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* From Evanston Northwestern Healthcare (Dr. Vender and Ms. Guilianelli), Evanston, IL; and Wellington Consulting Group, Ltd. (Drs. Dowling and Wang), Gold Canyon, AZ, and Chicago, IL.
Correspondence to: Jane Dowling, PhD, Wellington Consulting Group, 8406 E. Canyon Estates Circle, Gold Canyon, AZ 85218; e-mail: janedowling{at}msn.com
Key Words: critical care • family satisfaction • path analysis • quality of care
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Introduction |
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 TOP Introduction Overview of the Hypothesized...Materials and MethodsResultsDiscussionConclusionsReferences |
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Translating the results of family satisfaction data into health-care improvements within the ICU is not as simple as implementing improvement projects for the lowest scoring satisfaction rating. Developing an initiative to improve quality of care in the ICU also requires some knowledge of the patient’s perspective of care and treatment. Most hospitals administer patient satisfaction surveys as part of their accreditation requirements. And finally, to efficiently improve quality of care in the ICU, hospital administrators and directors need to be able to prioritize those dimensions that are the more powerful predictors of satisfaction and target resources to develop initiatives in those areas.
The present study is part of a program of research about family satisfaction in six hospitals implementing the Critical Care Family Assistance Program (CCFAP). The CCFAP emerged as a collaboration between The CHEST Foundation, the philanthropic arm of the American College of Chest Physicians, and the Eli Lilly and Company Foundation. The goal of the CCFAP is to respond to the unmet needs of families of critically ill patients in hospital ICUs through the provision of educational and family support resources. The primary objectives include the following:
The objective of this article was to cross-validate findings from the CCFAP Family Satisfaction Survey and the Press Ganey inpatient survey (Press Ganey Associates, South Bend, IN), which were administered over a 3-year period at one of the CCFAP pilot sites (Evanston Northwestern Healthcare, Evanston, IL). Constructs from family satisfaction studies, including our own, were used to develop two preliminary models of factors that predict family satisfaction and patient satisfaction.
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Overview of the Hypothesized Models |
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TOPIntroductionOverview of the Hypothesized...Materials and MethodsResultsDiscussionConclusionsReferences |
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Both models can thereby be summarized in terms of the following two primary components: (1) the central model describing the direct relationships of predictors of family/patient satisfaction and their effects on overall patient/family satisfaction; and (2) the expanded model that describes the indirect interrelationships of predictors of family/patient satisfaction and their effects on overall patient/family satisfaction. Figure 1 illustrates this two-component model.
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Materials and Methods |
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TOPIntroductionOverview of the Hypothesized...Materials and MethodsResultsDiscussionConclusionsReferences |
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Sample
Evanston Family Survey Data Sample:
The participants in this sample were 330 family members who had loved ones in the ICU at Evanston Northwestern Healthcare, Evanston, IL, between August 2002 and August 2004. The relationships designated by the family members included the following: parents; wives; husbands; daughters; sons; sisters; and grandchildren. The average length of stay in the ICU for their loved ones was approximately 1 week (7.14 days), ranging from 1 to 26 days. No other demographic data were collected on the families.
Press Ganey Survey Data Sample:
The participants in this sample for cross-time comparisons were 2,266 patients who had been admitted to Evanston Northwestern Healthcare between January 2002 and June 2004, and who had been patients in the ICU for at least 1 day. The average length of stay in the ICU was 6.23 days, and the range was from 1 to 66 days. The participants who were eligible for path analyses were 3,401 patients who had been admitted to the Evanston Northwestern Healthcare between January 2000 and June 2004, and who had been patients in the ICU for at least 1 day. Outliers were deleted to make the model more desirable.7 No other demographic data were collected on these patients.
Procedure
The CCFAP family satisfaction survey questionnaire, which is a modified version of the family satisfaction survey of Heyland et al8 and was validated by the evaluation team as a reliable instrument for evaluating the needs and level of satisfaction of family members with loved ones in the ICU, was administered to the families of patients. Items for the questionnaire included family satisfaction with care and communication provided by each ICU team member (eg, nursing staff, physician, social worker, chaplain, respiratory therapist, and dietician), together with items related to family needs and whether or not those needs were met. Family members were asked to provide a satisfaction rating on the treatment and care they received from ICU staff, and on their general comfort level related to feeling safe and secure in the hospital. Family members were also asked to report their level of stress/anxiety when their loved ones were admitted to the ICU and whether the hospital or ICU had provided any services or information to help reduce their stress and anxiety. If families received service/information, they were asked to indicate their level of stress/anxiety after receiving the service/information.
