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International Journal for Quality in Health Care 16:275-284 (2004)
International Journal for Quality in Health Care vol. 16 no. 4 © International Society for Quality in Health Care and Oxford University Press 2004; all rights reserved

Optimising care of acute coronary syndromes in three Australian hospitals

Ian A. Scott1, Charles P. Denaro2, Annabel C. Hickey1, Cameron Bennett2, Alison M. Mudge2, Daniela C. Sanders3, Justine Thiele4 and Judy L. Flores5

1 Department of Internal Medicine, Princess Alexandra Hospital, Brisbane, Queensland, 2 Department of Internal Medicine, Royal Brisbane Hospital, Brisbane, Queensland, 3 Department of Pharmacy, Princess Alexandra Hospital, Brisbane, Queensland, 4 Department of Pharmacy, Royal Brisbane Hospital, Brisbane, Queensland, 5 Department of Medicine, Queen Elizabeth II Hospital, Brisbane, Queensland, Australia

Objective. To improve quality of in-hospital care of patients with acute coronary syndromes using a multifaceted quality improvement program.

Design. Prospective, before and after study of the effects of quality improvement interventions between October 2000 and August 2002. Quality of care of patients admitted between 1 October 2000 and 16 April 2001 (baseline) was compared with that of those admitted between 15 February 2002 and 31 August 2002 (post-intervention).

Setting. Three teaching hospitals in Brisbane, Australia.

Study participants. Consecutive patients (n = 1594) admitted to hospital with acute coronary syndrome [mean age 68 years (SD 14 years); 65% males].

Interventions. Clinical guidelines, reminder tools, and educational interventions; 6-monthly performance feedback; pharmacist-mediated patient education program; and facilitation of multidisciplinary review of work practices.

Main outcome measures. Changes in key quality indicators relating to timing of electrocardiogram (ECG) and thrombolysis in emergency departments, serum lipid measurement, prescription of adjunctive drugs, and secondary prevention.

Results. Comparing post-intervention with baseline patients, increases occurred in the proportions of eligible patients: (i) undergoing timely ECG (70% versus 61%; P = 0.04); (ii) prescribed angiotensin-converting enzyme inhibitors (70% versus 60%; P = 0.002) and lipid-lowering agents (77% versus 68%; P = 0.005); (iii) receiving cardiac counselling in hospital (57% versus 48%; P = 0.009); and (iv) referred to cardiac rehabilitation (17% versus 8%; P < 0.001).

Conclusions. Multifaceted approaches can improve care processes for patients hospitalized with acute coronary syndromes. Care processes under direct clinician control changed more quickly than those reliant on complex system factors. Identifying and overcoming organizational impediments to quality improvement deserves greater attention.

Keywords: acute coronary syndrome, Australia, in-hospital care, quality improvement

Address reprint requests to Ian A. Scott, Department of Internal Medicine, Princess Alexandra Hospital, Brisbane, Queensland, Australia. E-mail: ian_scott{at}health.qld.gov.au

Accepted for publication February 25, 2004.

In Australia, ~12 400 deaths and 156 000 hospitalizations occur every year as a result of acute coronary syndromes (ACS), comprising acute myocardial infarction and unstable angina [1]. Seeking to ensure optimal in-hospital management of patients with ACS, the Cardiac Society of Australia and New Zealand and the National Heart Foundation have issued evidence-based clinical practice guidelines [2]. However, despite the release of similar guidelines from the American College of Cardiology/American Heart Association [3], studies in the US have shown persistent underutilization of effective therapies such as thrombolysis [4], aspirin [5], ß-blockers [6], angiotensin-converting enzyme (ACE) inhibitors [7], lipid-lowering agents (LLAs) [8], and cardiac rehabilitation [9].

Various investigators have shown that decision support (such as clinical guidelines, pathways, and checklists) [10,11], audit and feedback [12,13], opinion leaders [14], academic detailing [15], targeted education [16], and system redesign [17] can all assist in optimizing the quality of in-hospital care of ACS. Recently conducted studies in the US have shown that concurrent use of several of these interventions in multifaceted, multisite quality improvement programs can improve processes and outcomes of care [18,19].

