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International Journal for Quality in Health Care Advance Access originally published online on September 12, 2007
International Journal for Quality in Health Care 2007 19(6):358-367; doi:10.1093/intqhc/mzm045
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© The Author 2007. Published by Oxford University Press on behalf of International Society for Quality in Health Care; all rights reserved

Implementing a hospital guideline on pneumonia: a semi-quantitative review

Pieter-Jan Cortoos1, Steven Simoens1, Willy Peetermans2, Ludo Willems3 and Gert Laekeman1

1 Research Centre for Pharmaceutical Care and Pharmaco-economics, Faculty of Pharmaceutical Sciences, Katholieke Universiteit Leuven, Belgium
2 Department of Internal Medicine, Faculty of Medicine, Katholieke Universiteit Leuven, Belgium
3 Hospital Pharmacy, University Hospitals Leuven, Leuven, Belgium

Address reprint requests to: Pieter-Jan Cortoos, PharmD, Research Centre for Pharmaceutical Care and Pharmaco-economics, Faculty of Pharmaceutical Sciences, Katholieke Universiteit Leuven, Herestraat 49, O&N 2, PO 521, B-3000 Leuven, Belgium. Tel: +32 (0) 16 330407. Fax: +32 (0) 16 323468. E-mail: pieterjan.cortoos{at}pharm.kuleuven.be


   Abstract
Top
 Abstract
Methods
 Results
 Discussion
 Limitations
 Conclusion
 Supplementary material
 Funding
 References
 
Background and objective. To quantify the impact of different guideline implementation interventions to improve treatment of community-acquired pneumonia (CAP) in a hospital setting.

Methods. Pubmed, the Cochrane Library, the Cochrane Effective Practice and Organization of Care specialized register, EMBASE and CINAHL.

Study selection. Hospital-based trials studying the effect of guidelines on compliance with care processes, clinical and/or economic outcomes in the treatment of CAP together with a description of their implementation interventions.

Data extraction. Two independent reviewers extracted and categorized utilized implementation interventions, assessed intensity of use and calculated changes for process of care variables, clinical and economical outcomes. Correlations between interventions and improvement of outcomes were assessed by means of Spearman's rho-test and Mann–Whitney U-test.

Results. In 27 included studies, educational meetings (21/27) and distribution of written material (14/27) were the two most used interventions. Most individual studies show positive overall results, but taken together, no significant relation between number or type of implementation interventions and improvement of outcomes could be detected. Only audit and feedback showed a significant negative influence on the improvement rate of length of stay (p = 0.003; n = 20).

Conclusion. Other hospital-specific factors are likely to have a higher impact on the rate of improvement than the implementation interventions alone. Describing which interventions are most successful is unlikely to be correct without taking these hospital-specific factors into account. Future research should focus on how to identify and define these factors and how to adapt the intervention to hospital-specific factors.

Keywords: guideline adherence, implementation, pneumonia

Community-acquired pneumonia (CAP) is a major cause of death, health care resource use and costs. In the USA, CAP accounts each year for 10 000 000 physician visits, 500 000 hospitalizations and 45 000 deaths [1]. Hospital costs are estimated between 8.4 and 9.7 billion$/year.

Emerging antimicrobial resistance and growing need for optimal resource use force hospital authorities to a more efficient use of antibiotics and facilities. To guide this process without compromising patient safety and outcome, several CAP guidelines by authoritative organizations [2, 3] have been formulated and frequently updated. Issues of interest are the implementation of such guidelines and compliance with them. Numerous studies and reviews [4, 5] have been written about which interventions to use to ensure a successful implementation with prolonged use of the guideline. The general conclusion of these studies is that passive educational methods alone have low efficiency and that they should be combined with other, more active interventions to improve the implementation. Also, multiple interventions are recommended. However, these recommendations are based on qualitative evaluations. A quantitative comparison of interventions would be more useful to give correct advice to policy makers with a view to ensuring an efficient use of limited resources [6].

The aims of this review are to provide a classification of used CAP-guideline implementation interventions and to quantify the impact of different interventions and their intensity of use on several processes of care, clinical and/or economic outcomes. Thus, implementation interventions that ensure maximum compliance with the guideline can be identified. We focused on CAP management because of its specific importance as one of the most important infectious diseases, the multidisciplinary aspect of its treatment and the constant need for updates due to changing resistance patterns.


