Dabigatran and vitamin K antagonists’ use in naïve patients with non-valvular atrial fibrillation: a cross-sectional study of primary care-based electronic health records

2017 ◽  
Vol 73 (10) ◽  
pp. 1323-1330 ◽  
Author(s):  
Bogdan Vlacho ◽  
Maria Giner-Soriano ◽  
Edurne Zabaleta-del-Olmo ◽  
Albert Roso-Llorach ◽  
Ana García-Sangenís ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Primary Care ◽  
Electronic Health Records ◽  
Vitamin K ◽  
Vitamin K Antagonists ◽  
Cross Sectional Study ◽  
Sectional Study ◽  
Cross Sectional ◽  
Health Records ◽  
Electronic Health

scholarly journals Primary Care Medication Safety Surveillance with Integrated Primary and Secondary Care Electronic Health Records: A Cross-Sectional Study

Drug Safety ◽  
2015 ◽  
Vol 38 (7) ◽  
pp. 671-682 ◽  
Author(s):  
Artur Akbarov ◽  
Evangelos Kontopantelis ◽  
Matthew Sperrin ◽  
Susan J. Stocks ◽  
Richard Williams ◽  
...  
Keyword(s):  
Primary Care ◽  
Electronic Health Records ◽  
Secondary Care ◽  
Medication Safety ◽  
Cross Sectional Study ◽  
Sectional Study ◽  
Cross Sectional ◽  
Health Records ◽  
Safety Surveillance ◽  
Electronic Health

scholarly journals Soft clustering using real-world data for the identification of multimorbidity patterns in an elderly population: cross-sectional study in a Mediterranean population

BMJ Open ◽  
2019 ◽  
Vol 9 (8) ◽  
pp. e029594 ◽  
Author(s):  
Concepción Violán ◽  
Quintí Foguet-Boreu ◽  
Sergio Fernández-Bertolín ◽  
Marina Guisado-Clavero ◽  
Margarita Cabrera-Bean ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Electronic Health Records ◽  
Cross Sectional Study ◽  
Secondary Outcome ◽  
Sectional Study ◽  
Cross Sectional ◽  
Health Records ◽  
Soft Clustering ◽  
Fuzzy C Means ◽  
Electronic Health

ObjectivesThe aim of this study was to identify, with soft clustering methods, multimorbidity patterns in the electronic health records of a population ≥65 years, and to analyse such patterns in accordance with the different prevalence cut-off points applied. Fuzzy cluster analysis allows individuals to be linked simultaneously to multiple clusters and is more consistent with clinical experience than other approaches frequently found in the literature.DesignA cross-sectional study was conducted based on data from electronic health records.Setting284 primary healthcare centres in Catalonia, Spain (2012).Participants916 619 eligible individuals were included (women: 57.7%).Primary and secondary outcome measuresWe extracted data on demographics, International Classification of Diseases version 10 chronic diagnoses, prescribed drugs and socioeconomic status for patients aged ≥65. Following principal component analysis of categorical and continuous variables for dimensionality reduction, machine learning techniques were applied for the identification of disease clusters in a fuzzy c-means analysis. Sensitivity analyses, with different prevalence cut-off points for chronic diseases, were also conducted. Solutions were evaluated from clinical consistency and significance criteria.ResultsMultimorbidity was present in 93.1%. Eight clusters were identified with a varying number of disease values: nervous and digestive; respiratory, circulatory and nervous; circulatory and digestive; mental, nervous and digestive, female dominant; mental, digestive and blood, female oldest-old dominant; nervous, musculoskeletal and circulatory, female dominant; genitourinary, mental and musculoskeletal, male dominant; and non-specified, youngest-old dominant. Nuclear diseases were identified for each cluster independently of the prevalence cut-off point considered.ConclusionsMultimorbidity patterns were obtained using fuzzy c-means cluster analysis. They are clinically meaningful clusters which support the development of tailored approaches to multimorbidity management and further research.

scholarly journals Health of Spanish centenarians: a cross-sectional study based on electronic health records

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Antonio Gimeno-Miguel ◽  
Mercedes Clerencia-Sierra ◽  
Ignatios Ioakeim ◽  
Beatriz Poblador-Plou ◽  
Mercedes Aza-Pascual-Salcedo ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Cross Sectional Study ◽  
Sectional Study ◽  
Cross Sectional ◽  
Health Records ◽  
Electronic Health

scholarly journals Oral Anticoagulant Adequacy in Non-Valvular Atrial Fibrillation in Primary Care: A Cross-Sectional Study Using Real-World Data (Fantas-TIC Study)

