The Impact of Self-Rated Health Status on Patient Satisfaction Integration Process.

The purpose of this study was to investigate how patients' self-rated health status (SRHS) is associated with their attribute reaction integration process and, in turn, their overall ratings of hospitals. We collected patient satisfaction data from 70 hospitals by means of a patient satisfaction questionnaire. The sample included patients who were 18 years or older and discharged from the hospital from July 1, 2011, through June 30, 2012. Data for 36,528 patients were available for analysis. We conducted multiple linear regression analysis with patients' SRHS and interaction effects with nursing care, physician care, staff care, and room, while controlling for age, gender, race, and education. Study findings showed an association between SRHS levels and the patient's overall rating of the hospital; they also revealed interaction effects with nursing care, physician care, and staff care variables in the model. The statistically significant interaction effects indicate that for patients whose SRHS was less than excellent, physician care became more important and nursing care and staff care became less important compared with patients whose SRHS was excellent. When we consider the nature of medical care, this transition seems reasonable. We also found that it is reasonable to categorize patients into two groups: those whose SRHS is excellent and those whose SRHS is less than excellent (i.e., very good, good, fair, or poor). As the study findings show, these two groups of patients combined their attribute reactions differently.

Evaluating the influence of perceived pain control on patient satisfaction in a hospital setting.

The authors evaluated whether a patient's perceived pain control influenced the relationships between four attributes (nursing, physician, staff, and environment) and patient satisfaction. Multiple linear regression analyses were conducted to examine overall satisfaction and intention to recommend, controlling for race, gender, age, and education. The authors found that no matter the level of pain control, nursing was always the most influential attribute in patient satisfaction. The influence of the other attributes varied, depending on the patients' pain control. Hospital managers may improve patient satisfaction by focusing on pain management nursing care.

Increasing patient satisfaction: a new model development.

Most patient satisfaction studies put an emphasis on finding key drivers (attribute) to increase overall patient satisfaction. However, it is not clear how much health care managers need to improve certain attributes to attain the target overall patient satisfaction level. The study aims at finding not only what attributes, but also how much these attributes need to be improved to attain the target levels of patient satisfaction. The study uses an ordinal logistic regression model to analyze attribute reactions to salient drivers. This approach would significantly enhance health care managers' capabilities to develop a strategic plan to improve their patient satisfaction levels.

Patient satisfaction: how patient health conditions influence their satisfaction.

With increasing emphasis in healthcare on patient satisfaction, many patient satisfaction studies have been administered. Most assume that all patients combine their healthcare experiences (such as nursing care, physician care, etc.) in the same way to arrive at their satisfaction; however, no research has been conducted prior to the present study to investigate how patients' health conditions influence the way they combine their healthcare experiences. This study aims to determine how seriously ill patients differ from less seriously ill patients during their combining process. Data were collected from five large hospitals in the St. Louis area by administering a patient satisfaction questionnaire. Multiple linear regression analyses with a scatter term, a severity measure, and interaction effects of the severity measure were conducted while controlling for age, gender, and race. Two models (overall quality of care and willingness to recommend to others) were analyzed, and the severity of illness variable revealed interaction effects with physician care, staff care, food, and scatter term variables in the willingness to recommend model (six attributes were analyzed: admission process, nursing care, physician care, staff care, food, and room). With more seriously ill patients, physician care becomes more important and staff care becomes less important, and seriously ill patients are proportionately more likely to combine their attribute reactions only in the willingness to recommend model. All six attributes are not equally influential. Nursing care and staff care show consistent influence in both models. These findings show that if healthcare managers want to increase their patient satisfaction, they should enhance nursing care and staff care first to experience the most improvement.

Patient satisfaction and organizational impact: a hierarchical linear modeling approach.

