“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 research 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’s a good time, in other words, to update and upgrade the HIMSS Analytics Maturity Assessment Model, which first launched in 2016, as a benchmarking framework to help hospitals and health systems hone their analytics programs and data governance efforts.
Eight years ago, the eight-step AMAM helped healthcare organizations track their use of analytics technology from Stages 0 and 1 (fragmented point solutions and early efforts at data aggregation) all the way to Stages 6 and 7 (clinical risk intervention and predictive analytics; personalized medicine and prescriptive analytics).
With the original model, health systems such as UNC Health Care and Children’s Hospital Colorado showed the value of striving for and achieving Stage 7 – achieving big gains in process efficiencies and patient outcomes alike.
Now, with artificial intelligence and automation poised to transform every corner of healthcare delivery, the assessment model has been reimagined from the bottom up and has been made available for health systems across the globe.
‘What are you achieving?’
Launched officially earlier this month at the 2024 HIMSS APAC Health Conference & Exhibition, the new AMAM is not simply a measure of analytics adoption, but a means by which to measure the real impact of analytics, AI and data-driven decision-making on enterprise-wide operations and care quality.
The emphasis on patient outcomes is critical, 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 new AMAM is designed to measure the impact of analytics initiatives across a health system: how they’re impacting quality and safety, patient and population health, operational and financial performance, and more.
It now focuses on other areas, such as governance, privacy and security, analytics life cycle, and fostering a culture of responsible analytics – while including provisions for real-time prescriptive and predictive analytics, natural language processing, and other advanced 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 advance your use or you consider the use of things like artificial intelligence, do you have the data, data quality, so that that AI tool or technology is going to be accurate? 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 adds. “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 risks of bias and inaccuracies borne of bad data aren’t the only 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.
“Risk is multi-layered here, from an infrastructure data perspective, to a patient care and outcomes perspective, to an accuracy, fairness and data integrity perspective, AI tools are being used for forming decisions.
“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 new AMAM – which joins other HIMSS models, including the Infrastructure Adoption Model and the flagship EMR Adoption Model in being revamped in recent years – focus on helping participating health systems build basic data governance and quality measures, while amassing data repositories building expertise in dashboards and data visualizations to support decision-making that’s aligned strategically with organizational goals.
By the top of the ladder, Stages 6 and 7, healthcare organizations will be using predictive analytics to inform care decisions and integrating AI and machine learning into their analytics processes, with real-time clinical decision support. They’ll also have systems in place for monitoring population health outcomes and building health equity programs.
HIMSS (parent company of Healthcare IT News), notes that AMAM is designed to be a flexible framework, rather than a rigid checklist, and is meant to be used across care settings to help health systems refine and improve their data strategies 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 adds. “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.'”
Mike Miliard is executive editor of Healthcare IT News
Email the writer: [email protected]
Healthcare IT News is a HIMSS publication.
Source : Healthcare IT News