Determinants of Nursing Home Performance: Examining the Relationship Between Quality and Efficiency

Determinants of nursing home performance: examining the relationship between quality and efficiency By Nailya O. DeLellis, MPH, Ph.D. A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at Virginia Commonwealth University. Virginia Commonwealth University, 2010 Director: Dr. Yasar Ozcan, Professor, Department of Health Administration To assess the relationship between quality of care and efficiency of nursing homes this study used 10% random sample of non-hospital based nursing homes of size 20-360 beds and occupancy rate of 5-100% in OSCAR database 2008 (n=1430). Data Envelopment Analysis was used to calculate efficiency score and Structural Equations Modeling was used to assess the effect of environmental factors on efficiency score and quality measures as well as relationship between efficiency and quality of care. Logistic regression was performed to find the factors that affect high performance, defined as high efficiency and high quality. In the study’s sample, 149 facilities (10.4%) had an efficiency score of 1, which indicates perfect efficiency. The average efficiency score of nursing homes in the sample was 0.854 (0.079 min; 0.145 std). Competition positively affects efficiency, with a path coefficient 0.09 (t-value = 2.65). Although the path coefficients relating competition with process and with outcome quality were positive (0.08 and 0.04, respectively), the results were not statistically significant. Stronger position of payers in the market positively affects process quality of care (path coefficient = 0.15, (t-value = 2.48). Higher efficiency of nursing homes is associated with higher outcome quality (path coefficient of 0.06, t-value = 1.99), but lower process quality (path coefficient of –0.20 , t-value = –2.95). Only 7.4% of nursing homes in the sample could efficiently provide high quality services, which was defined as high performance in the study. Among the factors that demonstrated statistically significant coefficients in the regression were the size of a facility, the availability of registered nurses, excess demand, and for-profit status. The study provides evidence of the trade-off between efficiency and process quality, in which higher efficiency of a nursing home is associated with lower process quality of care. Findings in the study also suggested that higher efficiency is associated with higher outcome quality

Environmental Health Program Performance and its Relationship with Environment-Related Disease in Florida

This study used a unique approach to examine Florida county health department environmental health (EH) program performance of the 10 Essential Environmental Public Health Services (EEPHS) and its relationship with environment- related disease, described by enteric disease rates. Correlation analysis tested the association between performance of each EEPHS and five different enteric disease rates, while multivariate regression analysis further examined the relationships while considering program organizational characteristics as potential confounders. Correlation analysesrevealed cryptosporidiosis was associated with EEPHS 2 diagnose (Τb = .195, p = .027) and EEPHS 8 workforce (Τb= .234, p = .006), and salmonellosis with EEPHS 4 mobilize (Τb = .179, p = .042) and EEPHS 6 enforce (Τb = .201, p = .020). Multivariate regression results showed EEPHS 2 diagnose (p = .04) and EEPHS 4 mobilize (p = .00) had statistically significant associations with cryptosporidiosis and salmonellosis, respectively, and suggested that improved performance of these two EEPHS may have decreased disease incidence. EH programs may benefit from improving the performance of EEPHS to address the incidence of certain enteric diseases. Continued efforts to develop a robust understanding of EH program performance and its impact on environment-related disease could enhance EH services delivery and ability to improve health outcomes

A Consumer Health Information System to Assist Patients Select Quality Home Health Services

Patients evaluate the quality of home health agencies (HHAs) using the Health Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey. This paper describes a prototype community health information system to help patients select appropriate and quality HHAs, according to the location, proprietary status, type of service, and year of HHA establishment. Five HCAHPS indicators were selected: “summary rating”, “quality of care”, “professional care”, “communication”, and “recommend agency”. Independent t-test analysis showed that agencies offering Speech Pathology, Medical-Social, or Home Health Aide services, receive significantly worse HCAHPS ratings, while mean ratings vary significantly across different US states. Multiple comparisons with post hoc ANOVA revealed differences between and within HHAs of different proprietary status (p < 0.001): governmental HHAs receiving higher ratings than private HHAs. Finally, there was observed a relationship between all five quality rating variables and the HHA year of establishment (Pearson, p < 0.001). The older the agency is, the better the HCAPS summary ratings. Findings provided the knowledge to design of a consumer health information system, to provide rankings filtered according to user criteria, comparing the quality rankings of eligible HHAs. Users can also see how a specific agency is ranked against eligible HHAs. Ultimately, the system aims to support the patient community with contextually realistic comparisons in an effort to choose optimal HH service

CDSS-RM: a clinical decision support system reference model

Abstract Clinical Decision Support Systems (CDSS) provide aid in clinical decision making and therefore need to take into consideration human, data interactions, and cognitive functions of clinical decision makers. The objective of this paper is to introduce a high level reference model that is intended to be used as a foundation to design successful and contextually relevant CDSS systems. The paper begins by introducing the information flow, use, and sharing characteristics in a hospital setting, and then it outlines the referential context for the model, which are clinical decisions in a hospital setting. Important characteristics of the Clinical decision making process include: (i) Temporally ordered steps, each leading to new data, which in turn becomes useful for a new decision, (ii) Feedback loops where acquisition of new data improves certainty and generates new questions to examine, (iii) Combining different kinds of clinical data for decision making, (iv) Reusing the same data in two or more different decisions, and (v) Clinical decisions requiring human cognitive skills and knowledge, to process the available information. These characteristics form the foundation to delineate important considerations of Clinical Decision Support Systems design. The model includes six interacting and interconnected elements, which formulate the high-level reference model (CDSS-RM). These elements are introduced in the form of questions, as considerations, and are examined with the use of illustrated scenario-based and data-driven examples. The six elements /considerations of the reference model are: (i) Do CDSS mimic the cognitive process of clinical decision makers? (ii) Do CDSS provide recommendations with longitudinal insight? (iii) Is the model performance contextually realistic? (iv) Is the ‘Historical Decision’ bias taken into consideration in CDSS design? (v) Do CDSS integrate established clinical standards and protocols? (vi) Do CDSS utilize unstructured data? The CDSS-RM reference model can contribute to optimized design of modeling methodologies, in order to improve response of health systems to clinical decision-making challenges

Comparison of the Predictive Performance of Medical Coding Diagnosis Classification Systems

Health analytics frequently involve tasks to predict outcomes of care. A foundational predictor of clinical outcomes is the medical diagnosis (Dx). The most used expression of medical Dx is the International Classification of Diseases (ICD-10-CM). Since ICD-10-CM includes >70,000 codes, it is computationally expensive and slow to train models with. Alternative lower-dimensionality alternatives include clinical classification software (CCS) and diagnosis-related groups (MS-DRGs). This study compared the predictive power of these alternatives against ICD-10-CM for two outcomes of hospital care: inpatient mortality and length of stay (LOS). Naïve Bayes (NB) and Random Forests models were created for each Dx system to examine their predictive performance for inpatient mortality, and Multiple Linear Regression models for the continuous LOS variable. The MS-DRGs performed highest for both outcomes, even outperforming ICD-10-CM. The admitting ICD-10-CM codes were, surprisingly, not underperformed by the primary ICD-10-CM Dxs. The CCS system, although having a much lower dimensionality than ICD-10-CM, has only slightly lower performance while the refined version of CCS only slightly outperformed the old CCS. Random Forests outperformed NB for MS-DRG, and ICD-10-CM, by a large margin. Results can provide insights to understand the compromise from using lower-dimensionality representations in clinical outcome studies