“Analytics as a discipline has changed dramatically over the last five to 10 years, certainly in the last five years,” says Anne Snowdon, director of research at HIMSS. “With the explosion of artificial intelligence – the ChatGPT era, so to speak – large language models have really changed the way we think about where and how these advanced analytics tools offer value for healthcare.”
In other words, it’s a good time to update and modernize HIMSS Analytics Maturity Assessment Modelwhich first launched in 2016 as a benchmarking framework to aid hospitals and health systems improve their analytics programs and data management efforts.
Eight years ago, AMAM’s eight-stage project helped healthcare organizations track the apply of analytics technology from Stages 0 and 1 (piecemeal point solutions and early data aggregation efforts) through to Stages 6 and 7 (clinical risk intervention and predictive analytics; personalized medicine and prescription analytics).
Through the original model, health systems like UNC Health Care and Children’s Hospital Colorado have demonstrated the value of pursuing and achieving Stage 7 – making substantial gains in process efficiency and patient outcomes.
Now, with artificial intelligence and automation poised to transform every aspect of healthcare delivery, the assessment model has been redesigned from the ground up and made available to healthcare systems around the world.
“What are you achieving?”
Officially unveiled earlier this month at the HIMSS APAC Health 2024 conference and exhibition, the novel AMAM is not only a measure of analytics adoption, but also a way to measure the real impact of analytics, artificial intelligence and data-driven decision-making on enterprise-wide operations and quality of care.
The focus on patient outcomes is crucial, Snowdon says.
“It’s not, ‘Do you have artificial intelligence?’” he says. “It’s the question: ‘What can you achieve as an organization or system now, given your advanced maturity or analytical maturity?’ What are you achieving and for whom? This is a fundamental change compared to the previous model. »
The novel AMAM is here designed to measure the impact of analytics initiatives across the health care system: their impact on 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, the analytics lifecycle, and supporting a culture of responsible analytics, while also addressing regulations around real-time prescriptive and predictive analytics, natural language processing, and other advanced AI applications.
Modernizing AMAM “is not only about keeping pace with the rapid evolution of analytical technology and its potential value, but also about its potential risks,” Snowdon says. “As you develop applications or consider the apply of things like AI, do you have the data and the quality of data that will make the AI tool or technology precise? Will it be fair?
“Models can be trained on a lot of data from one sector, a large sector of the population, but it can actually be quite damaging to another sector of the population,” he adds. “In Canada, for example, we have a lot of data on Asian patients. We have much less data on our Indigenous community. How will an AI model work for this indigenous community if the model has never been trained on data representing them?”
The risk of bias and inaccuracy resulting from bad data is not the only one. The challenges with AI-powered analytics “are very different today compared to what we have seen in the past, given the nature of these technologies,” Snowdon says.
“The risk here is multi-layered, from an infrastructure data perspective, to a patient care and outcomes perspective, to a data accuracy, fairness and integrity perspective. Artificial intelligence tools are used to make decisions.
“It is a very multi-layered model, and the model develops and supports organizations in understanding all the variations and levels of risk as their analytics maturity evolves.”
The first few stages of the novel AMAM – which combines other HIMSS models, including the Infrastructure Deployment Model and the flagship EMR Deployment Model, which has been modernized in recent years – focus on helping participating health systems build foundational data management measures and quality metrics while collecting data repositories that build expertise in dashboards and data visualization to support decision-making strategically aligned with organizational goals.
At the top of the ladder, in Stages 6 and 7, healthcare organizations will apply predictive analytics to make care decisions and integrate artificial intelligence and machine learning into their analytics processes, providing real-time clinical decision support. They will also have systems in place to monitor population health outcomes and create health equity programs.
HIMSS (the parent company) notes that AMAM is designed as a versatile framework, not a immovable checklist, and is intended to be used across care settings to aid health systems refine and improve their data strategies and decision-making.
“It’s a really strategic plan to develop very sophisticated analytics, which at levels 6 and 7 in this model is very focused on artificial intelligence,” Snowdon says.
“We have thoroughly tested this new AMAM model with our partners and organizations that are well familiar with it,” he adds. “The overwhelming feedback we have received from customers who have used the current AMAM model is: ‘This is what I need. This gives me a roadmap to go to my CEO and senior management to help them see where we are today and where we’re going.” we have to get there.”