B

BIG DATA AND ONTARIO’S

PRIMARY CARE SECTOR

By

July 2014

Emmanuel Casalino, CPHIMS-CA

Radwan El Ali, MD, MBA

University of Toronto

School of Continuing Studies

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TABLE OF CONTENTS

ABSTRACT ......................................................................................................................................... 3

BACKGROUND .................................................................................................................................... 4

CURRENT STATE ................................................................................................................................ 6

PRIMARY CARE @ A GLANCE ........................................................................................................... 6

VALUE FOR MONEY ......................................................................................................................... 8

THE TRANSFORMATION JOURNEY .................................................................................................... 9

BEST PRACTICES ........................................................................................................................... 10

FUTURE STATE ................................................................................................................................. 12

IMAGINE FOR A MOMENT ................................................................................................................ 12

BACK TO REALITY .......................................................................................................................... 14

MOVING FORWARD ....................................................................................................................... 17

KEY TERMS ...................................................................................................................................... 20

END NOTES...................................................................................................................................... 21

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ABSTRACT

The paper looks at the potential role of Big Data in a primary care context, with a specific

Ontario focus. It looks at the impact of Big Data in helping to:

Support clinical decision making

Enhance practice workflow

Improve continuity of care from the patient‟s perspective

This paper examines concepts and opportunities beyond what is generally promised by

eHealth – i.e., having the right information, about the right person, at the right time. It examines

how data from the health sector, academia, social media, NGOs and a variety of non-traditional

health sources can help improve clinical outcomes for patients and society as a whole.

It compares and contrasts what data is readily available within a typical EMR-enabled family

practice against a similar yet hypothetical practice that is enabled with Big Data

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BACKGROUND

Primary care in Ontario has undergone significant change over the past several years, and has

evolved significantly from the days when most family physicians worked as solo practitioners,

used paper charts to manage patient information, and depended on their own professional

judgment to make clinical decisions. Today, family doctors typically work in multi-disciplinary

teams i including nurse practitioners, social workers, and dietitians. They use electronic

medical record (EMR) systems to electronically capture and share patient data and are moving

away from a „fee for service‟ compensation model towards an outcomes-based framework.

The province‟s Excellent Care for All Act (2010) is transformative in that it recognizes the

patient as the centre of Ontario‟s health system and acknowledges that healthcare decisions

should be based on evidence. The Act focuses on better access to care, enhancing

community-care delivery, improving integration by bringing services to the patient, and

ensuring that information follows them as they move through the system. Through policy

directives, family physicians are being asked to be ”more accountable for health outcomes by

embedding quality improvement initiatives in clinic settings”i.

Most modern healthcare system have and continue to invest in technology to assist in

automating clinical practice workflows, and records management – this is considered

foundational to achieving transformational objectives and promisesii.

Bending the cost curve is seen as a strategic imperative to sustaining a viable and effective

healthcare system – one that can meet the needs and expectations of Ontarians. Health care

spending in Canada is expected to double between 2000 and 2020, and is likely to outpace

growth in overall public expenditures by a 2:1 ratioiii. Canada‟s ageing population has a direct

relationship to this cost pressure, with the Conference Board of Canada predicting that per

capita expenditure will double over the same period for people currently aged 55 to 64. The

same report also outlines “advances in technology‟” such as diagnostics, and new drug

regimens that contribute to improved health outcomes. These are also expected to increase

overall health expenditures.

Big Data is being view as a quintessential game changer for primary care. Rather than bending

the cost curve, it may be used to get ahead of it by enhancing:

PREVENTATIVE CARE: Helping to avoid and/or delay the onset of chronic diseases;

improving patients‟ quality of life while having a direct and positive impact on health

system costs

QUALITY OF CARE: Interweaving clinical evidence and best practices into care

decisions; using the collective knowledge of leading experts at the point of care to help

guide care decisions

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PATIENT CARE CO-MANAGEMENT: Enabling a more robust dialogue and treatment

planning between individuals, family members and the care team

DECISION SUPPORT: Improving the variety, volume, and velocity of data available to

clinicians i.e. breaking down the silos, and contextualizing the data into meaningful

information to support more informed care decisions.

