Healthcare Knowledge Management

Introduction

The healthcare environment is changing rapidly, and effective management of the knowledge base in this area is an integral part of delivering high-quality patient care. People all over the world rely on a huge array of organizations for the provision of healthcare, from public-sector monoliths and governmental agencies to privately funded organizations, and consulting and advisory groups. It is a massive industry in which every organization faces a unique combination of operational hurdles. However, what every healthcare system has in common is the high price of failure. Faced with the prospect of failing to prevent suffering and death, the importance of continuously improving efficiency and effectiveness is high on the agenda for the majority of healthcare organizations (Brailer, 1999). Taking also into consideration that the amount of biological and medical information is growing at an exponential rate, it is not consequently surprising that knowledge management (KM) is attracting so much attention from the industry as a whole.

In a competitive environment like the healthcare industry, trying to balance customer expectations and cost requires an ongoing innovation and technological evolution. With the shift of the healthcare industry from a central network to a global network, the challenge is how to effectively manage the sources of information and knowledge in order to innovate and gain competitive advantage. Healthcare enterprises are knowledge-intensive organizations which process massive amounts of data, such as electronic medical records, clinical trial data, hospitals records, administrative reports, and generate knowledge. However, the detailed content of this knowledge repository is to some extent “hidden” to its users, because it is regularly localized or even personal and difficult to share, while the healthcare data are rarely transformed into a strategic decision-support resource (Heathfield & Louw, 1999). KM concepts and tools can provide great support to exploit the huge knowledge and information resources and assist today’s healthcare organizations to strengthen healthcare service effectiveness and improve the society they serve.

The key question which remains is the following: How can we make knowledge management work in healthcare? The answer is given in the following sections.

The Healthcare Industry: A Brief overview

The health care industry is one of the largest single industries all over the world and the largest one in the United States. It has increased by over 65% since 1990 and is expected to double by the year 2007.1 The IT industry is strategically positioned to become a powerful ally to the healthcare industry as it strives to adopt well-managed cost-efficient strategies. Advanced information technologies can give healthcare providers the opportunity to reduce overall healthcare expenses by lowering the costs of completing administrative and clinical transactions. Nevertheless, in comparison to other industry sectors, the healthcare industry has been slow to embrace e-business solutions and other advanced information technologies, as presented in Table 1.

The same study revealed that the healthcare industry spends substantially more on overhead and computer facility maintenance than other industry sectors. In 1997, for instance, the healthcare industry allotted 12% of its budget to maintain existing infrastructure—6% more than the industry norm. The high level of investment in this area by healthcare organizations indicates that many providers operate with the aid of old systems, which require constant repair and maintenance.

At this stage, it is worth emphasizing that the healthcare context differs from other information systems application domains in that it often concerns sensitive and confidential information and leads to critical decisions on people’s lives (or quality of life). Thus, stakeholder conflicts have more of an impact than in other areas such as business, trade, and manufacturing. Healthcare is an area with quite intense differences of values, interests, professional backgrounds, and priorities among key stakeholders. Given the complexity of the context, health informatics in general cannot simply focus on technical or information systems aspects alone. It has to take account of their relationship with clinical and managerial processes and practices, as well as deal with multiple stakeholders and organizational cultures and accompanying politics.

Concluding, it should be stressed that healthcare is not only a significant industry in any economy (Folland, Goodman, & Stano, 1997), but also a field that needs effective means to manage data as well as information and knowledge. Man aged care has emerged as an attempt to stem the escalating costs of healthcare (Wickramasinghe & Ginzberg, 2001) and improve the quality of services.

Table 1. Percentage of IT implementation in industry(Computer Economics, 1999)


Industry Sector

% in Place

Transportation

57.2

Banking and Finance

52.9

Insurance

48.1

State & Local Government

37.5

Trade Services

36.8

Retail Distribution

35.5

Process Manufacturing

34.9

Discrete Manufacturing

33.3

Wholesale Distribution

33.3

Utilities

26.9

Federal Government

25.0

Healthcare

21.8

Professional Services

21.7

The background of km in healthcare

An increasing concern with improving the quality of care in various components of the healthcare system has led to the adoption of quality improvement approaches originally developed for industry. These include Total Quality Management (Deming, 1986), an approach that employs process control measures to ensure attainment of defined quality standards, and Continuous Quality Improvement (Juran, 1988), a strategy to engage all personnel in an organization in continuously improving quality of products and services. Nowadays, the importance of knowledge management is growing in the information society, and medical domains are not an exception. In Yu-N and Abidi (1999), managing knowledge in the healthcare environment is considered to be very important due to the characteristics of healthcare environments and the KM properties. We should always keep in mind that medical knowledge is complex and doubles in amount every 20 years (Wyatt, 2001).

