Knowledge Management in Healthcare Essay

A health care system has two types of models in practice including; the traditional model and dispersed model. In particular, the traditional model employed in a health care setting involves primary care, secondary care, and tertiary care of patients. In that, a patient goes to primary care and receives treatment of the common problems diagnosed. The problems that go unsolved are referred to the secondary care, and the tertiary care is for those cases that require intensive care provision.
The knack to communicate ideas and information successfully is increasingly acknowledged as critical to the success of the health care system in general. For successful interactions between the health care providers and individuals but also in particular the health care providers themselves, effective communication is required.Knowledge Management in Healthcare Essay

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This function ensures an organization maintain productivity and efficiency. The importance of inter-departmental communication enables the growth of trust, improves the quality of service to customers hence ascertaining repeat business. In addition, it boosts efficiency in attaining delivery projections, and departmental conflicts in an organization are minimized where there is ease in flow of information.
Therefore, this paper aims at describing a health organization and its ineffectiveness in departmental sharing of information. Also to be illustrated are the proper assimilation sequence of the knowledge management model tools in the organization so as to improve on the limitations and increase efficiency of operation within.Knowledge Management in Healthcare Essay
Extensively, the patient centered medical home and the Chronic Care Management are integrated in the acute care system and the hospital in regards to delivering chronic illness care.

…health care providers: An antitrust analysis and practical guide. Chicago, Ill: Section of Antitrust Law, American Bar Association.
Bascetta, C. A., United States., & United States. (2005). VA and DOD health care: VA has policies and outreach efforts to smooth transition from DOD health care, but sharing of health information remains limited : testimony before the Committee on Veterans’ Affairs, House of Representatives. Washington, D.C.: U.S. Government Accountability Office.
Lytras, M. D. (2008). Knowledge management strategies: A handbook of applied technologies. Hershey: IGI Pub.

KM is a discipline that promotes an integrated approach to identifying, managing, and sharing all of an enterprise’s information assets, including database, documents, policies and procedures, as well as unarticulated expertise and experience resident in individual workers (Wickramasinghe, 2003). There are many dimensions around which knowledge can be characterized such as storage media, accessibility, typology and hierarchy.Knowledge Management in Healthcare Essay

Today, well-informed, demanding and more competitive persons are increasingly common. This is due to everyone’s easy and fast information access. Modern technology offers access to the internet, which has facilitated the immediate information search process. As a result, this accelerated process has clearly affected different work sectors, including health.

At the same time, health system users are increasingly aware of their rights and demand better conditions and increased quality of care delivery. In this context, distinguished techniques and tools, often used in the industrial sector, have been adapted and used for the health area, particularly care and service quality(1). Thus, one of the strategies adopted, based on large companies and industrial organizations’ experiences, is to apply the concepts of learning, knowledge and competency(2) in health institutions too.

Knowledge has been considered one of the most important resources in an organization, because it is capable of making organizational and individual actions more intelligent, efficient and effective. This stimulates the elaboration of innovative and continuously excellent products and services in terms of complexity, flexibility and creativity.Knowledge Management in Healthcare Essay

The Knowledge Management (KM) process covers any form of managing, storing, distributing and using knowledge). This process involves the treatment of large data volumes, demanding the use of information technologies to achieve acceptable efficiency. To enhance growth, development, communication and knowledge preservation in an organization, KM allows professionals to reach rapid and assertive responses, linked with the decisions they need to take(3).

In that sense, information systems, present in many health institutions, such as e-learning, allow professionals in this area to develop tacit knowledge storage capacities, which will later be converted into explicit knowledge(4).

Explicit knowledge comprises text represented in books and written documents, or taxonomies and rules(3). It is reliable, formal, systematic, easy and rapid to disseminate and connects people(4-5). Tacit knowledge results from personal experience accumulated for many years(4), marked by intuition, good sense and insights(5). In this type of knowledge, sharing is needed, which turns dialogue into an important learning mechanism. These two knowledge types intermingle in institutions(5).Knowledge Management in Healthcare Essay

In Nursing, various information technology tools exist that facilitate the decision making process, including: support for evidence-based practice, best practices stored in databases, clinical databases, distance education, electronic prescriptions and files(6). As health service managers, nurses should rapidly disseminate and share tacit and explicit knowledge with team members and other health professionals. Thus, they will contribute to enhance the team’s decision-making skills on care actions, which will directly affect the quality of care delivery(2).

When gaining further knowledge, nursing professionals manage to make safer and more effective decisions. This behavior leads to changes in the characteristics of the work performed, with a view to the rapid and assertive achievement of expected results(2). For these gained results to be high-quality, however, it is important for professionals to know how to manage and use this knowledge, obtained from countless existing information sources, in a correct and competent way(5).Knowledge Management in Healthcare Essay

Based on the above, this goal of this research is to develop a systematic literature review, guided by the following question: how do studies contribute that were published in Brazilian and international journals on Knowledge Management in Health between 2000 and 2010?

The use of information and communication technology (ICT) in healthcare is increasing (Ernstmann et al., 2009)because of its potential to improve the effectiveness and efficiency of healthcare (Kohn et al., 1999). Health information systems (HISs) help ensure that patients immediately receive appropriate treatment. Aggelidis and Chatzoglou (2009) mentioned that the use of information systems in the healthcare sector is widely accepted,

particularly in hospitals (Aggelidis and Chatzoglou, 2009). Information systems (ISs) improve the quality of services being provided (Scott, 2007). Researchers reported that the failure of hospitals to adopt new ISs increases inconvenience and loss of the trust among patients (Ammenwerth et al., 2003; Lu et al., 2005). Thus, HISs have gradually replaced traditional hospital procedures (Ammenwerth et al., 2003; Lu et al., 2005), and studies have proposed various frameworks for building trustworthy IS solutions for hospitals.Knowledge Management in Healthcare Essay

