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Delay in histologically confirming rectal cancer may lead to late treatment as histological confirmation is required prior to chemo-radiotherapy or surgical intervention. Multidisciplinary colorectal meetings indicate that there are patients who require multiple tissue biopsy episodes prior to histologically confirming rectal cancer. The purpose of this paper is to examine a quality improvement (QI) measure’s impact on tissue biopsy process diagnostic yield.

The authors performed the study in two phases (pre- and post-QI), between February 2012 and April 2014 in a district general hospital. The QI measures were derived from process mapping a rectal cancer diagnostic pathway. The primary outcome was to assess the tissue biopsy process diagnostic yield. The secondary outcome included total breaches for a 62-day target in the pre- and post-QI study phases.

There was no significant difference in demographics or referral mode in both study phases. There were 81 patients in the pre-QI phase compared to 38 in the post-QI phase, 68 per cent and 74 per cent were referred via the two-week wait urgent pathway, respectively. Diagnostic tissue biopsy process yield improved from 58.1 to 77.6 per cent after implementing the QI measure (p=0.02). The 62-day target breach was reduced from 14.8 to 3.5 per cent (p=0.42).

Simple QI measures can achieve significant improvements in rectal cancer diagnostic tissue biopsy process yields. A multidisciplinary approach, involving process mapping and cause and effect modelling, proved useful tools.

A process mapping exercise and QI measures resulted in significant improvements in diagnostic yield, reducing the episodes per patient before histological diagnosis was confirmed.

The purpose of this paper is to propose and describe a method that uses recurrent problems to increase the efficiency and effectiveness of the diagnosis of new product development (NPD) processes and supports the identification of improvement opportunities. The proposed method, called Diagile, is based on recurrent current reality trees (CRTs) and is a new way of building CRTs that includes best project management practices, and the identification and prioritization of improvement opportunities. To support the execution of the method, recurrent problems were identified and a computational tool to aid the diagnosis, a database of improvement opportunities and an automated spreadsheet to prioritize improvement projects were developed.

The proposed method was evaluated through a controlled experiment at a multinational manufacturer of office supplies.

The results achieved confirm that the use of the Diagile method increases the diagnostic efficiency and effectiveness when compared to diagnoses performed by the traditional CRT method.

The validity of the method must be tested on a larger scale, since this work involved only one controlled experiment for this purpose. The experiment involved the participation of postgraduate research assistants, who cannot be considered specialists in the diagnosis of NPD. One could question whether the method will be as helpful for proficient users as well. The authors did not have proficient users available to run the experiment. However, the authors believe that such a specialist would save time in carrying out a diagnosis with Diagile, and also be more effective in validating the diagnosis. However, this assumption could not be tested here and can therefore be considered a limiting factor of the experiment. Nevertheless, the positive results of the evaluations of the companies and users of the two case studies corroborate the statement that the objective of this work was attained.

The greater efficiency and effectiveness provided by the proposed Diagile method was also evident in the identification and prioritization of improvement opportunities. The experimental group drew up a more relevant and coherent list of improvement projects than the control group, and provided documentation for these projects in the form of project charts. The authors believe these results can be of a great impact if implemented by practitioners.

This paper proposes a new way to perform diagnostic of NPD process. In particular, this process is well known to be highly strategic, nevertheless, normally excluded out of improvement initiatives because of its complexity. The diagnostic method proposed is a powerful tool to assist practitioners finding systemic improvement opportunities, expanding the assessment to all dimensions of a business process, e.g. people, technology and process activities.

Suggests that there is justification for the use of a new methodology for process diagnosis which is simple to understand and realistic to implement. The control of quality of a process typically requires that multiple process variables be monitored simultaneously. Due to the multi‐dimensionality of the data collected, process diagnosis is complex and the data often are not efficiently integrated to capitalize on the wealth of available information. A two‐phased diagnostic approach for process diagnosis and identification of suspect causes for this multi‐dimensional problem is introduced in Krishnamurthi et al. (1993). Provides an in‐depth analysis of phase two of the statistical process control (SPC) diagnostic approach. Specifically, simulation is used to generate different cause and effect scenarios to determine the effectiveness of the SPC approach in correctly diagnosing a process disorder. The analysis utilizes analysis of variance to estimate the effect of various process variables, process steps, and associated out‐of‐control conditions on the performance of the SPC approach and its ability to diagnose correctly an out‐of‐control condition. As a result of these findings, the critical means are plotted and the findings are presented. Additionally, a comparison between the SPC approach and parsimonious covering theory (PCT) is made. Concludes that for the process scenarios considered, which are of practical size, the more simple approach of the SPC diagnostic approach is recommended.