The healthcare industry is nowadays trying to become a knowledge-based community that is connected to hospitals, clinics, pharmacies, physicians, and customers for sharing knowledge, reducing administrative costs, and improving the quality of care (Antrobus, 1997; Booth, 2001). The success of healthcare depends critically on the collection, analysis, and exchange of clinical, billing, and utilization information or knowledge within and across the organizational boundaries (Bose, 2003).

It is only recently that initiatives to apply KM to the healthcare industry have been undertaken by researchers. Firstly, in the second half of the 1980s, several authors tried to apply artificial intelligence (AI)—with doubtful success—to medicine (Clancey & Shortliffe, 1984; Frenster, 1989; Coiera, Baud, Console, & Cruz, 1994; Coiera, 1996). MYCIN is probably the most widely known of all medical (and not only) expert systems thus far developed (Shortliffe, 1976). And this is despite the fact that it has never been put into actual practice. It was developed at Stanford University solely as a research effort to provide assistance to physicians in the diagnosis and treatment of meningitis and bacteremia infections. PUFF, DXplain, QMR, and Apache III are also some of the most well-known medical expert systems that were developed and put into use (Metaxiotis, Samouilidis, & Psarras, 2000).

De Burca (2000) outlined the conditions necessary to transform a healthcare organization into a learning organization. Fennessy (2001) discussed how knowledge management problems arising in evidence-based practice can be explored using “soft systems methodology” and action research. Pedersen and Larsen (2001) presented a distributed health knowledge management (DKM) model that structures decision support systems (DSSs) based on product state models (PSMs) among a number of interdependent organizational units. The recurrent information for the DSS comes from a network-wide support for PSMs of the participating organizations.

Ryu, Hee Hp, and Han (2003) dealt with the knowledge sharing behavior of physicians in hospitals; their study investigated the factors affecting physicians’ knowledge sharing behavior within a hospital department by employing existing theories, such as the Theory of Reasoned Action and the Theory of Planned Behavior. Tor-ralba-Rodriguez and colleagues (2003) presented an ontological framework for representing and exploiting medical knowledge; they described an approach aimed at building a system able to help medical doctors to follow the evolution of their patients, by integrating the knowledge offered by physicians and the knowledge collected from intelligent alarm systems. Also, Chae, Kim, Tark, Park, and Ho (2003) presented an analysis of healthcare quality indicators using data mining for developing quality improvement strategies.

Reviewing the literature, it is concluded that a KM-based healthcare management system should have the following objectives (Shortliffe, 2000; Booth & Walton, 2000):

• To improve access to information and knowledge at all levels (physicians, hospital administrators and staff, consumers of health services, pharmacies, and health insurance companies) so that efficiencies and cost reductions are realized.

• To transform the diverse members (care recipients, physicians, nurses, therapists, pharmacists, suppliers, etc.) of the healthcare sector into a knowledge network/community of practice.

• To enable evidence-based decision making to improve quality of healthcare.

Table 2 presents important Web sites dedicated to the promotion and application of KM to healthcare.

The knowledge Management process in healthcare

In order to examine whether knowledge management can really succeed in healthcare, we can analyze this proposition in terms of examining the knowledge management process and the likelihood of success for the healthcare organizations in achieving these steps in the process. The KM process consists of four key stages, as shown in Figure 1 (Schwartz, Divitini, & Brasethvik, 2000).

Knowledge identification and capture refer to identifying the critical competencies, types of knowledge, and the right individuals who have the necessary expertise that should be captured.

Then, this captured knowledge is shared between individuals, departments, and the like. The knowledge application stage involves applying knowledge—which includes retrieving and using knowledge—in support of decisions, actions, and problem solving, and which ultimately can create new knowledge. As new knowledge is created, it needs to be captured, shared, and applied, and the cycle continues.

Knowledge Identification and capture in Healthcare

One way to identify the critical knowledge that should be captured and determine the experts in the healthcare organization who have the knowledge on a specific issue (e.g., disease, therapy) is to conduct a knowledge audit. The knowledge audit helps to identify the types of knowledge needed and the appropriate sources (e.g., patient records, medical research literature, medical procedures, drug references) in order to develop a knowledge management strategy for the organization.

On the other hand, the use of intranets is suggested as basic tools for the capture of implicit knowledge. St Helens & Knowsley Health Informatics Service—which covers 320,000 patients—designed and developed an intranet structure with the aim to generate the potential to capture organizational implicit knowledge (Mimnagh, 2002). The real challenge has been to create a health- community- wide intranet that implements directory services, communities of practice, and lessons learnedt in a way which builds on existing activity and looks for the synergistic effect of adding a KM focus to ongoing work.

