Posts Tagged ‘Medicaid and maternity care’

Medicaid Coverage for Doula Care: Re-Examining the Arguments through a Reproductive Justice Lens, Part One

March 28th, 2013 by avatar

by Christine H. Morton, PhD and Monica Basile, PhD, CPM, CD(DONA), CCE (BWI)

Last month there were great discussions after a study was published by the University of Minnesota, examining the potential cost savings to Medicaid if doulas worked with Medicaid clients, helping to reduce interventions and cesareans.  Today and next Tuesday, regular contributor, Christine Morton and her colleague Monica Basile, take a look at that study and another from Oregon, and share thoughtful insight about topics that might still need to be addressed if costs savings were to be effectively realized in a two part blog post. – Sharon Muza, Community Manager, Science & Sensibility




How can doula supported births help reduce the cesarean rate and realize cost savings within Medicaid-funded births? Two studies published last month offer the opportunity to address this complex question.

We support the goal of increasing access to doula supported care to childbearing people of diverse racial/ethnic and class backgrounds, and we are pleased that discussions are taking place about how doulas may be able to help reduce racial disparities in maternal and infant health. We recognize that work toward these goals requires policy advocacy, which depends heavily on economic arguments for the benefits of doula care.

However, by limiting the discussion of benefits to the economic impacts of reduced cesareans, advocacy for Medicaid funding of doula supported births—without specifying the doula model of care and without according true value to the doula’s impact—may have unintended consequences for individual doulas, and the organizations that represent them.  One such consequence may be that the resulting system will continue to perpetuate a model of economic marginality and potential exploitation for the doulas who serve a low income population of childbearing people.

The AJPH study by Katy Kozhimannil and colleagues in Minnesota received a lot of media attention when it appeared last month, even live coverage in the Huffington Post.  This study compared 1,079 selected Medicaid doula patients in Minnesota to Medicaid patients nationwide for their total cesarean rates.  They found that doula clients of a community program in Minnesota had a rate of 22.3% while national Medicaid had 31.5%.  The authors reported three scenarios, all assuming that if states reduced cesarean rates, by offering doula services, there would be varying levels of cost savings, depending on the cesarean rate achieved, and by reimbursing doulas between $100-300 per birth.

In our view, the Minnesota study design raises several methodological questions, which are applicable to this study and to future research on doula-attended births. We outline those questions here, as well as raise several more substantive concerns about the implications of the study’s stated conclusions.

  1. Why did the researchers not compare Minnesota Medicaid doula clients to Minnesota Medicaid women who gave birth?  Minnesota has a much lower rate of total cesarean that the US as a whole (27.4% during this time period), and this would have been a better matched comparison.  A better comparison would be doula attended births vs. non-doula attended births at the same facility.  It is not clear from the study whether the doula program whose data was utilized served women at one or multiple hospitals in Minneapolis. 
  2. Why did the researchers not limit their investigation to primary cesareans?  Doulas typically support women in labor rather than women undergoing repeat cesareans.  The total cesarean rate includes repeat cesarean so it will be much higher than the primary cesarean rate, which is more applicable to doula clients.  Including total cesarean rates means that the researchers are comparing a limited universe (doula support of women in labor) to all births (thus including repeat and primary cesarean).   The data source for this study, (Nationwide Inpatient Sample), however, does not have this information.
  3. Cesarean rates are very dependent on the parity distribution of the birthing population, so first time mothers need to be compared to first time mothers and multiparous women to multiparous women. This information is not available in the data source used by the researchers, but in future studies of this type, it is critical to verify that the proportion of each is the same in the intervention and control populations.
  4. States are implementing a number of payment reform models to reduce cesareans among women covered by Medicaid, with limited success.  In part, that is because cesareans are influenced by a number of factors, with payment incentives only one.  (Many of these issues are covered in the CMQCC white paper on improvement opportunities to reduce cesareans, which argues that a multi-pronged strategy is necessary). 
  5. Because hospital rates of cesarean have been shown to have high geographic variation in a number of studies (Baicker 2006; Main et al 2011; Caceres 2013; Kozhimannil 2013), it may be more feasible to have comparison groups of hospitals with similar primary cesarean rates.  Until we understand what accounts for variation in cesarean rates between institutions (unit culture; facility policies and protocols), it may be premature to assess the independent effect of labor support by a trained doula.

