Accurate assessment of technologies still is the heart of leaders’ tasks
Success or failure can fluctuate on a variety of factors, and magical thinking won’t make just any technology work in every instance.
Yesterday, I woke up to a snowplow growling down our street scraping away at a mid-March snowfall.
Today, I’ll be cutting short a long run because it’s a bit too hot to attempt a hard workout.
Ah, and that’s life in the Chicago area around the spring equinox. Every day is a surprise, and the watchword is flexibility. You can’t lock in on what your expectations are – going with the weather flow is indispensable if you’re going to manage life here.
It’s not much different in dealing with emerging technology in healthcare. Some presuppositions need to be put to the side – at least for the time being – while health leaders should be quick to implement approaches that are proven to work, can gain widespread support and yield real results.
This week, articles published by Health Data Management underscored the need for flexibility in making technology decisions, with some applications showing significant results while other potential uses requiring a wait-and-see approach.
Technology that can work
A targeted approach can make solid use of artificial intelligence in improving components of the revenue cycle, writes Ken Poray, CEO of Integrex Health and Chair of the AI Community of Practice at the American College of Health Data Management.
Poray notes that an orchestration layer, directing a team of four “specialist agents” can help an organization improve efforts to manage national provider identifier challenges. A coordinated approach can help speed the process and enable better specificity in communication with clients.
There’s also hope for enlisting clinical data in life sciences research, as long as certain capabilities exist in multi-cloud systems to handle the increasing volume, speed and diversity of data, writes Selvamurugan Ramamoorthy, a data engineering and cloud platform specialist.
However, that will require organizations to conduct a thorough evaluation process to ensure legacy pipelines for this data are ready for the load. “It is vital for pipeline operations to remain reliable and continuous as distributed cloud systems operate across variable latency conditions,” he writes.
“Integrating legacy pipelines with cloud-native platforms introduces security and operational risks that can affect system stability, data protection and regulatory alignment,” he adds, before listing mitigation strategies to ensure compliance.
Needs some work
But technology, no matter how fancy or sexy it is, won’t work without the necessary support.
For example, existing policies can thwart efforts to provide better care. That’s particularly true when it comes to behavioral health, writes Matt Miclette, vice president of innovation and policy at NeuroFlow, a behavioral health analytics company.
“Much of the public conversation reduces AI to consumer-facing tools, particularly large language models and chatbots,” he writes. “These tools represent one approach, but they do not address the underlying problem that surfaces repeatedly. Healthcare systems lack the clinical infrastructure to integrate data, surface risk and support decisions before patients reach crisis. AI-enabled clinical tools could address these gaps, but only if we build them on the right.”
While clinicians increasingly look to AI solutions to moderate burdens and help them improve care, the use of technology remains a useful tool to make important adjustments – not a magic wand that wipes away every challenge.
Like the weather in Chicago, healthcare organizations need to be ready for any eventuality – snowplows or sunscreen notwithstanding.
Fred Bazzoli is the Editor in Chief of Health Data Management.