Perinatal care gets a digital upgrade: From good intentions to measurable impact
By: Melissa Martin, MSN, MBA, RN, Strategic Partnership Leader, GE HealthCare
Most people think dying during childbirth is a thing of the past. We have more knowledge, more training, and more tools than ever before. Yet in the U.S., more than 80% of pregnancy related deaths are preventable.1 These are among the key themes we discussed at the HLTH Impact session last fall, as you can see in this video, and they continue to guide how we think about advancing digital innovation at GE HealthCare.
For those of us who have worked in or alongside labor and delivery teams, these realities are not abstract. Even in high-resource settings, something can change quickly. Early signs may be subtle, evolving, or difficult to interpret in real time. What a patient feels and what the care team sees does not always align immediately.
These moments are not about individual failure. They reflect the complexity of care delivery, where fragmented information, time pressure, and competing demands can make it difficult to fully understand what is happening in the moment.
What’s really broken: perception + process
Racial and ethnic disparities in pregnancy-related deaths persist across age and education levels and remain elevated even in states with the lowest mortality ratios.2
In labor and delivery, adverse outcomes rarely result from a single mistake. Instead, they often emerge at the intersection of human perception and system processes:
- Perception. Under pressure, clinicians rely on heuristics, or experience-based mental shortcuts. These can be efficient, but they can also introduce variability. Symptoms may be interpreted differently across clinicians, shifts or experience levels. Digital tools that surface standardized cues and objective summaries can help support more consistent interpretation in real time.
- Process. Care transitions remain a critical point of vulnerability. Analyses of malpractice claims attribute 1,744 deaths and $1.7B in costs over five years to miscommunication.3 In a multicenter study, the I‑PASS program, which included structured handoffs, reduced medical errors by 23% and preventable adverse events by 30% without increasing handoff time.4
Labor and delivery is one of the most complex and dynamic environments in the hospital. Improving outcomes requires systems that support clinical judgment while strengthening reliability across teams, shifts, and care settings.
From digitized to upgraded: design principles that actually help
Clinicians are working in increasingly complex environments, marked by staffing constraints, rising acuity, and fragmented information systems. Despite widespread digitization, gaps remain. Studies show that EHR decision support still misses up to one third of potentially harmful medication errors, highlighting that digital systems alone do not guarantee safer care.5-6
To improve outcomes, digital must function as an upgrade. The goal is straightforward: improve data visibility and support clinical workflows.
Three principles are key for success on the unit:
1. Human-centered design: support usability and workflow efficiency
- Unify the signal. A single, integrated view of maternal and fetal data is designed to support clinical interpretation of the patient’s condition. When fetal tracings, maternal vitals, medications, and key events are presented in a time-aligned format, clinicians may identify patterns more efficiently and make informed decisions.
- Standardize handoffs inside the workflow. Structured handoff summaries that communicate patient status, key risks, action items and contingencies help maintain continuity across shifts. Standardization supports consistency without adding complexity.
- Documentation designed to streamline workflows. Streamlined documentation and event review reduce administrative burden while supporting continuity of care. Efficient workflows allow clinicians to focus attention where it matters most.
2. Equity by design: standardize what “good” looks like
- Align standards and operationalize them. The ACOG 2025 guideline updates how intrapartum fetal heart rate patterns are defined, categorized ,and managed. Digital tools should reflect these standards directly within workflows, embedding guidance into documentation, surveillance and handoffs.
- Make risk and next actions objective. Protocol-linked prompts and pattern recognition reduce variability across clinicians and care settings. Standardizing how risk is identified and acted upon supports more consistent decision-making.
- Design and validate for equity. Digital capabilities must be evaluated across diverse populations and care environments to ensure reliability and consistency. When thoughtfully implemented, standardization supports equity by reducing variation in care.
3. AI that helps, not hinders: assist judgment, reduce variability
In perinatal care, emerging technologies are being applied to well-known challenges, particularly those related to subjectivity and cognitive load.
- FHR event detection and documentation. Multicenter research published in Frontiers in Digital Health demonstrates a dual-input approach that analyzes fetal heart rate and uterine activity together. This reinforces the same pattern recognition clinicians already perform, supporting more consistent interpretation under pressure.
