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Heath care measurement and evaluation is an integral piece of the health care system. The creation and assessment of care performance metrics are important and relevant for the obstetric community including both clinicians and patients. Careful deliberation is required to create a measurement system that results in optimal care for women and families. This article reviews the current approaches to measuring quality in obstetrics.
The practice of medicine continues to evolve, and individual circumstances will vary. This publication reflects information available at the time of its submission for publication and is neither designed nor intended to establish an exclusive standard of perinatal care. This publication is not expected to reflect the opinions of all members of the Society for Maternal-Fetal Medicine.
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.”
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.
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.
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 Leapfrog Group. Moreover, the lack of universally agreed upon metrics has resulted in a lack of standardized measures being consistently employed across hospitals. While some variation in metrics may be acceptable, certain metrics should be determined to be foundationally important to gain the momentum needed to improve quality of obstetric care at a larger level.
Table 1Current proposed quality metrics and data source
Measure endorser
Name
Type
Data source
AHRQ
Cesarean delivery
Process/outcome
ICD-9/CPT discharge data
AHRQ, NQF
VBAC
Process/outcome
ICD-9/CPT discharge data
AHRQ patient safety indicator
Birth trauma–injury to neonate
Outcome
ICD-9/CPT discharge data
AHRQ
Obstetric trauma–vaginal with instrument
Outcome
ICD-9/CPT discharge data
AHRQ
Obstetric trauma–vaginal without instrument
Outcome
ICD-9/CPT discharge data
AHRQ
Obstetric trauma–cesarean delivery
Outcome
ICD-9/CPT discharge data
NQF
Neonatal mortality
Outcome
NQF
Incidence of episiotomy
Process
ICD-9/CPT discharge data
NQF
Infant <1500 g delivered at appropriate level of care
Process/outcome
Abstraction
NQF
Healthy term newborn–absence of conditions or procedures reflecting morbidity during birth and nursery care to otherwise healthy infant
Appropriate DVT prophylaxis in women undergoing cesarean delivery
Process
Abstraction
NQF
Exclusive breast-feeding at hospital discharge
Process
Abstraction
NQF, Joint Commission
Nonmedically indicated delivery <39 wk
Process
Hospital-level chart abstraction
NQF, Joint Commission
Appropriate use of antenatal corticosteroids
Process
Hospital-level chart abstraction
Joint Commission
Severe maternal morbidity
Outcome
Hospital-level chart abstraction
AHRQ, Agency for Healthcare Research and Quality; CPT, Current Procedural Terminology; DVT, deep vein thrombosis; ICD-9, International Classification of Diseases, Ninth Revision; NQF, National Quality Forum; VBAC, vaginal birth after cesarean.
SMFM. Measuring quality of care in obstetrics. Am J Obstet Gynecol 2016.
Utilizing the AHRQ quality framework (Figure) of structure-process-outcome-access-patient experience allows us to consider the current metrics in a framework that allows for thoughtful evaluation of how suggested metrics actually reflect quality of care and what additional metrics may be used in the future.
Many of the current metrics being tracked are outcome measures, chosen due to their ease of measurement but criticized by many clinicians as being an end product that may not be truly reflective of quality of care. In obstetrics, unlike other areas of medicine, the outcomes of 2 patients (ie, the mother and her fetus), whose outcomes may involve tradeoffs with the other, need to be taken into account. Furthermore, when looking at outcomes, it may be important to take into account the differences in patient populations that can affect outcomes. Taking patient characteristics into account is known as risk-adjusting. There are many methods of doing this, some simpler than others, but for many outcome measures this is a critical step so hospitals are not penalized for taking care of the sickest patients.
Using only outcome metrics makes it difficult to determine which drivers (patient level, processes, systems) actually affect the outcome being measured. It is important to acknowledge that it is often not a single driver that impacts an outcome. Effective outcome metrics ideally would be those demonstrated to be impacted by changes in systems and process. An example is surgical site infections, which are affected by preoperative antibiotics and the abdominal preparatory process.
