3 Keys to Improving Patient Safety in Labor and Delivery

Plus updates on related new requirements from The Joint Commission

By Dr. Alana McGolrick, DNP, RNC-OB, C-EFM, CNO at PeriGen

Patient safety is a top priority in healthcare; but unlike most other service lines, where there is a single patient to worry about, the maternal-fetal dyad makes labor and delivery (L&D) particularly challenging to manage. The challenges don’t just double—they square.

Fortunately, many of the issues facing L&D clinicians today are avoidable with the right approach. Here are how three of the most common are being addressed.

1. Unexpected complications with otherwise expected healthy newborns
According to the most recent data from the Centers for Disease Control and Prevention (CDC), the national average rate of fetal and infant deaths (28 weeks to 7 days) is 6.6 per 1,000 live births. Healthy People 2020 has set a goal of reducing that number to 5.9 per 1,000 live births.

To reach this goal, we must first understand why complications occurred in births where there were no pre-existing fetal conditions or other indicators (such as maternal drug use exposure in-utero, birth weight under 2,499 grams, genetic disease and congenital malformations). That requires data.

To gather that data, The Joint Commission has added the PC.06 measurement to its requirements for accredited hospitals with more than 300 live births annually and/or any hospital or health system seeking Perinatal Care Certification regardless of birth volume. The new performance measurement requires hospitals to report the percentage of infants with unexpected newborn complications among full-term newborns with no pre-existing conditions. This performance measurement took effect January 1, 2019.

In creating this new performance measurement, The Joint Commission noted that, while there is extensive measurement of clinical outcomes and care in the pre-term population, there are far fewer metrics surrounding those who go to full term. The new performance measurement intends to correct that issue by requiring hospitals to report the percentage of full-term infants who were admitted to the newborn intensive care unit (NICU) with severe or moderate neonatal complications.

Once a sufficient amount of data has been gathered, The Joint Commission will establish national benchmarks that hospitals will be able to use to measure their own performance. However, this data will not be available until 2020; so in the meantime, The Joint Commission advises hospitals to use data from the California Maternal Quality Care Collaborative (CMQCC) for comparison purposes.

Still, there’s a difference between knowing the current rate and achieving Healthy People 2020’s goal. This is where analytics driven by artificial intelligence (AI) can be a game-changer.

By analyzing the data where outcomes are known in sufficient volume, AI will be able to uncover correlations between certain events, such as biometric readings of the mother and infant at different periods during labor progress, that are early indicators of potential complications. This information can then be incorporated into L&D technology to provide early, evidence-based warnings to clinicians during the intrapartum stage.

Clinicians will then be able to review the data and determine whether any actions need to be taken to prevent severe or moderate morbidities occurring among otherwise healthy infants. The ultimate goal is fewer babies admitted to the NICU with severe or moderate neonatal complications, enabling more families to take home healthy babies.

2. Risk factors associated with non-distinct newborn naming conventions
One of the most common avoidable issues among newborns is problems with patient identification. The typical hospital designation of “Baby Girl Smith” is often not precise enough to distinguish one patient from another, especially in hospitals with high L&D volume.

Patient identification errors among neonates can lead to issues such as incorrect diagnosis and procedures, breast milk administration errors, and medication events. They can also have an effect on the accuracy of laboratory, radiology, respiratory and surgical diagnostics.

The Joint Commission has addressed this issue with its National Patient Safety Goal 01.01.01, EP3. Effective January 1, 2019, the patient safety goal states that two distinct methods must be used to identify newborns when providing care, treatment and services. The recommendation is to consider using the mother’s first and last names as well as the newborn’s gender. This identification would be added to wristbands for both mothers and babies, as well as be used in the electronic health record (EHR) and other documentation.

For example, a newborn girl born to Sally Brown could be identified as “Brown, BabyGSally” with the G standing for girl. In the case of multiple births, numbers or letters could be added. In this case, the first of the multiples would be “Brown, BabyG1Sally.”

The goal is to add an extra measure of precision to ensure the right care is delivered to the right infant. While it may require changes to the technology being used (such as label printers that generate identification wristbands) as well as to processes, the upside more than outweighs any temporary disruptions.

3. Preventing maternal morbidity
These recent measures both focus on the baby. That makes sense, because babies are the most vulnerable patients in any hospital, since they cannot speak for themselves during the care process.

Yet when you look at the statistics, it is clear that mothers need additional patient safety protections as well—especially when you consider that the United States has one of the highest maternal mortality rates among all Western industrialized nations. It’s important to note that the U.S. rate has been rising in recent years, while the overall rate worldwide has been falling.

Moreover, a report by the CDC Foundation—a nonprofit created by Congress to support the CDC—found that more than 60 percent of pregnancy and childbirth-related deaths in the United States are preventable. Most of those preventable deaths occur because of a clinician’s “failure to recognize and delayed treatment of clinical warning signs.” What is needed is an improvement in the clinicians’ ability to recognize and treat warning signs which can prevent a developing situation from getting worse.

This is where AI-driven technology can make a difference. During L&D, modern devices gather huge data sets—more than humans can be reasonably expected to process. The dynamic nature of this data further complicates interpretation, making it even more difficult to recognize subtle warning signs.

This is what AI does well. It can sift through huge data sets and recognize patterns (based on past known outcomes) and then alert a clinician that a patient’s condition is worsening. Current AI technologies in obstetrics, that have been performance-tested in independent studies, have produced results that were consistent with expert evaluation of fetal heart tracings.

Once clinicians are alerted to a potential issue, they use their assessment and interpretation skill set to determine whether interventions are needed. Acting early can help them avoid unnecessary urgent interventions, producing better outcomes for mom, baby and family.

Keeping patient safety front and center
Virtually every health system in the United States will agree that patient safety is—and should be—a top priority. Fulfilling that commitment in L&D can be particularly challenging due to the dynamic, unpredictable nature of childbirth

The patient safety measures outlined here will go a long way towards keeping mothers and newborns safe today—to provide exponential benefits in the future.