Abstract
Triage and the timing of admission of low-risk pregnant women can affect the use of augmentation, epidural, and cesarean. The purpose of this analysis was to explore these outcomes in a community hospital by the type of provider staffing triage. This was a retrospective cohort study of low-risk nulliparous women with a term, vertex fetus laboring in a community hospital. Bivariate and multivariable statistics evaluated associations between triage provider type and labor and birth outcomes. Patients in this sample (N = 335) were predominantly White (89.5%), with private insurance (77.0%), and married (71.0%) with no significant differences in these characteristics by triage provider type. Patients admitted by midwives had lower odds of oxytocin augmentation (adjusted odds ratio [aOR] = 0.50, 95% confidence interval [CI] = 0.29–0.87), epidural (aOR = 0.29, 95% CI = 0.12–0.69), and cesarean birth (aOR = 0.308, 95% CI = 0.14–0.67), compared with those triaged by physicians after controlling for patient characteristics and triage timing. This study provides additional context to midwives as labor triage providers for healthy, low-risk pregnant individuals; however, challenges persisted with measurement. More research is needed on the specific components of care during labor that support low-risk patients to avoid medical interventions and poor outcomes.
Keywords: labor outcomes, nurse-midwives, obstetrical triage
Hospital admission for labor represents an important transition from outpatient to inpatient care. When admission is unscheduled, a triage process is conducted to determine whether the laboring person should be admitted to the hospital. This process can be complex and dynamic, even for individuals with low-risk pregnancies.1,2 Consequently, labor triage decision-making can change the odds of low-risk pregnant individuals receiving medical interventions.3,4 It has been well-established that those admitted to the hospital during the latent phase of labor are at higher risk for intervention use without associated benefit than those admitted during the active phase of labor.5–7 It is therefore critical to explore factors related to hospital triage and admission to understand how these care processes influence labor and birth outcomes. This is imperative in the United States because cesarean rates suggest that overuse is common and this could be part of the reason why the maternal mortality ratio is unacceptably high.8 A better understanding of optimal clinical practices around labor admission and patterns of intervention, particularly unplanned cesarean birth, is needed. The purpose of this analysis was to use contemporary data to compare labor processes and outcomes among low-risk nulliparous individuals with term, singleton vertex (NTSV) pregnancies who were triaged by either midwives or physicians in a hospital setting following spontaneous labor onset.
BACKGROUND
Components of perinatal care and cost savings relate to how triage systems are designed, managed, and/or implemented. Previous studies support that admission in active labor reduces costs for low-risk pregnancies,9 and reducing the NTSV cesarean rate similarly reduces costs.10 Triage systems also have significant implications for individuals’ labor course and birth outcomes. A 2014 review of the literature identified 7 key drivers of obstetrical triage decision-making. These included legal issues, risk stratification, utilization, patient flow, patient satisfaction, and interprofessional models, including models with midwifery.1 Since 2014, when both this review and new labor admission guidelines emphasizing later onset of active labor were published,11 there has been limited research on how decision-making at admission influences the use of medical interventions during labor as well as frequency of cesarean birth.
Intrapartum care in the United States is often provided by interprofessional teams that include nurses, midwives, and physicians.12 In previous studies, patients who received prenatal or intrapartum care from midwives or interprofessional teams including midwives had a lower risk of cesarean birth.1,13–18 Two other studies also suggested that individuals receiving care in hospitals with interprofessional care provided by midwives and doctors were admitted at more advanced cervical dilatation than those receiving intrapartum care in hospitals with physician-only care.15,16 Previous research on midwifery-led obstetric triage examined patient satisfaction and risk stratification; both indicate that women receiving care in midwifery-led triage experience high satisfaction and have positive birth outcomes.1,18
It is not currently understood what influences hospital admission or triage decisions and/or subsequent outcomes for spontaneous onset of labor for term, low-risk pregnancies.19,20 Older and non-US-based studies suggest that midwifery care at the moment of intrapartum triage improves a range of labor processes and outcomes, including decreased use of epidural, instrumental vaginal birth, and cesarean birth.21,22 However, these studies had methodologic issues that limit applicability to contemporary clinical practice. For example, these studies did not examine clinical factors influencing hospital admission for low-risk pregnant individuals, predate the recommendation to delay admission until after 6-cm dilation, or focus on triage processes without examining labor outcomes.23–25 The influence of nursing care in triage is another notable factor to consider. The Association for Women’s Health Obstetrical and Neonatal Nursing created a tool to identify and properly admit people with high-risk pregnancies, but there is no parallel tool for those with low-risk pregnancies.26 The purpose of this analysis was to use contemporary data to compare labor processes and outcomes among low-risk NTSV pregnancies who were triaged and admitted by either midwives or physicians in a hospital setting following spontaneous labor onset.