The survey was administered to families having loved ones in the ICU at Evanston Northwestern Healthcare. The survey was loaded onto a computer in a kiosk located in the family waiting room and could be completed online by a family member. When families entered the waiting room area, volunteers would provide directions on the use of the kiosk and encourage a family member to complete the survey before leaving. In addition, families were also offered the opportunity to complete paper surveys. Survey responses given on the computer were reviewed before analysis, and those determined to be incomplete (ie, < 50% of the questions had been answered) and those with inappropriate answers were deleted from the database. Family members completing the surveys included parents, wives, husbands, sons, daughters, grandchildren, brothers, and sisters. The answers for all surveys were reviewed, and it was determined that the individuals completing the surveys were knowledgeable about the communication and care received by the family member.
The Press Ganey survey was administered to all patients admitted to Evanston Northwestern Healthcare. Items for this survey included patient’s opinion of their experience during their hospital stay. The primary areas included the following: the room, diet and meals; intensive/critical care; nursing care; the physician; tests and treatments; hospital discharge; personal issues; special services; visitors and family; and overall rating of the hospital. The surveys were mailed to patients after discharge from the hospital. In the data preparation stage, descriptive statistics were run to identify missing data, possible data entry errors, and extreme outliers. Of the 3,581 total respondents who had experienced ICU services and who completed the survey during the time period of the study, 2,266 were included in the final sample of patients for the comparative analysis and 3,401 were included for path analysis.
Measures
This study examined multiple variables, most of which had been suggested in prior research.149 Except for the single-item variables, all variables included in the analyses were scales the values of which represent the standardized factor scores for constituent items. The scale construction was initiated by running factor analyses on items that were conceptually similar. The 
coefficients were obtained for items that factored together.
Safety Variable:
Hospital safety (single item) was assessed on a 5-point scale, from 1 (very poor) to 5 (very good), by asking families/patients the degree to which they felt safe and secure in the hospital.
Information Variables:
(1) Information provided to family (single item) was assessed by asking patients to indicate on a 5-point scale, from 1 (very poor) to 5 (very good), their perception of the information provided to their family while a loved one was in an ICU. (2) Communication (two items, = 0.75) by nurses and by physicians was assessed by asking family members to indicate on the same 5-point scale how well the nurse communicated with them and, separately, how well the physician communicated with them.
Staff Sensitivity Variable:
Staff sensitivity and responsiveness (single item) was assessed by asking both patients and family members to indicate on a 5-point scale, from 1 (very poor) to 5 (very good), the degree of sensitivity and responsiveness they had experienced from the ICU staff.
Staff Assistance Variable:
A single item was assessed by asking both patients and family members to indicate on a 5-point scale, from 1 (very poor) to 5 (very good), how well the staff had helped them to understand the treatment, test, or condition of the family member. In the case of the patient, the question was framed to assess how well the staff helped the patient understand his/her treatment, test, and condition.
Physician Care Variables:
(1) Physician care (single item) was assessed by asking family members to indicate on a 5-point scale, from 1 (very poor) to 5 (very good), their perception of the quality of care that had been provided by the physician. (2) Physician care (five items, = 0.92) was measured by asking patients to indicate, on the same 5-point scale, their assessment of the time the physician spent with them, the physician’s concern for their questions and worries, how well the physician kept them informed, the friendliness/courtesy of the resident physician, and the skill of the physician.
Visitor and Family Variables:
These variables (four items, = 0.90) were based on the patient’s assessment of the helpfulness of staff at the information desk, the accommodations and comfort for visitors, staff attitude toward the patient’s visitors, and the information given to the family about the patient’s condition and treatment. The scale ranged from 1 (very poor) to 5 (very good).
Nursing Care Variables:
(1) Nursing care (single item) was assessed by asking family members to indicate, on a 5-point scale from 1 (very poor) to 5 (very good), their perception of the quality of care provided by the ICU nurses. (2) Nursing care (six items, = 0.94) reflected the patient’s appraisal of the quality of nursing care, including the friendliness/courtesy of the nurse and promptness in responding to the call button. The scale ranged from 1 (very poor) to 5 (very good).
Personal Issues:
These issues (five items, = 0.91) were based on the patient’s report of how well the patient felt his/her pain had been controlled, how well the hospital staff had addressed his/her emotional/spiritual needs, how responsive staff had been to concerns/complaints made during his/her stay, staff concern for his/her privacy and dignity, and staff efforts to include the patient in decisions about his/her care. The scale ranged from 1 (very poor) to 5 (very good).