Process measures assess how the care system acts and do not assess the results of the care. Process measures are popular because they reflect actions of the care system and thus are able to be directly influenced by the health system. It is well known that even good care can be associated with bad outcomes and thus measuring the process of care ensures a hospital gets credit for what it did right. Process measures can be easy to measure, and are typically measured as a proportion, with 100% or 0 always being best, thus making them easy to understand.
Recent metrics such as severe maternal morbidity and appropriate use of antenatal corticosteroid use seem to have more face validity and wider acceptance by the provider community than do some older metrics (eg, third- and fourth-degree lacerations). To continue to work toward the national goal of improving and optimizing maternal and perinatal outcomes, selecting specific metrics as national priority areas may be an important and effective strategy.
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 also considered to be able to generate reliable data, but abstracting these data from text notes is also very expensive. Administrative data, typically data generated for billing or vital statistics, conversely, need no direct medical record abstraction and are thus relatively cheaper to use, but may not be as reliable. Billing codes can vary in quality between hospitals, and birth certificates have some fields that are very accurate and others that are weakly accurate at best.
Thus, understanding the data source being used to generate the quality measure is critical. Additionally, it is crucial to understand to whom the data apply, or attribution. For example, when a low-risk delivery occurs, to whom is it assigned: the prenatal care doctor, the admitting doctor, the doctor who manages the labor, the doctor who performs the delivery, or the doctor who discharges the patient? It is important to ensure, at a time when we are increasingly aware of the importance of team care, that individuals do not receive all of the blame or credit, and the application of quality measures to individuals who work in series or in parallel as part of a health care team is framed correctly. For some measures, attribution to individual health care providers may be inappropriate, but attribution to a practice or department may be appropriate.
It is also important to realize that single measures or suites of measures not well conceived may have unintended consequence. For example, if rates of third- and fourth-degree tear at operative delivery are measured in isolation, then avoiding all operative deliveries and delivering everyone who is a candidate for one by cesarean delivery instead would guarantee better results on this measure. Clearly this will not improve overall outcomes. Thus, the concept of balancing measures is important. As an example, if one is measuring adverse maternal outcomes that may be increased by vaginal delivery, one needs to have a balancing measure to assess cesarean rates. Another areas where balancing measures is critical relates to frequently discordant maternal and perinatal outcomes, where what is good for the mother may be bad for the baby and vice versa. For example, decreasing cesarean delivery may lead to adverse perinatal outcomes. If maternal outcomes are being measured, then one needs to balance them by measuring perinatal outcomes. Thus the combination of measures reported can be critical to improving care and not just maximizing the appearance of quality. What the balance should be however, is debatable. For example, how many maternal outcomes should be traded off for better neonatal outcomes? The calculus of how to balance between mother and baby or between types of outcomes remains undefined at present.
Another area where the inappropriate use of a quality measure may lead to unintended consequences is when such a measure can reflect good or bad care. As an example, early recognition of postpartum hemorrhage and early blood product use is essential to prevent maternal morbidity and mortality. On the other hand, blood transfusion may be a screen for postpartum hemorrhage that may have resulted from inappropriate care. Similarly, admission to the ICU may be life-saving for some patients, particularly in hospitals with limited critical care support on the obstetrical service, but may be a sentinel event for inappropriate care in other cases. The measure of “severe maternal morbidity,” which relies on blood transfusion and ICU admissions, has been proposed as a valuable tool hospitals can use to identify women who have had adverse pregnancy outcomes and to allow further investigation of these events. However, if used as a measure of quality without appropriate review, it may have the unintended consequences of disincentivizing appropriate care, leading to delay in the management of hemorrhage or transfer to the ICU, or even leading to avoidance of caring for high-risk patients. It should be noted that it has been expressly stated by the proposers that this measure should not be used to make interhospital comparisons of quality given the great differences in hospital populations.