METHODS
Setting
This institutional review board-approved, retrospective cohort study included medical record data of those giving birth at a suburban, community hospital in the Northeast United States. The labors of pregnant women included in this study occurred from August 2015 to December 2015, 1 year or more after the shift in the American College of Obstetricians and Gynecologist definition of active labor onset from 4-cm cervical dilation to 6 cm.19,20 Triage staffing during the day (7 AM to 8 PM) in this hospital is performed by either physicians or midwives. During the night (8 PM to 7 AM), midwives staff the triage unit and consult with their physician colleagues as needed. All laboring individuals in this study were triaged by either a midwife or physician, and all additionally received nursing care. Generally, triage care in this hospital does not involve continuity of providers from antenatal or intrapartum care, and triage providers use independent clinical judgment to make admission decisions. There are several private practices feeding into this hospital, each has at least 1 midwife providing prenatal care. Midwives also provide intrapartum care in the hospital, alongside obstetrician hospitalists who provide some care during the day shift. At this facility, once a laboring patient is admitted to a physician for intrapartum management, they do not change to a midwife provider for labor or birth. By contrast, laboring patients admitted to a midwife provider transfer to an obstetrician if they have a cesarean birth. For this study, provider type data for each participant at 2 time points were collected: during the triage visit (admitted provider type) and at birth. Intrapartum provider type was attributed as the delivery provider type for vaginal births for the outcome analysis.
Data abstraction
The first author reviewed the medical records of each birth in electronic medical record systems to screen for eligibility and extract variables of interest from eligible records. Abstracted data were stored in REDCap, a secure research database.27
Inclusion and exclusion criteria
Data from individuals who had an unscheduled triage visit for labor at the selected hospital and were NTSV were included. Cases were excluded if there was evidence of a scheduled hospital admission (induction or cesarean) and/or presence of any of the following: hypertension (pregestational or pregnancy-related), diabetes, any current placental issue, intrauterine fetal death, or any other condition that would be considered high risk at admission and thus require continuous fetal monitoring.28,29
Variables
Gestational age at hospital admission and cervical status (dilatation in centimeters, effacement in percent) were abstracted from the medical record for each individual. Cervical status at admission was the initial cervical dilatation in the hospital chart. The time of the initial cervical examination was compared with the admission time to ensure that both the examination and admission decision were within a 2-hour period; 84% of women in this study had at least 1 recorded cervical examination that met this criterion.
Labor process information such as the use of amniotomy, oxytocin augmentation, and interventions for pain management including narcotics, nitrous oxide, and epidural were collected. Lastly, data on the length of time from admission until birth, mode of birth, and reason for unplanned cesarean birth were also collected. Outliers for the labor length variable (>10 hours from median labor length) were confirmed by comparing the original time of admission and time of birth data points. Demographic variables collected for this study included race, marital status, admission body mass index (BMI), insurance type, and maternal age on admission.
Analysis methods
Pearson’s Chi-square, Fisher’s exact test, Student’s t test, and log-rank test were performed to assess bivariate associations between admitting provider type and outcomes and covariates of interest. All multivariable models were adjusted for the following demographics: maternal age, race, time of day of hospital admission, marital status, BMI, and insurance type. These covariates were chosen a priori based on practices at this hospital (triage provider shifts) and on published studies.30,31 Continuous outcome distributions were examined for skew and kurtosis. Dichotomous outcome variables were analyzed using logistic regression, continuous normally distributed outcomes were analyzed using linear regression, and time until event data were analyzed using proportional hazard regression. The variable time by admitting provider type was added to explore the length of labor analysis to test for violation of the proportional hazard assumptions. The assumptions were not violated, as the interaction term was not significant (P = .145). All analyses were performed using SAS software, Version 9.4 (SAS Institute Inc, Cary, North Carolina). Statistical significance was defined as a 2-tailed P value < .05.
RESULTS
A total of 455 births occurred during December 2015 to August 2015 in the source hospital to individuals with NTSV pregnancies. Of these, a total of 335 patients met criteria and were included in the final sample, as noted in Figure 1. Laboring patients in this sample were predominantly White (89.5%), with private insurance (77.0%), and married (71.0%). Just over half of the patients (54.2%) had a BMI of less than 30 kg/m2 at the time of admission. This sample was representative of the community the hospital serves, and there were no significant differences in these characteristics based on the admitting provider type.
