Despite the seemingly countless applications for artificial intelligence in data-driven health care, the industry has been particularly sluggish in adopting this rapidly advancing technology. Between hesitant staff members and outdated digital infrastructures, several obstacles stand in the way of widespread AI integration.
But that doesn’t mean that health care organizations don’t see AI coming. Odds are, many of them are well-aware of the massive operational and cultural change awaiting the industry. That’s why a growing number of organizations are currently preparing for this momentous transition by becoming increasingly digitized and data-driven. After all, AI’s decisions are based entirely on the data it ingests. For this reason, health care organizations would be wise to accustom themselves to prioritizing digital data collection and taking better care of this data across the board.
An organization that already understands the value of data and related technology is well positioned for a smooth integration when AI inevitably enters its daily operations. With this in mind, here are a few ways health care organizations can shift to a data-driven approach that will greet AI with open arms.
The first step towards adding more value to data is verifying its accuracy. Because health care data is compiled from a variety of sources and formats, this makes it difficult to structure and formulate the data into a comprehensive health history for any particular patient. The lack of structure combined with the need to constantly pull data from different sources and convert it into different formats also leaves more room for human error.
So, in order to become more data-driven, organizations must eliminate any data collection practices that could increase the risk of human error even further. That begins with eschewing paper documents and manual processes. Collecting data exclusively through digital means prevents small but significant pieces of information from getting lost in the daily chaos of a busy organization. Every bit of information about a patient could dramatically improve an organization’s ability to deliver the best possible care for that patient.
A fully digitized health care organization has already taken a big step towards preparing for AI, which has the power to ingest and aggregate vast swaths of digital data. When the organization introduces AI, it will already be accustomed to working primarily with digital data and will be able to verify the accuracy of the data it feeds into AI.
One major technology that health care organizations should be taking advantage of, if they haven’t already, is electronic health record (EHR) systems, which have evolved tremendously as of late.
Similarly, organizations can also develop specific strategies for accessing and curating what’s known as real-world data. This refers to data that is captured outside of clinical trials. Examples of real-world data include data collected from EHRs, wearable devices, claims and billing activities, or product and disease registries. Without advanced digital infrastructure, real-world data is extremely difficult to interpret. And when it’s interpreted correctly, real-world data can reveal more about the health of specific populations than data from clinical trials.
Organizing data for practical use can additionally help clinicians distinguish the most useful types of data for improving patient care, giving them a glimpse of the meaningful insights they’ll eventually see from AI. As it stands today, clinicians are so overloaded with information that they can’t narrow down what should and shouldn’t be incorporated into patient care, and organization is key for making these decisions.
Health care workers are not data scientists. In fact, only recently has technology begun to play a consistent role in their daily routines. So, current staff at most health care organizations are not qualified to implement an effective data collection and curation strategy. Which means a crucial step towards preparing for AI is training and the recruitment of new staff members with significant experience in managing unstructured data. If this isn’t a possibility, another option is partnering with a third-party organization that specializes in data curation and related services.
One of the biggest barriers towards the widespread adoption of AI in health care is the industry’s lack of familiarity with how the technology works. You can’t expect health care workers to trust the data-driven insights provided by AI if they have no idea how these insights are produced in the first place. While health care organizations might not have the resources to conduct in-house training that’s specific to AI, they can at least get the ball rolling by educating key employees about the different data points that will come to affect their daily routines. It’s important for employees to understand which processes will become more data-driven and why this transition is beneficial for patient care.
As you can see, prioritizing the collection and implementation of accurate data is essentially a precursor for the introduction of AI. This is an opportunity for health care organizations to learn how data can help them do their jobs before data becomes the very center of their jobs. A data-driven health care organization is ultimately more primed to achieve the main objectives of AI with minimal operational disruption and workplace stress.