Integrated Management Program Advancing Community Treatment of Atrial Fibrillation (IMPACT-AF)
Phase: NA
Conditions: Atrial Fibrillation
Sponsor: Jafna L Cox

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CONFIDENTIAL 
IMPACT-AF 
 
Integrated Management Program Advancing Community 
Treatment of Atrial Fibrillation 
 
Protocol #: IMPACT-AF-2013 
Pharmaceutical Study Support: Bayer Inc. 
Clinical Trials Identifier: NCT01927367 
Principal Investigator: Dr. Jafna Cox 
Version #: 4.2 
Date of Protocol: September 26, 2017 
 
 
 
 
Prepared by: 
Dr. Jafna Cox, Dr. Raza Abidi, Dr. Samina Abidi, Dr. Ratika Parkash; and, Dr. Feng Xie, 
McMaster University and Dr. Lehana Thabane, Population Health Research Institute, Hamilton 
Health Sciences, Ontario; with review by Dr. Jim MacKillop, CBDHA. 
c/o Dr. Jafna Cox 
Queen Elizabeth II Health Sciences Centre 
New Halifax Infirmary Site, Room 2147 
1796 Summer Street 
 
Halifax, Nova Scotia, Canada 
B3H 3A7 
Tel. 902-473-7811       
Fax. 902-473-8616 
jafna.cox@nshealth.ca 
 

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This document contains information which is of a confidential nature. It is not to be disclosed 
to any other person or party without the written approval of Dr. Jafna Cox. 

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IMPACT-AF 
Integrated Management Program Advancing  
Community Treatment of Atrial Fibrillation 
 
 
 
ABSTRACT:  Atrial fibrillation (AF) is the most common abnormality of cardiac rhythm.  It is also a disease 
of aging, affecting 3% of adults aged > 45 years- and- 12% of those aged > 75.  Individually, AF’s rapid 
and irregular heart beat is most frequently perceived as undesirable palpitations, but more threatening 
impacts are heart failure, catastrophic stroke and premature death.  At the public health level, the burden 
of managing AF is driven by our rapidly aging population and bears an annual cost of $5000 per patient.  
 
Optimal care of AF patients is also hindered by large gaps between usual care and best care.  For 
example, a large proportion of AF patients at moderate to high risk for stroke do not receive guideline 
recommended thromboprophylaxis; and of those that do, many are not optimally controlled.  The 
presence of these care gaps is particularly disappointing in light of recent therapeutic innovations such 
as more efficacious anti-coagulant drug therapies and evidence that an integrated multi-disciplinary 
approach to delivery of AF care facilitates improved care and outcomes, including large reductions in 
hospitalizations. Thus, opportunities to make things better are at hand.   
 
We propose a patient-centered and community-focused management program (IMPACT-AF) to grasp 
these opportunities and determine if patients with AF can be managed as effectively (clinical outcomes) 
and more efficiently (economic outcomes) in primary care as they are in specialized care.  The premise 
of IMPACT-AF is that primary care providers within the community when supported by an electronic AF 
patient management system that offers: a) clinical decision aids based on clinical guidelines for AF; and, 
b) a communication medium for data sharing and referrals, with the  specialized AF management centre 
in Halifax, will lead to improved diagnosis and standardized disease management, including increased 
use of proven therapies for AF rhythm and rate management, and better stroke prevention. We expect - 
frequent measurement and feedback of actual care practices, patient outcomes and other relevant 
disease / health information, to drive positive behaviour in both practitioners and patients.  The overall 
result will be cost-efficient improvement of care and outcomes for AF patients.   
 
The primary outcome is a decrease in CV hospitalizations, with secondary outcomes of clinical, process 
of care and economic relevance.  The cluster-randomized study design will permit measurement and 
comparison of the clinical decision support system pre and post intervention, and across cases.  A 
principal assumption is that IMPACT-AF will build on the principles and practices of benchmark disease 
management projects like ICONS, particularly in aligning with provincial and regional health policies that 
direct support to primary care and data management resources that address public health burdens.  We 
anticipate that IMPACT-AF will successfully extend a more clinically effective, cost-efficient and 
sustainable social-networking model of health management to a segment of our aging population who 
need things to be better.  In doing so, IMPACT-AF will also produce, markedly improved stakeholder 
outcomes and satisfaction – all of which can be modeled in other areas of health care priority.   

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Table of Contents 
List of Abbreviations ............................................................................................................... 5 
1.0 BACKGROUND AND RATIONALE ........................................................................................ 6 
1.1 The Problem:  Increasing Burden, Significant Risk and Care Gaps ................................................ 6 
1.2 Relevant New Knowledge ........................................................................................................... 9 
1.3 Public Health and IMPACT-AF in Nova Scotia:  A Synergy ........................................................... 11 
2.0 Hypotheses ...................................................................................................................... 12 
2.1 Study Objective .......................................................................................................................... 12 
2.2 Study Outcomes ......................................................................................................................... 13 
3.0 STUDY DESIGN ................................................................................................................. 14 
3.1 Description ................................................................................................................................ 14 
3.2 Study Flow: ................................................................................................................................ 14 
3.3 Usual Care ................................................................................................................................. 14 
3.4 Intervention Arm ....................................................................................................................... 15 
3.4.1 Proposed Technology Framework ........................................................................................... 18 
3.4.2 Other Interventions ................................................................................................................ 21 
3.5 Contamination and Co-intervention ........................................................................................... 22 
3.6 Sources of Bias ........................................................................................................................... 22 
3.7 Study Procedures ....................................................................................................................... 22 
3.7.1 Randomization ........................................................................................................................ 22 
3.7.2 Treatment Period and Follow-up ............................................................................................. 22 
3.7.3 Data Collection ....................................................................................................................... 22 
3.7.4 Data Quality ............................................................................................................................ 24 
3.7.5 Safety / Adverse Events .......................................................................................................... 24 
3.8 Sample Size ................................................................................................................................ 25 
3.9 Statistical Analysis: .................................................................................................................... 28 
4.0 Study Population .............................................................................................................. 29 
4.1 Recruitment of Clinics and Practitioners Managing AF Patients .................................................. 29 
4.1.1 Pharmacist Participation: ........................................................................................................ 30 
4.2 Patient ID / Recruitment / Inclusion / Exclusion ......................................................................... 30 
4.3 Ethical Considerations ................................................................................................................ 32 
5.0 STUDY ORGANIZATION ..................................................................................................... 32 
5.1 Medical Experts ......................................................................................................................... 32 
5.2 Health Informatics Experts ......................................................................................................... 33 
5.3 Statistician Expert ...................................................................................................................... 33 
5.4 Health Economist Expert ............................................................................................................ 33 
5.5 Committees ............................................................................................................................... 33 
5.5.1 Executive Committee: ............................................................................................................. 33 
5.5.2 Advisory Committee: .............................................................................................................. 34 
5.5.3 Adjudication Committee: ........................................................................................................ 34 
5.5.4 External Review Committee: ................................................................................................... 35 
5.5.5 Other Committees .................................................................................................................. 35 
8.0 REFERENCES ..................................................................................................................... 36 

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9.0 APPENDIX ......................................................................................................................... 48 
9.1. Project Organogram .................................................................................................................. 48 
 
 
List of Abbreviations  
AF: Atrial Fibrillation 
CDSS: Clinical Decision Support System 
IT: Information Technology 
FPN: Family Practice Nurse 
NP: Nurse Practitioner 
NS: Nova Scotia 
PCP: Primary Care Provider 
QEII HSC: Queen Elizabeth II Health Sciences Centre 
RN: Registered Nurse 
 
 

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1.0 BACKGROUND AND RATIONALE 
1.1 The Problem:  Increasing Burden, Significant Risk and Care Gaps   
Atrial fibrillation (AF) is the most common sustained cardiac rhythm abnormality.  It affects 1-2% 
of the entire population [1, 2]; and, is age related.  The incidence doubles with each decade 
beyond age 55, eventually afflicting 1 in 10 octogenarians [3, 4].  With the age distribution of the 
current Nova Scotian population (Figure 1), the prevalence of AF patients over 65 years is 
probably between 10,000 and 20,000 patients.  As North American populations continue to age, 
the number of AF patients will likely rise between 2- and 3-fold in the near future [4, 5].  Overall, 
the lifetime risk is about 26% for males; and, 23% for females [1].   
 
Figure 1.  Comparative age distributions in the Nova Scotian population 
 
AF is linked with increased mortality, substantial morbidity and high costs [7, 8].  The 
Framingham and other epidemiology studies have found AF to be associated with a 1.5 to 1.9-
fold risk of death even after adjustment for pre-existing cardiovascular conditions commonly 
associated with AF [9, 10].  AF is an important and independent risk factor for stroke, increasing 
the risk of such events by 5-fold [11-13].  Indeed, AF accounts for approximately 15-20% of all 
strokes [11-14].  Risk of stroke in AF patients increases with age, being around 2% in 50-59 year 
olds; and, increasing to about 24% in 80-89 year olds.  Importantly, the strokes associated with 
AF are more likely to be fatal or very disabling than are strokes associated with other etiologies 
[15].   
 
