Association Between Labor and Delivery Unit Census and Delays in Patient Management: Findings From a Computer Simulation Module

Novel Evidence-Based Labor Induction Algorithm Associated with Increased Vaginal Delivery within 24 Hours

OBJECTIVE

This study aimed to assess whether concordance with our proposed labor induction algorithm is associated with an increased rate of vaginal delivery within 24 hours.

STUDY DESIGN

We conducted a retrospective review of 287 induction of labors (IOLs) at a single urban, tertiary, academic medical center which took place before we created an evidence-based IOL algorithm. We then compared the IOL course to the algorithm to assess for concordance and outcomes. Patients age 18 years or over with a singleton, cephalic pregnancy of 36^6/7 to 42^0/7 weeks' gestation were included. Patients were excluded with a Bishop's score >6, contraindication to misoprostol or cervical Foley catheter, major fetal anomalies, or intrauterine fetal death. Patients with 100% concordance were compared with <100% concordant patients, and patients with ≥80% concordance were compared with <80% concordant patients. Adjusted hazard ratios (AHRs) were calculated for rate of vaginal delivery within 24 hours, our primary outcome. Competing risk's analysis was conducted for concordant versus nonconcordant groups, using vaginal delivery as the outcome of interest, with cesarean delivery (CD) as a competing event.

RESULTS

Patients with 100% concordance were more likely to have a vaginal delivery within 24 hours, n = 66 of 77 or 85.7% versus n = 120 of 210 or 57.1% (p < 0.0001), with an AHR of 2.72 (1.98, 3.75, p < 0.0001) after adjusting for delivery indication and scheduled status. Patients with 100% concordance also had shorter time from first intervention to delivery (11.9 vs. 19.4 hours). Patients with ≥80% concordance had a lower rate of CD (11/96, 11.5%) compared with those with <80% concordance (43/191 = 22.5%; p = 0.0238). There were no differences in neonatal outcomes assessed.

CONCLUSION

Our IOL algorithm may offer an opportunity to standardize care, improve the rate of vaginal delivery within 24 hours, shorten time to delivery, and reduce the CD rate for patients undergoing IOL.

KEY POINTS

· Studies on IOL have focused on individual steps. A labor induction algorithm allows for standardization.. · Algorithm concordance is associated with decreased time to delivery.. · Algorithm concordance is associated with decreased CD rate..

Facility Design: Reimagining Approaches to Childbirth in Hospital and Birth Center Settings

Few maternity care clinicians are aware of the current regulations that guide design standards for childbirth facilities in the United States or the regulatory history. There is considerable variance among state regulations as well as oversight of facility standards for healthcare settings. Understanding evidence-based recommendations on how facility design affects health outcomes is critical to reversing the rise in maternal mortality and morbidity. A variety of measures can be implemented that promise to improve user satisfaction, quality of care, and efficiency for all who engage in the childbirth environment. Recommendations for change include broader assessment to better understand how clinicians and consumers simultaneously maneuver within a complex system. Key metrics include evaluation of workflow within available space, patient acuity and census patterns, integration of evidence-based recommendations, and options that promote physiologic birth. For the changes to succeed, human centered design must be implemented and diverse clinicians and consumers engaged in all phases of planning and implementation. Exploring characteristics and outcomes of low-risk women who receive care in a freestanding birth center or the European alongside maternity unit provides opportunity to reimagine and address improvements for inpatient, hospital birth.

A conceptual framework for the impact of obesity on risk of cesarean delivery

Cesarean deliveries accounted for 32.2% of nearly 4 million births in the United States in 2014. Obesity affects a third of reproductive-age women and is associated with worse cesarean delivery outcomes. Studies have shown that increasing maternal body mass index correlates linearly with cesarean delivery rates, but little is known about the potential mediating and moderating mechanisms. Thus, a conceptual framework for understanding how obesity correlates with risk of cesarean delivery is crucial to determining safe ways to reduce the cesarean delivery rate among obese gravidas. Based on an extensive review and synthesis of the literature, we present a conceptual framework that posits how obesity may operate through several pathways to lead to a cesarean delivery. Our framework explores the complexity of obesity as an exposure that operates through potential mediating pathways, a moderator of cesarean delivery risk, and a covariate with other cesarean delivery risk factors. Among nulliparas, obesity appears to operate through 3 main proximal mediating mechanisms to increase risk of cesarean delivery including: (1) preexisting comorbidities and obstetric complications; (2) a slower progression of first-stage labor, potentially increasing the risk of cesarean delivery secondary to failure to progress; and (3) a prolongation of pregnancy, which is associated with risk of maternal postdates. For multiparas, a fourth proximal mediator of prior uterine scar may also increase cesarean delivery risk. Distal mediating mechanisms, which operate through one of the proximal mechanisms, may include an induction of labor or planned prelabor cesarean delivery. Obesity may also moderate the likelihood of cesarean delivery by interacting with clinician-level or hospital-level factors. Future research should assess the validity of this framework and seek to understand the relative contributions of each potential pathway between obesity and cesarean delivery. This will allow for evidence-based recommendations to reduce preventable cesareans among obese women by targeting modifiable mediators and moderators of the relationship between obesity and increased risk of cesarean delivery.

