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 Between hesitant staff members and outdated digital infrastructures, several obstacles stand in the way of widespread AI integration.
Key Takeaways
- The health care industry struggles to adopt AI due to staff hesitance and outdated technology.
- Transitioning to a data-driven approach requires phasing out physical documents and relying on digital data collection.
- Utilizing electronic health records (EHR) and real-world data can enhance patient care and operational efficiency.
- Investing in staff training and hiring experts in data management is crucial for a smooth AI integration.
- A focus on data-driven practices prepares health care organizations for future AI implementation, leading to improved patient outcomes.
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. They are 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. They should also take 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, it will be ready. With this in mind, here are a few ways health care organizations can shift to a data-driven approach. This will help them greet AI with open arms.
Phasing out physical documents
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, structuring the data into a comprehensive health history becomes challenging 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. This is crucial 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. Moreover, it will be able to verify the accuracy of the data it feeds into AI.
Using advanced technology
One major technology that health care organizations should be taking advantage of, if they haven’t already, is electronic health record (EHR) systems. These 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. When 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. It gives them a glimpse of the meaningful insights they’ll eventually see from AI. As it stands today, clinicians are so overloaded with information. They can’t narrow down what should and shouldn’t be incorporated into patient care. Organization is key to making these decisions.
Protecting Staff Wellbeing and Improving Patient Experience
As well as having the ability to reduce the physical documents needed in a hospital, there are also wider benefits you can expect from advanced hospital technology. For example, staff wellbeing can be taken more seriously. Furthermore, patient experience will be improved with the use of cutting-edge technology. A real-time location system (RTLS) is a key example. Implementing this type of solution into healthcare facilities is now a must-have. You’re aiming to improve communication, reduce nurse burnout, and smooth out challenges regarding underused equipment. Moreover, hiring travel CT techs knowledgeable of AI technology can help improve access to diagnostic services in underserved areas and mitigate healthcare professional shortages. This ultimately improves patient experience.
As soon as these strategies are introduced into a healthcare space, you can expect increased staff safety. There will be better care for patients due to the real-time element of this AI technology. On top of this, it can also make the waste in the workplace very clear. It can completely positively transform your health system. It’s clear to see that AI has a place in the healthcare system, and it’s not a matter of if you implement it, but when you introduce it to your establishment.
Hiring new staff and training employees
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. This 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. 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. They can educate key employees about the different data points that will come to affect their daily routines. Employees need to understand which processes will become more data-driven and why this transition is beneficial for patient care.
Final thoughts
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. This is 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. This can be done with minimal operational disruption and workplace stress.