POPULATION HEALTH MANAGEMENT: Leveraging the volumes of data that is

collected, managed, and stored across the health system to improve surveillance

capabilities, and to investigate and ultimately improve outcomes for condition / disease

specific patients.

“When you have extensive data you can query and analyze, you can

ask, what‟s going on? In healthcare, it means finding out what‟s

working and what‟s not working, and the latter is absolutely key.”

- Richard Alverez, CEO and President (former), Canada Health

Infoway

Yet, while healthcare remains one of the most information intense sectors iv, it continues to rely

heavily on paper and even when practices deploy systems they remain “islands of automation

in a sea of paper”v. Today, family physicians‟ care decisions about their patients is based

primarily on: what they know, what they observe and what they are told. What they don‟t know

is precisely that – What They Don‟t Know.

Consider the following: for a typical patient engagement, a family physician:

Knows what drugs they prescribed, but doesn‟t know

what drugs the Specialist also seeing the patient has

ordered

Knows that they have renewed a prescription, but

doesn‟t know that patient has never had the drug

dispensed at a pharmacy

Knows that they have ordered a lab test for blood sugar,

but doesn‟t know that this same test was just completed

during a recent hospital visit

Knows and is monitoring the patient‟s waist / hip ratio,

but doesn‟t know that recent evidence has discounted

this an effective diabetes risk factor

Knows that the patient typically presents with high blood

pressure, but doesn‟t know that the patient is actively monitoring their own pressure at

home and is showing normal ranges on a regular basis

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Knows that there is no family history of cancer, but doesn‟t know that the patient is

carrying a genetic marker that makes them predisposed to lymphoma

When you consider that “90 percent of all the data in the world has been generated over the

last two years”vi and the healthcare system in the United States reached 150 exabytes in

2011vii, it is clear that the debate is not if Big Data will play a role in enhancing primary care, but

how Big Data will be leveraged to transform the system.

CURRENT STATE

The Ontario healthcare sector is a large, complex and data rich sector in which the information

is fragmented and highly siloed. National and provincial leaders recognize the importance of

liberating patient data and thus making it available where and when it is needed to make more

informed clinical decisions that are based on the latest evidence.

To that end, Ontario is investing hundreds of millions towards the creation of Electronic Health

Records (EHRs). These investments will translate into better access to timely and

comprehensive data, enhanced coordination of care delivery, improved continuity of care and

health outcomes, and enhanced population health management. This paper will focus on the

data aspects of healthcare rather than the clinical characteristics of the healthcare sector in

Ontario.

PRIMARY CARE @ A GLANCE

Each and every day, Ontarians visit their family

doctor, nurse practitioner, dietitian or physiotherapist

to receive primary healthcare services – whether it is

to alleviate symptoms of the flu, maintain a chronic

disease such as diabetes, prevent diseases through

screening, or seek medical advice. Primary

healthcare is an important source of chronic disease

prevention and management. It is estimated that 56%

of patient encounters occur at the primary care

levelviii, making this sector a vital source of patient

data.

The province has invested millions into transforming

primary practices – moving away from paper files to

electronic records and has publically stated that the

promise of eHealth cannot be truly realized without automating the province‟s nearly 16,000

primary care providers ix.

Figure 1: Patients visits per day in Canada

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Consider:

13,000,000+ Ontarians

10,600 primary care physiciansx and 1,867xi nurse-practitioners are active across

Ontario

Every day Ontario‟s primary care clinicians see 150,000 patients

75% of primary care physicians have adopted or enrolled to adopt an Electronic

Medical Record (EMR) within their practicexii

More than 25% of primary care clinicians continue to use paper files to manage patient

records

There are 13 certified EMR products in Ontario

Primary care clinicians represent the first line of care for the vast majority of Ontarians, and

often care for patients over the course of many years –resulting in deep and trusted

relationships. As a result, primary care clinicians are in a unique position within the health

system of being able to collect and manage health data over a span of years. They are able to

witness how a patient‟s health progresses overtime and see patterns of behaviour that could

influence their health status. The challenge facing the health system is this vital data is housed

in thousands of sites, by thousands of unconnected systems, and many hundreds of disparate

applications.