Figure 1.
Consort diagram.
Admitting provider and labor characteristics
Approximately half of the individuals in this sample were admitted by a midwife (52.2%, n = 175), and the other half were admitted by a physician (47.8%, n = 160), as noted in Table 1. The mean cervical dilatation at hospital admission was 3.8 cm (SD 2.1). Over half of the sample (51.6%) had cervical dilatation between 0 and 3 cm at admission, while 29.5% had cervical dilatation between 4 and 5 cm, and the remaining (18.9%) had cervical dilation of 6 cm or greater. The majority of admissions were during the day (52.0%, n = 173).
Table 1.
Participant characteristics (N = 335)
| Total (N = 335) | Triaged by a midwife (n = 175) | Triaged by a physician (n = 160) | P | |
|---|---|---|---|---|
|
| ||||
| Demographics | ||||
| Maternal age, mean (SD), y | 28.8 (4.7) | 28.9 (4.7) | 28.8 (4.6) | .91 |
| Race, n (%) | .83 | |||
| White | 273 (89.5) | 142 (89.9) | 131 (89.1) | |
| Non-white | 32 (10.5) | 16 (10.1) | 16 (10.9) | |
| Marital status, n (%) | .90 | |||
| Married | 237 (71.0) | 124 (71.3) | 113 (70.6) | |
| Single | 97 (29.0) | 50 (28.7) | 47 (29.4) | |
| Health Insurance, n (%) | .16 | |||
| Public | 75 (22.4) | 35 (20.0) | 40 (25.0) | |
| Private | 258 (77.0) | 140 (80.0) | 118 (73.8) | |
| Self-pay/other | 2 (0.6) | 0 (0.0) | 2 (1.3) | |
| Birth admission information | ||||
| Gestational age, n (%) | .23 | |||
| Early term (370/7 to 386/7) | 54 (16.3) | 25 (14.5) | 29 (18.1) | |
| Full term (390/7 to 406/7) | 230 (69.3) | 117 (68.0) | 113 (70.6) | |
| Later term (410/7 to 416/7) | 48 (14.5) | 30 (17.4) | 18 (11.3) | |
| Postterm (>420/7) | 0 (0.0) | 0 (0.0) | 0 (0.0) | |
| Body mass index, mean (SD), kg/m2 | 30.4 (5.3) | 30.1 (5.1) | 30.6 (5.5) | .40 |
| Cervical dilation at admission, mean (SD), cm | 3.8 (2.1) | 4.1 (2.1) | 3.5 (2.1) | .01 |
| Cervical effacement, n (%) | 84.7 (19.7) | 88.2 (15.0) | 80.8 (23.4) | .003 |
| Length of labor, mean (SD), h | 14.7 (10.1) | 13.9 (9.3) | 15.6 (10.8) | .21 |
| Time of admission, n (%) | <.001 | |||
| Day (7:00 AM to 8:00 PM) | 173 (52.0) | 72 (41.6) | 101 (63.1) | |
| Night (8:01 PM to 6:59 AM) | 160 (48.0) | 101 (58.4) | 59 (36.9) | |
| Provider at birth, n (%) | <.001 | |||
| Midwife | 85 (25.8) | 74 (43.0) | 11 (7.0) | |
| Physician | 245 (74.2) | 98 (57.0) | 147 (93.0) | |
In bivariate analyses comparing key outcomes of interest, there were significant differences by admitting provider type in cervical dilatation at admission and in the time of day at admission, as noted in Table 2. Patients admitted by midwives were with more advanced cervical dilatation than those admitted by physicians (midwifery-led triage mean = 4.1 cm, SD 2.1, compared with physician-led triage mean = 3.5 cm, SD 2.1, P = .01). Those admitted by midwives were also more likely to be admitted at night than those admitted by physician (midwifery-led triage 58.4%, n = 101, compared with physician-led triage 36.9%, n = 59, P < .001). Following hospital admission, patients admitted by midwives were less likely than those admitted by physicians to receive oxytocin augmentation (39.8% vs 55.1%, P = 0.006) or epidural (77.0% vs 93.0%, P < .001). When examining vaginal births and admission provider, patients admitted by midwives were less likely to experience an epidural than those admitted by a physician, as noted in Figure 2. Among women admitted by midwives those who birthed with midwives were less likely to experience an epidural than those who birthed with a physician (P < .001). Patients who birthed with midwives were less likely to experience augmentation than those who birthed with physicians, and among those who birthed with a physician those admitted by a midwife were less likely to receive augmentation than those admitted by a physician (P = .008). Only vaginal births were included in Figure 2, as it was not possible to identify intrapartum provider type for cesarean birth from this hospital’s medical records.
