Measuring efficiency of community health centers: a multi-model approach considering quality of care and heterogeneous operating environments.

Over 1300 federally-qualified health centers (FQHCs) in the US provide care to vulnerable populations in different contexts, addressing diverse patient health and socioeconomic characteristics. In this study, we use data envelopment analysis (DEA) to measure FQHC performance, applying several techniques to account for both quality of outputs and heterogeneity among FQHC operating environments. To address quality, we examine two formulations, the Two-Model DEA approach of Shimshak and Lenard (denoted S/L), and a variant of the Quality-Adjusted DEA approach of Sherman and Zhou (denoted S/Z). To mitigate the aforementioned heterogeneities, a data science approach utilizing latent class analysis (LCA) is conducted on a set of metrics not included in the DEA, to identify latent typologies of FQHCs. Each DEA quality approach is applied in both an aggregated (including all FQHCs in a single DEA model) and a partitioned case (solving a DEA model for each latent class, such that an FQHC is compared only to its peer group). We find that the efficient frontier for the aggregated S/L approach disproportionately included smaller FQHCs, whereas the aggregated S/Z approach's reference set included many larger FQHCs. The partitioned cases found that both the S/L and S/Z aggregated models disproportionately disfavored (different) members of certain classes with respect to efficiency scores. Based on these results, we provide general insights into the trade-offs of using these two models in conjunction with a clustering approach such as LCA.

Expanding Strategic Opportunities in Nonprofits: Mapping the Interdependencies of Critical Performance Variables.

In this article, we demonstrate the method of participatory causal modeling to map the interdependencies of critical performance variables in a complex nonprofit health care provider with considerable financial and operational control challenges. Critical performance variables are output performance dimensions that are fundamental indicators of organizational success. Causal modeling provides an approach for nonprofit leaders to examine how critical performance variables dynamically and recursively affect each other and thereby offers a path to identify key points of leverage for organizational action. Using a case study, we show that participatory system dynamics modeling revealed assumptions, choices, and complexities and so helped a nonprofit health care organization recognize possible strategic opportunities. This study demonstrates an approach that other nonprofits may deploy in situations where they are experiencing competing objectives and constraints in managing critical performance variables.

Endoscopic Necrosectomy Through Percutaneous Self-Expanding Metal Stents May Be a Promising Additive in Treatment of Necrotizing Pancreatitis.

BACKGROUND: The recommended treatment of infected walled-off necrosis (WON) in necrotizing pancreatitis entails a step-up treatment approach starting with endoscopic necrosectomy (ETDN). AIMS: To report a small number of cases from 2013 to 2016 that were not amenable to or failed to respond to ETDN, and to describe a new, minimally invasive technique that may be a promising supplement to ETDN in this difficult patient population. METHODS: Using the Seldinger technique, a fully covered self-expanding metal stent (SEMS) was placed percutaneously in order to drain, irrigate, and debride WON. After resolution, the stent was removed. We reviewed electronic patient records and defined clinical success as complete WON resolution with removal of internal as well as percutaneous drains and stents. RESULTS: Five patients underwent treatment with SEMS placement. The mean length of the WON was 33.4 cm. Clinical success was achieved in four patients after an average of 5.75 necrosectomy sessions. One patient died from severe sepsis. Adverse events included severe abdominal pain and productive cutaneous fistulae (two patients). CONCLUSIONS: In our small case series, endoscopic necrosectomy through a percutaneous SEMS seemed beneficial and safe in the treatment of infected WON.

American Indians travel great distances for obstetrical care: Examining rural and racial disparities.

Rural, American Indian/Alaska Native (AI/AN) people, a population at elevated risk for complex pregnancies, have limited access to risk-appropriate obstetric care. Obstetrical bypassing, seeking care at a non-local obstetric unit, is an important feature of perinatal regionalization that can alleviate some challenges faced by this rural population, at the cost of increased travel to give birth. Data from five years (2014-2018) of birth certificates from Montana, along with the 2018 annual survey of the American Hospital Association (AHA) were used in logistic regression models to identify predictors of bypassing, with ordinary least squares regression models used to predict factors associated with the distance (in miles) birthing people drove beyond their local obstetric unit to give birth. Logit analyses focused on hospital-based births to Montana residents delivered during this time period (n = 54,146 births). Distance analyses focused on births to individuals who bypassed their local obstetric unit to deliver (n = 5,991 births). Individual-level predictors included maternal sociodemographic characteristics, location, perinatal health characteristics, and health care utilization. Facility-related measures included level of obstetric care of the closest and delivery hospitals, and distance to the closest hospital-based obstetric unit. Findings suggest that birthing people living in rural areas and on American Indian reservations were more likely to bypass to give birth, with bypassing likelihood depending on health risk, insurance, and rurality. AI/AN and reservation-dwelling birthing people traveled significantly farther when bypassing. Findings highlight that distance traveled was even farther for AI/AN people facing pregnancy health risks (23.8 miles farther than White people with pregnancy risks) or when delivering at facilities offering complex care (14-44 miles farther than White people). While bypassing may connect rural birthing people to more risk-appropriate care, rural and racial inequities in access persist, with rural, reservation-dwelling AI/AN birthing people experiencing greater likelihood of bypassing and traveling greater distances when bypassing.

