Shared decision making for acute respiratory infections in primary care

  • Protocol
  • Intervention


  • Peter Coxeter,

    Corresponding author
    1. Bond University, Centre for Research in Evidence-Based Practice (CREBP), Gold Coast, Queensland, Australia
    • Peter Coxeter, Centre for Research in Evidence-Based Practice (CREBP), Bond University, Gold Coast, Queensland, 4229, Australia.

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  • Tammy Hoffmann,

    1. Bond University, Centre for Research in Evidence-Based Practice (CREBP), Gold Coast, Queensland, Australia
    2. The University of Queensland, School of Health and Rehabilitation Sciences, Brisbane, Queensland, Australia
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  • Chris B Del Mar

    1. Bond University, Centre for Research in Evidence-Based Practice (CREBP), Gold Coast, Queensland, Australia
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This is the protocol for a review and there is no abstract. The objectives are as follows:

To assess whether shared decision making increases or reduces antibiotic prescribing for ARIs in primary care.


Description of the condition

Shared decision making

Shared decision making is the process of enabling a health professional and patient to make a joint decision about treatment or management based on the best available evidence and the patient's values and preferences (Charles 1997). It is one of the most important ways of bringing evidence to the point of clinical decisions and an effective strategy for reducing the overuse of ineffective treatments (Elwyn 2012). It is rapidly gaining recognition by health policy makers, health professionals and consumer groups.

Shared decision making for acute respiratory infections

The diagnostic uncertainty associated with acute respiratory infections (ARIs) and the trade-off between the benefits and harms of antibiotics mean that shared decision making can provide an important opportunity for making better clinical decisions (Butler 2001). It can enable health professionals and patients to choose appropriate treatment or management options, including the decision to not use an antibiotic. Systematic reviews conclude that antibiotics have modest benefit for reducing symptom duration or complications in patients with several ARIs, including sore throat (Spinks 2011), acute otitis media (Venekamp 2013), sinusitis (Ahovuo-Saloranta 2011) and bronchitis (Smith 2011), and no benefit for laryngitis (Gonzales 2001) or colds (Kenealy 2013). The limited benefits of antibiotics for ARIs are often outweighed by unnecessary exposure to common adverse reactions (e.g. rash, abdominal pain, diarrhoea and vomiting - although they are not well documented), increased healthcare costs and contribution to the growing problem of antibiotic resistance (Steinman 2009). Nevertheless, ARIs are one of the most common reasons for primary care consultations and account for approximately 75% of antibiotic prescriptions (Gill 2006; Gonzales 1997; Gonzales 2001), despite ARIs being predominantly viral and self limiting (Gonzales 2001).

The threat of antibiotic resistance

Antibiotic use creates biological pressure on selected bacterial populations to develop resistance (WHO 2012). Unnecessary prescribing of antibiotics for self limiting or viral infections and excessive use of broad spectrum antibiotics in place of narrower spectrum drugs are modifiable factors that contribute to resistance (WHO 2012). Influences on clinician's prescribing behaviours include clinical uncertainty and fear of disease progression; inadequate physician knowledge (Altiner 2012); underestimation of antibiotic resistance in clinical practice (Wood 2013) and perceived patient expectations for an antibiotic (Arroll 2002). Antibiotic prescribing for ARIs also creates a 'vicious cycle' by encouraging primary care patients to re-consult for similar conditions and reinforcing expectations for an antibiotic prescription (Butler 1998). Patients who are prescribed an antibiotic for a respiratory infection can develop bacterial resistance to that antibiotic for up to 12 months (Chung 2007; Costelloe 2010). The transient effect of antibiotic prescribing is sufficient to sustain a high level of antibiotic resistance in the community (Chung 2007). The development and spread of antibiotic resistance is an evolving global threat to public health (WHO 2012). The rational use of antibiotics is one of the most important strategies for preserving the therapeutic benefit of antibiotic treatment (WHO 2001; WHO 2012).

Description of the intervention

Shared decision making is the pinnacle of patient-centred care (Groves 2010). It describes health professionals and patients discussing the best available evidence for management options and a decision process in which patients are supported to choose options in accordance with their values and preferences (Makoul 2006). Some of the skills required of health professionals to facilitate shared decision making include proficient communication and rapport building skills as well as access to the best available evidence.

How the intervention might work

Shared decision making supports the principle of patient autonomy and the right to self determination (Elwyn 2012), and has been shown to improve patients' satisfaction with decisions and concordance of decisions with their values (Spatz 2012). As part of shared decision making, the potential benefits and harms of interventions, and the likelihood of each, are considered and discussed. Many patients elect for conservative treatment options after participating in shared decision making (Elwyn 2012). As shared decision making involves eliciting patients' expectations, clarifying any misperceptions and discussing the likely benefits and harms of interventions, this process may result in greater explicit consideration of the benefit-harm trade-off for antibiotics for ARIs, by both patients and health professionals. Antibiotic prescribing for ARIs may reduce as a result.

Why it is important to do this review

Several related Cochrane systematic reviews have been undertaken. Arnold and Straus (Arnold 2005) reviewed the effectiveness of interventions to improve antibiotic stewardship (including the decision to prescribe an antibiotic, and the type, dose and duration of antibiotic therapy) in outpatient care. However, broad inclusion criteria and subsequent heterogeneity of the identified interventions limited the generalisability of practice recommendations. Importantly, this review also did not focus on, or explicitly consider, shared decision making interventions for inclusion. The review by Stacey 2011 assessed the effectiveness of decision aids for people facing treatment or screening decisions. Decision aids are only one tool used to facilitate shared decision making in clinical care. Shared decision making may be enabled through methods other than, or in addition to, decision aids. Similarly, the review by Kinnersley 2007 evaluated the effect of interventions to encourage patient health communication and information seeking delivered prior to the primary care consultation, and shared some but not all components necessary for shared decision making to occur. Legare 2010 assessed the effectiveness of interventions to facilitate health professionals' uptake of shared decision making. However, these interventions were not restricted to a specific health condition and ARIs did not feature among the included studies. The growing interest in shared decision making for potential improvement in treatment decisions and patient outcomes is evident from Cochrane systematic reviews in other clinically important areas and diverse settings, including mental health (Duncan 2010) and paediatric oncology (Coyne 2013). Therefore, the effect of shared decision making on antibiotic prescribing for ARIs in primary care is an important addition to this body of literature. Moreover, shared decision making has become used as a means of reducing prescribing among primary care doctors (e.g. NPS MedicineWise, Australia) based on the assumption that people shown the evidence are less likely to demand antibiotics for common ARIs.


To assess whether shared decision making increases or reduces antibiotic prescribing for ARIs in primary care.


Criteria for considering studies for this review

Types of studies

Randomised controlled trials (RCTs) (individual level or cluster-RCTs) which evaluated the effectiveness of shared decision making in reducing antibiotic prescribing in primary care. Quasi-RCTs, quasi-experimental studies (controlled clinical trials), controlled before and after studies and interrupted time series analyses are not eligible.

Types of participants

  1. Clinicians who provide primary care (community practices, hospital-affiliated or government-run outpatient clinics); and/or

  2. Patients, of any age, diagnosed with an ARI (defined as an acute presentation of less than four weeks' duration, of any combination of symptoms of ARI) and parents and/or caregivers who make decisions on behalf of children who meet the criteria for diagnosis of an ARI.

Types of interventions

Shared decision making typically consists of a number of steps, including discussing the options, the pros/cons of each option and discussing the patient's understanding, values and preferences (Makoul 2006). It involves the patient and clinician partnering in the decision making process (Charles 1997). We will include trials where improving skills in shared decision making or facilitating shared decision making is stated by the authors, for example, as the focus or a component of the intervention. This may involve elements such as: a) training in skills (for example specific shared decision making communication skills training); b) providing tools which facilitate shared decision making (such as decision aids (Stacey 2011), Option Grids (Elwyn 2013), Decision boxes (Giguere 2012); or c) any combination of these or other elements. Interventions may be targeted at health professionals, patients and/or caregivers, or both. Interventions may be delivered in any primary care environment and there is no restriction on the mode or intensity of delivery.

We will exclude interventions that consist solely of the passive provision of patient information without the two-way sharing of information necessary for shared decision making, or which aim to enhance health professionals' and/or patients' general communication skills.

Types of outcome measures

Primary outcomes

Prescription of antibiotics (for example antibiotics prescribed per consultation, or a change in the population rate of antibiotic prescriptions per unit of time).

Secondary outcomes
  1. Number or rate of patient-initiated re-consultations for unresolved ARI (i.e. same illness episode).

  2. Incidence of colonisation with, or infection due to, antibiotic-resistant organisms.

  3. Incidence of hospital admission.

  4. Incidence of pneumonia (clinical with radiological confirmation).

  5. Incidence of acute otitis media complications (for example tympanic membrane perforation, contralateral otitis (in unilateral cases), mastoiditis, meningitis).

  6. Mortality due to respiratory illness or similar.

  7. All-cause mortality.

  8. Measures of patient and caregiver satisfaction.

  9. Measures of patient and caregiver satisfaction with the decision reached, decisional conflict and decisional regret.

  10. Measures of extent of patient involvement in the decision making process (for example OPTION; Elwyn 2003).

  11. Measures of treatment compliance or adherence to decision reached.

Search methods for identification of studies

Electronic searches

We will search the latest issue of the Cochrane Central Register of Controlled Trials (CENTRAL), which includes the Acute Respiratory Infections Groups' Specialised Register, MEDLINE (1966 to current date) and EMBASE (1990 to current date).

We will undertake a search of MEDLINE (Ovid) using appropriate MeSH terms and keywords and include the Cochrane Collaboration's search strategy (Higgins 2011) to identify RCTs (Appendix 1). We will use the MEDLINE search strategy to search CENTRAL and adapt it to search EMBASE. No language, publication date or publication status restrictions will be imposed on electronic database searches.

Searching other resources

We will search the bibliographies of retrieved articles and published reviews for additional studies. We will attempt personal communication with trial authors of significant publications and content experts to identify further published, unpublished or ongoing trials. We will search the National Institutes of Health registry of clinical trials ( and the World Health Organization's (WHO) clinical trials registry ( for completed and ongoing studies eligible for inclusion. We will search Web of Science and EMBASE to identify potentially relevant conference abstracts and proceedings.

Data collection and analysis

Selection of studies

We will merge the search results into reference management software (Endnote X6) and remove duplicate references. One review author (PC) will screen the titles and abstracts of retrieved records. We will identify multiple reports of single studies following the criteria recommended in the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2011). We will retrieve full-text copies of all potentially relevant articles for full-text evaluation. We will confirm the final list of eligible trials following discussion among the review authors (PC, TH, CDM). We will resolve disagreements by consensus. We will list excluded trials and reasons for their exclusion.

Data extraction and management

Two review authors (PC, TH) will independently extract data from each included trial using a specifically designed data extraction form. We will develop and pilot the data extraction form on a small number of studies. We will resolve disagreements by discussion and consensus, with the third author (CDM) acting as arbitrator if required. We will blind data extraction (i.e. without a priori knowledge of names of authors, institutions or publication title) and will include the following key study features.

  1. Trial characteristics and methodological quality – risk of bias (see below); trial design, including unit of randomisation and number of comparator arms; blinding; generation of allocation sequence; allocation concealment; number of participants; theoretical or conceptual basis of the intervention; number of intervention components; description of intervention and comparator arms; length of follow-up; sample size estimate (power calculation); and intention-to-treat (ITT) or per protocol analysis.

  2. Patient (and/or caregiver) characteristics - mean (or median) age, gender and socio-demographic variables; types of ARIs; duration of ARI prior to inclusion; co-morbidities; number of patients randomised to each intervention arm; number of patients completing the trial; reasons for withdrawal.

  3. Healthcare provider characteristics – mean (or median) age; gender; experience; primary care setting type.

  4. Outcome measures – all primary and secondary outcomes.

Assessment of risk of bias in included studies

The review authors (PC, TH, CDM) will assess the risk of bias of eligible studies using the 'Risk of bias' tool available in RevMan 2012 and the criteria explained in the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2011). We will assess the reliability of the sequence generation, allocation concealment, blinding (participants, personnel and outcome assessors), incomplete outcome data and selective outcome reporting bias, as well as other sources of bias. We will rank studies as high, low or unclear risk of bias as described in the Cochrane Handbook for Systematic Reviews of Interventions and present our assessments in a 'Risk of bias' summary figure (Higgins 2011). For cluster RCTs, we will assess additional sources of bias including recruitment bias, baseline imbalance between clusters, loss of clusters and incorrect analysis (Higgins 2011). We will perform a subgroup or sensitivity analysis comparing studies determined to have high versus low risk of bias to examine the effect of trial quality on the size and direction of effect.

Measures of treatment effect

Measures of treatment effect may include dichotomous (binary) and continuous primary or secondary outcome data. We will calculate the mean difference (MD) for continuous outcomes (for example number of antibiotic prescriptions) and risk ratio (RR) for dichotomous outcomes (for example mortality). We will use the standardised MD to combine trials that measure the same outcome on different measurement scales.

Unit of analysis issues

We will adjust the unit of analysis accordingly for cluster RCTs. We will avoid unit of analysis errors by conducting the analysis at the same level as the allocation, using a summary measurement from each cluster. This may reduce the power of the study depending on the number and size of the clusters. Analysis can occur at the level of the individual while accounting for the cluster in the data. We will seek statistical advice to determine the appropriate method (for example multilevel model, variance components analysis or generalised estimating equations).

Dealing with missing data

We will contact trial authors where reporting of data is incomplete in an attempt to retrieve the missing data. We will conduct an ITT analysis where possible and include all participants according to their originally randomised group regardless of the interventions they received. We will consider missing outcome data as treatment failures in the meta-analysis.

Assessment of heterogeneity

There may be considerable heterogeneity between included studies in terms of the specific interventions evaluated, participants, timing of the intervention and follow-up, and the measurement instruments and statistical techniques. We will use the I2 statistic to measure heterogeneity as recommended in the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2011). We will use a random-effects model for all meta-analyses.

Assessment of reporting biases

We will minimise reporting bias by conducting a comprehensive search for studies that meet the eligibility criteria, including grey literature and unpublished trials; and by contacting trials authors for missing information. If a sufficient number of studies are available we will create a funnel plot. If funnel plot asymmetry exists we will attempt to explain its possible causes.

Data synthesis

We will undertake meta-analysis where included trials are similar in design, interventions and outcomes. If possible, we will conduct a subgroup analysis of trials which incorporate shared decision making as part of multifaceted intervention and a subgroup analysis of trials in which shared decision making was the standalone intervention. If possible, we will combine trials in which the recipients of the intervention are health professionals in a subgroup analysis, as will those where the target intervention recipients are patients/parents. We will use RevMan 2012 to enter and analyse data to estimate a weighted treatment effect (with 95% confidence intervals (CIs)). We will analyse data using the random-effects model due to the heterogeneity expected from combined diverse shared decision making interventions.

However, meta-analysis will not be performed (with the exception of some subgroups) where significant heterogeneity is established between interventions. If it is not possible to conduct a meta-analysis, we will perform a narrative synthesis. The narrative synthesis will consider the four questions outlined in the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2011).

  1. What is the duration of the effect?

  2. What is the size of the effect?

  3. Is the effect consistent across studies?

  4. What is the strength of evidence for the effect?

Subgroup analysis and investigation of heterogeneity

If data allow, we will perform the following subgroup analyses:

  1. children versus adults;

  2. trials with low risk of bias versus high risk; and

  3. cluster-RCTs versus individually randomised studies.