   Methods
Top
 Abstract
 Methods
Results
 Discussion
 Limitations
 Conclusion
 Supplementary material
 Funding
 References
 
Data sources
Pubmed, the Cochrane Library, the Cochrane Effective Practice and Organization of Care (EPOC) specialized register, EMBASE and CINAHL were searched till March 2006. Search terms used included ‘guideline’, ‘guideline assessment’, ‘recommended process of care’, ‘implementation’, ‘implementation methods’, ‘quality improvement’, ‘quality care’, ‘intervention’, ‘protocol’ with ‘CAP’, ‘pneumonia’, ‘CAP’, ‘(lower) respiratory tract infection’ and ‘antibiotic’. The bibliography of retrieved articles was searched for relevant references.

Study selection
As this review focuses on the impact of different implementation interventions in the hospital setting, we searched for hospital-based trials that studied the effect of guidelines on compliance with care processes, clinical and/or economic outcomes in the treatment of CAP and that also gave a description of their implementation interventions. No restrictions with respect to study design were imposed. If the intervention was not clear, authors were contacted and asked for further information. In all studies, physicians responsible for treatment were free to follow the guidelines or not.

Defining type of implementation intervention and other influencing factors
For each study, the design, patient population, duration of intervention and outcomes were assessed according to the recommendations issued by the Cochrane Effective Practice and Organization of Care (EPOC) Review Group. On the basis of the retrieved articles, definitions used by the EPOC [6] and Weingarten et al. [7], we distinguished seven major classes of intervention divided into 15 subclasses (see Table 1). ‘Marketing’ and ‘Organizational’ as defined by the EPOC Group are not used because these interventions were not mentioned in any study. Other factors that could potentially influence outcomes or affect the ways of implementation were noted such as the presence of an external organization or coordinating hospital network, defined as Quality Improvement Organization. The setting (urban–rural), the hospital size and guideline complexity were considered as potentially influential but very few detailed data were available for sound assessment of their impact. If a subclass or Quality Improvement Organization was present, this was given a ‘1’ value and ‘0’ if absent. For each major class of interventions, summations of the number of subclasses used were made to reflect the intensity of use of that major class. Interventions were also classified according to whether their nature was more active or passive and whether they were more individually or generally perceived by the reviewers (see Table 1).


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  		Table 1 Classification of interventions with terminology and frequency

 
Data extraction
All articles were read, and interventions and changes in outcomes were extracted by two independent reviewers. If there was a disagreement in the presence or classification of an intervention or the mode of calculation, final decision was made by consensus. Data were abstracted concerning methodological quality of primary studies according to the criteria outlined by the Cochrane Collaboration [6].

In the context of a review, the validity of a study refers to the extent to which its design and conduct are likely to prevent systematic errors. Possible sources of systematic errors are selection bias (systematic differences in comparison groups), performance bias (systematic differences in care provided apart from the intervention being evaluated), attrition bias (systematic differences in withdrawals from the trial) and detection bias (systematic differences in outcome assessment).

Analysis of results
Articles were checked for data concerning compliance with processes of care, clinical and economic outcomes (see Table 2). Following the methodology proposed by Grimshaw et al. [6], the proportional change before–after the implementation was calculated for each outcome variable in each article. Across these changes, the median was calculated for the whole study and the different outcome types (clinical, economical and processes) to asses the impact of the implementation intervention. Improvements in process and outcome variables as enumerated in Table 2, are given as positive changes; negative changes reflect a deterioration of that variable.


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  		Table 2 List of relevant process of care and outcome variables in the treatment of CAP

 
Almost all studies used a retrospective before–after design (Supplementary data, Table S1). For the few controlled studies included, calculations and analyses of medians were carried out with only the intervention arm taken into account to make comparison possible.

