Agreement between self-report questionnaires and medical record data was substantial for diabetes, hypertension, myocardial infarction and stroke but not for heart failure

Medication adherence in patients with heart failure is very important. Previous study, the low level of medication adherence in patients with heart failure can increase the risk of recurrence of the patient, thereby increasing the rehospitalization and mortality. This study was conducted to evaluate existing programs in heart failure clinic. The purpose of this study is to determine the relationship between the incidence of medication adherence with the incidence of rehospitalization and mortality in patients with heart failure. Retrospective cohort study conducted using medical record data from the Heart Failure Clinic in dr Hasan Sadikin Hospital Bandung between October 2018 to July 2020. This study involved 77 people with a diagnosis of CHF and had attended a minimum of 6 months of the heart failure clinic program at Dr Hasan Sadikin Hospital, Bandung. Descriptive statistics were used to describe demographic characteristics such as age, gender, and NYHA class. Chi-square analysis was used to analyze the relationship between medication adherence, the incidence of rehospitalization and mortality. Result, demographic data, there are 55 (71.4%) adults and 22 (28.6%) elderly, male 50 (64.9%) and female 27 (35.1%), NYHA I 32 people (41.6%), NYHA II 26 (33.8%), NYHA III 7 (9.1%), NYHA IV 12 (15.6%). Non-adherence 8 (10,4%), rehospitalizayion 20 (26%), and mortality 18 (23,4%). Based on the chi-square statistical analysis, there was a relationship with the incidence of rehospitalization (p <0.001) and there was a relationship with mortality (p <0.002). This study has research limitations, because the data obtained is only based on medical record data. Medication adherence was associated with rehospitalization and mortality. It is important to develop interventions to improve medication adherence. This study is the most recent study conducted at the Heart Failure Clinic Dr Hasan Sadikin Hospital Bandung, and can be used as an evaluation of the program that has been carried out.

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768 Machine Learning Prediction Tools for All-Cause Readmissions in Patients Hospitalised for Heart Failure Using Routinely Collected Medical Record Data

Heart Lung and Circulation ◽

10.1016/j.hlc.2020.09.775 ◽

2020 ◽

Vol 29 ◽

pp. S382

Author(s):

A. Kawai ◽

H. Patel ◽

D. Kaye ◽

S. Nanayakkara

Keyword(s):

Machine Learning ◽

Heart Failure ◽

Medical Record ◽

Medical Record Data ◽

Prediction Tools ◽

Record Data

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Are medical record front page data suitable for risk adjustment in hospital performance measurement? Development and validation of a risk model of in-hospital mortality after acute myocardial infarction

BMJ Open ◽

10.1136/bmjopen-2020-045053 ◽

2021 ◽

Vol 11 (4) ◽

pp. e045053

Author(s):

Chaoqun Wu ◽

Danwei Zhang ◽

Xueke Bai ◽

Tiannan Zhou ◽

Yongfei Wang ◽

...

Keyword(s):

Myocardial Infarction ◽

Hospital Mortality ◽

Medical Record ◽

Risk Adjustment ◽

Validation Cohort ◽

Hospital Performance ◽

Front Page ◽

Medical Record Data ◽

Record Data ◽

Complete Medical Record

ObjectivesTo develop a model of in-hospital mortality using medical record front page (MRFP) data and assess its validity in case-mix standardisation by comparison with a model developed using the complete medical record data.DesignA nationally representative retrospective study.SettingRepresentative hospitals in China, covering 161 hospitals in modelling cohort and 156 hospitals in validation cohort.ParticipantsRepresentative patients admitted for acute myocardial infarction. 8370 patients in modelling cohort and 9704 patients in validation cohort.Primary outcome measuresIn-hospital mortality, which was defined explicitly as death that occurred during hospitalisation, and the hospital-level risk standardised mortality rate (RSMR).ResultsA total of 14 variables were included in the model predicting in-hospital mortality based on MRFP data, with the area under receiver operating characteristic curve of 0.78 among modelling cohort and 0.79 among validation cohort. The median of absolute difference between the hospital RSMR predicted by hierarchical generalised linear models established based on MRFP data and complete medical record data, which was built as ‘reference model’, was 0.08% (10th and 90th percentiles: −1.8% and 1.6%). In the regression model comparing the RSMR between two models, the slope and intercept of the regression equation is 0.90 and 0.007 in modelling cohort, while 0.85 and 0.010 in validation cohort, which indicated that the evaluation capability from two models were very similar.ConclusionsThe models based on MRFP data showed good discrimination and calibration capability, as well as similar risk prediction effect in comparison with the model based on complete medical record data, which proved that MRFP data could be suitable for risk adjustment in hospital performance measurement.