Indeed, the fact that most hospitals do not do well on all measure further emphasizes the need to consider a collection of quality measures in an effort to more fully characterize a given environment. Hospitals that are outstanding on one measure will do less well on other measures.
Thus, it is important to measure a variety of domains.
Lastly, it is crucial to recognize some measures are better for use within a hospital to track improvements over time and others are appropriate to use to make comparisons between providers or hospitals. Measures used to make comparisons between hospitals need to be validated and tested extensively. Such measures also need to follow validated risk-adjustment methods. While it is entirely responsible for a hospital to track an outcome year over year without risk adjustment, assuming the patient population remains roughly the same, that does not mean the same measure would be reasonable to compare different hospitals or practices. For example, the rate of “potentially preventable complications” depends on the patient population being cared for by the hospital or practice. Given that the phenotype for most obstetrical conditions is not easily ascertained, and that coding data are limited in defining the various risk factors for a particular patient, it may be difficult to risk-adjust using administrative obstetrical data. Research into how to appropriately risk-adjust in obstetrics is clearly needed.
In sum, a deep understanding to what is being measured, how it is applied, and how it should be contextualized are extremely important. A deep understanding of clinical obstetrics is clearly needed when developing any quality measures.
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.
Safer childbirth: minimum standards for the organization and delivery of care in labor. RCOG Press at the Royal College of Obstetricians and Gynecologists,
London2007
Royal Australian and New Zeland College of Obstetricians and Gynecologists. Obstetric clinical indicators users’ manual. Version 7. The Australian Council on Healthcare Standards and the Royal Australian and New Zealand College of Obstetricians and Gynecologists. Ultimo, New South Wales, Australia: 2011:1-51.
For example, the Royal College of Obstetricians and Gynecologists have proposed >290 clinical quality indicators that can be monitored at the discretion of the maternity unit.
Royal Australian and New Zeland College of Obstetricians and Gynecologists. Obstetric clinical indicators users’ manual. Version 7. The Australian Council on Healthcare Standards and the Royal Australian and New Zealand College of Obstetricians and Gynecologists. Ultimo, New South Wales, Australia: 2011:1-51.
Member states of Euro-Peristat monitor 10 core indicators with the option of 23 additional recommended indicators, and several future indicators under development.
Table 2 lists examples of proposed domains, and representative quality indicators used internationally, stratified in a conceptual framework for maternal quality indicator development.
National Perinatal Epidemiology Unit. International Network of Obstetric Survey Systems (INOSS). Available at: https://www.npeu.ox.ac.uk/inoss. Accessed July 5, 2015.
Table 2Potential future quality indicators stratified across reproductive life span; examples of existing indicators used internationally but not routinely in United States
Percent of pregnancies following fertility treatment (x)
±By plurality
Intrapartum
Severe maternal morbidity
Euro-Peristat US measure in pipeline
Mode of delivery by parity, plurality, presentation, previous cesarean delivery, and gestational age
Euro-Peristat
Induction of labor
UK consensus Process/outcome measure Subject to overutilization
Instrumental vaginal deliveries
UK consensus
Normal births
UK option to track
Ethnic diversity of care team
RCOG Ethnic/cultural diversity representative of patient population served
Postpartum
ICU admissions
UK consensus
Postpartum hemorrhage
UK consensus
Newborn
Fetal/neonatal death by selected congenital anomalies
Euro-Peristat
Severe neonatal morbidity
Euro-Peristat Vermont Oxford Network
NICU admissions at term
UK consensus
Newborn transfers
Newborn readmission
Interconception/well-woman visit
HPV, human papilloma virus; ICU, intensive care unit; NICU, neonatal intensive care unit; RCOG, Royal College of Obstetricians and Gynecologists; UK, United Kingdom.