This article presents the rationale for using multilevel analysis to address the broad environmental contexts in patient satisfaction research. This study utilized patient satisfaction data and the American Hospital Association Hospital Guide Book (2004). This study found significant contributions of individual patient attribute reactions (nursing care, physician care, etc.), and also clearly demonstrated hospital-level effects and cross-level interactions on patient satisfaction. Thus, it is clear that patient satisfaction is not solely explained by patients' attribute reactions and their demographic variables, but is also explained by patients' hospital levels. This approach would offer additional understanding in patient satisfaction research.

Factors Impacting Patients' Willingness to Recommend: A Structural Equation Modeling Approach.

Patient ratings of inpatient stay have been the focus of prior research since better patient satisfaction results in a financial benefit to hospitals and are associated with better patient health care outcomes. However, studies that simultaneously account for within- and between-hospital effects are uncommon. We constructed a multilevel structural equation model to identify predictors of patients' willingness to recommend a hospital at both within-hospital and between-hospital levels. We used data from 60 U.S. general medical and surgical hospitals and 12,115 patients. Multilevel structural equation modeling reported that patient ratings on the overall quality of care significantly affect the willingness to recommend within hospitals. Also, patients' perspectives on the hospital environment and nursing are the significant factors that predict the patient ratings on the overall quality of care. Overall patient satisfaction significantly predicts the willingness to recommend at the between-hospital level, whereas hospital size and location have marginal impacts.

Inpatients' willingness to recommend: a multilevel analysis.

BACKGROUND: Satisfaction with health care is one of the most widely assessed measures of hospital care quality, yet studies that account for clustering effects are uncommon. We constructed a multilevel model to identify predictors of willingness to recommend while controlling for clustering effects due to hospital and care unit. We also examined differences in predictors by care unit. PURPOSE: The aim of this study was to identify factors that both influence patient perceptions of care and are potentially modifiable by the hospital delivering care. METHODOLOGY: Our sample includes Hospital Consumer Assessment of Healthcare Providers and Systems survey data collected between July 1, 2007, and June 30, 2008, for 131 hospitals and 33,445 patients. The primary outcome was willingness to recommend the hospital to family and friends. Variables were collected at three levels: patient (Hospital Consumer Assessment of Healthcare Providers and Systems survey item responses and demographics), care unit, and hospital. Data were analyzed using multilevel modeling. We also ran a series of two-level models to explore differences in predictors by care type. FINDINGS: The strongest predictors of willingness to recommend, controlling for clustering effects, were items that generally reflected interpersonal aspects of care such as nursing and physician behaviors. In the two-level models, predictors of willingness to recommend overlapped across care units, but important differences were noted. PRACTICE IMPLICATIONS: Our results suggest that hospitals that wish to improve their performance would benefit most from focusing on interpersonal aspects of care. Hospitals that focus resources on improving in these areas, that assess care units separately, and that investigate the meaning and context of survey responses will be most likely to see improvements in satisfaction scores.

How patient reactions to hospital care attributes affect the evaluation of overall quality of care, willingness to recommend, and willingness to return.

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.

Patient satisfaction integration process: are there any racial differences?

BACKGROUND: Patient satisfaction studies have received strong attention from both health care managers and researchers. Many of them investigate how patients combine their health care attribute reactions to arrive at their overall satisfaction. These studies, however, did not specifically investigate a possible different combining process among different racial groups. Thus, these minority populations were statistically marginalized in the outcomes of the analyses. It means that parameter estimates of the attributes were assumed to be the same for all racial groups. PURPOSE: This study investigated a possible different combining process between Caucasian and African American patients. The objective of this study was to discover how differently Caucasian and African American patients combine their attribute reactions to arrive at their overall satisfaction. METHODOLOGY/APPROACH: Data used for this analysis were collected from 32 hospitals by using a patient satisfaction questionnaire, the Consumer Assessment of Healthcare Providers and Systems, Hospital version. There were 29,684 cases. A multiple linear regression analysis with a scatter term was conducted while controlling for age, gender, perceived health, and educational attainment. FINDINGS: Caucasian and African American patients recognize the same four attribute reactions (nursing care, staff care, physician care, and environment). However, when paying attention to the sizes of the parameter estimates (weights), these two groups show a clear difference. It is also found that Caucasian patients are risk averse. On the other hand, African American patients are likely to average out their attributes reactions proportionately. PRACTICE IMPLICATIONS: The critical differences found in this study provide a very important implication for health care managers developing an intervention program to improve patient satisfaction. A one-size-fits-all strategy would not work for all patients. Caucasian and African American patients are different, and each requires a different intervention program to improve their overall evaluation of their hospital stay.

