A complex real-time analytics system requires enormous amounts of processing power, which is why much of healthcare relies on cloud computing.
more than 16,000 cases of COVID-19 have gone undiagnosed because of an “IT error.” A hardware failure perhaps? a bug in the system? A bad backup?
None of that. Public Health England was using an automatic process in which CSV files (a data file format) were transformed into an XLS (old school Excel) file which can only handle 65,000 rows of data (instead of the over a million of lines of modern XLSX files). ). Therefore, due to file system limitations, all these cases were not counted.
I could write a whole article about all the data science sins committed here, but for now suffice it to say that the consequences were catastrophic. Tens of thousands of British citizens went about their daily lives without knowing they had been exposed to COVID-19.
In contrast, South Korea has been one of the biggest success stories during the pandemic, keeping the number of infected people under control without having to lock down the economy. The reasons for this success are three:
- Aggressive testing strategies with up to 15,000 to 20,000 tests per day.
- Focused quarantine of suspected cases and contact tracing.
- Rapid deployment of temporary hospitals in affected areas.
This may not seem very different from what other governments have done, except that the underlying decision-making was driven by an unprecedented level of data processing and analysis.
As a quick example, instead of just focusing on positive cases and verbal reports of their daily routines, epidemiologists have relied on credit card statements and CCTV footage to automatically cross-check who might have been in contact with a carrier. In turn, people at risk were notified by the system so they can go to the nearest health center and get tested.
Such a complex real-time analysis system requires enormous amounts of processing power, which is why much of this process relies on cloud computing. If there is one good thing we can learn from this comparison, it is that the healthcare sector has a lot to gain from adopting current trends in technology.
Patients as dynamic data
South Korea's adoption of cloud computing is a sign of a paradigmatic shift in the way we understand data. Patients have historically been treated as static data, meaning their information has always been stored on a server and virtually untouched. It may be updated periodically as new data is collected, but data analysis and transformation is mostly done on copies of the dataset by researchers and engineers.
There is another way to think about this data, rather than considering it as an archaeological fossil that needs to be protected and preserved in stasis. We should think of it as a living entity, growing, changing, adapting, and self-actualizing as new information is added.
AIs play a big role in dynamic systems as they monitor data and take actions based on conditions set by developers. For example, an AI could trigger an alarm and automatically contact a doctor when a patient reaches a risk threshold calculated by a predictive model.
Because much of the heavy lifting is done by AI (such as cleaning and filtering the data and tuning the models), analysts spend more time interpreting the results and expanding the system, implementing more and better models that can have a direct impact on both the patients and the institution.
With dynamic data, management can have intelligent models that help them make logistics decisions more efficiently. For example, with cloud analytics, you can calculate expected bed demands based on factors such as the infection rate of a disease in the area, the influx of new patients, and the expected discharge date of current patients.
Off-site data analysis
Healthcare systems have a lot to gain from using cloud storage and computing. Firstly, there is an issue of scalability. Because cloud services are on-demand, the system will naturally scale and accommodate as more processing power is needed. With on-premises servers, IT has to manually update hardware, which takes time and increases overall costs.
As more people purchase data collection devices such as smartwatches, refrigerators and cloud-based training equipment, new opportunities arise for the healthcare system to collect data directly from the user's home.
Willing users can share their daily routine and eating habits which in turn can be processed by the system. The end result? More accurate models and better diagnostic tools for the healthcare professional and more accurate data for the researcher to study human health, all without the hassle of having to increase bandwidth on a local server.
In addition to faster data collection, there is also the issue of more efficient data sharing, nearby facilities can share their data and the cloud can use the information to create more accurate models. This is what happened in South Korea. The private sector and government freely shared information with each other, which allowed them to identify centers of infection before they could spread.
Lastly, as for software development, cloud-based deployment tends to be faster and cleaner, new applications and technologies can be implemented and debugged in real time, this is a godsend, especially if your development team is working off site .
A closer relationship with the end user
So far we've talked about the behind-the-scenes benefits, but real-time analytics also means a user can have faster and easier access to test results, diagnoses and recommendations.
Imagine an interconnected world where a patient has a phone app that sends a notification when their test results are ready and at the same time this information is processed by the cloud and their profile is updated without the need for a human analyst and all In real time.
There is a lot to be gained here, especially for elderly patients or users with special needs who require close support. A caregiver can have remote access to clinical information as well as real-time measurements such as heart rate, which helps tremendously in their work.
In the event of a pandemic or flu season, cloud analytics can predict possible infection patterns and inform patients near outbreaks to exercise caution. This can also be done for other forms of health risks, such as air contamination.
In essence, healthcare is the wellness industry, and as technology evolves, it is necessary to look at how the world is changing and how it can change with it to provide a more flexible and efficient system that can have greater reach and serve the general population. .