Apart from death and stroke, AF markedly impairs quality of life [16, 17].  AF is symptomatic in 
39% patients with severity of symptoms may range from a ‘nuisance’ feeling of palpitations to 
debilitating symptoms that do not permit performance of activities of daily living, interfere with 
normal livelihood and significantly impair exercise tolerance [18]. The most severe symptoms 
are those resulting in hemodynamic compromise and heart failure, which are associated with 
poor prognosis and increased mortality.  Approximately 1/3 of patients with AF present to the 
emergency room (ER) at some time due to symptoms and they represent 3-6% of all medical 
admissions to hospital [19-23].  The overall hospitalization rate for AF in Canada is 
approximately 583 per 100,000 of population [21]; and, in an as yet unpublished pilot study 

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conducted at the QEII Health Sciences Centre in Halifax, 92% of patients with persistent atrial 
fibrillation had undergone prior cardioversion and 90% had experienced more than one ER visit.   
 
The annual cost of AF care to the Canadian health care system is estimated at $4840 per 
patient [24].  A recent study suggests that the costs associated with warfarin therapy, alone, may 
be as much as $1232-$2012 per patient, per year [25].  Care costs are distributed across 
outpatient encounters, ER visits, inpatient hospitalizations and drug therapies in roughly similar 
proportions in France and the United Kingdom, versus the United States where relatively more is 
spent on inpatient care (Figure 2) [26-28].  Overall, estimated care costs of hospitalized AF 
patients, compared to patients without AF, were 9 to 23 fold greater [29]. 
 
Figure 2.  Total and distribution of health care spending on AF 
 
 
 
 
 
 
 
 
 
 
While prevention of AF by controlling known risk factors is a long-standing and ever-increasing 
public-health target, the incidence and risk of AF are still increasing [30-36], despite advances in 
co-morbidity therapies, especially for hypertension and heart failure [37-54]. 
 
Traditionally, the major therapeutic interventions in AF patient care are directed at modifying or 
reversing the irregular rhythm and fast heart rate, as well as reducing the predisposition of the 
dysfunctional atria to form and expel clots that cause strokes.  Anticoagulant therapy in AF is 
influenced by the need to balance the benefit of this therapy in terms of stroke prevention with its 
side effect of enhancing the risk of bleeding.   
 
Most care is delivered by family physicians with eventual referral to specialists, although 
repeated ER visits and hospitalizations may occur prior to specialty assessment.  Figure 3 
illustrates recommended care steps for newly diagnosed patients [31].  Unfortunately, each 
referral step, whether for consultation, investigation or management, can be associated with its 
own wait time, or temporal care gap.  This can result in an overall wait for definitive diagnosis 
and treatment that extends beyond the norms endorsed by the CCS Access to Care Working 
Group and the Canadian Heart Rhythm Society, both of which suggest that outpatient 
assessment and investigations should be no longer than 4 to 12 weeks, depending on patient 
urgency [55].  
 
Although primary care settings are the first line of health care in the community, primary care 
providers (PCP) experience gaps in knowledge, skills and competencies with respect to best 

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evidence-based approaches concerning the management of complex chronic diseases, such as 
AF even though guidelines and best practice resources are freely available [56, 57].  Patients 
themselves have difficulty understanding their condition, treatment options and how to self-
manage their AF.  Despite the availability of evidence-based clinical guidelines, protocols and 
pathways, the reality is that such decision-support aids are often unavailable at the point-of-care 
to assist PCPs and patients in making evidence-based decisions regarding AF treatment and 
management.  There is a fundamental need to translate published medical knowledge more 
quickly and practically into patient care.  One way of doing so is through computerized decision 
support aids so that PCPs are better equipped with the clinical decision-making tools that will 
allow the best evidence-based, proactive and standardized care.  Such support is expected to 
reduce both the clinical and financial burdens of common chronic diseases, including AF. 
 
Several clinical trials comparing rate control to rhythm control as management for the rapid and 
irregular rhythm of AF have found no difference in mortality, rates of thromboembolism or 
bleeding between the two strategies [58].  Subsequent re-analysis of the largest of these trials, 
the Atrial Fibrillation Follow-up Investigation of Rhythm Management (AFFIRM) study, did, 
however, suggest a survival advantage in those who maintained sinus rhythm [59].   
 
 
Figure 3.  Recommended care flow for patient with newly-diagnosed AF 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Unfortunately, for those patients with longstanding AF, and the elderly, who form the major part 
of the whole AF population, cardioversion to, and then maintenance in, sinus rhythm can be 
especially challenging [60].  The outcome benefit gained by attaining sinus rhythm seems to be 
partly offset by an increased mortality risk associated with antiarrhythmic drugs.  The increasing 
attraction of stable sinus rhythm produced by successful percutaneous catheter ablation is, in no 
small measure, attributable to the hope that antiarrhythmic drugs may not be necessary.  

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There is ample data to suggest that current anticoagulant based stroke prevention therapy in AF 
patients is also sub-optimal.  According to one study, barely half (53%) of high-risk patients with 
atrial fibrillation receive warfarin therapy when hospitalized [61].  Another analysis showed that a 
large proportion of AF patients at moderate to high risk for stroke do not receive guideline 
recommended thromboprophylaxis; and of those that do, many are not optimally controlled [62].  
Most discouragingly, a Canadian study found that in patients with known AF and a prior stroke 
who were subsequently admitted with a second stroke, 15% were on no anticoagulation and 
only 18% were on warfarin and had a therapeutic INR [15].   
 
In summary, AF represents a large and growing public health burden, with large gaps between 
best and usual care.  For practitioners, managing care is complex and demanding; and, for 
patients, risk of significant morbidity and premature mortality are constant dangers.   
1.2 Relevant New Knowledge   
Fortunately, new and promising therapies and management strategies have recently emerged.  
In summary, they are: 
 
- 
Radiofrequency catheter ablation.  This technology has become more available and 
commonplace for treatment of AF since Haissaguerre et al. published their landmark paper [63, 
64].  Ablation is primarily targeted at AF patients with paroxysmal or persistent atrial fibrillation 
that is anti-arrhythmic drug resistant and symptomatic, despite therapy with rate or rhythm 
control agents [65].  It can produce a cost-efficient and dramatic improvement in symptoms and 
quality of life [66].     
 
- 
Antiarrhythmic drugs.  One new agent in this class, dronedarone, was approved for use in AF 
therapy in December 2009; and, has been incorporated into the new CCS AF treatment 
guidelines largely because of its ability to effectively decrease hospitalizations and their 
associated costs [67-71].  However, some debate remains about the cost-effectiveness of the 
drug [71-74] and its scope of use has been recently narrowed [75-76]. 
 
- 
Innovative oral anticoagulants.  Warfarin has been the mainstay for oral anticoagulation for 
decades.  However, there are very large gaps between optimal and actual applications of this 
therapy.  For example, optimal warfarin-based therapy to reduce stroke risk is achieved by only 
15% of outpatients and 53% of inpatients [15, 61].   
 
Very recently, large trials of 3 novel anticoagulant (NOAC) agents, dabigatran (a direct thrombin 
inhibitor), rivaroxaban and apixaban (both factor Xa inhibitors), in different populations of AF 
patients, have demonstrated these drugs to have a more favourable stroke prevention and 
bleeding profile than warfarin [77-79].  As compared with warfarin, all 3 drugs reduce the risk of 
stroke, particularly hemorrhagic stroke [77-79].  Moreover, the new drugs also share a similar 
tendency to reduce all-cause mortality risk [77-79]. 
 
- 
Health Social Networks: Team Care and AF Clinics.   Lately, multi-disciplinary approaches to 
AF management, centred on specialty AF clinics, have been reporting encouraging results, 
especially markedly decreased wait times for specialist assessment and decreased number of 

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ER visits and hospitalizations, compared to the time before the introduction of this model of care 
[80].  The patient flow-through chart for the clinic is demonstrated in Figure 4.   
 
Figure 4.  Patient flow through the AF clinic in Calgary, Alberta, Canada 
 
 
 
 
 
 
 
  
 
 
 
 
 
A clinical trial in the Netherlands reported a 33% reduction in CV hospitalizations, and more 
than a 70% relative risk reduction in cardiovascular mortality, with a nurse-led and 
cardiologist supervised AF clinic versus usual care [81, 132].  The beneficial outcomes from 
such care approaches are directionally similar to the successes reported with 
multidisciplinary, outpatient-based management of heart failure patients, leading to their 
widespread roll-out across the country [82-87].  And, new Canadian AF guidelines strongly 
reflect the value of a multidisciplinary management approach, including reduced wait times, 
greater coordination of care via nursing interventions, enhanced efficiency of resource 
utilization and better implementation of other cardiac therapies [55, 88-93].   
 
– Clinical Decision Support Systems. Computerized Clinical Decision Support Systems 
(CDSS) are increasing in popularity within the health informatics (or health IT) industry; 
however, there are no standout systems at the primary care level for providers and patients 
in particular.  The limited decision-support tools in the market today are largely bundled with 
large-scale health IT systems suitable for tertiary care hospitals, with functionality limited to 
relatively simple procedures such as: triggers for care activities, alerts for drug prescriptions 
and allergies, disease diagnosis, drug prescriptions support and reminders for forthcoming 
events.   Current systems are not based on computerized clinical guidelines and do not 
support long-term patient care, only episodic visits.   
 