Length of Stay:
This variable (single item) was a continuous variable for the inpatient survey and is represented by the following scale for the family survey: 1, 1 day; 2, 2 to 3 days; 3, 4 to 7 days, 4, 1 to 2 weeks; 5, 2 to 3 weeks; and 6, > 3 weeks. Length of stay was determined by asking patients and family members about the number of days spent in the hospital.
Overall Satisfaction Variables:
(1) Patient overall satisfaction (five items, = 0.94) was determined by asking patients to respond on a 5-point scale, from 1 (very poor) to 5 (very good), how well the staff had worked together to care for them, the likelihood that they would recommend the hospital to others, the likelihood that they would choose this hospital again for future medical care, and their overall satisfaction with the care given at the hospital. (2) Family needs met (two items, = 0.80) was measured with two items from the CCFAP family satisfaction survey, which reflected the family members’ appraisal of how well their loved one’s needs were met and how well their own needs were considered. For each item, the family members indicated on a 5-point scale, from 1 (very poor) to 5 (very good), how well their needs and their loved one’s needs had been met.
Statistical Analysis
Data analyses entailed the following four phases: (1) factor analysis and reliability analysis to confirm the predefined constructs embedded in the survey items that represented service areas of Evanston Northwestern Healthcare; (2) analysis of variance (ANOVA) to determine the statistical significance of the differences across the study time period; (3) standardized mean differences (effect sizes) to assess the magnitude of the observed effect or relationship; and (4) path analysis to understand the comparative strengths of direct and indirect relationships among the variables.
Principal components factor analysis was used to reduce the number of variables and to classify the variables. In order to condense the number of items, principal components factor analyses with eigenvalues of > 1 were performed (SPSS, version 11.5; SPSS; Chicago, IL). This type of analysis combines correlated variables into a single factor so that the multiple variables can be expressed by a single variable (or factor). Cronbach -coefficients were obtained to determine the level of reliability of each scale.
One-way univariate ANOVA was employed to compare the difference in mean scores across the study time period (2002 to 2004). The differences in satisfaction ratings before and after the program was implemented were also investigated by calculating the effect sizes for each scale.
Effect sizes were calculated using the d formula of Cohen,10 in which the difference in the two means, for each scale, is divided by the pooled SD. One issue that confronts any researcher using effect sizes is the question of what is a noteworthy effect. The field has not reached a definitive view on this matter.11 Cohen10 proposed some tentative benchmarks for what might be deemed small, medium, and large effects in regard to the d noted above. However, Cohen hesitated to present criteria for effect noteworthiness, stating that noteworthiness of an effect turns largely on what one is studying. Small but replicable effects for very important outcomes may be very noteworthy; extremely large effects may be needed for results to be noteworthy for relatively unimportant outcomes. For example, Gage12 pointed out that even though the relationship between cigarette smoking and lung cancer is relatively small (ie, h2 = 1 to 2%), he points out, "Sometimes even very weak relationships can be important... . [O]n the basis of such correlations, important public health policy has been made and millions of people have changed strong habits."
Thompson11 recommended that effect sizes be reported and explicitly interpreted in the context of effect sizes from prior related studies and not by invoking rigid benchmarks. Little research has been conducted using effect size in the study of family satisfaction in the ICU. It is believed that looking at effect sizes in the study of the impact of the CCFAP on family satisfaction will make a substantial contribution to the professional knowledge base. Simply knowing the direction of the effect is not sufficient to decide whether the CCFAP is effective. Determining statistical significance does not preclude the researcher from calculating effect sizes; effect sizes are useful in determining practical importance.13 The dilemma faced is how to treat effects when the p value is small but not statistically significant (eg, p > 0.05, but < 0.10). Following the advice of Tukey14 for describing results from single studies, additional words besides "significant" or "nonsignificant" are being used to describe the direction of the difference or relationship. If p > 0.05 but < 0.15, we say that the direction "leans" in a certain direction. If p > 0 0.15 but < 0 0.25, we say that there is a "hint" about the true direction. In other words, we are not treating statistical testing as an all-or-nothing procedure but, rather, using appropriate wording to describe degrees of uncertainty. In addition, we are in the process of replicating the satisfaction studies in all of the CCFAP sites and are focused on obtaining a reliable effect.
