Insights at Scale: A Guide for Healthcare Leaders – Blog

Healthcare leaders are investing in AI and predictive solutions to move from data overload to insights at scale. Yet, along this journey, each organization faces its own specific needs and challenges. Successful IT leaders take a platform approach, prioritize partnerships with clinical experts, and break down organizational goals into measurable (and achievable) steps.

In healthcare, no two digital transformations are the same, and this message is coming through loud and clear from healthcare IT leaders. For example, at the 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 struggling 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 article, we explored the difference between data and insights, and how AI and predictive analytics can unlock compound returns for today’s healthcare organizations to deliver insights to scale – from new operational efficiencies to more accurate clinical decisions and more efficient treatment pathways. .

The value of converting data into information is something that many IT managers agree on. In our 2022 Future Health Index report, which surveyed nearly 3,000 healthcare leaders in 15 countries, more than three-quarters of IT 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 recently published Press Center article on the Future Health Index report IT insights here.

So if moving from data to insights is a great way to advance the Quadruple Aim, then what does it take to get insights at scale, where insight generation is embedded into the very fabric of your workflow? organizational?

The journey begins with determining where your healthcare system sits on the digital maturity curve [1], 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 such as continuity of care, analytics, diagnostic imaging, and electronic health records (EHRs). Each model includes eight stages that broadly move from no digital infrastructure to multi-vendor, interconnected, and dynamic capabilities.

Yet even health officials who have just embarked on this journey will know that it is fraught with pitfalls, including difficulties in accessing, organizing and sharing data, as well as concerns about data privacy. data. Overcoming these challenges to smoothly move from data to actionable insights at scale requires four steps:


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