OBJECTIVE—We evaluated the association of different types of educational visits for diabetic patients of the eight Philadelphia Health Care Centers (PHCCs) (public safety-net primary care clinics), with hospital admission rates and charges reported to the Pennsylvania Health Care Cost Containment Council. RESEARCH DESIGN AND METHODS—The study population included 18,404 patients who had a PHCC visit with a diabetes diagnosis recorded between 1 March 1993 and 31 December 2001 and had at least 1 month follow-up time. RESULTS—A total of 31,657 hospitalizations were recorded for 7,839 (42.6%) patients in the cohort. After adjustment for demographic variables, baseline comorbid conditions, hospitalizations before the diabetes diagnosis, and number of other primary care visits, having had any type of educational visit was associated with 9.18 (95% CI 5.02–13.33) fewer hospitalizations per 100 person-years and $11,571 ($6,377 to $16,765) less in hospital charges per person. Each nutritionist visit was associated with 4.70 (2.23–7.16) fewer hospitalizations per 100 person-years and a $6,503 ($3,421 to $9,586) reduction in total hospital charges. CONCLUSIONS—Any type of educational visit was associated with lower hospitalization rates and charges. Nutritionist visits were more strongly associated with reduced hospitalizations than diabetes classes. Each nutritionist visit was associated with a substantial reduction in hospital charges, suggesting that providing these services in the primary care setting may be highly cost-effective for the health care system.

Objective. To use an advance in data envelopment analysis (DEA) called congestion analysis to assess the trade-offs between quality and efficiency in U.S. hospitals. Study Setting. Data Analysis. In the first step of the study, hospitals' relative output-based efficiency was determined in order to obtain a measure of congestion (i.e., the productivity loss due to the occurrence of patient safety events). The outputs were adjusted to account for this productivity loss, and a second DEA was performed to obtain input slack values. Differences in slack values between unadjusted and adjusted outputs were used to measure either relative inefficiency or a need for quality improvement. Principal Findings. Overall, the hospitals in our sample could increase the total amount of outputs produced by an average of 26 percent by eliminating inefficiency. About 3 percent of this inefficiency can be attributed to congestion. Analysis of subsamples showed that teaching hospitals experienced no congestion loss. We found that quality of care could be improved by increasing the number of labor inputs in low-quality hospitals, whereas high-quality hospitals tended to have slack on personnel. Conclusions. Results suggest that reallocation of resources could increase the relative quality among hospitals in our sample. Further, higher quality in some dimensions of care need not be achieved as a result of higher costs or through reduced access to health care.Key Words. Hospital efficiency, data envelopment analysis, congestion, patient safety, nurse-sensitive outcomesMuch is yet to be learned about the interaction of cost, efficiency, and quality. It is not clear that cost containment and quality improvement are mutually consistent objectives. Quality improvement can result in greater resource use because it may require more or better resources. Yet, proponents of total No claim to original U.S. government works r Health Research and Educational Trust

These findings suggest the need to further explore work among informal caregivers and associations with emotional stress, as well as consider work-based policy approaches, organizational and/or societal, to support informal caregivers.

This paper applies a new methodology to the study of hospital efficiency and quality of care. Using a data set of hospitals from several states, we jointly evaluate desirable hospital patient care output (e.g., patient stays) and the simultaneous undesirable output (e.g., risk-adjusted patient mortality) that occurs. With a DEA based approach under two different sets of assumptions, we are able to include multiple quality indicators as outputs. The results show that lower technical efficiency is associated with poorer risk-adjusted quality outcomes in the study hospitals. They are consistent with other studies linking poor quality outcomes to higher cost.

The cost, technical, allocative and scale efficiencies of a sample of rural U.S. hospitals are calculated via linear programming models. Tobit analysis is used to assess possible correlates of each of the efficiency measures. A large amount of dispersion in operating efficiency is found within our data set; the majority of the dispersion is due to technical inefficiency, In general, for-profit hospitals are found to outperform not-for-profit and public hospitals, Demand characteristics, quality of care, and the mix of services offered are also found to influence performance.