It is uncertain how the quality of care of ACS in Australia compares with that reported in other developed countries, and if quality improvement interventions (QIIs) such as those reported in the US are likely to exert favourable effects within the Australian health care system. In addressing these issues, a consortium of three teaching hospitals in Brisbane, Australia, was formed in mid-2000, with the aims of: (i) evaluating the quality of existent in-hospital care of patients admitted with ACS using explicit, agreed clinical indicators; (ii) devising and implementing quality improvement interventions that targeted poorly performing indicators; and (iii) assessing the impact of such interventions on quality of care over time.


   Methods
Top
 Methods
Results
 Discussion
 References
 
Setting
Study hospitals were two tertiary hospitals (hospitals 1 and 2) and one community hospital (hospital 3) in Brisbane, Australia: hospital 1 had 640 beds, and on-site coronary angiography, angioplasty, and cardiac surgery facilities; hospital 2 had 720 beds, and angiography and angioplasty only facilities; and hospital 3 had 165 beds and no on-site invasive cardiac facilities. In hospitals 1 and 2, patients were admitted under either cardiologists (the majority) or general physicians, while those admitted to hospital 3 received shared care from general physicians and a visiting cardiologist.

Subjects
Eligible subjects were consecutive patients between 1 October 2000 and 31 August 2002 presenting either acutely with ACS to the emergency department or by urgent transfer from another hospital, or who developed ACS whilst an in-patient for other reasons. Subjects were accepted if, following review of medical records and discussion with attendant clinical teams, the following criteria were fulfilled at 48 hours following presentation: diagnosis of ACS on the part of the admitting medical or cardiology registrar, and elevated serum cardiac markers (creatine kinase to more than twice, or troponin to >1.5 times the upper normal reference range). In uncertain cases, adjudication was provided by independent physician review of the case notes.

Patients who died within 12 hours of initial presentation were excluded. Cases were prospectively ascertained within 48 hours of presentation to study hospitals by review of admission logs in emergency departments and coronary care units, and liaison with on-take medical units. Patients transferred from one study hospital to another, and those developing ACS whilst already in hospital for other reasons, were evaluated only once as one episode of hospital care from the time of onset of declarative symptoms, and were assigned to whichever hospital accounted for most (>50%) of the total in-patient stay.

Data collection
Trained abstractors extracted the following data from hospital records of confirmed cases: baseline clinical characteristics; mode of clinical presentation (at either referring or study hospital); in-hospital interventions, complications and deaths; and medications, investigation results, and clinical parameters at, or ≤24 hours before, discharge or death. Data accuracy were confirmed by reabstraction of a randomly selected 5% of cases by independent physicians with excellent agreement (kappa score [20] = 1) for case definition, good agreement (>0.8) for medications and investigation results, and satisfactory agreement (>0.7) for all other data items.

Clinical indicators
Based on a review of published research in developing local clinical practice guidelines [2,3], an expert panel of cardiologists and general physicians developed a set of process indicators for assessing quality of care (Table 1). Each indicator was defined as either the proportion of eligible or of ideal patients (definite indication or no contra-indication, respectively) who received the stated intervention. Criteria for determining patient eligibility for each intervention were formulated by group consensus. Outcome indicators were overall rates of in-hospital death, reinfarction, and 30-day readmission (all-cause and same-cause) for those patients not discharged to another hospital, and median length of hospital stay (LOS) for patients discharged alive and not transferred.


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  		Table 1 Clinical indicators and eligibility criteria

 

Aggregated data from all hospitals for each indicator comprised the primary measure of quality in identifying suboptimal care, and in comparing our results with those of similar studies conducted elsewhere.

Quality improvement interventions
Research increasingly suggests that improvements in quality of care are greater if: (i) multiple, relatively simple, and low-cost interventions are employed concurrently as opposed to single, high-cost sophisticated interventions; (ii) quality improvement efforts are developed and driven by local ‘clinical champions’; (iii) regular feedback of credible, evidence-based performance data is a key component; and (iv) there is involvement of multidisciplinary stakeholders [21–23]. After reviewing published evidence [24], the following quality improvement interventions were chosen as worthy of evaluation (further details are available at http://www.health.qld.gov.au/bcc).

