Dataset Information

Can process mining automatically describe care pathways of patients with long-term conditions in UK primary care? A study protocol.

ABSTRACT:

Introduction

In the UK, primary care is seen as the optimal context for delivering care to an ageing population with a growing number of long-term conditions. However, if it is to meet these demands effectively and efficiently, a more precise understanding of existing care processes is required to ensure their configuration is based on robust evidence. This need to understand and optimise organisational performance is not unique to healthcare, and in industries such as telecommunications or finance, a methodology known as 'process mining' has become an established and successful method to identify how an organisation can best deploy resources to meet the needs of its clients and customers. Here and for the first time in the UK, we will apply it to primary care settings to gain a greater understanding of how patients with two of the most common chronic conditions are managed.

Methods and analysis

The study will be conducted in three phases; first, we will apply process mining algorithms to the data held on the clinical management system of four practices of varying characteristics in the West Midlands to determine how each interacts with patients with hypertension or type 2 diabetes. Second, we will use traditional process mapping exercises at each practice to manually produce maps of care processes for the selected condition. Third, with the aid of staff and patients at each practice, we will compare and contrast the process models produced by process mining with the process maps produced via manual techniques, review differences and similarities between them and the relative importance of each. The first pilot study will be on hypertension and the second for patients diagnosed with type 2 diabetes.

Ethics and dissemination

Ethical approval has been provided by East Midlands-Leicester South Regional Ethics Committee (REC reference 18/EM/0284). Having refined the automated production of maps of care processes, we can explore pinch points and bottlenecks, process variants and unexpected behaviour, and make informed recommendations to improve the quality and efficiency of care. The results of this study will be submitted for publication in peer-reviewed journals.

SUBMITTER: Litchfield I

PROVIDER: S-EPMC6286474 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature

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Publications

Can process mining automatically describe care pathways of patients with long-term conditions in UK primary care? A study protocol.

Litchfield Ian I Hoye Ciaron C Shukla David D Backman Ruth R Turner Alice A Lee Mark M Weber Phil P

BMJ open 20181204 12

<h4>Introduction</h4>In the UK, primary care is seen as the optimal context for delivering care to an ageing population with a growing number of long-term conditions. However, if it is to meet these demands effectively and efficiently, a more precise understanding of existing care processes is required to ensure their configuration is based on robust evidence. This need to understand and optimise organisational performance is not unique to healthcare, and in industries such as telecommunications ...[more]

PMID: 30518578

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Project description:BackgroundDespite effective treatments and long-standing management guidelines, there are approximately 1400 hospital admissions for asthma weekly in the United Kingdom (UK), many of which could be avoided. In our previous research, a secondary analysis of the intervention (ARRISA) suggested an improvement in the management of at-risk asthma patients in primary care. ARRISA involved identifying individuals at risk of adverse asthma events, flagging their electronic health records, training practice staff to develop and implement practice-wide processes of care when alerted by the flag, plus motivational reminders. We now seek to determine the effectiveness and cost-effectiveness of ARRISA in reducing asthma-related crisis events.MethodsWe are undertaking a pragmatic, two-arm, multicentre, cluster randomised controlled trial, plus health economic and process evaluation. We will randomise 270 primary care practices from throughout the UK covering over 10,000 registered patients with 'at-risk asthma' identified according to a validated algorithm. Staff in practices randomised to the intervention will complete two 45-min eLearning modules (an individually completed module giving background to ARRISA and a group-completed module to develop practice-wide pathways of care) plus a 30-min webinar with other practices. On completion of training at-risk patients' records will be coded so that a flag appears whenever their record is accessed. Practices will receive a phone call at 4 weeks and a reminder video at 6 weeks and 6 months. Control practices will continue to provide usual care. We will extract anonymised routine patient data from primary care records (with linkage to secondary care data) to determine the percentage of at-risk patients with an asthma-related crisis event (accident and emergency attendances, hospitalisations and deaths) after 12 months (primary outcome). We will also capture the time to crisis event, all-cause hospitalisations, asthma control and any changes in practice asthma management for at-risk and all patients with asthma. Cost-effectiveness analysis and mixed-methods process evaluations will also be conducted.DiscussionThis study is novel in terms of using a practice-wide intervention to target and engage with patients at risk from their asthma and is innovative in the use of routinely captured data with record linkage to obtain trial outcomes.Trial registrationISRCTN95472706 . Registered on 5 December 2014.

Project description:BackgroundBetter management of long-term conditions remains a policy priority, with a focus on improving outcomes and reducing use of expensive hospital services. A number of interventions have been tested, but many have failed to show benefit in rigorous comparative research. In 2016, the NHS Test Beds scheme was launched to implement and test interventions combining digital technologies and pathway redesign in routine health care settings, with each intervention comprising multiple innovations to better realise benefit from their 'combinatorial' effect. We present the evaluation of one of the NHS Test Beds, which combined risk stratification algorithms, practice-based quality improvement and health monitoring and coaching to improve management of long-term conditions in a single health economy in the north-west of England.MethodsThe NHS Test Bed was implemented in one clinical commissioning group in the north-west of England (patient population 235,800 served by 36 general practices). Routine administrative data on hospital use (the primary outcome) and a selection of secondary outcomes (data from both hospital and primary care) were collected in the intervention site, and from a comparator area in the same region. We used difference-in-differences analysis to compare outcomes in the NHS Test Bed area and the comparator after initiation of the combinatorial intervention.ResultsTests confirmed the existence of parallel trends in the intervention and comparator sites for hospital outcomes for the period April 2016 to March 2017, and for some of the planned primary care outcomes. Based on 10 months of post-intervention secondary care data and 13 months post-intervention primary care data, we found no significant impact on primary outcomes between the intervention and comparator site, and a significant impact on only one secondary outcome.ConclusionA combinatorial digital and organisational intervention to improve the management of long-term conditions was implemented across a whole health economy, but we found no evidence of a positive impact on health care utilisation outcomes in hospital and primary care.

Dataset Information

Can process mining automatically describe care pathways of patients with long-term conditions in UK primary care? A study protocol.

Introduction

Methods and analysis

Ethics and dissemination

Publications

Can process mining automatically describe care pathways of patients with long-term conditions in UK primary care? A study protocol.

Similar Datasets

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