A meta-analysis of primary studies was not suitable due to the heterogeneity of primary studies. Studies were not homogeneous in terms of setting, patient sample, nature of intervention, outcomes, outcome measurements and time of outcome measurements. Data on the number of interventions, individual types of interventions (total, active and personal interventions and major classes) and outcome variables (median across proportional changes in process, clinical and economic outcomes) were derived from each study. Correlations between the number/type of interventions and outcome variables were calculated using two-tailed Spearman's rho. As the presence–absence of Audit and feedback, Local Opinion Leader and presence of a Quality Improvement Organization are binary variables, analysis was conducted using a two-tailed Mann–Whitney U-test. Specific outcome variables were similarly analysed if more than 10 data points were available. No initial correction of the p-value was done due to the exploratory character of this review. The obtained medians were checked for outliers (more than three standard deviations from the mean) and analysed both with and without outliers. Statistical analysis was carried out using SPSS® 14.0 for Windows.


   Results
Top
 Abstract
 Methods
 Results
Discussion
 Limitations
 Conclusion
 Supplementary material
 Funding
 References
 
Selection of articles
Our search strategy yielded 10 998 articles from which 10 307 were excluded on the basis of the title. Main reasons for exclusion were articles about upper respiratory tract infections, nosocomial pneumonias, other respiratory infections, implementation interventions for other treatments and articles concerning cytostatics. The remaining 691 articles were checked on abstracts or if not available, on full article. We excluded 654 abstracts handling guidelines as such or their updates, implementation interventions for other pathologies or settings outside the hospital, economic analyses, importance of various treatment processes and description of current practices.

Thirty-seven articles were read, omitting seven titles for the following reasons. Three articles were excluded as the authors did not provide sufficient information on the method of implementation. Three other studies comparing interventions head-to-head in several hospitals provided little or no data on actual improvement and were used in the discussion section of this article [7–9]. One article described the implementation of a guideline in a developing country. This setting with limited resources and training was found to be too specific and was also omitted. We finally retrieved 30 articles concerning 27 different primary studies on interventions for the implementation of CAP guidelines [10–39].

Study characteristics
The characteristics of included studies are summarized in Table S1 (Supplementary data). The vast majority of trials were conducted in the USA (24/27) whereas the remainder was carried out in the UK [10], Canada [28–30] or Australia [39]. Three studies focused on patients above 65 years. The setting in which studies were conducted was variable. Fifteen studies [10–12, 14, 15, 22, 23, 25–27, 34, 35, 37–39] were conducted in a single institution, whereas other studies comprised multiple hospitals. Eight studies put a special emphasis on CAP management in the emergency department [11, 12, 14, 15, 24, 27, 37, 39]. Sample size showed large variations as did the duration of the intervention.

Intervention frequencies and outcomes
The distribution of the number of studies stating the intervention is given in Table 1. The two most frequently used interventions were educational meeting (17 studies) and distribution of written material (21 studies), 63.0 and 78%, respectively. The combination of these two interventions was used in 14 studies (52%).

Table S2 (Supplementary data) summarizes the interventions and outcomes per study. All studies except one showed at least a status quo for the median of all proportional changes following the implementation intervention(s) with improvements up to 103.6%. For processes of care and clinical outcomes, improvements varied between –39.1 and 194.9%; –24.9 and 56.7%, respectively. Decrease of mortality (–9.4 to 60.2%) and decrease in length of stay (–2.9 to 51.1%) showed an overall improvement with only few studies for which the performance of these variables worsened. With respect to the economic outcomes, positive results were seen, with improvements varying between 3.4 and 66.8%. Also, antibiotic guideline compliance showed overall improvement (6.8–277.0%).

There was no significant correlation between the total number of interventions or individual types of interventions and the median improvement on all outcome variables. Similar models were run for improvement of process-outcomes, clinical and economic improvements, improvement of guideline-concordance, length of stay and mortality (Tables 3 and 4). Most analyses showed no significant impact of implementation interventions on these variables. No difference was seen with or without outliers. When we compared the rate of improvement in length of stay, this improvement was significantly lower when audit and feedback was used (p = 0.003; n = 20).


