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How do organisations know which problems are worthy of their attention? Despite good intentions, many attempts to solve problems fail. One reason for this failure might be because of attempts to solve non-problems or to solve problems with insufficient means, a concept proposed by Deming as tampering. The purpose of this paper is to suggest a definition of tampering, outline what is currently known about possible practical implications of tampering and to suggest how to extend this knowledge by proposing an agenda for future research.

To fulfil the purpose, a narrative literature review was conducted.

Through this review, common aspects of what constitutes tampering are identified and the following definition is proposed: Tampering is a response to a perceived problem in the form of an action that is not directed at the fundamental cause of the problem, which leads to a deterioration of the process or the process output. In addition, recommendations are generated regarding how tampering manifests itself in practice and why tampering occurs. These recommendations could be studied in future research.

To the best of the authors’ knowledge, this is the first paper that suggests a revitalisation of tampering. The results presented in this paper form the basis for continued studies on how tampering in organisations can be understood, managed and prevented.

The Swedish health-care system currently implements cancer care pathways (CCPs) for better and more timely cancer diagnostics. The purpose of this paper is to elucidate and define “crowding out” effects associated with the CCP implementation.

A document study based on implementation reports and action plans from Swedish county councils (n = 21) and a case study in one county council were conducted. Qualitative data collection and analysis were used to acquire more knowledge about the “crowding out” effects associated with the CCP implementation.

Three effects discussed under “crowding out” were defined. The first effect, called the push-out effect, occurs when other patients have to wait for care longer in favour of CCP patients. Another effect is the inclusion effect, whereby “crowding out” is reduced for vulnerable patients due to the standardised procedures and criteria in the referral process. The final effect is the exclusion effect, where patients in need of cancer diagnostics are, for some reason, not referred to CCP. These patients are either not diagnosed at all or diagnosed outside CCP by a non-standard process, with the risk of longer waiting times.

“Crowding out” effects are an urgent topic related to CCP implementation. While these effects have been reported in international research studies, no shared definition has been established to describe them. The present paper creates a common base to measure the “crowding out” effects and support further development of CCPs to avoid the negative effects on waiting times.

Expert systems are computer-based systems that mimic the logical processes of human experts or organizations to give advice in a specific domain of knowledge. Fuzzy expert systems use fuzzy logic to handle uncertainties generated by imprecise, incomplete and/or vague information. The purpose of this paper is to present a comprehensive review of the methods and applications in fuzzy expert systems.

The authors have carefully reviewed 281 journal publications and 149 conference proceedings published over the past 37 years since 1982. The authors grouped the journal publications and conference proceedings separately accordingly to the methods, application domains, tools and inference systems.

The authors have synthesized the findings and proposed useful suggestions for future research directions. The authors show that the most common use of fuzzy expert systems is in the medical field.

Fuzzy logic can be used to manage uncertainty in expert systems and solve problems that cannot be solved effectively with conventional methods. In this study, the authors present a comprehensive review of the methods and applications in fuzzy expert systems which could be useful for practicing managers developing expert systems under uncertainty.