What Factors Affect Patient Perceptions on Their Hospital Experience?

Patient experience has been one of many significant drivers in determining hospitals' performance. Multivariate logistic regression was used to investigate the factors that impacted patients' likelihood to give top ratings for the overall hospital experience. Large hospitals with a lower percentage of Medicare/Medicaid patients and existing electronic health record system were more likely to receive top ratings. White, female patients with lower education levels were more likely to give top ratings for all quality domains (nurse, doctor, staff, and environment). All quality domains had positive impacts on patient experience, with the nursing domain being the most influential factor.

Patient Satisfaction, Quality Attributes, and Organizational Characteristics: A Hierarchical Linear Model Approach.

Patient satisfaction studies have gained more and more attention, and there are many patient satisfaction studies. These studies assume that patients were selected randomly and independently, but patient satisfaction surveys are described as a multistage or hierarchically structured sample. Thus, there is a need to conduct a hierarchical linear model (HLM) analysis with a large number of hospitals. This study utilized an HLM to investigate both the individual patient-level effect on the overall satisfaction rating and the effect of hospital characteristics on the combining process of patient's overall satisfaction rating. This study used patient satisfaction data collected from 100 hospitals with the sample size of 85 766. The hospital-level characteristics include total expense per personnel, payroll expense per personnel, number of staffed beds per personnel, and number of admission per personnel. This study found that hospital characteristics influence overall rating of the hospital through the doctor, staff, and room attributes. When considering the complex nature of the overall patient rating process of hospitals, it makes more sense to analyze hospital characteristics that are interacting with attributes rather than treat hospital characteristics as independent of these factors.

Patient satisfaction: focusing on "excellent".

In an emerging competitive market such as healthcare, managers should focus on achieving excellent ratings to distinguish their organization from others. When it comes to customer loyalty, "excellent" has a different meaning. Customers who are merely satisfied often do not come back. The purpose of this study was to find out what influences adult patients to rate their overall experience as "excellent." The study used patient satisfaction data collected from one major academic hospital and four community hospitals. After conducting a multiple logistic regression analysis, certain attributes were shown to be more likely than others to influence patients to rate their experiences as excellent. The study revealed that staff care is the most influential attribute, followed by nursing care. These two attributes are distinctively stronger drivers of overall satisfaction than are the other attributes studied (i.e., physician care, admission process, room, and food). Staff care and nursing care are under the control of healthcare managers. If improvements are needed, they can be accomplished through training programs such as total quality management or continuous quality improvement, through which staff employees and nurses learn to be sensitive to patients' needs. Satisfying patients' needs is the first step toward having loyal patients, so hospitals that strive to ensure their patients are completely satisfied are more likely to prosper.

Different Combining Process Between Male and Female Patients to Reach Their Overall Satisfaction.

Patient satisfaction and how it is measured is an important field of study in healthcare. Consequently, there have been many patient satisfaction studies. However, there is a lack of studies that focused on how male and female patients perceive, or weigh, their overall satisfaction. Determining the weights of these attributes by gender is an important outcome to determine overall patient satisfaction and quality of care. Data were collected by applying the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey instrument to record regular patient experience at 70 United States hospitals. The data were collected between July 1, 2011 and June 30, 2012, and there are 43,938 cases in the data set. This study found that all five of the attributes (Nursing Care, Physician Care, Staff Care, Room, and Help) had an association with patient satisfaction. Among them, nursing Care had the largest influence on patient satisfaction of all of the five attributes. This study also showed a statistically significant interaction effect by gender for the interaction between Nursing care x Gender, Physician care x Gender, and Help x Gender. Female patients rated their relationship with their nurses as being more important to their overall satisfaction, while male patients rated their relationship with their doctor as being more important to overall satisfaction. Male patients also rated their relationship with help as being more important to overall satisfaction when compared with the results from female patients. Suggestions to healthcare managers are also discussed.

