Variation in effective care Variation in effective care reflects differences in technical quality, i.e., in care that has been shown to be beneficial with few tradeoffs. High technical quality of medical services is identified as care with good scientific evidence of improved health outcomes. The Agency for Healthcare Research and Quality (AHRQ) describes quality measures as follows: “Quality measurements typically focus on structures or processes of care that have a demonstrated relationship to positive health outcomes and are under the control of the health care system… Health care quality measurement for children is the process of using a scientifically sound tool to assess the extent to which children are receiving quality health care in any of the IOM quality domains.”38 The “right rate” of effective care is usually known for a given population. Immunization for Hepatitis B is one perinatal example where the ideal rate should approach 100%. The rate of late sepsis (blood stream infection) or meningitis in very low birth weight newborns is another example; a lower rate is obviously better. Technical quality measures are limited in neonatal care,8 and some measures require clinical data that are not widely available. Variation in preference-sensitive care Preference-sensitive care refers to medical care for which the choice of treatment should reflect an informed patient or family decision, weighing the balance of possible benefits and harms for the different care options. For this type of medical care, there is no single “right rate” for every population or area. The right rate would reflect the decisions of fully informed patients and families, reached through a process of shared decision-making. It would be expected that care choices would differ across families and, in turn, across regions. The result would be variation warranted by patient and family preferences. A REPORT OF THE DARTMOUTH ATLAS PROJECT 9 The original analyses that led to this concept were studies of adult men facing treatment choices for benign prostatic hyperplasia,39 a decidedly non-perinatal problem. Most of the research in decision quality and shared decision-making has been for adult conditions, ranging from lower back pain to early stage breast cancer in women. Decision aids40 have been developed to assist patients and clinicians in choosing care that is consistent with the patient’s values. The introduction of decision aids usually—but not always—reduces utilization rates. Even when the overall rate remains unchanged, decision aids improve outcomes by ensuring that the right care is (or is not) provided to the right patient. A list of available decision aids and their sources can be found at the Ottawa Hospital Research Institute website.41 These differ greatly in quality, and only a few are available for pediatric illness. Decision aids for newborn care are limited to those discussing breastfeeding, circumcision, and the care of extremely premature newborns. Variation in supply-sensitive care Supply-sensitive care refers to medical services for which utilization rates are sensitive to the local availability of health care resources, such as hospital or intensive care beds, imaging units (e.g., MRI scanners), and physicians. While in some instances, effective care may be constrained by the lack of resources, this category is principally concerned with the many types of medical care for which there is weak theory and little evidence that more services are generally better. In such situations, regions with a greater supply of health care resources tend to have higher utilization rates—but not necessarily improved outcomes. Generally, the “right rate” is the lowest rate consistent with favorable outcomes. While this is a category of variation that has been studied extensively for adult patients, little research has been conducted in children’s health care.42 Variation in health care capacity Studies have shown striking population-based variation in pediatric and NICU health care capacity such as hospital beds, intensive care unit beds, and other specialized resources. Several studies have shown marked variation in the per capita (e.g., per child or newborn) number of general pediatricians and neonatologists.43,44 Pediatric capacity is generally not located where needs are greater. Chang et al showed a lack of association between general pediatrician supply and indicators of child health needs across states.45 Mayer observed a very high degree of variation across Dartmouth Atlas hospital referral regions for different pediatric subspecialists,46 and Goodman et al found little relationship between the supply of neonatologists and regional differences in perinatal risk.44 The irrational distribution of pediatric capacity has important implications for the health care system and the health of children and families. A Report of the Dartmouth Atlas Project 10 DARTMOUTH ATLAS OF NEONATAL INTENSIVE CARE A Preview… The report begins with two studies of the U.S. total birth cohort and then examines regional and hospital variation in NICU admissions, number of special care days, and imaging in different newborn populations. The magnitude of variation is strikingly high and is not explained by differences in newborn health status. The concluding section discusses the implications of these findings for families, health systems, and government and private payers. A REPORT OF THE DARTMOUTH ATLAS PROJECT 11 References 1.