Table 2.
Comparison of intrapartum practices (N = 335)
| Bivariate comparison |
Adjusted analysisa |
|||||
|---|---|---|---|---|---|---|
| Triaged by a midwife (n = 175) | Triaged by a physician (n = 160) | Bivariate P value | Regression estimate (midwife vs physician) | 95% CI | P value | |
|
| ||||||
| Intrapartum care | ||||||
| Amniotomy, odds ratio, n (%) | 49 (28.0) | 52 (32.5) | .37b | 0.96 | 0.55–1.68 | .88c |
| Oxytocin augmentation, odds ratio, n (%) | 68 (39.8) | 87 (55.1) | .006b | 0.50 | 0.29–0.87 | .01c |
| Epidural, odds ratio, n (%) | 134 (77.0) | 147 (93.0) | <.001b | 0.29 | 0.12–0.69 | .005c |
| Nitrous oxide, odds ratio, n (%) | 33 (19.1) | 22 (13.9) | .21b | 1.56 | 0.77–3.17 | .22c |
| Instrumental vaginal birth, odds ratio, n (%) | 17 (11.5) | 7 (5.7) | .09b | 2.99 | 0.69–12.93 | .14c |
| Cesarean birth, odds ratio, n (%) | 26 (14.9) | 36 (22.5) | .07b | 0.31 | 0.14–0.67 | .003c |
| Cesarean characteristics | ||||||
| Indication for cesarean, odds ratio, n (%) | ||||||
| Dystocia | 16 (61.5) | 22 (61.1) | Reference | |||
| Fetal distress | 9 (34.6) | 13 (36.1) | .99d | 0.79 | 0.11–5.60 | .81c |
| Vaginal birth vs cesarean because of labor dystocia, odds ratio, n (%) | ||||||
| Vaginal birth | 149 (90.3)e | 124 (84.9) | Reference | |||
| Cesarean due to dystocia | 16 (9.7) | 22 (15.1) | .15b | 0.29 | 0.10–0.84 | .02c |
| Cervical dilatation in cm at admission, beta estimate, mean (SD) | 4.12 (2.14) | 3.49 (2.07) | .01f | 0.51 | 0.04–1.06 | .07g |
| Length of time from admission to birth (hours), hazard ratio, median (IQR) | 12.1 (7.7–18.1) | 14.8 (9.0–20.1) | .21h | 1.24 | 0.95–1.62 | .12i |
| Mode of birth within the first 24 h after admission, odds ratio, n (%) | ||||||
| Vaginal | 138 (89.6) | 114 (81.4) | Reference | |||
| esarean | 16 (10.3) | 26 (18.6) | .045b | 0.29 | 0.12–0.69 | .005c |
Abbreviations: CI, confidence interval; IQR, interquartile rage; NS, not significant.
Analysis adjusted for age, race, marital status, time of day, insurance, and body mass index.
Chi-square.
Logistic regression (odds ratio).
Fisher’s exact test.
The difference in the percent calculation is because of the different denominator comparing these 2 vaginal birth groups.
Student’s t test.
Linear regression.
Log-rank test.
Proportional hazard regression (hazard ratio).
Figure 2.
Comparison of vaginal births in patients triaged by midwives versus physicians.
Adjusted analyses
Regression analyses to determine the influence of admission provider type on key outcomes were adjusted for the influence of age, race, marital status, time of day, insurance status, and BMI. Differences in cervical dilatation by provider type at admission did not remain significant in adjusted analyses. However, reductions in the use of oxytocin augmentation (P = .01) and epidural (P = .005) by midwife (compared with physician) admission provider remained significant in adjusted analyses. The odds of oxytocin augmentation among patients admitted by midwives was about half that of patients admitted by physicians (adjusted odds ratio [aOR] = 0.50, confidence interval [95% CI] = 0.29–0.87), and the odds of epidural among patients admitted by midwife was less than a third of that of patients admitted by physicians (aOR = 0.29, 95% CI = 0.124–0.692). Midwife-admitted patients also had decreased odds of unplanned cesarean birth (aOR = 0.31, 95% CI = 0.14–0.67), and unplanned cesarean due to dystocia compared with physician-admitted patients (aOR = 0.29 vs vaginal birth, 95% CI = 0.10–0.84). We specifically examined labor dystocia indications for cesarean delivery because they can be more reflective of overall labor management practices than other cesarean indications.32 There was no difference in the adjusted analysis for patients admitted by midwives compared with physicians in the use of amniotomy, nitrous oxide, or opioids during labor.