While doula support is associated with fewer cesareans across the board (Hodnett 2012), the methodological issues described above are likely to over estimate the benefits of doula-attended births in terms of reducing the cesarean rate for Medicaid covered births.  This, in turn, raises questions about the purported cost savings.  In the Minnesota study, the cost breakpoint is no more than $300 dollars for the doula per birth.  In most cities, doulas charge well above this amount for fee-for service care.

A cost-benefit analysis by Oregon Health & Science University researchers for the Oregon State Legislature was presented at the Society for Maternal Fetal Medicine in February 2013, which found that doula care in labor provides a cost benefit to payers only when doula costs are below $159.73 per case.  In that study, data sources are not entirely clear, but do seem to come from the OHSU facility where a hospital-based doula program is in place.  In that program, doulas are on call on weekends only and come to assist in a labor when requested by the woman during her prenatal care or when she arrives at the hospital.  A case-control study claiming the benefits of this doula model at OHSU was published as an abstract, and although it claims “women receiving doula care were statistically less likely to have an epidural during labor (p = 0.03), have an episiotomy (p = .03), or cesarean delivery (p = .006) and on average, doula attended women had a shorter hospital stay compared to the control group (p = .002),” nowhere does it show what the actual rates were.  This is important, because, they are likely to be relatively low overall, given that OSHU is a teaching hospital, with midwives and family practice physicians providing maternity care.

There are several types of doula models; not all have the same components.  The community-based doula model, as exemplified by the HealthConnectOne approach has a solid evidence base. This model employs doulas who are trusted community members, and provides extensive prenatal and postpartum support in addition to continuous labor support.  Doulas work collaboratively with community organizations, have extensive training in experiential learning and cultural sensitivity, and are paid a wage commensurate with their value and expertise, serving an important workforce development and grassroots empowerment function. Some so-called community doula programs do not incorporate all these components.

Hospital-based programs usually assign or utilize an on-call doula, who has not met the mother in advance and is not likely to follow up postpartum.  Some advocates of Medicaid doula programs utilize the community health worker (CHW) model, which seems to mirror the community-based doula (CBD) model but with important differences.  The American Public Health Association has defined CHWs as “frontline public health workers who are trusted members of and/or have an unusually close understanding of the community they serve.”  Yet, despite their widespread utilization in public health over the past several years, the conditions of their training, job opportunities, and even job description are idiosyncratic, and highly varied, and this “lack of CHW identity and standards of practice has led employers to contribute to the confusion about who CHWs are and what they do.” While the CHW and CBD models offer important job opportunities to members of under-resourced communities, their wages are often on the low side, with full time work paying $35,000 to $42,000 annually.  According to a health careers website, “CHWs often are hired to support a specific health initiative, which may depend on short-term funding sources. As a result, CHWs may have to move from job to job to obtain steady income.  This short-term categorical funding of health services is a challenge to the stability and sustainability of the CHW practice.”

In cost-benefit or cost effectiveness studies, it is critical to clearly specify the doula model of care on which the economic model is based.  It seems the doula model in the Minnesota study incorporates extensive pre and post partum contact and that there is an attempt to match doulas and clients in terms of race/ethnicity and language, but this is not always possible.   The study does not indicate what the doulas in the Minnesota program were paid, however, and that information was unavailable on their website.

Before we move to the topic of reimbursement, we want to note that the type of doula model is critical for assessing the benefits of doula-attended births.  The research clearly shows different outcomes for doulas who are affiliated with hospitals compared to those who work independently (Hodnett, 2012).  If a cost benefit model shows little gain in terms of outcomes, or yields a price point in the low hundreds of dollars, it may be that findings are affected by the assumptions embedded in the calculations.

