In fact, it has been an era of fast-progress AI in health care. AI-powered systems can, for instance, carry out medical imaging and diagnosis or provide prognosis analytics clinical decision support that equals – and every now and then even surpasses-humans in results.
Today, however, many medical AI endeavors fail to achieve consistent real outcomes.
The problem usually lies not with model accuracy.
More likely, it is finding the real cause of random data.
The main problem with healthcare AI is not that it cannot analyze data well. Rather, the problem is a data environment where the data itself is broken into pieces, arrives late or not at all, or exists in separate silos across systems.
The Real Problem Is No Longer Accuracy
Today’s AI models in health care are trained on vast datasets, and possess the capacity to far greater degree than before. They can find patterns in images and anomalies in lab values not known by human experts, and assist doctors with risk scoring at bouquet precision levels.
These systems work well under controlled conditions.
However, reality for healthcare professionals is not like that. Patients’ data doesn’t arrive as a clean stream-Either it comes from different hospitals and laboratories, different departments within the same hospital; Or alternatively emerges at some time after previous events have taken place (sometimes through various channels for multiple reasons); All this is stored by insurers etc in a variety going back.
We have to Emphasize Again That Precision Is the Key
Thus, an accurate model is only useful when it proves itself relevant.
Data Continuity in Healthcare: An understanding
Data continuity is the complete, timely, and connected flow of patient information throughout its experience in health practice.
This could involve:
Medical history from multiple providers
Diagnostic reports out of four or more laboratories.
Imaging data (e.g. x-rays and MRIs ) stored on one system Medication records revised at varying intervals
Notes on follow up which never end up getting back into any main system With this information not moving together, AI systems work off half a picture.
They are forced to make decisions based on snapshots instead of the full story of the patient being worked over by modern medical treatment.
Artificial Intelligence Deepens Fragmentation in Healthcare Data
Healthcare data fragmentation is nothing new. It had already appeared long before AI came on the scene. What has changed? Today we just think that AI could help us “fix” this problem.
In fact, AI magnifies the existing problems further.
For example, perhaps a predictive model will show a patient is at low risk simply because the recent test results don’t match what was put into the computer before a certain deadline on some Thursday morning or afternoon. A diagnostic AI misses crucial historical patterns because past records are all but unavailable from your hospital system. If underlying data is inconsistent, then clinical decision tools produce differing suggestions.
These are not algorithm failures. They are discontinuity failures.
But this in itself is neither here nor there. In their view, true interoperability is about getting systems to talk to each other rather than trying to convert incompatible pipes
By itself, interoperability will not do the trick.The patient must find his own way through time and rain. Whether in person or by phone on a network, this is essential.
You may encounter any of the following problems even when systems are technically connected: Data may arrive after the decision has been made and so have no influence upon it.
The first comprehensive reinternalization of history.Then, patient (or family) trains a video camera on twelve four-channel nocturnal studies for ten minutes each channel and receives back three hours of full-on sleeping lab science. No clinician attending upon him can recall such a thing as this in any hospital that he has ever seen.
Clinicians may not trust or act on AI outputs if data sources are unclearWithout continuity, AI outputs feel unreliable–even when they are statistically accurate.
The Human Cost Of Missed Continuity
When systems lack continuity, human clinicians are left to fill in the gaps by hand.
They carry out inspections for verification, and experience is relied on rather than the computer’s recommendations.
This increases the cognitive load and trust in AI tools drops.
Gradually, AI becomes an “added bonus” rather than a vital component of clinical workflow. Its adoption falters not because medical staff refuse technology but because this just does not match the real world of delivering patient care.
As healthcare AI today strides forward with ever more intricate and powerful models, it is important to address a vital point.Successful healthcare AI must take into account how care actually unfolds, not just how data is organized.This means knowing (or at least taking educated guesses about) things like: When and where in the care cycle information becomes available Who needs it and in what format How people make decisions under time pressure Where people have to hand work off from zone to another AI systems adapted to clinical workflows – and capable of handling imperfect data flows – are much more likely to work than those designed in isolation.
From Smart Models to Reliable Systems
Healthcare AI’s future is no longer to gain marginal increases in accuracy. Instead, it is all about building systems that work effectively and safely live up in all manner of messy real-world environments.
This calls for:
- Strong data governance and version control
- Context-aware data pipeline
- Full data provenance view
- Design right when some or all information is missing
If continuity improves, AI becomes reliable, powerful and not just for show.
Conclusion
Healthcare AI does not fail because to a deficiency in intellect. It doesn’t work because intelligence needs continuity to work.
As healthcare systems grow more digitized and connected, the real competitive edge will not be who has the most advanced model, but who can keep a full, trustworthy picture of the patient’s path.
AI will keep having problems, not with accuracy, but with reality, until data flows as smoothly as caring is supposed to.
Connect with Sifars today to schedule a consultation