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Why is information governance important for electronic healthcare systems? A Canadian experience

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Information governance becomes increasingly important due to the proliferation of data in various formats at different levels of an organization. A well-deeined information governance program improves data quality, enables quality care, enhances clinical research, and supports strategic decision making. However, despite the importance of information governance, there lacks a clear information gover-nance framework to guide the development, integration, and deployment of various healthcare technologies , resulting in dissatisfactory outcomes from expensive technology investments. Using the Canadian healthcare system as the backdrop, the study, drawing on the eight principles of information governance outlined by the Association of Records Managers and Administrators (ARMA) and the Data Governance Model, proposes an information governance framework detailing how information should be governed from four dimensions: people, process, policy, and technology. The model is then applied to analyze a case study on the 18-month well-baby visit program. After analyzing the indings from the case, the paper concludes with the implications for healthcare practitioners.
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Journal of Advances in Humanities and Social Sciences JAHSS
2016, 2(5): 250-260
PRIMARY RESEARCH
Why is information governance important for electronic healthcare systems? A
Canadian experience
Linying Dong 1*, Karim Keshavjee 2
Ryerson University, Toronto, Ontario
Key Words
Information governance
Electronic healthcare
Canadian experience
Governance model
Received: 25 May 2016
Accepted: 10 August 2016
Published: 27 October 2016
Abstract.Information governance becomes increasingly important due to the proliferation of data in
various formats at different levels of an organization. A well-deined information governance program im-
proves data quality, enables quality care, enhances clinical research, and supports strategic decision mak-
ing. However, despite the importance of information governance, there lacks a clear information gover-
nance framework to guide the development, integration, and deployment of various healthcare technolo-
gies, resulting in dissatisfactory outcomes from expensive technology investments. Using the Canadian
healthcare system as the backdrop, the study, drawing on the eight principles of information governance
outlined by the Association of Records Managers and Administrators (ARMA) and the Data Governance
Model, proposes an information governance framework detailing how information should be governed
from four dimensions: people, process, policy, and technology. The model is then applied to analyze a
case study on the 18-month well-baby visit program. After analyzing the indings from the case, the paper
concludes with the implications for healthcare practitioners.
©2016 TAF Publishing. All rights reserved.
INTRODUCTION
Since Electronic Medical Records (EMR) adoption program started in 2005, Canadian gov-
ernments at all levels have been pushing for the EMR adoption among physicians with
the aim to enhance the quality of care while reducing operating costs. There are 29,368
physicians in Ontario, a little over half (14,695) of which are family physicians who are
also known as primary care providers (Vuchnich, 2015). The recent progress report, pub-
lished in 2015 by eHealth Ontario, has indicated that 7 out of 10 primary care physicians
are using EMR software in their practice and that 2 out of 3 Ontarians are covered by EMR
software (eHealth Ontario, 2016). In addition, over 80% of healthcare data are digital-
ized (Ontario Medical Association, 2014). As the result of the prevalent EMR adoption,
physicians start to enjoy faster report transfer (reduced from 12 days to 30 minutes) and
easy access to lab results (over 3,000 types of lab results can now be accessed through
physician’s EMR software). Despite the remarkable accomplishments achieved, to what
extent the EMR adoption increases the quality of care remains unclear. As the matter of
fact, it has been argued that healthcare organizations may be facing “faster incorrect in-
formation” (Greene 2014), as different healthcare organizations have implemented their
own technology applications (Reeves and Rita, 2013), which use inconsistent metrics and
generate data in different formats (Greene, 2014), creating faulty data that could lead to
medical errors (Hripscak and David, 2012).
*Corresponding author: Linying Dong
Email: ldong@ryerson.ca
251 J. Adv. Hum. Soc. Sci. 2016
Health data are the lifeblood of a healthcare system. They serve as the foundation to
develop best practices and make critical clinical decisions (Bowen and Alisha, 2014). It
is argued that quality data support high quality care, accurate research, positive patient
outcomes, cost effective risk assessment, and strategic decision making” (Wood 2014). As
the result, the proliferation of electronic health data requires a well-deined systematic
data governance framework in place to ensure data quality. Without quality data, “orga-
nizations are spending more to be less eficient and less effective” (Wood, 2014), and the
objective of improving quality care would be elusive.
Data/information governance has just gained attention in the healthcaresector. Canada
Health Infoway has emphasized the need to develop a data/information governance frame-
work to govern information low in the interoperable (Canada Health Infoway, 2007). On-
tario Medical Association, while agreeing on the importance of having a data/information
governance model, indicates that there is no clear framework governing what information
should and should not low from a physician’s EMR to the system-wide EHR.
With the backdrop, the objective of this paper is to propose an information governance
model for the Ontario health care system. To achieve the goal, we irst deine informa-
tion governance, describe information governance maturity level, and introduce the data
governance model. Then we explain the information governance model for the Ontario
healthcare system, apply the model to a case study, and demonstrate how the model can
be applied to identify key problems and suggest the future action plan.
LITERATURE REVIEW
Information Governance
Information governance is denoted as “the exercise of decision making and authority on
matters related to data and information” (Ontario Medical Association 2014). Informa-
tion governance manages the entire life cycle of the information low, including how it is
created, stored, used, and archived (Greene, 2014), and deines who should have access to
certain information at what time and using what methods (Scardilli, 2014). It is argued
that information governance is “a strategic effort that requires executive ownership, lead-
ership support, and participation of everyone within the organization” (Isaacs, 2016).
Information governance differs from data governance in that data governance focuses
on processes and control related to information at the data level, and the goal of data gover-
nance is to ensure that data are accurate and secure (Smallwood, 2014). Data governance
falls under the domain of IT governance. How is information governance related to IT
governance? IT governance speciies the decision rights and accountability framework to
encourage desirable behavior in IT usage (Weill and Jeanne, 2004), and lies in the hands of
CIO. The objective of IT governance is to ensure value-delivery of IT investments through
“effective and eficient use of IT” (Gerrard, 2010). In contrast, information governance
is about governance of information, and the goal of information governance is to ensure
reliable, accurate, secure, and compliant information access to support/enhance existing
functions and/or to enable new functions of an organization to help achieve its business
goals. Managing information governance lies in the Information Governance ofice.
Table 1 summarizes the differences of the three governance concepts. Despite the dif-
ferences, information governance and IT governance are inseparable. An effective infor-
mation governance program relies on the support of information technologies to man-
age information governance policies and processes, engage stakeholders, and ensure data
quality. On the other hand, IT governance is dependent on information governance to
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identify information that needs to be managed, deine criteria for data quality, determine
decision rights, and identify stakeholders involved. The information is necessary for the
IT department to identify a suitable technology supporting information governance. Ulti-
mately, the technology investment should support the mission and vision of information
governance.
TABLE 1 .Comparison of information governance, data governance, and IT governance
Information governance Data governance IT governance
Focus area(s) Deine policies, involve people,
and manage processes related to
data and information
Data deinitions, rules, and secu-
rity
Decision rights and accountabil-
ity related to IT investments
Objective To ensure reliable, accurate, se-
cure, and compliant information
access
To ensure data quality (e.g. in-
tegrity, completeness) and secu-
rity
To ensure business value from IT
investment
Responsibility Information governance ofice IT department CIO
Information Governance Maturity
The Association of Records Managers and Administrators (ARMA) has developed eight
principles guiding information governance including accountability (i.e. a senior executive
is in charge of the information governance program, and information and records manage-
ment is delegated to appropriate individuals), transparency (i.e. processes and activities
including information governance program are documented and openly shared to all in-
volved personnel and interested parties), integrity (i.e. information generated is authentic
and reliable), protection (i.e. information generated is securely protected to ensure pri-
vacy and conidentiality), compliance (i.e. an information governance program should be
compliant with relevant laws and organizational policies), retention (i.e. records and in-
formation are maintained for a giventime based on the legal and regulatory requirements),
availability (i.e. timely, eficient, and accurate information retrieval), and disposition (i.e.
records and information are securely and appropriately disposed once they are no long
required).
Based on the eight guiding principles for information governance, the Association of
Records Managers and Administrators (ARMA) proposes ive levels of maturity of infor-
mation governance. Level one (Sub-standard) describes an organizational environment
where there is no program managing information governance and there lacks recognition
of the importance of information governance. In addition, there is no designated person in
charge of information governance, consequently there is unsystematic effort in managing
information across an entire organization. The ive levels are summarized in Table 2.
Level two (In Development) captures an organizational environment where the impor-
tance of information governance is somewhat recognized and the organization is beneit-
ting from the information governance program. A managerial position is created to man-
age information; however, the focus of information governance is limited to some format
of information, and there lacks systematic effort in managing information assets.
An organization at level three (Essential) has a clearly deined information governance
program. The importance of information governance is recognized by senior managers,
and as a result, there is a company-wide effort in information governance, and the man-
ager who is in charge of information governance is actively engaged in strategic initiatives
of the organization.
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At level 4 (Proactive), an organization has established well-deined procedures and
policies on various aspects of information governance and information governance prac-
tices have become an integral part of routine business operations. Information governance
is overseen by a senior manager, and plays a critical role in enhancing performance and
building competitiveness.
TABLE 2 .Summary of information governance maturity levels
Level Level one Level Two Level Three Level Four Level Five
Principle Substandard In development Essential Proactive Transformational
Accountability No senior executive
responsible; no
records manager
role
No senior executive
in charge; records
manager role recog-
nized
Records manager
actively engaged
in strategic info.
mgmt.
Records manager is
a senior oficer
Great emphasis
placed on the im-
portance of info.
governance
Transparency No emphasis on
transparency
A limited amount of
transparency
Written policy on
transparency
Transparency, an
essential part of
practice
Goals for trans-
parency are rou-
tinely reviewed and
revised
Integrity No systematic
audits or deined
processes
Some records
stored can demon-
strate authenticity
Formal process en-
suring integrity
Integrity,an integral
part of info. mgmt.
Goals for integrity
are routinely re-
viewed and revised
Protection No consideration
given to informa-
tion protection
Some protection is
applied
Formal policy on
info. protection
Systems in place to
protect info.
Goals for protection
are routinely re-
viewed and revised
Compliance Poor compliance Limited compliance Compliant to key
laws and regula-
tions
Effective auditing
process
Goals for compli-
ance are routinely
reviewed and
revised
Availability Info. not readily
available
Limited retrieval
info. mechanism
Established stan-
dards for info.
retrieval
Systems and control
in place to guard
info. access
Goals for availabil-
ity are routinely re-
viewed and revised
Retention No info. retention
policies
Limited policies on
info. retention
Established policy
for info. retention
Info. retention, a
major organiza-
tional objective
Goals for retention
are routinely re-
viewed and revised
Disposition No policies on info.
disposition
Limited policies on
retention
Established policies
for info. disposition
Procedures consis-
tently applied
Goals for disposi-
tion are routinely
reviewed and
revised
At level 5 (Transformational), an organization has built a continuous improvement
mechanism in its information governance program, and the initial goals of information
governance are routinely reviewed and revised to ensure that the organization is fully
compliant and continue to enjoy the beneits of information governance on a plenary basis.
Data Governance Model
The Data Governance Institute (DGI) proposes ten universal components of a data gov-
ernance model (see Table 3) which are grouped into three categories: rules and rules of
engagement, people and organizational bodies, and processes. Rules and rules of engage-
ment category describes rules (e.g. policies, standards) that need to be made and how
people work together to develop and enforce the rules. Components under the category
include mission and vision of a data governance program, metrics and success measures,
data rules and deinitions, decision rights, accountabilities, and controls (e.g. access).
The people and organizational bodies category describes the stakeholders who make
and enforce the rules deined above, and includes components such as data stakeholders,
a data governance ofice, and data stewards. The processes category denotes proactive,
ongoing processes that people follow to govern data.
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TABLE 3 .Data governance model
Category of components Description Detailed list
Rules and Rules of Engagement Describes rules (e.g. policies, re-
quirements, standards) and how
different people work together
to make these rules and enforce
them
1. Mission and Vision 2. Goals,
Governance Metrics and Success
Measures, and Funding Strategies
3. Data Rules and Deinitions 4.
Decision Rights 5. Accountabili-
ties 6. Controls
People and Organizational Bodies Stakeholders who make and en-
force the rules
7. Data Stakeholders 8. A Data
Governance Ofice 9. Data Stew-
ards
Processes Describe the processes that peo-
ple follow to govern data
10. Proactive, Reactive, and Ongo-
ing Data Governance Process
The DGI data governance model provides an overarching view of components that are
involved in data governance. While insightful, the model focuses mainly on data gover-
nance, instead of information governance. In addition, the model is generic, and targets
mainly at data governance within a single organization. The Canadian healthcare context
is complex in that information resides with healthcare providers who operate as individ-
ual businesses, but lows across organizations for the care to be provided. As a result, we
believe that it is imperative to develop an information governance model that relects the
complexity of the healthcare context.
Information Governance Model under the Canadian Healthcare Context
To deine the components of an information governance model for the Canadian health-
care context requires a clear deinition of purposes to be achieved through governance.
Therefore, we propose that under the Canadian healthcare context, the objective of infor-
mation governance is to achieve better quality care through (1) the governance of health-
related information for primary care and secondary information use, and (2) the gover-
nance of clinical data and research indings/hypotheses for collaboration among clini-
cians, researchers, and policy makers. In other words, we believe quality healthcare is
reliant upon quality health-related information as well as research outputs that inform
clinical decisions.
Drawing on the information governancemodel by the Digital Governance Institute (Dig-
ital Governance Institute, 2016), we propose that information governance encompasses
four basic components: people (e.g. information governance ofice, data stakeholders,
stewards), processes (e.g. establish accountability, determine decision rights, manage
change, stakeholder communication, evaluation and continuous improvement), and pol-
icy (e.g. information management, communication, issue resolution, decision rights, and
performance management), and technology (e.g. software, hardware, IT infrastructure).
Each of the components is elaborated in the following and illustrated in Figure 1.
People component
The focus of the people component is on identifying key stakeholders involved in the infor-
mation governance process, understanding their information needs, and creating a gover-
nance structure managing information.
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The key stakeholders of a healthcare system encompass the following:
1. Primary clinical data providers and users (e.g. family physicians, hospitals) who pro-
vide care to patients. They directly interact with patients, and collect and analyze clinical
data.
2. Secondary data users (e.g. governments, community services, researchers) who use the
collected clinical data to deine/review/revise policies, conduct relevant research, and of-
fer supplementary healthcare services.
3. IT service providers (e.g. e-health Ontario, EMR vendors) who develop and maintain
hardware, software, and infrastructures needed for healthcare services.
4. General public (e.g. patients) who receive care provided, and access and manage health
information collected through their direct interactions with healthcare providers.
Process component
In order to achieve high information governance maturity, wehave identiied the following
key processes:
FIGURE 1 .Information governance model for Canadian healthcare
1. Process of data element deinition
Data could include common data used across stakeholders. The data could include com-
mon demographic information such as age, gender, ethnic group, and clinical information
such as blood pressure, pulse, height, weight, etc. However, different stakeholders would
require different data. For example, clinicians are interested only in clinical data, and re-
searchers and policy makers are interested mainly in administrative data. All stakeholders
should be involved in deining data elements, data lows, and decision rights.
2. Process of data integration/harmonization
Currently data exist in different formats (e.g. paper, image, structured format), and ac-
cessing data is cumbersome. This process helps identify existing poor data integrations as
well as data that need to be integrated.
3. Process of information sharing and accountabilities
There are different agreements that need to be deined in order to achieve seamless in-
formation sharing across stakeholders. Therefore, there should be a central repository
for various sharing agreements and a process low that guides stakeholders in completing
the agreements. This process deines the procedure on information sharing, accountabil-
ity identiication, and agreements management.
4. Process of building governed information into technologies
Once information that needs to be governed is identiied and deined, the information
needs to be built into technologies to ensure that data collected are structure and stan-
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dard across EMRs. This process ensures that standard data deinitions, structures, and
formats are followed in technologies.
5. Process of issue/dispute resolution
This process deines the procedure to be followed in case of issues and disputes. 6. Pro-
cess of monitoring and change management
There should be a regular review process monitoring data collection, use, access, and stor-
age, and offering feedback to help continuous process improvement 7. Process of stake-
holder support (e.g. training) and communication (e.g. information)
This process deines trainings that need to be provided to stakeholders for them to
better manage information and use technologies, and how the trainings will be provided.
For example, when a new service is offered, the general public need to be educated about
how to access the service. Similarly, before the introduction of a new information system,
healthcare providers need to be trained and supported as to how to use the system effec-
tively.
The process also deines how the stakeholders are kept informed of the information
governance-related issues and how the information is to be communicated to stakehold-
ers. For example, senior citizens who do not access information online need to be informed
of new services provided via mail, and could be followed up by a phone call to ensure that
they understand the nature of the service and ways to access the service. For younger
generations, the service information could be pushed through social media. The ultimate
goal of the process is to ensure that stakeholders are involved, informed, and engaged in
information governance.
8. Process of measurements and report
In order to ensure effective information governance, continuous monitoring of informa-
tion governance activities is needed. So the process deines and measures outcomes of
information governance, and reports back to the stakeholders about the results.
Policy component
The policy component is necessary to legitimize the importance of information gover-
nance and regulates key responsibilities of key stakeholders. The policy component is
particularly important in the Canadian context as primary care providers operate individ-
ual businesses and are not directly managed by the government.
1. Policy on mandatory data entry and data collection. Quality care requires quality data.
One of the key quality criteria is the completeness of data. To obtain a comprehensive view
of a patient record, it is imperative that all necessary data are entered to the system. So
the governments need to deine policies to mandate data entry and associate data entry
with compensation.
2. Policy on mandatory incorporation of deined information requirements into IT sys-
tems. Advanced information technologies function as a conduit carrying health informa-
tion to various healthcare stakeholders. It is imperative that deined information require-
ments are captured in IT systems to ensure a smooth low of information that is needed
for providing quality care.
3. Policy on mandatory information governance committee, composition, and accountabil-
ity. As health-information is collected, accessed, and used by multiple healthcare providers,
an information governance committee needs to be formed to deine the processes elabo-
rated above. The policy needs to be in place to ensure that the information governance
committee represents the interests and needs of a wide spectrum of stakeholders, and ac-
countability is in place to ascertain desired results from information governance.
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Technology component
Information technologies have permeated many aspects of our healthcare system and play
an increasing role in provision of quality healthcare. As there are a myriad of healthcare
technologies available and many new developments emerge every day, the key focus of
the technology component is on compatibility and performance of software and hardware
deployed and openness, capacity, and scalability of IT infrastructure.
Case study of 18-month baby data from EMRs
Following the deinition of information governance and delineation of information gover-
nance components, we apply the concepts to a real case and demonstrate how the concepts
could be applied to identify key issues faced in information governance and suggest an ac-
tion plan to improve the level of information governance.
Case background
Each year there are approximately 140,000 children born in Ontario who, in the irst two
years of their life, are seen by family physicians, pediatricians, and nurse practitioners. Ap-
proximately 25% of these children will have some developmental issues when they start
school, which should have been recognized and treated much earlier. As a result, the On-
tario government provides the enhanced 18-month well-baby visit program, in which the
children’s information is collected by using two of three tools: the Rourke Baby Record
(RBR) or the Denver Developmental Screening Test, and the Nipissing District Develop-
mental Screen (NDDS).
In order to understand the current status of health information collected through the
18-month well-baby visit, the second author interviewed 11 groups of stakeholders (e.g.
family physicians, Pediatricians, hospitals) to understand various issues including infor-
mation collected, methods of information collection, technologies used, technology infras-
tructure, the type of data or information they would like to obtain, types of decisions they
would like to be able to make with the data obtained through a future 18-month data col-
lection system, and issues and problems encountered in collecting and using information
needed for diagnosis and analysis. The following reports the indings from the interviews.
RBR
The Rourke Baby Record is an evidence-based infant/child health record which includes
information about baby’s growth (e.g. length and weight within one week, two weeks, and
one month), physical examination (e.g. skin, ears, eyes and tongue mobility), immuniza-
tion, and nutrition (e.g.breastfeeding, formula feeding, stool pattern, urine output). Based
on the information, primary care providers monitor and assess the physical health of chil-
dren, and provide education (e.g. safe sleep position) and advice (e.g. parent/bonding).
The RBR is used mostly by Family Physicians. Data collected using the RBR are neither
extractable nor transmissible. Currently most data collected using the RBR are through a
paper form and kept in paper records. Although there are two of 17 EMR systems which
have electronic RBR (eRourke) installed, the data captured is not easily transferrable due
to the lack of standard data deinitions across the EMR vendors.
In addition, primary care provider representatives complained about poor integration
of ill-baby care and well-baby care in EMRs as compared to paper. Paper can be moved
around and multiple pages viewed at the same time, but a computer interface only allows
a single page to be viewed at one time, limiting user’s ability to view ill-baby visits while
viewing the well-baby visits in the eRourke.
Poor integration of the immunization module of the EMR and the eRourke form. Data
have to be entered in the immunization module for reminders and queries to work prop-
erly, but those data do not get sent to the eRourke. If a provider wishes to know the pa-
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tient’s immunization status when viewing the eRourke, they have to enter the immuniza-
tion information again in the eRourke form, creating a burden of double data entry.
Denver development screening tool (DDST)
The Denver Developmental Screening Tool is used mainly by Pediatricians to assess the
developmental progress of children. The test contains up to 125 items divided into four
categories including social/personal (i.e. aspects of socialization inside and outside the
home), ine motor function (e.g. eye/hand coordination), language (e.g. production of
sounds), and gross motor functions (e.g. sitting, walking) (Tidy and Colin, 2014).
Similar to the RBR, data collected through the DDST are neither extractable nor trans-
ferrable due to the fact that all data are collected through a paper-based form. The only
information available is the number of actual child visits billed from the OHIP fee code by
family physicians and pediatricians. However, the visit information is not entirely accu-
rate, as nurse practitioners do not have a fee code to bill, consequently nurse-practitioner
provided visits are not relected through the OHIP fee code.
NDDS
The NDDS is the most commonly used assessment tool completed by parents in Ontario.
It contains a checklist of questions designed to monitor a child’s behavioral development.
Some questions asked include: “By one month of age, does your child look at you, star-
tle to loud or sudden noise, calm down when comforted?” The factual data about a child’s
physical development are not captured in the NDDS.
Data captured by the NDDS lie in three formats. The common data format is paper-
based captured by the NDDS form which can be downloaded from the NDDS web site.
There is an electronic version of the NDDS that provides structured data extractable for
data transmission. However, since there is only a single EMR that incorporates the NDDS,
resulting in the limited availability of electronic data.
There is also a web-version NDDS (eNDDS) that allows web-illed data to be transferred
to EMRs. However, this practice is thwarted due to the complex requirements of authen-
ticating parents, reliably identifying practice providers, and securely transmitting data to
EMRs. As a result, most eNDDS completed forms are printed and scanned into the EMR,
defeating the purpose of developing the eNDDS in the irst place.
As illustrated above, the three tools capture baby information from three different per-
spectives; however, there is no integration of the collected information. In addition, most
information gathered is not transmissible or extractable. Furthermore, not all primary
care providers use the same set of tools, preventing consistent monitoring of a baby’s de-
velopment. Moreover, there is no mandatory submission of data when billing the well-
baby incentive code, which means that data may not be sent to a central repository. There
is no data sharing agreement infrastructure in place to allow for data to low, even if the
structure for data collection was in place.
Poor integration of the NDDS into the EMR. Currently, most providers have to scan in a
paper version of the completed NDDS into the EMR. This means that they have to exit the
eRourke to go view the NDDS; both cannot be viewed at the same time. Even in situations
where the NDDS is an electronically illed version, providers still have to exit the eRourke
to view it, causing fragmentation of information.
Case analysis
Under the context of a vigorous promotion of EMRs in Canada, the case offers a glimpse of
the reality of the EMR adoption, which in the end frustrates primary care providers, con-
fuses policy makers, and affects quality care. What went wrong?
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The stakeholders involved in the 18-month visit include primary care providers such as
family doctors, pediatricians, nurses, and hospitals and secondary data users include the
government, researchers, and communities who administer and manage the tools. Other
stakeholders such as EMR vendors and eHealth Ontario develop and maintain IT systems
needed to capture the information collected through the 18-month well-baby visit.
The case reveals the lack of the processes including the process of data element deini-
tion, the process of data integration/ harmonization, and the process of building governed
information into technologies.
In particular, the lack of the process of data element deinition results in the lack of a
clear deinition of health information collected in the 18-month journey. As illustrated in
the case study, there are three tools that capture different information; however, the use
of these tools by healthcare providers is inconsistent, resulting in an incomplete picture of
a patient’s condition.
The fact that the information collected through these tools is stored in various formats
including paper, image and electronic which are not easily extractable or transferrable in-
dicates poor data integration and harmonization.
In addition, different EMR vendors are inconsistent in the tools that they support and
the structure of the information. This situation exacerbates the issue of data integration
and harmonization. Furthermore, it reveals the lack of the process ensuring that the health
information needed is incorporated into, and captured consistently in, the EMR technolo-
gies.
From the policy perspective, despite the realization of the importance of information
governance in quality healthcare, there is no information governance committee to over-
see information governance-related issues. Ontario’s EMR certiication process is still
evolving and not geared for certifying speciic forms. In addition, there is no plan to in-
clude forms as part of the certiication process in the near future.
From the technology perspective, there is an evaluation process of performance of EMR
systems. The low usability and the lack of integration among EMR systems indicates the
lack of control of integration and compatibility across EMRs.
DISCUSSION NAND CONCLUSION
As illustrated above, we have applied the information governance model to analyze the
case and pinpoint the key problems underlying the issues faced in the 18-month well-baby
visit. The model helps identify the lack of information governance processes that results
in disintegrated information that is dificult to extract and transmit, which affects quality
care.
Based on our information governance model, we propose the next step moving for-
ward. First of all, an information governance committee composed of key stakeholders
needs to be formed. Second, the committee needs to review the existing practices involved
in information governance, and clearly deine key information governance processes, poli-
cies, and procedures. Third, a chief information governance ofice and key personnel need
to be assigned to implement the identiied processes, policies, and procedures. As Cana-
dian healthcare providers are operated as individual businesses, it is imperative that peo-
ple who are experienced with information governance work closely with the providers to
facilitate the implementation. In addition, training and support have to be provided to
obtain buy-in. More importantly, policies legitimizing information governance and sup-
porting information governance implementation are essential to ensure the success of in-
formation governance.
ISSN: 2414-3111
DOI: 10.20474/jahss-2.5.1
2016 L. Dong, K. Keshavjee - Why is information governance .... 260
Health data are the lifeblood of a healthcare system, and critical to high quality care.
Despite the importance of health data, however, a well-deined information governance
model for the Canadian healthcare context is lacking. To make up for the gap, we have
proposed an information governance model that consists of four components: people,
processes, policies, and technologies. Applying the model to the case study on the 18-
month well-baby visit, we have highlighted key problems underlying the issues faced in
the 18-month journey, and suggested an action moving forward. The study contributes to
the academic study on information governance by offering a well-deined model to prac-
titioners by suggesting effective approaches to information governance.
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— This article does not have any appendix. —
ISSN: 2414-3111
DOI: 10.20474/jahss-2.5.1
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It's working for you. URL: goo.gl/itExhn
  • Ehealth Ontario
eHealth Ontario. 2016. It's working for you. URL: goo.gl/itExhn (accessed October 10, 2016).
IT governance. URL: goo.gl/7IsNQh
  • Michae Gerrard
Gerrard, Michae. 2010. IT governance. URL: goo.gl/7IsNQh (accessed September 8, 2015).
Data governance: The public sector's next big frontier. Government Technology. URL: goo.gl/Wmkgmf
  • Colin Wood
Wood, Colin. 2014. Data governance: The public sector's next big frontier. Government Technology. URL: goo.gl/Wmkgmf (accessed September 8, 2015).
Information governance strategy isn't a project
  • Leigh Isaacs
Isaacs, Leigh. 2016. Information governance strategy isn't a project. Computer Weekly, 11-15.
White paper on information governance of the interoperable electronic health record (EHR) URL: goo.gl/S2zwka
  • Canada Health
Canada Health Infoway. 2007. White paper on information governance of the interoperable electronic health record (EHR). URL: goo.gl/S2zwka (accessed August 4, 2016).
Number of doctors on the rise in canada and payments hit $24 billion: Report. URL: goo.gl/oSbxCb
  • Allison Vuchnich
Vuchnich, Allison. 2015. Number of doctors on the rise in canada and payments hit $24 billion: Report. URL: goo.gl/oSbxCb (accessed October 10, 2015).
Better data through IT governance. Hospitals & Health Networks. URL: goo.gl/0OYKyX
  • Greene
Greene, Jan. 2014. Better data through IT governance. Hospitals & Health Networks. URL: goo.gl/0OYKyX (accessed August 11, 2016).