With many economic hurdles this year including rising costs and inflation, the healthcare industry has had a challenging backdrop in which to operate. In a sector dedicated to improving and saving lives, healthcare has so much to gain by adopting new AI and machine learning (ML) technologies. The World Economic Forum sums up the transformative potential of AI and ML in this setting: from propelling the development of drugs and vaccines to improving medical diagnosis and treatment, it can virtually be applied to any stage of the value chain, boosting efficiencies across the overall healthcare system.
Like in all organizations today, leading with data and building it into a formal strategy should no longer be viewed as an adjunct to an existing healthcare business model – digital and data must be considered part of it. There are certainly more hoops to jump through in this industry than many others when it comes to privacy, security and governing rules. However, if you look at other highly regulated industries like finance, you can see significant strides by taking a gradual approach with great care. McKinsey’s analysis of data from the banking sector shows that digital and AI transformations created bottom-line value. Healthcare can achieve similar results by applying technology thoughtfully and with purpose.
Digital modernization in healthcare
Healthcare is an industry with great digital promise, but one that has traditionally been hamstrung by its largely legacy IT systems and data practices. This is compounded by IT staff shortages and budget constraints. According to Gartner, top items for increased investment in healthcare include cybersecurity, business intelligence/data analytics and cloud platforms. However, without the supporting IT staff many healthcare systems lag in technological advancement.
Modernizing processes and technology in healthcare is also difficult because the margin of error in healthcare is slim to none, as it many times involves life-or-death scenarios. This means that experimentation and trialling technology is not a common or widely viable option. Additionally, with the immediate and growing financial demands surrounding patient care delivery, executives aren’t always willing to allocate budget to new initiatives where the use case is not crystal clear. This means that adoption of any new technology must be easy to implement, extremely reliable and provide fast results immediately.
How AI and ML can strengthen healthcare organizations
For an industry built on patient experience, the promise of what AI and ML can deliver is significant. Healthcare systems have an enormous amount of personalized data that could be compared or contrasted with the vast amounts of external studies to design more effective treatments and staffing. The only way this data becomes useful, however, is if a solid data strategy is in place to harness it for action. AI and ML can help immensely in this area. Healthcare companies can better manage patient care by forecasting patient admissions and readmissions and leveraging insights to design precision medicine and preventative strategies, not to mention the operational efficiencies.
Exploring “what-if” scenarios is at the heart of making predictions, and is one of the most impactful ways that AI and ML are being applied in modern medicine today. It reduces provider burden and clinical variation by leveraging statistical techniques that learn from large amounts of training data. For example, when looking to improve patient care, AI and ML can help predict what kind of treatment plan will be most successful, based on their unique characteristics and situation as compared with other patients. In the case of assessing the urgency of care, it can identify gaps in medical history and predict which patients need care first. Appalachian Regional Healthcare (ARH) saw this firsthand, utilizing tailored automated ML and cloud solutions to better identify at-risk patients and encourage them to keep appointments. There are numerous other use cases on the care side and of course on the operational side – such as reducing time-consuming administrative tasks from physicians, like documenting appointment notes and summaries as just one example.
A smarter, simpler healthcare setting for all
Everyone involved in the healthcare system – from patients to providers to payers – will benefit from the data-driven insights generated by AI and ML models. The results can guide business practices, policies and field operations, thus creating a smarter and simpler setting for all. When applied in a safe and controlled way, AI and ML technologies can cost-effectively target valuable use cases where both patient care and operations can quickly see vast improvements.
About Nick Magnuson
Nick Magnuson is the Head of AI at Qlik, executing the organization’s AI strategy, solution development, and innovation. Nick joined the company through the acquisition of Big Squid, where he was the firm’s CEO, and has previously held various executive roles in the field of machine learning and predictive analytics.