Nested-Solution Facility Location Models.

Classical facility location models can generate solutions that do not maintain consistency in the set of utilized facilities as the number of utilized facilities is varied. We introduce the concept of nested facility locations, in which the solution utilizing p facilities is a subset of the solution utilizing q facilities, for all i ≤ p < q ≤ j, given some lower limit i and upper limit j on r, the number of facilities that will be utilized in the future. This approach is demonstrated with application to the p-median model, with computational testing showing these new models achieve reductions in both average regret and worst-case regret when r ≠ p facilities are actually utilized.

Evaluating disparities in access to obstetric services for American Indian women across Montana.

PURPOSE: Pregnant women across the rural United States have increasingly limited access to obstetric care, especially specialty care for high-risk women and infants. Limited research focuses on access for rural American Indian/Alaskan Native (AIAN) women, a population warranting attention given persistent inequalities in birth outcomes. METHODS: Using Montana birth certificate data (2014-2018), we examined variation in travel time to give birth and access to different levels of obstetric care (i.e., the proportion of individuals living within 1- and 2-h drives to facilities), by rurality (Rural-Urban Continuum Code) and race (White and AIAN people). FINDINGS: Results point to limited obstetric care access in remote rural areas in Montana, especially higher-level specialty care, compared to urban or urban-adjacent rural areas. AIAN women traveled significantly farther than White women to access care (24.2 min farther on average), even compared to White women from similarly rural areas (5-13 min farther, after controlling for sociodemographic characteristics, risk factors, and health care utilization). AIAN women were 20 times more likely to give birth at a hospital without obstetric services and had less access to complex obstetric care. Poor access was particularly pronounced among reservation-dwelling AIAN women. CONCLUSIONS: It is imperative to consider racial disparities and health inequities underlying poor access to obstetric services across rural America. Current federal policies aim to reduce maternity care professional shortages. Our findings suggest that racial disparities in access to complex obstetric care will persist in Montana unless facility-level infrastructure is also expanded to reach areas serving AIAN women.

Diabetes management at community health centers: Examining associations with patient and regional characteristics, efficiency, and staffing patterns.

A major source of primary health care for millions of Americans, community health centers (CHCs) act as a key point of access for diabetes care. The ability of a CHC to deliver high quality care, that supports patients' management of their diabetes, may be impacted by the unique set of resources and constraints it faces, both in terms of characteristics of its patient population and aspects of operations. This study examines how patient and regional characteristics, staffing patterns, and efficiency were associated with diabetes management at CHCs (percentage of patients with uncontrolled diabetes, HbA1C > 9%). Data on a sample of 1229 CHCs came from multiple sources. CHC-level information was obtained from the Uniform Data System and regional-level information from the Behavioral Risk Factor Surveillance System and the US Census American Community Survey. A clustering methodology, latent class analysis, identified seven underlying staffing patterns at CHCs. Data envelopment analysis was performed to evaluate the efficiency of CHCs, relative to centers with similar staffing patterns. Finally, generalized linear models were used to examine the association between staffing patterns, efficiency, and patient and regional-level characteristics. Findings from this study have sociological, practical, and methodological implications. Findings highlight that the intersection of patient racial composition with regional racial composition is significant; diabetes control appears to be worse at CHCs serving racial minorities living in predominantly White areas. Findings suggest that CHCs that incorporate more behavioral health care into their staffing mix have lower rates of uncontrolled diabetes among their patients. Finally, greater efficiency in CHC operations is associated with better diabetes control among patients. By identifying sociodemographic and operational characteristics associated with better hemoglobin control among diabetes patients, the current study contributes to our understanding of both health care operations and health inequalities.

Operational efficiency, patient composition and regional context of U.S. health centers: Associations with access to early prenatal care and low birth weight.

Community health centers (CHCs) provide comprehensive medical services to medically under-served Americans, helping to reduce health disparities. This study aimed to identify the unique compositions and contexts of CHCs to better understand variation in access to early prenatal care and rates of low birth weights (LBW). Data include CHC-level data from the Uniform Data System, and regional-level data from the US Census American Community Survey and Behavioral Risk Factor Surveillance System. First, latent class analysis was conducted to identify unobserved subgroups of CHCs. Second, data envelopment analysis was performed to evaluate the operational efficiency of CHCs. Third, we used generalized linear models to examine the associations between the CHC subgroups, efficiency, and perinatal outcomes. Seven classes of CHCs were identified, including two rural classes, one suburban, one with large centers serving poor minorities in low poverty areas, and three urban classes. Many of these classes were characterized by the racial compositions of their patients. Findings indicate that CHCs serving white patients in rural areas have greater access to early prenatal care. Health centers with greater efficiency have lower rates of LBW, as do those who serve largely white patient populations in rural areas. CHCs serving poor racial minorities living in low-poverty areas had particularly low levels of access to early prenatal care and high rates of LBW. Findings highlight that significant diversity exists in the sociodemographic composition and regional context of US health centers, in ways that are associated with their operations, delivery of care, and health outcomes. Results from this study highlight that while the provision of early prenatal care and the efficiency with which a health center operates may improve the health of the women served by CHCs and their babies, the underlying social and economic conditions facing patients ultimately have a larger association with their health.