Vast amounts of medical knowledge reside within text documents, so that the automatic extraction of such knowledge would certainly be beneficial for clinical activities. Valencia-Garcia and colleagues et al. (2004) presented a user-centered approach for the incremental extraction of knowledge from text, which is based on both knowledge technologies and natural language processing techniques. The system was successfully used to extract clinical knowledge from texts related to oncology and capture it.

Table 2. Important Web sites dedicated to KM in healthcare

Web Site

Description

www.nelh.nhs.uk/knowledge_management.asp

The National Electronic Library for Health has a link dedicated to knowledge management. It describes how to manage explicit knowledge and outlines revolutions in KM in healthcare.

www.who.int

The World Health Organization has launched the Health Academy, which aims to demystify medical and public health practices, and to make the knowledge of health specialists available to all citizens through Web-based technology. The academy will provide the general public with the health information and knowledge required for preventing diseases and following healthier lifestyles.

www. cochrane. org

The Cochrane Collaboration is an international non-profit and independent organization, dedicated to making up-to-date, accurate information about the effects of healthcare readily available worldwide. The major product of the collaboration is the Cochrane Database of Systematic Reviews, which is published quarterly.

www.AfriAfya.org

AfriAfya, African Network for Health Knowledge Management and Communication, is a consortium formed by well-known agencies such as Aga Khan Health Service in Kenya, CARE International, SatelLife HealthNet, PLAN International, and the Ministry of Health in Kenya to harness the power of information and communication technology for community health.

www.hc-sc.gc.ca/iacb-dgiac/kmgs/english/kmhome.htm

The goal of knowledge management at Health Canada is to use the knowledge that resides in the department—in the minds of its staff, in the relationships they have with other organizations, and in their repositories of information—to fulfill their mission: to help the people of Canada maintain and improve their health.

www. ucl.ac. uk/kmc/index.html

The Knowledge Management Centre is part of the School of Public Policy of University College London (UCL). The Knowledge Management Centre’s aim is to improve clinical practice, patient outcomes, and health service innovation and efficiency by promoting better health knowledge management by serving as a resource center and making efficient use of its resources internally and across a network of collaborators.

Concluding, a key question is whether people would be willing to give up their competitive edge to have their knowledge captured via online repositories, lessons learned, best practices, and the like. This possible dilemma is especially valid in the healthcare sector.

Knowledge sharing in Healthcare

Productive organizations have the ability to create an environment where specialized knowledge, skills, and abilities of all employees are leveraged to achieve advancements in service industry. However, healthcare organizations cannot be considered as a good example of such organizations. A healthcare organization is a collection of professional specialists who contribute to the delivery of patient care, but also often act competitively inside the organization, without being willing to transfer knowledge because of associated status and power within the organization and the society.

Taking also into account that people in general are not likely to share their knowledge unless they think it is valuable and important, it becomes clear why doctors and physicians are not willing to share and transfer their knowledge. In addition, due to minimal interdisciplinary training, the transfer of tacit knowledge which occurs through apprenticeship style work patterns—for example, internships wherejunior doctors work alongside a senior clinician in surgery or intensive care—remains problematic (Beveren, 2003).

Effective knowledge management requires a “knowledge sharing” culture to be successful. Especially in healthcare, it is crucial that doctors and physicians understand the benefits of knowledge sharing on a number of levels: benefits to the organization, benefits to patients, and benefits to them personally. The more you can clearly demonstrate these benefits, the more people are likely to be open to change. Doctors and physicians need to be recognized and rewarded in a formal way (e.g., promotions, cash awards) to make knowledge sharing a reality in healthcare.

The Wisecare (Workflow Information Systems for European Nursing Care) project—an EC-funded initiative (1997-1999)—has promoted knowledge sharing using the Internet and online communities. Wisecare provided nurses with a vast amount of information and knowledge about clinical practice through both the Wisecare Web site and data collection tool. This information has been specifically selected to meet their clinical needs and meant nurses had access to relevant knowledge extremely quickly.

Figure 1. The knowledge management process cycle

The knowledge management process cycle

Lesson learned systems can also be an effective knowledge sharing approach to be used in healthcare (Yassin & Antia, 2003).

knowledge Application in Healthcare

Knowledge application refers to taking the shared knowledge and internalizing it within one’s perspective and worldviews. For the healthcare organizations the reality is that technology can only fulfill some of their needs. And how well it fulfills them depends critically on managing the knowledge behind them—content management, assigning knowledge roles, and so forth. Tom Davenport (2002), a prominent author on knowledge management, is often quoted as offering the following rule of thumb: your investment in technology in terms of both cost and effort should stay under one-third of the total knowledge management effort—otherwise you are going wrong somewhere.

