As we kick off 2024, we wanted to start the new year with a series of 2024 Health IT predictions. We asked the Healthcare IT Today community to submit their predictions and we received a wide ranging set of responses that we grouped into a number of themes. In fact, we got so many that we had to narrow them down to just the best and most interesting. Check out our community’s predictions below and be sure to add your own thoughts and/or places you disagree with these predictions in the comments and on social media.
All of this year’s 2024 health IT predictions (updated as they’re shared):
- John and Colin’s 2024 Healthcare IT Predictions
- Health Equity Predictions
- Healthcare Cybersecurity Predictions
- Telehealth and VR Predictions
- Value Based Care Predictions
- Pharma IT and AI Predictions
- Healthcare Interoperability, Data, and Cloud Predictions
- Healthcare Workforce Predictions
- Healthcare Generative AI and Data Predictions
- Healthcare AI Regulations and Ethics Predictions
- Cautionary Views of AI
- Patient Preditions
- Uses of AI in Healthcare Predictions
- General Health IT Predictions
- Health IT Predictions (on video)
- Healthcare AI Predictions
And now, check out our community’s Cautionary Views of healthcare AI predictions.
Joseph Zabinski, Ph.D., Managing Director, AI & Personalized Medicine at OM1
The Anti-Prediction of AI in 2024: The healthcare industry likes to compare artificial intelligence (AI) to a standard of perfection – we want it to get the answer ‘right’ every time, and sometimes, we’ll even hear this claimed as a ‘goal.’ The reality is, this won’t happen next year (or the next, or the next!). In 2024, we must compare what AI can do to the world as it is – real patient journeys, real diagnostic procedures, and (lack of) uptake, and real treatment decision-making under tremendous uncertainty. AI will always disappoint if we expect it to be perfect, but it can be incredibly helpful if we use patients’ lived experiences as our baseline to create well-supported solutions that address real problems.
The key to AI adoption: acceptance among patients: As we move into 2024, a key challenge to artificial intelligence (AI) adoption will be acceptance among patients, particularly as cases of AI ‘getting it wrong’ are widely publicized in the media. In 2023, AI became a familiar concept, but next year, the industry needs to take the next step of supplying clarity, transparency, repeatability, and answers around AI’s strengths and weaknesses directly to patients. To do so successfully comes down to using AI when it makes sense – not always – and framing the added value AI can create. The gap between what AI can do in theory, and what it actually does in the real world will only be closed if we address barriers to access and acceptance among patients. In 2024, the industry needs to be specific about the value AI creates; efficient and seamless in its delivery for providers and patients; and resolute in the use of insights to help make decisions – only then will we see acceptance and the next step of adoption.
Miroslav Klivansky, Global Practice Leader – Analytics & AI at Pure Storage
In 2024, we will start to creep into GenAI’s trough of disillusionment (Gartner’s Hype Cycle defines this as a period when interest wanes when experiments and implementations fail to deliver) and will eventually industrialize the use of AI. As we shift from the hype brought on by AI tools with a consumer-friendly UX, we’ll see companies better understand, invest, and apply AI-specific solutions to their business needs.
In fact, one of the industries most ripe for innovation with the help of AI in general is healthcare. Not only does it have the potential to improve diagnostics, but it also can improve medical devices and automate administrative tasks. The latter will likely be disrupted first because these systems are electronically managed and quickly automate tasks.
Matt Eisendrath, President and Chief Commercial Officer at Full Spectrum
Medtech companies will continue to pour money into AI. AI’s near-term impact outside of diagnostic imaging is uncertain, and the hype-cycle surrounding it echoes past experiences with emerging technologies like analytics. Instead of hastily pouring resources into AI initiatives despite the unknown returns, wait for clear and specific use cases around AI and consider the advantages of investing in cybersecurity first.
