8 Signs You Need Help with AI in Healthcare

8 Signs You Need Help with AI in Healthcare

Healthcare is an industry that stands to receive help from the development of Artificial Intelligence (AI). From chatbots powered by natural language processing (NLP) and chat GPT (generative pre-trained transformer) to machine learning algorithms that can help diagnose illnesses, AI has the potential to revolutionize the healthcare sector.?


Statistically speaking, Frost & Sullivan’s research found that global revenue from healthcare due to the use of artificial intelligence is expected to reach $6.7 billion (about $21 per person in the US) by 2021, a marked increase from the $811 million seen in 2015. But with such exciting potential comes great responsibility; without the right guidance and aid, healthcare organizations may struggle to make the most of AI and its associated technologies.??


In this blog post, I will discuss eight signs that you need help implementing AI in your healthcare organization.?


1) You're feeling overwhelmed?

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If you are feeling overwhelmed with AI in healthcare, you are not alone. The rapid advancement of technology and the increasing complexity of AI systems can make it difficult to stay on top of the latest developments. It is natural to feel overwhelmed when there is so much to learn, and you may even want to give up before you start.?


However, feeling overwhelmed should not be a reason to give up. It could be a sign that you need help. Many resources are available to help healthcare professionals understand and use AI, including AI consultants, training courses, and online tutorials. Getting help can make the process of learning AI in healthcare much easier and more manageable. With the right support, you can gain confidence in your understanding and use of AI.?


There are recent developments of deep learning AI-powered platforms that help speed up radiology image examination and reduce missed detections by up to 70% while automating mundane tasks and supplying always-connected, 24/7 access to patient data.


2) You're Struggling to Use the Right Tools?

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Knowing which tool is right for your organization can be a daunting task. It's important to find a tool that best suits your needs and the needs of your team.??


The right tool for you will depend on the specific goals you want to achieve. When it comes to AI tools, there are three main categories: automation, predictive analytics, and deep learning.??


Automation?

Automation tools can automate certain processes and tasks, such as patient scheduling or claims processing. Automation tools may be helpful if you want to streamline processes or free up time for staff to focus on other tasks.?


Predictive Analytics?

Predictive analytics tools use data-driven models to predict future events or trends. Predictive analytics tools may be helpful if you want to understand customer behavior or name potential health problems.?


Deep Learning?

Finally, deep learning tools use artificial neural networks to recognize patterns and draw insights from large amounts of data. Deep learning tools may be helpful if you need to analyze large amounts of data quickly and accurately.?


It's important to research each tool carefully and compare its features and capabilities before deciding. Additionally, speaking with an expert who can supply guidance and advice on the best AI tool for your organization may be helpful. Seeking help can ensure that you choose the right tool and get the most out of your AI implementation.?


3) You're Unable to Leverage AI For Health Care Data?

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If you’re struggling to make the most of your healthcare data and cannot use AI to help you use it, then you need help. AI can be a powerful tool in the healthcare industry, but it’s only useful if you know how to apply it correctly. Without the right training and ability, using AI for healthcare data cannot be easy.??


The key is to know what kind of data you’re looking for, how to access it, and how to analyze it. AI can help you find patterns in the data that may not be at once obvious, helping you to understand the patient population better or create more effective treatments.??


Once you learn how to fully use AI technology the opportunities are endless. For example, Sanofi, together with several company collaborators uses AI technology and supercomputers to predict which potential medicines will work and which won't. They have recently signed a $1.2 billion research collaboration with Sanofi to research small molecules aimed at up to five drug targets.?


To get started, it’s important to understand the basics of AI and how it works. This is the part where the Think AI team helps you make informed decisions about which data to use, what type of analysis is proper, and how best to use AI for your healthcare data.??


4) You're Unable to Integrate AI with Existing IT Infrastructure?

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Integrating AI into your existing IT infrastructure can be a challenging task. As AI algorithms become increasingly complex, they require more sophisticated hardware and software to run effectively. Also, if you don’t have the right ability, it can be difficult to properly design the infrastructure and ensure all the pieces fit together.?


The good news is that plenty of resources are available to help you integrate AI into your existing IT infrastructure. For example, many tutorials, white papers, and research studies offer insight into the process. Additionally, you can find experienced engineers and consultants who specialize in integrating AI with existing IT infrastructure.??


