Knowledge Management Tools in the Healthcare


Services rendered by healthcare organizations primarily operate on the decisive collection, analysis, and exchange of clinical information. In the healthcare industry in the 21st century, knowledge and connection between healthcare organizations and parties of interest like public hospitals, clinics, and patients and administrators have become a necessity. There is sufficient evidence that effective information and knowledge sharing leads to improved healthcare delivery, reduction in healthcare administrative costs.

The flow of information and knowledge between and within departmental boundaries is crucial to the effective functioning of health organizations. More importantly, the process of decision-making is critical in determining the success of these organizations. Public health organizations especially in the developing countries have specifically been singled out as some of the areas experiencing severe decision-making breakdowns attributed primarily to lack of or limited information and knowledge flow within the organization. Stanhope & Lancaster (2000, p. 57) cite among other causes the inadequate systems of information as the primary contributors to red tape thus leading to slow decision-making processes in third world countries’ public hospitals. Because of this reason, knowledge management has been billed as one of the most promising solutions that can be applied in resolving the situation as described above.

In a bid to improve the decision making processes in public hospitals especially in situations like the ones described above, this discussion will focus on a description of a conceptual analysis of data warehousing as a knowledge management tool that it is hoped when implemented will enhance decision making processes in the healthcare settings. Most of the entities targeted above still employ the traditional decision-making system where managers have little autonomy and most of the processes involve paperwork which hinders the quality of healthcare delivery. The sometimes chaotic knowledge management in these public hospitals leads to unilateral decision making potentially creating unnecessary conflicts. Data warehousing according to (Khan, 2005, p. 132), drastically shortens the decision-making time vis a vis process if proper design and implementation take place.

To understand the process, this discussion will in the following sections discuss the theoretical underpinnings of data warehousing and specifically the process that will be implemented in a public hospital to enhance decision making.

Data warehousing

One assumption that informs data warehousing is the belief that the quality of decisions made by the managers is a reflection of the quality of the information used in the process. Data warehousing, therefore, involves the storage of information including competitive intelligence in a central system where administrators can easily access it to help them make sound decisions. The tools and techniques applicable to these tools help healthcare administrators to search and locate data that will easily reveal important patterns that are crucial decision support systems (Ponniah, 2010, p. 18).

To understand the implementation of the data warehousing tool that will be used, it is important to have a look at the process of data warehousing itself. According to (Thierauf 1999, p. 98), data warehousing, extraction, and distribution are the principal components of the process. After data is extracted, it is taken to the warehouse database where the server hosts it together with the decision management system. The warehouse database then receives the extracted data from the server enabling users to extract it with the aid of special software. Figure 1.0 below illustrates a simple illustration of the data warehousing process.

Data warehousing process. Source: Thierauf (1999)
Figure 1. Data warehousing process. Source: Thierauf (1999)

Implementation of data warehousing involves three stages. They include identification of the factors critical to the success of the organization – in this case the public hospital, identification of the informational needs that decision-makers critically need, and making sure that the end-users appreciate the importance of the systems and acknowledge the benefits (Hall, 2010, p. 497). Identification of the factors of success will come in handy in determining the aims of the system that forms the basis of the warehouse. Identifying the informational needs of the decision-makers will aid in determining the information gaps that exist and the stages involved in the decision-making process in the health organization. It is also important that clarification be made on issues such as reception of information, automation of routine decision-making skills level of IT skills of the users of the system.

In a public hospital setting, the implementation of data warehousing as a knowledge management process can be implemented using three tools i.e. the Online Analytical Processing System (OLAP), data mining as well as data visualization. According to Kainz et al. (2005, p. 279), OLAP simplifies the decision-making process through three functions. They include the initiation of queries and generation of reports even without a basic understanding of the programming language. OLAP also helps users to carry out multidimensional analysis that is crucial in generating analyses from different perspectives that a decision-maker needs. This function is advantageous in that it helps users to compare data relationships from different settings using multiple dimensions such as time and location. Furthermore, OLAP aids in the statistical analysis which helps in simplifying large quantities of data to simple formulas that are used to generate answers (Istepanian & Pattichis, 2010, p. 593).


The implementation of OLAP involves complex technical processes that may be incomprehensible to the average person. The process described here involves the theoretical approach that can be implemented in an organization that is experiencing knowledge sharing and information flow problems. As said earlier, public hospitals especially in developing countries still depend on paper-based processes to exchange information for decision making. This process will therefore involve the storage of all existing information in an electronic server, designing and installing an OLAP system that will mainly work through data warehousing processes and training of personnel especially key decision-makers on using the system. The main aim here will be the creation of a hospital information system that will utilize computers and other related equipment of communication to aid in the collection, storage, processing, retrieval, and communication of information regarding hospital activities. Additionally, the system will be aiming at satisfying the functional requirements of the users. Implementation will entail three steps: the creation of a database, creation of a knowledge warehouse, and Training of personnel.

Creation of a database

This will entail the electronic storage of all the hospital records to create a central server that can be accessed by users as needed. The conversion will be done systematically through the creation of patient-oriented modules that will detail all information related to admission, discharge order of entry, and laboratory modules. Besides, there will be administrative modules that will detail the financing and billing systems as well as information management and decision support modules.

Creation of a Knowledge Data Warehouse

This will primarily act as a repository for data that may be historical and/or current. This knowledge data warehouse will facilitate the presentation and wide distribution of data in different formats as will be needed by decision-makers. The OLAP system will be installed to help managers make decisions on specific health issues in the hospitals. In essence, the knowledge data warehousing will be helping in disseminating business intelligence, access to decision support systems, and communication through OLAP support.

Decision-makers will be able to obtain information through queries that will return multidimensional data. This will be numerical data that may include patient volumes, diagnosis, and procedure, location of physicians, and area of service. Additionally, OLAP will enable the users to receive more specific data views through drill-downs. Through drill-downs, decision-makers will be able to obtain detailed analyses based on the feedback they get from OLAP.

Training of personnel

All hospital staff will be trained on how to operate the installed system especially on entering the data patient data that will help managers in decision making. Both junior and senior staff will be taken through the operation of the metadata document source that will be used to access the OLAP data for analysis with OLAP.

In many cases, the implementation of OLAP experiences hiccups owing to the relatively low technical know-how of the target populations. However, continuous training and support always overcome the problem. It is important to note that OLAP may need assistance from other warehousing tools such as data mining for better delivery of results. That is however not to say that it cannot deliver on its own.


  1. Hall, A.J. (2010). Accounting information systems. New York: Cengage Learning.
  2. Istepanian, H.S., & Pattichis, C. (2010). M-health: emerging mobile health systems. London: Birkhäuse.
  3. Kainz, W. et al. (2005). Advances in spatial analysis and decision making. Lisse: Sweets & Zeitlinger.
  4. Khan, A. (2005). SAP and BW data warehousing: How to plan and implement. Lincoln: iuniverse.
  5. Ponniah, P. (2010). Data warehousing fundamentals for IT professionals. Hoboken: John Wiley & Sons Publishing.
  6. Stanhope, M., & Lancaster, J. (2000). Community and public health nursing. Boston: Mosby.
  7. Thierauf, R. J. (1999). Knowledge management. New York: Quorum Books.

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