Significant adverse events (AE) during cancer therapy disrupt treatment and escalate to emergency admissions. Approaches to improve the timeliness and accuracy of AE reporting may improve safety and reduce health service costs. Reporting AE via patient reported outcomes (PROs), can improve clinician–patient communication and making data available to clinicians in ‘real-time’ using electronic PROs (ePROs) could potentially transform clinical practice by providing easily accessible records to guide treatment decisions. This manuscript describes the development of eRAPID (electronic patient self-Reporting of Adverse-events: Patient Information and aDvice) is a National Institute for Health Research-funded programme, a system for patients to self-report and manage AE online during and after cancer treatment. A multidisciplinary team of IT experts, staff and patients developed using agile principles a secure web application interface (QStore) between an existing online questionnaire builder (QTool) displaying real-time ePRO data to clinicians in the electronic patient record at Leeds Teaching Hospitals NHS Trust. Hierarchical algorithms were developed corresponding to Common Terminology Criteria for Adverse Events grading using the QTool question dependency function. Patient advocates (N = 9), patients (N = 13), and staff (N = 19) usability tested the system reporting combinations of AE. The eRAPID system allows patients to report AE from home on PC, tablet or any web enabled device securely during treatment. The system generates immediate self-management advice for low or moderate AE and for severe AE advice to contact the hospital immediately. Clinicians can view patient AE data in the electronic patient record and receive email notifications when patients report severe AE. Evaluation of the system in a randomised controlled trial in breast, gynaecological and colorectal cancer patients undergoing systemic therapy is currently underway. To adapt eRAPID for different treatment groups, pilot studies are being undertaken with patients receiving pelvic radiotherapy and upper gastrointestinal surgery. ISRCTN88520246.

There is growing interest in the use of routinely collected electronic health records to enhance service delivery and facilitate clinical research. It should be possible to detect and measure patterns of care and use the data to monitor improvements but there are methodological and data quality challenges. Driven by the desire to model the impact of a patient self-test blood count monitoring service in patients on chemotherapy, we aimed to (i) establish reproducible methods of process-mining electronic health records, (ii) use the outputs derived to define and quantify patient pathways during chemotherapy, and (iii) to gather robust data which is structured to be able to inform a cost-effectiveness decision model of home monitoring of neutropenic status during chemotherapy. Electronic Health Records at a UK oncology centre were included if they had (i) a diagnosis of metastatic breast cancer and received adjuvant epirubicin and cyclosphosphamide chemotherapy or (ii) colorectal cancer and received palliative oxaliplatin and infusional 5-fluorouracil chemotherapy, and (iii) were first diagnosed with cancer between January 2004 and February 2013. Software and a Markov model were developed, producing a schematic of patient pathways during chemotherapy. Significant variance from the assumed care pathway was evident from the data. Of the 535 patients with breast cancer and 420 with colorectal cancer there were 474 and 329 pathway variants respectively. Only 27 (5%) and 26 (6%) completed the planned six cycles of chemotherapy without having unplanned hospital contact. Over the six cycles, 169 (31.6%) patients with breast cancer and 190 (45.2%) patients with colorectal cancer were admitted to hospital. The pathways of patients on chemotherapy are complex. An iterative approach to addressing semantic and data quality issues enabled the effective use of routinely collected patient records to produce accurate models of the real-life experiences of chemotherapy patients and generate clinically useful information. Very few patients experience the idealised patient pathway that is used to plan their care. A better understanding of real-life clinical pathways through process mining can contribute to care and data quality assurance, identifying unmet needs, facilitating quantification of innovation impact, communicating with stakeholders, and ultimately improving patient care and outcomes.

There is a growing body of literature on process mining in healthcare. Process mining of electronic health record systems could give benefit into better understanding of the actual processes happened in the patient treatment, from the event log of the hospital information system. Researchers report issues of data access approval, anonymisation constraints, and data quality. One solution to progress methodology development is to use a high-quality, freely available research dataset such as Medical Information Mart for Intensive Care III, a critical care database which contains the records of 46,520 intensive care unit patients over 12 years. Our article aims to (1) explore data quality issues for healthcare process mining using Medical Information Mart for Intensive Care III, (2) provide a structured assessment of Medical Information Mart for Intensive Care III data quality and challenge for process mining, and (3) provide a worked example of cancer treatment as a case study of process mining using Medical Information Mart for Intensive Care III to illustrate an approach and solution to data quality challenges. The electronic health record software was upgraded partway through the period over which data was collected and we use this event to explore the link between electronic health record system design and resulting process models.

Process mining, an emerging data analytics method, has been used effectively in various healthcare contexts including oncology, the study of cancer. Cancer is a complex disease with many complicated care requirements and there is an urgent need to improve the cost and clinical effectiveness of cancer care pathways. Process mining of the e-health records of cancer patients may play an important future role and this paper presents a literature review of process mining in oncology as a contribution to this research. The search produced 758 articles which were manually reviewed by title, abstract, and full paper text review to develop the original pool of papers. An in-depth ancestor search was used to gather additional articles from the references of the original pool. These steps resulted in 37 papers. Through a thematic review process, the papers were analysed and five themes emerged. These were: 1) process and data types; 2) research questions; 3) techniques, perspectives and tools; 4) methodologies; 5) limitations and future work. This review can: (i) highlight the potential value of process mining for improving cancer care processes (ii) provide a useful overview of the current work undertaken; (iii) help researchers to choose process mining algorithms, techniques, tools, methodologies and approaches; and (iv) identify research opportunities in this new field of study.