Job Satisfaction in the Home Health Care Context: Validating a Customized Instrument for Application, Grant B. Morgan, John J. Sherlock, EdD and William J. Ritchie, PhD
The purpose of this study is to present the development and validation process of a customized job satisfaction instrument within the home health care setting. During development, home health care agency directors selected items from preexisting scales to create a new 15-item instrument designed to measure job satisfaction, supervisory relationships, and job environment. The instrument’s reliability and validity were examined using a two-step approach. First, exploratory factor analysis (EFA) was carried out with a sample of 398 aides, and three factors were identified. Second, confirmatory factor analysis was conducted with an independent sample of 328 aides using the three-factor solution that was selected from the EFA. The reliability estimates for the job satisfaction, supervisory relationships, and job environment scales were 0.84, 0.93, and 0.74, respectively.
Results from this study yield a statistically sound instrument that offers potential advantages to those interested in better understanding job satisfaction and two related constructs of employees in health-related fields. Both research and practical implications are discussed. The final instrument will be a valuable tool for collecting information from home health care workers, a population that has historically been difficult to access and measure.
How Patient Reactions to Hospital Care Attributes Affect the Evaluation of Overall Quality of Care, Willingness to Recommend, and Willingness to Return, Koichiro Otani, PhD, Brian Waterman, Kelly M. Faulkner, Sarah Boslaugh, PhD, and Clairborne Dunagan, MD
Patient satisfaction is a critical part of the quality outcomes of healthcare. Every industry is interested in customer satisfaction because satisfied customers are loyal customers. Healthcare is no exception. Many research studies assume that satisfied patients are more likely to recommend their providers to their friends and to return when they need care again. Although this assumption sounds logical, we argue that three dependent variables—the Evaluation of Overall Quality of Care, Willingness to Recommend, and Willingness to Return—are unique constructs. Thus, we examine how patient reactions (experiences) to different hospital care attributes (factors or dimensions) influence these dependent variables.
Our study analyzed a comprehensive patient satisfaction data set collected by BJC HealthCare. We used a multiple linear regression model with a scatter term to analyze 14,432 cases. In Evaluation of Overall Quality of Care model, we found that the nursing care attribute showed the strongest influence, followed by staff care. In assessing the other two models—Willingness to Recommend and Willingness to Return—we found that staff care showed the strongest influence, followed by nursing care. Patients put a different emphasis or a different priority on their reactions to hospital care attributes, depending on which outcome they arrive at. In addition, we found that patients are disproportionately influenced by a weak or poor attribute reaction, which is a conjunctive strategy (risk averse). In general, nursing care and staff care should be the first priority for improvement. This may be good news because these areas are under the control of hospital managers.
How Healthcare Organizations Use the Internet to Market Quality Achievements, Lee Revere, PhD, and Leroy Robinson, Jr., PhD
The increasingly competitive environment is having a strong bearing on the strategic marketing practices of hospitals. The Internet is a fairly new marketing tool, and it has the potential to dramatically influence healthcare consumers. This exploratory study investigates how hospitals use the Internet as a tool to market the quality of their services.
Significant evidence exists that customers use the Internet to find information about potential healthcare providers, including information concerning quality. Data were collected from a random sample of 45 U.S. hospitals from the American Hospital Association database. The data included hospital affiliation, number of staffed beds, accreditation status, Joint Commission quality awards, and number of competing hospitals. The study’s findings show that system-affiliated hospitals do not provide more, or less, quality information on their websites than do non–system-affiliated hospitals.
The findings suggest that the amount of quality information provided on a hospital website is not dependent on hospital size. Research provides evidence that hospitals with more Joint Commission awards promote their quality accomplishments more so than their counterparts that earned fewer Joint Commission awards. The findings also suggest that the more competitors in a marketplace the more likely a hospital is to promote its quality as a potential differential advantage. The study’s findings indicate that a necessary element of any hospital’s competitive strategy should be to include the marketing of its quality on the organization’s website.
Using Existing Case-Mix Methods to Fund Trauma Cases, Julia Monakova, Irene Blais, Charles Botz, PhD, Yuriy Chechulin, MD, Gino Picciano, and Antoni Basinski, PhD, MD
Policymakers frequently face the need to increase funding in isolated and frequently
heterogeneous (clinically and in terms of resource consumption) patient subpopulations.
This article presents a methodologic solution for testing the appropriateness
of using existing grouping and weighting methodologies for funding subsets of
patients in the scenario where a case-mix approach is preferable to a flat-rate based
payment system. Using as an example the subpopulation of trauma cases of Ontario
lead trauma hospitals, the statistical techniques of linear and nonlinear regression
models, regression trees, and spline models were applied to examine the fit of the
existing case-mix groups and reference weights for the trauma cases.
The analyses demonstrated that for funding Ontario trauma cases, the existing
case-mix systems can form the basis for rational and equitable hospital funding,
decreasing the need to develop a different grouper for this subset of patients. This
study confirmed that Injury Severity Score is a poor predictor of costs for trauma
patients. Although our analysis used the Canadian case-mix classification system
and cost weights, the demonstrated concept of using existing case-mix systems to
develop funding rates for specific subsets of patient populations may be applicable