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

Creating a real-world linked research platform for analyzing the urgent and emergency care system

Background This article describes the development of a system-based data platform for research developed to provide a detailed picture of the characteristics of the Urgent and Emergency Care system in 1 region of the United Kingdom. Data Set Development CUREd is an integrated research data platform that describes the urgent and emergency care system in 1 region of the United Kingdom on almost 30 million patient contacts within the system. We describe regulatory approvals required, data acquisition, cleaning, and linkage. Data Set Analyses The data platform covers 2011 to 2017 for 14 acute National Health Service (NHS) Hospital Trusts, 1 ambulance service, the national telephone advice service (NHS 111), and 19 emergency departments. We describe 3 analyses undertaken: 1) Analyzing triage patterns from the NHS 111 telephone helpline using routine data linked to other urgent care services, we found that the current triage algorithms have high rates of misclassifying calls. 2) Applying an algorithm to consistently identify avoidable attendances for pediatric patients, we identified 21% of pediatric attendances to the emergency department as avoidable. 3) Using complex systems analysis to examine patterns of frequent attendance in urgent care, we found that frequent attendance is stable over time but varies by individual patient. This implies that frequent attendance is more likely to be a function of the system overall. Discussion We describe the processes necessary to produce research-ready data that link care across the components of the urgent and emergency care system. Making the use of routine data commonplace will require partnership between the collectors, owners, and guardians of the data and researchers and technical teams. Highlights -This article describes the development of a system-level data platform for research using routine patient-level data from the urgent and emergency care system in 1 region of the United Kingdom. -The article describes how the data were acquired, cleaned, and linked and the challenges faced when undertaking analysis with the data. -The data set has been used to understand patient use of the system, journeys once in the system, and outcomes following its use, for example, patterns of frequent use within urgent care and accuracy of referral decisions within the system

Transactions, Transformations, Translations: Metrics that Matter for Building, Scaling, and Funding Social Movements

This report provides an evaluative framework and key milestones to gauge movement building. Aiming to bridge the gap between the field of community organizing that relies on the one-on-one epiphanies of leaders and the growing philanthropic emphasis on evidence-based giving, the report stresses three main insights. The first is that any good set of movement metrics should capture quantity and quality, numbers and nuance, transactions and transformations. They are related -- an energized leader with a clear power analysis (a transformative measure) may turn out more members for a coalition rally (a transactional measure) -- and the report offers a matrix that weaves together both types of metrics across ten different movement-building strategies. The second is that a movement is more than one organization -- and if the whole is to be greater than the sum of its parts, we must measure accordingly. While report includes measures of success at the organizational level, it attempts to move beyond and focus on whether groups can align and work together to create a more powerful force for social change -- suggesting that in the same way that movements need to scale up to face the challenges of our times, metrics, too, must expand to capture the whole. The third is that metrics must be co-created, not imposed. Recognizing the gravity of the times and hoping to gauge their effectiveness, movement builders are eager to come up with a common language and framework for themselves -- and are developing the tools and capacities to do so. The report suggests that the funder-grantee relationship can build on this wisdom in the field and develop a set of evaluative measures that are not onerous requirements but tools for mutual accountability. The report also offers a set of recommendations to funders and the field, ranging from practical steps (like building a new toolbox of measures, improving the capacity to use them, and documenting innovation and experimentation) to more far-reaching suggestions about leadership development, the connection of policy outcomes with broader social change, and the need to generate movement-level measures. We, at USC PERE, hope this report contributes to a conversation about how to best capture transformations as well as transactions in social movement organizing, and how to build the broader public and philanthropic support necessary to realize the promise of a more inclusive America

Identifying and appraising promising sources of UK clinical, health and social care data for use by NICE

This report aimed to aid the National Institute of Health and Care Excellence (NICE) in identifying opportunities for greater use of real-world data within its work. NICE identified five key ways in which real-world data was currently informing its work, or could do so in the future through: (i) researching the effectiveness of interventions or practice in real-world (UK) settings (ii) auditing the implementation of guidance (iii) providing information on resource use and evaluating the potential impact of guidance (iv) providing epidemiological information (v) providing information on current practice to inform the development of NICE quality standards. This report took a broad definition of ‘real-world’ data and created a map of UK sources, informed by a number of experts in real-world data, as well as a literature search, to highlight where some of the opportunities may lie for NICE within its clinical, public health and social care remit. The report was commissioned by the NICE, although the findings are likely to be of wider interest to a range of stakeholders interested in the role of real-world data in informing clinical, social care and public health decision-making. Most of the issues raised surrounding the use and appraisal of real-world data are likely to be generic, although the choice of datasets that were profiled in-depth reflected the interests of NICE. We discovered 275 sources that were named as real-world data sources for clinical, social care or public health investigation, 233 of which were deemed as active. The real-world data landscape therefore is highly complex and heterogeneous and composed of sources with different purposes, structures and collection methods. Some real-world data sources are purposefully either set-up or re-developed to enhance their data linkages and to examine the presence/absence/effectiveness of integrated patient care; however, such sources are in the minority. Furthermore, the small number of real-world data sources that are designed to enable the monitoring of care across providers, or at least have the capability to do so at a national level, have been utilised infrequently for this purpose in the literature. Data that offer the capacity to monitor transitions between health and social care do not currently exist at a national level, despite the increasing recognition of the interdependency between these sectors. Among the data sources we included, it was clear that no one data source represented a panacea for NICE’s real world data needs. This does highlight the merits and importance of data linkage projects and is suggestive of a need to triangulate evidence across different data, particularly in order to understand the feasibility and impact of guidance. There exists no overall catalogue or repository of real-world data sources for health, public health and social care, and previous initiatives aimed at creating such a resource have not been maintained. As much as there is a need for enhanced usage of the data, there is also a need for taking stock, integration, standardisation, and quality assurance of different sources. This research highlights a need for a systematic approach to creating an inventory of sources with detailed metadata and the funding to maintain this resource. This would represent an essential first step to support future initiatives aimed at enhancing the use of real-world data

Modelling blue-light ambulance mobility in the London metropolitan area

Actions taken immediately following a life-threatening incident are critical for the survival of the patient. In particular, the timely arrival of ambulance crew often makes the difference between life and death. As a consequence, ambulance services are under persistent pressure to achieve rapid emergency response. Meeting stringent performance requirements poses special challenges in metropolitan areas where the higher population density results in high rates of life-threatening incident occurrence, compounded by lower response speeds due to traffic congestion. A key ingredient of data-driven approaches to address these challenges is the effective modelling of ambulance movement thus enabling the accurate prediction of the expected arrival time of a crew at the site of an incident. Ambulance mobility patterns however are distinct and in particular differ from civilian traffic: crews travelling with ashing blue lights and sirens are by law exempt from certain traffic regulations; and moreover, ambulance journeys are triggered by emergency incidents that are generated following distinct spatial and temporal patterns. We use a large historical dataset of incidents and ambulance location traces to model route selection and arrival times. Working on a road routing network modified to reflect the differences between emergency and regular vehicle traffic, we develop a methodology for matching ambulances Global Positioning System (GPS) coordinates to road segments, allowing the reconstruction of ambulance routes with precise speed data. We demonstrate how a road speed model that exploits this information achieves best predictive performance by implicitly capturing route-specific patterns in changing traffic conditions. We then present a hybrid model that achieves a high route similarity score while minimising journey duration error. This hybrid model outperforms alternative mobility models. To the best of our knowledge, this study represents the first attempt to apply data-driven methodologies to route selection and estimation of arrival times of ambulances travelling with blue lights and sirens