More fundamentally, however, we argue that doula benefits cannot be captured solely through an economic model.  Neither should doulas be promoted as a primary means to reduce cesarean rates.  Both strategies (economic benefits and cesarean reduction) for promoting doulas have significant barrier.  In part two of this topic, running on Tuesday, April 2nd,  we discuss our concerns about reimbursement and program sustainability alongside a caution against relying too heavily on arguments that position the doula as primarily a money saver and cesarean reducer.


Baicker, K, Kasey S. Buckles, and Amitabh Chandra. Geographic Variation In The Appropriate Use Of Cesarean Delivery: Do higher usage rates reflect medically inappropriate use of this procedure? Health Affairs 25 (2006): w355–w367; doi: 10.1377/hlthaff.25.w355

Caceres, Isabel A., Mariana Arcaya, et al., Hospital Differences in Cesarean Deliveries in Massachusetts (US) 2004–2006: The Case against Case-Mix Artifact, PLoS ONE 8(3): e57817. doi:10.1371/journal.pone.0057817

Hodnett ED, Gates S, Hofmeyr GJ, Sakala C. Continuous support for women during childbirth. Cochrane Database of Systematic Reviews, 2012, Issue 10. Art. No.: CD003766. DOI: 10.1002/14651858.CD003766.pub4.

Kozhimannil, Katy Backes, Michael R. Law, and Beth A. Virnig. Cesarean Delivery Rates Vary Tenfold Among US Hospitals; Reducing Variation May Address Quality And Cost Issues, Health Affairs 32, NO. 3 (2013): 527535; doi: 10.1377/hlthaff.2012.1030

Main EK, Morton CH, Hopkins D, Giuliani G, Melsop K and Gould JB. 2011.  Cesarean Deliveries, Outcomes, and Opportunities for Change in California: Toward a Public Agenda for Maternity Care Safety and Quality.  Palo Alto, CA: CMQCC.  (Available at http://www.cmqcc.org/white_paper)

Pilliod, Rachel; Leslie, Jennie; Tilden, Ellen; et al. Doula care in active labor: a cost benefit analysis. Abstract presented at 33rd Annual Meeting/Pregnancy Meeting of the Society-for-Maternal-Fetal-Medicine (SMFM), San Francisco, CA, February 11-16, 2013, American Journal of Obstetrics and Gynecology, Volume: 208 (1); S348-S349.

About the authors


Monica Basile

Monica Basile has been an active birth doula, childbirth educator, and midwifery advocate for 17 years, and holds a PhD in Gender, Women’s and Sexuality Studies. Her 2012 doctoral dissertation, Reproductive Justice and Childbirth Reform: Doulas as Agents of Social Change, is an examination of emerging trends in doula care through the lens of intersectional feminist theory and the reproductive justice movement.


Christine Morton

Christine Morton

Regular contributor Christine H. Morton, PhD, is a sociologist whose research on doulas is the topic of her forthcoming book, with Elayne Clift, Birth Ambassadors: Doulas and the Re-emergence of Woman-Supported Birth, which will be published by Praeclarus Press in Fall 2013. For more on Christine, please see Science & Sensibility’s Contributor page.

Cesarean Birth, Doula Care, Guest Posts, Healthy Birth Practices, Healthy Care Practices, Maternity Care, Research, Uncategorized , , , , ,

Measures, Organizations and their Relationships

June 2nd, 2011 by avatar


Quality measures, transparency, quality improvement –these “buzz words” are proliferating in the blogosphere, reflecting increased activity and interest around improving the quality of health care in the United States.  How does maternity care fit into this picture?  This blog post series contains three parts: Part 1 provided an introduction to the history of the general quality measure landscape.  Today, in Part 2, we will deconstruct and demystify the alphabet soup of indicators, measures and organizations involved and explain their relationship to one another. Part 3 will review the current National Quality Forum (NQF) perinatal measures and discuss The Joint Commission (TJC) Perinatal Core Measure Set, describe how these measures are being used by various organizations and/or states, and discuss their limitations as well as their potential.  We will conclude with suggestions on how maternity care advocates can engage with maternal quality improvement efforts on national and local levels.




