Fuzzy nature of decision-making process in healthcare enforces technology producers and researchers to employ creative and smooth solutions. Conversion from fuzzy concepts and ideas to crisp values causes loss of precision and weakens the output decisions. A promising bundle of techniques, soft computing, is a fast developing and popular area that helps meet this creative and smooth need in healthcare. In this study, fuzzy logic (FL) application in healthcare decision-making is examined. The number of publications is rising each year related to FL application in healthcare. FL can be used as a classifier, or in a selection process of a certain type of disease, or diseased patients, or determining the risk ratio of a disease, or in a data mining algorithm, or in constructing a decision support system. This study is a descriptive study aiming to examine and explain FL applications in healthcare.

Abstract:Hospital information system (HIS) evaluation frameworks have largely been discussed in the literature. framework is not a rival of but is rather an alternative or complementary to the existing frameworks. It is a different approach and has a different computation methodology, supported by fuzzy logic. The acceptable meeting ratio depends on the evaluator; we do not propose a threshold.

Healthcare is a technology-intensive area where all the healthcare organizations use an information system. Managing daily works and providing the continuity of healthcare is impossible without healthcare information systems. Healthcare information systems have to be well supported and managed, also have to be improved to meet the changing needs, they must be living systems. In this respect, evaluations are carried out to reveal the weak and strong sides of information systems in operation. Evaluation is an important subject for medical informatics domain. The investors and managers need to know the success level and poor sides of their information system to make improvements. In this sense, a case study is performed in this study, to evaluate a healthcare information system. Particularly, the recently deployed laboratory information system (LIS) is evaluated by means of questionnaires, applied to both patients and users of the laboratory information system. Laboratory information system is evaluated on the basis of Function sufficiency, Decreasing Work Load, Speed, Learning Ease, Improving Service Quality, Availability, Help Manuals, User Satisfaction, and Patient Satisfaction features. The features needing to be improved in terms of the effectiveness and efficiency of LIS are measured based on the threshold value. The results are presented in a variable table according to the threshold value selected by the evaluator. As the target threshold value increases, the number of features needing to be improved also increases. Sağlık Bilgi Sistemlerinin zayıf yönlerinin belirlenmesi: Bir Deneysel Esağlık Değerlendirme Çalışması ÖZ Sağlık sektörü, tüm sağlık teşkillerinin bir bilgi sistemi kullandığı teknoloji-yoğun bir alandır. Sağlık hizmetinin devamının sağlanması ve günlük sağlık bakımı işlemlerinin yürütülmesi sağlık bilgi sistemleri olmaksızın imkansız bir hale gelmiştir. Sağlık bilgi sistmleri sürelki

The medical decision-making process is fuzzy in its nature. The physician handles linguistic concepts in deciding the diagnosis and prognosis. The conversion from this fuzzy nature into crisp real world outcome causes the loss of precision. Fuzzy logic is a suitable way to provide the physician with the support he needs in handling linguistic concepts and get rid of the loss of precision. Fuzzy logic technologies are applied to each area of medicine, and they have been proven to be successful. The literature shows that the medical area has a great compatibility with fuzzy logic technology. Fuzzy cognitive maps, fuzzy expert systems, fuzzy medical image processing, fuzzy applications in information retrieval from medical databases, fuzzy medical data mining, and hybrid fuzzy applications are the common and most known fuzzy logic usage areas in the medical field. This chapter is a descriptive study that examines and explains the common fuzzy logic applications in the medical field after an introduction to fuzzy logic.

Health institutions invest huge amounts in Information Systems (IS). Despite the huge budgets of investments, it is estimated that nearly 60-70% of Information Technology (IT) implementation projects fail in healthcare. In the literature, success factors and the failure reasons have largely been discussed. One of these, both in failure reasons and success factors, is the User Expectations. Expectation Failure, which can be defined as the gap between expectations of the end users from the system and actual performance of it, is introduced as one of the failure reasons of IS. The expectations of users must be well understood and discreetly worked out to design and implement a successful, acceptable, and useful IS. There is no study about the expectations from Healthcare Information System (HCIS) in the literature. The aim of this chapter is to investigate the end user expectations from HIS and their rankings. Seventeen potential end user expectations in four dimensions are examined and ranked according to the importance of expectations to the users.