Healthcare information systems (HISs) in healthcare organizations such as hospitals is important for providing and sharing healthcare information among medical staff, especially physicians and researchers (Yang et al., 2010). In addition, collaboration is an important requirement for HISs (Ahmed and Yasin, 2012). The term “collaboration” in the field of healthcare is defined as the communication that occurs among healthcare practitioners when sharing information and skills regarding patient care (Gaboury et al., 2009; Scandurra et al., 2008; Weir et al., 2011). Furthermore, healthcare information is valuable to many organizations for scientific research or analysis (Chen et al., 2012). Sharing these healthcare data among different organizations can significantly benefit both medical treatment and scientific research in relevant sectors (Hillestad et al., 2005; Wang et al., 2003; Yang et al., 2010). Nevertheless, healthcare data typically contains considerable private information. Sharing this data directly would pose a threat to patient privacy. Thus, developing practical models to balance healthcare data sharing utility and privacy preservation is necessary in order to improve collaboration among physicians (Chen et al., 2012; Fung et al., 2010; Gkoulalas-Divanis and Loukides, 2011; LeFevre et al., 2006; Wang and Yang, 2011). In this context, collaborative in sharing healthcare information using HISs based on privacy preservation rarely handles healthcare information sharing among physicians and researchers at different places need to collaborate and communicate with each other to provide safer and more accessible to improve research findings that lead to enhanced care to patients. The need to address such collaboration among physicians and researchers in research activities based on privacy preservation is of utmost importance. A number of studies on the benefits of HISs have been conducted in the healthcare sector. These studies determined their effect on outcomes, including quality, efficiency, and provider satisfaction. Knowledge Management in Healthcare Essay Three systematic reviews of peer-reviewed studies about the benefits of adopting HISs in healthcare systems have been conducted and covered from 1994 to 2010 (Buntin and Burke, 2011; Goldzweig et al., 2009; Wu et al., 2006). Buntin and Burke (2011) cover the findings of these reviews and mentioned that 92% of recent articles on health IT reached conclusions that were generally positive (Buntin and Burke, 2011). Moreover, they found that the benefits of this technology were beginning to emerge in smaller practices and organizations as well as in large organizations that were early adopters. However, dissatisfaction with EMRs among some providers continued to hinder the potential of health IT. These realities highlight the need for studies that document the challenging aspects of the more strategic implementation of health IT and how these challenges may be addressed. Figure 1 summarizes the aforementioned findings on the benefits of health IT to the healthcare sector.Knowledge Management in Healthcare Essay

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The collaboration among physicians in sharing information using HISs in the patient treatment or research activities within the hospital environment in many developing countries is very weak (Organization, 2010; Reddy et al., 2011). This weak occurs due to decentralized and autonomous units and lack of shared goals within healthcare systems; many HISs are isolated from one another because of the fragmented nature of healthcare systems (Fried et al., 2011). Disintegrated HISs and manual systems hinder information sharing and collaboration among physicians, thus impeding optimal use of healthcare resources and delaying because large amounts of data are difficult to manage and control in a system that uses paper (Tierney et al., 2010; Van Vactor, 2012)introduced another important factor that affects collaboration among physicians, that is, privacy concerns raise the necessity of improving collaboration among medical staff through HISs. Effective implementation of HISs requires trust from both the providers who use them and the patients they serve (Blumenthal, 2009;Chen et al., 2012; Goldzweig et al., 2009). In such cases, sharing information regarding patients’ treatment and medical researches among hospitals is difficult. The aforementioned factors critically affect technology acceptance in hospitals and collaboration among physicians, which can lead to poor patient outcomes (Reddy et al., 2011). The bigger challenge is strengthening sharing of healthcare information among physicians and researchers in same or different hospital, many of which still rely on paper-based records. As such, introducing new activities to hospitals is a difficult process. These activities are important in enhancing healthcare services. Knowledge Management in Healthcare Essay Collaborative HISs based on privacy preservation rarely handles healthcare information sharing among physicians and researchers at different places need to collaborate and communicate with each other to provide safer and more accessible to improve research findings that lead to enhanced care to patients. The need to address such collaboration among physicians and researchers in research activities based on privacy preservation is of utmost importance.

The privacy preservation is an important issue when dealing with personal data and can be considered as the backbone for the sharing data process. There are numerous real-world applications which require sharing data while meeting specific privacy constraints. Consequently, the literature review in this section aims to clarify the privacy preservation data sharing challenges.Knowledge Management in Healthcare Essay

The recent studies refer to the increase privacy and security consciousness has lead to increased research and development of methods that compute useful information in a secure fashion (Clifton et al., 2004; Fung et al., 2010). Data sharing have been a long standing challenge for the database community. This need has become critical in numerous contexts, including integrating data on the Web and at enterprises, building ecommerce market places, sharing data for scientific research, data exchange at government agencies, monitoring health crises, and improving homeland security (Clifton et al., 2004). Additional to large amounts of personal health data are being collected and made available through existing and emerging technological media and tools. While use of these data has significant potential to facilitate research, improve quality of care for individuals and populations, and reduce healthcare costs, many policy-related issues must be addressed before their full value can be realized. These include the need for widely agreed-on data stewardship principles and effective approaches to reduce or eliminate data silos and protect patient privacy (Hripcsak et al., 2014).Knowledge Management in Healthcare Essay

Unfortunately, data integration and sharing are hampered by legitimate and widespread privacy concerns (Clifton et al., 2004; Fung et al., 2010). Companies could share information to boost productivity, but are prevented by fear of being exploited by competitors or antitrust concerns. Sharing healthcare data could improve scientific research, but the cost of obtaining consent to use individually identifiable information can be prohibitive and these efforts must engage patients as partners (Hripcsak, et al., 2014). Sharing healthcare and consumer data enables early detection of disease outbreak (Tsui et al., 2003), but without provable privacy protection it is difficult to extend these surveillance measures nationally or internationally. Besides effective public safety and health care, collaboration and sharing between public agencies, and public and private organizations, can have a strong positive impact on public safety.Knowledge Management in Healthcare Essay

The continued exponential growth of distributed personal data could further fuel data integration and sharing applications, but may also be stymied by a privacy backlash. It is critical to develop techniques to enable the integration and sharing of data without losing privacy. As noted above, there is widespread agreement on the value of personal health data for many uses beyond direct patient care and treatment. Thus, discussions about the privacy preservation data sharing are more important than ever. As part of the overall problem, the literature review in this study aims to cover the privacy preserving data sharing as mentioned in the recent studies. The recent studies indicate to the emergent privacy issues of healthcare data are important issue. According to Gkoulalas and Loukides (2011) mentioned that 62% of individuals worry that their electronic medical records will not remain confidential (Gkoulalas-Divanis and Loukides, 2011), and 35% expressed privacy concerns regarding the collaboration (publishing and sharing) of their data (Ludman et al., 2010), Figure 2 shows the motivation for this work.Knowledge Management in Healthcare Essay