In today’s post,  we underscore three key points: First, the utility of a measure is dictated by the quality of the data being measured.  A huge amount of human and financial resources go into constructing, implementing, analyzing, and making improvements based on results of measures.  For a measure to achieve maximum utility, it must be carefully constructed to successfully meet its stated purpose as well as contain a high degree of reliability and validity.  However, this is not enough; our second point is that the measure must also utilize data that is feasible to collect and analyze.  Measures, therefore, are highly constrained by the actual data that is available.  Administrative data sets may contain birth certificate information or patient discharge diagnoses (ICD-9 or ICD-10 codes) or procedure codes (diagnoses related group-DRG) data.   Because this data was not designed or collected with quality improvement measures in mind, there may not be a natural fit between data and measure.  Additionally, the types of administrative data available and the methods for collecting and ensuring the accuracy of that data can vary from hospital to hospital, state to state, making it difficult to extract the uniform data necessary to compare “apples to apples.”   Third, there are a variety of organizations that intersect in this landscape, each with their own set of organizational and professional interests regarding quality measure development, endorsement and implementation, adding another layer of complexity into this picture (see Figure 1 below).


Figure 1: The Measurement Enterprise: Key Organizations

Quality measures, when reported in a timely and accurate way, can exert a large influence over practice, even in the face of these complexities and challenges.  What is measured comes to stand for overall “quality,” however limited in focus any particular quality measure may be.  This is why it’s so important for maternity care advocates to understand where we are and where we need to go.  There are many gaps in the measurement landscape for maternity care, for example.  We currently lack maternity care measures that address the full episode of care, including prenatal visits and tests, coordination of care, intrapartum, and postpartum care[1].  Longitudinal aspects of ongoing morbidity are not captured, including postpartum re-admissions or follow up visits (of the woman and/or infant) via the Emergency Department or provider’s office.  A major goal of maternal quality measure developers is to identify and fill these gaps.  However, both developers and consumers of measures need to evaluate how measures are constructed for maximum utility and benefit, be aware of instances in which measures are not feasible and appropriate, and consider how and when to use quality measures in concert with other quality improvement strategies, such as provider benchmarking and public reporting.


Figure 2: PCPI Cycle of Measure Development, adapted




Situating Measures in the overall Safety & Quality Landscape
The patient safety and quality improvement umbrella includes a wide range of activities: quality measures, quality indicators, reporting of adverse events, and risk management are broad categories of various QI activities.  Many of these rely on labor-intensive medical chart review.  However, chart review is not feasible for large scale comparisons across organizations and over time.  In this blog post, we will focus on defining quality measures.  It is important to note that data measurement is used in both quality measures and quality indicators. This can be confusing since sometimes there is overlap in the specific data being measured.  For instance, the rate of primary cesarean section among low risk women can be both a quality indicator and a quality measure.  The distinction between indicators and measures lies in how the data is used.  Quality indicators are not direct measures of quality; they identify potential problem areas that need further review and investigation, usually through a peer review processes.  In contrast, quality measures are designed to be both direct measures of quality and a way to address identified problem areas.  Agencies that use quality indicators to identify potential problems include American Congress of Obstetricians and Gynecologists (ACOG), the Agency for Healthcare Research and Quality (AHRQ) Quality Indicators (QIs), and The Institute for HealthCare Improvement (IHI).  In contrast, quality measures are interventions that create change by providing organizations with timely feedback and provide a vehicle for consumers to evaluate and choose healthcare providers, which in turn pressures organizations to change practices in ways that are reflected in quality measures (see Table 1).


TABLE 1: Quality Indicators and Quality Measures Compared

Indicators identify potential problems in one of three areas Measures address identified problem areas where quality is lacking in one of five domains
Prevention Quality Indicators- prevention of hospitalization for certain conditions, i.e. diabetesInpatient Quality Indicators- quality of care inside hospital, i.e.