Driven by the development in sensing techniques and information and communications technology, and their applications in the manufacturing system, data-driven quality control methods play an essential role in the quality improvement of assembly products. This paper aims to review the development of data-driven modeling methods for process monitoring and fault diagnosis in multi-station assembly systems. Furthermore, the authors discuss the applications of the methods proposed and present suggestions for future studies in data mining for quality control in product assembly.

This paper provides an outline of data-driven process monitoring and fault diagnosis methods for reduction in variation. The development of statistical process monitoring techniques and diagnosis methods, such as pattern matching, estimation-based analysis and artificial intelligence-based diagnostics, is introduced.

A classification structure for data-driven process control techniques and the limitations of their applications in multi-station assembly processes are discussed. From the perspective of the engineering requirements of real, dynamic, nonlinear and uncertain assembly systems, future trends in sensing system location, data mining and data fusion techniques for variation reduction are suggested.

This paper reveals the development of process monitoring and fault diagnosis techniques, and their applications in variation reduction in multi-station assembly.

The purpose of this study is to develop a map for the holistic business process management (BPM) diagnosis in order to guide the choice of techniques that encompass all dimensions of the business process.

The design science research method was used, with the elaboration of seven steps to project solutions to empirical problems: (1) identification of the problem, (2) awareness of the problem, (3) definition of expected results, (4) design and development, (5) demonstration, (6) evaluation of artifacts and (7) communication. These steps were organized in different analyzes: descriptive, experimental and observational. The descriptive analysis comprised steps one to three (identification of the problem, awareness of the problem, definition of expected results) and made use of the systematic literature review procedure for proposing artifacts. The experimental analysis comprised steps four to five (design and development, and demonstration), where the consultation with specialists' procedures and then the Delphi procedure for the construction of the artifacts were carried out. In the observational analysis, steps six (evaluation of artifacts), where two case studies were performed, and step seven (communication), in which the map for the holistic BPM diagnosis was presented were carried out.

The article systematizes the BPM diagnostic techniques scattered throughout the literature and relates how these techniques relate to dimensions. A map for the holistic BPM diagnosis is generated containing 21 techniques and 9 dimensions, with 45 relationships between these techniques and tools. Another aspect is that the map shows that in BPM promotion projects, techniques are not restricted to any specific phase of the life cycle.

Professionals can use the map to form a blend with selected techniques and use them for holistic BPM diagnosis according to the skills and other resources of the project team.

The map developed is innovative because it relates a set of consolidated techniques for each dimension of the process to provide the holistic diagnosis for the organization. It is important to highlight that these techniques and dimensions were scattered in the literature.

The purpose of the study is to identify and ascertain if there were any validated tools for diagnosing or screening autism spectrum disorder in adults with ID. The estimated prevalence of intellectual disability (ID) in the general population is about 10.37/1,000 population (Maulik et al., 2011). In total, 1 out of 4 individuals with ID suffers from an autism spectrum disorder (ASD) (Sappok et al., 2010). Early diagnosis and support for ASD is key to having a good quality of life. The diagnosis of ASD in people with an ID presents its own challenges and it is likely under-identification of ASD amongst adults with ID by about 20% to 30% (Emerson and Baines, 2010).

Studies were selected based on the following criteria: studies that reported either screening or diagnostic tools for ASD, participants had an ID i.e. a mean IQ of <70, adults i.e. participants were >18 years of age at the time of entry to the study and articles reported either sensitivity, specificity or area under the curve. Relevant studies that were published up to January 2020 were identified from EMBASE, PsychINFO, CINAHL and PubMed. In total, 75 papers were identified of which 15 papers met the criteria.

The screening or diagnostic tools currently in use is dependant on the degree of ID. A number of the tools had good psychometric properties and utility when used in people with specific degrees of ID or when used in combination with another screening or diagnostic tool. The authors could not identify a diagnostic tool that could be used across all levels of severity of ID unless used in combination. Hence, concluded that there is a need for a diagnostic tool with good psychometric properties for the assessment of ASD in adults with all degree of ID within a reasonable time period without the need for an additional tool to be used in conjunction.

