have an audio EHR system installed, AI capabilities can be added to the EHR system as many EHR vendors have opened their platforms to allow data exchange and system connectivity . Additionally, many vendors are adding AI features to their EHR systems. For most hospitals, working with EHR vendors and other AI technology companies to develop the solutions they need is probably the best option.
For organizations that have the expertise and resources to build their own AI capabilities or want to become AI players in the healthcare industry, they can do so using commercially available AI cloud platforms and services. To keep the business running as usual, they can build new AI infrastructure and process it independently, and then link it to the old infrastructure. This gives medical organizations complete control over initiating a new process while avoiding interference with ongoing operations.
Successful ML relies on access to large volumes of quality data; The source, size, and quality of the data can significantly impact the ML models developed. Collecting large-scale data that is complete, accurate, up-to-date and representative of typical populations is a major challenge for analysts. Part of the bias in AI is due to the lack of diverse data available to train the algorithms. So the ability to collect, store, and learn from data is critical to AI success, and often AI staff spend most of their time cleaning up data to ensure the quality of the models. ML model they are developing. Some data scientists believe that it is better to collect new data that meets current data standards than to clean up cluttered old data. This is a valid view because stale data in an EHR system often contains noise, bias, errors, and unusable data.
Not including enough meaningful and representative data during training and validation is a common problem in ML. Health organizations need to understand those limitations and provide complete, balanced, diverse, and representative data from their populations to retrain and validate ML models as they deploy them. Decision-makers need to keep in mind that most AI technologies are not “off-the-shelf” products that you can simply plug into your digital system to make it work. On-site small-scale pilot testing is a good way to validate any AI application.
Ensuring patient safety, privacy and well-being requires conducting hazard analysis, assessing the consequences of potential false positives and false negatives, and developing risk prevention procedures. muscle. For important clinical processes that could lead to serious consequences (e.g., making medical treatment and diagnostic decisions), a dual safety mechanism is required. In such cases, the physician is the one making the call, using the insights generated by the data as a reference. Furthermore, to deploy any AI product, it is necessary to do on-site piloting and validation. Ultimately, it is important to collect real-world evidence and develop a mechanism to continuously monitor system performance, ensuring the safety and effectiveness of AI products deployed on an ongoing basis.
In all of these processes, it is important to establish policies and protocols to ensure privacy, security, and ethics of the use of AI. However, we must strike a balance between patient privacy and data sharing, and between regulation and innovation. Artificial intelligence professionals need to work with large volumes of real patient data to ensure the accuracy and safety of ML models, so patients need to know that AI can only be enhanced when they share data more freely and this can be done in a secure environment. Meanwhile, medical organizations need to ensure that their AI approaches are legal, ethical, and robust, demonstrating complete transparency about what they do with patient data.
Assessing AI approaches takes time but it will enable health organizations to discover problems and fix them before it is too late. Before implementation, it is essential to define performance evaluation metrics, then measure AI success accordingly at different stages of the development and implementation (eg, pilot testing, scaled implementation, and validation). Such performance metrics should reflect the values, priorities, and vision of your organization. There are many ways to assess AI technologies. Generally speaking, things to consider in the evaluation should include improved clinical effectiveness (quality, efficiency, and safety), extended access and expanded services to patients, improved patient experience and outcomes, optimized operational processes, improved staff satisfaction with the work environment, and reduced costs and increased revenue.
Given the high complexity and costs involved in developing the various types of AI technologies needed for improving the effectiveness, access, and affordability of healthcare, each country needs to have a national AI strategy for building a nationwide AI-powered digital healthcare ecosystem that benefits both health organizations and patients. Currently, the majority of funding is used for developing ML on big EHR data, mostly for the benefits of health professionals. As the total health can only be achieved through joint efforts between health professionals and patients, patients need to have AI-powered tools for self-monitoring and self-managing their chronic conditions.