SMFM. Measuring quality of care in obstetrics. Am J Obstet Gynecol 2016.
Perhaps more important than the identification of specific future measures is an understanding of what is needed for measurement to occur.
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 surveillance system that collects information on a range of rare disorders of pregnancy that includes both clinical and health system–level information.
Because it is a population-based registry, it is less prone to case ascertainment bias and can be used to benchmark and compare hospital-level disease incidence and outcomes, inform and audit national guidelines, and monitor the effect of changes in policy or practice.
The International Network of Obstetric Survey Systems has been formed to allow for future collaborative studies that aim to improve patient safety and clinical outcomes.
These collaborative efforts emphasize the second thing needed to enhance measurement reporting, namely, core indicators, with standard definitions that can be tracked monthly via dashboards. As mentioned previously there are >290 proposed indicators, but there is little consensus about core measures. While there have been numerous international calls for the systematic monitoring of childbirth outcomes using a comprehensive set of quality indicators for hospitals providing maternity services, this goal has yet to be realized.
In the United States, to achieve ongoing accreditation by the Joint Commission, hospitals are reporting on 5 perinatal indicators, but there are other measures in the pipeline. Finally, and perhaps most importantly, more randomized and comparative effectiveness trials evaluating obstetrical practices are needed to establish evidence for best practices. The majority of clinical guidelines and quality indicators being advanced in obstetrics are based on expert opinion.
An evaluation of potential quality indicators based solely on systematic reviews yielded only 18 quality indicators of which 6 are clinical practices that should not be done (eg, 0 events: episiotomy, enemas before labor, perineal shaving).
Clearly there are more important clinical practices for which there are individual, institutional, and regional variation that would benefit from clinical comparative effectiveness trials and more rigorous high-quality clinical research.
Nevertheless, while trials are the gold standard, monitoring and reporting in the absence of trials still has led to discernable improvements in care. For example, the UK surveillance system, using ongoing monitoring and quality audits, identified high rates of eclampsia. They implemented a policy of magnesium sulfate prophylaxis for women with severe preeclampsia and were able to quickly demonstrate a nationwide decline in seizures after this policy change. Similarly, international comparisons with Scandinavia and The Netherlands showed high rates of eclampsia relative to the UK after the policy was implemented suggesting an opportunity for best practices in the Nordic countries.
In the United States, efforts are moving in the right direction. Efforts are in place to harmonize measures so that there is a uniform definition that satisfies the needs of all stakeholders. Efforts for uniform data definitions within electronic medical records (EMR) are being coordinated through the American Congress of Obstetricians and Gynecologists (ACOG), Society for Maternal-Fetal Medicine (SMFM), coders, clinicians, and EMR vendors. ACOG and ACOG/SMFM practice bulletins and clinical consensus statements often suggest proposed quality metrics. However, given: (1) that childbirth is the number-1 reason for hospital admission,
(2) the evolving recognition of the importance of pregnancy as a source of long-term fetal health (eg, fetal origin of adult diseases), and (3) the current concept that prenatal care should include preconception/interconception care, more appropriate measurement/quality indicators are needed across the spectrum of care, including health status and access to care, ambulatory (preconception/interconception), inpatient (antepartum/intrapartum), postpartum, newborn, and even parenting indicators.
National Perinatal Epidemiology Unit. International Network of Obstetric Survey Systems (INOSS). Available at: https://www.npeu.ox.ac.uk/inoss. Accessed July 5, 2015.
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 be calculated and reported at the patient, provider, hospital, health system, and population level
; (2) external pressure about cost, efficiency, and value will increase the likelihood that these numbers will become widely available, if not publicly released; and (3) learning collaboratives are allowing for rapid change based on best available evidence and expert opinion (Table 3).
