Capacity-Building for Collecting Patient-Reported Outcomes and Experiences (PRO) Data Across Hospitals.

PURPOSE: Patient-reported outcomes and experiences (PRO) data are an integral component of health care quality measurement and PROs are now being collected by many healthcare systems. However, hospital organizational capacity-building for the collection and sharing of PROs is a complex process. We sought to identify the factors that facilitated capacity-building for PRO data collection in a nascent quality improvement learning collaborative of 16 hospitals that has the goal of improving the childbirth experience. DESCRIPTION: We used standard qualitative case study methodologies based on a conceptual framework that hypothesizes that adequate organizational incentives and capacities allow successful achievement of project milestones in a collaborative setting. The 4 project milestones considered in this study were: (1) Agreements; (2) System Design; (3) System Development and Operations; and (4) Implementation. To evaluate the success of reaching each milestone, critical incidents were logged and tracked to determine the capacities and incentives needed to resolve them. ASSESSMENT: The pace of the implementation of PRO data collection through the 4 milestones was uneven across hospitals and largely dependent on limited hospital capacities in the following 8 dimensions: (1) Incentives; (2) Leadership; (3) Policies; (4) Operating systems; (5) Information technology; (6) Legal aspects; (7) Cross-hospital collaboration; and (8) Patient engagement. From this case study, a trajectory for capacity-building in each dimension is discussed. CONCLUSION: The implementation of PRO data collection in a quality improvement learning collaborative was dependent on multiple organizational capacities for the achievement of project milestones.

Using Potentially Preventable Severe Maternal Morbidity to Monitor Hospital Performance.

BACKGROUND: The Centers for Disease Control and Prevention (CDC) measure of severe maternal morbidity (SMM) quantifies the burden of SMM but is not restricted to potentially preventable SMM. The authors adapted the CDC SMM measure for this purpose and evaluated it for use as a hospital performance measure. METHODS: Guidelines for defining performance SMM (pSMM) were (1) exclusion of preexisting conditions from outcome; (2) exclusion of inconsistently documented outcomes; and (3) risk adjustment for conditions that preceded hospitalization. California maternal hospital discharge data from 2016 to 2017 were used for model development, and 2018 data were used for model testing and evaluation of hospital performance. Separate models were developed for hospital types (Community, Teaching, Integrated Delivery System [IDS], and IDS Teaching), generating model-based expected pSMM values. Observed-to-expected (O/E) ratios were calculated for hospitals and used to categorize them as overperforming, average performing, or underperforming using 95% confidence intervals. Performance categories were compared for pSMM vs. CDC SMM (excluding blood transfusion). RESULTS: The overall 2016-2018 pSMM rate was 0.44%. All hospital types had over- and underperformers, and the proportions of Community, Teaching, IDS, and IDS Teaching hospitals whose performance differed from their performance on the CDC SMM measure were 12.1%, 25.0%, 38.9%, and 66.7%, respectively. CONCLUSION: The rate of potentially preventable SMM as defined by pSMM (0.44%) was less than half the previously published rate of CDC SMM (1.03%). pSMM identified differences in performance across hospitals, and pSMM and CDC SMM classified hospitals' performances differently. pSMM may be suitable for hospital comparisons because it identifies potentially preventable, hospital-acquired SMM that should be responsive to quality improvement activities.

Severe Maternal Morbidity in California Hospitals: Performance Based on a Validated Multivariable Prediction Model.

BACKGROUND: Severe maternal morbidity (SMM) is under development as a quality indicator for maternal health care. The aim of this study is to evaluate California hospital performance based on a standardized SMM measure. METHODS: California maternal hospital delivery discharge data from 2016 to 2017 were used to develop logistic regression models for SMM, adjusted for clinical risk factors at admission. Data from 2018 were used to test the models and evaluate hospital performance. SMM was defined per the Centers for Disease Control and Prevention, including (excluding) blood transfusion. Independent models were developed for each hospital type: community, teaching, integrated delivery system (IDS), and IDS teaching. Within each type, model-based expected SMM values and observed-to-expected (O/E) ratios were calculated for each hospital. For each hospital type, hospitals were ranked by O/E ratio, and over- and underperforming hospitals were identified using 95% confidence intervals. RESULTS: Rates of SMM including (excluding) transfusion by hospital type were 1.7% (0.9%) for community, 2.7% (1.5%) for teaching, 2.3% (1.2%) for IDS, and 3.0% (1.6%) for IDS teaching hospitals. In higher-volume community hospitals (≥ 500 births/year), the proportion of underperformers including (excluding) transfusion was 20.7% (11.0%). Summing over all hospital types, 25.3% (14.9%) of hospitals were identified as underperformers in that they experienced significantly more SMM events than expected including (excluding) transfusion. CONCLUSION: California hospital discharge data demonstrated significant hospital variation in standardized childbirth SMM. These data suggest that a standardized SMM measure may help guide and monitor statewide quality improvement efforts.

A scoping review of severe maternal morbidity: describing risk factors and methodological approaches to inform population-based surveillance.

