In healthcare, no two digital transformations are alike, and this message comes across loud and clear from health informatics leaders. For example, at HIMSS 2022 conference, we heard from some healthcare leaders who have teams of experts applying machine learning to 35 years of healthcare data to generate clinical and operational insights, and others who were still grappling with a fragmented IT system that leaves them spending most of their budget just keeping the lights on.
Whatever your transformation journey, the opportunities offered by machine-generated insights are clear. In my previous post, we explored the difference between data and insights, and how AI and predictive analytics can unlock compounding returns for today’s healthcare organizations to deliver insights at scale – from newfound operational efficiencies, to more accurate clinical decisions, and more effective treatment pathways.
The value of converting data to insights is a point that many informatics leaders agree on. In our 2022 Future Health Index report, which surveyed nearly 3,000 healthcare leaders across 15 countries, more than three quarters of informatics experts believe predictive analytics can have a positive impact on the cost of care (78%) and overall staff experience (76%), two of the vital components of the Quadruple Aim. You can read a newly published news center article on informatics insights from the Future Health Index report here.
So, if moving from data to insights is a great way to advance the Quadruple Aim, then what does it take to achieve insights at scale, where insight generation is embedded in the very fabric of your organizational workflow?
The journey starts with figuring out where your health system sits on the digital maturity curve , which refers to a range of global frameworks created by HIMSS to enable healthcare organizations to benchmark and improve their digital transformation progress. There are seven frameworks, called maturity models, in areas that include the continuity of care, analytics, diagnostic imaging, and electronic health records (EHRs). Each model comprises eight stages that broadly move from no digital infrastructure up to multi-vendor, interconnected, and dynamic capabilities.
Yet even healthcare leaders who are just embarking on this journey will know that it is fraught with challenges, including difficulties accessing, organizing, and sharing data, as well as concerns over data privacy. Overcoming these challenges to move smoothly from data to actionable insights at scale takes four steps: