Process Mining in Primary Care: A Literature Review

Process mining is the discipline of discovering processes from event logs, checking the conformance of real world events to idealized processes, and ultimately finding ways to improve those processes. It was originally applied to business processes and has recently been applied to healthcare. It can reveal insights into clinical care pathways and inform the redesign of healthcare services. We reviewed the literature on process mining, to investigate the extent to which process mining has been applied to primary care, and to identify specific challenges that may arise in this setting. We identified 143 relevant papers, of which only a small minority (n=7) focused on primary care settings. Reported challenges included data quality (consistency and completeness of routinely collected data); selection of appropriate algorithms and tools; presentation of results; and utilization of results in real-world applications

Business Process Intelligence Berdasarkan Graph Model (Studi Kasus: Enterprise Resource Planning Retail)

Event Log merupakan salah satu aset penting di perusahaan. Tugas akhir ini berusaha untuk memodelkan model proses bisnis di event log sehingga bisa menganalisa proses model agar bisa membawa berbagai manfaat bagi perusahaan seperti mengurangi biaya perusahaan, membuat keuntungan meningkat, dan banyak lagi. Salah satu masalah dalam pemodelan event log secara otomatis adalah penambahan non-free choice. Tugas akhir ini bertujuan untuk mengembangkan sebuah sistem yang dapat memodelkan proses bisnis dari event log yang berisi non-free choice dengan menggunakan graph database di Neo4j. Pertama-tama, penelitian ini menghasilkan event log ke dalam link list berupa grafik di Neo4j. Selanjutnya, penelitian ini mengusulkan sebuah metode untuk meningkatkan link list dari event log menjadi graph model yang mengandung non-free choice. Hasil graph model yang diperoleh dengan menggunakan Neo4j dibandingkan dengan hasil yang diperoleh dengan menggunakan LTL yang diturunkan dari aturan dalam model deklaratif. Makalah ini dapat membuktikan bahwa metode yang diusulkan dengan menggunakan graph database adalah metode yang tepat karena hasil grafik memiliki hasil yang lebih baik daripada Heuristic Miner. Hasil Neo4j bisa langsung menghasilkan relasi yang benar meski event log mengandung non-free choice dimana tidak bisa dilakukan pada Heuristic Miner. ======================================================================================================== The event log is one of the important assets in the company. This research strives to model the business process model in event log so that can analyze the model process in order to bring various benefits to companies such as reducing company costs, increasing profits, and more. One of the problem in modeling event log automatically is the addition of non-free choice. This research aims to develop a system that can model a business process of an event log that contains non-free choice using a graph database in Neo4j. First, this research model the event log into a link list in the form of a graph in Neo4j. Next, this research proposes a method to enhance the link list of event log to be a graph model that contains the non-free-choice. The results of the obtained graph model using Neo4j are compared with the results that are obtained using LTL that is derived from rules in a declarative model. This research can prove that the proposed method using graph database is a right method because the graph result has a better result than a Heuristic Miner. The result of Neo4j can directly generate relation which is true even though event log containing non-free choice where can’t be done in the Heuristic Miner