Table 3Examples of future indicators using available/emerging big data sources and electronic medical records
Time
Indicator
Definition/comment
Health status and access
Appropriate level of care (maternal)
Mother delivered at risk-appropriate facility MOD, ACOG/SMFM
Preconception/well-woman visit
Folic acid
Percent taking folic acid at time of conception WHO
Documentation of reproductive life plan
Percent of women with documentation in medical record as proportion of provider practice (IOM)
Education to optimize medical conditions (diabetes, hypertension)
Percent HbA1C <6% when positive beta sub Percent HTN started on low-dose ASA in first trimester
Prenatal/antepartum
Prenatal diagnosis of selected congenital anomalies
Percent “confirmed” Percent “missed” By center, clinician Process, outcome measure Addresses accuracy, false positives, false negatives, and potential harm from overutilization
Percent of pregnancies following fertility treatment
By treatment type, facility, clinician Process, outcome measure, identify best practices
Percent progestin treatment
Percent of women with prior preterm delivery receiving progestin Track patient-level scripts by provider Process, outcome measure At risk for underutilization
Intrapartum
Mode of delivery by parity, plurality, presentation, previous cesarean delivery, and gestational age; age, race/ethnicity, clinician (MD, RN, CNM), intended location, etc
Requires stratification or risk adjustment Known variation; identify risk factors, high utilizers and interactions
Induction of labor
By indication/provider Process, outcome measure At risk for overutilization
Instrumental vaginal deliveries
By hospital/provider Process, outcome measure Risk for underutilization and overutilization
Postpartum
DVT prophylaxis
Current inpatient hospital indicator but excludes pregnancy
Hemorrhage
Definition needs to be harmonized; trend resource utilization, outcomes, best practices (UK, MQI)
ICU admissions
Part of evolving composite measure (severe maternal morbidity)
Readmissions
Current inpatient hospital indicator
Percent eclampsia or stroke in preeclamptics
Potential system error Underutilization of magnesium
Percent gestational diabetics with postpartum glucose tolerance test
Opportunity for screening and behavioral risk modification to decrease type 2 diabetes
Newborn
Fetal/neonatal death by selected congenital anomalies
Potential for undertreatment/overtreatment; futile care
NICU admissions at term
Term newborn measure in pipeline
Delivery at appropriate level of care
By hospital, provider
Interconception/well-woman visit
Documentation of reproductive life plan; prescriptions for contraception
Birth spacing improves maternal, family outcomes
ACOG, American Congress Obstetricians and Gynecologists; ASA, aspirin; HbA1C, hemaglobin A1c; HTN, hypertension; ICU, intensive care unit; IOM, Institute of Medicine; MOD, March of Dimes; MQI, maternal quality indicator work group; NICU, neonatal intensive care unit; SMFM, Society for Maternal-Fetal Medicine; UK, United Kingdom; WHO, World Health Organization.
SMFM. Measuring quality of care in obstetrics. Am J Obstet Gynecol 2016.
Patient engagement and shared decision-making needs to be addressed/documented and part of the process, especially in obstetrics where there are 2 patients, a concerned and interested partner, birth plans, patient autonomy and right to refusal, and the frequent expectation of a perfect outcome since childbirth is a normal physiologic event.
For example there is increased interest in home births to avoid interventions or medicalization of a natural process. Correspondingly, quality indicators should be developed to track success, transfers, and adverse outcomes. Similarly, low cesarean rates for low-risk nulliparas is considered good quality care at the provider level (hospital and physician), however some patients may prefer to undergo cesarean due to personal preference without medical indication. Likewise, a high rate of exclusive breast-feeding is considered good quality care at the hospital level, but some women prefer not to breast-feed and there are persistent regional and cultural differences regarding this preference. Systematic efforts to capture these patient preferences and allow for risk adjustment or exclusion is needed to make benchmarking and comparisons meaningful and equitable. The burden of data collection has been noted as a barrier in the UK and Europe, and has been a significant deterrent in the United States.
