Society for Maternal-Fetal MedicineSociety for Maternal-Fetal Medicine (SMFM) Special Report: Current approaches to measuring quality of care in obstetrics
Introduction
Increasingly there has been pressure on hospitals and physicians to measure quality and prove the adequacy of the care they are delivering. This pressure comes from insurers and consumers who want to be sure they are not only obtaining good outcomes but also obtaining good value for dollars spent. While the need to spend wisely is understandable, the dilemma remains of how to prove the quality of care provided is high. That is to say, is quality of care measurable?
The Institute of Medicine defines quality of care as “the degree to which heath care services for individuals and populations increase the likelihood of desired health outcomes and are consistent with current professional knowledge.”1 According to Agency for Healthcare Research and Quality (AHRQ), “quality measures” are mechanisms that enable the user to quantify a selected aspect of care by comparing it to an evidence-based criterion.2 A “clinical performance measure” is a type of quality measure that assesses the degree to which a provider competently and safely delivers a clinical service to a patient within the optimal time period. Performance measures have been created by a number of advocacy coalitions, patient safety institutions, government agencies, and professional organizations. To measure performance adequately and accurately, process, structure/capacity, access, patient satisfaction, and outcome measures must not only be created, but must be relevant, scientifically sound, feasible, actionable, accurately measurable (reliable and valid), and ultimately result in improved outcomes for the population. In the case of outcome measures, they may need to be risk-adjusted as well. To paraphrase Einstein, everything should be made as simple as possible, but not any simpler.
Various types of quality measures exist as summarized in the Figure.
Structure/capacity measures are designed to assess whether the capacity to perform a service or function exists in a particular system (eg, what proportion of providers have undergone a certain postpartum hemorrhage (PPH) training or whether a particular service is available at an institution such as massive transfusion policy or PPH cart).
Process measures are designed to assess the frequency of usage of a particular clinical process; they are calculated using the number of patients eligible for a particular service in the denominator and the number of patient who actually receive the service in the numerator (eg, the proportion of GBS carriers who received antibiotics during labor).
Outcome measures are created by assessing the frequency or prevalence of a specific outcome in a given population (eg, number of third- or fourth-degree tears, brachial plexus injuries, or postpartum intensive care unit [ICU] admissions per 1000 deliveries).
Access measures assess the attainment by patients of timely care as well as the delays and barriers (educational, financial, prejudiced, geographic, or environmental) that may result in failure to obtain care.
Finally, patient experience/satisfaction measures assess the patient’s perception and experience of health care delivery. These measures are typically dependent on patient survey data and are not obtainable by typical administrative data generated by a hospital.
Using measures for quality improvement involves 6 steps: identifying deficiencies or areas for improvement, selecting measures to assess these areas, obtaining preintervention baseline data, performing an intervention, performing postintervention measurement, and finally, refining the measurement and the intervention. Quality improvement may involve assessment of internal processes at a single institution or may involve assessments across different institutions that result in regional, state, or national comparisons.
Having established that it is desirable and conceptually possible to measure quality of care, this article will review current approaches to measure quality of care in obstetrics and preview new measures on the horizon. Additionally, systems for using and maintaining quality measures will be discussed.
Section snippets
Current measures of obstetrical quality
There are multiple different metrics currently being suggested or employed in an effort to measure quality of obstetric care. Current metrics for obstetrics endorsed by national organizations, such as the AHRQ, National Quality Forum (NQF), and Joint Commission, are shown in Table 1. However, the lack of an obstetric national database has resulted in measurement difficulty leading to high resource use for certain types of metrics required by organizations such as the Joint Commission and
Responsible use of quality measures
While it is possible that aspects of quality may be measurable, the ability of obstetrical quality measures to translate into clinical improvements depends greatly on how they are applied. The data source, the group of measures chosen, and the way they are reported can all affect whether the measures can lead to benefits vs unintended harms.
Data sources vary greatly in quality. While direct observation is the gold standard for data collection, it is prohibitively expensive. Medical records are
Future obstetric quality measures
It is difficult to predict the new obstetric quality measures that will emerge in the United States over the next 2-5 years, which is the approximate time required for a new indicator to be vetted and validated through the quality indicator development process. Even an indicator with a broad consensus and clear evidence of link to improved outcome requires a minimum of 20–24 months from conception to implementation.10
Much can be learned from other countries that have a political mandate to
What is needed for measurement to occur?
Three things are needed to enhance measurement reporting. First, a uniform core data set, perhaps even a registry of all births with a unique medical identification number that can allow for longitudinal follow-up, would facilitate quality indicator reporting. This is the model utilized by Denmark and other Scandinavian countries allowing for epidemiologic studies and population-based outcomes. Because many adverse events in childbirth are rare, the United Kingdom (UK) has a national obstetric
What will make quality metric reporting in obstetrics inevitable?
We must embrace the need for improvement and continue to engage all stakeholders in measurement to define the purpose of the measures. Several phenomena are occurring, making the need for consensus about what to measure crucial: (1) EMR, wearable bioapplications, and other such health data ultimately will allow for documentation and monitoring of health (clinical data, laboratory data, vital signs, behaviors, clinician notes, orders, referrals, charges) enabling data (processes and outcomes) to
References (30)
- et al.