Memperbaiki Business Process Mengandung Invisible Task Menggunakan Graph Model

Event Log merupakan salah satu aset penting di perusahaan. Penelitian ini berusaha untuk memodelkan model proses bisnis di event log sehingga bisa menganalisa proses model agar bisa membawa berbagai manfaat bagi perusahaan seperti mengurangi biaya perusahaan, membawa keuntungan meningkat, dan banyak lagi. Salah satu masalah dalam pemodelan event log secara otomatis adalah penambahan invisible task. Tugas akhir ini bertujuan untuk mengembangkan sebuah sistem yang dapat memodelkan proses bisnis dari event log yang berisi invisible task dengan menggunakan graph database di Neo4J. Pertama-tama, penelitian ini menghasilkan event log into a link list berupa graph di Neo4J. Selanjutnya, tugas akhir ini mengusulkan sebuah metode untuk meningkatkan link list dari event log menjadi model graph yang mengandung invisible task. Hasil model graph yang diperoleh dengan menggunakan Neo4J dibandingkan dengan hasil yang diperoleh dengan menggunakan LTL yang diturunkan dari aturan dalam model deklaratif. Tugas akhir ini dapat membuktikan bahwa metode yang diusulkan dengan menggunakan graph database adalah metode yang tepat karena hasil graph memiliki hasil yang lebih baik daripada Heuristic Miner. Hasil Neo4J bisa langsung menghasilkan relasi yang benar meski event log mengandung invisible task dimana tidak bisa dilakukan pada Heuristic Miner. =========================================================================================== Event log is one of the important assets in the company. This research strive to model the business process model in event log so we can analyze the model process in order to bring various benefits to companies such as reducing company costs, increasing profits, and more. One of the problem in modeling event log automatically is the addition of invisible tasks. This research aims to develop a system that can model a business processes of an event log that contains invisible tasks using a graph database in Neo4J. First of all, this researchmodels the event log into a link list in the form of graph in Neo4J. Next,this research proposes a method to enhance the link list of event log to be a graph model that contains the invisible task. The results of the obtained graph model using Neo4Jare compared with the results that are obtained using LTL that is derived from rules in declarative model. This research can prove that the proposed method using graph database is a right method because the graph result have a better result than a Heuristic Miner. The result of Neo4J can directly generate relation which is true even though event log containing invisible task where can’t be done in Heuristic Miner

Pola Pencocokan Workflow untuk Memulihkan Anomali Pada Proses Bisnis

Belakangan ini, banyak perusahaan yang membutuhkan program untuk memodelkan log data mereka. Dikarenakan ini dapat memiliki banyak keuntungan salah satunya untuk mengecek error dari tiap aktivitas yang ada pada log data. Masalahnya log data biasanya sangatlah besar dan tidak dapat dimodelkan manual karena akan sangat membuang waktu dan energi. Maka dari itu, tugas akhir ini ditujukan untuk memunculkan model bisnis proses dari Linear Temporal Logic dan membandingkannya dengan model yang diciptakan langsung dari event log menggunakan metode Pattern Matching. Tugas akhir kali ini menunjukkan kelanjutan dari algoritma declarative miner, untuk membuat pola dalam Linear Temporal Logic (LTL) model dari log data yang dimiliki perusahaan, memiliki performa yang sangat bagus. Itu dibuktikan dari besarnya akurasi perbandingan antara LTL dan Event Log dengan menggunakan Neo4j. ============================================================================================================= Nowadays, many companies need a program to model their data logs. As this can have many benefits such as to check the errors of every activity in the data log. The problem is log data is usually very large that it can’t be modelled manually because it will be such a waste of time and energy. Therefore, this research generate a business process model from Linear Temporal Logic and compare it to model generated directly from event log using Pattern Matching. This research shows that an extended algorithm of declarative miner, to create pattern in Linear Temporal Logic (LTL) rule model from Log Data owned by the company, has a very good performance. It is proven by the high accuracy of the matching of LTL and event log using graph in Neo4j