  1. Development and active implementation of locally endorsed, evidence-based clinical practice guidelines for all in-hospital care [25]. In addition to on-line full-text guidelines, summarized versions with flow diagrams were distributed as pocket guides, wall charts, and laminated desk top cards.
  2. Reminder tools to support adherence to guideline recommendations: bedside checklists; chart stickers; detailing of doctors by clinical pharmacists; and case-based education meetings and presentations.
  3. Clinical indicator reports distributed (by e-mail, as hard copies and in hospital newsletters) to all clinicians involved in ACS care at each hospital every ~6 months. Indicator reports were discussed in open forums in emergency departments, cardiology units, and general medicine wards, were mediated by opinion leaders, and involved doctors, nurses, and (with the exception of emergency departments) clinical pharmacists.
  4. A pharmacist-mediated patient education program that promoted patient self-management and included patient resource packages, management plans, and medication lists [26].
  5. Electronically generated discharge summaries containing management targets and medication changes, which, in the process of being signed off, reminded junior medical staff of any omissions in care.
  6. Establishment of multidisciplinary teams to introduce strategies aimed at improving suboptimally performing clinical indicators. Examples of practice reform that occurred in one or more hospitals included: (i) fast-track triaging of patients in the emergency department using a rapid assessment cubicle staffed by a dedicated nursing team; (ii) flow-chart protocols for assessing and managing chest pain aimed at ensuring more appropriate referrals to cardiology units and prevention of inappropriate discharge [27]; (iii) senior nurse- or intern-initiated protocols for electrocardiograms (ECGs), lysis, and haemodynamic monitoring; and (iv) standardized ascertainment and referral of patients to outpatient cardiac rehabilitation programs.

The majority of interventions listed (1–5) commenced within 4 weeks of the official start date of 17 April 2001 (except for checklists and chart stickers which commenced on 1 December 2001), were implemented in a standardized fashion, and underwent little change throughout the intervention period. In contrast, the formation of health care teams and instigation of practice reforms (intervention 6) occurred in a more ad hoc fashion at different times in response to identified problems within specific contexts. On average, these system-of-care changes did not occur until 12 months after the beginning of the intervention period.

Program governance and personnel
Program activities were targeted at ~280 health care providers working within emergency, cardiology, and general medicine departments of participating hospitals. The program governance structure comprised a steering executive (n = 5), a multidisciplinary leadership group (n = 25), and five separate working groups each comprising 10–20 individuals: decision support; performance measurement and feedback; clinical information systems; patient self-management and consumer support; and hospital general practice integration. Program support staff included a senior program manager, two project managers, four full-time equivalent (FTE) data abstractors/research assistants, and 2.5 FTE clinical pharmacists. Many other practising clinicians assisted in the design and implementation of program interventions.

Outcome measures
We used a prospective before and after design to evaluate improvement in care following the implementation of quality improvement interventions. The proportions of eligible or ideal patients who received specific forms of care during a baseline period (between 1 October 2000 and 17 April 2001) were compared with those observed during the last 6 months of the intervention period (between 15 February 2002 and 31 August 2002; hereafter referred to as the post-intervention cohort).

Statistical analysis
Proportions, means, and medians were compared using chi-square, parametric (t-test), and non-parametric (Mann–Whitney U-test) methods, respectively. Statistical significance was denoted by P values ≤0.05.

Ethical approval
Study methods were approved by the Princess Alexandra and Royal Brisbane Hospital Research Ethics Committees.


   Results
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 Methods
 Results
Discussion
 References
 
Patient characteristics
The quality of care of 1594 patients with ACS was evaluated over a 23 month period, with 428 patients enlisted during the baseline period and 1166 following initiation of QII, the last 435 of which comprised the post-intervention cohort. Patient characteristics of the entire cohort are listed in Table 2. The mean age of patients was 68 years, 65% were male, 40% had previous history of ACS, and the prevalence of hypertension, hyperlipidaemia, diabetes and smoking was 56%, 40%, 22%, and 25%, respectively. The majority (63%) of patients received care from a cardiologist and most (72%) were direct presentations to hospital from the community. There were no significant differences in patient characteristics, specialty of admitting physician, or source of admission between baseline, intervention, and post-intervention cohorts.