Comparison of risk-adjustment methodologies
Obstet Gynecol
(2003) - et al.
Accuracy of obstetric diagnoses and procedures in hospital discharge data
Am J Obstet Gynecol
(2006) - et al.
Risk-adjusted models for adverse obstetric outcomes and variation in risk-adjusted outcomes across hospitals
Am J Obstet Gynecol
(2013) - et al.
The UK obstetric surveillance system: impact on patient safety
Best Pract Res Clin Obstet Gynaecol
(2013) - et al.
Intrapartum care quality indicators: a systematic approach for achieving consensus
Eur J Obstet Gynecol Reprod Biol
(2013) An urgent call to implement systematic monitoring of a comprehensive set of quality indicators for maternity services
Women Birth
(2010)- et al.
User engagement in the delivery and design of maternity services
Best Pract Res Clin Obstet Gynaecol
(2013) - et al.
Development of maternity dashboards across a UK region: current practice, continuing problems
Eur J Obstet Gynecol Reprod Biol
(2013) - et al.
A strategy for quality assurance in Medicare
N Engl J Med
(1990) - Agency for Health care Research and Quality. Tutorials on quality measures. Available at:...
Comparing postcesarean infectious complication rates using two different skin preparations
Obstet Gynecol
Can administrative data be used to compare postoperative complication rates across hospitals?
Med Care
Coding of perineal lacerations and other complications of obstetric care in hospital discharge data
Obstet Gynecol
Facility-based identification of women with severe maternal morbidity: it is time to start
Obstet Gynecol
Cited by (27)
Society for Maternal-Fetal Medicine Special Statement: Clinical quality measures in obstetrics
2024, American Journal of Obstetrics and GynecologySociety for Maternal-Fetal Medicine Special Statement: Curriculum outline on patient safety and quality for maternal-fetal medicine fellows
2023, American Journal of Obstetrics and GynecologySociety for Maternal-Fetal Medicine Special Statement: Telemedicine in obstetrics—quality and safety considerations
2023, American Journal of Obstetrics and GynecologyEarly postpartum readmissions: identifying risk factors at birth hospitalization
2022, AJOG Global ReportsCitation Excerpt :The causes for later readmission were more dispersed overall with perinatal, infectious, and digestive causes covering more than half (69%) of the later readmissions. Maternal care during the immediate postpartum period has received significant attention.6,19 Based on the United States vital statistics records from 2011 to 2015, 18.6% of pregnancy-related mortalities occurred within 1 to 6 days postpartum and 21.4% occurred within 7 to 42 days postpartum.5
Society for Maternal-Fetal Medicine Special Statement: A quality metric for evaluating timely treatment of severe hypertension
2022, American Journal of Obstetrics and GynecologyCitation Excerpt :Thus, concern about potential transient maternal hypotension should not delay the treatment of severe HTN, which carries far more morbid risks of maternal stroke or death. One way to evaluate the potential adverse effects of a quality metric is to track balancing measures.15 In this case, good balancing measures might include the rates of maternal hypotension-related complications and cesarean delivery for fetal indications.
Association between hospital-level cesarean delivery rates and severe maternal morbidity and unexpected newborn complications
2021, American Journal of Obstetrics and Gynecology MFMCitation Excerpt :From a practical application of measuring and tracking hospital-level complication rates, UNCs occur rarely, and thus, the measurements may be subject to high variability (ie, noise in the estimates); this limits their utility in reflecting the quality of care. The challenges with the measurement and comparison of obstetrical quality have been previously noted, and we acknowledge that these measures of morbidity are not perfect (eg, issues with coding reliability and the inclusion of individual components considered morbidity, such as neonatal transfer).23–26 However, we chose to measure morbidity using these metrics, as these data are now being routinely measured and tracked at local, state, and national levels.
A listing of articles in this series that were published in other journals before #36 appeared in the June 2015 issue of AJOG is available at smfm.org/publications/.
All authors and Committee members have filed a conflict of interest disclosure delineating personal, professional, and/or business interests that might be perceived as a real or potential conflict of interest in relation to this publication. Any conflicts have been resolved through a process approved by the Executive Board. The Society for Maternal-Fetal Medicine has neither solicited nor accepted any commercial involvement in the development of the content of this publication.
Committee Members: Quality and Safety Committee: Alfred Abuhamad, Peter Bernstein, Meredith Birsner, Steven Clark, C. Andrew Combs, Carey Eppes, Jennifer McNulty, Brian Mercer, Peter Napolitano, Daniel O’Keeffe, Christian Pettker, Patrick Ramsey, Larry Shields
Committee Members Health Policy: John Albert, Joanne Armstrong, Dana Block-Abraham, Mark Clapp, Rebekah Gee, William Grobman, Lisa Hollier, Irogue Igbinosa, Men Jean Lee, Sarah Little, James Meserow, Emily Miller, George Saade, Katie Schuber, Erika Werner