DISCUSSION
Key findings
This analysis suggests that midwife-led assessment during triage may involve care or shift processes that decrease intervention use during labor. In prior studies, investigators have shown similar results, such as reduced use of epidural, oxytocin augmentation, and cesarean birth among those cared for by midwives compared with physicians during labor.13,15,17 This study adds to the literature by specifically examining the influence of midwifery care during triage, when the critical decision is made of whether to admit or not, with multiple effects on later labor processes and outcomes.11,19,20 Finally, this analysis supports that midwife-led triage assessment and admission decision-making are potentially beneficial for low-risk pregnant individuals admitted to a community hospital with spontaneous labor onset.
Admission after triage is a critical decision point, as the laboring person moves from their home environment into the birth facility. In this community hospital, midwives cover triage during the night. However, even when controlling for time of day in analyses, the influence of midwife compared with physician triage care on intrapartum interventions remained the same. Laboring individuals may benefit from services provided in the hospital, but the hospital environment may not have advantages for low-risk individuals in early labor. A triad of individuals—the pregnant person, the nurse, and the provider—are centrally involved in hospital admission decision-making.2,33 In many settings and circumstances, this triad continues for decision-making throughout labor. What merits consideration is, depending on the length of time in labor and practice setting characteristics, a patient might be cared for by multiple different nurses and providers, thus shifting the interpersonal dynamics of the triad. This dynamic situation can affect communication and labor support depending on the nurse and the provider.
Previous research comparing midwife and physician providers is often limited by selection bias, as individuals who seek midwifery care may be different from those who select physician care.34 Several differences in labor processes and outcomes between groups persisted, even though the provider for the remainder of labor and during birth may or may not have been a midwife. This analysis expands what is known about the benefits of interprofessional care between midwives and physicians; however, it is limited in that the collected clinical data did not provide the attribution of care practices during labor for patients who went on to have cesarean birth.
Challenges
The primary challenge revealed by this work relates to understanding how triage care by midwives might improve outcomes, including how the care triad may function differently depending on triage provider type. This limits the ability to define components of optimal triage care, which might be replicated elsewhere. In addition, it is important to acknowledge that for evaluation of systems and care, data collection needs to be addressed to appropriately attribute labor outcomes to care providers, including nurses and midwives. This is challenging due to the way that healthcare systems tend to focus around the identity of only the physician providers. Changes are needed in medical records to better document care practices and interventions, as they relate to labor outcomes and cesarean rates to each part of the care triad. For example, when care by nurses or midwives like continuous labor support or intermittant auscultation is not defined nor correctly attributed in the patient’s medical records, this care becomes invisible and more difficult to assess and disseminate. This has national implications because low-risk, cesarean births are a quality measure for hospitals.
Additionally, this research reveals the lack of information being collected in medical records regarding the content of intrapartum care. Until the interventions implemented by physicians, midwives, and nurses in this study are defined and measured, it will be difficult to define optimal care of low-risk laboring patients. For example, this data set contains information about epidural use but does not contain information about interventions common to midwifery and nursing practice, such as touch or support. This limited the analysis to correlate triage care characteristics with outcomes during labor. Finally, this study points to the healthcare challenge of collecting information on how patients’ preferences and experiences inform care. Did the laboring patients in this study feel empowered to negotiate for the care they wanted, and did the interprofessional model of care they encountered in triage change how they felt? Was there 1:1 nursing support provided? Was a doula present? As healthcare systems are examined for racism, was there racial concordance with the patient with either the nurse or provider? These questions are not usually measured within many electronic medical record systems; yet continuous support and racial concordance are important when considering the quality of care and patient engagement.35,36
Midwives may improve systems-level outcomes by decreasing intervention overuse, which has both cost and health outcome implications. For example, they may optimize hospital admission for individuals who stand to benefit the most from inpatient care, decreasing the risks of morbidity and mortality from “too much too soon” medical intervention.37 Although there was no difference by triage provider type on cervical dilation in this study, it is possible that midwifery triage providers delayed hospital admission until laboring persons were experiencing more active labor symptoms, despite having similar cervical dilation, compared with physician triage providers. Unfortunately, the data set used for this analysis did not allow for testing this hypothesis, as the information on contraction frequency, intensity, or symptoms of labor was not consistently or clearly documented in the triage record.