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  		Table 2 Patient characteristics

 

Changes in care
The baseline audit showed that >80% of eligible patients received thrombolysis, ß-blockers, and antiplatelet agents (Table 3). However, in a quarter or more of cases there were delays in performing ECGs and administering thrombolysis in the emergency department, and failure to measure serum lipids, prescribe ACE inhibitors, provide in-hospital cardiac counselling, refer to an outpatient cardiac rehabilitation program, and undertake non-invasive risk stratification. In addition, almost a fifth of eligible patients were not receiving LLAs at discharge.


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  		Table 3 Comparisons of clinical indicators before and after implementation of quality improvement interventions

 

Following quality improvement interventions, there were significant improvements in: the timeliness of ECG performance at presentation (70% of patients undergoing ECG within 10 minutes versus 61%; P = 0.04); prescribing of ACE inhibitors (70% versus 60%; P = 0.002) and LLA (77% versus 68%; P = 0.005) in all patients at discharge; use of in-patient cardiac counselling (57% versus 48%; P = 0.009); and referral to outpatient rehabilitation (17% versus 8%; P < 0.001) (Table 3). These improvements occurred in association with reduced median length of stay (6 versus 7 days; P = 0.01) and no increase in readmission rates. For some indicators that showed significant change, such as discharge prescribing of ACE inhibitors and LLAs, improvement occurred within 6 months of QII initiation and continued in a linear fashion over the duration of the program, whereas for others such as time to first ECG on arrival at emergency department and referral to cardiac rehabilitation, improvement was delayed until after a lag period of ≥12 months following commencement of QII (Figure 1).



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  		Figure 1 Temporal trends in clinical indicators showing significant change.

 

No significant change was seen in the proportions of eligible or ideal patients undergoing lipid assessment, being prescribed ß-blockers or antiplatelet agents, or undergoing coronary angiography or non-invasive risk stratification. In the case of thrombolysis all ideal patients received this treatment, and in the case of ß-blockers or antiplatelet agents, rates in the baseline period were already >75%. The vast majority (>90%) of ideal patients in both periods underwent coronary angiography, in contrast to no more than 30% of ideal patients who underwent non-invasive risk stratification.


   Discussion
Top
 Methods
 Results
 Discussion
References
 
This study suggests that a multifaceted quality improvement program can achieve significant improvements in the quality of care of patients hospitalized with ACS.

Comparing our results with those reported elsewhere, it is worth noting that quality of routine care before QII implementation in study hospitals was similar to that reported contemporaneously from US hospital registry data [28] and, in the case of ß-blockers and ACE inhibitors, superior to results reported from European patient survey data collected after hospital discharge [29] (Table 4). After initiation of QII, the proportions of highly eligible patients who received adjunctive therapies were equivalent to those achieved in quality improvement programs similar to ours, and, in the case of lipid measurement and coronary angiography, were increased further (Table 5). The results of the Co-operative Cardiovascular Project (CCP) [18] are less impressive than those reported here and from other studies [19,30]. This may be due to CCP using performance feedback as the key intervention, whereas our study and the GAP (Guidelines Applied to Practice) initiative [19] combined feedback with intense decision support and educational interventions.


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  		Table 4 Comparison of selected quality indicators with other contemporary reports of routine care (% eligible patients)

 

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  		Table 5 Comparison of selected quality indicators with other contemporary reports of quality improvement interventions (% ideal patients)

 

Understanding variation in indicator improvement
Changing the behaviour of clinicians with respect to processes of care over which they had direct control (e.g. drug prescribing) was easier to achieve than cross-disciplinary collaboration and/or organizational changes (e.g. changes in acute emergency department management, cardiac rehabilitation referral, and risk stratification requests). Questionnaire surveys and focus group discussions (data not shown) indicated that many clinicians welcomed feedback and that program activities had influenced their practice. The involvement of discipline-specific opinion leaders, the sharing of resources and ideas between participating hospitals, the use of indicator data in non-judgemental fashion, and engagement with practising clinicians in detailing problems and solutions were key success factors. Based on our experience, we would recommend that dissemination of decision support tools and feedback of accurate performance data need to be coupled with the nurturing of multidisciplinary groups that focus on identifying and overcoming system-of-care barriers to best practice. Indicator feedback by itself may not be sufficient to generate momentum for system change.