The application of process mining to care pathway analysis in the NHS

Background: Prostate cancer is the most common cancer in men in the UK and the sixth-fastest increasing cancer in males. Within England survival rates are improving, however, these are comparatively poorer than other countries. Currently, information available on outcomes of care is scant and there is an urgent need for techniques to improve healthcare systems and processes. Aims: To provide prostate cancer pathway analysis, by applying concepts of process mining and visualisation and comparing the performance metrics against the standard pathway laid out by national guidelines. Methods: A systematic review was conducted to see how process mining has been used in healthcare. Appropriate datasets for prostate cancer were identified within Imperial College Healthcare NHS Trust London. A process model was constructed by linking and transforming cohort data from six distinct database sources. The cohort dataset was filtered to include patients who had a PSA from 2010-2015, and validated by comparing the medical patient records against a Case-note audit. Process mining techniques were applied to the data to analyse performance and conformance of the prostate cancer pathway metrics to national guideline metrics. These techniques were evaluated with stakeholders to ascertain its impact on user experience. Results: Case note audit revealed 90% match against patients found in medical records. Application of process mining techniques showed massive heterogeneity as compared to the homogenous path laid out by national guidelines. This also gave insight into bottlenecks and deviations in the pathway. Evaluation with stakeholders showed that the visualisation and technology was well accepted, high quality and recommended to be used in healthcare decision making. Conclusion: Process mining is a promising technique used to give insight into complex and flexible healthcare processes. It can map the patient journey at a local level and audit it against explicit standards of good clinical practice, which will enable us to intervene at the individual and system level to improve care.Open Acces

Applying Process-Oriented Data Science to Dentistry

Background: Healthcare services now often follow evidence-based principles, so technologies such as process and data mining will help inform their drive towards optimal service delivery. Process mining (PM) can help the monitoring and reporting of this service delivery, measure compliance with guidelines, and assess effectiveness. In this research, PM extracts information about clinical activity recorded in dental electronic health records (EHRs) converts this into process-models providing stakeholders with unique insights to the dental treatment process. This thesis addresses a gap in prior research by demonstrating how process analytics can enhance our understanding of these processes and the effects of changes in strategy and policy over time. It also emphasises the importance of a rigorous and documented methodological approach often missing from the published literature. Aim: Apply the emerging technology of PM to an oral health dataset, illustrating the value of the data in the dental repository, and demonstrating how it can be presented in a useful and actionable manner to address public health questions. A subsidiary aim is to present the methodology used in this research in a way that provides useful guidance to future applications of dental PM. Objectives: Review dental and healthcare PM literature establishing state-of-the-art. Evaluate existing PM methods and their applicability to this research’s dataset. Extend existing PM methods achieving the aims of this research. Apply PM methods to the research dataset addressing public health questions. Document and present this research’s methodology. Apply data-mining, PM, and data-visualisation to provide insights into the variable pathways leading to different outcomes. Identify the data needed for PM of a dental EHR. Identify challenges to PM of dental EHR data. Methods: Extend existing PM methods to facilitate PM research in public health by detailing how data extracts from a dental EHR can be effectively managed, prepared, and used for PM. Use existing dental EHR and PM standards to generate a data reference model for effective PM. Develop a data-quality management framework. Results: Comparing the outputs of PM to established care-pathways showed that the dataset facilitated generation of high-level pathways but was less suitable for detailed guidelines. Used PM to identify the care pathway preceding a dental extraction under general anaesthetic and provided unique insights into this and the effects of policy decisions around school dental screenings. Conclusions: Research showed that PM and data-mining techniques can be applied to dental EHR data leading to fresh insights about dental treatment processes. This emerging technology along with established data mining techniques, should provide valuable insights to policy makers such as principal and chief dental officers to inform care pathways and policy decisions

