Knowledge is a common way to create value. Its role is critical in terms of corporate value and improving organisational performance, especially in the case of the creation of new knowledge through sharing with colleagues (Carlile 2002).
As such, there is a popular tendency to implement knowledge management technologies in order to share knowledge and improve economic outcomes of companies and facilities. In order to introduce efficient technologies effectively, a comprehensive and detailed strategy, as well as considerable investment, are essential. However, not all companies can afford to develop such strategies and embrace these necessary changes. For this reason, it is critical to deploy decision-making methods in order to choose the most appropriate knowledge management technology available that would help an organisation achieve a desired level of performance and create corporate value within their particular resource constraints.
This report will focus on satisfying the needs of a small healthcare facility. The motivation for choosing this organisation is the fact that the introduction of knowledge management technologies is one of the latest trends in the healthcare sector due to the so-called contingency effect (Bordoloi & Islam 2012). In the case of regional medical centres, there are no significant challenges when it comes to choosing appropriate technology and investing in its implementation.
Still, the issue is critical for small facilities due to their limited resources and, at the same time, the belief that knowledge management is inseparable from improving the overall efficiency of practices (Kahouei et al. 2015).
Therefore, the objective of this report is to describe some common knowledge management technologies that can be applied in the healthcare environment and select the one that is the most appropriate and potentially efficient in the case of a small healthcare facility. Due to the great number of knowledge management technologies available, only a selection will be reviewed here, such as knowledge-sharing technologies (Web 2.0 platforms), knowledge-storing technologies (knowledge bases), document management systems, decision support systems, and executive information systems.
Review of Knowledge Management Technologies
As mentioned above, there are numerous knowledge management technologies. The objective of this report is to select the most appropriate among these options.
Knowledge-Sharing Technologies (Web 2.0 Platforms)
Knowledge sharing is one of the main ways of creating value. In most cases, this knowledge management technology is associated with the use of Web 2.0 platforms as networks for exchanging knowledge and experience (Alkhuraiji et al. 2016). The idea is to utilize already developed networks or design a new one for facilitating communication and interaction among colleagues. The central benefit of this technology is the fact that there is no need for investing in an individually designed platform. Instead, one of the already extant social networks can be the perfect option, especially in case of limited financial resources (Balubaid 2013).
In this way, the time necessary for implementing any changes are minimal and there is no need for training personnel because in most cases everyone is already familiar with the specifics of using social networks. Nevertheless, the impact on the overall performance of personnel is not guaranteed; the common challenges related to the potential of poor engagement of employees in the knowledge sharing process are still present (Alkhuraiji et al. 2016).
Knowledge-Storing Technologies (Knowledge Bases)
Creating knowledge bases is commonly associated with developing and launching electronic libraries and databases. This knowledge management technology is particularly valuable in an academic environment due to the need for constant access to the latest information on issues of interest. It also contributes to the continuous improvement of personnel that use it. This technology is beneficial because it can be developed in a way to meet the specific needs of an institution (Kahouei et al. 2015).
However, as such, it does require work on the implementation of the individual architecture. In addition, the scope of its benefits is limited to knowledge-intensive industries (Sajid & Ahsan 2016). In this way, it can help improve the overall performance only in cases of active involvement of personnel where it is a common challenge. Moreover, it is linked to a broad time frame, needed for its implementation, due to the necessity to develop the architecture and train personnel.
Document Management Systems
The foundation of this knowledge management technology is developing a unique system for storing and organising electronic documents. It is based on the extensive database that incorporates all information and data about an organisation and its customers. Regardless of the essential need for allocating resources in developing an individual system, the benefits commonly outweigh the costs, especially in the long run, due to decreasing the risks of important data loss and errors in conducting operations (Finger & Sultana 2012). In addition, this system does not require active personnel involvement in order to have a positive influence on performance. Instead, performance improvement is an automatic by-product of the introduction of this technology due to the specificities mentioned above.
Decision Support Systems
Decision support systems incorporate different web-based technologies aimed at supporting the decision-making process within an institution. The specificity of this type of knowledge management technology is the support of group activities and enhancement of collaboration among team members. It is also beneficial for managing individual cases of customers regardless of the complexity of issues related to them (Zarate & Liu 2016).