Study limitations
In the absence of a control group, the improvements in care observed here may simply reflect secular trends in care rather than effects of our quality improvement program. However, randomized trials of quality improvement in health care are difficult to perform because of contamination bias, clustering effects, and problems with choosing appropriate units of randomization and analysis [31]. We contend that our interventions were exerting a real effect given the significant change in a number of indicators over a relatively short period of time, particularly when temporal trends seen here are compared with those recently reported from registry data.

Since late 1999, the GRACE (Global Registry of Acute Coronary Events) registry has collected data about management of ACS from 95 hospitals in 14 countries, including patient samples from six Australian hospitals located in Bathurst, Sydney, and Melbourne [32,33]. The Australian population has shown no significant variation in process indicators over time compared with hospitals from other countries. In the current study, the proportions of ideal patients receiving LLAs increased over 17 months by 7%, compared with 4% reported from GRACE over an 18 month period from July 2000 to December 2001 [33].

As another example, the proportion of eligible patients receiving ACE inhibitors at discharge in our study increased by 10% over 17 months compared with only a 4% increase over 4 years observed from data collected for the Medicare Health Quality Improvement Program, which analysed care of 270 467 patients admitted with acute myocardial infarction to US hospitals between 1995 and 1999 [34].

There are other limitations to our program methods that need to be considered when replicating them in other sites. Firstly, we are unable to discern the differential effects on quality of care of individual quality improvement interventions and therefore cannot recommend that certain interventions be implemented in preference to others. Secondly, our use of 16 indicators required data collection and analysis systems for 200 variables abstracted from hospital records. Existing administrative and clinical datasets did not provide the required information. Such systems, by consuming ~50% of program resources, are difficult to sustain despite their advantage in outputting ‘ideal’ patient indicators that assist in driving change [35]. Thirdly, the use of a 6 month cycle length for indicator feedback, designed to minimize random variation due to small sample sizes at the individual hospital level, leads to a significant lag time for clinicians to respond to identified problems.

Further developments
We are attempting to address some of the above mentioned limitations in a current extension of our work to 12 other public hospitals in the state of Queensland under the auspices of the Queensland Health Cardiac Collaborative [36]. This collaborative is using electronically scannable data abstraction forms that collect only 35 data items, and provides instant clinician access to indicator results as they are being collected using web-based reporting tools. Regular forums involving lead clinicians and project officers from all sites have been convened for the purposes of discussing comparative indicator data and devising hospital-specific quality improvement plans that target the most suboptimal indicators. Initial results show significant improvements in six of 11 process-of-care indicators among participating hospitals over an intervention period of <12 months [37].

Conclusions
Quality improvement programs based on decision support and performance measurement and feedback achieved favourable, albeit modest changes in Australian practice and accords with international experience [14,18,19]. The challenge to optimizing care is to establish and sustain multidisciplinary teams that have a mandate to review and redesign the way care is delivered [38]. Issues of sustainability persist, but with organizational and technical support for decision aids and performance measurement we believe that the strategies used here are durable and generalizable to other sites and to other clinical conditions within the Australian health care system.

We thank the staff of all three hospitals and the following members of the Brisbane Cardiac Consortium Clinical Leadership Group for their assistance in this project: Ian Coombes, Dr Paul Garrahy, Professor Thomas Marwick, Associate Professor Charles Mitchell, Melodie Downey, Karen Watson, Lisa Mitchell, Neil Stewart, Dr John Atherton, Neil Cottrel, Dr Mark Dooris, Therese Theile, Alison Ruiz, Katie Foxcroft, Jane Davey (deceased), Gaby Bitcon, Dr Michael Ward, Dr Judy Flores, Adam La Caze, Kieran Behan, and Dr Malcolm Wright. The program was funded by the Commonwealth Department of Health and Ageing, conducted under the auspices of the Royal Australasian College of Physicians Clinical Support Systems Program, and was supported by Queensland Health.


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 Results
 Discussion
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