A Data Analysis Methodology for Process Diagnosis and Redesign in Healthcare

Department of Management EngineeringDespite the disruptive and continuous development of healthcare environments, it still faces numerous challenges. Many of these are connected to clinical processes within the healthcare environment, which can be resolved through process analysis. At the same time, through the digitalization of healthcare, information from the various stakeholders in hospitals can be collected and stored in hospital information systems. On the basis of this stored data, evidence-based healthcare is possible, and this data-driven approach has become key to resolving medical issues. However, a more systematic data analysis methodology that covers the diagnosis and the redesign of clinical processes is required. Process mining, which aims to derive knowledgeable process-related insights from event logs, is a promising data-driven approach that is commonly used to address the challenges in healthcare. In other words, process mining has become a way to improve business process management in healthcare. For this reason, there have been numerous studies on clinical process analysis using process mining. However, these have mainly focused on investigating challenges facing clinical processes and have not reached a virtuous cycle until process improvement. Thus, a comprehensive data analysis framework for process diagnosis and redesign in healthcare is still required. \ud We identify three challenges in this research: 1) a lack of guidelines for data analysis to help understand clinical processes, 2) the research gap between clinical data analysis and process redesign in healthcare, and 3) a lack of accuracy and reliability in redesign assessment in healthcare. Based on these problem statements, this doctoral dissertation focuses on a comprehensive data analysis methodology for process diagnosis and redesign in healthcare. In particular, three frameworks are established to address important research issues in healthcare: 1) a framework for diagnosing clinical processes for outpatients, inpatients, and clinical pathways, 2) a framework for redesigning clinical processes with a simulation-based approach, and 3) a framework for evaluating the effects of process redesign. The proposed methodology has four steps: data preparation, data preprocessing, data analysis, and post-hoc analysis. The data preparation phase aims to extract data in a suitable format (i.e., event logs) for process mining data analysis. In this step, a method for obtaining clinical event logs from electronic health record data mapped using the common data model needs to be developed. To this end, we build an event log specification that can be used to derive event logs that consider the purpose, content, and scope of the data analysis desired by the user. After compiling the event logs, they are preprocessed to improve the accuracy and validity of the data analysis. The data analysis phase, which is the core component of the proposed methodology, consists of three components for process mining analysis: clinical process types, process mining types, and clinical perspectives. In the last phase, we interpret the results obtained from the data analysis with domain experts and perform a post-hoc analysis to improve clinical processes using simulations and to evaluate the previous data analysis results. For the first research issue, we propose a data analysis framework for three clinical process types: outpatients, inpatients, and clinical pathways. For each category, we provide a specific goal and include suitable fine-grained techniques in the framework which are either newly developed or based on existing approaches. We also provide four real-life case studies to validate the usefulness of this approach. For the second research issue, we develop a data-driven framework in order to build a discrete event simulation model. The proposed framework consists of four steps: data preparation and preprocessing, data analysis, post-hoc analysis, and further analysis. Here, we propose a mechanism for obtaining simulation parameters from process mining analysis from a control flow and performance perspective and automatically build a reliable and robust simulation model based on these parameters. This model includes realistic arrival rates and service times in a clinical setting. The proposed framework is constructed with a specific goal in mind (e.g., a decrease in waiting times), and the applicability of the framework is validated with a case study. For the final research issue, we develop a framework for evaluating the effects of process redesign. Two types of indicators are used for this: best practice implementation indicators to assess whether a specific best practice has been applied well or not and process performance indicators to understand the impact of the application of best practices. These indicators are explicitly connected to process mining functionalities. In other words, we provide a comprehensive method for assessing these indicators using clinical event logs. The usefulness of the methodology is demonstrated with real-life logs before and after a redesign. Compared to other existing frameworks in healthcare, this research is unique in constructing a healthcare-oriented data analysis methodology, rather than a generic model, that covers redesign in addition to diagnosis and in providing concrete analysis methods and data. As such, it is believed that this research will act as a motivation to extend the use of process mining in healthcare and will serve as a practical guideline for analyzing and improving clinical processes for non-experts.clos

Front-Line Physicians' Satisfaction with Information Systems in Hospitals

Day-to-day operations management in hospital units is difficult due to continuously varying situations, several actors involved and a vast number of information systems in use. The aim of this study was to describe front-line physicians' satisfaction with existing information systems needed to support the day-to-day operations management in hospitals. A cross-sectional survey was used and data chosen with stratified random sampling were collected in nine hospitals. Data were analyzed with descriptive and inferential statistical methods. The response rate was 65 % (n = 111). The physicians reported that information systems support their decision making to some extent, but they do not improve access to information nor are they tailored for physicians. The respondents also reported that they need to use several information systems to support decision making and that they would prefer one information system to access important information. Improved information access would better support physicians' decision making and has the potential to improve the quality of decisions and speed up the decision making process.Peer reviewe

