Health technology is an ancient concept. There were prosthetics in ancient Egypt a thousand years before the birth of Jesus, stethoscopes and x-rays emerged in the 19th century, and in the mid-19th century, transistors were developed to aid in implants and computers (which, like most of what we’re discussing in this post, facilitated data-sharing).

Now, the genie is out of the bottle on automated health care systems, from using AI for diagnoses to robots for surgery. The overriding importance of data management in that evolution is undeniable. Healthcare professionals have unprecedented amounts of data “at their fingertips,” but fingertips alone can never effectively manage such data. The promise of automated, or even AI-based management of that data is appealing because it helps those in the profession do what they have set out to do—provide the best possible care to patients.

The challenge, however, is that knowledge is power, and the optimal distribution of knowledge is not something that just happens by itself. Automation can exacerbate that maldistribution of information because “automation proposals involve solutions that focus on highly structured data,” organizing it takes human resources, and machine-to-machine interfacing involves “complex clinical data flows” that need reliable application programming interfaces. The very complexity of those processes makes systems vulnerable to information blocking—interfering with legitimate access to medical information.

Enter the 2016 Cures act, also called the Increasing Choice, Access, and Quality in Health Care for Americans Act, which does many things including making information blocking punishable by fines of up to $1 million per violation.

The goal here is the facilitation of informational communication: “The Cures Act looked to facilitate communication between the diverse patchworks of healthcare providers and between providers and their patients” by requiring “the electronic players in this space to provide open APIs that can be used ‘without special effort on the part of the user.’”

It is the proliferation of data that makes automation optimal in so many facets of care. The development of low-code frameworks for healthcare workers to build their own applications is another part of this process. There are low-code platforms for databases, business processes, web applications, and low-code “can also work alongside emerging technologies like robotic process automation and artificial intelligence to streamline drug development processes and leverage data to inform life-saving decision making.”

The results of this interactivity are not just glamorous or exceptional lifesaving methodologies. At Health TechJosh Gluck writes that AI is automating basic administrative and other tasks that ultimately ought to be the easiest and most automatic parts of the profession.

It’s fascinating to see this interactivity of tech developments, legal changes, and new approaches to data-sharing, which is such a big part of health technology. We’ve come a long way since ancient Egyptian prosthetics.