The literature review in this study aims to cover the privacy preserving data sharing as mentioned in the recent studies, in order to improve the collaboration among medical staff (relation management) with regard to medical data sharing for research through review and classification methods of privacy protection. The recent studies indicate to the emergent privacy issues of healthcare data are important issue. In the sections that follow, we briefly explain the related works and highlight related literature, collaboration in sharing healthcare information based on privacy preservation (relation between sharing and privacy), state of the art privacy preserving, privacy preservation and technical contribution, privacy preservation models, and proposed model to sharing healthcare information based on control privacy preservation.Knowledge Management in Healthcare Essay

RELATED WORKS

Privacy protection is an important issue particularly with regards to personal data that must have stringent policies on sharing. A definition on privacy protection has specified that access to published data should not allow potential attackers to learn anything beyond what target victims had permitted to disclose, which is in contrast to having no access to the database or the background knowledge of the potential attacker that he has obtained from other sources (Dalenius, 1977). The development of information technology and the collection of electronic information by data owners, such as governments, corporations, and individuals, have facilitated higher instances of data sharing and knowledge management. Driven by mutual benefits, these data owners have created broad opportunities for knowledge management and for information retrieval. Recent developments have helped improve decision making, particularly in the fields of medical information, research, and public health organization, among others. Many approaches have been proposed for different data publishing needs in different fields.Knowledge Management in Healthcare Essay Data sharing requires control and management to ensure system integration. Integration is required specifically in the management of patient data to secure sensitive information such as the identity of the patients (Gkoulalas-Divanis and Verykiosc, 2009; Qi and Zong, 2012). Several studies had focused on the management of data, such as in medical applications, to ensure system integration. However, management and sharing of data in different fields can lead to misuse of information, disclosure of the identification of the data owner, and other related problems (Clifton et al., 2004; Rashid et al., 2012). The primary goal in privacy preservation is the protection of sensitive data before they are released for analysis or for re-publication. Data may be kept at centralized or at distributed data storage areas. In this scenario, appropriate algorithms or techniques should be used to protect any sensitive information during the knowledge discovery process. Many approaches can be adopted for privacy-preserving data mining (Kaye et al., 2010).Knowledge Management in Healthcare Essay

An important aspect on privacy-preserving data mining algorithms and on tools for development and evaluation is to select the appropriate evaluation criteria. The reality, however, is that privacy-protected data mining algorithms with a variety of indicators are not better than other algorithms. Generally, an algorithm may be practical in terms of performance or may be slightly better than others. Users must be provided with a set of metrics to enable them to choose the best appropriate algorithms for data privacy preservation. Subsequently, we formulated a simple introduction on algorithm performance, data utility, privacy protection degree, and on the difficulty of different data mining techniques (Qi and Zong, 2012). In algorithm performance, the algorithm with O(n2) complexity polynomial time is more efficient than those with O(en) index of complexity. An alternative approach is necessary to evaluate time requirements in terms of average number of operations to reduce the frequency of sensitive information appearing below a specified threshold. Possibly, this value does not provide an absolute measure, but it can be capable of performing a fast comparison among different algorithms (Qi and Zong, 2012). Data utility is a very important issue in the implementation of data privacy protection. To hide sensitive information, false information may be inserted into the database or data values can be blocked. Although sample techniques do not modify the information stored in the database, they can exhibit a reduction because of the presentation of incomplete information (Qi and Zong, 2012). In the degree of privacy protection, the privacy protection policy prevents the downgrade of information to a certain threshold, though hidden information can be derived by some uncertainty. The uncertainty reconstructed by hidden information can evaluate the sanitation algorithm. Knowledge Management in Healthcare Essay A solution can set a maximum on perturbation information from the execution perspective, and then consider achieving the degree of uncertainty by measuring the constraints of different purification methods. We intend to define an algorithm that can achieve the highest uncertainty and that is better than all other algorithms (Qi and Zong, 2012). In difficulty of different data mining techniques, we must measure the difficulty of data mining algorithms, which differ from the purification method, to provide full estimation on the purification method called parameter horizontal difficulty. Parameter estimation must consider the data mining classification, which is important to the test. Alternatively, we may need to develop a formal framework that can ensure privacy assurance for an entire class of sanitization algorithms upon testing one against pre-selected data sets (Qi and Zong, 2012).

The recent studies refer to the increase privacy and security consciousness has lead to increased research and development of methods that compute useful information in a secure fashion (Clifton et al., 2004; Fung et al., 2010). Data sharing have been a long standing challenge for the database community. In other words, great concern has been directed on the control of data and it’s sharing to make it available to their owners. Some reviewers and researchers have even suggested the use of covert techniques which isolate data such as encryption technology. Different ways of protecting data have been dealt with in recent research. The methods previously introduced include information on how to spread and use data in research, decision making, scientific analyses, and other purposes (Fung et al., 2010). First, the concern is how to control data sharing and management and avoid the risk of publishing data that may lead to revealing the real data. Second, there is lack of unity among the collected data, and their sources vary as they are collected from various points such as governments, hospitals, companies, and so on. Third, the data collected may contain errors. How data are processed and formatted before access requires a high level of analysis techniques to extract and determine knowledge and relationships hidden.Knowledge Management in Healthcare Essay

To identify the relationships among different data and their influence on the results, they must be accurate and correct, as one type of data relies on the results of the analysis. Examples are the reasons for the spread of a particular disease in a particular area in the medical field, the losses incurred by a company after a change in business strategy, and the low standards of living in a society. The main objective of the present research is to control management and sharing of data in the medical field, which mainly involves “patient data”. The main objectives of the present research is to sharing healthcare information based on privacy preservation and keep data utility for secondary purposes such as research.Knowledge Management in Healthcare Essay

COLLABORATION HEALTHCARE INFORMATION BASED ON PRIVACY PRESERVATION

Recently, many healthcare organizations are adopting Customer relationship management (CRM) as a strategy, which involves using technology to organize, automate, and coordinate business processes, in managing interactions with their patients. CRM with the Web technology provides healthcare providers the ability to broaden their services beyond usual practices, and thus offers suitable environment using latest technology to achieve superb patient care (Anshari and Almunawar, 2012).