Ontario has what could be classified as the perfect storm from Big Data perspective – lots and

lots of available data that cannot be readily accessed or analyzed.

“We have been referring to Ontario‟s collection of healthcare

providers as a „system‟. In reality, the province has a series of

disjointed services working in many different silos. The Ministry of

Health and Long-Term Care must work with its healthcare providers,

administrators and stakeholders to co-ordinate roles, simplify the

pathways of care and improve the overall patient experience”

Don Drummon, Commission on the Reform of Ontario‟s Public

Services

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VALUE FOR MONEY

According to the Ontario Budget for the year 2013,

38.3% of the total expenses are related to the

healthcare sector which accounts to $48.9 Billionxiii.

Furthermore, the Canadian Institute for Health

Information (CIHI) forecasted that the health

expenditure in Ontario will be $79.7 Billion by 2013,

of which the public sector expenditure will be $54

Billion or 67.7% of the total health expenditurexiv.

There is growing concern over the affordability of

Ontario‟s health system, with related expenditures

consistently outpacing the growth in GDPxv.

In spite of the level of investment, there are

questions as to whether sufficient value is being

realized. Ontario does not fare well when compared against other Organization for

Economic Co-operation and Development (OECD) jurisdictions who can offer comparable

or even enhanced health outcomes while investing less on a per capital basis. A recent

OECD study of 34 countries placed Canada as the sixth most expensive system in 2009.

Despite the level of expenditures, the Canadian health system does not fare well from a

value for money perspective according to a 2010 Commonwealth Fund Reportii. It placed

Canada second last, ranking only ahead of the United States.

Consider:

Canadian health expenditures are expected toxvi:

o rise by 5.2% in annual growth by 2020 due to primarily demographic factors

o account for 42% of governmental revenues by 2020, up from 31% in 2000

o grow by 58% by 2020, compared to growth in other government spending of

17%

Those 65 and older are estimated to double to more than 4 million by 2036 xvii

80% of Ontarians above the age of 45 suffer from at least one chronic disease, with the

prevalence and cost expected to grow as our population ages xviii

8,700 Ontarians will have been diagnosed with colorectal cancer, with approximately

3,350 dying from it each year. 30% of targeted Ontarians were properly screened for

the disease.xix

61% of women in Ontario aged 50 to 74 were screened for breast cancer with

mammographyxx

Figure 2: Ontario Budget 2013

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55% of healthcare costs can be attributed to the management of chronic diseases

across Ontario xxi

5% of patients account for two-thirds of healthcare costs xxii

Ontario must do more to offer more comprehensive services at more affordable rates – the

success of our very economy requires it, and Ontarians demand it. There is consensus among

subject matter experts that technology is key to getting ahead of the cost curve; allowing

Ontario to offer high quality health services at affordable rates.

THE TRANSFORMATION JOURNEY

Today, according to OntarioMD more than 75% of community based physicians, “have or are in

the process of adopting an Electronic Medical Record (EMR)”. This is an important

advancement that will allow the liberation of primary care data from the traditional confines of

what was the family physician file room. EMR data contains highly pertinent information to

clinicians outside of the practice. Sadly, it is the norm that when a patient is admitted into an

emergency room or even a long-term care facility, attending clinicians have little to no

knowledge of the patient‟s: current medications, family history, existing diagnoses, past

surgeries, allergies, or procedures performed.

The transformation journey is akin to OntarioMD‟s Maturity Model as depicted in Figure 3:

OntarioMD EMR Maturity Model The first step along the transformation journey is to move away from

paper records and to equip primary care clinicians with an EMR to support the electronic

capture, management and sharing of patient data. As the practice matures it adopts more

advanced capabilities to automating workflow, managing diseases, analyzing and reporting on

patient cohorts, and improving quality outcomes through advanced analytics.

Consider:

Approximately a quarter of Ontario‟s primary

care physicians operate in a purely paper-

based environment

Ontario currently lacks a provincial:

o Medication repository, preventing

access to comprehensive

prescription histories for patients

o eReferral offering, preventing

advances on how patients are

referred to specialists on how data

can follow the patient

o Immunization repository, preventing access to comprehensive history of

immunizations

Figure 3: OntarioMD EMR Maturity Model

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Primary care clinicians can now access lab results from a provincial repository (i.e. the

Ontario Laboratories Information System), but it currently holds 75% of labs completed

across the province

Provincial infrastructure to allow and support the exchange of information between care

delivery organizations / clinicians is not readily available

Personal Health Information Protection Act (PHIPA) imposes consent obligations on the

sharing of health information

The lack of key provincial assets is inhibiting the flow of patient data between care providers.

Today, primary care clinicians have restricted access to a full view of the patient‟s relevant

medical history and procedures. The promise of Big Data does not seem to be a realistic

objective for this sector of the health system due to the current restrictions.

BEST PRACTICES

The picture is not entirely bleak as the province has several initiatives underway to help

improve data quality. Health Quality Ontario (HQO) is an independent government agency that

is currently working with different stakeholders across Ontario to “provide leadership on a

coordinated and sustainable approach to measure and report on primary care performancexxiii”.

Their goal is ambitious in terms of determining indicators for quality, efficiency, effectiveness

and other considerations. In addition they are aiming to define and capture these indicators

from their different sources and create the technological infrastructure required to capture,

analyze and report on these indicators at both a system and practice level.

Recent advancements are enabling the extraction of EMR data to support secondary uses

such as clinical research. Some examples include:

Better Outcomes and Registry Network (BORN) is currently extracting maternal and

child data to support its mandate surrounding: new born screening and researchxxiv

Ontario Best Practices Research Institute (OBRI) is currently capturing data from

practice locations treating patients with Rheumatologic diseases xxvfor research and

best practices purposes.

The Canadian Cardiovascular Harmonization of National Guidelines Endeavour (C-

Change) initiative is a national initiative focused on the advancement, promotion and

harmonization of cardio vascular disease guidelines. C-CHANGE is working on

promoting these guidelines and implementing them at both a patient and primary care

physician levelxxvi

Canadian Primary Care Sentinel Surveillance Network (CPSSN) is an initiative to work

with participating primary care physicians to capture, analyze and provide insight on the

quality of care provision and how selected chronic diseases and neurologic conditions

are treatedxxvii.

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When you have extensive data you can query and analyze, you can

ask, what‟s going on? In healthcare, it means finding out what‟s

working and what‟s not working, and the latter is absolutely key.”

- Richard Alverez, CEO and President (former), Canada Health

Infoway

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FUTURE STATE

IMAGINE FOR A MOMENT

Mark is a sixty year old who was recently admitted into hospital for congestive heart failure,

where he was treated and released two days later. The hospital recommended that Mark be

referred to a cardiologist, be placed into a Telehomecare program, and was ePrescribed an

ACE inhibitor.

The eReferral system identifies Dr. Strul as the most appropriate cardiologist to handle the

referral – based on models that take into consideration factors such as proximity, wait time,

qualifications, patient ratings on social media, etc. An eReferral is sent to Dr. Strul‟s practice

accompanied with relevant details.

As part of the Telehomecare program, Mark is required to measure his weight and blood

pressure twice a day via Bluetooth-enabled devices, which send results to a Telehomecare

Nurse Practitioner.

Three days after being discharged, Mark‟s weight has suddenly increased by 3 lbs. New

evidence from the Heart and Stroke Foundation is suggesting= – based on Mark‟s height,

weight and age – that a weight gain of 2.5 lbs. or more is an indicator of fluid retention and

highly dangerous for CHF patients. The Telehomecare Nurse Practitioner‟s Case Management

System (CMS) monitors emerging evidence, notices the weight gain pattern, and issues an

immediate alert. When reviewing the alert she notices that while Mark was prescribed

medication at the hospital, there is no indication that it was filled.

Given the severity of the situation, the CMS automatically redirects the alert to Dr. Rodin who is

Mark‟s family doctor. Five days ago, Dr. Rodin was sent an electronic notification that Mark

was admitted into hospital and was able to remotely monitor his situation as he received both

hospital and Telehomecare reports on a daily basis directly into his Electronic Medical Record

(EMR).

Dr. Rodin, the Nurse Practitioner, Dr. Strul, and Mark convene a web-based consultation to

discuss his sudden wait gain. On the videoconference, Mark confirms that he did not have time

to get his prescription filled – both the Nurse Practitioner and Dr. Rodin agree this is the likely

cause for his weight gain. Both doctors‟ EMRs received the ePrescription notice, and based on

recent evidence from the United States as well as Mark‟s genetic make-up, they recommend

the drug digoxin instead. It has less risk of a negative interaction with his current medications

and medical history.

The care team agrees on the course of action, and the ePrescription is updated and the closest

pharmacy is notified and delivers the medication to Mark within hours. Mark is told to continue

monitoring his weight and blood pressure and for the moment there is no need to go to the

emergency room. Mark‟s follow-up visit with Dr. Strul is cancelled as the consultation was

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completed electronically and his medical record is updated – and readily available should his

situation change.

Dr. Rodin participates in a clinical research studies into clinical guidelines for CHF and

Diabetes and has agreed to submit anonymized patient data on a monthly basis. In return he

receives a report that demonstrates how well he is doing against established targets and in

comparison with other providers with similar patient cohort profiles.

Later that month, Dr. Rodin reviews his on-line performance report. It shows that his diabetic

patients are being effectively managed and there is a positive trend regard compliance with

clinical guidelines. However, the report has identified an anomaly with respects to patients who

are booked for their annual foot exam. It forecasts that he will be out of compliance on this key

measure. He opens the list of patients that are due for a foot exam, and sends each a text

reminding them to book an online appointment.

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BACK TO REALITY

While progress is being made, for the vast majority of

patient interactions across Ontario‟s health system,

information does not readily flow between provider

organizations. Figure 4: Islands of Data depicts the reality

of the current health system. Patient data captured during

a patient encounter, for the most part, remains captive

within the organization‟s four walls.

The current reality is preventing many of the advantages

and benefits described with the fictional Mark‟s

experience. The inability to amass timely and relevant

data are creating „white spaces‟ or gaps of important

information that could be used to advance healthcare

delivery via enhanced analytics and machine learning

opportunities. Current information gaps are preventing /

inhibiting:

a. EFFECTIVE DATA EXCHANGES

What was a near real-time referral in the future state, can currently take days if not weeks.

Family doctors are forced to fax referral requests, wait possibly days to hear back and only

to hear that the specialist is not accepting new patients. Data is not being exchanged in a

manner that can be readily repurposed unless manually re-entered by the receiving

practice.

Patient‟s personal and healthcare information remains fragmented across the continuum of

care, due to the lack of effective system integration capabilities. Machine learning, whether

structured or unstructured, is therefore limited to a finite set of data. Even the highly

respected Institute of Clinical Evaluative Sciences (ICES) which has the mandate gaining

insights and learnings on clinical practices and health outcomes has resorted to using

encrypted discs to gain access to primary care data. Also, due to systemic barriers and

limited budgets, they are restricted to gathering data from 300 of 10,600 primary care

physicians; representing 300,000 of 13,000,000 Ontariansxxviii.

For an industry that is so information intensive the lack of affordable / suitable data

analytics is truly surprising. EMRs typically have limited and unsophisticated data analytics

/ reporting tools. All too often primary care clinicians are forced to download EMR data into

Excel spreadsheets or Access databases to perform rudimentary analytics. In some cases,

EMRs actually prevent data extracts and force clinicians to use vendor-offered services.

What is not surprising is the business intelligence industry that is starting to form around

primary care here in Ontario and across North America. While a welcome addition, these

start-ups are not likely to have access to province-wide data thus restricting the amount

and variety of data sets they can leverage.

Figure 4: Islands of Data

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Bottom line: The lack of end-to-end integration within the primary care sector is a key

barrier to achieving the promise and hope of Big Data.

b. DATA ANALYSIS / MACHINE LEARING

In the fictional future state scenario data was being consumed and used to detect patterns

and EMRs used machine learning techniques to suggest care plan recommendations.

Today clinical guidelines are typically stored on the doctor‟s office shelf, and are usually out

of date as new guidelines are published as new evidence is discovered. These paper-

based artifacts typically are not consumable by health applications. As such, the latest

guidelines or „business rules‟ cannot be readily used to support analytics or other reporting

functions. Mark was able to avoid complications and re-admittance into hospital because

the CMS was able to: notice patterns in the data and with updated clinical rules in play

facilitate an action to issue an alert.

The Canadian Institute for Health Information (CIHI) piloted the Primary Health Care

Voluntary Reporting System (PHC VRS) with 300 EMR-enabled primary care clinicians to

capture, analyze and report on compliance against established guidelines. In 2013 CIHI

concluded that “the process of extracting, manipulating and analyzing non-standardized

EMR data is not sustainable or affordable” xxix as it is labour intensive and might introduce

“data quality risks” The PHC VRS was eventually decommissionedxxx.

Current reports cannot be produced in a timely manner due to data collection barriers, and

often analysis is looking at events that occurred 6 months ago. Analysis is often highly

retrospective and cannot be effectively used to support quality improvement initiatives.

Data collected to inform clinical guidelines is also dated, and is not from a representative

sample.

The quality of the data collected and managed in EMRs is also a big challenge as data is

typically captured in a non-standardized and often unstructured (e.g., free form) manner.

For example, if a patient is diabetic, the physician can put the diagnosis as DM, DM type 2,

Diabetic, or diabetes. The adage “garbage in, garbage out” comes into play in the current

state and as a result makes data extraction / analysis highly burdensome and costly.

Data quality issues are a key contributing factor for the inability to exchange primary care

data in support of clinical and administrative decisions being made across the health

sector. The amount of effort and costs needed to „cleanse‟ the data for reuse is

insurmountable. And, it is unclear if clinicians would rely on or even trust data that was

extracted from other sources.

Bottom line: The inability to collect primary care data and the quality of the data itself are

key barriers in realizing the benefits that machine learning could offer.

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c. POPULATION HEALTH MANAGEMENT

Surveillance is at the heart of avoiding / mitigating any outbreak of a contagious disease.

Key to effective surveillance is data from sentinel systems such as primary care EMRs,

hospital information systems, social media, laboratory systems and drug information

systems. The current health system is populated with disparate systems that are not

interconnected or easily interoperable.

The SARS scare in 2003 was a clear example of our health systems limitations. We could

not ask or answer basic questions such as who is sick, who is at risk, who was / was not

immunized, what is the absenteeism rates at work and school, is the virus changing, how

have patients responded to care, what care was provided and will there be enough supply

of vaccines to meet tomorrow‟s demand?

Pockets of data warehouses exist across the health system, but each contains data to help

satisfy the objectives and mandates of the associated organizations (i.e., public health,

hospitals, MOHLTC). Each is not interconnected or able to exchange data for analysis or

monitoring.

Bottom line: Ontario does not have access to the depth nor breadth of information

necessary for a truly effective public / population health surveillance capacity - a key barrier

to an effective population health capability in Ontario.

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MOVING FORWARD

The promise that Big Data holds in enhancing primary care is both compelling and motivating.

Big Data will allow care to be delivered in ways that we have not yet begun to imagine, and this

potential cannot be ignored.

Currently, healthcare providers are faced with data from multiple data sources for the purpose

of primary and secondary healthcare provision, quality and research. With the huge volume of

data, the exponential velocity, and the different data format and standards, Big Data tools can

play a tremendous role in the consolidation of this data, the extraction, transferring and loading

(ETL) of this data into the proper data warehouses and spreading it across different servers,

which eliminate data loss and system failure. Furthermore, big data tools can facilitate real time

retrieval of information by the primary care physicians, and real time decision support at the

point of care. This will eventually lead to improvement in healthcare provision, reduction if not

elimination of waste in healthcare such as redundant diagnostic tests and imaging, wait time,

resource underutilization and thus lead to reduction of healthcare costs.

However, it is important to choose the proper Big Data tools and take into consideration the

anticipated responsiveness that is related to the utilization and retrieval of data (i.e. how fast

would you like the data query to take place). Furthermore, there are lots of challenges that

need to be taken into consideration in the implementation of Big Data; some are specific to big

data and some are common to data in general. Some of the main challenges are the growing

need for security and privacy protection, yet the need to be in compliance with the increased

scrutiny of the legal and regulatory requirements such as the Personal Health Information

Protection Act. Some examples to clarify this point are as follows:

Consolidation of this huge volume of data from different sources, faces more privacy

concerns than if the data is dispersed over its initial multiple repositories;

Figure 5: Bridging the Information Gap

Acute Care

Primary Care

Public HealthAcademia

MedicalDevices

Social Media

Rx

Labs

Continuing Care

Helping to enhance:• population health management • personalized medicine• evidence-based care• health system management• surveillance / outbreak prevention• continuity of care• patient safety• patient satisfaction

Big Data :Bridging the Information Gap

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Data transmission from different sources needs to happen through secure channels.

The bigger the number of the data sources and transmission channels, the higher the

change of a breach in security; and

Composable risk is associated with the consolidation of data from different data sources

which might lead to personal information identification

Furthermore, a huge challenge that faces implementation of big data in healthcare is related to

data governance and patient consent. iWho is the custodian of the data?Who can share the

data and under which circumstances? Is the purpose to obtain, provision of care, quality

improvement or research? Is patient consent, implicit, explicit or required?

In conclusion, big data can help improve the efficiency of the healthcare system, improve the

quality of care, enhance patient safety and bend the cost curve. An all-inclusive approach

needs to be implemented at the provincial, regional, system and practice level to deal with the

proper questions and challenges the healthcare system faces. An appropriate solution needs

to be implemented in order to meet the increasing demand for healthcare services from an

aging population with complex care demands.

Where is all the knowledge we lost with information?

- T.S. Elliot

Technology is dominated by two types of people: those who understand

what they do not manage, and those who manage what they do not

understand.

- Archibald Putt

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KEY TERMS

BORN Better Outcomes and Registry Network

CHF Congestive Heart Failure

CIHI Canadian Institute for Health Information

CMS Case Management System

EHR Electronic Health Record

EMR Electronic Medical Record

HQO Health Quality Ontario

ICES Institute for Clinical Evaluative Sciences

PHIPA Personal Health Information Protection Act

VRS Voluntary Reporting System

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END NOTES

i Ministry of Health and Long-Term Care, Family Health Teams ii Commonwealth Fund, 2012 Report

iii The Conference Board of Canada, The Future Cost of Health Care in Canada, 2000 to 2020

iv Health Care Information Systems: A Practical Approach for Health Care Management

v Physician eHealth Technology Roadmap, August 2010

vi Big Data, for better or worse, SINTEF, May 2013

vii Transforming Health Care Through Big Data, Institute for Health Technology Transformation

viii The CMA‟s 5-year strategy for health information technology investment in Canada; 2010; Canadian

Medical Association ix Physician Technology Roadmap, eHealth Ontario, 2010

x Primary Care in Ontario: reforms, investments and achievements, OMA

xi CIHI Nursing Data 2012

xii OntarioMD

xiii 2013 Ontario Budget

xiv National Health Expenditure Trends, 1975 to 2013, Canadian Institute for Health Information

xv Ontario needs better value for money in healthcare, Toronto Star, April 20, 2014

xvi The Future Cost of Canada

xvii 2012 – 2036 Ontario and Its 49 Census Divisions; Spring 2013, Ministry of Finance, Ontario

xviii Preventing and Managing Chronic Disease: Ontario‟s Framework; May 2007, Ministry of Health &

Long Term Care. xix

Cancer Care Ontario, Colorectal Cancer Screening Program web site xx

Cancer Care Ontario, Breast Cancer Screening Program web site xxi

Preventing and Managing Chronic Disease: Ontario‟s Framework; May 2007, Ministry of Health &

Long Term Care. xxii

Ministry of Health and Long-Term Care, Health Link‟s web site xxiii

Health Quality Ontario xxiv

eHealth Ontario Annual Business Plan (2014-15) xxv

Ontario Best Practices Institute

xxvi Canadian Cardiovascular Harmonization of National Guidelines Endeavour

xxvii Canadian Primary Care Sentinel Surveillance Network

xxviii Institute of Clinical Evaluative Sciences, Electronic Medical Record Administrative Data Linked

Database) xxix

Insights and Lessons Learned From the PHC VRS Prototype; 2013; Canadian Institute for Health

Information xxx

President‟s Quarterly Report and Review of Financial Statements as at December 31, 2013;

Canadian Institute for Health Information