The fourth analysis conducted on the Evanston family satisfaction survey and the Press Ganey inpatient survey data was the application of path analysis. To test the hypothesized models shown in Figures 1 and 2
, we utilized recursive path analysis, which is estimated by ordinary least squares regression. This is a statistical technique that allows the testing of both direct and indirect relations among variables, confirming the presence and identifying the magnitude of each relation hypothesized in the models.15 Compared to other linear equation models, it is unique, in that it allows mediating variables in the pathway (X 
Y Z). The pathways in the path model represent the hypotheses but cannot be statistically tested for directionality. Although providing a test of whether the data are congruent with our hypothesized causal model, it does not demonstrate causality.
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levels for the variables used here provide some assurance. Structural equation-modeling software (EQS, version 6.1; Multivariate Software; Encino, CA) was used for the path analysis.7 The structural equation-modeling software analyzed the model in terms of its reliability in generating reliable output. Both of the path models that were developed for this study yielded the statement, "no special problems were encountered during optimization," which indicates that the analysis of both path models that was done using the software yielded reliable output. It was also determined that the data were normally distributed. There were several indicators all showing that, statistically, the model works well. First, in both models, almost all of the standardized residual matrix values are < 0.1. Second, the independence model does not fit the data (ie, the data are related). In addition, the Bentler-Bonnet normed fit index is close to 1 in both cases, which confirms that the proposed model works well. The relative sizes of the path coefficients in the resulting path diagram yield the answer to which of the hypotheses are better supported by the data. For example, the direct effect of the variable "nursing care on overall satisfaction" is depicted by the arrow leading directly from nursing care to overall satisfaction. The magnitude of this effect is quantified by a standardized regression coefficient (0.42). The indirect effect is depicted by the pathway leading from nursing care to physician care and then leading to overall satisfaction. The indirect effect is quantified by the product of these two paths (0.20 x 0.14 = 0.028).
In both of the path models seen in Figures 1 and 2, the variables in squares represent observed variables. In other words, these are raw scores directly reported by respondents. Variables in ovals represent composite or latent variables. These are condensed factor scores that represent more than one item. Significant relationships between variables are represented by solid lines. For purposes of clarity, the presentation of the results that follow is organized around the two major components of the hypothesized models presented in Figures 1 and 2, as follows: (1) predictors of family satisfaction (ie, needs being met); and (2) predictors of patient satisfaction.
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Results |
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TOPIntroductionOverview of the Hypothesized...Materials and MethodsResultsDiscussionConclusionsReferences |
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Family Members:
Treatment by ICU Team and Services Received: Table 2
reports the means, pooled SDs, and effect sizes for items related to family member perception of how they were treated by ICU staff and the availability of special services needed by a family member. The means for the first set of items were calculated on a 5-point scale. The second half of Table 2 is representative of special services or information needed and received by family members. The special services items were recoded, with "didn’t need" coded as missing data, "needed, but didn’t receive" coded as 0, and "needed and received" coded as 1. The means, SDs, and effect sizes were calculated.
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ICU Patient:
Care and Treatment by ICU Team and Overall Satisfaction: Differences in patient perception of the seven variables before and after implementation of the CCFAP were explored using a one-way ANOVA. In all cases, the differences were not found to be statistically significant. Effect sizes were calculated for each item, and the results are presented in Table 3
. The effect sizes were relatively small and were both positive and negative. The mean ratings for all areas were high (> 4.0 on a 5-point scale) before the CCFAP was implemented, which indicates that patients were generally satisfied with the care and treatment they were receiving from the hospital. The two areas where there were no negative effect sizes were in the items listed under "visitors and family" and the physician-related items.
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Predictors of Family and Patient Satisfaction
Table 4
presents, separately for family members and patients, the bivariate correlations between the variables. Coefficients below the diagonal are for family members, and those above it are for patients.
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2 (28) = 14109.03; family, 2 (28) = 2168.86). In addition, the Bentler-Bonnet normed fit index7 is > 0.9 and close to 1.0 for both models (patient, 0.94; family, 0.92), which confirms that the proposed model works well. The path analyses conducted on both the family model and the patient model allow the examination of the predictive power of the models in relation to the variables that contributed to family/patient satisfaction and the relative importance of the variable in increasing or improving family/patient satisfaction. Several models were tested, and the sequence of the variables was reordered in order to discover the most powerful model. Table 5 reports the amount of variance explained for each of the variables in the family path model and the patient model.