There are two basic types of healthcare CRMs, one is for a healthcare organization to stay in contact with their patients, and the other is for a healthcare organization to stay in contact with referring organizations. In other hand, privacy is critical factor when patients’ information used in other treatment purposes (Fung et al., 2010; Gkoulalas-Divanis and Loukides, 2011).Knowledge Management in Healthcare Essay

One of the most interesting aspects in medical care is how to manage the relationship between healthcare providers and patients (Anshari and Almunawar, 2012). Fostering relationship leads to maintain loyal customer, greater mutual understanding, trust, patient satisfaction, and patient involvement in decision making (Glanz et al., 2008). Furthermore, effective communication is often associated with improved physical health, more effective chronic disease management, and better health related quality of life (Arora, 2003). On the other hand, failure in managing the relationship will affect to the patient dissatisfaction, distrust towards systems, patient feels alienated in the hospital, and jeopardize business survivability in the future.Knowledge Management in Healthcare Essay

In this context, Usually, CRM is applied in the business field but not in the medical one. The application of the CRM model can result in desirable results through collaboration among hospital in patients treatment and other purposes such as data analysis, research. In other hand, Data mining has been used intensively and extensively by many organizations (Anshari and Almunawar, 2012). In healthcare, data mining is becoming increasingly popular, if not increasingly essential. Data mining applications can greatly benefit all parties involved in the healthcare industry. For example, data mining can help healthcare insurers detect fraud and abuse, healthcare organizations make customer relationship management decisions, physicians identify effective treatments and best practices, and patients receive better and more affordable healthcare services The huge amounts of data generated by healthcare transactions are too complex and voluminous to be processed and analyzed by traditional methods. Data mining provides the methodology and technology to transform these mounds of data into useful information for decision making (Koh and Tan, 2011). In healthcare, data mining is becoming increasingly popular, if not increasingly essential. Several factors have motivated the use of data mining applications in healthcare. Knowledge Management in Healthcare EssayThe existence of medical insurance fraud and abuse, for example, has led many healthcare insurers to attempt to reduce their losses by using data mining tools to help them find and track offenders (Anshari and Almunawar, 2012; Christy, 1997). Fraud detection using data mining applications is prevalent in the commercial world, for example, in the detection of fraudulent credit card transactions. Recently, there have been reports of successful data mining applications in healthcare fraud and abuse detection (Milley, 2000). Another factor is that the huge amounts of data generated by healthcare transactions are too complex and voluminous to be processed and analyzed by traditional methods. Data mining can improve decision-making by discovering patterns and trends in large amounts of complex data (Biafore, 1999). Insights gained from data mining can influence cost, revenue, and operating efficiency while maintaining a high level of care (Silver et al., 2001).

Healthcare organizations that perform data mining are better positioned to meet their long-term needs, Benko giving an illustration of a healthcare data mining application; and finally, highlighting the limitations of data mining and offering some future directions Cios and Moore31 have argued that data problems in healthcare are the result of the volume, complexity and heterogeneity of medical data and their poor mathematical characterization and non-canonical form. Further, there may be ethical, legal and social issues, such as data ownership and privacy issues, related to healthcare data. The quality of data mining results and applications depends on the quality of data (Koh and Tan, 2011).Knowledge Management in Healthcare Essay

Recent studies have shown that the development of effective collaborative HISs to support collaborative work among medical staff, especially among physicians and researchers, requires the use of real data. This result is based on the fact that the collaborative HIS approach requires appropriate, flexible, and comprehensive healthcare information based on user (Kuziemsky et al., 2012; Kuziemsky and Varpio, 2011; Lezzar et al., 2012; Reddy et al., 2011; Ruxwana et al., 2010; Scandurra et al., 2008). The findings of the review here indicate strong relationship between collaboration in sharing healthcare information and privacy preservation as mentioned in recent studies, in order to development of effective collaborative HISs to support collaborative work and improve patients outcome. Many researchers in this area proposed healthcare system models for healthcare information sharing among medical staff, and few studies focused on the research on healthcare system and privacy preservation in health sector. However, such models are not flexible in structure and are difficult to manage and control because of the enormous data in complex healthcare systems. The Figure 3 shows the Integration HISs.Knowledge Management in Healthcare Essay

In the past few years, research communities have responded to the challenges of privacy preservation through collaborative activities in sharing data as mentioned in (Clifton and Atallah, 2007)to eliminate privacy concerns from patients and help medical institutions or participants comply with privacy protection regulations. These approaches encompass several fields of research. The problems they are trying to address could be classified into three categories:
The first category focuses on privacy protection in data sharing during data usage. These kinds of approaches attempt to protect patient privacy by transforming the healthcare data before they are shared. The privacy information may be wiped or reduced after the transforming process. The de-identification approach simply detects the private data and deletes them (Neamatullah et al., 2008). To retain the usability of the transformed data as much as possible, many new models and methods are proposed. Privacy-preserving data publishing models, such as K-anonymity and l-diversity (Fung et al., 2010), and privacy-preserving data mining models and methods, such as privacy-preserving decision trees and associate rule mining (Aggarwal and Philip, 2008), have been developed as a result of these studies. The second category focuses on privacy data management. Many access control models and systems have been developed to enhance the flexibility of privacy data management and compliance with regulations. Elements such as access purpose, data content, and personal preferences have been brought into these data access management models (Byun et al., 2005; Smith, 2001). The third category focuses on privacy data storage and management. Privacy for data storage and management in a cloud environment has attracted plenty of attention in recent years. Approaches for privacy-aware data storage and auditing in a cloud environment are proposed to protect private data (Itani et al., 2009; Wang et al., 2010).Knowledge Management in Healthcare Essay

All approaches listed above may be used in privacy data sharing or management in some way. Many abstract frameworks have been proposed to realize privacy protection during data sharing, such as a framework for privacy preserving data sharing proposed by Chen (2004). Kennelly (2009) developed an Internet data-sharing framework for balancing privacy and utility. However, to the best of our knowledge, few research works about healthcare data sharing frameworks that preserve the privacy of users offer a practical view for real life application (Chen et al., 2012).Knowledge Management in Healthcare Essay