BACKGROUND: Current interest in using severe maternal morbidity (SMM) as a quality indicator for maternal healthcare will require the development of a standardized method for estimating hospital or regional SMM rates that includes adjustment and/or stratification for risk factors. OBJECTIVE: To perform a scoping review to identify methodological considerations and potential covariates for risk adjustment for delivery-associated SMM. SEARCH METHODS: Following the guidelines for Preferred Reporting Items for Systematic Reviews and Meta-analyses Extension for Scoping Reviews, systematic searches were conducted with the entire PubMed and EMBASE electronic databases to identify publications using the key term "severe maternal morbidity." SELECTION CRITERIA: Included studies required population-based cohort data and testing or adjustment of risk factors for SMM occurring during the delivery admission. Descriptive studies and those using surveillance-based data collection methods were excluded. DATA COLLECTION AND ANALYSIS: Information was extracted into a pre-defined database. Study design and eligibility, overall quality and results, SMM definitions, and patient-, hospital-, and community-level risk factors and their definitions were assessed. MAIN RESULTS: Eligibility criteria were met by 81 studies. Methodological approaches were heterogeneous and study results could not be combined quantitatively because of wide variability in data sources, study designs, eligibility criteria, definitions of SMM, and risk-factor selection and definitions. Of the 180 potential risk factors identified, 41 were categorized as pre-existing conditions (e.g., chronic hypertension), 22 as obstetrical conditions (e.g., multiple gestation), 22 as intrapartum conditions (e.g., delivery route), 15 as non-clinical variables (e.g., insurance type), 58 as hospital-level variables (e.g., delivery volume), and 22 as community-level variables (e.g., neighborhood poverty). CONCLUSIONS: The development of a risk adjustment strategy that will allow for SMM comparisons across hospitals or regions will require harmonization regarding: a) the standardization of the SMM definition; b) the data sources and population used; and c) the selection and definition of risk factors of interest.

Childbirth-specific patient-reported outcomes as predictors of hospital satisfaction.

BACKGROUND: Under value-based payment programs, patient-reported experiences and outcomes can impact hospital and physician revenue. To enable obstetrical providers to improve the childbirth experience, a framework for understanding what women expect and desire during childbirth is needed. OBJECTIVE: The purpose of this study was to identify key predictors of childbirth hospital satisfaction with the use of the Childbirth Experiences Survey. STUDY DESIGN: This study builds on a larger effort that used Patient-Reported Outcomes Management Information System methods to develop a childbirth-specific preliminary patient-reported experiences and outcomes item bank. These efforts led to the development of an antepartum and postpartum survey (Childbirth Experiences Survey Parts 1 and 2). All phases of the study were conducted with the participation of a community-based research team. We conducted a prospective observational study using national survey response panels that was organized through Nielsen to identify women's antepartum values and preferences for childbirth (Childbirth Experiences Survey Part 1). Eligible participants were pregnant women in the United States (English or Spanish speaking) who were ≥18 years old and ≥20 weeks pregnant. Women were recontacted and invited to participate in a postpartum follow-up survey to collect information about their childbirth patient-reported experiences and outcomes, which included childbirth satisfaction (Childbirth Experiences Survey Part 2). In bivariate analyses, we tested whether predisposing conditions (eg, patient characteristics or previous experiences), values and preferences, patient-reported experiences and outcomes, and the "gaps" between values and preferences and patient-reported experiences and outcomes were predictors of women's satisfaction with hospital childbirth services. Multivariable logistic regression models were fitted to examine the simultaneous effect of predictors on hospital satisfaction, which were adjusted for key predisposing conditions. RESULTS: From 500 women who anticipated a vaginal delivery at the time of the antepartum survey, who labored before delivery, and who answered the postpartum survey, key findings included the following responses: (1) the strongest predictors of women's satisfaction with hospital childbirth services were items in the domains of staff communication, compassion, empathy, and respect, and (2) 23 childbirth-specific patient-reported experiences and outcomes were identified. Examples of these patient-reported experiences and outcomes (such as being told about progress in labor and being involved in decisions regarding labor pain management) appeared especially relevant to women who experienced childbirth. A final model that predicted women's satisfaction with hospital childbirth services included a total of 8 items that could be optimized by doctors, midwives, and hospitals. These included the patient's report of how well she coped with labor pain, whether the hospital provided adequate space and food for their support person, and whether she received practical support for feeding the newborn infant. CONCLUSION: This study identified 23 childbirth-specific patient-reported experiences and outcomes that were predictors of childbirth hospital satisfaction. The implementation of the Childbirth Experiences Survey Parts 1 and 2 in a multihospital setting may lead to the development of childbirth hospital performance measures and strategies for improvement of the childbirth experience.

The Development of a Conceptual Framework and Preliminary Item Bank for Childbirth-Specific Patient-Reported Outcome Measures.

OBJECTIVE: To develop a conceptual framework and preliminary item bank for childbirth-specific patient-reported outcome (PRO) domains. DATA SOURCES: Women, who were U.S. residents, ≥18 years old, and ≥20 weeks pregnant, were surveyed regarding their childbirth values and preferences (V&P) using online panels. STUDY DESIGN: Using community-based research techniques and Patient-Reported Outcomes Management Information System (PROMIS® ) methodology, we conducted a comprehensive literature review to identify self-reported survey items regarding patient-reported V&P and childbirth experiences and outcomes (PROs). The V&P/PRO domains were validated by focus groups. We conducted a cross-sectional observational study and fitted a multivariable logistic regression model to each V&P item to describe "who" wanted each item. PRINCIPAL FINDINGS: We identified 5,880 V&P/PRO items that mapped to 19 domains and 58 subdomains. We present results for the 2,250 survey respondents who anticipated a vaginal delivery in a hospital. Wide variation existed regarding each V&P item, and personal characteristics, such as maternal confidence and ability to cope well with pain, were frequent predictors in the models. The resulting preliminary item bank consisted of 60 key personal characteristics and 63 V&P/PROs. CONCLUSIONS: The conceptual framework and preliminary (PROMIS® ) item bank presented here provide a foundation for the development of childbirth-specific V&P/PROs.