- Contextual care companion experiences. Large Language Model (LLM)-enabled tools can synthesize vitals, medications, labs and policies into concise, situation-aware summaries embedded directly into clinical screens. Emerging research from GE HealthCare on multi-agent AI architectures for perinatal decision support shows how coordinating information across data sources and roles can improve orientation and handoffs amid staffing strain and uneven experience levels.
From blueprint to bedside: a cloud‑enabled perinatal platform
Translating these principles into practice requires an enterprise platform that unifies data, supports teams with near real-time data integration*, and evolves safely as evidence changes. CareIntellect™ for Perinatal** was designed with that goal in mind. The cloud first application brings maternal and fetal data—such as uterine activity, fetal heart rate, maternal blood pressure, and SpO₂—into a single, chronological view that supports both at a glance surveillance and deeper clinical review. Developed with clinician input, including collaboration with health systems such as HCA Healthcare (reference does not imply endorsement), it is part of a broader family of applications built to integrate once and expand over time.
See CareIntellect for Perinatal in action.
Sources
- Centers for Disease Control and Prevention. (n.d.). Maternal mortality. https://www.cdc.gov/womens-health/features/maternal-mortality.html
- Petersen, E. E., Davis, N. L., Goodman, D., Cox, S., Syverson, C., Seed, K., Shapiro‑Mendoza, C. K., Callaghan, W. M., & Barfield, W. (2019). Racial/ethnic disparities in pregnancy‑related deaths — United States, 2007–2016. MMWR. Morbidity and Mortality Weekly Report, 68(35), 762–765. https://www.cdc.gov/mmwr/volumes/68/wr/mm6835a3.htm
- CRICO Strategies. (2015). Malpractice risks in communication failures (Annual benchmarking report). Harvard Risk Management Foundation. https://www.rmf.harvard.edu/CRICO-Strategies/Publications/malpractice-risks-in-communication-failures
- Starmer, A. J., Spector, N. D., Srivastava, R., West, D. C., Rosenbluth, G., Allen, A. D., Noble, E. L., Tse, L. L., Dalal, A. K., Keohane, C. A., Lipsitz, S. R., Rothschild, J. M., Wien, M. F., Yoon, C. S., Zigmont, K. R., Wilson, K. M., O’Toole, J. K., Solan, L. G., … Landrigan, C. P. (2014). Changes in medical errors after implementation of a handoff program. New England Journal of Medicine, 371(19), 1803–1812. https://doi.org/10.1056/NEJMsa1405556
- Classen, D. C., Holmgren, A. J., Co, Z., Newmark, L. P., Seger, D. L., Danforth, M., Bates, D. W., & Kahane, D. C. (2020). National trends in the safety performance of electronic health record systems for medication ordering. JAMA Network Open, 3(5), e205379. https://doi.org/10.1001/jamanetworkopen.2020.5379
- The Pew Charitable Trusts. (2020). Study shows electronic health records improving, but safety concerns remain. https://www.pew.org/en/research-and-analysis/articles/2020/06/11/study-shows-electronic-health-records-improving-but-safety-concerns-remain
- Jacques, S., & Williams, E. (2016). Reducing the safety hazards of monitor alert and alarm fatigue. AHRQ Patient Safety Network (PSNet). https://psnet.ahrq.gov/perspective/reducing-safety-hazards-monitor-alert-and-alarm-fatigue
- Pardasani, R., Vitullo, R., Harris, S., Yapici, H. O., & Beard, J. (2025). Development of a novel artificial intelligence algorithm for interpreting fetal heart rate and uterine activity data in cardiotocography. Frontiers in digital health, 7, 1638424. https://doi.org/10.3389/fdgth.2025.1638424
*Requires real-time HL7 data feed from all devices.
** CareIntellect Perinatal formerly Mural Perinatal Surveillance, a 510(k)-cleared device in the U.S. is the product name. CareIntellect for Perinatal may appear as descriptive content.
Maternal & Infant Care Products & Accessories: Helping to send moms and babies home healthy
The material presented in this blog represents the opinion of the author(s) and not necessarily the views of Synova Associates. Synova Associates does not endorse any specific products or organizations but strives to connect its industry partners with leaders interested in product/educational innovation.