However, one of the most important steps you can take is carefully to analyze your existing IT infrastructure. This will help you decide what changes need to be made to accommodate the AI technologies you plan to deploy. With a comprehensive evaluation of your current IT setup, you can easily find areas where changes must be made and set up the best way to do so.?


Investing in a strong team of experts, like the very team I have built at Think AI, who can support your AI integration project is also a good idea. Having people who understand both AI and existing IT infrastructure can help ensure a successful implementation. They should be able to present you with a solid strategy and roadmap and able to guide you on how best to configure your hardware, software, and other components to work together effectively.??

By understanding the complexities involved in integrating AI with existing IT infrastructure and being prepared with the right resources and personnel, you'll be able to make the process smoother and more efficient. Doing so will give you an edge over competitors and put you on the path to harnessing the full potential of AI in healthcare.?


By understanding the complexities involved in integrating AI with existing IT infrastructure and being prepared with the right resources and personnel, you'll be able to make the process smoother and more efficient. Doing so will give you an edge over competitors and put you on the path to harnessing the full potential of AI in healthcare.?


5) Inability to Create Reliable Algorithms?

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Creating reliable algorithms is one of the most challenging aspects of AI in healthcare. An algorithm is a set of instructions that tells a computer how to solve a problem. For AI in healthcare to be effective, it needs to be able to interpret and analyze data sets accurately. This requires an understanding of the underlying principles of machine learning and the ability to design and create algorithms that can effectively learn from and process healthcare data.??


Creating algorithms for healthcare can be difficult because of the complexity of the data and the need for accuracy. Healthcare data often contains many variables and is often subject to change. As a result, it isn't easy to design algorithms that can interpret and make accurate decisions based on this data. Furthermore, many healthcare datasets are incomplete or have missing data points, making it even more difficult to create reliable algorithms.??


A great example of the successful implementation of machine learning and data algorithms is the cardiology-focused healthcare technology company, Tempus. They are revolutionizing cancer research, collecting and analyzing an abundance of medical and clinical data to craft personalized treatments for patients. By utilizing its library of data with AI-enabled algorithms, TempusJust like Tempus has done, your healthcare company may be able to contribute to genomic profiling, clinical trial matching, diagnostic biomarking, and academic research.?


When creating algorithms for AI in healthcare, it is important to understand how the data is structured and how different variables interact. In addition, it is important to test and confirm the algorithm using a wide range of scenarios before deploying it. By doing this, you can ensure that the algorithm can interpret the data and make accurate decisions correctly. Without doing this, you risk creating an algorithm that may not be exact or reliable.?


6) Issues Managing Patient Data Security?

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When it comes to using AI for healthcare, one of the key components is ensuring that patient data is secure. Any time data is collected and stored in a digital format, there is potential for misuse or hacking. The U.S. Department of Health and Human Services found that among the 337 healthcare incidents reported, 19,992,810 individuals were affected. With the number of breaches occurring each year, healthcare organizations need to use AI and machine learning and must be aware of the potential risks and how to protect patient data.?

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One of the key issues with managing patient data security is having the right processes in place to ensure data is protected. This means having systems to limit access to only authorized personnel, watching all access to patient data, and having the necessary compliance standards in place. Additionally, it is important to understand how the data will be used and stored and to consider encryption or other measures to protect the data.?


There are solutions developed to extract data from physician-patient conversations and convert it to medical notes for EHR systems. One example is the recently launched Chart Prep, which prepares a patient note?structure and content for the physician based on the unique visit type and the patient's records, including demographics, medications, history, imaging, and more. These EHR systems alleviate the manual task of physicians and can help reduce burnout symptoms of medical personnel. ?


Managing patient data security when using AI and machine learning can be complicated and time-consuming, but it is a critical part of any healthcare organization. Just like the Chart prep, the right processes, and systems should be in place to protect patient data should be a priority for any organization using AI in healthcare.?


?7) Struggling with Interoperability and Interconnectivity?

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Interoperability and interconnectivity are essential for any successful AI-enabled healthcare system. Without the ability to effectively share data, transfer information, and coordinate care, it’s impossible to get the most out of your AI technology. Unfortunately, many healthcare organizations still struggle with these issues, making it hard to take full advantage of the potential that AI has to offer.?