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In the patient model, four of the six measures of ICU environmental conditions were significantly related to the measure of overall patient satisfaction, ranging from a standardized regression coefficient of 0.14 (physicians care) to 0.42 (nursing care). The model explained 56% of the variance in patient satisfaction, which indicates that the patient model has a lower level of predictive power than the family model. The difference may be due to the makeup of the construct in the patient model, which consisted of five items that measured additional areas of patient experience and future plans. These additional items were not part of the measure of satisfaction in the family survey. However, the model can still be used to identify specific variables or constructs that should be addressed jointly with variables that would also improve family satisfaction. For example, the same three variables of (1) hospital safety, (2) staff help in understanding tests and treatments, and (3) staff sensitivity and responsiveness accounted for 62% of the variance in the communication construct in the patient path model. In both the family and patient models, actions taken to increase or improve family and patient satisfaction with staff behaviors and the perception of hospital safety are likely to result in higher satisfaction ratings.
As expected, hospital safety and security is important to both family and patient satisfaction, both directly and indirectly. If the paths from the construct of hospital safety are followed, six significant relationships are seen on the patient model, and six are found on the family model. In the family models, the path coefficients are somewhat stronger than in the patient model. For families, feelings of being safe and secure in the hospital are significantly related to high ratings of ICU staff, both in terms of their sensitivity and responsiveness (0.37), as well as communication, particularly nurse and physician communication (0.21). Feeling safe and secure is also a predictor of higher ratings of nursing care (0.17), physician care (0.16), and overall family satisfaction (0.30). For patients, hospital safety is significantly related to nursing care (0.40), physician care (0.18), staff sensitivity and responsiveness (0.17), the communication construct (ie, information provided to family) [0.03], and, ultimately, to overall patient satisfaction (0.24).
The two constructs reflecting staff behaviors (ie, helping to understand tests and treatments, and being sensitive and responsive) are statistically significant, with a relatively strong relationship with the communication construct in the family model, which emphasizes the importance of staff interactions with family members and, ultimately, with family satisfaction. Both staff help in understanding test results, and staff sensitivity and responsiveness are significantly related to family satisfaction, with coefficients of 0.26 and 0.32, respectively. Similar to the family model, there are strong relationships in the patient path model between staff help with understanding test results and treatments and the communication construct (0.44), and staff sensitivity/responsiveness (0.70), and between the communication construct and staff sensitivity/responsiveness (0.42). This finding further supports the importance of staff interactions.
Physician care (0.14) and nursing care (0.22) are significantly related to family satisfaction. There is not a significant relationship between the communication construct and family satisfaction, although significant indirect relationships were demonstrated by the paths on the model between communication and nursing care, and between communication and physician care. In other words, families who are satisfied with communication are also satisfied with care and are satisfied overall.
A moderately strong relationship was also found between the communication construct and physician care (0.53), and between the communication construct and nursing care (0.63) in the family model, indicating a direct connection between the family perception of communication and their subsequent perception of the quality of care. Although not as strong, there was also a significant relationship between the communication construct and physician care (0.22) in the patient model. There was not a significant relationship between the communication construct and nursing care in the patient model. More significant relationships between physician care and the other constructs were reported in the patient model than in the family model.
Four of the five constructs in the patient model were significantly related to physician care, suggesting that, from the point of view of the patient, physician care is a higher priority than it is for families. Only the following two constructs were significantly related to physician care in the family model: hospital safety (0.16) and communication (0.53).
One of the hypothesized patient outcomes of the CCFAP model was the reduction of length of stay; however, neither the family path model nor the patient model fully supported this prediction. There was not a significant relationship between length of stay and overall family satisfaction. In fact, the family rating of staff sensitivity and responsiveness was significantly reduced as the length of stay increased (–0.26). Length of stay was significantly related to hospital safety (0.17), suggesting that the longer families are associated with the hospital, the more likely they are to observe the security measures in effect and, thus, to rate security higher. This finding is disputed in the patient path model. The longer the patient was in the hospital, the lower was the rating of hospital safety (–0.08). This may be related to a level of frustration felt by the patient in not being able to leave the hospital; however, there was a significant relationship between the length of stay and overall satisfaction (0.07). The longer a patient was in the hospital, the higher were the satisfaction ratings. There was also a significant relationship between the communication construct and length of stay (0.06), but there was a negative relationship between length of stay and nursing care (–0.08). All of the path coefficients related to length of stay were marginally significant, and, as stated earlier, the amount of variance explained for length of stay was only 7% in the family path model and 1% in the patient path model. Further study is warranted before drawing any conclusions from the findings related to length of stay.