  • Mortality associated w/ certain conditions
  • Utilization of certain procedures (i.e. c-    section)
  • Volume of certain procedures

Safety Indicators- provide information about complications and adverse events

Structure- reflect a provider or organization’s capacity to give quality care (i.e. provider staffing ratios)Process- reflect generally accepted recommendations for practice (i.e. administering antenatal steroids for lung maturity)

Outcome- reflect impact of the health service on the health of a patient (i.e. birth injuries, neonatal mortality)

Access- access to services and disparity in receiving services

Patient Experience: derives from patient satisfaction surveys


Key Considerations for Quality Measures

When constructing measures, there are additional key considerations that impact feasibility of data collection and also the potential impact of a measure.  The first is Level of Measurement– which level of care will the collected data reflect?  Measures provide information about quality of care at the level of the provider, medical group, hospital, or population (such as a state’s overall performance).  At the micro-level, data may be harder to collect, but the information may be more specific and thus more useful for bringing about practice change.  Increasingly, there is pressure to move towards measures of care of individual providers, but this level of measurement can be controversial in maternity care because care is typically conceptualized in modern QI theories as being a team effort comprised by individual contributions of medical providers and staff members, and during the course of a labor, many different clinicians are likely to have made consequential process and outcome decisions, yet only the physician whose name is on the birth certificate would be associated with the ultimate outcomes of the case.  The second key consideration is level of release of the data– who will have access to the data?  Data may be collected and publicly reported, it may be collected by states or other organizations and then made available to individual hospitals for QI purposes, or it may be used for internal benchmarking purposes only and never leave the walls of the hospital where it is collected.


Defining and Developing Quality Measures
Although early measures typically reported outcomes of hospital or provider care, more recently, types of measures have expanded to include those that assess process and patient experience as well as structural capacity as shown in Table 1 above. Many organizations have specified criteria for assessing quality measures, drawing heavily on the definition of quality outlined by the Institute of Medicine in its 1999 report, To Err is Human, as the Six Aims of Quality.  Here, quality refers to clinical care that is uniformly:

Safe: Patients not harmed by care intended to help them

Effective: Based on evidence and produces better outcomes than alternatives

Patient-Centered: Focuses on patient’s experiences, needs and preferences

Timely: Provides seamless access to care without delays

Efficient: Avoids waste including unnecessary procedures and re-work

Equitable: Assures fair distribution of resources based on patients’ needs

AHRQ and NQF have both specified criteria for developing and evaluating quality measures.   The process whereby a measure is constructed and evaluated is very technical, dependent on an intimate familiarity with the data points that can be used by hospitals to construct these measures. The desirable attributes of a measure can be grouped according to three key broad conceptual areas within which narrower categories provide more detail. These three areas are (1) importance of a measure, (2) scientific soundness of a measure, and (3) feasibility of a measure.  (See Table 2 below.)


TABLE 2:  AHRQ Quality Measures Desirable Attributes

Importance of the Measure – Five Aspects Relevance to stakeholders Health importance Applicable to measuring the equitable distribution of health care Potential for improvement Susceptibility to being influenced by the health care system
Scientific Soundness: Clinical Logic Explicitness of evidence 


Strength of evidence

Scientific Soundness: Measure Properties

Reliability – the results should be reproducible and reflect results of action when implemented over time; reliability testing should be documented. Validity – the measure is associated with what it purports to measure; Allowance for patient/consumer factors as required – the measure allows for stratification or case-mix adjustment. Comprehensible – the results should be understandable for the user who will be acting on the data.


Explicit specification of numerator and denominator – statements of the requirements for data collection should be understandable and implementable. Data availability – the data source that is needed to implement the measure should be available, accessible, and timely. The burden of measurement should also be considered, where the costs of abstracting and collecting data are justified by the potential for improvement in care.


