The healthcare industry is in a transformative era driven by artificial intelligence (AI). This technology is revolutionizing healthcare delivery, from accelerating drug discovery and streamlining clinical workflows to improving patient diagnosis and treatment plans.
In fact, AI is anticipated to potentially save healthcare professionals up to $110 billion within the next five years. This can translate to even further cost optimizations and more efficient healthcare systems on a global scale.
However, with critical patient data and lives at stake, AI in healthcare must be done right. A hasty rollout of the technology in the industry can have dire consequences – proving wasteful, disruptive and possibly even deadly.
Successfully harnessing the power of AI requires a strategic approach to IT infrastructure. Healthcare IT leaders face the challenge of balancing innovation with cost considerations and robust security measures, particularly when dealing with sensitive patient data. This article explores how strategic investment in healthcare organizations’ infrastructure can empower leaders to navigate these challenges and unlock the full potential of AI for their organizations – and for their patients.
Prioritize Knowledge Over Hype
AI in healthcare can mean a lot more than bigger profits – it has the promise to save and transform lives for countless patients across the globe. For centuries, major medical innovations have transformed our quality of life. However, data from these developments may not have been adequately documented or centralized, slowing progress and results. AI has the power to expedite innovation – speeding up research, discovery and testing at an unprecedented rate, and at a lower cost.
While AI can offer immense benefits to the healthcare industry and especially to end users, IT leaders must prioritize building a strong foundation in core data and AI principles before diving headfirst into complex solutions. These principles include:
- Build a foundation of good, clean data: AI runs on data, which means having clean, verified data is imperative. The adage “garbage in, garbage out” reminds us that faulty data will create a surefire mess.
- Start small: It’s important to take measured steps with AI implementation. There’s no need to incorporate a monolithic solution simply to obtain a few features that can be tested and implemented from trusted AI vendors.
- Set realistic expectations: Understanding the basic concepts of AI will empower leaders to set realistic expectations of its outcomes. It also allows them to be confident when vendors present their AI offerings, as leaders will be better able to distinguish big, flashy promises (often accompanied by equally big invoices) from other solutions that can add better value.
- Seek peer advice: It can be invaluable to get guidance from industry colleagues or trade organizations that have practical experience implementing AI in healthcare settings. Their insights can provide a clearer picture of the challenges and opportunities associated with AI adoption, offering a more grounded perspective than the inflated projections often presented by vendors.
By gaining core knowledge and collaborating with experienced peers, healthcare IT leaders can approach AI implementation with a healthy dose of realism and a strategic plan for success.
It All Starts With Data Quality
As mentioned, AI output is only as good as data input. Without high-quality data, ROI from AI integrations can go unrealized.
Currently, there’s a data quality problem among organizations. One major factor is the price of data storage plummeting over the past 10 years, which led to IT leaders rapidly purchasing storage, without proper data governance. Now, as IT teams extract data for their AI applications, they’re finding their data is a mess.
The importance of data governance and management can be exemplified in Oracle’s bungled acquisition of EHR (electronic healthcare records), Cerner. Oracle planned to employ its generative AI capabilities on Cerner’s massive trove of EHR data. However, operations went south. Flaws in Cerner’s system “caused over 11,000 orders for medical care to disappear into an ‘unknown queue,’ resulting in thousands of patients not receiving the treatment their doctors had ordered. These errors were contributing factors in three deaths.”
In the healthcare industry, data quality is critical not only to operations but to patient welfare as well. To assess the quality of their data, IT leaders can ask themselves key questions such as:
- Is my data centralized in a safe, accessible location?
- Is my data accurately collected?
- Is my data compliant?
Ensuring data integrity from the outset is crucial, as healthcare leaders integrate advanced AI solutions within their enterprise.
AI Beyond ERP: A Strategic Approach
AI holds immense potential to revolutionize healthcare, save lives and enhance quality of life globally. However, a successful AI implementation requires a strategic approach that extends beyond technology adoption. Healthcare IT leaders must carefully evaluate cloud architecture options, considering factors such as cost, flexibility and the specific needs of their business.
While partnering with AI vendors can alleviate the challenges of AI implementation and data quality assurance, excessive dependence on external providers may compromise patient care.
Today, ERP software vendors are heavily touting AI features available only with an upgrade to a newer version or with migration to a new product altogether. Both options can come with hefty price tags, disruption to the business and a host of risks. Some IT leaders embark on the journey, only to never make it to the completion they anticipated, as the project set off with unrealistic expectations, higher expenses and greater complexities.
Analysts specializing in ERP have been vocal about their hesitations also. They’re hearing directly from customers who aren’t seeing the ROI of such an undertaking, and some who say they don’t see the value of losing their on-premises solution to move to the next solution only available on the cloud – all just to get one or two new AI features that may or may not turn out to be what they need.
By maintaining current systems (many of which are customized to fit their unique business needs) and leveraging the data that comes from those systems to feed the AI machine outside of ERP, healthcare organizations can achieve the following benefits:
- Adopt AI innovations immediately vs. delaying for years by taking on a lengthy upgrade or migration track.
- Choose only the AI features they need vs. being forced to purchase features in the software vendor bundle. Many AI-driven applications are available as standalone solutions, providing focused functionality without the need to overhaul existing systems.
- Stay flexible and in control of their IT roadmap. Overreliance on a single vendor and product can limit future options and hinder innovation.
This approach by IT leaders incorporates tactics such as leveraging enterprise software support and services expertise to help maximize the potential of existing systems, adopting best-of-breed solutions and saying “no” to activities that don’t help increase their top and bottom line.
By prioritizing core AI knowledge, meticulously managing data quality and centralization and staying flexible in their ERP strategy to take on AI innovations of their choosing, healthcare organizations can harness the power of machine learning to drive greater efficiency and business growth. And most importantly, they can help deliver higher quality patient care.
About Emmanuelle Hose
Emmanuelle Hose has over 25 years of proven experience in the IT industry, leading enterprise software sales, service, and development operations across 3 continents: Europe, North America, and Oceania. After serving in several executive roles with global software companies, Ms. Hose was recently leading Rimini Street business operations across Australia and New Zealand, delivering exceptional service to our clients, accelerating sales growth, and extending the leadership position of Rimini Street in the software support market across the region.
