Artificial intelligence (AI) has the potential to reinvent operational models and drive consequential change in healthcare. From primary care and triage to patient risk identification and radiology, AI can increase productivity and enhance the efficiency of care delivery.
However, as with any opportunity for improvement, AI comes with its own set of complexities. AI transformations in the healthcare industry can be especially challenging, but they are absolutely necessary to remain competitive. In this article, I offer a playbook for healthcare organizations looking to adopt AI in a seamless manner. I explain why AI transformations matter, and share a step-by-step methodology for beginning your successful AI adoption journey.
AI transformation: Sooner is better than later
Healthcare organizations have many issues to overcome when it comes to AI adoption. To name a few:
- Lack of understanding of how AI works, making it difficult to find the right AI use cases to adopt, let alone to implement and apply AI solutions
- Adoption of AI requires high-end hardware and software, and AI development demands greater than average time and resources
- High-quality data is hard to come by: patient privacy, the ethics of data ownership, and the variability of data types play a significant role
But beginning your healthcare organization’s AI transformation will be worth the effort. Just consider that the AI in healthcare market was valued at $7.9B in 2021. It is estimated to grow to $201.3B by 2030, with a CAGR of 43.4%.
The fact is, AI will become a game changer in the next 30 years. If you do not get on the AI train today, your business may be completely out of the picture in the near future.
From discovering AI opportunities to building a robust foundation for scaling AI
AI transformation is a complex process initiated to improve and modernize the technology, processes, and culture of an organization. Aside from AI, it often involves building and implementing the systems and services required for automation and advanced analytics, and eventually aims to reinvent how healthcare is delivered.
The AI journey starts with the recognition of AI as a competitive advantage by your leadership team. The stakeholders, including your IT team, should be ready to accommodate change while acknowledging that the speed of AI innovation may fail to meet their short-term expectations.
A crucial prerequisite for a successful AI transformation in healthcare is data. You should have large volumes of accurate, objective, and easy-to-interpret data. Consider these factors when evaluating for data readiness:
- You can have EHRs stored in neat tables, but also thousands of scanned clinician notes. To make AI work, you need to find a way to integrate and process multiple data types from various sources.
- Even if you have well-structured EHRs, you will have to check your datasets for duplicates, missing values, syntax errors, format errors, etc., so you should allocate time and resources for this.
- Getting access to the right dataset(s) can be problematic if multiple service providers are a part of the medical service delivery lifecycle. Remember: no data means no AI.
The next step is identifying AI opportunities. Consider the following:
- AI use cases can come from top management, business units, and from grassroots. The latter may come up with the most hands-on applications that can drive efficiencies in the short term.
- Starting with customer-centric use cases is recommended, but you can also work on workforce-centric and operation excellence use cases (e.g. intelligent document processing). The key is to select use cases that can begin to generate value as soon as possible, to garner trust and buy-in across your organization.
- When ideating, keep data in mind, but also be aware of such factors as model accuracy, adoption timeframe, and other Go/No-Go KPIs. Imagine that you have an eye-screening app that can detect vision impairments with AI. If its models are just 80% accurate, one of five screens will be erroneous, meaning excessive overhead cost to catch misidentified screens manually. Implementing such a solution will not be feasible.
You are now ready to build your first AI pilot, and when it has proven its value in one area or department, you can consider building a more robust foundation for scaling AI across your organization.
By robust foundation, I mean a fully automated, end-to-end machine learning (ML) infrastructure with machine learning operations (MLOps). A solid foundation will enable your engineers to deliver ML models from research to production, faster and at scale, with minimal handoff. It dramatically accelerates time-to-value for AI initiatives.
Once the foundation is established, scaling AI across your organization entails all of the above: evaluating the available data, ideating on AI use cases, having subject matter experts and engineers on the ground to train and release models, and assessing the value that every AI use case brings for Go/No-Go.
The transformation of processes and culture go hand-in-hand with technology transformation. AI is not a silver bullet, and you risk ending up with a meager ROI if your employees do not see the value of AI, or find it hard to use AI solutions.
AI adoption in healthcare is difficult but necessary
Healthcare is not the easiest industry for adopting AI. Clinical workflows are complex, disjointed, and hard to integrate; the right data is often impossible to access; privacy and ethics of AI use remain significant barriers.
But AI is a must-have to improve patient experience and outcomes, streamline operations, and strengthen innovation. AI front-runners are already positioned to achieve the level of customization, personalization, and operational efficiency necessary to compete in a fast-paced digital environment, while late adopters risk massive declines in performance and profits.
Embracing AI transformation is better done sooner than later. Take your first steps today.
Rinat Gareev is an AI/ML Practice Lead at Provectus. He has more than 10 years of experience in AI and machine learning, in both business applications and academic research. Rinat’s expertise enables him to cover the whole ML lifecycle, from problem framing to model deployment and monitoring. At Provectus, Rinat applies his vast experience to design, development, and operationalization of AI/ML solutions for customers.
