AI and Analytics paving the Future of Healthcare
How DATA and AI can improve Technology?
- Streamlining administration: AI has the ability to speed up and automate certain tasks, which could lead to a more efficient workflow in a healthcare setting. Additionally, AI can help to identify potential issues and problems that may arise, which can help to prevent or mitigate them before they become bigger issues.
- Unlocking precision care: AI has various use cases when it comes to care delivery. It is already being used for intelligent symptom checkers which is reducing valuable provider time at the diagnostics stage. Since the advent of covid-19, providers have also started using AI-powered assistants to offer personalized recommendations. Additionally AI can take the concept of precision medicine to whole another level by analyzing data and providing informed conclusions. Lastly, healthcare AI can also be used to predict the health risks of a specific population.
- Improving patient engagement: AI can automate communication with patients, which can help keep them updated on their care and reduce the chances of them becoming disengaged. Healthcare providers can utilize AI to better identify and personalize treatments for patients, increasing the likelihood that patients will stick to their treatment plans. AI-powered Chatbots and virtual assistants usually make care delivery more personalized and convenient for patients.
Roadblocks to Data Analytics Journey
- Data silos that are difficult to integrate: Data silos are roadblocks in healthcare because they can lead to inaccurate or incomplete patient data. This can lead to suboptimal care and even patient harm. For example, a patient’s medication history may be incomplete if it is only stored in the pharmacy’s data silo. This could lead to the patient being prescribed a medication that interacts with one of their other prescriptions.
- Scalability due to the volume and velocity of data sets: Provider networks working on legacy systems have huge volumes of data stored away which they rarely use. This causes inaccurate data points. As an AI needs a substantial set of accurate data to start working automatically, inaccurate data hinders will make the system prone to errors and also hinder future upgradation.
- Educational deficit: It is not cost-effective for a provider to keep an AI developer in their network. The best option is to outsource such a product. But this also is tricky because the process involves a third party who needs to understand your specific problems.
How to build a Strategy for Modern Cloud Data Analytics?
- Enabling use cases through data unification: identify the data sources you will need to power your analytics. In many cases, this data may be spread across multiple systems in your organization. The second step is to build a data lake or data mart that can aggregate this data in a single location. Once you have the data in a centralized location, you can begin using machine learning and artificial intelligence algorithms to find patterns and insights.
- Identifying high-value ML opportunities: Healthcare providers can take advantage of the economies of scale offered by the cloud to store and process large amounts of data more efficiently. This data can then be used to identify high-value ML opportunities and also be used to power ML algorithms that can automate important tasks such as diagnosis and treatment recommendation. Some examples of such data types include patient histories, medical images, genomic data, and data on drug interactions.
- Enabling best-of-breed technologies: A modern cloud data analytics platform, can provide a wide range of services that you can use to collect, process, and analyze your data. You can also them to deploy machine learning models and create predictive dashboards to provide better care.
AI and Machine learning are still evolving but the recent trends sway toward the adoption of these technologies. As the technologies are still relatively new, providers are finding it hard to locate experts who can guide them in taking the next step. At Nalashaa, our data experts can perfectly analyze your data and provide you with insights for better tomorrow. We specialize in all three main cloud platforms which include AWS, Microsoft Azure, and Google cloud platform. We have been a healthcare AI development partner for major US healthcare providers for more than a decade now.
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A writer with a keen interest in the Healthcare domain and B2B content marketing. He enjoys writing and creating pieces around the latest Healthcare IT trends using the simplest of words.All stories by: Mitrajit Das