An Electronic Health Record Tool Designed to Improve Pediatric Hospital Discharge has Low Predictive Utility for Readmissions

29 Predicting Unplanned Readmission and Death After Hospital Discharge: How Do Frailty Tools Compare to Electronic Health Record Frailty Markers?

Age and Ageing ◽

10.1093/ageing/afab029.08 ◽

2021 ◽

Vol 50 (Supplement_1) ◽

pp. i7-i11

Author(s):

A Anand ◽

Y Yong Tew ◽

J Hao Chan ◽

P Keeling ◽

S D Shenkin ◽

...

Keyword(s):

Electronic Health Record ◽

Hospital Discharge ◽

Royal Infirmary ◽

Unplanned Readmission ◽

Health Record ◽

Clinical Frailty Scale ◽

Standard Deviation Increase ◽

Face To Face ◽

Hospitalised Patients ◽

Electronic Health

Abstract Introduction Numerous frailty tools and definitions have been described. Amongst hospitalised patients, the validity of face-to-face instruments may be confounded by acute illness. However, patient assessment after recovery at the point of hospital discharge, or recognition of electronic health record (EHR) frailty markers, may overcome this issuep. Methods In a consented, prospective observational cohort study, we recruited patients ≥70 years old within 24 hours of expected discharge from the cardiology ward of the Royal Infirmary of Edinburgh. Three established frailty instruments were tested: the Fried phenotype, Short Physical Performance Battery and nurse-administered Clinical Frailty Scale (CFS). An unweighted 32-item EHR score was generated using frailty markers (e.g. falls risk, continence, cognition) recorded within mandated admission documentation. Comorbidity was assessed by count of chronic health conditions. Outcomes were a 90-day composite of unplanned readmission or death and 12-month mortality. Adjusted Cox modelling determined the hazard ratio (HR) per standard deviation increase in each frailty score. Results 186 patients (mean age 79 ± 6 years, 64% male) were included, of whom 55 (30%) had a 90-day composite outcome, and 21 (11%) died within 12 months. All four frailty tools were moderately correlated with age and comorbidity (Pearson’s r 0.21 to 0.43, all p < 0.05). The Fried phenotype (HR 1.47, 95% CI 1.18–1.81), CFS (HR 1.24, 95% CI 1.01–1.51) and EHR score (HR 1.26, 95% CI 1.03–1.55) independently predicted 90-day readmission or death, after adjustment for age, sex and comorbidity. All frailty instruments were independent predictors of 12-month mortality, with age, sex and comorbidity losing predictive power (p > 0.05) once frailty was included in modelling. Conclusions At hospital discharge, the Fried phenotype and CFS added to age and comorbidity in risk prediction for future unplanned readmission or death. EHR frailty markers appeared comparable to face-to-face assessment. An automated trigger for high-risk patients using routine EHR data merits prospective evaluation.

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Earlier Hospital Discharge With Prospectively Designated Discharge Time in the Electronic Health Record

PEDIATRICS ◽

10.1542/peds.2019-0929 ◽

2019 ◽

Vol 144 (5) ◽

pp. e20190929

Author(s):

Daniel J. Sklansky ◽

Sabrina Butteris ◽

Kristin A. Shadman ◽

Michelle M. Kelly ◽

M. Bruce Edmonson ◽

...

Keyword(s):

Electronic Health Record ◽

Hospital Discharge ◽

Health Record ◽

Discharge Time ◽

Electronic Health

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Engaging End-Users in Electronic Health Record (EHR) Updates: Social Workers’ Opinions on Integrating a Psychosocial Acuity Scale at a Large Urban Pediatric Hospital

Journal of Technology in Human Services ◽

10.1080/15228835.2021.1972503 ◽

2021 ◽

pp. 1-21

Author(s):

Abigail M. Ross ◽

Matthew Tvedte ◽

Susan Shanske ◽

Nicole Lamontagne ◽

Paige Sable ◽

...

Keyword(s):

Electronic Health Record ◽

Social Workers ◽

End Users ◽

Health Record ◽

Pediatric Hospital ◽

Electronic Health

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Predicting readmission and death after hospital discharge: a comparison of conventional frailty measurement with an electronic health record-based score

Age and Ageing ◽

10.1093/ageing/afab043 ◽

2021 ◽

Author(s):

Yong Yong Tew ◽

Juen Hao Chan ◽

Polly Keeling ◽

Susan D Shenkin ◽

Alasdair MacLullich ◽

...

Keyword(s):

At Risk ◽

Electronic Health Record ◽

Hospital Discharge ◽

Risk Score ◽

Primary Outcome ◽

Risk Scores ◽

Health Record ◽

Co Morbidity ◽

Patients At Risk ◽

Electronic Health

Abstract Background frailty measurement may identify patients at risk of decline after hospital discharge, but many measures require specialist review and/or additional testing. Objective to compare validated frailty tools with routine electronic health record (EHR) data at hospital discharge, for associations with readmission or death. Design observational cohort study. Setting hospital ward. Subjects consented cardiology inpatients ≥70 years old within 24 hours of discharge. Methods patients underwent Fried, Short Physical Performance Battery (SPPB), PRISMA-7 and Clinical Frailty Scale (CFS) assessments. An EHR risk score was derived from the proportion of 31 possible frailty markers present. Electronic follow-up was completed for a primary outcome of 90-day readmission or death. Secondary outcomes were mortality and days alive at home (‘home time’) at 12 months. Results in total, 186 patients were included (79 ± 6 years old, 64% males). The primary outcome occurred in 55 (30%) patients. Fried (hazard ratio [HR] 1.47 per standard deviation [SD] increase, 95% confidence interval [CI] 1.18–1.81, P < 0.001), CFS (HR 1.24 per SD increase, 95% CI 1.01–1.51, P = 0.04) and EHR risk scores (HR 1.35 per SD increase, 95% CI 1.02–1.78, P = 0.04) were independently associated with the primary outcome after adjustment for age, sex and co-morbidity, but the SPPB and PRISMA-7 were not. The EHR risk score was independently associated with mortality and home time at 12 months. Conclusions frailty measurement at hospital discharge identifies patients at risk of poorer outcomes. An EHR-based risk score appeared equivalent to validated frailty tools and may be automated to screen patients at scale, but this requires further validation.

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Design and Implementation of a Visual Analytics Electronic Antibiogram within an Electronic Health Record System at a Tertiary Pediatric Hospital

Applied Clinical Informatics ