The use of electronic alerts in primary care computer systems to identify the excessive prescription of short-acting beta2-agonists for people with asthma: a systematic review

Computers are increasingly used to improve prescribing decisions in the management of long-term conditions however the effects on asthma prescribing remain unclear. We aimed to synthesise the evidence for the use of computerised alerts that identify excessive prescribing of short-acting beta2-agonists (SABAs) to improve asthma management for people with asthma. MEDLINE, CINAHL, Embase, Cochrane and Scopus databases (1990–2016) were searched for randomised controlled trials using electronic alerts to identify excessive prescribing of SABAs for people with asthma in primary care. Inclusion eligibility, quality appraisal (Cochrane risk of bias tool) and data extraction were performed by two independent reviewers. Findings were synthesised narratively. A total of 2035 articles were screened and four trials were eligible. Three studies had low risk of bias: one reported a positive effect on our primary outcome of interest, excessive SABA prescribing; another reported positive effects on the ratio of inhaled corticosteroid (ICS)-SABA prescribing, and asthma control; a third reported no effect on outcomes of interest. One study at high risk of bias reported a reduction in exacerbations and primary care consultations. There is some evidence that electronic alerts reduce excessive prescribing of SABAs, when delivered as part of a multicomponent intervention in an integrated health care system. However due to the variation in health care systems, intervention design and outcomes measured, further research is required to establish optimal design of alerting and intervening systems.


INTRODUCTION
Asthma affects an estimated 300 million individuals worldwide and almost 30 million people below 45 years of age in Europe. 1 With a prevalence of 6% in 2016-2017 2 and an estimated 5.4 million people receiving treatment, 3 asthma is the most common long-term condition in the United Kingdom (UK). 4 In 2015-2016 there were approximately 1.4 million hospital admissions for asthma in England and Wales 5 and whilst the number of asthma deaths has fallen by five percent from 2015 to 2016, this remains higher than the 15-year average. 6he National Review of Asthma Deaths (NRAD) identified that, of 195 deaths from asthma between 2012 and 2013, 39% of those who died were prescribed more than 12 short-acting beta 2agonist inhalers (SABAs) in the previous year, with 4% prescribed more than 50 SABAs in the same time period. 7][15][16][17] Asthma control is defined as the extent to which the manifestations of asthma, commonly wheeze, shortness of breath, chest tightness, cough and variable expiratory airflow limitation, can be observed in the patient, or have been reduced or removed by treatment. 18,19Control can be assessed by current symptoms and future risk of adverse outcomes; 8 patients with good asthma control have less need for SABAs and require no emergency visits. 20Following the National Review of Asthma Deaths, the electronic surveillance of prescription refill frequency was recommended to alert clinicians to people with asthma prescribed excessive quantities of SABAs. 7eneral practice computer systems increasingly use reminders and alerts for preventative care and disease management 21,22 including asthma. 23,24Computer decision support systems (CDSSs), defined as 'active knowledge systems which use two or more items of patient data to generate case-specific advice,' 25 have the potential to influence prescribing behaviour.Efforts to automate reminder systems and improve efficiency in both prevention and chronic disease management have yielded some improvements when assessed using randomised trials. 26However, evaluations suggest CDSSs do not consistently improve prescribing behaviour and clinical outcomes 27 and the role of electronic alerts to identify and reduce excessive SABA prescribing remains unclear.This review aims to provide a systematic overview of the extent to which electronic alerts in primary care computer systems can identify excessive prescribing of SABAs, and assess the impact of these interventions on SABA prescribing, asthma management and asthma control.

Study selection
Fig. 1 details the systematic search and eligibility assessment.9][30][31] No ongoing or unpublished trials were identified.Risk of bias is reported in Table 1.

Study characteristics
RCTs were conducted between 2001 and 2015; two recent studies (published in 2014 and 2015) were carried out within integrated healthcare systems in the United States 30 and Canada 31 respectively, whilst two older studies were from the United Kingdom (published in 2001 and 2002). 28,29The features of interventions are summarised in Table 2.A detailed description of the interventions can be found in Supplementary Appendix 1. Methods of alerting included computerised prompts, 28,29 an electronic message to physicians 30 and a dashboard alert. 31Three of the four studies included people with asthma under-18 years of age, of which the lower age range for inclusion was 5 years of age, 31 12 years of age 30 and one not reported. 28No studies stratified findings by age range.The characteristics of studies are presented in Table 3.

Primary outcome
A brief summary of findings is presented in Table 4.A detailed description of findings can be found in Table 5.
Study-defined excessive SABA prescribing.Zeiger et al. 30 reported a reduction in the number of patients being dispensed excessive SABAs (p = 0.007) and an increase in length of time between SABA prescriptions (p = < 0.001).These effects were noted in the subgroup of patients without prior asthma specialist care who received the intervention (p = < 0.001).Tamblyn et al. 31 reported excessive SABA (expressed as fast-acting b-agonist) dispensing as a composite primary outcomethe rate of out-of-control asthma episodes-which included emergency department (ED) attendances and hospitalisations.It was therefore not possible to determine the effect of the intervention on SABAs alone.
Secondary outcomes SABA prescribing.Zeiger et al. 30 reported a reduction in the number of SABAs dispensed at 3 months (p = < 0.001), 6 months (p = < 0.001) and 12 months (p = < 0.001) in the subgroup of patients without prior specialist asthma care.Eccles et al. 29 reported no significant effect of a computerised decision support system on SABA prescription in the 12 months before and after the intervention (odds ratio (OR) 1.04, 95% CI 0.83-1.31).
ICS prescribing.Zeiger et al. 30 reported no difference in the number of patients dispensed ICS (not as a combination inhaler), whilst Eccles et al. 29 reported no difference in the number of patients prescribed ICS before and after the intervention.McCowan et al. 28 reported no between-group difference in maintenance prescribing patterns and no difference in the proportion of patients classified by management step.
Ratio of ICS-SABA prescribed.Tamblyn et al. 31 reported an increase in the ratio of ICS-SABAs dispensed (mean difference (MD) 0.27, p = 0.03; 95% CI 0.02-0.51)with higher ratios reported in both subgroups of patients whose asthma was controlled and out of control at the start of the study.Zeiger et al. 30 reported a controller (ICS) to total medication ratio of greater or equal to 0.5 at 3, 6 and 12 months, in particular for those without prior asthma specialist care.As the ICS to total medication ratio was calculated by the number of ICS canisters or 30-day supplies of oral controller medications dispensed, divided by the total number of controller Fig. 1 PRISMA flow chart units and SABA inhalers, it was not possible to determine the ICS-SABA ratio specifically.
ICS/LABA prescribing.Zeiger et al. 30 reported an increase in the number of patients in the subgroup without prior asthma specialist care dispensed an ICS-LABA inhaler at 3 months (p = 0.004), 6 months (p = < 0.001) and 12 months (p = 0.03).

Asthma reviews.
McCowan et al. 28 reported no reduction in the number of patients attending practice-initiated asthma reviews.
Study-defined asthma exacerbations.McCowan et al. 28 observed a reduction in asthma exacerbations, with 8% of patients who received the intervention reporting an acute asthma exacerbation compared to 17% in the control group (OR 0.42; 95% CI 0.21-0.85).However, there was no difference in the use of oral steroids to manage these attacks in the intervention and control group.Zeiger et al. 30 reported no difference in the numbers of patients prescribed oral steroids for an exacerbation irrespective of prior asthma specialist care status.Neither McCowan et al. 28 nor Zeiger et al. 30 explicitly defined an asthma exacerbation.Eccles et al. 29 reported no difference in the numbers of patients prescribed oral steroids before and after the intervention but did not specifically report asthma exacerbations.
Unscheduled consultations for asthma.Eccles et al. 29 found no between-group reduction in the number of primary care asthma consultations, whilst McCowan et al. 28 reported that patients who received the intervention initiated fewer primary care consultations (OR 0.59; 95% CI 0.37-0.95).However neither study clarified whether consultations were scheduled or unscheduled.Both McCowan et al. 28 and Zeiger et al. 30 reported no effect of the intervention on ED attendances or hospitalisations for asthma.Tamblyn et al. 31 reported ED visits and hospitalisations for asthma as a composite outcome defined as 'rate of out-of-control asthma episodes,' therefore secondary care consultations for asthma could not be specifically determined.
Asthma control.Tamblyn et al. 31 reported a reduction in the rate of out-of-control asthma events, defined as a composite outcome of excessive SABA use, ED attendance and hospitalisations for asthma, in the sub-group of patients whose asthma was out-ofcontrol at the beginning of the study (MD −28.4,p = 0.04; 95% CI −55.6, −1.2).When stratified by intervention component, the rate of out-of-control asthma events further reduced when patients were treated with CDSS alone (rate difference (RD) −36.9/100 per year, p = 0.01) in comparison to those threated with both CDSS and the asthma home care monitoring programme (RD -28.4,p = 0.04; 95% CI −55.6,−1.2).

Main findings
Given the few studies identified, the evidence to support the use of alerts to reduce excessive SABA prescribing in primary care is limited but promising.This review found that electronic alerts, when delivered as a multicomponent intervention in an integrated health care system, have the potential to successfully identify and reduce excessive SABA prescribing.The greatest effect on our outcomes of interest occurred when an alert, delivered in an integrated health care system, flagged excessive SABA prescribing to clinicians and prompted/facilitated intervening actions including referral to an allergy specialist and a patient information letter. 30here is however no consensus definition on excessive SABA use in the literature.Definitions of excessive SABA use vary from three or more SABAs per quarter 34 to 12 or more SABAs a year. 7Of the two included studies in which excessive SABA use was reported, definitions varied from greater or equal to seven canisters per year (at least four puffs per day per year) 30 to greater than 250 doses of SABA in the past 3 months. 31he identification and reduction of excessive SABA use in Zeiger et al. 30 study was facilitated by alerts that were not restricted to point-of-care presentation.Such methods of alerting may offer a solution to the dilemma that automatic provision of decision support at point of decision making neither guarantees clinician uptake or engagement 27 nor predicts improvements in process of care or patient outcomes. 35The two studies that showed greatest effects on our outcomes of interest were those carried out recently (in the past three years), in which decision support was integrated with electronic health record (EHRs). 30,31Although research has indicated that advice presented within EHRs is less likely to improve care or outcomes than stand-alone programmes, 35 our findings support the evidence that computer decision support integrated with clinician workflow is associated with improved outcomes. 22,32Zeiger et al's finding of a reduction of excessive SABA use supports the evidence that electronic health records and electronic messaging in an integrated health care system increases clinician adherence to evidence-based guidelines. 36n one study, users failed to engage with decision support 29 whilst in another, clinicians failed to interact with the CDSS in approximately 60% of cases. 31However it is not clear whether levels of engagement were consistent between clinicians and whether clinician interaction declined over time.There may be valid reasons to account for the variability in decision support engagement which include technical design of the CDSS, the setting in which the system is deployed and the characteristics of users and the patients treated. 27The higher user engagement in Tamblyn et al. 31 is likely due to the increased ease of use associated with more recent, sophisticated decision support integrated within a comprehensive EHR system that accesses pharmacy, as well as primary and secondary care data.This is in comparison to older interventions, such as that of Eccles et al. 29 where the intervention was not integrated with the EHR, and in which pharmacy and secondary care data was not captured.
Alerts integrated within EHRs may interrupt clinician workflow and result in "alert fatigue" with up to 96% of alerts over ridden or ignored in one study. 37Following user feedback, Eccles et al. 29 altered decision support to trigger when a clinician entered a relevant morbidity code rather than being automatically activated upon entering a patient's medical record.Whilst this may have been an attempt to minimise alert fatigue it did not improve CDSS user interaction.It is likely that very low CDSS interactions reflected clinical guidelines being located in a separate system not supported within clinician workflow.
Qualitative research used in conjunction with RCTs has the potential to beneficially influence intervention design and delivery 38 however none of the included studies reported using qualitative methods to complement intervention design.Such methods may optimise alert design, improve clinician interaction with decision support and aid the interpretation of results.

Strength and limitations
As interventions to improve prescribing volumes/rates do not necessarily result in more 'appropriate' prescribing or improved patient outcomes 27 both process and clinical outcomes were assessed in this review.However few studies met our inclusion criteria, with only one study reporting our primary outcome of interest.Due to the limited number of published reports of randomised controlled trials in our analyses, there may be possibility of publication bias or selective reporting.Interventions in the two older studies 28,29 were poorly described which may have limited our interpretation of the findings.We were unable to conduct a meta-analysis due to heterogeneity in intervention design and outcomes evaluated.Due to a lack of reporting no conclusions could be made on health economics.