However, one set of methods that would allow health information to be used and disclosed under existing legal frameworks is de-identification. De-identification refers to a set of methods that can be applied to data to ensure that the probability of assigning a correct identity to a record in the data is very low (El Emam and Fineberg, 2009; El Emam et al., 2011). Recent studies (Bayardo and Agrawal, 2005; Campan and Truta, 2009; El Emam et al., 2012; El Emam and Dankar, 2008; El Emam et al., 2009; Goryczka et al., 2011; Jiang and Clifton, 2006; Jurczyk and Xiong, 2009; LeFevre et al., 2005; Parmar et al., 2011; Sacharidis et al., 2010; Sokolova et al., 2012; Sweeney, 2002a, b; Tassa and Gudes, 2012; Truta and Vinay, 2006) indicate that the K-anonymity model provides a formal way of generalizing this concept because K-anonymity provides a measure of privacy protection by preventing the re-identification of data to fewer than a group of K data items. As stated in Sweeney and Samarati (Samarati, 2001;Sweeney, 2002a, b), a data record is K anonymous if and only if it is indistinguishable from its identifying information from at least K-specific records or entities. The key step in making data anonymous is to generalize a specific value. Generalized data can be beneficial in many situations as stated in (Chen et al., 2012; Jiang and Clifton, 2006). Many applications are used to generalize data in a many areas, including medical research, education studies, and targeted marketing.Knowledge Management in Healthcare Essay

STATE-OF-THE-ART PRIVACY PRESERVING

This study covers a review of the most relevant areas below and discuss how our work levels up with recent state-of-the-art systems.

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Privacy preservation in data publication

The preservation of privacy when publishing data for centralized databases has been examined intensively in recent years. One thread of work aims at devising privacy principles such as k-anonymity and subsequent principles that address problems, which in turn serve as criteria for judging whether a published data set enables privacy protection (Nergiz and Clifton, 2007; Sweeney, 2002b). Another body of work has contributed to the development of an algorithm that transforms a data set to meet one of the privacy principles (dominantly k-anonymity). However, most of these works have focused only on structured data (Gardner and Xiong, 2009; Li et al., 2007; Xiao and Tao, 2007).Knowledge Management in Healthcare Essay

Medical text de-identification

In the medical informatics community, there have been efforts in de-identifying medical text documents (Gardner and Xiong, 2009; Sweeney, 2002b; Zhong et al., 2005). Most of them use a two-step approach which extracts the identifying characters first and then removes or masks the attributes for de-identification purposes. Most of them are specialized for specific document types, for example, pathology reports only (Gardner and Xiong, 2008; Zhong et al., 2005). Some systems focus on a subset of Health
Insurance Portability and Accountability Act (HIPAA) identifiers, for example, name only (Aramaki et al., 2006; Gardner and Xiong, 2009), whereas others focus on differentiating protected health information (PHI) from non-PHI (Gardner and Xiong, 2009). Most importantly, most of these studies rely on simple identifier removal or grouping techniques, and they do not take advantage of recent research developments that guarantee a more formalized notion of privacy while increasing data utility.Knowledge Management in Healthcare Essay

Information extraction

Extracting atomic identifiers and sensitive characters (such as name, address, and disease) from unstructured text such as pathology reports can be seen as an application of the named entity recognition (NER) problem (Neumann, 2010). NER systems can be roughly classified into two categories, both of which are applied in medical domains for de-identification. The first uses grammar-based or rule-based techniques (Gardner and Xiong, 2008). Unfortunately, such hand-crafted systems may take months of work by experienced domain experts, and the rules will likely change for different data repositories. The second category uses statistical learning approaches such as support vector machine (SVM)-based classification methods. However, an SVM-based method such as that introduced by Sibanda and Unuzer (Sibanda and Uzuner, 2006)only performs binary classification of the terms into PHI or non-PHI. It does not also allow statistical de-identification which requires knowledge on different types of identifying characters.Knowledge Management in Healthcare Essay

PRIVACY PRESERVATION AND TECHNICAL CONTRIBUTION

In the following, the researcher explains technical contributions of the survey to data privacy through the control and sharing of data in knowledge management. We focus on six aspects of technical contributions, which we consider to be the most interesting (Xiao, 2009).

Personalized privacy preservation

We examined the work of (Xiao and Tao, 2006)on the publication of sensitive data using generalization, the most popular anonymization methodology in the literature. The existing privacy model for generalized tables (that is, noisy microdata obtained through generalization) exerts the same amount of protection on all individuals in the data set without catering to their concrete needs.Knowledge Management in Healthcare Essay  For example, in a set of medical records, a patient who has contracted flu would receive the same degree of privacy protection as a patient suffering from cancer, despite the willingness of the former to reveal his/her symptoms directly (mainly because flu is a common disease) (Xiao and Tao, 2006). Motivated by this, we propose a personalized framework that allows each individual to specify his/ her preferred privacy protection in relation to his/her data. Based on this framework, we devised the first privacy model that considers personalized privacy requests. We also developed an efficient algorithm for computing generalized tables that conform to the model. Through extensive experiments, we show that our solution outperforms other generalization techniques by providing superior privacy while incurring the least possible information loss (Xiao and Tao, 2006).Knowledge Management in Healthcare Essay

Republishing dynamic data sets

Data collection is often a continuous process, where tuples are inserted into and deleted from the microdata as time evolves. Therefore, a data publisher may need to republish the microdata at multiple times to reflect the most recent changes. Such republication is not supported by conventional generalization techniques because microdata are assumed to be static (Xiao and Tao, 2007). We address this issue by proposing an innovative privacy model called m-invariance which secures the privacy of any individual involved in the republication process, even against a rival who exploits the correlations between multiple releases of the microdata. The model is accompanied by a generalization algorithm whose space and time complexity are independent of the number n of generalized tables that have been released by the publisher. This property of the algorithm is essential in the republication scenario, where n increases monotonically with time (Xiao and Tao, 2007).