Center for Health Policy/Center for Primary Care and Outcomes Research and Battelle Memorial Institute Quality indicator measure development, implementation, maintenance, and retirement (prepared by Battelle, under contract no. 290-04-0020).
Agency for Healthcare Research and Quality,
Rockville (MD)2011
Hence an integrated data system with patient preferences and behaviors, clinician notes, orders, referrals, laboratory tests, vital signs, clinical conditions, complications, and outcomes, as well as a party who is responsible for ensuring data are entered and correct should be the basis for monitoring future quality indicators in obstetrics.
In addition to the creation of the actual measures, the obstetric community needs to create evaluation mechanisms to periodically assess the adequacy of the measures themselves, ie, their relevance, evolving data collection instruments, underlying population changes, and evolving patient desires. It has been suggested that several questions should be asked about any developed metrics, as follows.
Center for Health Policy/Center for Primary Care and Outcomes Research and Battelle Memorial Institute Quality indicator measure development, implementation, maintenance, and retirement (prepared by Battelle, under contract no. 290-04-0020).
Agency for Healthcare Research and Quality,
Rockville (MD)2011
How strong is the scientific evidence supporting the validity of the measure? Are all individuals in the denominator equally eligible and have access to care processes that lead to being included in the numerator? Is the measure result under control of those whom the metric evaluates? How well does the measurement specification capture the desired metric or clinical process? Does the measure provide for fair comparisons across various providers and populations? Does the measure adequately risk-adjust? Does the data source adequately reflect and capture the relevant clinical processes? What are the demands on financial and human resources during data collection? Additional questions should include the following. When should a measure be retired, either because it is no longer relevant or because it is universally employed? What usage should be considered ideal? Should performance measurement ever be punitive from either a regulatory or financial perspective? Who establishes the hierarchy when competing measures result in conflict? Who ultimately determines the social importance of clinical care: patients or providers? Who determines the relative financial remuneration for care improvement, as overcompensation may result in too great an incentive?
Agencies such as the NQF do some of this critical work by evaluating measures proposed to them. The old lists are reviewed and new measures added from time to time on a regular schedule. However, NQF only evaluates measures put before them and does not review all possible measures. Thus, a more comprehensive measure evaluation solution is still needed.
In summary, heath care measurement and evaluation is an integral piece of the health care system, equivalent to the scientific process utilized during the creation of new scientific discovery. The creation and assessment of care performance metrics are important and relevant for the obstetric community including both clinicians and patients. Careful deliberation is required to create a measurement system that results in optimal care for women and families.
Moving quality measurement forward in obstetrics will inevitably require multidisciplinary collaboration including subspecialty groups (ACOG, SMFM); health service researchers and clinicians interested in indicator development; clinical trialists interested in establishing best practice; external agencies that facilitate public reporting and credentialing such as the NQF, Leapfrog Group, and Joint Commission; government agencies such as the US Department of Health and Human Services; as well as payers and patients. Stakeholders will need to support the arduous process of indicator development, 1 criterion at a time and 1 indicator at a time. We urge organizations that care for pregnant women and their families to join together to agree upon metrics we can all be held accountable to in efforts to improve quality of care for mothers and their infants. If we can align efforts to collaborate and agree upon goals to measure, improvement in quality of care for both mother and baby is not just inevitable, but rather a foregone conclusion.
Safer childbirth: minimum standards for the organization and delivery of care in labor. RCOG Press at the Royal College of Obstetricians and Gynecologists,
London2007
Royal Australian and New Zeland College of Obstetricians and Gynecologists. Obstetric clinical indicators users’ manual. Version 7. The Australian Council on Healthcare Standards and the Royal Australian and New Zealand College of Obstetricians and Gynecologists. Ultimo, New South Wales, Australia: 2011:1-51.
National Perinatal Epidemiology Unit. International Network of Obstetric Survey Systems (INOSS). Available at: https://www.npeu.ox.ac.uk/inoss. Accessed July 5, 2015.
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
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