Effect of Automated, Point-of-Care Electronic Medical Record Screening for Appropriate Implantable Device Use in Heart Failure Patients

2012 ◽  
Vol 27 (6) ◽  
pp. 524-528 ◽  
Author(s):  
Eugene S. Chung ◽  
Lisa Dye ◽  
Andrea Feldmann ◽  
Donna Conley ◽  
Cheryl Bartone ◽  
...  
Keyword(s):  
Heart Failure ◽  
Medical Record ◽  
Electronic Medical Record ◽  
Point Of Care ◽  
Implantable Device ◽  
Heart Failure Patients ◽  
Device Use

An Automated Model to Identify Heart Failure Patients at Risk for 30-Day Readmission or Death Using Electronic Medical Record Data

Medical Care ◽  
2010 ◽  
Vol 48 (11) ◽  
pp. 981-988 ◽  
Author(s):  
Ruben Amarasingham ◽  
Billy J. Moore ◽  
Ying P. Tabak ◽  
Mark H. Drazner ◽  
Christopher A. Clark ◽  
...  
Keyword(s):  
Heart Failure ◽  
At Risk ◽  
Medical Record ◽  
Electronic Medical Record ◽  
Medical Record Data ◽  
Heart Failure Patients ◽  
Electronic Medical Record Data ◽  
Record Data ◽  
Patients At Risk

scholarly journals Comprehensive electronic medical record implementation levels not associated with 30-day all-cause readmissions within Medicare beneficiaries with heart failure.

2014 ◽  
Vol 05 (03) ◽  
pp. 670-684 ◽  
Author(s):  
P. Marken ◽  
Y. Zhong ◽  
S. D. Simon ◽  
W. Ketcherside ◽  
M. E. Patterson
Keyword(s):  
Heart Failure ◽  
Electronic Health Record ◽  
Medical Record ◽  
Electronic Medical Record ◽  
Incidence Rates ◽  
Health Record ◽  
Medicare Beneficiaries ◽  
Readmission Rates ◽  
Heart Failure Patients ◽  
Electronic Health

SummaryBackground: Regulatory standards for 30-day readmissions incentivize hospitals to improve quality of care. Implementing comprehensive electronic health record systems potentially decreases readmission rates by improving medication reconciliation at discharge, demonstrating the additional benefits of inpatient EHRs beyond improved safety and decreased errors.Objective: To compare 30-day all-cause readmission incidence rates within Medicare fee-for-service with heart failure discharged from hospitals with full implementation levels of comprehensive EHR systems versus those without.Methods: This retrospective cohort study uses data from the American Hospital Association Health IT survey and Medicare Part A claims to measure associations between hospital EHR implementation levels and beneficiary readmissions. Multivariable Cox regressions estimate the hazard ratio of 30-day all-cause readmissions within beneficiaries discharged from hospitals implementing comprehensive EHRs versus those without, controlling for beneficiary health status and hospital organizational factors. Propensity scores are used to account for selection bias.Results: The proportion of heart failure patients with 30-day all-cause readmissions was 30%, 29%, and 32% for those discharged from hospitals with full, some, and no comprehensive EHR systems. Heart failure patients discharged from hospitals with fully implemented comprehensive EHRs compared to those with no comprehensive EHR systems had equivalent 30-day readmission incidence rates (HR = 0.97, 95% CI 0.73 – 1.3)Conclusions: Implementation of comprehensive electronic health record systems does not necessarily improve a hospital’s ability to decrease 30-day readmission rates. Improving the efficiency of post-acute care will require more coordination of information systems between inpatient and ambulatory providers.Citation: Patterson ME, Marken P, Zhong Y, Simon SD, Ketcherside W. Comprehensive electronic medical record implementation levels not associated with 30-day all-cause readmissions within Medicare beneficiaries with heart failure. Appl Clin Inf 2014; 5: 670–684http://dx.doi.org/10.4338/ACI-2014-01-RA-0008

scholarly journals Electronic Medical Record-Based Appointment Order Improves 30-Day Post-Hospital Discharge Follow-Up in Heart Failure Patients

2019 ◽  
Vol 25 (8) ◽  
pp. S155-S156
Author(s):  
Kartik Telukuntla ◽  
Chetan Huded ◽  
Tim Sobol ◽  
Michael Hulseman ◽  
Ben Barzilai ◽  
...  
Keyword(s):  
Heart Failure ◽  
Medical Record ◽  
Hospital Discharge ◽  
Electronic Medical Record ◽  
Heart Failure Patients

Abstract 17060: Machine Learning and Video Recognition for Automated Detection of Fluid Status in Heart Failure Patients

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Pratik Doshi ◽  
John Tanaka ◽  
Jedrek Wosik ◽  
Natalia M Gil ◽  
Martin Bertran ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Failure ◽  
Point Of Care ◽  
Machine Learning Algorithms ◽  
Right Atrial ◽  
Heart Catheterization ◽  
Heart Failure Patients ◽  
Non Invasive ◽  
Fluid Status ◽  
Video Recognition

Introduction: There is a need for innovative solutions to better screen and diagnose the 7 million patients with chronic heart failure. A key component of assessing these patients is monitoring fluid status by evaluating for the presence and height of jugular venous distension (JVD). We hypothesize that video analysis of a patient’s neck using machine learning algorithms and image recognition can identify the amount of JVD. We propose the use of high fidelity video recordings taken using a mobile device camera to determine the presence or absence of JVD, which we will use to develop a point of care testing tool for early detection of acute exacerbation of heart failure. Methods: In this feasibility study, patients in the Duke cardiac catheterization lab undergoing right heart catheterization were enrolled. RGB and infrared videos were captured of the patient’s neck to detect JVD and correlated with right atrial pressure on the heart catheterization. We designed an adaptive filter based on biological priors that enhances spatially consistent frequency anomalies and detects jugular vein distention, with implementation done on Python. Results: We captured and analyzed footage for six patients using our model. Four of these six patients shared a similar strong signal outliner within the frequency band of 95bpm – 200bpm when using a conservative threshold, indicating the presence of JVD. We did not use statistical analysis given the small nature of our cohort, but in those we detected a positive JVD signal the RA mean was 20.25 mmHg and PCWP mean was 24.3 mmHg. Conclusions: We have demonstrated the ability to evaluate for JVD via infrared video and found a relationship with RHC values. Our project is innovative because it uses video recognition and allows for novel patient interactions using a non-invasive screening technique for heart failure. This tool can become a non-invasive standard to both screen for and help manage heart failure patients.

Sign in / Sign up

Export Citation Format

Share Document