Unsupervised Abstraction for Reducing the Complexity of Healthcare Process Models

Healthcare processes are complex and may vary considerably among the same cohort of patients. Process mining techniques play a significant role in automating the construction of healthcare models using a system's event log. An event log is a data type that records any event that occurs within the process. It is a basic element of any information system and has three main components: process instance id, event and time when an event has occurred. Using ordinary techniques of process mining in healthcare produces `spaghetti-like' models which are difficult to understand and thus have little value. Previously published studies have highlighted the importance of event abstraction which is considered as a central tool for reducing complexity and improving efficiency. Although studies have successfully improved the understandability of process models, they have generally relied on involvement from a domain expert. Untangling these `spaghetti-like' models with the help of domain experts can be expensive and time-consuming. Machine learning techniques such as Hidden Markov Model (HMM) has been used for modelling sequential data for a long time. State transition modelling has also been explored by process mining research and is advocated for sequence clustering purposes where a model is trained over a group of sequences and then used to evaluate if a process instance is more likely to be generated from this model or not. However, state transition models can also be utilised for detecting hidden processes which can be used subsequently for process abstraction. In this thesis, we aim to address healthcare process complexity using unsupervised abstraction. We adopt an unsupervised method for detecting hidden processes using HMM and the Viterbi algorithm. The method in this research includes eight stages; event logs extraction, preprocessing, learning, decoding, optimisation, selection, visualisation and lastly model evaluation. One of the main contributions of this research is the design of two different types of process model optimisation which are strict and soft optimisations. Models that are selected by the proposed optimisation address the limitations of other standard metrics that can be used for model selection in HMM such as Bayesian Information criteria (BIC). Two different real healthcare data sources are used in this research namely the Medical Information Mart for Intensive Care (MIMIC-III) from Boston, USA and the Patients Pathway Manager (PPM) from Leeds, UK. Models are trained using the MIMIC-III medical event log and then tested using the PPM dataset to be evaluated later by a domain expert. Three breast cancer case studies that range in complexity are extracted. The results of our method have significantly improved model complexity and provided a conceptually valid abstraction for several care patterns. Promising results are demonstrated in the improvement of the precision and fitness of the abstracted models. The abstracted models can then be used as a middle step for bringing structure to unstructured processes which helps in finding cohorts of patients based on similar healthcare processes. The healthcare processes of a cohort of patients can then be modelled using any process mining tool where their process similarity could not be captured in the complex models

Exploring the use of routine healthcare data through process mining to inform the management of musculoskeletal diseases

Healthcare informatics can help address some of the challenges faced by both healthcare providers and patients. The medical domain is characterised by inherently complex and intricate issues, data can often be of poor quality and novel techniques are required. Process mining is a discipline that uses techniques to extract insights from event data, generated during the execution of processes. It has had good results in various branches of medical science but applications to musculoskeletal diseases remain largely unexplored. This research commenced with a review of the healthcare and technical literature and applied a variety of process mining techniques in order to investigate approaches to the healthcare plans of patients with musculoskeletal conditions. The analysis involved three datasets from: 1) a private hospital in Boston, US, where data was used to create disease trajectory models. Results suggest the method may be of interest to healthcare researchers, as it enables a more rapid modelling and visualisation; 2) a mobile healthcare application for patients receiving physiotherapy in Sheffield, UK, where data was used to identify possible indicators for health outcomes. After evaluation of the results, it was found that the indicators identified may be down to chance; and 3) the population of Wales to explore knee pain surgery pathways. Results suggest that process mining is an effective technique. This work demonstrates how routine healthcare data can be analysed using process mining techniques to provide insights that may benefit patients suffering with musculoskeletal conditions. This thesis explores how strict criteria for analysis can be performed. The work is intended to expand the breadth of process mining methods available to the data science community and has contributed by making recommendations for service utilisation within physiotherapy at Sheffield Hospital and helped to define a roadmap for a leading healthcare software company