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Discussion |
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Staff being helpful in the explanation of tests and treatment and the resultant understanding was directly related to both family satisfaction and patient satisfaction. Physician care was also directly related to both family satisfaction and patient satisfaction, although the relationship is somewhat small at 0.14 in both models. Although the communication construct is not directly related to satisfaction in either model, there are significant indirect relationships through the two care constructs, which in turn are directly related to satisfaction. Communication remains a powerful predictor of satisfaction and should be a key component of any initiative.
Hospital safety was a strong predictor, both directly and indirectly, of patient and family satisfaction, with a total of 10 significant relationships among the variables in both models. This positive effect size that was calculated from the family satisfaction survey when families were asked whether they felt safe and secure in the hospital indicates that the hospital has been effectively addressing this variable. The components of the CCFAP model appear to be linked to making families feel more safe and secure in the hospital. It would seem appropriate to suggest that, given this strong correlation between certain family satisfaction predictor variables and hospital safety, these data be used by hospitals to further examine the linkages and relationships between the family perception of hospital safety and specific components of the CCFAP. Future research is needed to identify why families feel safe and secure, and, conversely, why they may not feel safe in the hospital.
The path analysis enables further examination of the effects, both direct and indirect, on the final outcome variable (satisfaction) and determine which variables should be prioritized based on the magnitude of their impact on satisfaction. To determine priorities, both significant and nonsignificant indirect pathways (ie, those paths traveling through other variables to reach the final outcome variable) and the direct pathway are identified for each variable. The magnitude of the direct effect is quantified by the standardized coefficient, and the indirect effect is calculated by taking the product of all the pathways from an independent variable leading to the final outcome variable. For example, in both the family and patient path models, the variable "hospital safety" has one direct and five indirect paths leading to the variable "overall satisfaction." Table 6 presents the results from an analysis of the direct and indirect paths for the family path model and the patient path model. Table 6 also compares the rankings before and after the inclusion of the indirect effects, as indicated in the parentheses.
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Regardless of the strategy selected to prioritize the predictor variables, the data can be studied profitably around specific program components as part of an action plan to address family and patient satisfaction. Future analysis should examine how specific processes (eg, staff training to increase skill level to help families understand tests and treatments) lead to increased family understanding of tests and treatments, and how these strategies affect family satisfaction. What about other ways of presenting the information to help families understand tests and treatments? Does the presentation of an animated video increase understanding and increase the ratings of family satisfaction? Or does the presentation of the information in a booklet or at a computerized information kiosk result in increased family understanding?
Equally important will be research on the multidisciplinary team approach that is embedded in the CCFAP model. This aspect of the model was not included in this present study and is an important underlying component that is most likely to impact staff behavior and communication variables.
Several limitations of this study and some caveats must be noted. First, the proposed model was not intended to be exhaustive. Models that include different parameters than those included in the present model could also account for variation in the outcome assessments. In addition, although the paths between variables in the model imply causality, at this point one can only test the extent to which the observed variables can be predicted from the hypothesized model without respect to the direction of effects. The sample is nonrandom; it does, however, represent families and patients who have had experiences in the ICU before and after the implementation of the CCFAP. And finally, the patient sample for 2004 was incomplete. Less than half of the inpatient survey data for 2004 were made available for the study. The average sample size for 2002 and 2003 was 1,000, but the sample size for 2004 was 317. Further study of the effect sizes using the total data set for 2004 may yield more reliable results.
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Conclusions |
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New insights have been provided by this study about family and patient satisfaction and their relationship within the ICU environment. They are articulated as follows:
The CCFAP study has contributed to the growing knowledge base regarding the needs and opinions of family members of the critically ill. In general, these findings that have emerged from the research on family satisfaction in ICUs have supported the fundamental principles on which a family-centered program in an ICU could be built. Some of the key implications are enumerated below:
In conclusion, family satisfaction constructs have been studied over the past 20 years, but little research has been reported that has investigated the interrelationships among patient and family satisfaction in an ICU. The path models reported in the CCFAP study represent an initial attempt to specify and test those interrelationships. The results of the analyses provide support for the theoretical model examined and also provide an avenue for translating the data into quality improvement.
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Footnotes |
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References |
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This article has been cited by other articles:
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  R. J. Wall, J. R. Curtis, C. R. Cooke, and R. A. Engelberg Family Satisfaction in the ICU: Differences Between Families of Survivors and Nonsurvivors Chest, November 1, 2007; 132(5): 1425 - 1433. [Abstract] [Full Text] [PDF]
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