Currently, the “gold standard” for diagnosing ASD is a lengthy and time-consuming process carried out by trained multi-disciplinary team members who assess historical, behavioural and parent/carer report to arrive at a diagnosis. There are a number of tools that have been developed to aid diagnosis. However, it is important to identify the tools that can optimise the procedures and are also time-efficient.

This chapter explores how “discourse,” as a process concerned with the production and consumption of talk and text, has been embraced within the field of organizational change and development (OCD). We present six ways of thinking about the role of discourse in OCD (namely: “discourse as component,” “discourse as process,” “discourse as analysis,” “discourse as method,” “discourse as mindset,” and “discourse as style”). Although the advent of dialogic OD has raised awareness of discourse, we demonstrate that it remains a marginal and under-utilized area of interest. We conclude by making a case for a more expansive role for discursive modes of analysis and engagement within OCD.

This paper focuses on organizational change through the business process management approach. While “business process modeling” permits understanding process activities and their activities with other participants, “current reality tree (CRT)” technique promotes the identification of process constraints. The purpose of this study is to compare the results from applying both diagnostic techniques, process modeling, using the business process modeling notation, and root cause analysis, using the CRT.

The comparison is made using a pre-experiment in which two teams conducted diagnoses concomitantly in the information technology management (ITM) process of one unit of the biggest and prestigious higher education institution (HEI) in Brazil.

The modeling technique and the CRT should be considered complementary techniques, since applying one does not diminish or exclude the importance of using the other. Results were compared analyzing which dimensions of the process each technique highlighted: strategy, organization, activity/information and resources.

A possible limitation of this research is that the experiment was conducted in a single process and the result cannot be generalized to other processes.

It may be noted that the main contribution of this study is the presentation of the steps of two techniques for process diagnosis. It is expected that with the reports on diagnoses outcomes, team's assessment and the perception of the managers presented here other improvement teams may use the results of this research as an inspiration to perform process diagnosis, and as basis for decision making to define which technique to use according to the specific needs of process improvement.

The paper stands out the comparison of the technique application's outcomes. This study offers valuable insights to the organizations that are interested in restructuring their processes. It delineates many important benefits of such a diagnosis techniques. It also identifies possible pitfalls and recommends guidelines for the successful conduction of process diagnoses initiatives.

This paper aims to apply the Six Sigma methodology to improve workflow by eliminating the causes of failure in the medical imaging department of a private Turkish hospital.

Implementation of the design, measure, analyse, improve and control (DMAIC) improvement cycle, workflow chart, fishbone diagrams and Pareto charts were employed, together with rigorous data collection in the department. The identification of root causes of repeat sessions and delays was followed by failure, mode and effect analysis, hazard analysis and decision tree analysis.

The most frequent causes of failure were malfunction of the RIS/PACS system and improper positioning of patients. Subsequent to extensive training of professionals, the sigma level was increased from 3.5 to 4.2.

The data were collected over only four months.

Six Sigma's data measurement and process improvement methodology is the impetus for health care organisations to rethink their workflow and reduce malpractice. It involves measuring, recording and reporting data on a regular basis. This enables the administration to monitor workflow continuously.

The improvements in the workflow under study, made by determining the failures and potential risks associated with radiologic care, will have a positive impact on society in terms of patient safety. Having eliminated repeat examinations, the risk of being exposed to more radiation was also minimised.

This paper supports the need to apply Six Sigma and present an evaluation of the process in an imaging department.

Low-cost microcomputers such as the Raspberry Pi are common in library makerspaces. This paper aims to create an OBD-II technology to diagnose a vehicle’s condition.

An OBD-II scanner plugged into the OBD-II port or usually called the data link connector (DLC), sends diagnostics to the Raspberry Pi.

Compared with other microcontrollers such as Arduino, the Raspberry Pi was chosen because it sustains the application to receive real-time diagnostics, process the diagnostics and send commands to automobiles at the same time, rather than Arduino that must wait for another process finished to run another process.

This paper also represents the history of mobile technology and OBD-II technology, comparison between Arduino and Raspberry Pi and Node.