A Method to Improve the Early Stages of the Robotic Process Automation Lifecycle

The robotic automation of processes is of much interest to organizations. A common use case is to automate the repetitive manual tasks (or processes) that are currently done by back-office staff through some information system (IS). The lifecycle of any Robotic Process Automation (RPA) project starts with the analysis of the process to automate. This is a very time-consuming phase, which in practical settings often relies on the study of process documentation. Such documentation is typically incomplete or inaccurate, e.g., some documented cases never occur, occurring cases are not documented, or documented cases differ from reality. To deploy robots in a production environment that are designed on such a shaky basis entails a high risk. This paper describes and evaluates a new proposal for the early stages of an RPA project: the analysis of a process and its subsequent design. The idea is to leverage the knowledge of back-office staff, which starts by monitoring them in a non-invasive manner. This is done through a screen-mousekey- logger, i.e., a sequence of images, mouse actions, and key actions are stored along with their timestamps. The log which is obtained in this way is transformed into a UI log through image-analysis techniques (e.g., fingerprinting or OCR) and then transformed into a process model by the use of process discovery algorithms. We evaluated this method for two real-life, industrial cases. The evaluation shows clear and substantial benefits in terms of accuracy and speed. This paper presents the method, along with a number of limitations that need to be addressed such that it can be applied in wider contexts.Ministerio de Economía y Competitividad TIN2016-76956-C3-2-

Business process variant analysis based on mutual fingerprints of event logs

Comparing business process variants using event logs is a common use case in process mining. Existing techniques for process variant analysis detect statistically-significant differences between variants at the level of individual entities (such as process activities) and their relationships (e.g. directly-follows relations between activities). This may lead to a proliferation of differences due to the low level of granularity in which such differences are captured. This paper presents a novel approach to detect statistically-significant differences between variants at the level of entire process traces (i.e. sequences of directly-follows relations). The cornerstone of this approach is a technique to learn a directly-follows graph called mutual fingerprint from the event logs of the two variants. A mutual fingerprint is a lossless encoding of a set of traces and their duration using discrete wavelet transformation. This structure facilitates the understanding of statistical differences along the control-flow and performance dimensions. The approach has been evaluated using real-life event logs against two baselines. The results show that at a trace level, the baselines cannot always reveal the differences discovered by our approach, or can detect spurious differences.This research is partly funded by the Australian Research Council (DP180102839) and Spanish funds MINECO and FEDER (TIN2017-86727-C2-1-R).Peer ReviewedPostprint (author's final draft

VISUAL PPINOT: A Graphical Notation for Process Performance Indicators

Process performance indicators (PPIs) allow the quantitative evaluation of business processes, providing essential information for decision making. It is common practice today that business processes and PPIs are usually modelled separately using graphical notations for the former and natural language for the latter. This approach makes PPI definitions simple to read and write, but it hinders maintenance consistency between business processes and PPIs. It also requires their manual translation into lower-level implementation languages for their operationalisation, which is a time-consuming, error-prone task because of the ambiguities inherent to natural language definitions. In this article, Visual ppinot, a graphical notation for defining PPIs together with business process models, is presented. Its underlying formal metamodel allows the automated processing of PPIs. Furthermore, it improves current state-of-the-art proposals in terms of expressiveness and in terms of providing an explicit visualisation of the link between PPIs and business processes, which avoids inconsistencies and promotes their co-evolution. The reference implementation, developed as a complete tool suite, has allowed its validation in a multiple-case study, in which five dimensions of Visual ppinot were studied: expressiveness, precision, automation, understandability, and traceability

Using Cognitive Pre-Testing Methods in the Development of a New Evidenced-Based Pressure Ulcer Risk Assessment Instrument

Background: Variation in development methods of Pressure Ulcer Risk Assessment Instruments has led to inconsistent inclusion of risk factors and concerns about content validity. A new evidenced-based Risk Assessment Instrument, the Pressure Ulcer Risk Primary Or Secondary Evaluation Tool - PURPOSE-T was developed as part of a National Institute for Health Research (NIHR) funded Pressure Ulcer Research Programme (PURPOSE: RP-PG-0407-10056). This paper reports the pre-test phase to assess and improve PURPOSE-T acceptability, usability and confirm content validity. Methods: A descriptive study incorporating cognitive pre-testing methods and integration of service user views was undertaken over 3 cycles comprising PURPOSE-T training, a focus group and one-to-one think-aloud interviews. Clinical nurses from 2 acute and 2 community NHS Trusts, were grouped according to job role. Focus group participants used 3 vignettes to complete PURPOSE-T assessments and then participated in the focus group. Think-aloud participants were interviewed during their completion of PURPOSE-T. After each pre-test cycle analysis was undertaken and adjustment/improvements made to PURPOSE-T in an iterative process. This incorporated the use of descriptive statistics for data completeness and decision rule compliance and directed content analysis for interview and focus group data. Data were collected April 2012-June 2012. Results: Thirty-four nurses participated in 3 pre-test cycles. Data from 3 focus groups, 12 think-aloud interviews incorporating 101 PURPOSE-T assessments led to changes to improve instrument content and design, flow and format, decision support and item-specific wording. Acceptability and usability were demonstrated by improved data completion and appropriate risk pathway allocation. The pre-test also confirmed content validity with clinical nurses. Conclusions: The pre-test was an important step in the development of the preliminary PURPOSE-T and the methods used may have wider instrument development application. PURPOSE-T proposes a new approach to pressure ulcer risk assessment, incorporating a screening stage, the inclusion of skin status to distinguish between those who require primary prevention and those who require secondary prevention/treatment and the use of colour to support pathway allocation and decision making. Further clinical evaluation is planned to assess the reliability and validity of PURPOSE-T and it’s impact on care processes and patient outcomes

Understanding process behaviours in a large insurance company in Australia: A case study

Having a reliable understanding about the behaviours, problems, and performance of existing processes is important in enabling a targeted process improvement initiative. Recently, there has been an increase in the application of innovative process mining techniques to facilitate evidence-based understanding about organizations' business processes. Nevertheless, the application of these techniques in the domain of finance in Australia is, at best, scarce. This paper details a 6-month case study on the application of process mining in one of the largest insurance companies in Australia. In particular, the challenges encountered, the lessons learned, and the results obtained from this case study are detailed. Through this case study, we not only validated existing `lessons learned' from other similar case studies, but also added new insights that can be beneficial to other practitioners in applying process mining in their respective fields

Root cause analysis with enriched process logs

In the field of process mining, the use of event logs for the purpose of root cause analysis is increasingly studied. In such an analysis, the availability of attributes/features that may explain the root cause of some phenomena is crucial. Currently, the process of obtaining these attributes from raw event logs is performed more or less on a case-by-case basis: there is still a lack of generalized systematic approach that captures this process. This paper proposes a systematic approach to enrich and transform event logs in order to obtain the required attributes for root cause analysis using classical data mining techniques, the classification techniques. This approach is formalized and its applicability has been validated using both self-generated and publicly-available logs

Prolonged hypocalcemia following denosumab therapy in metastatic hormone refractory prostate cancer

Prostate cancer is a leading cause of cancer death, frequently associated with widespread bone metastases. We report two cases of hypocalcemia following the first dose of denosumab in metastatic hormone refractory prostate cancer, the first case requiring 26 days of intravenous calcium therapy. This is the first report of prolonged hypocalcemia following denosumab in a patient with normal renal function.F. Milat, S. Goh, L.U. Gani, C. Suriadi, M.T. Gillespie, P.J. Fuller, H.J. Teede, A.H. Strickland, C.A. Alla