Implications for clinical care and future research
There is an increased focus on the digitalisation of the NHS in an attempt to improve safety and quality of care. 39Recommendations have called for the national use of electronic alerts to identify excessive prescribing of SABAs in the UK. 7,40Due to the few studies identified in this review, the role of alerts to reduce excessive SABA prescribing in the UK's publically funded national health service (NHS) remains unclear.Integrated care can take many forms involving collaboration between policy providers and commissioners and between service providers, however benefits arise primarily when clinical teams and services are brought together and incentives are aligned to support service improvement. 36It is likely that a combination of design, technical capabilities and variety of intervention components, when delivered in an integrated health care system, facilitated the improvements to SABA prescribing and asthma management identified in recent studies.In a publicly funded health care system such as the NHS it remains challenging to deliver such improvements.However this review identifies a number of areas where potential exists and where further research is recommended.
In the UK, 78% of bronchodilators are issued on repeat prescription 41 yet research fails to address the use of alerts at this point in the prescribing process.Furthermore, two studies from the UK, carried out over a decade ago, did not integrate interventions within EHRs, in contrast to more recent studies from North America.Future research should consider novel ways to

Features in addition to alert
McCowan et al. 28 Eccles et al. 29 Zeiger et al. 30 Tamblyn et al. 31 Guideline decision support Allergy Specialist Referral Specialist asthma nurse home-care monitoring Self-management plan Patient advice sheet

Patient information letter
The use of electronic alerts in primary care computer S McKibben et al.  deliver SABA alerts as a sole intervention and/or as part a multicomponent intervention in care.Furthermore, the point in the prescribing process at which a SABA alert will have greatest impact should be explored.Interventions should be trialled both in and outside of the consultation to target clinicians and people with asthma.
Standardised methods for the design and reporting of CDSS interventions are recommended to enable a thorough evaluation of process and clinical outcomes.We support previous recommendations that studies use a taxonomy or framework such as Kawamoto et al. 21and Berlin et al. 42 to theoretically underpin the design and reporting of interventions. 25,43An explicitly defined outcome set that includes more standardised endpoints, e.g., excessive SABA prescribing and asthma exacerbations, may help the translation of research findings into clinical practice.We recommend that future studies report on both the implementation process and health economics outcomes associated with CDSS-based alerts.Third party external validation of CDSSs is recommended. 35Systems evaluation involving academiccommercial collaborations and user testing should be explored to aid the translational research process.End-users should be involved in the design of alerts to optimise interventions and trial design.Future studies should consider mixed methods designs that incorporate qualitative methods before, during and/or after an RCT.Such methods may help determine the barriers and facilitators to alert usage in practice, as well as assisting in the development of alerts that are transferable to the real-world clinical setting.

CONCLUSION
There is some evidence that electronic alerts integrated with EHRs and delivered as part of a multicomponent intervention reduce excessive SABA prescribing.Due to variations in health care systems, intervention designs and outcomes measured, further research is required to determine the effects of alerts on excessive SABA prescribing in a publically funded health system.Future research should determine the point at which novel alerts will most effectively reduce excessive SABA prescribing and be accepted by users.

MATERIALS AND METHODS
The study design was a systematic review, performed following PRISMA-guidelines. 44Methods of analysis and inclusion criteria were specified in advance and documented in a protocol 45 and registered on PROSPERO (International Prospective Register of Systematic Reviews; www.crd.york.ac.uk/PROSPERO/) with identifier CRD 42016035633.

Selection criteria
Studies were considered for inclusion in this systematic review according to the following criteria.
Participants.Studies that delivered care to adults and/or children with asthma, in a primary care setting.Primary care was defined as healthcare delivered in a community setting, most commonly in general practice, by a clinician, nurse or pharmacist.Non-clinical staff for example administrators and/or receptionists were also included.
Intervention.CDSSs were included if they incorporated an alert initiated by the excessive prescribing or dispensing of SABAs for asthma.Alerts used in secondary or tertiary care, for other respiratory conditions that were not asthma were excluded.
Comparison.The comparator was 'usual care.'The use of electronic alerts in primary care computer

Table 1 .
Risk of biasInterpretations in relation to published literature Our findings support previous research on the use of computer decision support for long-term conditions including asthma, chronic obstructive pulmonary disease, diabetes and osteoporosis which found that interventions consisting of multiple components are associated with greater improvement in outcomes than singletarget interventions with fewer components.

Table 3 .
Characteristics of included studies

Table 4 .
Summary of

Table 5 .
Detailed description of

Table 5 continued
This intervention increased the mean ratio ICS-SABA use and reduced the rate of outof-control asthma episodes.Greatest effect were seen in the subgroup of patients with out-of-control asthma at study entry as treated by CDS alone.In 60% of outof-control visits the decision support was not accessed by clinicians.No data was available on use over time.