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Healthcare software development is moving rapidly to create innovative personalized healthcare plans.
One of the greatest current challenges? There are varying degrees of user willingness to share data with healthcare software development. This data may include personal health, social, and demographic information, which many people aren’t willing to share.
So how can healthcare software developers use personal data to its greatest advantage when creating personalized healthcare plans? And how can we encourage users to trust us with their data?
Significo’s Dr. Jana Schmidt, Chief Innovation Officer, discusses the power and pitfalls of data and privacy in healthcare software development and what this means for those who develop this software.
User Concerns About Sharing Personal Data for Healthcare Software Development
It’s no secret that many people are concerned about the way their data is used. In many cases, there is the assumption data will be misused before it is even handed over.
There are generational and cultural differences between those who are most and least willing to hand over their health data, says Schmidt.
“We need to collect not only medical data but also some sort of demographic social information about the lifestyle of people,” she says.
“Having a very transparent data usage protocol and having an open ear for the user is super beneficial. And on the other side, [the industry needs to] show the users that if they share their data they can also influence how products are shaped.”
Schmidt adds that the healthcare software development industry still lacks in providing this opportunity to users. “I think we need to change our perception of the user who is only the consumer currently,” she says. “I think the user could be more engaged or included in the process.”
Ethics & Solutions with Anonymized vs. Synthetic Data
Personal data is central to a system which relies on real-world evidence for the development of healthcare software. We will look at three main types of data: anonymized, pseudonymized, and synthetic.
Any form of real-world data can lead to data protection issues, as combining information from different sets that are publicly available can risk identifying the data’s owner. Anonymized personal data is therefore often used because it runs a lower risk of identifying a patient.
Anonymization is the processing of personal data that makes it impossible for the person to be identified from the data alone. This impossibility of identification must be permanent.
One study found that 71% of Americans were willing to share their health data as long as it was anonymized. Many of these people did not even classify their data as personal, since it did not include their name or address.
Pseudonymized data is the process of attributing another name, or pseudonym, to a person whereby their identity cannot be deciphered from looking at the data. The data set will have a “key holder” who knows the true identity of the participants. Encoded information is a form of pseudonymized data.
The consensus among GDPR officers is that no matter how robust your data sets are in the present, there is no guarantee more data will not come out in the future that can be linked with an existing data set to identify the patient.
Anonymization or pseudonymization alone cannot solve all problems, because gathering the information requires the consent of the owner. It also requires many of the data rows to be exactly the same to avoid any chance of identification. An alternative to this is synthetic data.
Synthetic data uses an algorithm to create new data sets. Currently, algorithms are being tested to anonymize real data for research purposes. “We can combine healthcare data sets to drive completely new research,” says Schmidt.
Synthetic data offers patterns that can be analyzed in medical research and used in healthcare software development. It is a cost-effective alternative to real clinical data. It also carries no risk of data breaches to the patient, because there are no real-world patients providing this data.
“It is the only form of data that can be truly classed as anonymous,” says Schmidt. This then eliminates many ethical issues and GDPR concerns that researchers would find when dealing with personalized anonymous data.
However, a pitfall of synthetic data is it can be difficult to ensure that these data sets are just as reliable as real-world data.
What the Future May Hold for Data, Privacy & Innovation in Healthcare Software Development
When it comes to health, humans are not as unique as we like to think we are. Our bodies react to certain bacteria in similar patterns. These patterns are pivotal in healthcare software development research.
Improving healthcare development software requires us to understand these patterns. The key is to capture these big patterns using real data. Using this, we can test the algorithm that produces the synthetic data to make sure it is creating accurate data to be studied.
The user can then input their data to see where they lie within these patterns and personalized healthcare plans can be created for them.
“We are still at the beginning of this era,” says Schmidt. This is a hot topic among researchers currently – there is still so much more to learn.
“I would love it if the users could receive proper education from each service they are using so that they can make this informed decision about how much of their data they want to give,” says Schmidt.
This will also make users more aware of how their data could be used to inform synthetic data sets. As we learn more about synthetic data and develop
more and more sets, the need for real-world data will naturally diminish.
This can only be achieved through increased collaboration between the user and the healthcare software developers. This involves building trust, using what Schmidt calls The Principle of Solidarity.
For example, in the case of Recco – a personalized well-being and primary preventative healthcare software development kit created by Significo – users are given the option to allow Recco to use their personal data to help improve the accuracy of the tool. This means that users are collaborating with the tool developers in solidarity with the shared commitment of helping people build a healthier lifestyle, even if the person donating their data is relatively healthy.
A 2020 study showed 73% of those surveyed would be willing to share their personal health data if it helped healthcare providers improve patient care. So perhaps more people than we think are adopting the Principle of Solidarity.
We have the power to create personalized health plans, not only through collecting data on health but also on social and demographic information. The challenge lies in the willingness of people to share their data.
This power to tailor healthcare development software using real world data leads to risks of data breaches. Synthetic data holds fewer ethical issues, but the data itself may be less reliable. This is why researchers need real-world data to influence these algorithms.
With healthcare software development research evolving fast, there is so much that can be done to deliver personalized plans that vastly improve a person’s life. Using social and demographic data will only enhance this.
As healthcare software developers, if we maintain responsible data utilization practices and prioritize patient and employee privacy, there really is no ceiling on where we can go with this enhanced patient profile. Staying on top of the latest research is key in this ever-developing sector.
Schedule a demo with Significo today to see what we can do to help your organization shape a brighter future for healthcare software development.