How Measures Become Endorsed
In the midst of the accreditation, standardization and quality movement outlined in our previous post, there were numerous organizations vying to define and promote particular quality measures.   In 1999, pressures from payers, consumers and providers coalesced into the creation of a single agency, the National Quality Forum (NQF), to consider, evaluate and endorse any particular quality measure submitted to it for consideration.  NQF also provides a set of criteria used for evaluating measures. NQF is solely responsible for endorsing measures- they are not involved in developing, collecting or evaluating measures.  There are clinically-specific divisions within NQF, including perinatal services.   Historically, most of the perinatal measures have been focused on quality measures for neonatal outcomes, both in general and in the NICU. However, in 2008, the NQF released National Voluntary Consensus Standards for Perinatal Care, which included 17 measures, 9 reflecting obstetrics care and outcomes, of which two are of interest here – the NTSV (nulliparous term singleton vertex) and the elective delivery(ED)<39 weeks as well as eight other perinatal quality measures, again, mostly related to neonatal care issues.

Once the NQF endorses quality measures, organizations can then select from among them for their own purposes.  These organizations include the Joint Commission, the leading hospital accreditation organization, which shortly after NQF endorsed their latest set, included five of them into their revised perinatal core measures set, which hospitals could begin reporting on in April 2010.  It is important to note that to receive JC accreditation, hospitals have to report on a certain number of core measure sets, but they have the choice to select which ones.  Hospitals that have a lot of births may or may not elect to report on the perinatal core measures.

Individual states can utilize the NQF measures as well.  And, as noted above, the ACA 2010 mandated that ADULT measures of quality must be included in evaluations of Medicaid services.   So now states are looking at these issues.  A notable example here has been Ohio.   While their efforts predated the ACA, their experience has been highly successful by all accounts, in reducing the number of elective deliveries prior to 39 completed weeks gestation.

Finally, organizations, such as Leapfrog, a consortium for purchasing health care that represents Fortune 500 companies are able to utilize the NQF endorsed measures.  Leapfrog administers a voluntary survey among hospitals, which focuses on practices that have been identified as promoting patient safety.  Survey results are made publicly available to influence decisions made by large buyers of health care.  Currently, the survey includes process questions (borrowed from the NQF endorsed perinatal standards) that relate to both high-risk and normal deliveries.


Public Reporting of Quality Measures

As noted above, quality measures have been developed for several years using data from Medicare, a federally funded health care program, which has long required hospitals and providers to report these to the public, insurance companies and the government.   Thus data from these ongoing efforts can be found online through the HospitalCompare.org project – some states have their own version, in California, it’s CalHospitalCompare.org.  Organizations like the Dartmouth Atlas have conducted many useful studies using this data, showing the extent of regional variation in certain procedures and concluding this variation is based on provider, not patient characteristics or practices.  The scores of individual hospitals on TJCs quality measure sets are available on the Joint Commission’s free public quality report known as Quality Check®, which is available at (http://www.qualitycheck.org).  Users can view results that are organization-specific online or download results for free.  For each set of measures, the site reports composite scores for the individual hospital and national and state data for comparison.


PART 3:  Maternal Quality Care – How do we measure AND achieve it?

In Part 3 of our primer, we will review the current NQF perinatal measures and discuss The Joint Commission Perinatal Core Measure Set, using a case study to describe how these measures are being used by various organizations and/or states, and discuss their limitations as well as their potential.  We will conclude with suggestions on how maternity care advocates can engage with maternal quality improvement efforts on national and local levels.

 [Editor’s note:  to read Part Three of this series–which is segmented into five sections, spanning 12/26/11-12/30/11, go here.]


