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

ACCESS DATA

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

Similar Datasets

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.

Project description:OBJECTIVE:The aim of this study was to validate a new generic patient-reported outcome measure, the Long-Term Conditions Questionnaire (LTCQ), among a diverse sample of health and social care users in England. DESIGN:Cross-sectional validation survey. Data were collected through postal surveys (February 2016-January 2017). The sample included a healthcare cohort of patients recruited through primary care practices, and a social care cohort recruited through local government bodies that provide social care services. PARTICIPANTS:1211 participants (24% confirmed social care recipients) took part in the study. Healthcare participants were recruited on the basis of having one of 11 specified long-term conditions (LTCs), and social care participants were recruited on the basis of receiving social care support for at least one LTC. The sample exhibited high multimorbidity, with 93% reporting two or more LTCs and 43% reporting a mental health condition. OUTCOME MEASURES:The LTCQ's construct validity was tested with reference to the EQ-5D (5-level version), the Self-Efficacy for Managing Chronic Disease scale, an Activities of Daily Living scale and the Bayliss burden of morbidity scale. RESULTS:Low levels of missing data for each item indicate acceptability of the LTCQ across the sample. The LTCQ exhibits high internal consistency (Cronbach's ?=0.95) across the scale's 20 items and excellent test-retest reliability (intraclass correlation coefficient=0.94, 95%?CI 0.93 to 0.95). Associations between the LTCQ and all reference measures were moderate to strong and in the expected directions, indicating convergent construct validity. CONCLUSIONS:This study provides evidence for the reliability and validity of the LTCQ, which has potential for use in both health and social care settings. The LTCQ could meet a need for holistic outcome measurement that goes beyond symptoms and physical function, complementing existing measures to capture fully what it means to live well with LTCs.

Project description:BACKGROUND:The Chihuahua, the world's smallest dog breed, is a popular breed in the UK today. The VetCompass™ Programme collates de-identified clinical records from primary-care veterinary practices in the UK for epidemiological research. This study aimed to characterise the demography, age at death and common disorders of Chihuahuas under primary veterinary care during 2016 in the UK. RESULTS:Chihuahuas comprised 11,647/336,865 (3.46%) dogs under veterinary care during 2016. The annual proportional birth rate for Chihuahuas rose from 1.01% in 2005 to 5.35% in 2016. Median adult bodyweight was 3.4?kg (interquartile range [IQR]: 2.7-4.3, range 0.8-9.8).). Median age was 2.8?years (interquartile range [IQR] 1.6-4.9). Median age at death from this young expanding population was 8.2?years (IQR 3.5-12.1). Females (10.2?years) outlived males (6.9?years) (Mann-Whitney U test: P =?0.005). The most common grouped causes of death were heart disease (18.8%, 95% CI: 10.9-29.0), lower respiratory tract disorder (16.3%, 95% CI: 8.9-26.2) and traumatic injury (13.8%, 95% CI: 7.1-23.3). The most common specific disorders were periodontal disease (13.5%, 95% CI: 12.6-14.4), obesity (5.9%, 95% CI: 5.3-6.5), retained deciduous dentition (5.7%, 95% CI: 5.1-6.4), anal sac impaction (4.9%, 95% CI: 4.4-5.5) and aggression (4.2%, 95% CI: 3.7-4.8). Among the 28 most common fine-level disorders, males had statistically (P <? 0.005) higher probability than females for 5 disorders (aggression, heart murmur, otitis externa, conjunctivitis and upper respiratory tract infection). There were no disorders with statistically (P <? 0.005) higher prevalence in females. CONCLUSIONS:This study documented rising ownership and a currently youthful population of Chihuahuas in the UK. These results suggest that the Chihuahua is currently undergoing a popularity boom but veterinarians need to be watchful for welfare issues related to impulse purchase of Chihuahua puppies by owners with limited experience of dog care. Periodontal disease, obesity, retained deciduous dentition, anal sac impaction and aggression were identified as common health issues within the breed. The unique veterinary care needs of this popular miniature breed suggest that veterinarians should consider the value of advanced training in anesthesia and dentistry in small-sized dogs.

Project description:BackgroundNormalisation Process Theory (NPT) provides a framework to understand how interventions are implemented, embedded, and integrated in healthcare settings. Previous reviews of published literature have examined the application of NPT across international healthcare and reports its benefits. However, given the distinctive clinical function, organisational arrangements and the increasing management of people with a wide variety of conditions in primary care settings in the United Kingdom, it is important to understand how and why authors utilise and reflect on NPT in such settings to inform and evaluate implementation processes.MethodsA systematic review of peer-reviewed literature using NPT in primary care settings in the United Kingdom (UK) was conducted. Eight electronic databases were searched using replicable methods to identify articles published between January 2012 and April 2018. Data were analysed using a framework approach.ResultsThirty-one articles met the inclusion criteria. Researchers utilised NPT to explore the implementation of interventions, targeting a wide range of health services and conditions, within primary care settings in the UK. NPT was mostly applied qualitatively; however, a small number of researchers have moved towards mixed and quantitative methods. Some variation was observed in the use of NPT constructs and sub-constructs, and whether and how researchers undertook modification to make them more relevant to the implementation process and multiple stakeholder perspectives.ConclusionNPT provides a flexible framework for the development and evaluation of complex healthcare interventions in UK primary care settings. This review updates the literature on NPT use and indicates that its application is well suited to these environments, particularly in supporting patients with long-term conditions and co-morbidities. We recommend future research explores the receipt of interventions by multiple stakeholders and suggest that authors reflect on justifications for using NPT in their reporting.

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

OmicsDI is part of the ELIXIR infrastructure

Tweets