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  		Table 3 Spearman correlation between total number of interventions or intensity of individual types of interventions and improvement of calculated or specific outcomes

 


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  		Table 4 Correlation analysis using two-tailed Mann–Whitney U-test between presence of the intervention and improvement of calculated or specific outcomes

 
A significant positive correlation between increased improvement of process outcomes and clinical outcomes was detected (Spearman's rho = 0.452; p = 0.040; n = 22). There was no significant correlation between improvement in compliance and median improvement on clinical outcome variables (Spearman's rho = 0.551; p = 0.16; n = 8). Such correlations were not calculated with respect to other outcomes because of a lack of data.


   Discussion
Top
 Abstract
 Methods
 Results
 Discussion
Limitations
 Conclusion
 Supplementary material
 Funding
 References
 
The two objectives of this article were to classify available implementation interventions and to quantify their impact. For this purpose, articles describing interventions were searched, median changes were calculated and correlations between median improvements and number of interventions were examined. Our results showed that there does not seem to be a correlation between the type or number of interventions used to implement a CAP guideline and improvement on several CAP-related outcomes. Only audit and feedback was significantly correlated with a lower improvement in length of stay. On the basis of these results, no optimal intervention to implement CAP guidelines can be selected.

Audit and feedback
Results relating to the impact of audit and feedback have to be interpreted cautiously. The main objective was to detect those interventions that yield the highest improvement. Outcomes (see Table S2, Supplementary Data) show that overall negative results are scarce, so it is likely that there are no interventions with an implicit negative effect. Concluding that audit and feedback results in worse outcomes would be premature given the possibility of confounding due to the different study settings and the fact that we did not correct the p-value for multiple testing. However, this finding illustrates that the benefit of this commonly used intervention is modest [6] and, in our review, not necessarily balanced by a high decrease in mortality or better process improvement. If audit and feedback is to be used, Hysong et al. argue that the prerequisites to make it successful are timeliness, individualization, non-punitiveness and customizability which increase even more its complexity [40].

Multiple interventions
Tu et al. [8] showed a possible correlation between number of interventions and outcomes in CAP management. They described a ‘social influence approach’ where human actions and efforts are used to implement the pathway and an ‘epidemiological approach’ that emphasizes the use of an evidence-based clinical pathway or other quality improvement projects. A significant difference was attained only when the maximum number of interventions per approach was used. In case of number-dependent improvement, however, a gradual response would be expected, but this could not be seen. Yealy et al. [9] compared three intervention strategies according to their intensity in a study of 32 hospitals. For several recommended processes of care for in- and out-patient setting, a significant difference was seen for the high-intensity strategy only, with just small differences between low and moderate intensity. This gives an indication that the relation between intervention intensity and improvement is not as strong as repeatedly stated [4, 5]. The primary conclusion of our review is that in a hospital setting, a strategy of multiple interventions does not automatically result in higher improvements. Reviewing the strategies for improving antibiotic selection in outpatient setting, Steinman et al. reached the same conclusion [41]. In a multi-state study of Medicare beneficiaries, Weingarten et al. [7] concluded that multiple interventions are not always superior to single intervention strategies. Grimshaw et al. [6] reached the same conclusion in a large review on effectiveness of implementation strategies.

Variables
The absence of a clear correlation between implementation interventions and improvement rate in outcomes points to the existence of other influential parameters. In a series of interviews with experienced guideline implementers, Solberg et al. [4] already saw 87 variables with an important effect on the ability to implement guidelines with an emphasis on various organizational and change management characteristics such as implementation infrastructure or change capabilities. Factors that are usually stated in such articles (e.g. location, organization size or patient mix) are graded with lower importance. Also, patient and disease characteristics have been shown to have a limited impact on guideline-compliant prescribing behaviour, where the physician's behaviour and attitude towards certain therapies seem to be more important [42]. The fact that process improvement and guideline compliance are only weakly or not correlated to clinical outcomes in our analysis indicates the presence of other factors, medical and non-medical, in addition to pointing to the limited importance of process parameters. A rigorous selection procedure should be used if one wishes to follow-up these parameters as an indicator for quality [43] and even then final conclusions are to be drawn from clinical and economical outcomes.

Our findings have implications for hospital managements who are considering implementation interventions because more resources are required for a strategy comprising multiple interventions than for a limited intervention, while the clinical and economic benefits of such a strategy are uncertain. These findings suggest that factors other than the number of interventions may have an effect on successful guideline implementation. And even when successful process improvements have been made, this is at most partly reflected in higher improvement of clinical outcomes.