In an interprofessional system, midwives may optimize the use of medical interventions in low-risk labors by facilitating physiologic birth when appropriate and engaging additional providers and resources if needed.15,16,37 Midwives from the hospital of this investigation report that they frequently provide encouragement to women in triage about the labor process; however, these conversations are not captured in the record. While research supports midwives as effective and person-centered members of perinatal healthcare teams,12,13,17,18,21 hospital regulations and state practice legislation may limit their ability to provide independent management of low-risk women in the hospital, decreasing their influence on perinatal outcomes.
Strengths and limitations
This investigation had several limitations. First, there was no way to ascertain whether patients saw their primary prenatal provider in triage. If patients did see their prenatal provider, their decision-making on hospital admission and labor interventions might have been influenced by this preexisting relationship. A strength of this study is that, at this community hospital, midwives are integrated into an interprofessional team: laboring individuals did not have a choice of their triage provider. An additional limitation is that the medical records did not attribute who provided care (midwife or physician) during postadmission labor, although provider type for both triage and birth was recorded. Thus, it was not possible to control for intrapartum care provider type as a potential mediator between triage care processes and birth outcomes. Although the lack of intrapartum provider attribution prevented us from further evaluating the influence of triage care on labor processes for cesarean births, intrapartum interventions and outcomes following triage care for vaginal births could be assessed. This study was also limited by lack of information on prelabor rupture of membranes in the sample, which could change downstream risks for labor interventions like oxytocin augmentation and surgical birth. Future research on the influence of provider type at different time points in labor (triage, labor, and birth) on labor processes and outcomes is warranted, including additional measurement of patients’ perspectives of their care, the content of care, and how these factors interact.
CONCLUSION
In this observational study, low-risk nulliparous pregnant patients triaged and admitted by midwives, compared with teams led by physicians, experienced vaginal birth more frequently and received fewer labor interventions. The clinical challenge for dissemination of this model of care could be supported by research measuring and describing the specific nonmedical components of triage care that precede better patient outcomes.
Acknowledgments
Dr Breman was supported by grant #1UL1TR003098-01 from the University of Maryland, Baltimore, Institute for Clinical and Translational Research. Dr Carlson was supported by grant number K01NR016984 from the National Institute of Nursing Research during manuscript production.
The authors would like to thank Pamela Patterson and Dr Kim Dever for their support of this work.
Footnotes
Disclosure: The authors have disclosed that they have no significant relationships with, or financial interest in, any commercial companies pertaining to this article.
Each author has indicated that he or she has met the journal’s requirements for Authorship.
Contributor Information
Rachel Blankstein Breman, University of Maryland School of Nursing, Baltimore.
Julia C. Phillippi, Vanderbilt School of Nursing, Nashville, Tennessee.
Ellen Tilden, School of Nursing and School of Medicine, Oregon Health and Science University, Portland.
Julie Paul, Perinatal Behavioral Health Clinic, Weymouth, Massachusetts.
Erik Barr, University of Maryland School of Nursing, Baltimore.
Nicole Carlson, Emory University School of Nursing, Atlanta, Georgia.
References
- 1.Angelini D, Howard E. Obstetric triage: a systematic review of the past fifteen years 1998–2013. MCN Am J Matern Nurs. 2014;39(5):284–297. doi: 10.1097/NMC.0000000000000069. [DOI] [PubMed] [Google Scholar]
- 2.Breman RB, Iobst SE, Paul JA, Low LK. Clinicians’ perspectives on admission of pregnant women: a triad. MCN Am J Matern Nurs. 2019;44(5):260–268. doi: 10.1097/NMC.0000000000000550. [DOI] [PubMed] [Google Scholar]
