About the Author
Paul Clark is the director of Healthcare Research at Digital Reasoning, a research firm that supports artificial intelligence solutions that reduce risk, seize opportunities, and save lives. Prior to this, Paul served as Vice President of Research and Education at the School of Health Management, where he led the research agenda and educational development of the C-Suite Executive Committee of the leading health systems in the United States.
Artificial Intelligence (AI) has enormous potential to increase and amplify human labor to change the health care services in repetitive cognitive tasks through automation.
However, like any tool, if the method used is not appropriate or imperfect, it may do more harm than good.
Once it causes harm, it will not only affect the company's income, but also the life of the patients served by the organization.
When the beginning of 2018, we can expect more dialogue about the value of AI.
While the hype of the technology is justified, it is important that companies take the time to understand the potential pitfalls and common pitfalls that often arise when investing in this technology.
As with any new technology, the likelihood of a successful problem resolution depends on the definition of the problem, the evaluation of the problem, the goals set, and the way in which the new tool is implemented and managed.
To ensure that the AI ​​solution can be applied in the right way, consider the following:
Don't try to solve all problems with artificial intelligence
While machines can learn complex solutions in an efficient time, the omniscient cognitive machines that really solve all the challenges don't exist yet, and they won't appear in the short term.
Therefore, setting artificial intelligence to solve problems requires precise definition; problems must be analyzed experimentally or quasi-experimentally to analyze discrete structures; you can't simply let the machine go and let it explore the answers freely.
A "golden" data set that can be used for artificial intelligence technology to learn how to solve problems should be carefully designed.
Simply put, you must ask the right questions in the right way to get the right answer.
It's important to take the time to identify problems and assign specific questions to guide machine learning, and it's worth taking the time and effort to research and ensure success.
A common mistake is to choose a topic that is too big.
Artificial intelligence can't cure cancer next year, but it can solve the discrete problems and processes that create value in healthcare companies, including saving lives through better, faster, and higher quality medical services.
Coordinating and promoting the health care value chain
Healthcare providers and data science partners must understand the health care value chain and how data flow and technology infrastructure affect care services.
The value chain begins before the patient enters the main entrance and includes all information accumulated by the patient, their consultation experience, and interactions with members of the medical facility, and is stored as a permanent residual data asset for the organization.
This is the first time that healthcare companies have discovered the tremendous value of their historical data—especially unstructured data.
When you understand how structural problems affect the value chain, you move from a transactional perspective to a vertical perspective, where machines are applied to patterns, predictions, and effects on cognitive problems. Expensive manual processing.
Just like drawing a patient's consultation process, companies need to digitize the patient's process and determine which data needs to be processed manually to determine how to modernize.
Machines can operate more efficiently and effectively before they need to manually read, skim, and scan large amounts of unstructured data (eg, text, notes, messages, records, etc.).
The result will be a lot of potential data, but companies must take steps to put the data that corresponds to the expected value into the value chain.
For example, a radiology report can be read by multiple doctors, nurses, coders, registrars, and patient care coordinators.
Setting up machine operation analysis in this report can save everyone's work and create great value in labor efficiency and labor quality.
At the same time, this process can also be seen as an actual risk assessment of the machine in the event of a possible error - in this process, the machine is more likely to find bugs.
Pay special attention to unstructured data
Most predictive algorithms use structured data in large numbers, while ignoring unstructured data is often richer, more complex, and more subtle.
This poses a problem because dealing with large amounts of unstructured data and analyzing human languages ​​requires a powerful natural language processing analysis and data science techniques.
Clinical literature and patient-specific narrative feedback can provide many clues for disease prediction, recovery, health behaviors, and other services that can also be used to analyze vital signs and social factors.
Instead of just using artificial intelligence to acquire structured resources, it's better to take the time to solve the problem of how unstructured data provides new value.
In many cases, this may give health care companies a host of new opportunities to optimize processes and improve patient care.
More importantly, it may reveal significant problems in the system before the problem gets worse.
Many health systems are discussing “data monetization,†but few have taken action to turn their remaining data into valuable assets.
For healthcare companies, the future is bright, new technologies will create an exponential leap forward, and work with data science to achieve truly groundbreaking innovation.
As healthcare companies use artificial intelligence, don't let rumors cover up the company's real goals.
If used properly, artificial intelligence can bring success and a huge return on investment, while the healthcare industry needs innovation to create value.
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