scholarly journals Improving the management of type 2 diabetes through large-scale general practice: the role of a data-driven and technology-enabled education programme

2021 ◽  
Vol 10 (1) ◽  
pp. e001087
Author(s):  
Tarek F Radwan ◽  
Yvette Agyako ◽  
Alireza Ettefaghian ◽  
Tahira Kamran ◽  
Omar Din ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Primary Care ◽  
Large Scale ◽  
Education Programme ◽  
Educational Programme ◽  
Data Driven ◽  
Treatment Targets ◽  
Care Processes ◽  
Data Driven Approach

A quality improvement (QI) scheme was launched in 2017, covering a large group of 25 general practices working with a deprived registered population. The aim was to improve the measurable quality of care in a population where type 2 diabetes (T2D) care had previously proved challenging. A complex set of QI interventions were co-designed by a team of primary care clinicians and educationalists and managers. These interventions included organisation-wide goal setting, using a data-driven approach, ensuring staff engagement, implementing an educational programme for pharmacists, facilitating web-based QI learning at-scale and using methods which ensured sustainability. This programme was used to optimise the management of T2D through improving the eight care processes and three treatment targets which form part of the annual national diabetes audit for patients with T2D. With the implemented improvement interventions, there was significant improvement in all care processes and all treatment targets for patients with diabetes. Achievement of all the eight care processes improved by 46.0% (p<0.001) while achievement of all three treatment targets improved by 13.5% (p<0.001). The QI programme provides an example of a data-driven large-scale multicomponent intervention delivered in primary care in ethnically diverse and socially deprived areas.

Efficacy of a self-management education programme on patients with type 2 diabetes in primary care: A randomised controlled trial

2019 ◽  
Vol 13 (2) ◽  
pp. 122-133 ◽  
Author(s):  
Estibaliz Gamboa Moreno ◽  
Maider Mateo-Abad ◽  
Lourdes Ochoa de Retana García ◽  
Kalliopi Vrotsou ◽  
Emma del Campo Pena ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Primary Care ◽  
Randomised Controlled Trial ◽  
Management Education ◽  
Controlled Trial ◽  
Education Programme ◽  
Self Management ◽  
Randomised Controlled

scholarly journals Multimorbidity and achievement of treatment goals among patients with type 2 diabetes: a primary care, real-world study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Eveliina Heikkala ◽  
Ilona Mikkola ◽  
Jari Jokelainen ◽  
Markku Timonen ◽  
Maria Hagnäs
Keyword(s):  
Type 2 Diabetes ◽  
Primary Care ◽  
Health Care ◽  
Primary Health Care ◽  
Treatment Target ◽  
Adequate Treatment ◽  
Treatment Targets ◽  
Primary Health

Abstract Background Type 2 diabetes (T2D), with its prevalence and disability-causing nature, is a challenge for primary health care. Most patients with T2D are multimorbid, i.e. have one or more long-term diseases in addition to T2D. Multimorbidity may play a role in the achievement of T2D treatment targets, but is still not fully understood. The aims of the present cross-sectional, register-based study were to evaluate the prevalence and the most common patterns of multimorbidity among patients with T2D; and to study the potential associations between multimorbidity and treatment goal achievement, including measurements of glycosylated haemoglobin A1c (HbA1c), low-density lipoprotein (LDL) and systolic blood pressure (sBP). Methods The study population consisted of 4545 primary care patients who received a T2D diagnosis between January 2011 and July 2019 in Rovaniemi Health Centre, Finland. Data on seven long-term concordant (T2D-related) diseases, eight long-term discordant (non-T2D-related) diseases, potential confounders (age, sex, body mass index, prescribed medication), and the outcomes studied were collected from patients’ records. Logistic regression models with odds ratios (ORs) and 95 % confidence intervals (CIs) were assessed to determine the associations between multimorbidity and the achievement of treatment targets. Results Altogether, 93 % of the patients had one or more diseases in addition to T2D, i.e. were considered multimorbid. Furthermore, 21 % had only concordant disease(s) (Concordant subgroup), 8 % had only discordant disease(s) (Discordant subgroup) and 64 % had both (Concordant and discordant subgroup). As either single diseases or in combination with others, hypertension, musculoskeletal (MS) disease and hyperlipidaemia were the most prevalent multimorbidity patterns. Being multimorbid in general (OR 1.32, CI 1.01–1.70) and belonging to the Concordant (OR 1.45, CI 1.08–1.95) and Concordant and discordant (OR 1.31, CI 1.00–1.72) subgroups was associated with achievement of the HbA1c treatment target. Belonging to the Concordant and discordant subgroup was related to meeting the LDL treatment target (OR 1.31, CI 1.00–1.72). Conclusions Multimorbidity, including cardiovascular risk and the musculoskeletal disease burden, was extremely prevalent among the T2D patients who consulted primary health care. Primary care clinicians should survey the possible co-existence of long-term diseases among T2D patients to help maintain adequate treatment of T2D.

scholarly journals Impact of a self-care education programme on patients with type 2 diabetes in primary care in the Basque Country

2016 ◽  
Vol 16 (6) ◽  
pp. 98
Author(s):  
Estibaliz Gamboa Moreno ◽  
Emma Del Campo Pena ◽  
Lourdes Ochoa de Retana Garcia ◽  
Juan Carlos Arbonies Ortiz ◽  
Koldo Piñera Elorriaga ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Primary Care ◽  
Basque Country ◽  
Self Care ◽  
Education Programme ◽  
Care Education

scholarly journals Implementation and Effectiveness of an Interprofessional Support Program for Patients with Type 2 Diabetes in Swiss Primary Care: A Study Protocol