This presents an opportunity to develop integrated and interactive CDSS that are 
knowledge-driven (based on clinical guidelines), agile in knowledge management and 
knowledge updatability, web- and smart-device accessible for broader outreach, targeted to 
primary care providers but also with provisions for nurse and specialist use, interactive 
educational tools for patients, able to handle multiple chronic diseases and even co-morbid 
diseases, integrate data streams from different sources and interoperate with existing 

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medical record systems, compliant to specialized clinical workflows and offer integrated 
patient care planning and surveillance capabilities. 
 
Whether AF patient care and outcomes will improve at the population level with the 
implementation of one, or a combination, of these promising therapies and strategic care 
approaches remains uncertain.   
 
1.3 Public Health and IMPACT-AF in Nova Scotia:  A Synergy    
As discussed above, AF is an important public health care issue and a growing priority of 
comparative effectiveness research [94].   The rationale for doing such work in Nova Scotia is 
compelling.   
 
Nova Scotia provides a near-perfect real-world laboratory for health services and outcomes 
research seeking to identify alternative models of care.  It has a population of approximately 
940,000 [95], with one large academic centre and well-known referral patterns, that allow for the 
straightforward study of whole patient populations.  It is especially suited for the study of 
cardiovascular diseases, whose prevalence in Nova Scotia is amongst the highest in Canada 
[96].  Moreover, Nova Scotia has one of the oldest populations in Canada, a desirable feature if 
the target disease is age-related, like AF.   
 
The Nova Scotia geography provides an opportunity for study sites in diverse regions, with 
differing rural/urban mix, ensuring that successful test approaches are broadly portable, feasible 
and effective; yet travel distances are relatively short, thereby ensuring manageability and cost-
efficiency.  Nova Scotians are more ethnically homogeneous than many other provincial 
populations, serving to minimize racial differences in disease manifestations and outcomes, 
while also reducing some of the background effect of culture-specific approaches to care [97].  
On the other hand, socio-economic disparities are broad, allowing, for example, insights into the 
utility of proposed interventions according to patient ability to pay [98].    
 
The structure of the health care system in Nova Scotia is also conducive to health services 
research.  The province is divided into 9 health care regions with some autonomy in service 
prioritization and delivery but, ultimately, all functioning as part of a single system.  Tertiary 
cardiovascular diagnostic and therapeutic services for the entire province are centralised at the 
Queen Elizabeth II Health Sciences Centre (QEII HSC), the main teaching hospital of Dalhousie 
University, where virtually 100% of the provincial population requiring expert electrophysiological 
(EP) assessment or interventional services are referred.  This facilitates significantly the process 
of tracking patient referrals, including any impediments to these, for EP consultation, AF ablation 
and other specialty-directed therapies. 
 
In summary, Nova Scotia provides a unique environment for researching health services, 
outcomes, and alternative care models that are specific priority needs identified in the Canadian 
Heart Health Strategy [99] and by the Nova Scotia Department of Health and Wellness [100].  
The Department of Health and Wellness has previously collaborated on successful multi-partner 
population health projects, including ICONS [101] and ANCHOR [102].  The participant 

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satisfaction [103], clinical effectiveness [104] and cost efficiencies [105,106] demonstrated by 
ICONS led, in fact, to its evolution from a research project to a publicly funded program – 
Cardiovascular Health Nova Scotia (CVHNS).  CVHNS is a provincial program of the Nova 
Scotia Department of Health and Wellness which aims to improve the cardiovascular health and 
care of Nova Scotians.  The Program's scope includes cardiac disease and stroke.  CVHNS is 
responsible for: 
§ developing guidelines and service delivery models,  
§ working with District Health Authorities to improve cardiovascular health,  
§ monitoring and reporting cardiovascular health outcomes,  
§ facilitating professional development opportunities for health providers, and  
§ working with others to reduce the risk and burden of cardiovascular disease. 
 
CVHNS is accountable to the Acute and Tertiary Care Branch of the Nova Scotia Department of 
Health and Wellness and receives advice from a Provincial Advisory Council.  The Council 
advises the Program and subsequently the Department of Health and Wellness on pertinent 
health care issues and priorities related to Cardiovascular Health Nova Scotia's mandate.  The 
Advisory Council consists of physicians, senior leaders from the District Health Authorities, 
researchers, health professionals, and non-governmental organizations. 
 
The proposed project, IMPACT-AF, offers similar opportunities for demonstrating leadership in 
collaborative health policy research and the optimal translation of results to patient care.   
2.0 Hypotheses  
 
The main hypothesis is as follows: 
 
In the primary care setting, implementation of a patient-centered, disease management platform, 
which supports practitioners and patients with evidence-based management strategies, 
including the optimal use of proven innovative therapies and repeated measuring and sharing of 
practices and outcomes information and education, will generate and reinforce beneficial 
behaviours to improve, and sustain, care and outcomes of AF patients in a cost effective 
manner.   
2.1 Study Objective 
The primary research question is: 
 
Among community-based patients with AF, does providing an integrated Clinical Decision 
Support System (CDSS) to providers and patients improve process of care and clinical 
outcomes, and decrease the healthcare costs and resource utilizations over 12 months, as 
compared to usual care? 
 

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2.2 Study Outcomes 
The primary study outcome is a composite of unplanned cardiovascular hospitalization at 12 
months and AF-related emergency department visits.   
 
Cardiovascular hospitalization is defined as the following: 
 
Any unplanned hospitalization (admission with an overnight stay in hospital) due to one of the 
following causes:  acute coronary syndrome, presyncope / syncope, transient ischemic attack / 
stroke, atrial fibrillation, flutter, pulmonary embolism / deep vein thrombosis /systemic embolism, 
worsening congestive heart failure including pulmonary edema or dyspnea of cardiac origin. 
[81,107] 
 
The primary safety outcome is major bleeding.  
Major bleeding is to be defined as fatal and/or symptomatic / intracranial bleeding in a critical 
area or organ, such as brain, intraspinal, intraocular, retroperitoneal, intra-articular or pericardial, 
or intramuscular with compartment syndrome and /or overt bleeding causing a fall in hemoglobin 
level of 20 g/L or more, or leading to transfusion of two or more units of whole blood or red cells. 
[108] 
 
Secondary outcomes address clinical, process of care, quality of life, and cost effectiveness. 
 
Clinical: 
• 
Individual hospitalization elements as noted above. 
• 
Any bleeding 
• 
All cause mortality 
• 
Appropriateness of oral anticoagulant therapy (based on current CCS guidelines, [23]) 
 
Process of Care: 
• 
Access to specialist consultation 
• 
Access to echocardiograms 
• 
Access to catheter ablations for AF and atrial flutter 
 
Health-related quality of life (HRQoL)  
• 
HRQoL measured using the EQ-5D-5L questionnaire 
 
Costs: 
• 
The costs associated with the development, implementation, and maintenance of CDSS 
• 
The costs associated with managing and treating patients with AF 
 
Cost effectiveness 
• 
Incremental cost effectiveness ratio between the interventional arm and the control arm 

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3.0 STUDY DESIGN  
 
3.1 Description 
The proposed project falls into the scope of what has been termed a pragmatic or practical 
clinical trial [109-111].  This means that the hypothesis and study design have been developed 
specifically to answer questions faced by policy makers as they seek to develop new and 
effective population health management approaches.  The first goal is to develop a CDSS that 
has undergone preliminary assessment of its clinical usefulness by knowledge opinion leaders 
as well as practical field-testing of its utility and dependability in practice settings.  Following this, 
a rigorous analytical assessment of efficacy will be undertaken.  
 
Specifically, IMPACT-AF will include a prospective, randomized, unblinded cluster design clinical 
trial, proposed to take place within Nova Scotia over the time period of 2014-2018, that will 
investigate whether a Clinical Decision Support System (CDSS) improves the clinical outcomes 
of patients with AF.  Primary care providers (n=200) will be randomized to usual care versus use 
of the CDSS in a 1:1 fashion, at the level of the clinic practice.  In order to facilitate the study, 
primary care providers will be required to have high speed internet access.  The study will follow 
patients over a minimum of 12 months after enrolment.   
 
Patients Included in the Analysis: 
1. 
Age >/= 18 years 
2. 
Electrocardiographically confirmed atrial fibrillation or documentation of past 
diagnosis or management of AF in the patient’s medical record 
3. 
Able to provide informed consent in English 
4.  
Resident of Nova Scotia 
 
Exclusion Criteria: 
1. 
Patients unable to provide informed consent 
2. 
Patients who have a terminal illness and are not expected to be alive at the end 
of follow up. 
 
3.2 Study Flow: 
See Figure 5 for a Summary of Study Flow. 
 
3.3 Usual Care 
Primary care providers in the usual care arm will be asked to identify eligible patients from their 
practice and obtain informed consent for the patients to participate in the study.  Aside from 
obtaining informed consent and completion of patient questionnaires there will be no other study 
specific intervention imposed on this arm. It is anticipated, that usual care will be provided 
according to current Canadian and international guidelines for the treatment and management of 
AF and represent the accepted standard care as established by the major medical specialty 
organizations dealing with AF management. 
 

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3.4 Intervention Arm 
Participating practices in the intervention arm will receive access to the clinical decision support 
system (CDSS) intervention in their primary care setting. The CDSS is an integrated 
management system that involves many facets of the healthcare system.  It will focus on four 
key elements:   
 
1) 
Translation of best-evidence to Primary Care Providers (PCP) through computerized 
decision support aids designed to enable PCP to handle the identification and rapid 
treatment of AF patients at the primary care level; 
2) 
Engagement and empowerment of patients in the self-management of their disease; 
3) 
Remote surveillance of AF patients through a range of data streams with proactive 
response to critical trends and situations; 
4) 
Leveraging innovative health informatics and computer science technologies to develop 
an integrated knowledge-centric patient-centered disease management platform (DMP). 
 
Interventions most frequently cited as efficacious to improve care and outcomes in disease 
management include: stakeholder education and reminders of best care practices; financial 
incentives to follow best practices; and, regular measurement and feedback of actual practices 
and outcomes [101].   In the successful ICONS project, financial incentives were not used; 
instead, a combination of education, reminders and repeated measurement and feedback 
communication of practices and outcomes were all employed, including the sharing of region-
specific and sub-group data [103-106]. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

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Figure 5. Study Flow Diagram 
 
PCPs in NS join IMPACT-AF
N=203
Intervention Group
104 PCPs
Use CDSS for 
12 months
597 Patients
Complete Baseline 
Survey
Use AF Partner Tool for 
12 months
Complete Health 
Economics Survey monthly
Complete Follow-up 
Survey
Control Group
99 PCPs
Provide usual care 
for 12 months
548 Patients
Complete Baseline 
Survey
Complete Health 
Economics Survey 
monthly
Complete Follow-up 
Survey
Ps identify and invite eligible
Randomization

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Over the 5-year course of ICONS, it became evident that the network, itself, was also a likely 
efficacious intervention [104, 106].  Recent, basic science data support the observations and 
hypotheses generated by ICONS [112,113].  Briefly, this evidence suggests human behaviours 
are spread via networks of social contact, which add value by facilitation and reinforcement of 
beneficial behaviours.  A few characteristics appear to be of key importance in relation to 
effectiveness.  One is the number of reinforcing messages required to foster uptake of new 
behaviours, which will differ between individuals.  This appears to be more important than who is 
delivering the message.  Another is that the structure of the network also determines the extent 
and speed of behaviour adoption [113].  In this latter regard, the so-called clustered lattice 
network structure, which resembles the community-based organisation of ICONS, and is 
proposed for IMPACT-AF, is more efficient, or efficacious, than more random network structures 
[113]. 
 
For IMPACT-AF, we propose a broad community-based network of overlapping and shared 
interactions among providers, patients, their families and community-based referral services, all 
communicating with academic partners, patient advocacy organizations and policy makers in a 
clustered lattice type of network model integrated through a formalized electronic web.  Such a 
network will facilitate the specific interventions outlined in Table 1 to overcome perceived 
impediments to optimal care. 
 
 
Education will be provided to AF patients via the study website and paper-based materials 
included in patient mail outs. Similar to what was offered in ICONS, [101,103] stakeholder 
education will be offered via patient advocacy organizations, an IMPACT-AF Newsletter, web 
site and bi-annual meetings.  
 
The academic hospital-based specialized clinic is planned to serve as an additional, albeit non-
randomized comparator, of care.  Indeed, it is hypothesized that the management and outcomes 
of community-based patients in the intervention arm of the IMPACT-AF study will not only be 
superior to what is seen in the usual care arm, but it will be as good as the care provided for 
those patients being managed uniformly through the specialized AF clinic. 
 
Table 1.  Barriers to optimal care and core interventions 
 
Identified barriers 
Interventions 
Lack of knowledge 
Interactive education sessions 
Perception/reality mismatch 
Audit and feedback 
Lack of motivation 
Network reinforcement 
Beliefs/attitudes 
Peer influence / opinion leaders 
Systems of care 
Process design 

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3.4.1 Proposed Technology Framework 
 
This research program involves systemic integration of advance health informatics technologies, 
best evidence, health models and knowledge translation strategies and best evidence to 
develop a technology-assisted Atrial Fibrillation (AF) management framework.  The project will 
adopt a knowledge management approach to both develop and deploy the proposed clinical 
decision support framework.  The proposed research encompasses a patient-centered care 
delivery paradigm, targeting both patients and primary care physicians (PCP) that leverages 
state-of-the-art computer technologies to design personalized, interactive, pre-emptive, socially 
cognizant, contextually-aware, evidence-based AF strategies delivered through electronic 
media.  A patient-centered approach to the translation of evidence into practice will be taken, 
addressing the knowledge gaps of PCP and the engagement of AF patients to develop a patient 
centered program of research and care delivery for the management of AF. 
 
Our intent is to develop a four-layered research and care delivery program to address Atrial 
Fibrillation management as a longitudinal care process.  The clinical care layer will investigate 
the theoretical framework of the Expanded Chronic Care Model to develop, implement and 
integrate innovative models of evidence-based patient care regarding Atrial Fibrillation into 
clinical practice.  This model is well suited to this program because of the persistent nature of 
arrhythmic and thromboembolic issues over a lifetime and the link between AF and underlying 
chronic diseases such as chronic heart failure [114].  The patient engagement layer will 
investigate commitment and empowerment models (Social Cognition Theory, Health Belief 
Model and Shared Decision Making Model) to develop a high-level patient engagement model 
that can help us understand and establish patient perceptions on AF management in terms of 
personal goals, environmental, behavioural, functional and economic factors.  The knowledge 
translation layer will build on the synergy of integrated knowledge translation models with web 
technologies to develop a collaborative, ubiquitous and immersive knowledge translation, 
sharing and training environment for PCP.  The knowledge-modeling layer aims to leverage 
knowledge management methodologies to achieve the transformation and translation of paper-
based clinical guidelines into knowledge-driven clinical decision support systems.  
 
From an E-health research perspective, we will pursue the following research components: 
 
1. Healthcare Knowledge Modeling 
Healthcare knowledge modeling involves the abstraction and representation of medical 
concepts, relationships, constraints and rules to realize a formal computable knowledge 
model defining the domain [115, 116].  The model will be generic, scalable, robust and 
flexible and will serve as a generic logical and executable template for the computerization of 
multiple co-morbid guidelines in addition to AF guideline.  Cutting edge Semantic Web 
technologies such as highly expressive and computer interpretable OWL-Web Ontology 
Language [117] will be used to model the health care knowledge as Ontology.  
 

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The model will be based on three different, yet interrelated knowledge objects as follows: (a) 
Clinical knowledge about AF management encapsulated within evidence-based clinical 
guidelines; (b) Behavior change strategies that are represented in terms of health behavior 
models—such as social cognition, health belief, shared decision making and so on; and (c) 
Clinical workflows that capture the function and operational workings of healthcare 
institutions, eliciting the processes, decisional choices and resources.  These three unique 
knowledge objects will be modeled as three independent ontologies—namely the AF 
Knowledge Ontology, Behavior Strategy Ontology and Care Workflow Ontology—yet their 
active interplay based on our existing knowledge morphing method [118] will align these 
ontologies to renders a holistic knowledge base that will be used for AF decision support 
purposes.  
 
2. Clinical Decision Support System for Primary Care Providers  
Healthcare knowledge modeled in terms of specialized ontologies can be executed through 
CDSS to provide case-specific decision support [119,120].  In this project, we propose to 
build on our existing research covering the development of ontology-driven CDSS using 
Semantic Web Technologies [117,121] to develop a guideline-mediated AF CDSS to assist 
family physicians to manage patients with AF.  Our approach is to leverage state-of-the-art 
Semantic Web technologies [117] that provide methods to systematically translate 
ontologically-modeled medical knowledge into point-of-care interventions.  We will use a 
logic-based knowledge morphing method to integrate the different ontologies, explicating 
the domain and process-specific decision logic, conceptual constructs, process workflows 
and constraints in terms of explicit logical rules.  We will employ OWL-based reasoning 
methods, using logic-based proof engines, to infer guideline-mediated patient-specific 
diagnostic and therapeutic recommendation based on specific patient information provided 
by the physician.  The novelty of our decision support approach is that PCP can request 
justifications and explanations for the recommendations suggested by the CDSS. The AF 
CDSS will be accessible through a secure web portal over the internet (with potential for a 
mobile app). 
 
 
3. Patient AF Self-Management Aids  
This research program aims to deliver personalized, proactive and pervasive AF self-
management and monitoring aids to help with self-management via health behavior change.  
In this regard, we propose to develop a mobile app – called AF Care Partner – that will 
feature a range of patient-specific AF self-management aids as follows:  
 
1. 
AF self-management plan adherence through (a) AF management goal setting and 
strategy design; (b) delivery of motivational, behaviour modification and emotion 
management educational messages; (c) vitals, diet, exercise, stress and mood 
recording diaries; (d) plan related alerts and reminders.  

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2. 
AF self-management plan monitoring through (a) weekly progress reports reflecting 
on goal attainment; (b) daily and weekly trends of risk factors based on diary data; (c) 
observed risk related alerts; (d) mood/stress related alerts. 
3. 
Communication with care providers, with potential for peer support. Patients will be 
able to access the app through smart phones and tablets. 
 
The project’s deliverable will be a robust, scalable and flexible clinical decision support 
framework (Figure 6) for managing AF with a suite of enabling health informatics services.  The 
short-term goal is to develop and implement care pathways for managing AF based on best 
evidence in primary care settings. This component will be designed prior to patient enrollment, 
with end-user testing of functionalities prior to the start of the clinical trial.  The long-term goal is 
to extend design and functionality of the framework in order to incorporate and streamline 
patient-specific care pathways for multiple co-morbid diseases that commonly occur with AF.  
Once implemented, such a framework will be able to provide evidence-based decision support 
services for a wide range of co-morbid scenarios that are deemed treatable in the primary care 
setting.  The project will impact health outcomes in terms of improving evidence-based care 
delivery, patient safety and satisfaction, care quality, patient-centeredness and cost / resource-
effectiveness. 
 
Anticipated challenges include: (a) Adoption of the intervention by Primary Care Providers 
(PCP); (b) Privacy and security of patient information that is collected and managed by the 
clinical decision support system; (c) Recruitment of PCP across the rural and urban regions of 
Nova Scotia; and, (d) Integration of the clinical decision support system with existing health IT 
systems. 
 
 

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Figure 6. CDSS Visual. 
 
 
 
3.4.2 Other Interventions 
 
A Change Management plan may be developed and deployed to ensure the technical 
solution (CDSS) is embraced, adopted and utilized effectively by providers and patients, so 
that the intended benefits (improved patient outcomes and enhanced patient and provider 
satisfaction) can be realized. 
 

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As per the Prosci® Change Management Process and ADKAR® Model, the 3 Phases of 
change will be addressed: Preparing for change, Managing Change and Reinforcing 
change™.  Example activities would include: implementation of a readiness assessment, 
creation of a sponsorship model, change management plans developed to address ADKAR 
(Awareness, Desire, Knowledge, Ability and Reinforcement™) including plans for: 
Communication, Coaching, Training, Sponsor Roadmap and Resistance Management. 
Feedback would be collected and analyzed in order to diagnose gaps and or manage 
resistance. As necessary, corrective actions would be deployed and successes celebrated.  
(Prosci®, ADKAR®, Preparing for change, Managing Change and Reinforcing change ™ are 
trademarks of Prosci Inc. 2012). 
3.5 Contamination and Co-intervention 
Contamination of the usual care arm will be limited by having separate investigator meetings for 
PCPs enrolled into each arm.  Discussion amongst PCPs that may occur outside of study 
meetings will not be able to be monitored, however this type of contamination is unlikely to have 
a significant impact on the usual care arm as they will not have access to the CDSS.   
3.6 Sources of Bias 
In cluster-randomized trials, there are important sources of bias to consider:  recruitment bias, 
baseline imbalance and loss of clusters [122].   Recruitment bias should be limited as all patients 
in each PCP practice will be included, thereby minimizing bias in types of AF patients.  Baseline 
imbalance is unlikely to be an issue as there are a large number of clusters in this study.  Loss of 
clusters is possible but unlikely since there is little migration of PCPs from Nova Scotia; thus, 
loss of an entire cluster over the period of follow-up is improbable.   
3.7 Study Procedures  
3.7.1 Randomization 
Primary care centres will obtain their randomized assignment (intervention versus control) at the 
time of recruitment.  (See Section 4.0 for Study Recruitment Procedures.) Randomization will be 
stratified by whether the PCP is urban or rural.  Urban is defined as a population > 10,000, while 
rural is defined as < 10,000 population [123]. 
3.7.2 Treatment Period and Follow-up 
The study will recruit PCPs until the goal of 200 is achieved, then follow patients for a minimum 
of 12 months.  The intervention will be set up with appropriate training in the PCP practice prior 
to testing the intervention.  There are no specific study visits for this trial; however, any 
unplanned PCP visits, emergency department visits and hospitalizations will be examined for 
important data (See Table 2). 
3.7.3 Data Collection 
Once informed consent has been obtained by the patient’s PCP, a process to record 
retrospective baseline and ongoing clinical data prospectively will be implemented.   Patients will 
be also requested to complete a patient case report form and quality of life questionnaire (either 
paper-based or electronic format) to gather relevant demographic and socioeconomic data.  
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participants will chose not to complete online surveys or interact with the web-based tool. 
Completion of the web-based survey tools and / or interacting with the web-based tool, the AF 
Care Partner, is not mandatory for participation; all survey tools will be available to participants 
in a paper-based format. 
In both study arms, baseline clinical data will be captured from the patient’s chart by a data 
abstractor via on-site visit(s).  Abstractors will utilize a laptop computer or tablet to record and 
transmit study data to secure and password protected servers located behind a firewall at Health 
Information Technology Services Nova Scotia (HITS-NS).  This process will be repeated at 12 
months for follow-up data collection in both study arms.  Follow-up data for the intervention arm 
will also be captured through examination of data gathered during use of the CDSS.  
The majority of patients at the start of the study are expected to have a past diagnosis of AF.  As 
the study progresses, newly diagnosed patients are anticipated to be enrolled.  
Review of PCP records will be done at 12 months to ensure completeness of outcome events. 
Clinical event / outcomes data will also be captured via review of hospitalization records for 
consented patients.  Patients visiting the AF clinic in Halifax will be consented and data captured 
during their clinic visit. See Section 4.2 for further details. 
Information to be collected on all patients will include contact details, demographic and 
socioeconomic characteristics, and, clinical, process of care and health outcome variables such 
as:  Name, year and month of birth, health card number, mobile phone number, mailing address, 
internet access / smart-phone and social media usage, type of residence, gender, ethnicity, date 
of AF diagnosis, means of documentation of AF (ECG, Holter, other), medications, risk factors 
for AF, bleeding and stroke (based on HASBLED, CHADS2 and / or CHA2DS2-Vasc score), 
prior cardiovascular or other hospitalizations and ER / AF clinic / GP office visits for AF care, 
laboratory investigations (e.g., thyroid studies, renal function, hemoglobin, INR), referrals, 
alcohol use, smoking status, cardiac risk factors, other co-morbidities / past interventions and 
information on the patient’s pharmacies of choice.  Additional information that will be sought will 
relate to socioeconomic, quality of life and satisfaction with care (e.g., marital / employment 
status, annual household income, education level, care giver support, missed work, EQ-5D-5L 
questionnaire).  See supporting data collection materials for a complete list of variables that will 
be captured from the patient and or the provider via Case Report / Data Collection Forms. 
 
Table 2. Overview of variables collected during the study 
Schedule Procedure 
Initial 
Primary Care / 
AF Clinic Visit 
Follow-up 
Visits 
Tertiary Care 
(if applicable) 
Visit date 
X 
X 
X 
Demographic data 
X 
 
 
Medical history  
X 
 
 
Investigations / Labs / Vital signs 
X 
X 
X 
Medications / Interventions 
X 
X 
X 
Socioeconomic / HRQoL 
X 
X 
 

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Costs 
The relevant costs for this study may include the cost associated with the development, 
implementation, and maintenance of the CDSS. All the health care resource utilization 
associated with managing and treating participating patients with AF will also be recorded. This 
health care resource utilization may occur in the participating primary care providers and the 
tertiary AF care clinic.    
 
HRQoL 
HRQoL will be measured using health utility, a single index anchored at 0 representing death 
and 1 representing full health. Health utilities can be used to calculate quality adjusted life year 
(QALY) for economic evaluation. The EQ-5D is a prominent example of the utility-based 
instruments, with some indication that it has become the most widely used multi-attribute 
instrument in the world [124]. It consists of 5 dimensions: mobility, self-care, usual activities, 
pain/discomfort, and anxiety/depression. Each dimension defines 3 levels indicating no 
problems (level 1), some problems (level 2), and extreme problems (level 3)(referred to as EQ-
5D-3L) [123]. A new version of the EQ-5D has recently developed by refining the response 
options of the EQ-5D-3L to reflect 5 levels of impairment: no, slight, moderate, severe, and 
extreme problems in the five dimensions (the EQ-5D-5L) [125]. The EQ-5D-5L tends to improve 
descriptive richness and discriminatory power as it defines a total of 3125 health states 
compared to only 243 by the EQ-5D-3L. The EQ-5D-5L will be used in our study. 
3.7.4 Data Quality 
A Data Quality Coordinator will monitor data quality to ensure accuracy.  This will be done via 
random audits of case report forms.  All data captured in the study database will be validated at 
time of entry.  Missing or implausible data will be queried. 
3.7.5 Safety / Adverse Events 
The IMPACT-AF study is not involved with examining novel therapies, but rather innovative 
approaches to optimizing proven, evidenced-informed care.  Study outcomes such as bleeding 
will be monitored as a safety outcome, but a data safety and monitoring committee is not 
anticipated given the study is supporting uptake of Canadian guideline-based approaches to 
care. 
Events (including details) 
X 
X 
X 

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3.8 Sample Size 
The primary objective of the proposed study is a composite of unplanned cardiovascular 
hospitalizations in AF patients and any AF-related emergency department visits.  The sample 
size calculation is based on the test of the null hypothesis that the percentages of patients with 
cardiovascular hospitalizations in the two populations (CDSS and usual care) are equal.  The 
primary measure of effect is the difference in proportions of patients with composite of any AF-
related emergency department visit or unplanned CV hospitalization over 12 months.   The 
criterion for significance (alpha) has been set at 0.05.  With the proposed sample size of 1075 
primary care patients (537 per intervention/control arm, assuming a 1:1 allocation ratio), the 
study will have power of 80% to yield a statistically significant absolute difference of 10% (or 20 
[RRR] relative risk reduction) between the two percentages of patients with composite of any 
AF-related emergency department visit or unplanned CV hospitalization in the control and 
intervention groups, assuming 50% annual rate of CVD hospitalizations for the control group.  
This sample size was adjusted for a potential clustering of patient outcomes within a physician, 
by assuming an intra-physician correlation coefficient (ICC) of 0.05 with an average cluster size 
of 9 patients/physician.  The ICC is based on studies done in the primary care sector reporting 
this magnitude of ICC.  This extent of reduction was felt to represent a minimal clinically 
important achievement by a consensus of study investigators and has been an endpoint target 
in other studies in AF populations of similar design [127-129].   
Table 3 provides a summary of the sample size calculation for different values of the baseline 
control rate (50%, 58%), RRR (15%, 20%, 25%, 30%), intra-class correlation coefficient [ICC] 
(0.05, 0.10) and average cluster size (m) (9,12,15). The primary measure of effect is the 
difference in the proportions or percentages of patients with cardiovascular hospitalizations and 
AF-related emergency room visits across study arms over an active study period of 12 months.  
The criterion for significance (alpha) has been set at 0.05.  The test is 2-tailed, which means that 
an effect in either direction will be interpreted. The statistical power is set at 80%. 
The above computation assumes a binomial distribution for the number of patients with 
cardiovascular hospitalizations and AF-related ED visits, a baseline control rate of about 50% 
and hypothesized RRR of 20%. We also adjusted the sample size for a potential clustering of 
patient responses within a practice group, varying the intra-class correlation coefficient (ICC)—
which measures the degree of clustering – from 9-15 (see Table 3).  Therefore, the required 
sample size of 1075 patients corresponds to an ICC of 0.05 and average cluster size of m= 9.   
 
P0= the proportion of patients in the control group 
 
 
MCID=minimal clinically important difference/improvement 
 
P1=proportion in the intervention group 
 
 
 
n=Total unadjusted sample size  
 
 
 
ICC=intra-cluster/practice correlation coefficient 
 
 
m=average cluster size 
 
 
 
 
VIF=1+(m-1)ICC=variance inflation factor 
 
 
 
n_adj=n*VIF=Total adjusted sample size (adjusted for clustering) 
RRR = relative risk reduction 
 
 
 
 
The calculations are based on 
 
 
 
 

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Control rate of 50% and 58% based on the NS data 
 
 
RRR varying from 15% to 30% 
 
 
 
 
Average cluster sizes (m) of 20, 25 and 30 
 
 
 
ICC values of 0.05 and 0.10 
 
 
 
 
Alpha = 0.05 
Beta = 0.20 
 
 
 
 
 
 
 
 
 
 
 
Table 3. Sample size Calculation 

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P0 
RRR 
MCID 
P1 
n 
ICC 
m 
VIF 
n_adj 
k 
0.58 
0.3 
0.174 
0.406 251.0602299 
0.05 
9 
1.4 351.4843218 39.05381354 
0.58 
0.3 
0.174 
0.406 251.0602299 
0.05 12 
1.55 389.1433563 32.42861303 
0.58 
0.3 
0.174 
0.406 251.0602299 
0.05 15 
1.7 426.8023908 28.45349272 
0.58 
0.25 
0.145 
0.435 364.9655172 
0.05 
9 
1.4 510.9517241 56.77241379 
0.58 
0.25 
0.145 
0.435 364.9655172 
0.05 12 
1.55 565.6965517 47.14137931 
0.58 
0.25 
0.145 
0.435 364.9655172 
0.05 15 
1.7 620.4413793 41.36275862 
0.58 
0.2 
0.116 
0.464 573.6717241 
0.05 
9 
1.4 803.1404138 89.23782375 
0.58 
0.2 
0.116 
0.464 573.6717241 
0.05 12 
1.55 889.1911724 74.09926437 
0.58 
0.2 
0.116 
0.464 573.6717241 
0.05 15 
1.7 
975.241931 65.01612874 
0.58 
0.15 
0.087 
0.493 1022.444138 
0.05 
9 
1.4 1431.421793 159.0468659 
0.58 
0.15 
0.087 
0.493 1022.444138 
0.05 12 
1.55 1584.788414 132.0657011 
0.58 
0.15 
0.087 
0.493 1022.444138 
0.05 15 
1.7 1738.155034 115.8770023 
0.58 
0.3 
0.174 
0.406 251.0602299 
0.1 
9 
1.8 451.9084138 50.21204598 
0.58 
0.3 
0.174 
0.406 251.0602299 
0.1 12 
2.1 527.2264828 43.93554023 
0.58 
0.3 
0.174 
0.406 251.0602299 
0.1 15 
2.4 602.5445517 40.16963678 
0.58 
0.25 
0.145 
0.435 364.9655172 
0.1 
9 
1.8 
656.937931 72.99310345 
0.58 
0.25 
0.145 
0.435 364.9655172 
0.1 12 
2.1 766.4275862 63.86896552 
0.58 
0.25 
0.145 
0.435 364.9655172 
0.1 15 
2.4 875.9172414 58.39448276 
0.58 
0.2 
0.116 
0.464 573.6717241 
0.1 
9 
1.8 1032.609103 114.7343448 
0.58 
0.2 
0.116 
0.464 573.6717241 
0.1 12 
2.1 1204.710621 100.3925517 
0.58 
0.2 
0.116 
0.464 573.6717241 
0.1 15 
2.4 1376.812138 91.78747586 
0.58 
0.15 
0.087 
0.493 1022.444138 
0.1 
9 
1.8 1840.399448 204.4888276 
0.58 
0.15 
0.087 
0.493 1022.444138 
0.05 12 
1.55 1584.788414 132.0657011 
0.58 
0.15 
0.087 
0.493 1022.444138 
0.05 15 
1.7 1738.155034 115.8770023 
0.5 
0.3 
0.15 
0.35 332.7644444 
0.05 
9 
1.4 465.8702222 51.76335802 
0.5 
0.3 
0.15 
0.35 332.7644444 
0.05 12 
1.55 515.7848889 42.98207407 
0.5 
0.3 
0.15 
0.35 332.7644444 
0.05 15 
1.7 565.6995556 
37.7133037 
0.5 
0.25 
0.125 
0.375 
486.08 
0.05 
9 
1.4 
680.512 75.61244444 
0.5 
0.25 
0.125 
0.375 
486.08 
0.05 12 
1.55 
753.424 62.78533333 
0.5 
0.25 
0.125 
0.375 
486.08 
0.05 15 
1.7 
826.336 55.08906667 
0.5 
0.2 
0.1 
0.4 
768.32 
0.05 
9 
1.4 
1075.648 119.5164444 
0.5 
0.2 
0.1 
0.4 
768.32 
0.05 12 
1.55 
1190.896 99.24133333 
0.5 
0.2 
0.1 
0.4 
768.32 
0.05 15 
1.7 
1306.144 87.07626667 
0.5 
0.15 
0.075 
0.425 1378.097778 
0.05 
9 
1.4 1929.336889 214.3707654 
0.5 
0.15 
0.075 
0.425 1378.097778 
0.05 12 
1.55 2136.051556 178.0042963 
0.5 
0.15 
0.075 
0.425 1378.097778 
0.05 15 
1.7 2342.766222 156.1844148 
0.5 
0.3 
0.15 
0.35 332.7644444 
0.1 
9 
1.8 
598.976 66.55288889 
0.5 
0.3 
0.15 
0.35 332.7644444 
0.1 12 
2.1 698.8053333 58.23377778 
0.5 
0.3 
0.15 
0.35 332.7644444 
0.1 15 
2.4 798.6346667 53.24231111 
0.5 
0.25 
0.125 
0.375 
486.08 
0.1 
9 
1.8 
874.944 
97.216 
0.5 
0.25 
0.125 
0.375 
486.08 
0.1 12 
2.1 
1020.768 
85.064 
0.5 
0.25 
0.125 
0.375 
486.08 
0.1 15 
2.4 
1166.592 
77.7728 
0 5
0 2
0 1
0 4
768 32
0 1
9
1 8
1382 976
153 664

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3.9 Statistical Analysis:  
The analysis and reporting of the trial results will follow the CONSORT Statement for cluster-
randomization trials [129].  The baseline characteristics of the practices and patients will be 
reported by group as mean (standard deviation) or median (first quartile, third quartile) for 
continuous variables, depending on the distribution, and count (percent) for categorical 
variables.  The analysis will follow the intention-to-treat (ITT) principle.  The unit of randomization 
will be the practice.  The unit of analysis will be the patient.  We will use multiple imputation to 
handle missing data to enable ITT analysis.  Multiple imputation is a Monte Carlo technique in 
which the missing values are replaced by m > 1 simulated versions, where ‘m’ is typically small. 
Each of the simulated complete datasets will be analyzed by standard methods, and the results 
will be combines to produce estimates and confidence intervals that incorporate missing data 
uncertainty [133]. We will use generalized estimating equations (GEE) – assuming 
exchangeable correlation structure for patients within the same practice, and adjusting for urban 
and rural practice types to analyse all outcomes [130].  Unlike ordinary regression techniques, 
GEE allows us to model the intra-practice correlation among patients within each practice.  The 
results will be reported as the estimate of the effect, corresponding 95% confidence interval and 
associated p-values.  All p-values will be reported to three decimal places with those less than 
0.001 reported as p<0.001.  The criterion for statistical significance will be set a priori at alpha = 
0.05, and adjusted using the Bonferroni method for multiple secondary analyses.  All analyses 
will be performed using SAS 9.4 (Cary, NC). 
 
Sensitivity analyses: 1) There are several methods for analyzing cluster RCTs [131].  Therefore, 
we will perform sensitivity analyses using some of the commonly used patient-level methods 
such as random-intercept model and cluster-level (i.e. random- and fixed-effects meta-analytic) 
methods to assess the robustness of the results. 2) Given that the unit of randomization is the 
practice, there is a higher chance of potential imbalance of patient-level baseline characteristics. 
We will perform some sensitivity analysis to adjust for any potential imbalance. 
 
HRQoL 
The proportion of patients with reported impairment in each of the five EQ-5D-5L dimensions will 
be calculated for each arm. Health utilities will be calculated using the Canadian EQ-5D-5L 
scoring algorithm. QALYs accumulated over the study period will be calculated using the area 
under the curve method.  
 
Costs 
Total cost will be calculated using the sum of the product of resource utilization items (e.g. length 
of hospital stay, medications) and corresponding unit costs.   
 
Cost effectiveness   
The incremental cost effectiveness ratio (ICER) will be calculated by dividing the incremental 
difference in the mean cost between the treatment arms by the incremental difference in the 
mean QALYs between the arms. Sampling uncertainty is a critical issue that needs to be 
sufficiently and adequately dealt with in any economic evaluation alongside a clinical trial. 

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Nonparametric bootstrap with replacement will be used to estimate the confidence intervals 
around the ICER. The decision uncertainty will be presented using the cost effectiveness 
acceptability curves in which maximum willingness to pay value from the policy maker’s 
perspective is explicitly considered.  Both base case and uncertainty analyses will be stratified 
by demographic and socioeconomic variables (such as age, gender and income).  These 
stratified analyses are important for any economic evaluation to identify subpopulations that 
could potentially receive the most benefit from the intervention.  
4.0 Study Population 
4.1 Recruitment of Clinics and Practitioners Managing AF Patients 
 
IMPACT-AF will seek participation from a wide variety of primary care providers managing AF 
patients within Nova Scotia.  All patients with a prior or new electrocardiographically confirmed 
diagnosis of AF during the study period will be eligible for inclusion as part of the study.   
 
Inclusion Criteria: Any licensed medical provider of community-based adult patients with a 
minimum of 15 diagnosed AF patients enrolled in the study from their practice will be eligible to 
participate.  To avoid bias based on technological availability or proficiency, all eligible providers 
are required to have access to high-speed internet access in their office for the duration of this 
study.  As well, participating providers must sign a site agreement which outline the following 
terms: 
 
• 
Agree to participate for the duration of the study;  
• 
Notify the research team if your practice: 
• 
Is located in an urban (population ≥10,000) or rural (population < 10,000) 
setting; and, 
• 
Operates as a collaborative practice (i.e., shares in the care of patients with 
at least one other health care provider); 
• 
Attend and participate in investigator meetings; 
• 
Identify all eligible patients in your practice who meet the study inclusion criteria; 
• 
Invite all eligible participants to join in the study (either in person or via mail outs); 
• 
Perform informed consent discussions with eligible participants who visit your practice 
for routine care; 
• 
Accept randomization into either the control or intervention group; 
• 
Agree to store informed consent forms and completed patient questionnaires in locked 
filing cabinets in an access-controlled location within your practice (after faxing a copy 
to the study office); 
• 
Agree to be trained on and use the web-based decision support tool, if assigned to the 
intervention group; 
• 
Allow the study team access to medical records for consented study participants at 
baseline and follow-up; 
• 
Facilitate the distribution of patient questionnaires (all materials supplied by the 
research team); 

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• 
Notify the Privacy Officer at HITS-NS that the study team is to be granted access to 
data for all patients who consent to study inclusion and provide copies of patient 
signed consent forms as requested. 
• 
Report any problems that you experience related to your participation in the study.  
• 
Agree not to disclose or use any study related information for purposes other than the 
delivery of standard AF patient care and study activities.  
• 
Agree that all information disclosed by the research team regarding the study will be 
considered confidential information and not disclosed, publish or disseminated to any 
third party other than your employees (e.g., nursing and administrative staff).  
 
 
Exclusion Criteria: Providers who do not meet the above criteria will be excluded from the 
study. 
 
Provider Recruitment: Doctors Nova Scotia, the medical association for Nova Scotia 
physicians, has been invited to identify a primary care physician to participate as a peer 
Champion on the project Advisory Committee.  Doctors Nova Scotia and the peer champion will 
also be engaged and consulted to support primary care provider recruitment across Nova 
Scotia.  Proactive provider recruitment activities are expected to include: electronic and paper-
based notices (e.g., in the Doctors Nova Scotia magazine and other relevant discipline-based 
publications / websites), letters of invitation to participate (fax, e-mail and or hard-copy via 
hospital mail-boxes) sent to all primary care practices across Nova Scotia, study display booths 
at local medical conventions, face-to-face information sessions during community based CME 
events, and at specific provider meetings (e.g., family practice meetings).  Recruitment will begin 
following confirmation of ethics approval and is anticipated to continue for 6 months, or until the 
desired number of practices is achieved.  Promotional study materials (e.g., branded brochures, 
post-card, wall posters, FAQ documents, study website) will be used during the provider 
recruitment phase. 
 
4.1.1 Pharmacist Participation: Consented patients will be requested to identify those 
pharmacies which they frequent for their medication needs. With the patient’s consent, individual 
pharmacies will be notified by the Project Office that the patient is an IMPACT-AF study 
participant and invited to visit the study website for further details on the study.  Pharmacy 
directed communications are also anticipated via regular communication channels (e.g., 
pharmacy websites, newsletters, etc). 
 
4.2 Patient ID / Recruitment / Inclusion / Exclusion    
 
Primary Care ID / Recruitment:  Provider recruitment will aim to be broad to help ensure that 
the findings of the study can be generalized.  Based upon NS billing data, the average family 
practice in NS is estimated to have between 20-30 adult (aged > 18 years) patients with 
diagnosed AF.  Practices will be provided with detailed written and verbal instructions on patient 
identification methods during the investigator orientation session entitled ‘How to Create an AF 
Patient Registry’.  EMR or billing systems can be queried (e.g., search for: ICD-9 diagnostic 
code 427.3 (atrial fibrillation) and or 427 (Cardiac dysrhythmias) OR ICD-10 code I48 (atrial 

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fibrillation and flutter) billings in the last 24 months, AF diagnosis in the past history or problem 
list, INR monitoring / flow sheet use, use of specific medications, etc.), as well as paper-based 
methods utilized (e.g., tracking patients on a daily basis or using stickers / index cards) to 
identify potential patients with AF.  Practices will be asked to create and maintain a confidential 
electronic registry of patient names, preferably versus a paper-based registry, that can track 
relevant clinical information on AF for both the practice and IMPACT-AF purposes.  The 
expectation is for participating practices to explore their total patient population for patients with 
AF, not just those known to have the condition.   
 
Other options for patient identification include: providers can request from MSI a list of those 
patients whom the provider has billed for an AF-related visit or INR monitoring in the last 12-24 
months.  Project support staff, or other peer champions, will be available after investigator 
orientation to support practices requiring assistance in patient identification, consenting and 
registry creation. 
 
Once eligible patients are identified, a consent brochure will be mailed or handed to the patient.  
If the patient is seen in the provider’s office, consent will be sought at the time of visit.  Each 
patient will be assigned a study number for coding purposes; otherwise, unique identifiers for 
health record identification and linkage will remain with the health facility at which they were 
recruited and where their AF care is routinely provided.  This affords a mechanism to allow 
follow-up on long-term care procedure and events through subsequent chart review or database 
linkage without having to include unique health identifiers in the IMPACT-AF study registry. 
 
AF Clinic ID / Recruitment:  Patients visiting the AF Clinic at the QEII Health Science Centre 
will also be asked to participate and sign a consent form.  The patient’s primary care provider 
would be notified that the patient has agreed to participate via the AF clinic site. 
 
Inclusion Criteria:   
Primary Care: All new and previously diagnosed patients ≥ 18 years of age with AF and a life 
expectancy > 12 months will be eligible for study enrollment.  In addition, patients will need to 
be: 
 
• 
A registered and regular patient at a primary care practice participating in the 
IMPACT-AF study; 
• 
Able to read, understand and communicate in English. 
• 
A resident of Nova Scotia. 
 
Patients in the Intervention Arm will have the option to interact with the CDSS through the 
patient portal, the AF Care Partner tool.  These patients will therefore need access to the 
Internet and be willing to input information and receive feedback and reminders from the AF 
Care Partner tool over the duration of the study.  Patients can revoke their AF Care Partner tool 
participation if desired.   
 

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All AF Clinic (QEII HSC) patients will be invited to participate in the IMPACT-AF study by signing 
an informed consent form.  
 
Exclusion Criteria:  Patients with a terminal illness (expected death within 12-months) and 
those having cognitive impairment or who are unable to communicate in English will be 
considered ineligible for participation.  Also ineligible for inclusion are AF patients whose care is 
being significantly overseen by a specialist as well as those who are patients of the hospital-
based AF clinic at the QEII Health Sciences Centre. 
 
4.3 Ethical Considerations 
IMPACT-AF is a non-interventional study.  There is no assignment of a patient to a particular 
therapeutic strategy; instead, the treatment decision falls within the boundaries of acceptable 
current practice and treatment options remain the decision of the primary care provider.   
Ethics approval will be solicited through the Nova Scotia Multisite Research Ethics Board.  
Providers will be required to sign a site agreement. Patient consent will also be sought from all 
participants at the beginning of their involvement in the study.  Consent forms will be 
standardized to adhere to local ethics criteria and good clinical practices, with reference to all 
planned data capture including clinical, demographic, socioeconomic, economic and health care 
utilization information.  The consent process will be as previously outlined.  
 
Provider and patient confidentiality will be maintained via encryption of all electronic study 
data.  Only Canadian servers will be utilized to host the study registry and clinical decision 
support system with the servers housed at the Nova Scotia government’s provincial data 
center. The CDSS, AF Care Partner and participant data will be stored on firewall and 
password protected servers located at HITS-NS. A unique Study ID is created for each patient 
following enrollment in the study. A participant registry stores the name and corresponding 
Study ID for each participant. All study data collected will be stored by Study ID and names 
will be removed and kept separate from the participant registry. The intervention tool (CDSS) 
will also be stored on servers at HITS-NS; when a Primary Care Provider and / or patient 
interacts with the intervention tool, all data is immediately sent and stored on these servers. 
All study researchers, staff, Site Investigators and participants will access the servers through 
a secure login requiring unique usernames and passwords. Individuals will be permitted 
access to only the information required for their role in the study. 
Hard copy study forms maintained at the Study Coordinating Centre (e.g., any patient data or AF 
clinic consent forms, etc) will be maintained in a secured locked cabinet in a secure location on 
the QEII HSC property. 
5.0 STUDY ORGANIZATION 
5.1 Medical Experts 
Medical Experts for the study are: 

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• 
Dr. Jafna Cox 
• 
Dr. Ratika Parkash 
• 
Dr. Jim MacKillop (family physician) 
5.2 Health Informatics Experts 
Health Informatics Experts for the study are: 
• 
Dr. Raza Abidi 
• 
Dr. Samina Abidi 
5.3 Statistician Expert 
Statistician Expert for the study is: 
• 
Dr. Lehana Thabane 
5.4 Health Economist Expert 
Heath Economic Expert for the study is: 
• 
Dr. Feng Xie 
5.5 Committees 
IMPACT-AF will be organized similar to other successful health management / clinical studies. 
See Appendix 6.1 
5.5.1 Executive Committee: 
The project Executive Committee is responsible for study oversight. Participation includes: 
• 
Dr. Jafna Cox, Dr. Raza Abidi, Dr. Ratika Parkash, Dr. Samina Abidi, Dr. Lehana Thabane, 
Dr. Feng Xie, Dr. Jim MacKillop;  
• 
NS Department of Health and Wellness; and, 
• 
Dr. Shurjeel Choudhri, Bayer. 
 
A Project Chairperson from the investigator team will be responsible for overall project 
supervision and chairing of all committee meetings.  Executive Committee members are 
expected to: actively participate in committee meetings; respect confidentiality of all 
information discussed; serve for the duration of the project, but may resign by providing the 
Committee Chair with written notice two months in advance of the effective date of their 
resignation.  Each member has one vote on committee decisions.  The quorum for all 
meetings shall consist of a majority of the voting members in attendance.  Committee 
expenses / honoraria, if applicable, are provided from within the project budget. 
• 
Mandate: Responsible for overall project governance including all operational and budget 
issues.  
o The Project Office coordinates day-to-day management issues.  
• 
Meets frequently, usually bi-monthly at project start and monthly thereafter (or as 
required). Meeting minutes will serve as record of Committee activities to be distributed 
within 15 days following the meeting. 
• 
Reports to the Advisory Committee. 
• 
Administrative support provided by the Project Office. 

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5.5.2 Advisory Committee: 
The project Advisory Committee, a broad-based, multi-stakeholder group reflective of all 
partners, will be responsible to provide expertise, guide strategic decisions, inform and 
communicate with their representative organization(s) regarding project development, 
implementation, evaluation and or knowledge translation. This committee of 15-20 
participants is expected to include representatives from: 
 
• 
NS Department of Health and Wellness/Provincial Programs 
• 
Heart and Stroke Foundation Nova Scotia 
• 
Patient representative  
• 
Doctors Nova Scotia 
• 
Pharmacy (retails chains, independents and or the Association) 
• 
Long Term Care organization(s) 
• 
Bayer Healthcare 
• 
Clinical Advisors (Neurology / Hematology / Geriatrics) 
• 
Canada Health Infoway 
• 
Health IT vendors (TELUS Health) 
• 
Canadian Cardiovascular Society 
• 
Provider representatives (FPN, NP or RN rep) 
• 
NS Health Research Foundation 
• 
One or more District Health Authority representatives 
• 
Other participants may be added e.g. NSCAD. 
 
Advisory Members are expected to: actively participate in committee meetings; respect 
confidentiality of all information discussed, as appropriate; serve for the duration of the 
project, but may resign by providing the Committee Chair with written notice two months in 
advance of the effective date of their resignation.  Each member has one vote on committee 
decisions.  The quorum for all meetings shall consist of a majority of the voting members in 
attendance.  Committee expenses / honoraria, if applicable, are provided from within the 
project budget. 
• 
Mandate: Concerned with project development, providing input, guidance and direction 
on the project, its goals, implementation and any major policy issues / decisions. 
Responsible for promoting and driving the project in their organizations and across the 
province. 
• 
Meetings twice yearly. 
• 
Meeting minutes will serve as record of Committee activities to be distributed within 30 
days / months following each meeting. 
• 
Administrative support provided by the Project Office. 
5.5.3 Adjudication Committee: 
An adjudication committee may be created to provide oversight regarding adjudication of 
certain study outcomes.  Anticipated members include: a clinical cardiologist, an 
electrophysiologist, a neurologist and a haematologist.  The committee will be blinded to 

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treatment allocation if applicable.  Meetings will be held as needed subsequent to the 
collection of data. 
5.5.4 External Review Committee: 
An external review committee of 4-5 participants may be created to provide visionary 
oversight and peer-review on a yearly basis.  Consideration will be given to non-Nova Scotia, 
non-Canadian participants.  Representatives are likely to include computer scientists, clinical 
experts, knowledge translation researchers, health services researchers, and health policy 
makers or researchers. 
5.5.5 Other Committees 
Other project collaborators / co-investigators (e.g., subject matter experts) are expected to 
participant in additional project committees (e.g., Technology Development, Sub-Studies, 
Economics and Knowledge Translation). Participants may include: 
• 
Health Economist, Behavior / Health Psychologist, Communications / Media (e.g., 
Dalhousie Faculty of Management), Adult / Health Education, Computer Science – Human 
Interaction 
• 
Membership: Composed of a core group of individuals with particular subject matter 
expertise. 
o Members are expected to: actively participate in committee meetings; respect 
confidentiality of all information discussed, as appropriate; serve for the duration 
of the project, but may resign by providing the Committee Chair with written 
notice two months in advance of the effective date of their resignation.  
o Ad hoc members as required. 
o Each member has one vote on committee decisions. The quorum for all meetings 
shall consist of a majority of the voting members in attendance. Committee 
expenses / honoraria, if applicable, are provided from within the project budget. 
• 
Mandate: Charged with providing subject matter expertise regarding project-related 
issues / implications of the study. 
• 
Meets as required to address planning and discuss results of economic evaluations. 
Meeting minutes will serve as record of Committee activities to be distributed within 15 
days following the meeting. 
• 
Administrative support provided by the Project Office.  
 
 
 
 
 

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9.0 APPENDIX 
9.1. Project Organogram    
 
 
 
Committee
Technology 
Development
Technical
Team
Study Servers 
(HITS-NS)
Clinical Trial 
Project Office
Research 
Staff
Biostatistics
Administrative 
Support
Advisory 
Committee
Project 
Manager
Health 
Economics
Knowledge 
Translation