Introduction: Influenza vaccination campaigns have difficulty in reaching the 75% uptake in healthcare workers (HCWs) that public health organizations target. This study runs a campaign across 42 primary care centers (PCCs) where for every HCW vaccinated against influenza, a polio vaccine is donated through UNICEF for children in developing nations. It also analyses the efficacy and cost of the campaign. Method: This observational prospective non-randomized cohort study was conducted across 262 PCCs and 15.812 HCWs. A total of 42 PCCs were delivered the full campaign, 114 were used as the control group, and 106 were excluded. The vaccine uptake in HCWs within each of those PCCs was registered. The cost analysis assumes that campaign costs remain stable year to year, and the only added cost would be the polio vaccines (0.59€). Results: We found statistically significant differences between both groups. A total of 1423 (59.02%) HCWs got vaccinated in the intervention group and 3768 (55.76%) in the control group OR 1.14, CI 95% (1.04–1.26). In this scenario, each additional HCW vaccinated in the intervention group costs 10.67€. Assuming all 262 PCCs had joined the campaign and reached 59.02% uptake, the cost of running this incentive would have been 5506€. The potential cost of increasing uptake in HCWs by 1% across all PCC (n = 8816) would be 1683€, and across all healthcare providers, 8862€ (n = 83.226). Conclusions: This study reveals that influenza vaccination uptake can be innovative by including solidary incentives and be successful in increasing uptake in HCWs. The cost of running a campaign such as this one is low.
Funding Acknowledgements Type of funding sources: Private grant(s) and/or Sponsorship. Main funding source(s): Sanofi Aventis S.A. Introduction Optimisation of the care of patients after an acute coronary syndrome (ACS) is a fundamental step to improve health outcomes and avoid consecutive cardiovascular events however data on how care is provided is often absent. Purpose Our objective was to analyse the main quality indicators in the post-ACS patient pathway so as to determine the actions which avoid future CDV events. Methods In a random sample of 100 patients between January 2018 and December 2019, we selected the indicators which most affect secondary prevention in patients post-ACS. All patients had been diagnosed with ACS within a tertiary-care hospital with a 24h interventional cardiology lab. The indicators were retrospectively analysed using the patients’ health record. Results The main results are presented in Table 1. Conclusions Based on this, we proposed an integrated protocol for all patients post-ACS which will begin in 2021 within this tertiary-care hospital. Within this protocol, the information contained in the discharge report will be improved and automatized as much as possible so as re-evaluate at a later date. Table 1: Demographics and results Title of the indicator Yes (%) No (%) Men 76 24 STEMI 40 60 NSTEMI 60 40 Dual antiplatelet therapy included in discharge report 100 0 High doses statins at discharge 98 2 BMI included in discharge report 0 100 LDL objective included in discharge report 14 86 HbA1c objective included in discharge report 13 87 Physical activity included in discharge report 15 85 Flu vaccination recomendations included in discharge report 0 100 Complete blood analysis completed 4-6 weeks after discharge 100 0 Blood pressure is measured on first post-discharge consultation 100 0 Blood pressure medication is changed on first post-discharge consultation 28 78 Patient arrives with measurement for HbA1c on first post-discharge consultation 78 22 Antidiabetic medication is modified on first post-discharge consultation 8 92 BMI is registered on first post-discharge consultation 0 100 Patients with LDL below 55mg/dl on first post-discharge consultation 29 71 Lipid-lowering medications is changed on first post-discharge consultation 29 71
Introduction and purpose Atrial fibrillation (AF) is the most common arrhythmia worldwide, with a considerable prevalence, high morbidity, mortality, and finantial cost in Europe. To optimise the quality of medical care received in patients with AF, we need to know and investigate their accurate demographic and clinical typology and the actual patient journey, which involves many data to review and a high number of patients included. The CHA2DS2Vasc score classifies the risk of stroke in patients with atrial fibrillation, one of the most critical complications of this arrhythmia, and assists decision-making. A part of Big Data, Data mining, process mining, and business intelligence techniques can analyse a high volume of clinical and non-clinical data. Methods Big Data pre-processing, data mining, process mining and business intelligence techniques were applied. Databases storing clinical and administrative information related to hospital discharges, highly complex procedures, emergency care and specialised practices from 2016 to 2020 were used. Patients with a principal or secondary diagnosis of AF were selected. Data sources included the Basic Minimum Set of Data (BMSD), containing administrative and clinical information at hospital discharge and resources at the Cardiology outpatient clinic. Once the databases were free of noise, inconsistencies, anomalies and duplicates, they were simultaneously reduced into smaller datasets. CHA2DS2Vasc score at the time of the patient's first contact with the system was calculated using BMSD information. Data integration techniques were applied to combine the extracted datasets into a single data source. Data transformation and reduction techniques were used, and a global dataset was generated for exploitation with process mining and business intelligence tools. Results After analysing 10942 individual BMSD, the CHA2DS2Vasc score was calculated for 6870 unique patients from 2016 to 2020. The most prevalent score was 5 (36.07%) (Figure 1), 4.19% of patients had a score of 1, and the first quartile was score 4. Nearly half of the patients (49.6%) were women. A large portion of patients (69.46%) were aged ≥75 (Figure 2). Diabetes mellitus was present in 25.21% of patients, high blood pressure in 64.76%, previous stroke in 8.59%, and none had a history of arterial disease. Conclusions Patients with atrial fibrillation treated in a tertiary university institution show a very high risk of stroke, according to the CHAD2DS2Vasc score. Almost 70% of them are aged 75 years and over. A high number of data and a high volume of patients can be analysed through data mining and business intelligence. Funding Acknowledgement Type of funding sources: Private grant(s) and/or Sponsorship. Main funding source(s): BMS-PfizerBoehringer