Complexity of data anonymization

We have presented the first study on the complexity of producing generalized tables, which conform to ?-diversity, the most commonly adopted privacy model. We note that achieving ?-diversity with minimum information loss is NP-hard for any ? larger than two and any data set that contains at least three distinct sensitive values. Considering this, we developed an O(?.d)-approximation algorithm, where d is the number of QI characters contained in the microdata (Xiao, 2008). Aside from its theoretical guarantee, the proposed algorithm works fairly well in practice and considerably outperforms state-of-the-art techniques in several aspects (Xiao, 2008).Knowledge Management in Healthcare Essay

Transparent anonymization

Previous solutions for data publication consider the idea that the rival controls certain prior knowledge about each individual. However, they overlook the possibility that the rival may also know the anonymization algorithm adopted by the data publisher. Thus, an attacker can compromise the privacy protection enforced by the solutions by exploiting various characteristics of the anonymization approach (Xiao, 2008). To address this problem, we propose the first analytical model for evaluating the disclosure risks in generalized tables under the assumption that everything involved in the anonymization process, except the data set, is public knowledge. Based on this model, we developed three generalization algorithms to ensure privacy protection, even against a rival who has a thorough understanding of the algorithms. Compared with state-of-the-art generalization techniques, our algorithms not only provide a higher degree of privacy protection but also satisfactory performance in terms of information distortion and overhead estimation (Xiao, 2008).Knowledge Management in Healthcare Essay

Anonymization via anatomy

While most previous work adopts generalization to anonymize data, we propose a novel anonymization method anatomy which provides almost the same privacy guarantee as generalization does. However, it significantly outperforms it in terms of the accuracy of data analysis on the distorted microdata (Xiao and Tao, 2006). We provide theoretical justifications for the superiority of anatomy over generalization and develop a linear time algorithm for anonymizing data via anatomy. The efficiency of our solution was verified through extensive experiments.Knowledge Management in Healthcare Essay

Dynamic anonymization

We propose dynamic anonymization which produces a tailor-made anonymized version of the data set for each query given by users; the anonymized data increases the accuracy of the query result. Privacy preservation is achieved by ensuring that no private information is revealed despite combining all anonymized data (Xiao, 2008). For example, even if the rival obtains every anonymized version of the data set, he/she would not be able to infer the sensitive value of any individual. Through extensive experiments, we show that compared with existing techniques, dynamic anonymization significantly improves the accuracy of queries on the anonymized data (Xiao, 2008).

PRIVACY PRESERVATION MODELS

Recent developments in healthcare technology enable the collection, storage, management, and sharing of massive amounts of medical data (Lau et al., 2011). HISs
are increasingly adopted in the healthcare sector (Dean et al., 2010; Makoul et al., 2001). The use of HISs allows specialists to access comprehensive medical information, to extract knowledge and reduce medical errors, as well as to collaborate with other specialists and healthcare entities to improve the diagnosis and treatment of diseases. At the same time, reusing medical data offers the potential to improve medical research findings. However, reusing medical data must be performed in a way that addresses important privacy concerns.Knowledge Management in Healthcare Essay

Preserving the privacy of medical data is not only an ethical but also a legal requirement that is posed by several data sharing regulations and policies worldwide. For example, in 1996, the Health Insurance Portability and Accountability Act (HIPAA) title II was enacted in the USA (Act, 1996; Nosowsky and Giordano, 2006). One of the purposes of this act is to increase the protection of patients’ medical records against unauthorized usage and disclosure. Hospitals, clinical offices, health insurance companies, and other entities governed by HIPAA are asked to comply with regulations. In 1997, the European Council announced Recommendation R (97) 5 regarding the protection of medical data to enhance the protection of personal healthcare data (DIRECTIVE, 1997). Similar regulations have been enacted in many other countries (Chen et al., 2012). For example, contracts and agreements cannot guarantee that sensitive data will not be carelessly misplaced and end up in the wrong hands. A task of the utmost importance is developing methods and tools for publishing data in a more hostile environment, so that the published data (shared data) remains practically useful while preserving individual privacy. This undertaking is termed privacy-preserving data publishing (Fung et al., 2009; Gkoulalas-Divanis and Loukides, 2011; Gkoulalas-Divanis and Verykiosc, 2009). Privacy-preserving data publishing and information security communities have recently begun addressing these issues. Numerous techniques have been developed to address the first problem, which is avoiding potential misuse posed by an integrated data warehouse (Vaidya et al., 2006). Many abstract frameworks have been proposed to realize privacy protection during data sharing, such as a framework for privacy preserving data sharing proposed by Chen (2004). Kennelly (2009) developed an Internet data-sharing framework for balancing privacy and utility. However, to the best of our knowledge, few research works about healthcare data sharing frameworks that preserve the privacy of users offer a practical view for real life application (Chen et al., 2012).Knowledge Management in Healthcare Essay

The finding form this section indicates to K-anonymity model is suitable methods in sharing information in healthcare sector. The main features of the K-anonymity model as mentioned in recent literature: K-anonymity is a simple and effective (Sweeney, 1997, 2002b)model that provides a measure of privacy protection by preventing the re-identification of data to fewer than a group of K data items (Jiang and Clifton, 2006; Narayanan and Shmatikov, 2009), providing a formal way of generalizing this concept (Samarati, 2001; Sweeney, 2002a, b), and minimizing data utility loss while limiting disclosure risk to an acceptable level (Morton et al., 2012). In addition, the K-anonymity model is a simple and practical model for data privacy preservation (Chiu and Tsai, 2007), and it guarantees that the data released are accurate (Barak et al., 2007).Knowledge Management in Healthcare Essay

COLLABORATIVE HEALTHCARE INFORMATION SYSTEM: PROPOSED MODEL

The collaborative healthcare information management system, which was based on the k-anonymization model and generalization technique, was developed to achieve the objective of improving collaboration and outcomes based on a privacy preservation approach. The proposed framework comprises four phases. The first phase involves collecting data from different HISs, and then sending the data to a central database. The second phase involves data pre-processing, such as missing values, inconsistent data, data integration, data selection, and data transformation. The third phase involves processing data based on the anonymization engine, which applies the anonymization operation based on the data generalization technique; this phase involves “a strategy for protecting individual privacy in released microdata records”. The fourth phase involves sharing data among researchers based on privacy preservation as shown in Figure 4.Knowledge Management in Healthcare Essay

The idea is that by reconstructing a more “general” and semantically consistent domain for the attributes and transforming its values to this domain, identifying individuals by linking this attribute with external data would be much more difficult. From the perspective of information communication technology (ICT), the CHIMS construction was developed on the basis of an agent- based technique for linking the CHIMS units in different departments at hospitals using Web-based application tools; in this stage collecting healthcare data from different HISs departments, and then sending the data to a central database. The second stage pre-processing data in this study the researcher assume the collected data of hospital departments is clear. Stage three collected healthcare data send to anonymization engine in order to privacy preservation; to anonymize data was applied generalization, which transforms attribute values of non-sensitive attributes in the data into values ranges, so as to prevent an adversary from identifying individuals by linking these attributes with public available information. In hospital environment the collaboration among medical staff increases the awareness of team members regarding their respective knowledge and skills, which leads to further improvements in decision making and improve the research findings in healthcare sector. Consequently, Collaboration is an important requirement in health information systems (HISs) because it produces reliable and rigorous evidence that can inform critical decisions related to healthcare services. It aids in the provision of proper, fast treatment to patients, and healthcare information for research.Knowledge Management in Healthcare Essay

