This document discusses the challenges of using AI and machine learning in healthcare due to issues with data silos and lack of data sharing. Healthcare data is fragmented across different organizations, making it difficult to access enough high-quality data needed to train accurate AI models. This fragmentation was caused by organizations independently developing their own IT systems, and impacts the ability to develop personalized medicine models that need access to diverse patient data sources. Overcoming these challenges will require addressing issues of data governance like ownership, responsibility and privacy.

1. Precision medicine and AI - data problems ahead
https://diginomica.com/precision-medicine-and-ai-data-problems-ahead
by Neil Raden
December 17, 2019
SUMMARY: The promise of personalized medicine has sparked a proliferation of AI hype. But
the obstacles AI faces in the healthcare industry are daunting. Look no further than data silos -
and the factors that spawned them.
Machine Learning (ML), as a focus in commercial applications, has hit a wall.
Successful commercial application of ML is hampered by the difficulty
sourcing adequate, clean data for the models. Machine Learning needs
significantly more data for training the models than previous quantitative
disciplines.

2. Too small or too dirty datasets, as well as datasets that do not represent the
population under consideration, can yield biased results, inappropriate
conclusions, and host of other problematic results.
Exciting innovations are happening in research facilities for AI and ML, but
very few of them are operating in production because of the data problem.
While this issue appears across the board in every industry, nowhere else is
this problem as severe as it is in the healthcare industry.
What’s the problem with healthcare?
Healthcare is defined by Investopedia as “… businesses that provide medical
services, manufacture medical equipment or drugs, provide medical
insurance, or otherwise facilitate the provision of healthcare to patients .”
It's that last word, "patients" that is problematic. Pharmaceutical/Biotech have
their own data problems, but they are mostly in control of the data sources.
The same is true of insurance companies and medical equipment
manufacturers. But when you get down to the patient level, and even the
components of patient care, the data is everywhere, it's balkanized.
A single clinical operation, to the extent it has analytical data, has treatment
protocols, population demographics, and other variables that must be part of
AI training data for personalized medicine. It cannot be merged and
integrated or aggregated with enough other operations to reach the needed
volume for machine learning without losing its local character.

3. Can AI provide opportunities in clinical care to yield better diagnosis? Can it
offer a potential leap in both patient care and delivery efficiency? Can it lead
to the “precision medicine” approach, customizing treatments for individuals
to dramatically improve outcomes, data is hindering the process?
A paper in Nature, The Inconvenient Truth about AI in Healthcare, describes
the situation for AI in clinical medicine:
In the 21st Century, the age of big data and artificial intelligence (AI), each
healthcare organization has built its own data infrastructure to support its
individual needs, typically involving on-premises computing and storage.
and the obstacle:
Data is balkanized along organizational boundaries, severely constraining the
ability to provide services to patients across a care continuum within one
organization or across organizations. This situation evolved as individual
organizations had to buy and maintain the costly hardware and software
required for healthcare, and has been reinforced by vendor lock-in, most
notably in electronic medical records (EMRs).
Why the adoption of new AI algorithms is slow to catch on in clinical
healthcare is, as the authors stated, an issue of data, but there are other
factors as well. It's the old culture walnut. The AI offerings cannot address
existing incentives that support existing ways of working. AI models are not
that smart. They provide reliable inferencing, but they cannot ensure people

4. will adopt them. Besides, most healthcare organizations lack the data
infrastructure required to collect the data needed to optimally train algorithms
to “fit” the local population and to interrogate them for bias
Clinical practices can avail themselves of novel AI models, but only those that
are developed elsewhere, where adequate data is available for training the
models. For example, a well-trained pathology model that can recognize
malignant skin lesions from images with high accuracy can be used anywhere.
But to practice personalized medicine, a model has to be aware of local
differences: in the population itself, in the provenance and semantics of the
data and practice differences between locations, and even practitioners within
a situation, that bleed into how the data was captured.
Within a practice or a hospital or even a small group of hospitals, the most
detailed and most valuable store of data is in EMRs. To date, providers of
EMR software have not been able to raise clinician satisfaction, which remains
at a low point.
As a result, completeness and availability of data lack the quality and
governance that other enterprise applications possess. Most difficult of all,
interoperability between different EMR providers is low, and even data
extraction is challenging.
Where is there hope? The article in Nature cited above mentions “islands of
aggregated healthcare," such as data in the ICU, and in the Veterans
Administration. Useful efforts, but not sufficient. What is needed is a data

5. infrastructure far beyond these “silos” of data. The authors of the article cited
above suggest:
To realize this vision and to realize the potential of AI across health systems,
more fundamental issues have to be addressed: who owns health data, who is
responsible for it, and who can use it? Cloud computing alone will not answer
these questions—public discourse and policy intervention will be needed. The
specific path forward will depend on the degree of a social compact around
healthcare itself as a public good, the tolerance to public-private partnership,
and crucially, the public's trust in both governments and the private sector to
treat their healthcare data with due care and attention in the face of both
commercial and political perverse incentives.
My take
If you are an IT manager in a clinical healthcare operation, you have to ask
yourself the following questions:
1. What is the state of data available within our purview?
2. Is it adequate for fueling AI models?
3. Do we have the infrastructure and/or cloud expertise to host AI
modeling?
4. Who is responsible for assuring the output of the models is correct?
5. What ethical issues do we face sharing patient and activity data with
others?
The enthusiasm for AI to solve previously unsolvable problems is in
opposition to the limited data in a clinical setting. To provide

6. precision/personalized medicine, models cannot be trained with data from
other sites that are not a match for local conditions. This is the conundrum.