JOGNN
IN FOCUS
Key Findings from the AWHONN Perinatal Staffing Data Collaborative Benjamin Scheich and Debra Bingham
Correspondence Benjamin Scheich, MS, Association of Women’s Health, Obstetric and Neonatal Nurses, 2000 L Street NW, Suite 740, Washington, DC 20036. [email protected]
ABSTRACT The Association of Women’s Health, Obstetric and Neonatal Nurses (AWHONN) created the Perinatal Staffing Data Collaborative in response to the release of its Guidelines for Professional Registered Nurse Staffing for Perinatal Units. In total, 183 surveys were submitted from 175 birthing hospitals in the United States. These findings represent the largest set of data available to describe current patterns in perinatal registered nurse (RN) staffing. In this article we summarize the findings of the AWHONN Perinatal Staffing Data Collaborative from 2011 through 2012.
JOGNN, 44, 317-328; 2015. DOI: 10.1111/1552-6909.12548
Keywords RN staffing perinatal staffing staffing guidelines
Benjamin Scheich, MS, is the Associate Director of Data Analytics, Association of Women’s Health, Obstetric and Neonatal Nurses, Washington, DC.
Accepted July 2014
n recent years, the complex nature of improving health care has often led to the formation of quality collaboratives to gather data and understand issues. In the perinatal arena, these collaboratives, networks of health care professionals who work to improve care through data collection, discussion, and collaboration, have focused on such problems as reducing elective deliveries prior to 39-weeks gestation (Donovan et al., 2010), reducing central-line associated blood stream infections in newborns (Schulman et al., 2011), and improving rates of antenatal steroid administration in neonates (Lee et al., 2011). In 2011, the Centers for Disease Control and Prevention (CDC) recognized the utility of collaboratives by supporting state-based collaborative organizations through the Division of Reproductive Health (Henderson, Suchdev, Abe, Johnston, & Callaghan, 2014). Although collaboratives such as the California Maternal Quality Care Collaborative (CMQCC), the New York State Perinatal Quality Collaborative (NYSPQC), and the Ohio Perinatal Quality Collaborative (OPQC) have demonstrated an improvement in outcomes (Henderson et al., 2014), there has been limited focus on solving the unique problems associated with nurse staffing.
tal Units (Guidelines), surveyed AWHONN nurse members working in the United States, and hosted a series of perinatal leadership summits on staffing (AWHONN, 2010; Simpson, Lyndon, Wilson, & Ruhl, 2012). The introduction of the AWHONN Guidelines presented a unique opportunity for AWHONN to gather staffing data through the formation of a perinatal staffing collaborative. In this article, we describe the AWHONN Perinatal Staffing Data Collaborative findings from 2011 through 2012.
The authors report no conflict of interest or relevant financial relationships.
To address this limitation, in 2010 the Association of Women’s Health, Obstetric and Neonatal Nurses (AWHONN) published the Guidelines for Professional Registered Nurse Staffing for Perina-
A total of 183 surveys were submitted from 175 U.S. birthing hospitals during the 2011 (n = 119) and 2012 (n = 64) timeframes; eight hospitals that participated in the 2011 collaborative chose
http://jognn.awhonn.org
C 2015 AWHONN, the Association of Women’s Health, Obstetric and Neonatal Nurses
Debra Bingham, DrPH, RN, FAAN, is the Vice President of Nursing Research, Education and Practice, Association of Women’s Health, Obstetric and Neonatal Nurses, Washington, DC.
I
Methods Data Collection AWHONN staff developed a survey in Microsoft Excel to collect hospital staffing data. This survey was reviewed by seven nurses identified as experts in the field of perinatal nurse staffing. A final version of the survey received an exempt review determination from Western Institutional Review Board. AWHONN collected staffing data from hospitals that chose to submit data during three separate timeframes: spring 2011, fall 2011, and fall 2012.
Data Collaborative Participants
317
IN FOCUS
Key Findings from the AWHONN Perinatal Data Staffing Collaborative
The introduction of the AWHONN staffing guidelines presented a unique opportunity for AWHONN to gather staffing data in the form of a perinatal staffing collaborative.
to participate again in the 2012 collaborative. For reporting purposes, hospitals were grouped according to their annual birth volumes; volume categories were determined by experts on nurse staffing (Table 1). The hospitals self-selected to participate in the data collaborative and were located throughout the United States in 43 of the 50 states (Figure 1). Ohio had the largest number of participating hospitals (22), followed by California (18), New York (17), Texas (12) and Utah (11). No hospitals from Alaska, Mississippi, Nevada, North Dakota, Oklahoma, Vermont, Wyoming, or Washington, DC participated.