The Nurse-Engineer: A New Role to Improve Nurse Technology Interface and Patient Care Device Innovations

PURPOSE

The purpose of this article is to describe two innovative biomedical engineering and nursing collaborations designed to educate a new cadre of professionals and develop new knowledge and innovations (robots, patient care devices, and computer simulation).

ORGANIZING CONSTRUCT

Complex health problems demand a highly skilled response that uses teams of professionals from various disciplines. When the biomedical engineering lens is expanded to include the practical perspective of nursing, opportunities emerge for greater technology-nurse interface and subsequent innovation. A joint nursing-engineering degree program provides the ideal preparation for a well-informed nurse-engineer who can explore new and innovative solutions that will improve care and patient outcomes.

APPROACH

A review of the literature provides the background on innovation and engineering in nursing and a rationale for the development of two innovative joint degrees, as well as a description of those programs.

FINDINGS

These innovative programs will advance healthcare-related technology and maximize the potential contribution of the nursing profession in the design and implementation of creative solutions. They also have the potential to increase the skills and knowledge for students enrolled in biomedical engineering or Bachelor of Science in nursing programs individually, providing them with interdisciplinary training and exposure.

CLINICAL RELEVANCE

Important patient care improvement opportunities are missed when nurses are not actively engaged in patient care device innovation and creation. Innovative nurse and engineer collaborations are needed in various forms to leverage nurse ingenuity and create patient care innovations.

Simulation model of the relationship between cesarean section rates and labor duration

Cesarean delivery is the most common major abdominal surgery in many parts of the world, and it accounts for nearly one-third of births in the United States. For a patient who requires a C-section, allowing prolonged labor is not recommended because of the increased risk of infection. However, for a patient who is capable of a successful vaginal delivery, performing an unnecessary C-section can have a substantial adverse impact on the patient's future health. We develop two stochastic simulation models of the delivery process for women in labor; and our objectives are (i) to represent the natural progression of labor and thereby gain insights concerning the duration of labor as it depends on the dilation state for induced, augmented, and spontaneous labors; and (ii) to evaluate the Friedman curve and other labor-progression rules, including their impact on the C-section rate and on the rates of maternal and fetal complications. To use a shifted lognormal distribution for modeling the duration of labor in each dilation state and for each type of labor, we formulate a percentile-matching procedure that requires three estimated quantiles of each distribution as reported in the literature. Based on results generated by both simulation models, we concluded that for singleton births by nulliparous women with no prior complications, labor duration longer than two hours (i.e., the time limit for labor arrest based on the Friedman curve) should be allowed in each dilation state; furthermore, the allowed labor duration should be a function of dilation state.

Robotic assistance in the coordination of patient care

We conducted a study to investigate trust in and dependence upon robotic decision support among nurses and doctors on a labor and delivery floor. There is evidence that suggestions provided by embodied agents engender inappropriate degrees of trust and reliance among humans. This concern represents a critical barrier that must be addressed before fielding intelligent hospital service robots that take initiative to coordinate patient care. We conducted our experiment with nurses and physicians, and evaluated the subjects’ levels of trust in and dependence upon high- and low-quality recommendations issued by robotic versus computer-based decision support. The decision support, generated through action-driven learning from expert demonstration, produced high-quality recommendations that were accepted by nurses and physicians at a compliance rate of 90%. Rates of Type I and Type II errors were comparable between robotic and computer-based decision support. Furthermore, embodiment appeared to benefit performance, as indicated by a higher degree of appropriate dependence after the quality of recommendations changed over the course of the experiment. These results support the notion that a robotic assistant may be able to safely and effectively assist with patient care. Finally, we conducted a pilot demonstration in which a robot-assisted resource nurses on a labor and delivery floor at a tertiary care center.