Managing primary care using patient satisfaction measures.

Our study aimed to identify which attributes of a primary healthcare experience have the most impact on patient satisfaction as well as which aspects of each attribute are most significant in patients' response to the services they receive. The three attributes examined in this study were access, staff care, and physician care. Analyses of the aspects of each attribute controlled for age, gender, and race. Data used in this study were obtained through a survey questionnaire with random sampling, resulting in the sample size of 8,465. The psychometric properties of the questionnaire were also examined and showed appropriate reliability and validity. The multiple regression analysis showed that among the three attributes, physician care was most influential, closely followed by staff care, with access having much less influence. Further analyses revealed that specific aspects of each attribute were more influential on patient satisfaction. Within the physician care attribute, patients were found to be rational consumers who were looking for surrogate indicators of correct diagnosis and treatment options among the measures available to them. They were much less likely to be influenced by so-called bedside manner. Within the staff care attribute, willingness and compassionate behaviors of staff and prompt service were most important. Within the access attribute, patients sought caring interaction with appointment personnel. After considering the findings, we discuss possible actions for healthcare managers.

Impact of Pain on Patient Satisfaction Integration Process: How Patients With Pain Combine Their Health Care Attribute Reactions.

CONTEXT: Health care environments have been changing rapidly, and one of the changes is to emphasize patient satisfaction. However, most studies assume that all patients integrate their health care attribute reactions in the same way to arrive at their satisfaction. OBJECTIVE: The objective of this study is to investigate how patients' experience of pain influences their attribute reaction integration process and their overall rating of the hospital. DESIGN: Patient satisfaction data were collected using a mailed questionnaire. Multiple linear regression analyses with a dichotomous (yes/no) pain variable and its interaction effects with nursing care, physician care, staff care, and hospital room were conducted with control variables. MAIN OUTCOME MEASURES: The pain variable was statistically significant and also revealed interaction effects with the physician care and the staff care variables in the model. Patients who needed medicine for pain showed lower overall rating of the hospitals than patients who did not need medicine. RESULTS: The statistically significant interaction effects indicate that for patients who needed medicine for pain, staff care becomes more important and physician care becomes less important compared to patients who do not need medicine for pain. All 4 attributes (nursing care, physician care, staff care, and hospital room) are not equally influential. CONCLUSION: Implementing policies and procedures related to these interaction effects would lead to the most efficient and effective improvement outcomes. These findings suggest that future policies should be modified to enhance nursing and staff care to provide more direct care for patients with pain.

A paradigm shift in patient satisfaction assessment.

The authors investigated the relationships between patients' reactions to health care attributes and their overall satisfaction with primary care. The study found the following: (1) patients' overall satisfaction levels are disproportionately influenced by low levels of their reactions (less satisfied) to the primary care attribute, rather than simply averaged out among attribute reactions. This is a noncompensatory relationship. (2) The marginal impact of primary care attributes on overall satisfaction decreases at higher levels of patients' reactions (more satisfied) to primary care attributes, indicating a nonlinear relationship. Patients combine their reactions to the health care attributes by means of noncompensatory and nonlinear models to form their overall satisfaction. Decision makers should selectively concentrate training resources on those areas of attributes showing high dissatisfaction rather than attempt to improve an attribute that showed the largest parameter estimate. This approach would not only save resources but result in better outcomes of patient satisfaction.