Posted by: Christine Morton, PhD (CMQCC) and Kathleen Pine, (University of California, Irvine)





Elliott Main.  Quality Measurement in Maternity Services: Staying One Step Ahead.  November, 2010.  Childbirth Connection.  Available at: http://transform.childbirthconnection.org/2011/03/performancewebinar/

R. Rima Jolivet and Elliott Main.  Quality Measurement in Maternity Services: Staying One Step Ahead.  November, 2010.  Childbirth Connection.  Available at: http://transform.childbirthconnection.org/2011/03/performancewebinar/



Healthcare Reform, Maternal Quality Improvement, Maternity Care, Uncategorized , , , , , , , , ,

The Maternal Quality Landscape – A primer in Three Parts

April 15th, 2011 by avatar

Quality measures, transparency, quality improvement –these “buzz words” are proliferating in the blogosphere, reflecting increased activity and interest around improving the quality of health care in the United States.  How does maternity care fit into this picture?  This blog post series will contain three parts: Part 1 will provide an introduction to the history of the general quality measure landscape.  Part 2 will deconstruct and demystify the alphabet soup of indicators, measures and organizations involved and explain their relationship to one another. Part 3 will review the current National Quality Forum (NQF) perinatal measures and discuss The Joint Commission (TJC) Perinatal Core Measure Set, describe how these measures are being used by various organizations and/or states, and discuss their limitations as well as their potential.  We will conclude with suggestions on how maternity care advocates can engage with maternal quality improvement efforts on national and local levels.

Maternal Quality Care – A brief historical overview

Childbirth Connection, with their Transforming Maternity Care project, has been a strong voice in the emerging maternal quality landscape, as have other key stakeholders.   Maternal quality measures have been highlighted as one way to track maternal outcomes of interest and have these outcomes become transparent for health care decision-making.   But to understand how these can work for advocates of evidence-based maternity care, we have to understand how maternal quality fits into the larger landscape of quality improvement.

Quality improvement is not a new phenomenon.  Efforts to improve healthcare quality started a century ago, largely due to the general disorganization of American medicine in the early 19th century.  Care and conditions varied widely from one hospital to the next.  Medical schools were largely for-profit and there was little consistency in curriculum between one school and the next.  In the early 1900s, two professional organizations, the American Medical Association (AMA) and the American College of Surgeons (ACS) began developing and enforcing minimum standards for medical schools (AMA) and for hospitals (ACS).  While this consolidated and increased the professional authority of these organizations and helped establish necessary qualifications of hospital staff, specifications for the collection and maintenance of patient-centered medical records, and particular kinds of diagnostic and treatment units such as laboratory services, these efforts focused on the training and education of medical professionals but not the performance of professionals themselves.

This set the stage for activities and organizations related to hospital accreditation, leading to the creation of the Joint Commission on Accreditation of Hospitals (JCAHO) in 1951 (JCAHO shortened its name to The Joint Commission (TJC) in January 2007).  In the mid 1960s, two developments shifted TJC’s focus away from certifying minimum standards and toward determining quality.  Most hospitals were meeting the minimum standards and new theoretical conceptions of quality were garnering attention.  One such model, proposed by Donabedian (JAMA 1988;260(12):1743-1748), emphasized a three-pronged approach to quality measurement.  In order to measure quality, he argued, you must assess the process of caring for patients and the outcomes this care achieves in addition to the structure of the organization itself (staffing and physical characteristics).  The final important factor pushing quality improvement along was creation of the federally-funded Medicare program in 1965. This created a need for the US federal government to determine hospital eligibility for Medicare reimbursement. The legislation introducing Medicare specified that any hospital accredited by the Joint Commission was automatically “deemed” eligible for Medicare reimbursement.  Thus, the federal government became a primary user of accreditation, and later, of quality assessment of the care provided under Medicare.

This process evolved from the mid 1960s to the mid 1980s when TJC adopted a new model of quality improvement.  Based largely on the research of W. Edward Deming and adapted from industry to the health care arena, this model emphasized that most errors were propagated by systems, not people.  Inspecting records to identify errors was likened to “scraping the toast after it’s burnt” – it was more important to address the systems that contributed to errors before the errors occurred.  TJC adopted this approach and called it “continuous quality improvement” (CQI).  The focus moved from auditing individual records to promoting quality in the organizational structure of the hospital.  Proponents contrasted CQI to the “theory of bad apples,” the name given to earlier quality improvement efforts that attempted to make things better by unearthing and ousting outlier practitioners and hospitals who were poisoning the well.