2021 ◽  
Vol 18 (5) ◽  
pp. 2244
Author(s):  
M. Rosa Dalmau Llorca ◽  
Carina Aguilar Martín ◽  
Noèlia Carrasco-Querol ◽  
Zojaina Hernández Rojas ◽  
Emma Forcadell Drago ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Primary Care ◽  
Real World ◽  
Vitamin K Antagonists ◽  
Oral Anticoagulants ◽  
Cross Sectional Study ◽  
Sectional Study ◽  
Real World Data ◽  
Cross Sectional ◽  
World Data

Background: Oral anticoagulants (OAs) are the treatment to prevent stroke in atrial fibrillation (AF). Anticoagulant treatment choice in non-valvular atrial fibrillation (NVAF) must be individualized, taking current guidelines into account. Adequacy of anticoagulant therapy under the current criteria for NVAF in real-world primary care is presented. Methods: Cross-sectional study, with real-world data from patients treated in primary care (PC). Data were obtained from the System for the Improvement of Research in Primary Care (SIDIAP) database, covering 60,978 NVAF-anticoagulated patients from 287 PC centers in 2018. Results: In total, 41,430 (68%) were treated with vitamin K antagonists (VKAs) and 19,548 (32%) NVAF with direct-acting oral anticoagulants (DOACs). Inadequate prescription was estimated to be 36.0% and 67.6%, respectively. Most DOAC inadequacy (77.3%) was due to it being prescribed as a first-line anticoagulant when there was no history of thromboembolic events or intracranial hemorrhage (ICH). A total of 22.1% had missing estimated glomerular filtration rate (eGFR) values. Common causes of inadequate VKA prescription were poor control of time in therapeutic range (TTR) (98.8%) and ICH (2.2%). Conclusions: Poor adequacy to current criteria was observed, being inadequacy higher in DOACs than in VKAs. TTR and GFR should be routinely calculated in electronic health records (EHR) to facilitate decision-making and patient safety.

scholarly journals Physician Specialty and Variations in Adoption of Electronic Health Records

2013 ◽  
Vol 04 (02) ◽  
pp. 225-240 ◽  
Author(s):  
S. Banerjee ◽  
R. Kaushal ◽  
L.M. Kern ◽  
Z. M. Grinspan
Keyword(s):  
Electronic Health Records ◽  
Reference Group ◽  
Cross Sectional Study ◽  
Sectional Study ◽  
Physician Specialty ◽  
Cross Sectional ◽  
Health Records ◽  
Ehr Adoption ◽  
Electronic Health ◽  
Over Time

SummaryObjective: Efforts to promote adoption of electronic health records (EHRs) have focused on primary care physicians, who are now expected to exchange data electronically with other providers, including specialists. However, the variation of EHR adoption among specialists is underexplored.Methods: We conducted a retrospective cross-sectional study to determine the association between physician specialty and the prevalence of EHR adoption, and a retrospective serial cross-sectional study to determine the association of physician specialty and the rate of EHR adoption over time. We used the 2005–2009 National Ambulatory Medical Care Survey. We considered fourteen specialties, and four definitions of EHR adoption (any EHR, basic EHR, full EHR, and a novel definition of EHR sophistication). We used multivariable logistic regression, and adjusted for several covariates (geography, practice characteristics, revenue characteristics, physician degree).Results: Physician specialty was significantly associated with EHR adoption, regardless of the EHR definition, after adjusting for covariates. Psychiatrists, dermatologists, pediatricians, ophthalmologists, and general surgeons were significantly less likely to adopt EHRs, compared to the reference group of family medicine / general practitioners. After adjustment for covariates, these specialties were 44 – 94% less likely to adopt EHRs than the reference group. EHR adoption increased in all specialties, by approximately 40% per year. The rate of EHR adoption over time did not significantly vary by specialty.Conclusions: Although EHR adoption is increasing in all specialties, adoption varies widely by specialty. In order to insure each individual’s network of providers can electronically share data, widespread adoption of EHRs is needed across all specialties.Citation: Grinspan ZM, Banerjee S, Kaushal R, Kern LM. Physician specialty and variations in adoption of electronic health records. Appl Clin Inf 2013; 4: 225–240http://dx.doi.org/10.4338/ACI-2013-02-RA-0015

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