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Construction of a Heart Failure Database Collating Administrative Claims Data and Electronic Medical Record Data to Evaluate Risk Factors for In-Hospital Death and Prolonged Hospitalization

Circulation Reports ◽

10.1253/circrep.cr-19-0051 ◽

2019 ◽

Vol 1 (12) ◽

pp. 582-592

Author(s):

Kazuhisa Kodama ◽

Tomohiro Sakamoto ◽

Toru Kubota ◽

Hideyuki Takimura ◽

Hiroshi Hongo ◽

...

Keyword(s):

Risk Factors ◽

Heart Failure ◽

Medical Record ◽

Electronic Medical Record ◽

Claims Data ◽

Hospital Death ◽

Administrative Claims ◽

Medical Record Data ◽

Electronic Medical Record Data ◽

Record Data

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Enhancing ICD-Code-Based Case Definition for Heart Failure Using Electronic Medical Record Data

Journal of Cardiac Failure ◽

10.1016/j.cardfail.2020.04.003 ◽

2020 ◽

Vol 26 (7) ◽

pp. 610-617 ◽

Cited By ~ 1

Author(s):

Yuan Xu ◽

Seungwon Lee ◽

Elliot Martin ◽

Adam G. D'souza ◽

Chelsea T.A. Doktorchik ◽

...

Keyword(s):

Heart Failure ◽

Medical Record ◽

Electronic Medical Record ◽

Case Definition ◽

Medical Record Data ◽

Electronic Medical Record Data ◽

Record Data

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Influential Factors in The Law Enforcement Process of Sexual Violence Cases in Children in The City of Semarang

SOEPRA ◽

10.24167/shk.v6i2.2912 ◽

2020 ◽

Vol 6 (2) ◽

pp. 4

Author(s):

Liya Suwarni

Keyword(s):

Law Enforcement ◽

Sexual Violence ◽

Medical Record ◽

Child Protection ◽

Influential Factors ◽

Medical Record Data ◽

Violence Against Children ◽

Record Data ◽

The Law ◽

The City

Background. Cases of sexual violence increase every year, victims ranging from adolescents, children to toddlers. Based on data from the Indonesian Child Protection Commission, abuse and violence against children in Indonesia in 2013 were 23 cases, in 2014 there were 53 cases, in 2015 there were 133 cases, 2017 reached 1,337 cases, and as of July 2018 there were 424 cases. Purpose. Knowing the factors that influence the law enforcement process of sexy violence cases in Semarang City. Method This study uses descriptive analytical methods for cases of violence against children, based on medical record data in hospitals, documents in Mapolrestabes, the District Attorney's Office and the Semarang City Court for the period of January 2015 to December 2018. Results. Based on research results obtained 213 experimental cases section from medical record data in hospitals in the city of Semarang. Most cases of child abuse occurred in 2018 with 72 cases. Most victims are 12-14 years old age group, female. Most types of cases are cases of intercourse. The majority of violations are persons known as victims, perpetrators not working, and most of the places of occurrence are in the defendant's house. At the time of prosecution and trial, the number of cases was significantly reduced to only 8 cases. Factors related to this include lack of evidence, difficulty in obtaining information from victims, convoluted statements of coverage, lack of election, and obtaining diversion rates. Conclusion Cases of sexual violence have increased from year to year. The process of law enforcement on this problem still has many difficulties in each manufacturing process which is still difficult to overcome.

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