CONCLUSION

Collaboration in HISs is important in providing proper and fast treatment to patients and suitable medical data for research. Collaboration among current healthcare departments is important in addressing most HISs problems and in satisfying all system requirements. These requirements must maximize information flows and storage among HISs units to provide information in an appropriate and timely manner based on privacy preservation. Anonymization approach has been successfully used to provide privacy preservation and to maintain data utility. Therefore, this study improved the collaboration research among physicians and researchers by developing CHIMS based on the k-anonymization model, which in turn addressed privacy preservation and improved healthcare services through adoption in HISs.Knowledge Management in Healthcare Essay

HISTORY OF KM
Knowledge Management (KM) is an essential tool in today’s emerging healthcare system. Hospitals that seek to deploy KM systems need to understand the human element in the process. Earlier, success factors were only restricted to a few healthcare variables such as patient care and cost, but over the years, technology (both clinical and administrative) has evolved as a differentiating variable, thus redefining the doctrines of competition and the administration of healthcare treatments. One of the key objectives of a KM system is to insulate a hospital’s intellectual knowledge from degeneration (Elliot, 2000).

The UK public sector now typically spends an estimated £2 billion per annum on IT, equating to around 1% of the public purse (Holmes & Poulymenakou, 1995), while the NHS spends around £220 million annually on IT in hospitals (Audit Commission, 1995). Information technology is transforming the healthcare environment in ways that go beyond simple consumer health information Web sites (Hoagland, 1997).Knowledge Management in Healthcare Essay

SWOT ANALYSIS
Various Strengths of such organizational structure are:

There is a strong control over the employees with clearly defined rules and regulations.
The system is highly centralized because of which various decisions can be monitored efficientley..
There is standardization in the organization and everyone is following same procedures and thus there is no scope for any confusion.
Weaknesses of bureaucratic form of organizations:

The biggest weakness of such form of organization is that there is too much control leading to a lack of innovation initiatives and thus making the jobs dull and boring. Also, this has an adverse impact on the level of morale of employees which is clearly seen in case of Reddix trust hospital.
Another weakness is that though decisions can be efficiently monitored it takes a lot of time to take any decision as there are only few people in whose hands such a power rests. In case this group of people is overloaded decision making will become too slow.Knowledge Management in Healthcare Essay
The chain of communication is too long which generally leads to distortion of the message
Bureaucracy itself encourages political behavior in the organization and people try to use wrong means to go up the hierarchy.
KEY ISSUE OF REDDIX HOSPITAL
Reddix Hospital does have an information system in place. It comprises of Radiology Information System, Patient Administration System, Laboratory Information System and Clinical Patient Record System, Pharmacy Systems and Nursing System. But there is a lack of interoperability between these systems and there is no clinical information governance. Further due to an inefficient Hospital Information System patient files are not available to the concerned caregivers when required. These caretakers are not aware of patients’ medical history and if some wrong medicines are given patients suffer from severe reactions. According to NHS performance report 60% of patients were suffering from life-threatening consequences of improper care.

Furthermore nurses and caregivers are not aware of the best practices. Also Reddix is using a centralized computer architecture where softwares being used are 30-40 years old. Such outdated softwares have limited interfaces with other healthcare information systems. Moreover they did not have the ability to interconnect with other desktop applications.Knowledge Management in Healthcare Essay

In most of the NHS hospitals a distributed form of computer architecture is followed. Moreover Reddix does not use a secure information security mechanism which is again an important point of consideration.

Due to above reasons and to ensure an efficient and innovative working of Reddix Hospital Trust it was decided to adopt a proper Knowledge Management System at Reddix.

Thus, a combination of all the three systems may be used to address the requirements of various stakeholders to the KM project.

SUGGEST CHANGE
Reddix can move to divisional form of organization as it will be easy to handle the complexities associated with a complex nature of hospital functions and divisions.

In order to promote learning and development in the organization Reddix can use following methods:Knowledge Management in Healthcare Essay

Learning Culture
Reddix need to develop a learning culture in the organization. There should be a free flow of information within the organization. People should be able to share and exchange information and knowledge without any barriers. Senior team should people at all levels to learn regularly and learning should also be rewarded.

Key Management processes
Learning and development can be fostered through proper capability planning, reinforcing teams, developing values and vision for such teams and maintaining an efficient performance reward system.

Tools and Techniques
Open communication, mentoring and supporting colleagues, making people learn to see team and organizational goals as same are some tools to maintain learning in the organization.

Thus, from above mentioned process Reddix can ensure learning and development of its staff so as to implement KM in an efficient manner.

RESOURCES
These organizations decentralize decision making to the business units, thereby allowing the corporate office to concentrate its focus on corporate strategy, capital allocation, and monitoring of the operational and strategic performance of business units. This creates the advantage of increasing accountability, given that common/comparable measures can be established across different divisions and internal competition for available capital can be stimulated.Knowledge Management in Healthcare Essay

Along with its various merits this system may bring about certain disadvantages for Reddix Trust Hospital:

First, there is the problem of duplication of services—that is, redundant marketing, manufacturing, and other functional services that are established within each unit. Costs can escalate when functions are repeated in multiple areas.

Corporate executives in decentralized organizations can too easily distance themselves from their divisional operations and thus find that they lack the needed insights and skills to understand their disparate businesses. Corporate leaders can also focus so much on capital allocation and corporate strategy (e.g., mergers, divestitures, acquisitions) that they lose touch with the operational side of their businesses.Knowledge Management in Healthcare Essay

Organizational Fit
2nd Learning outcome:
SCOPE OF CHANGE AND VISION
Healthcare organizations are facing many challenges in the 21st Century due to changes taking place in global healthcare systems. Spiraling costs, financial constraints, increased emphasis on accountability and transparency, changes in education, growing complexities of biomedical research, new partnerships in healthcare and great advances in IT suggest that a predominant paradigm shift is occurring. This shift is necessitating a focus on interaction, collaboration and increased sharing of information and knowledge which is in turn leading healthcare organizations to embrace the techniques of Knowledge Management (KM) in order to create and sustain optimal healthcare outcomes. This report describes the importance of using Information Technology knowledge management systems for healthcare organizations and provides an overview of knowledge management technologies and tools that may be used by healthcare organizations with a special focus on Reddix Hospital Trust.Knowledge Management in Healthcare Essay

RESISTANCE
Reddix hospital is overloaded with work. Doctors are working for double the stipulated time. The information system at Reddix is centralized and nurses and caretakers have no direct and easy access to patient records. Also there is low level of morale, lack of motivation, lack of innovation and high rate of absenteeism and staff turnover and also high rate of sickness among hospital staff.