Survey Categories The AWHONN Perinatal Staffing Data Collaborative survey tool contained the following categories: Section 1: Hospital demographic information; Section 2: Patient volume and acuity data and nurse staff characteristics; and Section 3: RN-to-woman, RN-to-baby, or RN-to-mother/baby couplet ratios. Based upon feedback obtained in 2011 from participants, a number of data elements were added to the 2012 survey, including registered nurse (RN) full-time equivalent (FTE), questions stratified by bachelor of science in nursing (BSN) versus nonBSN, and typical orientation hours stratified by years of nursing experience.
Procedures The survey tool and instructions document were e-mailed to the self-designated hospital point of contact. Data were loaded into a database that was linked with Microsoft Excel. Each hospital survey was coded with a unique, randomly generated identification code that was stored in a passwordprotected database at AWHONN headquarters. Data analysis was performed in Microsoft Excel, and graphical visualization and statistical testing was performed in R 3.0. Graphical manipulation was performed in Inkscape 0.48. Data entry outliers that did not appear to follow typical patterns were identified, and hospitals leaders were contacted to confirm whether their data were correct. If hospitals did not respond to data inquiries, their data were included as submitted.
Data Reduction Techniques In Section 3 of the survey, hospital leaders were asked to enter their typical RN-to-woman, RNto-baby, or RN-to-mother/baby couplet staffing ratios for each of the categories of the Guidelines (AWHONN, 2010). The Guidelines are divided into three main categories: antepartum, intrapartum, and postpartum and newborn care. Subcategories of patient types within each main category are identified resulting in a total of 28 different patient care scenarios. Hospital leaders entered a whole number that defined the typical (most prevalent) ratio, and these ratios were compared to the recommendations of the Guidelines. Coding logic was developed by AWHONN staff and was then used to classify the hospital ratio as “Yes, the guideline was being met” or “No, the guideline was not being met” for each of the subcategories.
Table 1: Hospitals Participating in the Perinatal Staffing Data Collaborative Annual Delivery
Number of Hospitals in the Collaborative by Collaborative Enrollment Period
Category
Spring 2011
318
Fall 2011
Fall 2012
Total
IN FOCUS
Key Findings from the AWHONN Perinatal Staffing Data Collaborative Benjamin Scheich and Debra Bingham
Correspondence Benjamin Scheich, MS, Association of Women’s Health, Obstetric and Neonatal Nurses, 2000 L Street NW, Suite 740, Washington, DC 20036. [email protected]
ABSTRACT The Association of Women’s Health, Obstetric and Neonatal Nurses (AWHONN) created the Perinatal Staffing Data Collaborative in response to the release of its Guidelines for Professional Registered Nurse Staffing for Perinatal Units. In total, 183 surveys were submitted from 175 birthing hospitals in the United States. These findings represent the largest set of data available to describe current patterns in perinatal registered nurse (RN) staffing. In this article we summarize the findings of the AWHONN Perinatal Staffing Data Collaborative from 2011 through 2012.
JOGNN, 44, 317-328; 2015. DOI: 10.1111/1552-6909.12548
Keywords RN staffing perinatal staffing staffing guidelines
Benjamin Scheich, MS, is the Associate Director of Data Analytics, Association of Women’s Health, Obstetric and Neonatal Nurses, Washington, DC.
Accepted July 2014
n recent years, the complex nature of improving health care has often led to the formation of quality collaboratives to gather data and understand issues. In the perinatal arena, these collaboratives, networks of health care professionals who work to improve care through data collection, discussion, and collaboration, have focused on such problems as reducing elective deliveries prior to 39-weeks gestation (Donovan et al., 2010), reducing central-line associated blood stream infections in newborns (Schulman et al., 2011), and improving rates of antenatal steroid administration in neonates (Lee et al., 2011). In 2011, the Centers for Disease Control and Prevention (CDC) recognized the utility of collaboratives by supporting state-based collaborative organizations through the Division of Reproductive Health (Henderson, Suchdev, Abe, Johnston, & Callaghan, 2014). Although collaboratives such as the California Maternal Quality Care Collaborative (CMQCC), the New York State Perinatal Quality Collaborative (NYSPQC), and the Ohio Perinatal Quality Collaborative (OPQC) have demonstrated an improvement in outcomes (Henderson et al., 2014), there has been limited focus on solving the unique problems associated with nurse staffing.
tal Units (Guidelines), surveyed AWHONN nurse members working in the United States, and hosted a series of perinatal leadership summits on staffing (AWHONN, 2010; Simpson, Lyndon, Wilson, & Ruhl, 2012). The introduction of the AWHONN Guidelines presented a unique opportunity for AWHONN to gather staffing data through the formation of a perinatal staffing collaborative. In this article, we describe the AWHONN Perinatal Staffing Data Collaborative findings from 2011 through 2012.