Introduction In an autonomous region in Spain, the heads of cardiovascular disease from four hospitals, forty-three primary care centres and the ambulance provider, have come together to collectively reduce variability, improve outcomes and patient experience, increase efficiency and focus on population health through prevention. As a result, a cardiovascular network was founded to address the needs of the population through eight projects, each with a group of professionals dedicated to its completion. This region has Beveridge-type universal healthcare and the clinical network must operate under a choice model in which patients can choose where they wish to be treated. This approach has never been carried forward in Spain previously nor, as far as we are aware, in Europe from the bottom-up with clinical leaders achieving buy-in from the political level. Purpose With the well-known demographic challenge putting the system under strain, our hypothesis is that this innovative collaborative approach where by hospitals pool resources and implement collective improvements from the bottom-up will results in better health for its population of over one million people. Methods The Institute for Healthcare Improvement's Assessment Scale for Collaboratives is used to track project progress through the valuation of project leads assigned to each project and the senior project manager. This scale divides progress from: forming team, activity but no changes, modest improvement, significant improvement, and outstanding sustainable results; some are further subdivided as shown in the image. Projects each have their set of indicators to ensure project objectives are achieved. Results The network has 8 working projects which include different specialists, professional groups and organizations including primary care and the main ambulance provider. The four hospitals have pulled together their resources to recruit two project managers. Progress measured with the Assessment Scale for Collaboratives indicates that projects have moved forward 18% in a three month period, reaching an average progress of 35%. Conclusion The closer collaboration across four hospital sites has put this network at the forefront of Spanish health policy by focusing work on diseases rather than by geographical areas. This allows clinical leaders to decide and focus on objectives that most suit the needs of its population whilst building a culture of continuous improvement across multiple care sites and professional groups. Funding Acknowledgement Type of funding source: Foundation. Main funding source(s): Fundaciόn Interhospitalaria para la Investigaciόn Cardiovascular - Fundaciόn FIC
Introduction and purpose Atrial fibrillation (AF) is the most common arrhythmia worldwide, with a considerable prevalence, high morbidity, mortality, and finantial cost in Europe. To optimise the quality of medical care received in patients with AF, we need to know and investigate their accurate demographic and clinical typology and the actual patient journey, which involves many data to review and a high number of patients included. The CHA2DS2Vasc score classifies the risk of stroke in patients with atrial fibrillation, one of the most critical complications of this arrhythmia, and assists decision-making. A part of Big Data, Data mining, process mining, and business intelligence techniques can analyse a high volume of clinical and non-clinical data. Methods Big Data pre-processing, data mining, process mining and business intelligence techniques were applied. Databases storing clinical and administrative information related to hospital discharges, highly complex procedures, emergency care and specialised practices from 2016 to 2020 were used. Patients with a principal or secondary diagnosis of AF were selected. Data sources included the Basic Minimum Set of Data (BMSD), containing administrative and clinical information at hospital discharge and resources at the Cardiology outpatient clinic. Once the databases were free of noise, inconsistencies, anomalies and duplicates, they were simultaneously reduced into smaller datasets. CHA2DS2Vasc score at the time of the patient's first contact with the system was calculated using BMSD information. Data integration techniques were applied to combine the extracted datasets into a single data source. Data transformation and reduction techniques were used, and a global dataset was generated for exploitation with process mining and business intelligence tools. Results After analysing 10942 individual BMSD, the CHA2DS2Vasc score was calculated for 6870 unique patients from 2016 to 2020. The most prevalent score was 5 (36.07%) (Figure 1), 4.19% of patients had a score of 1, and the first quartile was score 4. Nearly half of the patients (49.6%) were women. A large portion of patients (69.46%) were aged ≥75 (Figure 2). Diabetes mellitus was present in 25.21% of patients, high blood pressure in 64.76%, previous stroke in 8.59%, and none had a history of arterial disease. Conclusions Patients with atrial fibrillation treated in a tertiary university institution show a very high risk of stroke, according to the CHAD2DS2Vasc score. Almost 70% of them are aged 75 years and over. A high number of data and a high volume of patients can be analysed through data mining and business intelligence. Funding Acknowledgement Type of funding sources: Private grant(s) and/or Sponsorship. Main funding source(s): BMS-PfizerBoehringer
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