Still, this technology is associated with numerous risks and challenges because time and vast investment are necessary for its implementation. Moreover, its operation is inseparable from the need for constant technological innovation of the process and training personnel to run the system (Zarate & Liu 2016). Still, due to being a comprehensive technology, performance improvement potential is significant (even though it may not be as notable in small companies) (Liu et al. 2010).
Executive Information Systems
An executive information system is another technology available for supporting the decision-making process. The specificity of this system is that it is the network for communicating decisions of senior management to everyone working for an institution.
In this way, it is beneficial for assuring that all personnel are kept informed of the latest developments in organisational practices. In addition, it is valuable for estimating the overall performance due to making the process of collecting information less troublesome. From this perspective, it is helpful in the case of large companies and where there are limited options for one to one communication between individuals. On the down side, it is expensive to design and implement, even despite it not needing any special technological skills to operate it (Gelinas, Dull & Wheeler 2015).
Multi-Criteria Decision Analysis Methods
Multi-criteria decision analysis (MCDA) is a helpful tool for making critical decisions (Ishizaka & Nemery 2013). Within the context of this report, this general approach is beneficial for selecting the best knowledge management technology corresponding with the needs and resources of the company. The different MCDA methods available are reviewed below:
Quantitative and Qualitative Methods
The quantitative MCDA method is based on analysing numerical data, while the qualitative approach focuses on individual behaviours. Within the context of this research, the quantitative method is preferable because of the limited resources and the investment-related nature of the problem (Rao 2013). Furthermore, the analytic hierarchy process and scoring model are the two approaches to select from due to their practicability and usability in complex decision-making.
Analytic Hierarchy Process
The foundation of the Analytic Hierarchy Process (AHP) is quantifying the relevant significance of different criteria related to the problem under consideration. The idea is to determine primary and secondary objectives of an innovation and grade all of them based on their relevance to the selected criteria. The decision is made on the basis of criteria grades, as well as the importance of achieving a particular objective (Mu & Pereyra-Rojes 2017; Pauer et al. 2016).
Scoring Model
A scoring model is a quantitative method based on identifying criteria relevant to the issue under consideration and grading all of them. It is advisable to choose those options with the highest scores for all the criteria or, at least, the most critical ones (Faulin et al. 2013; Ragsdale 2015).
Choosing a Decision Method and Illustrating Its Application
For the purposes of this research, the scoring model is seen as the most appropriate one to adopt. The rationale for selecting this method is based on the fact that it is not complicated to develop the model to address all necessary criteria, as it does not require specific skills or vast resources (Matta et al. 2016). In addition, there are no limits to adding determinants of interest.
The model will address several important criteria that are central in choosing knowledge management technology. The first determinant is the cost of the implementation. This is illustrated by the limited resources of the healthcare facility and interest in minimising expenditure for any process innovations. The second criterion is the intensiveness of skills necessary for using the introduced system. It is associated with the potential need for allocating additional resources for training personnel in order to cope with the complexities of the technology.
Another aspect that will be assessed is the access to the system. It is closely connected to the skills needed to use it, but the issue also involves the scope of the novelty – the processes it will change and personnel who will deploy it. One more criterion is the time necessary for implementing the system and setting up its operation accordingly. Finally, it is essential to assess the potential consequences of the implemented system, such as the overall performance, time for working with one patient, errors in medication and treatment, etc.
To develop the scoring model, it is paramount to understand the specificities of the design and the choice process. In order to make it easier to select the best option, the idea is to develop a comprehensive table that will incorporate all criteria and their grades for each of the proposed knowledge management technologies. In addition, it will include the total line that will demonstrate which of the technologies is the most appropriate; it is beneficial to have the visual representation of the technologies’ attractiveness. Nevertheless, the choice can be made based on the overall excellence of the technology or, at least, in the case of the most critical criteria, and not necessarily on the basis of the total score.
Selecting the Most Appropriate Knowledge Management Technology: Developing a Scoring Model
A comprehensive scoring table incorporates all criteria of interest with their weight (W) – the criticality – and rating (R) – the score (see Table 1 below). At the same time, it involves a total line – the overall score of the criteria (G) – that is obtained by multiplying weight and rating. Weight is identical for all options, while rating differs, based on the specificities of implementing the technology and its potential outcomes. The criteria are graded from 1 (not significant/minimal) – 5 (significant/maximum).