If you are facing problems with interoperability and interconnectivity, you need to address them quickly. Not doing so can affect the efficiency and effectiveness of your organization’s healthcare operations. Consider investing in an interoperability solution like HL7 and integrating it with your IT infrastructure. This will allow you to securely exchange data between systems, giving you access to the insights and analytics necessary to make informed decisions.?

Consider partnering with a vendor specializing in this field for more complex interconnectivity needs

. They can supply ability and guidance and integrate their solutions into your existing architecture. With the right partner, you can ensure that your healthcare organization can take advantage of all AI's benefits.?


8) Struggling With Business Needs

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Integrating artificial intelligence (AI) into your healthcare organization can be challenging, especially if you do not have the right ability and resources. One of the most important things to consider is your business needs. AI has the potential to revolutionize how healthcare is delivered, but it must be integrated into the existing business model to be effective.?


First, organizations must consider how AI can help meet their business goals. This could involve improving patient care, streamlining processes, or even reducing costs. In any case, it is important to thoroughly analyze the potential benefits of introducing AI before making any decisions.?


Organizations should also consider the potential risks involved with using AI in healthcare. If not responsibly managed, AI can cause significant disruption to existing processes and procedures. It is important to understand how AI can affect data security and privacy, for example, and any potential ethical implications.?


Finally, organizations need to consider how to best manage and support their AI systems over time. While it may initially seem like a cost-effective solution, AI can quickly become expensive if it is not effectively managed. Organizations should look for solutions that are designed for long-term use, such as cloud-based platforms that are regularly updated and kept.?


In conclusion, integrating AI into healthcare is a complex process that requires thoughtful planning and consideration. Before making any decisions, organizations should carefully evaluate their business needs and weigh the potential risks and rewards of introducing AI. Additionally, organizations should ensure they have the right tools and ability to manage and keep their AI systems successfully. With the right resources and strategies in place, organizations can take advantage of the many benefits AI offers in healthcare.?


Key Takeaways?

AI has the potential to revolutionize the healthcare industry by streamlining processes, improving patient outcomes, and enabling more correct diagnoses. As a business owner, I know how using this technology for healthcare can be daunting for any organization. From developing reliable algorithms to managing patient data security, a range of complex challenges need to be addressed.??

Some signs that show a need for AI help include feeling overwhelmed, struggling to use the right tools, inability to create reliable algorithms, and issues with interoperability and interconnectivity. To overcome these challenges, you must partner with an experienced AI consultant or solutions provider who can help you define your goals and develop a plan to integrate AI into your existing IT infrastructure. With their ability, you can ensure you are making the most of AI for your healthcare operations.?



From the Author??

Dear Fellow Thought Leaders,??

Please feel free to ask me your questions in the comments section below. And I am always happy to engage in a healthy discourse as much as I can or explore any topic – or this topic further in my next weekly article release.??

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Do you need to explore what AI applications or which tech stack solution is right for your Healthcare Organization? Our team at Think AI can help ensure that you make the right business decision in solving the most complex problem for your business. To learn more about how my team can help or if you have a related concept you wish to explore with me, send me a message here on LinkedIn.?

Hope Frank

Global Chief Marketing & Growth Officer, Exec BOD Member, Investor, Futurist | AI, GenAI, Identity Security, Web3 | Top 100 CMO Forbes, Top 50 Digital /CXO, Top 10 CMO | Consulting Producer Netflix | Speaker

4 周

Devendra, thanks for sharing! How are you doing?

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Scott Bartnick

#1 PR Firm Clutch, G2, & UpCity - INC 5000 #33, 2CCX, Gator100 ?? | Helping Brands Generate Game-Changing Media Opportunities ??Entrepreneur, Huffington Post, Newsweek, USA Today, Forbes

1 个月

Great share, Devendra!

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Lara Rosales

VP of Media Relations at Otter Public Relations

1 个月

Great share, Devendra!

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Ryan Bass

Orlando Magic TV host, Rays TV reporter for FanDuel Sports Network, National Correspondent at NewsNation and Media Director for Otter Public Relations

3 个月

Great share, Devendra!

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Rob Fegan

Demystify selling with Microsoft. Learn what it takes to partner with Microsoft field sellers.

1 年

When it comes to #data and #healthcareinnovation topics 3 and 6 from your article are so deeply intertwined and critical. It's the data that can be used to improve or revelotionize patient care but at the same time that needs to be balanced with protecting that data and ensuring patient privacy. Well written article Dave Goyal. #microsoftai #microsoftpartner

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