All these factors prove that Reddix Hospital Trust is following a bureaucratic form of organization.

The bureaucratic hierarchy is by far the most abundant organization form as we start the new millennium. They are everywhere all of the time and it is hard to envision a world without them, or indeed any other kind of organization form that will work as well.Knowledge Management in Healthcare Essay

Elliott Jaques (1989, 1990), firmly believed that the bureaucratic hierarchy’s only problem is that it still lacks complete perfection, and Hammer and Champy (1993), asserted that bureaucracy is a glue that holds organizations together.

OPTIONAL APPRAISAL
Reddix can use intranet to make the stakeholders properly understand what is KM and how it can enable them to work efficiently. A dedicated blog can be created where staff can exchange their understanding of the concept and that of project a whole and can learn through shared experiences. Also it will help them in understanding the flow of information within the organization and how to use the new method efficiently. A proper detailed view of the new system along with some relevant examples can be easily provided on the portal which will help in a detailed understanding of the concept.

However in this system people will learn as per their understanding levels. In case there is some misunderstanding on their part it cannot be cleared and people will start working on their individual assumptions about the concept and project. This may lead to conflict and disputes while implementing the concept.

Justify your planned changes?Knowledge Management in Healthcare Essay

COMMUNICATION TO STAKEHOLDER
The project of Knowledge Management affects a number of people related with the organization. These are- doctors, nurses, administrative staff, etc. All these people need to be properly aware of the need of KM in their organization and also how they will be benefited from such a change in the organization. The success of any KM program depends upon the clear understanding of concepts by these stakeholders.

FINALIZE CHANGE PLAN
Continuous use of knowledge leads to generation of new ideas which can be recorded in the system and again and again use of such idea further leads to generation of new ideas. Thus, KM will give a scope of innovation to hospital staff.

Proper storage and availability of information about a patient’s health will allow the team of doctors to communicate easily and take decisions on further treatment in an efficient manner. Also the medication prescribed to patient, allergic records, surgery records, etc are readily available which can form the basis of further treatment.Knowledge Management in Healthcare Essay

Another important system is to develop a program for providing training with regard to use of new system. Such programs or training workshops should be designed in a manner that each and every person in the organization is properly aware about his/her role in KM and can also help his/her subordinates in achieving efficiency through such a system.

This system is good for imparting knowledge about the concept but lack practical approach. For proper implementation of the concept such workshops should be continued for some time after the introduction of KM in the hospital. It will help the staff to get real time experience of getting trained while working.

Instant flow of information and improved communication leads to quick decision making. Doctors can communicate with each other regarding the treatment of some critical patient, refer to the case history available at a single place and take decision in a short period of time

Reddix can take the groups of staff for some tours to other hospitals using Knowledge management effectively. This will help the staff in getting a real-world idea about use and benefits of KM. This will act as a catalyst to prepare them for them for the next stages of the project. However, this method does not ensure a deep understanding of the concept as methodology of implementing KM varies from organization to organization.Knowledge Management in Healthcare Essay

IMPLEMENTING KM AT REDDIX
Various steps involved in implementation of KM program at Reddix Trust Hospital are discussed as follows:

Objectives
Reddix need to implement a KM program so as to improve patient care, reduce accidents, increase the morale level of the hospital staff, efficient decision making and improve the flow of information within the organization.

Strategic model to achieve these objectives
The achievement of an efficient KM program depends upon the designing of an efficient Application Architecture. The key features of such an architecture or model is discussed as follows:Knowledge Management in Healthcare Essay

Patient Admission Process
First step in implementing Knowledge Management in Reddix is the automation of Hospital administration and registration systems that are used to “register” patients into the hospital. A powerful first point-of-contact (point-of-sale) approach for the hospital can be used in the form of embedded-chip smart cards. These cards are capable of holding compact patient medical record and biometrics identifiers. This would enable quick, automated registration and admitting, as well as information for health and health insurance purposes such as eligibility, referral, and pharmacy approval.

Communication of Patient Admission Data
Next is to automate the data associated with the admission of a patient which is of a relatively generic nature. Made up of standard patient demographic data, insurance particulars, and the patient’s location (department, room number, and bed), the information associated with the event of admitting a patient is of interest to most if not all of the other information systems used in the hospital. In an e-hospital, this patient information is communicated with all other applications in the hospital. Hospitals organize themselves around specialized diagnostic methods, focused medical interventions, and various therapeutic care strategies.Knowledge Management in Healthcare Essay

Supporting Diagnostic and Therapeutic Sciences
According to Becich (2000), it is estimated that 50% to 70% of the major decisions that affect patients are based on information available from clinical pathology (laboratory tests) and anatomical pathology (tissue samples).

Thus it is necessary to computerize laboratories, radiology, cardiovascular laboratories, nuclear medicine, etc. For Example: The classical x-ray film processing has been replaced with “film-less” imaging processes that produce digital images in many hospitals worldwide. Hospital Pharmacy also need to be right from automated drug dispensing devices to robotic workstations used to package and barcode patient medication.Knowledge Management in Healthcare Essay

Point-of-Care Data Entry
Further there is a need to automate the point-of-care data. Procedures (e.g., surgeries, laboratory tests, or x-rays) can be scheduled in an enterprise scheduling system to better allocate many types of resources. Integration between the admitting and orders systems makes the process more efficient and accurate. These orders can be communicated to the appropriate clinical system (e.g., radiology or laboratory) electronically if interfaced or integrated with the order management system. Once an order is placed in a clinical system, the process of performing the ordered diagnostic test or delivering the specified medication or service begins. If the physician could consistently digitize these “paper instructions,” the improvements in the accuracy, the timeliness, and the appropriateness of patient care would be staggering. In addition, the patient vital sign data (e.g., blood pressure, fluid input and output, temperatures) are written on the patient’s chart. Technology can be used to convert physician voice dictations to digital text (typically the patient’s admitting history and physical and the discharge plan and diagnosis). Knowledge Management in Healthcare Essay