Kari Miller, Senior Director of Product Management at IQVIA
Quality management has historically strayed away from the rapid adoption of artificial intelligence (AI) tools, as with such sensitive information in quality management systems (QMSs), they have been cautious of implementing an additional element of intelligence into their process. However, many are starting to turn a new leaf with technology, as they begin to leverage large language models (LLM) and other capabilities of AI, which is a trend expected to continue in 2024. Additionally, the use of recommendation engines will increase, as they allow the combining of AI with personal experience, focusing on augmented intelligence as opposed to solely AI. Recommendation engines can greatly improve responsiveness, cycle times, accuracy, and efficiency, making a significant impact on productivity. The use of public generative AI (GenAI) will take longer to enter the space due to the risk of leaked intellectual property, which is critical in life sciences, however, as private GPT becomes possible use of GPT will increase. As these trends increase, there will be an expansion of investment in predictive and preventative analytics.
Additionally, as organizations work to break down siloes between quality and other departments AI can be used to connect and work across systems, which will greatly benefit everyone improving productivity and efficiency. This connectivity will allow greater access to data across disciplines such as enterprise resource planning (ERP), supply chain management (SCM), and product lifecycle management (PLM) reducing errors and duplication of effort.
Joerg Schwarz, Sr. Director of Healthcare Interoperability at Infor
Healthcare executives are cautious to use generative AI for clinical content, and we can expect this apprehension to extend well into 2024, primarily because of the overabundance of unstructured data. Healthcare CIOs and CTOs will begin implementing a data analytics platform strategy combining data from different sources (IoT, EMR, ERP) in 2024, with hopes of using this platform to feed the many AI algorithms that promise improved operational and clinical outcomes. While healthcare providers recognize the potential of generative AI, 2024 will be the year when organizations come together and work to train algorithms for reliable results, in parallel with good governance models.
Michael King, Senior Director of Product and Strategy, Technology Solutions at IQVIA
While the excitement and innovation of artificial intelligence (AI), inclusive of language learning models (LLMs), natural language processing (NLP), and generative AI (GenAI), are groundbreaking and advance the capabilities of technology, organizations are beginning to realize that success is not solely predicted by the latest and greatest technology solutions. Not only will organizations realize the limitations of this technology, but there will be some impeding forces, such as regulations, the availability of industry/product-specific data to train the AI tools, and the commercial viability of such AI solutions which will tailor the use of these technologies in life sciences.
Especially in the healthcare and life sciences industry, organizations are realizing that technology is not the answer to every problem. Rather, it is the proper implementation and utilization of technology, driven and understood by people and supported by processes, that will produce the most effective outcomes for patients. Organizations often neglect people and process portions of technological advancement and the improved interfaces between these parties will bring about greater outcomes for companies and patients alike.
Increasing regulations and standards are advancing the complexity of quality management in the life sciences industry. Organizations are being asked to provide improved results while working with constrained investment and resources, driving the need for efficient, compliant business operations with controlled investment in headcount. The way in which organizations will counter these difficulties is with the provision of consulting services, outsourced services, and technology. The real value of quality management is in the ability to combine these services to address pain points at a given point in time and as the organization scales in its company evolution, not just through deploying out-of-the-box technology or software.
Jason Handza, Chief Medical Officer at Nextech Systems
Enthusiasm levels among healthcare stakeholders for artificial intelligence vary, and navigating the thoughtful integration of this powerful technology is crucial as we approach 2024. Many see AI as a catalyst for improved patient outcomes, supported by emerging trends and prospects. However, the surge in AI’s presence has prompted federal government action, with President Joe Biden signing an executive order to mitigate potential risks in AI systems, particularly in healthcare applications. The urgency is clear–it’s time for meaningful discussions on ethical AI use and strategic implementation within healthcare. As we’re still in the early stages, these conversations must commence promptly. In healthcare, change, even under the guise of technological progress, waits for no one.
Be sure to check out all of Healthcare IT Today’s Healthcare AI content and all of our other 2024 healthcare IT predictions.