   Limitations
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 Abstract
 Methods
 Results
 Discussion
 Limitations
Conclusion
 Supplementary material
 Funding
 References
 
Even though primary studies were heterogeneous, this heterogeneity is typical of this area of research and multiple reviews have attempted to summarize the evidence. Previous reviews for evaluation of interventions and methods were carried out by using a vote counting method. But this method is only useful for making general conclusions and is not capable of providing effect sizes, which was the aim of this review. Another problem with vote counting is its sensitivity to methodological errors [6]. Instead, we adopted a semi-quantitative approach; we calculated effect sizes of process, clinical and economic outcomes; and we explored the possible association between the number and type of interventions on the one hand and effect sizes on the other hand.

The main focus of our approach was the calculation of effect sizes in terms of the median across proportional changes in outcome variables. This approach did not consider statistical significance of changes in outcomes given difficulties in correctly estimating statistical significance when primary studies suffer from methodological biases [44]. Also, when calculating effect sizes for all outcome variables reported in a study, it should be noted that an intervention may be directed at one specific outcome and not necessarily at all reported outcomes. By comparing an outcome across studies that may not have been designed to address this outcome, the impact of an intervention may be diluted.

The fact that most articles describe a combination of interventions precludes a sound quantification of the effect size of separate interventions. Furthermore, data about the effectiveness of the interventions, whether they were implemented as intended together with reasons for possible changes is missing in all primary studies. Indeed, it is likely that the impact of an intervention is determined not only by the intervention itself, but also by the way that the intervention has been implemented. However, as no data on the degree of implementation of an intervention was provided in primary studies, we were not able to assess the impact of implementation of an intervention on effect size.

We carried out a thorough assessment of the methodological quality of primary studies. For instance, our review includes a majority of before–after studies that do not correct for an underlying secular trend and show a high risk for selection and performance biases. For studies with a smaller sample size, there is a possibility that small changes are overestimated. The great diversity in the quality of primary studies implies that our semi-quantitative results need to be interpreted with caution. Studies with good quality have been conducted mostly in larger organizations with more academic, study-orientated settings and presumably higher know-how and resources. Future research needs to focus on developing analytical frameworks that adjust effect sizes for the methodological quality. A possible approach is to weight effect sizes by the level of quality of the underlying study. However, the optimal method of weighting effect sizes is unclear.

The reader should note the risk of publication bias but the extent of publication bias in this area of research has been poorly studied [44]. Further, if many unsuccessful trials go unreported, this reflects even more the complexity and the presence of unforeseen conditions.


   Conclusion
Top
 Abstract
 Methods
 Results
 Discussion
 Limitations
 Conclusion
Supplementary material
 Funding
 References
 
In conclusion, our analysis shows that with respect to the implementation of a CAP guideline, multiple interventions do not automatically lead to higher improvements in CAP management. In most cases, improvement can be seen following an intervention but there is no significant correlation between the implementation intervention and outcome improvement. For audit and feedback, the benefit of this commonly used but complex and expensive intervention has been shown to be low or absent and not necessarily balanced by higher outcome improvement. Other hospital-specific factors are likely to have a much greater impact on the rate of improvement than the used interventions alone. In order to identify the most efficient implementation interventions, hospital-specific factors need to be taken into account. Future research should focus on how to define these factors and how to adapt the strategy to the specific situation. Another focus should be reducing variability in the studies' methodology and providing sufficient quantitative and qualitative data in regards to the interventions for a better understanding on how to implement new insights.


   Supplementary material
Top
 Abstract
 Methods
 Results
 Discussion
 Limitations
 Conclusion
 Supplementary material
Funding
 References
 
Supplementary material is available at IJQHC Journal online.


   Funding
Top
 Abstract
 Methods
 Results
 Discussion
 Limitations
 Conclusion
 Supplementary material
 Funding
References
 
No funding was received in the preparation of this manuscript.


   References
Top
 Abstract
 Methods
 Results
 Discussion
 Limitations
 Conclusion
 Supplementary material
 Funding
 References
 

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Accepted for publication August 17, 2007.


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