THE INFLUENCE OF KNOWLEDGE MANAGEMENT ON EHR TO IMPROVE THE QUALITY OF HEALTHCARE SERVICES

European, Mediterranean & Middle Eastern Conference on Information Systems 2010 (EMCIS2010)

April 12-13 2010, Abu Dhabi, UAE

Anjum Razzaque and Akram Jala-Karim.

Knowledge Management facilitates Electronic Health Records to improve Quality of Healthcare 1

THE INFLUENCE OF KNOWLEDGE MANAGEMENT

ON EHR TO IMPROVE THE QUALITY OF

HEALTHCARE SERVICES

Anjum Razzaque, School of Management, School of Management,

New York Institute of Technology, Adliya, Kingdom of Bahrain

[email protected]

Akram Jalal-Karim, College of Business and Finance, Department of Management Information Systems,

Ahlia University, Manama, Kingdom of Bahrain

[email protected]

Abstract –

Background & Purpose: The healthcare (HC) sector, globally, invests huge amounts of funds

in an attempt to attain international quality standards but the structure and content barriers

make the electronic patient records (EPRs) and electronic health record (EHRs) fall short.

This study seeks to illuminate theories and practices of HC knowledge management (KM) so

this process can facilitate the narrowing of EPR and EHR gap, that ultimately will cascade to

improving the HC quality which is therefore assessable by the quality management system

(QMS) model.

Design/methodology/approach: This research is theoretical in nature. It examines relevant

theories and reviews literature on (1) HC quality assessment and tools, (2) HC KM, (3) EPRs

and EHRs, (4) knowledge representation and (5) the Symantec web. This study passed

through phases of research. In the first phase, EPR and EHR were researched to analyze why

they fall short in facilitating the HC’s initiative to improve quality. Next, HC KM was studied

to analyze and establish a viable link between (1) EPR and EHR and (2) KM. Technologies

and techniques like the Symantec web and Knowledge Representation were also analyzed to

make the facilitation of HC KM possible.

Findings: The findings indicate that, by this paper contributing a conceptual, integrative, and

strategically viable HC KM facilitator model for EPRs and EHRs, this paper answers its

research question that HC KM can facilitate EPR and EHR to improve HC quality.

Research limitations/implications, Originality & Value: This research provides an integrated

and a conceptual model grounded in theory. This model needs to be tested in a real or

simulated HC environments. This paper is one of the first studies to solve the barriers of EPR

and EHR using KM.

Keywords: Electronic Patient Record, Electronic Health Record, Electronic Medical Record,

Healthcare Knowledge Management, Knowledge Representation, Symantec Web,

Quality Management System, Knowledge Management facilitator model.

1 INTRODUCTION

With countries worldwide spending ample funds in their HC industries, it comes to no

surprise that such a complex service-oriented industry suffers in quality; due to medical errors

(besides other reasons). Therefore such countries experience a rise in HC costs and hence

dissatisfied patient (A Research Center of the University of Sheffield and CITY Liberal





Studies 2005). This paper points out that even though EHR and EPR assist dramatically in

reducing medical errors they too fall short due to barriers reported in the area of their non-

uniform structures that lead to the hindrance in interoperability (Jalal-Karim and

Balachandran 2008). This study attempts to find a solution from the discipline of HC KM to

narrow the, just mentioned, gap by KM facilitating EHR to tackle the interoperability barrier

caused due to an immature EHR structure. Once this issue is resolved; HC professionals can

access information as well as knowledge to make sound decisions that would help reduce

medical errors, satisfy patients and save patients lives and hence improve HC quality.

2 LITERATURE REVIEW

This paper researches the current situation of HC quality by analyzing its current status in

developing and developed countries worldwide. Since EPR and EHR is a well-researched

study, the proposed solution to improving HC quality is also studied to assess its limitations.

Once this paper pinpoints the gap that produce a snag in the initiative to improve HC quality,

HC KM is researched to seek a solution to narrow the gap using data mining, knowledge

representation and decision support systems as the proposed tools and techniques.

2.1 Definition and Important of HC Quality

Quality in HC is stakeholder dependent as pointed out by (Duke University Medical Center,

2005). While the provider looks for quality and cost efficiency the payer looks for quality of

diagnoses as well as cost efficiency. The patient looks for a clear communication and

compassion from the HC provider. At this point quality can be defined as a way of looking

forward to customers' future needs by involving everyone in providing a service at a lower

cost. Quality is a cost effective and capital-intensive way to improve productivity and reduce

complaints. However its absence increases patient length of stay, inefficiency and cost

(Stewart, 2003). Tools like total quality control (TQC) or total quality management (TQM),

are utilized to enhance quality improvement to: (1) bring forth the cultural revolutions within

an organization, (2) establish a market customer oriented focus, (3) enable a fact-based

management strategy, (4) initiate an industrial democracy by workers participation, (4)

facilitate continues improvement, (5) initiate process management, (5) begin a new approach

to problem solving, etc (Ovretveit, 2001).

2.2 The Reality of Medical Errors that degrade HC Quality

HC is an expensive investment and various countries allocate varying amounts of funds

towards HC. USA spends the most % of GDP on HC with UK second and Canada third in

ranking. Still HC experience a breach in its quality caused due to medical errors (A Research

Center of the University of Sheffield and CITY Liberal Studies, 2005).

In USA, on an average, 7,000 patients die annually, due to HC medical errors (Kaiser Family

Foundation, 2008). As many as 4% - one out of every 25 patients are victims of medical

errors (AHRQ, 2000). Approximately, medical errors cost USA approximately $37.6 billion.

54% of these errors are preventable (IOM, 1999). This is not only a problem in the USA (The

Sun, 2009). The National Health Services (NHS) in the UK was reported to cover-up medical

error-related mistakes, which ended up costing 72,000 patients‟ lives. 90,000 to 24,000

patients die in Canada for the same reason (CBC, 2004). 0.05% of 100,000 patents fall victim

of medical errors in Saudi Arabia (Saudi government report, nd). Emergency doctors, in the

Kingdom of Bahrain, reported that patients‟ lives are risked because clinics fail to attain

proper patient records (Singh, (2008). These are just a few instances of many mentioned

above that prove a breach in the HC quality. This has turned out to be a serious matter for

consideration and hence an important objective that demands a solution.





2.3 Definition and The Importance of EPR and EHR

EPR by definition is a periodic record of patient‟s care provided by an acute hospital during a

specific period of time. Currently, each hospital‟s multiple departments have their own

system. Each system has its own patient record. EPR makes it possible for all these records to

intersect into one record per patient, instead of multiple records floating around in different

departments that cause repetition of the same record and hence cause inefficiencies when HC

parishioners need to maintain these records to enhance their decision-making power (Jalal-

Karim and Balachandran, 2008). EPR is also referred in many studies as electronic medical

record (EMR) (Hayrinena, Sarantoa and Nykanenb, 2008). EHR, on the other hand, is a

record of a patient‟s lifelong medical health record, composing all summaries of EPRs.

Demands for EPR arose when HC professionals were seeking a solution to reduce medical

errors and hence improve HC quality. Three reasons are a cause for medical errors being: (1)

difficulty in accessing HC information, (2) un-reliable accessed HC information and (3) un-

applicable accessed HC information. EHR allows HC institutions to share and create

knowledge when multiple HC locations communicate through interpretability (with improved

24/7 accessibility of HC information) (Jalal-Karim and Balachandran, 2008).

EHR is a secure web-based digital patient health information usable by: (1) hospitals, (2)

patients and their relatives, (3) HC professionals e.g.: nurses, library technicians or physicians

and (4) HC administration. EHR systems are advantageous for management decision-making

and for setting up policies. The content and structure of an EHR holds (1) information

summary of patient, (2) summary of their progressing care and (3) result of patient care.

However, EHR‟s structure and content is not up to the data quality standards. It is important

to note that good documentation improves patient care. EHR systems are classified and

organized by their structure and matter; differentiated by (1) time - where data ordered by

time and date and collected using qualitative and quantitative methodology (as stated by most

studies even though other methods get used too), (2) problem - where data is organized by

each problem and (3) source - where data is organized by material, e.g.: blood test or x-ray

report (Hayrinena, Sarantoa and Nykanenb, 2008). Hence EPR and EHR systems can be

utilized as tools for crafting a solution to facilitate an improvement in the HC quality.

2.4 EPR and EHR –History and Limitations

Data was of little value when it was a paper-based, hand written and an un-structured record;

except for the person who wrote it. Through an act in the UK, since the 21st century by Kioyd

George, the written records started disappearing. This is when data starred getting coded that

gave rise to a need for computerization. This coded data started getting formatted in the 1950s

by introducing colored summary cards. At that period structure of such records improved,

when detail got incorporated, by adding additional cards to the initial set of summary cards

(Lusignan and Robinson, 2007). As history repeats itself, now, detailed EHR can be utilized

in this modern era to improve the structure of an EPR when details are added to it (Jalal-

Karim and Balachandran 2008).

The need for a proper structure was noticed after computerization took birth. There is a

reported barrier in structure as quoted "there are no national standards about what should be

coded or how many items in a consultation.” In addition; another quote states, "creation of the

readily searchable computerized medical record has greatly improved the potential for KM

activity" and "the evolution of the primary care record from paper to computer is a key

enabled of KM" (Jalal-Karim and Balachandran 2008). Hence, this proves the importance and

potential of KM as a facilitator towards EPR and EHR. The next step is to come up with a

viable solution that illustrates how HC KM can facilitate EPR and EHR to improve HC

quality.

2.5 Defining Data, Information, Knowledge, KM, Knowledge Discovery and HC KM





This section proposes an explanation as to why HC KM is the solution to the above-

mentioned barriers in EPR and EHR. By HC KM facilitating to narrow the EPR and EHR

barriers this will be a stepping-stone that will allow EPRs and EHRs to better facilitate their

initiative to improve HC quality. The HC industry has not yet adapted HC KM, which is a

key business process along with data mining technologies (Wickram et al. (2009, p. 1).

Data, within the context of this study, refers to patient data that is a raw fact spread through

out a HC organization's departments in differing operational systems and also utilized for

reporting and business intelligence, e.g.: data warehousing, data mart-ing and online

analytical processing (OLAP) systems in databases so HC practitioners are able to make

better decisions (Alshawi, Missi and Eldabi, 2003). Information is possible when data is

translated into a more useful form. Information systems (ISs) are developed to facilitate the

collection and analyzing of information (Herring, 1992). Knowledge is an integration of

know-what, know-how, know-where, know-who and know-why processed from information

(White, 2000). KM is where knowledge is creatively, effectively and efficiently applied to

benefit patients (Prince, 2000).

The core of HC KM is organizational knowledge. This knowledge exists when people,

process and technology merge together. By harnessing HC KM, with its tools, processes and

technologies one can combine data, information and knowledge to allow an organization to

reach it's goals. This is possible because knowledge is at the core of an organization's

performance. HC can no longer cope with the rising costs and miss-management of patient

care. HC lacks the technology and faces increasing numbers of dissatisfied information-

hungry patients. Therefore HC needs to adapt HC KM. Also HC KM is becoming important

since an organization needs to manage its data, information and knowledge assets as well as

retain vital expertise. HC KM is defined as a "generation, representation, storage, transfer

and transformation of knowledge." Knowledge exists in 2 aspects: (1) objective - process-

based where knowledge is of 2 types being: (a) explicit (tangible in documents and

expressible) and (b) tacit (know-how - experience that is not expressible) and (2) subjective -

evolving phenomenon shaped by a community of social practice (Wickram et al., (2009, p. 1).

A knowledge management system (KMS) supports both aspects of knowledge. When

managing knowledge the knowledge spiral is a process of changing knowledge from one

form to another, through 4 possible modes being: (1) combination - new explicit knowledge is

created from body of old explicit knowledge, (2) externalization - new explicit knowledge is

created from tacit knowledge, (3) internalization - new tacit knowledge is created from

explicit knowledge and (4) socialization - new tacit knowledge is created from existing tacit

knowledge. (Mohmed, Stanosky and Murray, 2006).

Knowledge spiral is experienced when ICT along with EMR are utilized by HC professions

from the time a patient gets a symptom to the time when where he/she is treated. Knowledge

transforms from explicit to explicit when learning takes place. It transforms from explicit to

tacit when it is getting used to broaden tacit knowledge. Knowledge transforms from tacit to

explicit when evidence is asked for as to the question why. It is transformed from tacit to tacit

when enhancing tacit knowledge base when physicians integrate treatment patterns and

interact amongst each other. Since there is a gap between data collection (huge amount) and

data comprehension; data mining becomes and important tool along with HC KM. Data

mining associates with databases and statistics to look for useful patterns and support both

objective and subjective knowledge and a facilitator of knowledge spiral for it assists in

expanding the knowledge base. When these patterns are found this is referred to as

knowledge discovery where data gets transformed to knowledge using data mining models

like clustering, regression, classification, neural networks, sequence analyses, etc. This

knowledge can then be managed using HC KM tools (Wickram et al., (2009, p. 1). HC KM is

a long-term project that needs to be embedded within the organization‟s culture, processes

and management style (e.g.: quality) and a contributor, facilitator and implementer of HC KM





strategy (Wickram et al., (2009, p. 23).

2.6 Our Analyses

Up, till now, we have not fully shown how really HC KM fills the structure-based gap in the

EPR and EHR. Therefore this paper also looks at knowledge representation and the Semantic

web. Through our analyses we have realized that ample research describes the breath and

depth of data required by the structure of a medical record. However, it seems that such

records get to be shown as if they are static even though in reality they are dynamic and are

subjective to their ability to view their data based upon the requirements of a HC professional.

Hence it is not the actual data whose syntax is of concern. The meaning of such data that

needs to be commonly and automatically understood by humans and computers combined is

of importance. Hence this shows that it is important for us to research in the area of

knowledge representation.

2.7 Defining Knowledge representation and the Symantec Web

Data “is organized into information by combining data with prior knowledge and the person's

self-system to create a knowledge representation. This is normally done to solve a problem or

make sense of a phenomenon.” A KMS captures a snap shot of an experts knowledge

representation also referred as knowledge harvesting (Knowledge Management, 2004).

Knowledge representation is storing and processing information and knowledge so it can be

used by applications. The main topics in knowledge representation are: (1) language and

notation, (2) ontology languages, (3) links and structures, (4) notation and (5) storage and

manipulation. These topics integrate to represent knowledge. The development of HC KM

and then knowledge representation has introduced the development of semantic web. Earlier

applications were program centric with data that was processed within the format only

understandable by these applications. Modern applications are data centric with data

distributed on the web where technologies, e.g.: XML – extensible markup language, define

the structure (syntax) of data. XML is limited since to a corporate Intranet that utilizes data

with web services. To achieve open interoperability within and across Intranets, one has to

look beyond XML. This brings us to talk about models and techniques developed in the

knowledge engineering field. Semantic web - an extension of the current web where

information gets meaning beyond data that is just getting defined structurally using XML. A

number of new technologies are researching into the area of semantic web e.g.: RDF, OWL,

RDFS, etc (Wickram et al., (2009, p. 23).

2.8 Assessing HC using Quality Management System (QMS)

For HC to improve quality it needs quality initiatives also referred as: (1) TQM, (2) total

quality in service (TQS) or (3) continues quality improvement (CQI). EFQM – European

foundation for quality management is a tool to portray management theories to attain total

quality by using self-assessment via a statistical approach to assess root causes of problems

due to management but not company workers. This model works with the support of

leadership, within a non-blaming organizational culture, team-based incorporating training,

planning and customers at the heart of the philosophy of quality improvement (Stewart,

2003).

Another model that is found most advantageous is the quality management system (QMS)

due to its narrower scope and broader supporting functionality cater able to solving HC poor

quality problem at hand (businessballs.com, 2004-2009). QMS is a process based

management strategy as illustrated below in figure 1, dependable upon the participation of its

members, to improve HC service quality. Dashed Arrows direct information flow and the

plain bold lined arrow point point the main elements for executing a process. Information

provides feedback of the client‟s requirements and expectations inputted to the service





realization process that hold account of all services and their relevant processes and

procedures (Posadas, 2007). QMS focuses on customer satisfaction through processes and

procedures by measuring and monitoring the operations of QMS and striving for continual

improvement (ISO, 2009),. This model intends to fulfill customer‟s requirements when

evaluating a business (in this case HC) as a process with inputs and outputs. This model is

made up of four interfaces being: (1) service realization, (2) resource management, (3)

management responsibilities and measurement and (4) analysis and improvement. Each

interface is composed of its own processes, inputs and outputs inter-relating with each other

(Smith, 2008). QMS‟s management responsibilities interface is for executive management to:

(1) establish and enforce QMS while establishing quality policies and objectives and (2)

provide resources needed to endorse the standards placed forth. The resource management

interface distinguishes resources like people, suppliers, information, work environment or

even personal training that would be needed at the highest level of quality and customer

satisfaction. Product or service realization utilizes feed back from customers ensuring that

product or service meets customer expectations. The recognized services are re-engineered

for any improvements taking under consideration customer‟s feedbacks. The measurement,

analysis and improvement interface seeks continual improvement of QMS and are driven by

quality policies and objectives. Past performance data and customer feedback are analyzed to

determine areas, i.e.: services in HC for improvement (Relex Software Corporation 2009).

Figure 1. QMS model

Adopted from – ( ISO, 2009); (Posadas, 2007); (Smith, 2008)

3 METHODOLOGY

This study started with an assumption after prior of research done on EPR, EHR and KM; that

KM as a tool will be able to facilitate EPRs and EHRs to untimately improve HC qality. The

question of this research was how this is possible. This was a pragmatic research that studied

consequences of actions taken in the context if this researched. There was a knowledge claim

where the most talked about problems (barriers) were of the essence in terms of finding and

proposing a solution (a viable model of KM as a facilitator to improve HC quality – figure 2

below). The choice of the research design was qualitative research methodologies utilized to

come up with this solution. This methodology began with a narrative research to analyze the

applicability and barriers in EPRs and EHRs. This was followed by a phenomenon research

where this study understood the consequences of difference HC scenarios where EPRs and





EHRs were attempted for utilizations but were hindered due to their barriers. In the next step

case study (e.g.: NHS – national health service of UK, EPR application utilization barriers

incurred by doctors in a NY hospital, etc) were assessed to understand how the barriers in

EPR and EHR snagged daily work and treatment procedures. Ethnographic data and theory

was applied to understand and study observations made when HC professionals attempted to

adapt EPRs, HER, HC KM and quality assessment tools. This was followed by utilizing a

grounded theory of (1) EPRs, (2) EHR, (3) KM application and models as well as (4) quality

assessment models to refine, integrate and inter-relate these theories to come up with our

solution. This proposed model is ready to be rested in a simulated or a real HC environment.

All literature review was extracted from (1) journal articles especially from databases (like

ProQuest, Science Direct, Emerald, etc..), (2) articles published in conference proceedings,

(3) books relating to KM and those that compile several HC KM articles and (4) also reports,

case studies and surveys posted on the world wide web. Our contribution is a very important

for the main reason being that very little research is done that relates KM and EHR as well as

how HC KM can facilitate in narrowing the above-mentioned barriers/gaps.

4 SIGNIFICANT CHALLENGE

A number of significant challenges got encountered during our research. These challenges

have been described in the literature review, above, so this paper would be better

communicated across to its readers. In summary, referred from the literature review the

following barriers were come across being:

There is a barrier in the structure of EPR since there are no reported national

standards laying policies to standardize what needs to be coded and not coded and to

what depth and breath must EHR and EPR be stretched.

Another resource mentions content-base barriers in EHR that is vitally essential so

HC professionals can communicate and share medical data.

Yet another report states that HC industry has not yet adapted HC KM and data

mining. Hence HC in general lacks the sound organizational structure required to

improve the quality of HC services.

5 PROPOSED SOLUTION

Clinical systems, compatible with EPRs, can integrate with the clinical records system (EPR)

on one end and link departments by a master patient index on the other hand. This complete

system can be integrated with an electronic clinical results reporting system. This system can

be extended with knowledge and decision support activities by linking this system with (i)

knowledge bases, (ii) embedded rules and guidelines and (iii) expert systems. This whole

system would be capable of integration with specialized clinical modules and document

imaging systems to achieve specialized specific support. To achieve an advanced multi-media

and telemedicine this whole system needs to be integrated with telemedicine, other

multimedia applications and communication applications (Jalal-Karim and Balachandran,

2008).

As per figure 2 - HCKM facilitator model to improve quality of HC, below, is a solution to

the explanations and barriers mentioned above, HC KM is a solution and a facilitator that

helps support EPRs and EHRs to improve the quality of HC by the support of knowledge

representation and the Symantec web. HC quality improvement is measurable using the QMS

model that assures patient and HC professional centric-based satisfaction.





Figure 2. HC KM facilitator model to improve quality of healthcare

Adopted from – (Jalal-Karim and Balachandran, 2008).

6 DISCUSSION

The threat of rising patient deaths, rising HC costs and increasing medical errors posed a

significant challenge bring the assessment of the quality in HC slumping low. This posed

EPRs and EHRs as an attractive and effective solution towards solving this problem.

However barriers pertaining to EPR and EHR, reported above, produced a snag in the HC‟s

initiative to improve its quality.

Our paper introduces KM as a new merging business process with its tools and techniques to

not only improve EPR and EHR structure and content but brings along a new thought-based

approach showing the EPRs and EHRs no longer need to be dealt statically with what data

they should or should not hold. By harnessing knowledge representation, an area of study

branched out of artificial intelligence knowledge is created, stored and shared using KMSs

within a framework of the Symantec web. Therefore data in an EHR becomes dynamic and is

shared based not on the content an EHR bares but such data holding not only definition but

also meaning is mined automatically by both humans (HC professionals or patients) and

computer systems. Knowledge representation with the Symantec web bypass the structural

and content-based barriers, circling the EPRs and EHRs, and brings about a new way of

thinking considering that new technologies like RDF, OWL, RDFS, etc are being researched

and are new to the scientific world of KM and knowledge representation.

Even though KM facilitates EPR and EHR and thus faciliates to improve HC quality, this

quality needs to be assessed keeping customer (patient) in the center of the qualitative

philosophy. Therefore the QMS model is a process-based management strategy to measure,

analyze and improve quality of HC to satisfy its customers based upon a constant

improvement policy.

7 CONCLUSION





This paper describes a literature review of EPR, EHR, and their applications in different

countries, barriers that are widely spoken about in the current research (EHR interoperability).

The paper pushes ahead to describe the theory of knowledge and HC KM and proves in detail

how this is a facilitators to EHR and EPR. In addition the QMS Model -figure 1 (HC KM

facilitator model to improve the quality of HC) is proposed as a viable solution to assess the

quality of HC considering the reasons mentioned regarding its suitability to the context of our

research. A final solution-based model is crafted (figure 2) which thus answers our research

question proving that KM has a positive influence on EHR and EPR to improve the quality of

HC services.

Our finding is in support with the theories mentioned in this paper and hence prove the

viability of our solution model of figure 2. Even though there is ample research done in the

areas of: (1) EPR and HER, (2) HC KM and (3) quality in HC, there is very little study

conducted that would hold the research question we have gapped out. Therefore our approach

that led us to contribute our solution model is new a correct as per opinions of many

researchers described in the literature review. This point proves the importance of our

intellectual contribution to the large body of research.

Now our future plan is to perform a survey of HC practitioners to gain their insights on the

applicability and usefulness of our proposed model. Based upon our survey results we intend

to implement the model using ample theoretical knowledge available that can show us how to

apply KM in practice (towards implements our solution model).





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THE INFLUENCE OF KNOWLEDGE MANAGEMENT ON EHR TO IMPROVE THE QUALITY OF HEALTHCARE SERVICES

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