- 3.Neal JL, Lowe NK, Ahijevych KL, Patrick TE, Cabbage LA, Corwin EJ. “Active labor” duration and dilation rates among low-risk, nulliparous women with spontaneous labor onset: a systematic review. J Midwifery Womens Health. 2010;55(4):308–318. doi: 10.1016/j.jmwh.2009.08.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Kauffman E, Souter V, Katon J, Sitcov K. Cervical Dilation on admission in term spontaneous labor and maternal and newborn outcomes. Obstet Gynecol. 2016;127(3):481–488. doi: 10.1097/AOG.0000000000001294. [DOI] [PubMed] [Google Scholar]
- 5.Rahnama P, Ziaei S, Faghihzadeh S. Impact of early admission in labor on method of delivery. Int J Gynecol Obstet. 2006; 92(3):217–220. doi: 10.1016/j.ijgo.2005.12.016. [DOI] [PubMed] [Google Scholar]
- 6.Bailit JL, Dierker L, Blanchard MH, Mercer BM. Outcomes of women presenting in active versus latent phase of spontaneous labor. Obstet Gynecol. 2005;105(1):77–79. doi: 10.1097/01.AOG.0000147843.12196.00. [DOI] [PubMed] [Google Scholar]
- 7.Neal JL, Lowe NK, Phillippi JC, et al. Likelihood of cesarean delivery after applying leading active labor diagnostic guidelines. Birth. 2017;14(2):128–136. doi: 10.1111/birt.12274. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.MacDorman MF, Declercq E, Cabral H, Morton C. Recent increases in the U.S. maternal mortality rate. Obstet Gynecol. 2016;128(3):447–455. doi: 10.1097/AOG.0000000000001556. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Tilden EL, Lee VR, Allen AJ, Griffin EE, Caughey AB. Cost-effectiveness analysis of latent versus active labor hospital admission for medically low-risk, term women. Birth. 2015; 42(3):219–226. doi: 10.1111/birt.12179. [DOI] [PubMed] [Google Scholar]
- 10.DeJoy SA, Bohl MG, Mahoney K, Blake C. Estimating the financial impact of reducing primary cesareans. J Midwifery Womens Health. 2020;65(1):56–63. doi: 10.1111/jmwh.13010. [DOI] [PubMed] [Google Scholar]
- 11.Spong CY, Berghella V, Saade GR, Wenstrom KD, Mercer BM. Preventing the first cesarean delivery. Obstet Gynecol. 2012; 120(5):1181–1193. doi: 10.1097/AOG.0b013e3182704880. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Jennings J, Nielsen P, Buck M, et al. Collaboration in practice: implementing team-based care. Obstet Gynecol. 2016;127(3): 612–617. [DOI] [PubMed] [Google Scholar]
- 13.Carlson NS, Corwin EJ, Hernandez TL, Holt E, Lowe NK, Hurt KJ. Association between provider type and cesarean birth in healthy nulliparous laboring women: a retrospective cohort study. Birth. 2018;45(2):159–168. doi: 10.1111/birt.12334. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Loewenberg Weisband Y, Klebanoff M, Gallo MF, Shoben A, Norris AH. Birth outcomes of women using a midwife versus women using a physician for prenatal care. J Midwifery Womens Health. 2018;63(4):399–409. doi: 10.1111/jmwh.12750. [DOI] [PubMed] [Google Scholar]
- 15.Neal JL, Carlson NS, Phillippi JC, et al. Midwifery presence in United States medical centers and labor care and birth outcomes among low-risk nulliparous women: a Consortium on Safe Labor study. Birth. 2019;46(3):475–486. doi: 10.1111/birt.12407. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Carlson NS, Neal JL, Tilden EL, et al. Influence of midwifery presence in United States centers on labor care and outcomes of low-risk parous women: a Consortium on Safe Labor study. Birth. 2019;46(3):487–499. doi: 10.1111/birt.12405. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Attanasio LB, Kozhimannil KB. Relationship between hospital-level percentage of midwife-attended births and obstetric procedure utilization. J Midwifery Womens Health. 2018;63(1):14–22. doi: 10.1111/jmwh.12702. [DOI] [PubMed] [Google Scholar]
- 18.Paul J, Jordan R, Duty S, Engstrom JL. Improving satisfaction with care and reducing length of stay in an obstetric triage unit using a nurse-midwife-managed model of care. J Midwifery Womens Health. 2013;58(2):175–181. [DOI] [PubMed] [Google Scholar]
- 19.American College of Obstetricians and Gynecologist. Obstetric Data Definitions Issues and Rationale for Change. ACOG Revitalize. https://www.acog.org/-/media/Departments/Patient-Safety-and-Quality-Improvement/2014reVITALizeObstetricDataDefinitionsV10.pdf?dmc=1&=20190121T1426484392. Published 2014. Accessed January 21, 2019. [Google Scholar]