Sensitivity analysis

We will perform a sensitivity analysis by excluding those studies found to have a higher risk of bias.


We thank the staff and editors of the Cochrane Acute Respiratory Infections Group, and sincerely give thanks to the Trials Search Co-ordinator (Sarah Thorning). We are grateful to Adrian Edwards and Sandra Arnold for their informed appraisal and suggested improvements to the protocol draft, and to N. Sreekumaran Nair for statistical review. Finally, we would also like to thank Sally Crowe, Anne Lyddiatt and Inge Axelsson for their consideration and feedback on the draft protocol.


Appendix 1. MEDLINE search strategy


  1. exp Respiratory Tract Infections/

  2. (respiratory adj2 (infection* or inflam*)).tw.


  4. sinusit*.tw.

  5. (acute adj2 rhinit*).tw.

  6. (rhinosinusit* or nasosinusit*).tw.

  7. common cold*.tw.


  9. (throat* adj2 (sore* or inflam* or infect*)).tw.

  10. laryngit*.tw.

  11. tonsillit*.tw.

  12. bronchit*.tw.

  13. bronchiolit*.tw.

  14. pneumon*.tw.

  15. (bronchopneumon* or pleuropneumon*).tw.

  16. Cough/

  17. cough*.tw.

  18. exp Otitis Media/

  19. otitis

  20. (aom or ome).tw.

  21. Croup/

  22. (croup or pseudocroup or laryngotracheobronchit* or laryngotracheit*).tw.

  23. or/1-22

  24. exp Anti-Bacterial Agents/

  25. antibiotic*.tw,nm.

  26. or/24-25

  27. 23 and 26

  28. exp Decision Making/

  29. exp decision support techniques/

  30. exp Decision Theory/

  31. (decision* or decid* or option* or choice* or choose*).tw.

  32. exp Informed Consent/

  33. (informed adj3 (consent* or agree* or assent*)).tw.

  34. Health Knowledge, Attitudes, Practice/

  35. "Attitude of Health Personnel"/

  36. professional-patient relations/ or physician-patient relations/

  37. exp Consumer Participation/

  38. ((patient* or consumer* or carer* or parent* or child* or individual* or person* or interpersonal*) adj5 (participat* or involv* or deliberat* or collabor* or cooperat* or co-operat* or engag* or consult* or feedback* or interaction*)).tw.

  39. (values* or prefer*).tw.

  40. exp Communication/

  41. (communicat* or negotiat* or facilitat* or discuss*).tw.

  42. health education/ or exp consumer health information/ or patient education as topic/

  43. ((patient* or consumer* or parent*) adj3 (educat* or informat*)).tw.


  45. ((patient* or client* or subject or person or consumer* or family or families or carer* or care giver*) and (professional* or physician* or clinician* or practitioner*)).tw. )

  46. Risk Assessment/

  47. ((check or clarify) adj3 understanding).tw.

  48. (patient adj2 (understanding or expect*)).tw.

  49. problem defin*.tw.

  50. (ask adj2 question*).tw.

  51. (assess* adj2 risk*).tw.

  52. self-manag*.tw.


  54. checklist*.tw.

  55. (goal adj2 set*).tw.



  58. (action* adj2 plan*).tw.

  59. or/28-58

  60. 27 and 59

  61. 27 and 60

Contributions of authors

Tammy Hoffmann (TH) conceived the original idea for the review.
Peter Coxeter (PC) was responsible for drafting the protocol.
TH and Chris Del Mar (CDM) contributed content and methodological expertise and provided advice and guidance on the development of the draft protocol and final editing.

Declarations of interest

None known.

Sources of support

Internal sources

  • No sources of support supplied

External sources

  • National Health and Medical Research (NHMRC), Australia.

    The Centre for Research Excellence in Minimising Antibiotic Resistance from Acute Respiratory Infections (CREMARA; NHMRC grant APP1044904).