I have been in health care and worked as a bedside nurse for more than 30 years now. Early in my career a surgeon said to me, “I could have made changes if I had that information sooner. I’ve had 200 people on my operating room table during this time.”

The surgeon was referring to critical data within medical registries, which are used by clinicians to improve care delivery. Registries have always collected valuable data on outcomes for populations based on a specific disease or condition, and health systems are able to increasingly harness that value thanks to emerging digital technologies.

However, it has been challenging for physicians to realize the value of their clinical and nonclinical registry data, despite its potential to help inform improved processes and quality of care. When I first started working with registries back in the early ’90s, clinicians were required to populate paper forms with patient information and submit them to the registries. The registry bodies would then compile and return the data from participating providers – six months later.

Registries are about more than submitting data to a database. If a patient comes into the ED unconscious, registry information can be invaluable in informing immediate care while saving the attending nurse from searching for that information or having to contact a family member.

Another big change I have seen is the heightened shortage of nurses and heavy increases in workloads – both of which are exacerbated by an aging population. Long gone are the days when a nurse was responsible for one patient in the ICU. Today, an ICU nurse may be caring for three to four patients at a time, while also training new staff and performing other administrative tasks like clinical data abstraction.

Unfortunately, the nursing shortage is only expected to worsen as older nurses leave the workforce and will further contribute to burnout and attrition – resulting in a hit to the quality of patient care. According to a study published by Medical Care on June 10 of this year, “Reducing the proportion of RNs in hospitals, even when total nursing personnel hours are kept the same, is likely to result in significant avoidable patient deaths, readmissions, longer lengths of stay, and decreased patient satisfaction, in addition to excess Medicare costs and forgone cost savings to hospitals.”

Data is the most important ingredient for improving health care.

Nurses have a strong impulse to help others. Trust me, no one gets into nursing for the money. Still, that doesn’t mean a full-time nurse isn’t resentful of a travel nurse, who often makes up to three times their annual pay. This understandable resentment adds to the low morale that already impacts too many nurses.

To fulfill their primary mission of caring for patients, nurses need help amid continued short-staffing and growing demand. If we implement available technologies to help alleviate the manual charting burden and other administrative functions, nurses immediately have more time for direct patient care.

Nurses also don’t enter the field because they have a burning desire to perform manual clinical data abstraction—a process that is time-consuming, labor-intensive, and costly for health systems. High-quality data is essential for improving and transforming health care, so abstracting data is a critically important function within every hospital—but it takes time away from the real mission, which is to deliver care to patients.

Automation enabled by artificial intelligence (AI) can free nurses from the time-consuming and arduous tasks, such as clinical data abstraction. This allows them to work at the top of their license providing bedside care that no technology could ever replace. As the study published in Medical Care shows, a nurse’s ability to spend additional time and focus at the point of care improves patient safety and outcomes.

Not only does automation help relieve administrative burden on clinical staff, hospitals and health systems are able to realize valuable cost savings. AI-based automation can cut clinical data abstraction costs by up to 50 percent, all while abstracting data exponentially faster than the traditional, manual approach.

AI and automation also helps lower costs by reducing the need for travel nurses, which also helps alleviate the aforementioned resentment of the in-house nursing staff.

Listen to your nurses.

Instead of solely focusing on slashing spending and improving margins, health systems should ask the frontline what support they could use to best do their jobs. Provider organizations must actively listen to their nursing bedside staff and identify what is taking away most of their time from patient care, monitoring, and coordination.

Reducing clinical data abstraction and charting responsibilities would be a tremendous help to nurses, and available forms of AI-enabled automation do that.

Conclusion 

The surgeon who years ago regretted not being able to get registry data faster knew that clinicians needed trustworthy data to deliver better care. The goal remains the same today. But a nursing shortage exacerbated by the retirement of older RNs, combined with growing manual abstraction tasks and chart responsibilities, is making it difficult for these dedicated health care professionals to focus on patient care. AI and automation can improve accuracy and efficiency in data collection, allowing time to be spent on process improvements. The result is improved patient outcomes, which is ultimately what health care should be all about.

Betsy Castillo is a nurse executive.