scholarly journals Mismatch between the prevalence of overweight and obese children and adolescents and recording in electronic health records: a cross-sectional study

2011 ◽  
Vol 19 (2) ◽  
pp. 75-82
Author(s):  
Paula Otero ◽  
Pablo Durán ◽  
Débora Setton ◽  
Alfredo Eymann ◽  
Julio Busaniche ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Children And Adolescents ◽  
Cross Sectional Study ◽  
Sectional Study ◽  
Obese Children ◽  
Cross Sectional ◽  
Health Records ◽  
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 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 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 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

scholarly journals Predictive value of genomic screening: cross-sectional study of cystic fibrosis in 50,788 electronic health records

2019 ◽  
Vol 4 (1) ◽  
Author(s):  
J. P. Sugunaraj ◽  
H. M. Brosius ◽  
M. F. Murray ◽  
K. Manickam ◽  
J. A. Stamm ◽  
...  
Keyword(s):  
Cystic Fibrosis ◽  
Electronic Health Records ◽  
Predictive Value ◽  
Cross Sectional Study ◽  
Sectional Study ◽  
Cross Sectional ◽  
Health Records ◽  
Genomic Screening ◽  
Electronic Health

scholarly journals QT interval and antidepressant use: a cross sectional study of electronic health records

BMJ ◽  
2013 ◽  
Vol 346 (jan29 3) ◽  
pp. f288-f288 ◽  
Author(s):  
V. M. Castro ◽  
C. C. Clements ◽  
S. N. Murphy ◽  
V. S. Gainer ◽  
M. Fava ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Qt Interval ◽  
Cross Sectional Study ◽  
Sectional Study ◽  
Cross Sectional ◽  
Health Records ◽  
Antidepressant Use ◽  
Electronic Health

scholarly journals Effect of electronic health records in ambulatory care: retrospective, serial, cross sectional study

BMJ ◽  
2005 ◽  
Vol 330 (7491) ◽  
pp. 581 ◽  
Author(s):  
Terhilda Garrido ◽  
Laura Jamieson ◽  
Yvonne Zhou ◽  
Andrew Wiesenthal ◽  
Louise Liang
Keyword(s):  
Electronic Health Records ◽  
Ambulatory Care ◽  
Cross Sectional Study ◽  
Sectional Study ◽  
Cross Sectional ◽  
Health Records ◽  
Electronic Health
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