- 20.Caughey AB, Cahill AG, Guise JM, Rouse DJ. Safe prevention of the primary cesarean delivery. Am J Obstet Gynecol. 2014; 210(3):179–193. doi: 10.1016/j.ajog.2014.01.026. [DOI] [PubMed] [Google Scholar]
- 21.Sandall J, Soltani H, Gates S, Shennan A, Devane D. Midwifeled continuity models versus other models of care for childbearing women (Review). Cochrane Database Syst Rev. 2016;4:CD004667. doi: 10.1002/14651858. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Johantgen M, Fountain L, Zangaro G, Newhouse R, Stanik-Hutt J, White K. Comparison of labor and delivery care provided by certified nurse-midwives and physicians: a systematic review, 1990 to 2008. Womens Health Issues. 2012; 22(1):1–9. doi: 10.1016/j.whi.2011.06.005. [DOI] [PubMed] [Google Scholar]
- 23.Angelini DJ. The utilization of nurse-midwives as providers of obstetric triage services results of a national survey. J Nurse Midwifery. 1999;44(5):431–438. doi: 10.1016/S00912182(99)00082-8. [DOI] [PubMed] [Google Scholar]
- 24.O’Rourke K, Teel J, Nicholls E, Lee DD, Colwill AC, Srinivas SK. Improving staff communication and transitions of care between obstetric triage and labor and delivery. J Obstet Gynecol Neonatal Nurs. 2018;47(2):264–272. doi: 10.1016/j.jogn.2017.11.008. [DOI] [PubMed] [Google Scholar]
- 25.Lee DD, Colwill AC, Teel J, Srinivas SK. Safe passage: improving the transition of care between triage and labor and delivery. Qual Manag Health Care. 2018;27(4):223–228. doi: 10.1097/QMH.0000000000000191. [DOI] [PubMed] [Google Scholar]
- 26.Ruhl C, Scheich B, Onokpise B, Bingham D. Content validity testing of the maternal fetal triage index. J Obstet Gynecol Neonatal Nurs. 2015;44(6):701–709. doi: 10.1111/15526909.12763. [DOI] [PubMed] [Google Scholar]
- 27.Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap)—a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2009;42(2):377–381. doi: 10.1016/j.jbi.2008.08.010. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Centers for Disease Control and Prevention. Data on Selected Pregnancy Complications in the United States. https://www.cdc.gov/reproductivehealth/maternalinfanthealth/pregnancy-complications-data.htm#codes. Published 2019. Accessed August 13, 2020.
- 29.Dzakpasu S, Deb-Rinker P, Arbour L, et al. Severe maternal morbidity surveillance: monitoring pregnant women at high risk for prolonged hospitalisation and death. Paediatr Perinat Epidemiol. 2020;34(4):427–439. doi: 10.1111/ppe.12574. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Teachman J Body weight, marital status, and changes in marital status. J Fam Issues. 2016;37(1):74–96. doi: 10.1177/0192513X13508404. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Neal JL, Lowe NK, Phillippi JC, Carlson NS, Knupp AM, Dietrich MS. Likelihood of cesarean birth among parous women after applying leading active labor diagnostic guidelines. Midwifery. 2018;67:64–69. doi: 10.1016/j.midw.2018.09.007. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Jackson S, Gregory KD. Management of the first stage of labor: potential strategies to lower the cesarean delivery rate. Clin Obstet Gynecol. 2015;58(2):217–226. doi: 10.1097/GRF.0000000000000102. [DOI] [PubMed] [Google Scholar]
- 33.Moore JE. Women’s voices in maternity care: the triad of shared decision making, informed consent, and evidence-based practices. J Perinat Neonatal Nurs. 2016;30(3):218–223. doi: 10.1097/JPN.0000000000000182. [DOI] [PubMed] [Google Scholar]
- 34.Snowden JM, Tilden EL, Odden MC. Formulating and answering high-impact causal questions in physiologic childbirth science: concepts and assumptions. J Midwifery Women Health. 2018;63(6):721–730. doi: 10.1111/jmwh.12868. [DOI] [PubMed] [Google Scholar]
- 35.Association of Women’s Health, Obstetric and Neonatal Nurses. Continuous labor support for every woman. J Obstet Gynecol Neonatal Nurs. 2018;47(1):73–74. doi: 10.1016/j.jogn.2017.11.010. [DOI] [Google Scholar]
- 36.Shen MJ, Peterson EB, Costas-Muñiz R, et al. The effects of race and racial concordance on patient-physician communication: a systematic review of the literature. J Racial Ethn Health Disparities. 2018;5(1):117–140. doi: 10.1007/s40615-017-0350-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Miller S, Abalos E, Chamillard M, et al. Beyond too little, too late and too much, too soon: a pathway towards evidence-based, respectful maternity care worldwide. Lancet. 2016;388(10056):2176–2192. doi: 10.1016/S0140-6736(16)31472-6. [DOI] [PubMed] [Google Scholar]