The authors report no conflict of interest or relevant financial relationships.
To address this limitation, in 2010 the Association of Women’s Health, Obstetric and Neonatal Nurses (AWHONN) published the Guidelines for Professional Registered Nurse Staffing for Perina-
A total of 183 surveys were submitted from 175 U.S. birthing hospitals during the 2011 (n = 119) and 2012 (n = 64) timeframes; eight hospitals that participated in the 2011 collaborative chose
http://jognn.awhonn.org
C 2015 AWHONN, the Association of Women’s Health, Obstetric and Neonatal Nurses
Debra Bingham, DrPH, RN, FAAN, is the Vice President of Nursing Research, Education and Practice, Association of Women’s Health, Obstetric and Neonatal Nurses, Washington, DC.
I
Methods Data Collection AWHONN staff developed a survey in Microsoft Excel to collect hospital staffing data. This survey was reviewed by seven nurses identified as experts in the field of perinatal nurse staffing. A final version of the survey received an exempt review determination from Western Institutional Review Board. AWHONN collected staffing data from hospitals that chose to submit data during three separate timeframes: spring 2011, fall 2011, and fall 2012.
Data Collaborative Participants
317
IN FOCUS
Key Findings from the AWHONN Perinatal Data Staffing Collaborative
The introduction of the AWHONN staffing guidelines presented a unique opportunity for AWHONN to gather staffing data in the form of a perinatal staffing collaborative.
to participate again in the 2012 collaborative. For reporting purposes, hospitals were grouped according to their annual birth volumes; volume categories were determined by experts on nurse staffing (Table 1). The hospitals self-selected to participate in the data collaborative and were located throughout the United States in 43 of the 50 states (Figure 1). Ohio had the largest number of participating hospitals (22), followed by California (18), New York (17), Texas (12) and Utah (11). No hospitals from Alaska, Mississippi, Nevada, North Dakota, Oklahoma, Vermont, Wyoming, or Washington, DC participated.
Survey Categories The AWHONN Perinatal Staffing Data Collaborative survey tool contained the following categories: Section 1: Hospital demographic information; Section 2: Patient volume and acuity data and nurse staff characteristics; and Section 3: RN-to-woman, RN-to-baby, or RN-to-mother/baby couplet ratios. Based upon feedback obtained in 2011 from participants, a number of data elements were added to the 2012 survey, including registered nurse (RN) full-time equivalent (FTE), questions stratified by bachelor of science in nursing (BSN) versus nonBSN, and typical orientation hours stratified by years of nursing experience.
Procedures The survey tool and instructions document were e-mailed to the self-designated hospital point of contact. Data were loaded into a database that was linked with Microsoft Excel. Each hospital survey was coded with a unique, randomly generated identification code that was stored in a passwordprotected database at AWHONN headquarters. Data analysis was performed in Microsoft Excel, and graphical visualization and statistical testing was performed in R 3.0. Graphical manipulation was performed in Inkscape 0.48. Data entry outliers that did not appear to follow typical patterns were identified, and hospitals leaders were contacted to confirm whether their data were correct. If hospitals did not respond to data inquiries, their data were included as submitted.
Data Reduction Techniques In Section 3 of the survey, hospital leaders were asked to enter their typical RN-to-woman, RNto-baby, or RN-to-mother/baby couplet staffing ratios for each of the categories of the Guidelines (AWHONN, 2010). The Guidelines are divided into three main categories: antepartum, intrapartum, and postpartum and newborn care. Subcategories of patient types within each main category are identified resulting in a total of 28 different patient care scenarios. Hospital leaders entered a whole number that defined the typical (most prevalent) ratio, and these ratios were compared to the recommendations of the Guidelines. Coding logic was developed by AWHONN staff and was then used to classify the hospital ratio as “Yes, the guideline was being met” or “No, the guideline was not being met” for each of the subcategories.
Table 1: Hospitals Participating in the Perinatal Staffing Data Collaborative Annual Delivery
Number of Hospitals in the Collaborative by Collaborative Enrollment Period
Category
Spring 2011
318
Fall 2011
Fall 2012
Total
