Decision-making is vital for any organization; therefore, it can lead to success or failure. Organizations are working their best to make the right decisions to help in growth and yield more profits. Presently, information systems are extensively incorporated into the organization’s functions. Both private and public institutions have realized the importance of using technology in the decision-making process, where almost all organizations are opting to use information systems applications to make well-thought decisions. Healthcare facilities are amongst the institutions that have realized the significance of using technology in making effective decisions.
One technology application used in health care to facilitate decision-making
Healthcare institution uses several decision-making systems and amongst them is the MYCIN diagnostic support. The system is identified as a computer-based consulting application used by physicians to diagnose and select the best treatment for patients infected with bacteria. In addition to its consulting ability, the MYCIN system contains an embedded explanation intelligence system used to answer simple English questions to educate the user or justify advice. This system’s intelligence is encoded in some 350 production rules that encompass the criteria used in infectious disease experts’ clinical decisions (Rai, Sahu & Sawant). A lot of this technology’s power is derived from the highly stylized nature of the decision rules identified as modular. The modular allows the system to dissect its reasoning and allow some modification to the base of its knowledge.
The application’s impact on the quality of decision making
The use of MYCIN in making decisions in healthcare provides high-quality decisions with no or fewer errors than healthcare givers’ decisions. The system provides consistent solutions and answers for repetitive tasks, processes, and decisions. As long as the system’s rule base is maintained, the system will give the same conclusion despite the number of times similar problems get tested. The system leads to quality decisions through the provision of reasonable explanations. After advising on the best decision to be taken, MYCIN can clarify the reason for its conclusion and why it considers that reason as the most coherent choice among other options (Shilo & Kanina, 2019). The system has the intelligence to note any doubt, and in such a case, it prompts some questions to the healthcare givers to process and answer its logical conclusion.
The use of MYCIN is significant in leading to quality decisions in healthcare because it can overcome human limitations like tiresome, boredom, and being discouraged. The system can perfectly work across the crock due continuously because it is not limited like humans. Unlike humans, as long as there is a power supply, the system does not get tired of diagnosing and providing the best solution that can best solve the problem. Therefore, the healthcare users of MYCIN enjoy the advantage of using the system frequently in seeking critical solutions associated with several diseases. This expert system’s knowledge is invaluable for healthcare institutions because it can store the knowledge and avails it every time the organizations require it (Shilo & Kanina, 2019). The system, therefore, relieves humans from routine tasks that lead to tiredness and boredom. A tired and bored nurse can make poor decisions in disease treatment, leading to a severe outcome.
The MYCIN expert system is also significant in situations where changes are experienced. The system is designed in a way that it can adapt to new conditions. Unlike the system, human beings have the weakness of making it difficult to adapt to new environments. The system is associated with a high level of adaptability where it can easily meet new requirements per user requests quickly (Shilo & Kanina, 2019). Additionally, the system can perfectly capture new knowledge from a medical expert and use it as a conclusion rule for solving new medical conditions.
The MYCIN system is also significant in providing medical experts with a way of developing and testing ideas and theories. This reason is accompanied by the fact that medical knowledge is increasingly becoming more unmanageable and complex. Therefore, there is a need for technological systems that can store information and reason with medical experts in making quality decisions in disease diagnosis and treatment procedures and methods (Shilo & Kanina, 2019). The system can also provide a convenient and economical means of spreading the medical expert’s knowledge and making it more accessible.
The process for selecting and implementing the application
Selecting the best system for decision-making in healthcare facilities is a difficult task that requires carefulness. The people with the power to select the best expert system for the hospital need to follow the proper steps to avoid making costly mistakes. First, the management conferred to select the kind of system that should understand the system’s needs. They are then supposed to collect and analyze the medical activities the system is supposed to solve.
The managers should evaluate their technical side and analyze the specification of the software required, after which they are supposed to analyze the future expectations and the need of the system. They should also consider the economic perspective and cost factors involved in getting the system. The organization should consider the knowledge impact of the system and the reputation of institutions already using it. After all the above steps, the expert system that meets the management criteria should be identified and purchased. To implement the system, the managers should use the correct technical personnel to implement after purchasing. The implementation comprises the installation process and procedures required in using the system (Abu-Nasser, 2017). The management should follow the implementation guidelines as providers by the user manual.
The costs associated with the application
According to Gil Press (2020), the essential core cost of the MYCIN ranges from $200000 to $500000. This amount is exclusive to the cost used to interview the expert and train personnel on using the system. Therefore, the cost for the first stage of buying the installation totals $ 1million, and integrating the system with the hospital healthcare facility data can accumulate the cost to $1.5 or $2 million.
Nurses’ role(s) in selecting and evaluating the application
The role of nurses in selecting and evaluating the best expert system is significant in the entire process. Nurses are involved in the process of making a shared decision that leads to the best results. They also have a role in assessing the improvement achieved by having the system in the healthcare facility for patient care. Additionally, nurses are involved in the selection and evaluation process to stay updated on the newer heath technologies (Hashi, Zaman & Hasan, 2017). Nurses are known to expose possible problems associated with the system, therefore, requesting necessary changes.
Clinical expert systems have undeniably become a game-changer in healthcare with their ability to make well-articulated decisions. The expert system has helped the industry in making perfect decisions in complex situations. They can work for a long time therefore reliable whenever experts need to make decisions concerning certain medications. Nurses play a critical role in selecting and evaluating expert systems before implementing healthcare systems.
Abu-Nasser, B. (2017). Medical Expert Systems Survey. International Journal of Engineering and Information Systems (IJEAIS), 1(7), 218-224.
Gil Press, (2020). 12 AI Milestones: 4. MYCIN, an Expert System For Infectious Disease Therapy.
Hashi, E. K., Zaman, M. S. U., & Hasan, M. R. (2017, February). An Expert Clinical Decision Support System to Predict Disease Using Classification Techniques. In 2017 International Conference on Electrical, Computer and Communication Engineering (ECCE) (pp. 396-400).IEEE.
Rai, R. B., Sahu, S., & Sawant, S. An Analysis on MYCIN Expert System.
Shilo, P., & Kanina, A. (2019). Development of Expert System that Improves the Decision-Making Process with an Emphasis on Health-Related Quality of Life in Elderly Patients with a Colon Cancer.