Conclusion
Based on the results of the scoring model, the technologies are rated in the following way: document management system (194), knowledge-storing technology and group support system (101 both), knowledge-sharing technology (95), and executive information system (89). From this perspective, the introduction of a document management system is the recommended option in the case of a small healthcare facility.
This choice is motivated not only by the highest score but also the significance of improving the overall performance and patient satisfaction with the provided care. In the case of implementing a document management system, it is evident that the risk of errors will be minimised due to storing all data in a digital version. The same is true for reducing the time necessary for treating one patient because all necessary information will be easily available and less at risk of being misplaced or lost.
Finally, notwithstanding the time and expenditure needed to design and introduce the system, the potential benefits outweigh the costs, especially if a long term view is taken. At the same time, it is essential to note that the other options are also of merit due to their potential positive impact on the facility’s performance. If, in the case that the institution manages to find additional resources, the other reviewed knowledge management technologies could also be implemented, according to their rating – from the highest to the lowest grades. The combination of all these technologies is the very best option open to any company or facility, to ensure operational success.
Reference List
Alkhuraiji, A, Liu, S, Oderanti, F O & Megicks, P 2016, ‘New structured knowledge network for strategic decision-making in IT innovative and implementable projects’, Journal of Business Research, vol. 69, no. 5, pp. 1534-1538.
Balubaid, M A 2013, ‘Using Web 2.0 technology to enhance knowledge sharing in an academic department’, Procedia – Social and Behavioural Studies, vol. 102, no. 22, pp. 406-420.
Bordoloi, P & Islam, N 2012, ‘Knowledge management practices and healthcare delivery: a contingency framework’, Electronic Journal of Knowledge Management, vol. 10, no. 2, pp. 110-120.
Carlile, C 2002, ‘A pragmatic view of knowledge and boundaries: boundary objects in new product development’, Organisation Science, vol. 13, no. 4, pp. 442-455.
Faulin, J, Juan, A A, Grasman, S E & Fry, M J 2013, Decision making in service industries: a practical approach, CRC Press, Boca Raton.
Finger, M & Sultana, F N 2012, E-governance, a global journey, IOS Press, Amsterdam.
Gelinas, U J, Dull, R B & Wheeler, P R 2015, Accounting information systems, Cengage Learning, Stamford.
Ishizaka, A & Nemery, P 2013, Multi-criteria decision analysis: methods and software, Wiley, Chichester.
Kahouei, M, Molanoroozi, Z, Habibiyan, M & Sedghi, S 2015, ‘Health information technology in knowledge management of health care organisations’, Rehabilitation and Health, vol. 2, no. 3, pp. 1-6.
Liu, S, Duffy, A H B, Whietfield, R I & Boyle, I M 2010, ‘Integration of decision support systems to improve decision support performance’, Knowledge Information Systems, vol. 22, no. 3, pp. 261-286.
Matta, A, Sahin, E, Li, J, Guinet, A & Vnadaele, N J 2016, Health care systems engineering for scientists and practitioners, Springer, Cham.
Mu, E & Pereyra-Rojes, M 2017, Practical decision-making: an introduction to Analytic Hierarchy Process using super divisions v2, Springer, Cham.
Pauer, F, Schmidt, K, Babac, A, Damm, K, Frank, M & Schulenburg, M G 2016, ‘Comparison of different approaches applied in Analytic Hierarchy Process – an example of information needs of patients with rare diseases’, BMC Medical Informatics and Decision-Making, vol. 16, no. 1, pp. 117-128.
Ragsdale, C T 2015, Spreadsheet modelling and decision analysis: a practical introduction to business analytics, Cengage Leaning, Boston.
Rao, R V 2013, Decision making in the manufacturing environment using Graph theory and fuzzy multiple attribute decision making methods, Springer-Verlag, London.
Sajid, M & Ahsan, K 2016, ‘Role of enterprise architecture in healthcare organisations and knowledge-based medical diagnosis system’, Journal of Information Systems and Technology Management, vol. 13, no. 2, pp. 181-192.
Zarate, P & Liu, S 2016, ‘A new trend for knowledge-based decision support systems design’, International Journal of Information and Decision Sciences, vol. 8, no. 3, pp. 305-324.