The impact of nursing care and other healthcare attributes on hospitalized patient satisfaction and behavioral intentions.

Healthcare organizations in the United States are struggling to find ways to survive in their uncertain and competitive environments. One of the survival strategies used by those organizations is to increase patient satisfaction. This article presents research on factors that influence hospitalized patients' satisfaction and their intention to return to and recommend the hospital. The first objective of this study was to find out, using a comprehensive set of healthcare attributes, which attributes play a more important role in increasing patient satisfaction and behavioral intentions. The second objective was to analyze the relative importance of those attributes and the nature of the relationships across the values of the attributes. More specifically, this study attempted to identify any existing curvilinear relationships among these variables. If any curvilinear relationships exist, do they show an increasing or a decreasing marginal-utility function? Included in this article is an example, featuring a hospital-discharged patient, that explains the importance and uniqueness of this curvilinear relationship. This study found that among six attributes, nursing care showed the largest parameter estimate for the patient satisfaction and behavioral intentions models. Thus, simply improving the nursing care attribute seems to be the most effective manner to enhancing patient satisfaction and behavioral intentions. However, nursing care also showed a diminishing marginal-utility function for both models. To assess the effect of this diminishing marginal-utility function, the impact of nursing care was computed for each unit of improvement together with other attributes. The finding from this study provides information needed to increase patient satisfaction and behavioral intentions and should result in more effective and efficient healthcare management.

Improving patient satisfaction in hospital care settings.

Health-care managers have to address many aspects of the organization, and patient satisfaction is clearly one of the critical aspects for managers. To respond to the need of health-care managers, there have been many patient satisfaction studies. However, these studies focus on which attributes (factors such as nursing care and physician care) are more influential; they do not provide specific aspects for each attribute. In order to develop an effective intervention programme to improve patient satisfaction, more specific research outcomes are needed. This study utilized data collected between January 2007 and June 2008 from 32 hospitals representing a large, national private not-for-profit hospital system. The patient satisfaction survey included the Consumer Assessment of Healthcare Providers and Systems, Hospital version questionnaire items, and there are 31,471 cases. Two-stage multiple linear regression analyses were conducted with control variables (age, gender, perceived health, education and race). It was found that patients' highest priority is to be treated with courtesy and respect by nurses and physicians. An effective intervention programme to improve patient satisfaction would include a training programme, where care providers understand that patients want them to show courtesy and respect. Then, well-trained and empathetic nurses and staff members can comfort patients, and consequently improve patient satisfaction.

Improving patient evaluation of hospital care and increasing their intention to recommend: are they the same or different constructs?

Many patient satisfaction studies consider patient satisfaction and intention to recommend as the same constructs. However, we propose to investigate the two constructs separately. This study has utilized patient satisfaction data with 32 hospitals in different locations to investigate how hospital discharged patients combine their attribute reactions to arrive at their evaluation of hospital care and their intention to recommend to others. A multiple linear regression analysis with a scatter term was conducted while controlling for age, gender, perceived health, educational attainment and race. The interpretation of the scatter term is presented with a story. There were 31,471 cases for the analysis, and the results show some similarities and critical differences between the two models. In conclusion, hospital managers are well advised to focus on improving nursing care first, and then staff care and environment in order to increase patient evaluation of hospital care and intention to recommend.

Healthcare cost and predictive factors: high- and low-utilization model development.

Healthcare costs in the U.S. are the highest in the world and are increasing rapidly. With increasing costs, employers and health insurance companies are trying to contain the cost of healthcare. This study aims at developing a predictive model relating enrollees' healthcare insurance claims to their health risks while controlling for gender, age, and their previous year's claims. Our study findings suggest that age, gender, and a previous year's healthcare expenditure are strong predictors of healthcare cost and that none of the six biomarkers was a significant predictor. Even though these biomarkers failed to contribute to the predictive model, they are, nonetheless, important predictors of future chronic diseases, many of which are leading causes of death in the U.S.