Knowledge-enabling technologies which can effectively be applied to healthcare organizations are:

• Groupware

• Intranet

• Collaborative tools (e.g., discussion boards, videoconferencing)

• Portals

• Taxonomies

Abidi (2001) presented the Healthcare Enterprise Memory (HEM) with the functionality to acquire, share, and operationalize the various modalities of healthcare knowledge. Davenport (2002) outlined how Partners Health Care System in Boston implemented an enormously successful expert-intervention KM solution. Case studies from the UK’s National Health Service (NHS) and the Department of Health illustrated the drive towards modernization and more effective collaborative working among public-sector healthcare systems (Ark Group, 2002).

Knowledge creation in Healthcare

In general, knowledge creation may take the form of new products or services, increased innovation, and improved customer relationships. In the healthcare setting, knowledge creation can take place in terms of improved organizational processes and systems in hospitals, advances in medical methods and therapies, better patient relationship management practices, and improved ways of working within the healthcare organization. Given the various constraints and barriers occur in the healthcare sector, it takes longer for a new idea to be implemented in the healthcare setting versus that in the business sector.

A few examples of knowledge creation technologies that can be used in healthcare are:

• Data Mining: Tools that analyze data in very large databases, and look for trends and patterns that can be used to improve organizational processes.

• Information Visualization: Computer-supported interactive visual representations of abstract data to help improve understanding.

conclusion

Knowledge is a critical tool for health, and knowledge management is the capacity to translate research results (knowledge) into policies and practices that can improve the quality of life and lengthen survival. Managing knowledge in a healthcare organization is like trying to knit with thousands of strands of knotted wool; data is held in a number of locations, managed by a variety of people, and stored in every imaginable format. Perhaps in no other sector does knowledge management have such a high promise.

Delivering healthcare to patients is a very complex endeavor that is highly dependent on information. Healthcare organizations rely on information about the science of care, individual patients, care provided, results of care, as well as its performance to provide, coordinate, and integrate services. The traditional single physician-patient relationship is increasingly being replaced by one in which the patient is managed by a team of health care professionals each specializing in one aspect of care. Hence, the ability to access and use the electronic healthcare record (EHCR) of the patient is fundamental. In addition, the transformation of healthcare data into a strategic decision-support resource is fundamental too.

KM can be approached in numerous ways to serve particular needs and conditions. Successful KM practices typically need to be supported by complementary efforts in different domains. IT-related support activities and infrastructures are very important. They serve vital functions, are complex, costly, and often take time to design and implement. In the case of healthcare, building the infrastructure for a KM practice requires extensive effort due to the peculiarities of the health sector (e.g., legal and ethical issues, complex procedures for provision of healthcare, doctors’ behavior, etc.).

Coming back to the original question—How can we make knowledge management work in healthcare?—and by examining the knowledge management process, we can see that there are positive and negative points as to whether KM will truly work in the healthcare sector. Some people in healthcare think that KM is a passing fad like Total Quality Management, Business Process Reengineering, and other administration-backed initiatives. It is unfortunate to think in this light, as knowledge sharing should be encouraged so that lessons can be learned. KM solutions can facilitate the transfer of patient medical information, access to new treatment protocols as they emerge, knowledge exchange among experts, and so on.

Future research needs to be devoted to measuring the success of KM in healthcare organizations, showing quantitative benefits, and producing a “Return on Investment” index. Measurement is the least-developed aspect of KM because of the inherent difficulty to measure something that cannot be seen, such as knowledge (Bose, 2004). However, this is a very crucial issue since the future usage of KM is heavily dependent on both the quality of the metrics and whether output generated by this metric management would provide tangible value addition to the healthcare organizations. Integration of KM with e-health is also another direction for further research.

key terms

Collaborative Tools: Electronic tools that support communication and collaboration—people working together; essentially they take the form of networked computer software.

Distributed Knowledge Management Model: The model which combines the interdependence of one partial product state model to others with the idea of knowledge acquisition rather than just the operational exchange relationship.

Evidence-Based Medicine: Healthcare based on best practice which is encoded in the form of clinical guidelines and protocols.

Groupware: Specific software which allows groups of people to share information and to coordinate their activities over a computer network.

Healthcare Enterprise Memory: A KM info-structure which supports the functionality to acquire, share, and operationalize the various modalities of knowledge existent in a healthcare enterprise.

Information Visualization: Computer-supported interactive visual representations of abstract data which help improve understanding.

Taxonomy: A hierarchical structure for organizing a body of knowledge; it gives a framework for understanding and classifying knowledge.

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