New HIMSS Analytics Maturity Assessment Model supports smart AI deployments

“Analytics as a discipline has changed dramatically in the last five to 10 years – and for sure in the past five,” says Anne Snowdon, chief scientific review officer at HIMSS. “With the explosion of artificial intelligence – the ChatGPT era, if you will – large language models have really shifted the needle on where and how these advanced analytics tools offer value for healthcare.”

It is an real time, in a range of words, to change and beef up the HIMSS Analytics Maturity Evaluation Modelwhich first launched in 2016, as a benchmarking framework to abet hospitals and health methods hone their analytics programs and records governance efforts.

Eight years ago, the eight-step AMAM helped healthcare organizations music their use of analytics technology from Phases 0 and 1 (fragmented point solutions and early efforts at records aggregation) the total technique to Phases 6 and 7 (scientific difficulty intervention and predictive analytics; personalized treatment and prescriptive analytics).

With the customary mannequin, health methods comparable to UNC Correctly being Care and Kid’s Hospital Colorado confirmed the price of striving for and reaching Stage 7 – reaching substantial gains in job efficiencies and affected person outcomes alike.

Now, with artificial intelligence and automation poised to noticeably change every nook of healthcare provide, the review mannequin has been reimagined from the backside up and has been made accessible for health methods across the globe.

‘What are you reaching?’

Launched formally earlier this month at the 2024 HIMSS APAC Correctly being Convention & Exhibition, the fresh AMAM is now now not simply a measure of analytics adoption, however a technique in which to measure the exact impact of analytics, AI and records-driven decision-making on project-huge operations and care quality.

The emphasis on affected person outcomes is severe, says Snowdon.

“It’s not, ‘Do you have AI?'” she says. “It’s, ‘What are you now able to achieve as an organization or system, given your advanced maturity or your analytics maturity? What are you achieving, for whom? That’s a fundamental shift from the prior model.”

The fresh AMAM is designed to measure the impact of analytics initiatives across a health machine: how they’re impacting quality and safety, affected person and population health, operational and financial performance, and more.

It now specializes in a range of areas, comparable to governance, privateness and safety, analytics life cycle, and fostering a culture of responsible analytics – while alongside with provisions for exact-time prescriptive and predictive analytics, natural language processing, and a range of evolved AI applications.

The AMAM modernization “is all about not just keeping pace with this rapid evolution of analytics technologies and its potential value, but also potential risks,” says Snowdon. “As you come your use or you keep in mind the use of things fancy artificial intelligence, blueprint you would possibly possibly perchance possibly absorb the records, records quality, in tell that that AI instrument or technology goes to be simply? Is it going to be equitable?

“Models can be trained on a lot of data from one sector, the large sector in the population, but it may actually be quite harmful to a different sector of the population,” she provides. “For example, in Canada, we have a lot of data on Asian patients. We have much less data on our Indigenous community. How is an AI model going to work for that Indigenous community when the model has never been trained on data that represents them?”

And dangers of bias and inaccuracies borne of hideous records usually are now not primarily the most easy ones. The challenges of AI-enabled analytics are “very different now compared to what we’ve seen in the past, given the nature of these technologies,” says Snowdon.

“Possibility is multi-layered here, from an infrastructure records perspective, to a affected person care and outcomes perspective, to an accuracy, fairness and records integrity perspective, AI tools are being customary for forming choices.

“It’s very multi-layered, and this model advances and supports organizations to understand all of the variations and levels of risk as their maturity in analytics evolves over time.”

The first few stages of the fresh AMAM – which joins a range of HIMSS items, alongside with the Infrastructure Adoption Model and the flagship EMR Adoption Model in being revamped in present years – focal point on serving to taking part health methods assemble current records governance and quality measures, while accumulating records repositories constructing expertise in dashboards and records visualizations to toughen decision-making that’s aligned strategically with organizational desires.

By the head of the ladder, Phases 6 and 7, healthcare organizations could be the use of predictive analytics to repeat care choices and integrating AI and machine studying into their analytics processes, with exact-time scientific decision toughen. They are going to furthermore absorb methods in procedure for monitoring population health outcomes and constructing health fairness programs.

HIMSS (guardian company of Healthcare IT Files), notes that AMAM is designed to be a flexible framework, rather than a inflexible checklist, and is supposed to be customary across care settings to abet health methods refine and beef up their records recommendations and decision-making.

“It’s really a strategic roadmap for advancing, very sophisticated analytics, which at Levels 6 and 7 in this model is heavily focused on artificial intelligence,” says Snowdon.

“We have tested this new AMAM model extensively with our partners and organizations that are quite familiar with the AMAM model,” she provides. “The overwhelming feedback we got from clients who have used the current AMAM model is, ‘This is what I need. It gives me my roadmap to go to my CEO and C-suite executives to help them see where we are today, and where we need to get to.'”