Pharmacy ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 106
Author(s):  
Noura Bawab ◽  
Joanna C. Moullin ◽  
Clémence Perraudin ◽  
Olivier Bugnon
Keyword(s):  
Type 2 Diabetes ◽  
Primary Care ◽  
Large Scale ◽  
Quantitative Methods ◽  
Implementation Process ◽  
Support Program ◽  
Patient Support ◽  
Patient Support Program ◽  
Patient Reported

This research protocol illustrates the use of implementation science to support the development, dissemination and integration in primary care of effective and sustainable collaborative pharmacy services for chronic care management. The objective is to evaluate the implementation and the effectiveness of a pharmacist-led patient support program including regular motivational interviews; medication adherence, patient-reported outcomes, and clinical outcomes monitoring; and interactions with physicians, for patients with type 2 diabetes taking at least one oral antidiabetic medication in the French-speaking part of Switzerland. This is a prospective, multi-centered, observational, cohort study using a hybrid design to assess the patient support program. The evaluation includes three levels of analysis: (1) the implementation strategies, (2) the overall implementation process, and (3) the effectiveness of the program. Qualitative and quantitative methods are used, and outcomes are assessed at each stage of the implementation process: exploration, preparation, operation, and sustainability. This research project will provide key insights into the processes of implementing patient support programs on a large scale and adapting the traditional community pharmacy practices towards the delivery of person-centered and collaborative services.

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

BMJ Open ◽  
2018 ◽  
Vol 8 (12) ◽  
pp. e019947 ◽  
Author(s):  
Ian Litchfield ◽  
Ciaron Hoye ◽  
David Shukla ◽  
Ruth Backman ◽  
Alice Turner ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Primary Care ◽  
Process Mining ◽  
Process Models ◽  
Ageing Population ◽  
Long Term Conditions ◽  
Care Processes ◽  
The Uk

IntroductionIn 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 analysisThe 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 disseminationEthical 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.

scholarly journals Diagnostic Criteria for Depression in Type 2 Diabetes: A Data-Driven Approach

PLoS ONE ◽  
2014 ◽  
Vol 9 (11) ◽  
pp. e112049 ◽  
Author(s):  
Sergio E. Starkstein ◽  
Wendy A. Davis ◽  
Milan Dragovic ◽  
Violetta Cetrullo ◽  
Timothy M. E. Davis ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Diagnostic Criteria ◽  
Data Driven ◽  
Data Driven Approach

scholarly journals Multidisciplinary Collaboration in the Treatment of Patients With Type 2 Diabetes in Primary Care: Analysis Using Process Mining (Preprint)

2017 ◽  
Author(s):  
Tania Conca ◽  
Cecilia Saint-Pierre ◽  
Valeria Herskovic ◽  
Marcos Sepúlveda ◽  
Daniel Capurro ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Primary Care ◽  
Health Care ◽  
Health Care Professionals ◽  
Process Mining ◽  
Care Processes ◽  
Collaboration Patterns ◽  
Primary Health Care Centers ◽  
Health Care Centers

BACKGROUND Public health in several countries is characterized by a shortage of professionals and a lack of economic resources. Monitoring and redesigning processes can foster the success of health care institutions, enabling them to provide a quality service while simultaneously reducing costs. Process mining, a discipline that extracts knowledge from information system data to analyze operational processes, affords an opportunity to understand health care processes. OBJECTIVE Health care processes are highly flexible and multidisciplinary, and health care professionals are able to coordinate in a variety of different ways to treat a diagnosis. The aim of this work was to understand whether the ways in which professionals coordinate their work affect the clinical outcome of patients. METHODS This paper proposes a method based on the use of process mining to identify patterns of collaboration between physician, nurse, and dietitian in the treatment of patients with type 2 diabetes mellitus and to compare these patterns with the clinical evolution of the patients within the context of primary care. Clustering is used as part of the preprocessing of data to manage the variability, and then process mining is used to identify patterns that may arise. RESULTS The method is applied in three primary health care centers in Santiago, Chile. A total of seven collaboration patterns were identified, which differed primarily in terms of the number of disciplines present, the participation intensity of each discipline, and the referrals between disciplines. The pattern in which the three disciplines participated in the most equitable and comprehensive manner had a lower proportion of highly decompensated patients compared with those patterns in which the three disciplines participated in an unbalanced manner. CONCLUSIONS By discovering which collaboration patterns lead to improved outcomes, health care centers can promote the most successful patterns among their professionals so as to improve the treatment of patients. Process mining techniques are useful for discovering those collaborations patterns in flexible and unstructured health care processes.

scholarly journals Impact of a self-care education programme on patients with type 2 diabetes in primary care in the Basque Country

2013 ◽  
Vol 13 (1) ◽  
Author(s):  
Estibaliz Gamboa Moreno ◽  
◽  
Álvaro Sánchez Perez ◽  
Kalliopi Vrotsou ◽  
Juan Carlos Arbonies Ortiz ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Primary Care ◽  
Basque Country ◽  
Self Care ◽  
Education Programme ◽  
Care Education
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