To summarize, underlying modern quality improvement efforts is the application of business principles to healthcare—hospitals take inputs and transform them into products.  To assess “quality,” it is necessary to define the products and find ways to measure them (Wiener, 2000).   An essential part of CQI is the identification of preferred ways of doing things.  Rather than setting minimum acceptable standards for performance, CQI calls for “specifications of process,” described as “clear, scientifically grounded, continuously reviewed statements of how one intends to behave” (Berwick, 1989 p. 56).

Once quality measures have been developed, and reported, several stakeholder groups can then use this information:

  • Consumers can use performance data to make decisions when choosing providers, hospitals, and health care plans.
  • Purchasers (employers, health plans and Medicare/Medicaid) can leverage performance data to increase value. In the near future, performance measures may be used to determine reimbursement rates for care, and there is a lot of discussion in policy circles about this strategy.
  • Hospitals and Healthcare providers can use the data to target areas in need of improvement, assess the effectiveness of quality improvement programs on an ongoing basis, and advertise the high-quality care they give
  • Policy makers can use data to determine the overall performance of the health-care system and identify under-performing areas in need of policy interventions

Quality advocates believe that measurement, while essential, must be carefully applied.  Donald Berwick (formerly of the Institute for Healthcare Improvement and Administrator for the federal Centers for Medicare & Medicaid Services (CMS) since July 2010) warns that measurement and publication of performance data is not a sufficient strategy for improvement: “The danger lies in a naive and atheoretical belief, rampant today in the orgy of measurement involved in health care regulation, that the assessment and publication of performance data will somehow induce otherwise indolent care givers to improve the level of their care and efficiency” (Berwick, 1989 p. 55).  In a later paper Berwick and colleagues (2003) take a more balanced view.  Measurement is a necessary component of quality improvement but not sufficient to bring about change by itself.

What does all this have to do with Maternal Quality Improvement?

Why has maternity care been left out of the larger movement toward quality improvement in medicine?  One reason is that as we have seen, Medicare, as a federally administered health insurance program, has dominated much of the development and utilization of quality measures.  Although public insurance pays for nearly half (41% in 2003) of all births in the U.S., the program responsible is Medicaid, which is administered at the state level.  Until recently, states have not invested much in measuring quality or coordinating their efforts, but this is changing rapidly for two reasons.  First, in an economic climate with declining state revenues, administrators of Medicaid programs are increasingly seeking reliable ways to assess quality of care to justify expenditures.  Second, the 2010 Patient Protection and Affordable Care Act (PPACA) included several provisions relevant to childbearing women.  The one most applicable to this issue is Section 2701, which provides a directive to develop a health care quality measurement program for adult beneficiaries of Medicaid.  <Section 2701 of PPACA notes that “not later than January 1, 2011, the Secretary shall identify and publish for comment a recommended core set of adult health quality measures for Medicaid eligible adults.”> While not explicitly identifying maternal quality measures, it is reasonable to think that since Medicaid covers a significant proportion of US births, and childbearing women comprise a significant adult population within Medicaid, this is an opportunity for the newly adopted quality measures in maternity care to be used for evaluation of Medicaid programs covering maternity benefits.

So now you have a sense of the historical backdrop, in Part 2 we will review the many ways to look at quality and the organizations involved, and in Part 3 we will review the maternity quality measures in greater detail.   A great resource for this is the Childbirth Connection webinar by Dr. Elliott Main of the California Maternal Quality Care Collaborative (Christine’s colleague).  If you listen to this presentation before the next posts, you’ll have done your homework!

Posted by:  Christine Morton, PhD (CMQCC) and Kathleen Pine, (University of California, Irvine)



Berwick, D. M. (1989). Continuous improvement as an ideal in health care. New England Journal of Medicine, 320(1), 53-56.

Berwick, D. M., James, B., & Coye, M. J. (2003). Connections between quality measurement and improvement. Medical Care, 41(1), I30-I38.

Wiener, C. L. (2000). The Elusive Quest: Accountability in Hospitals. New York: Walter de Gruyter.

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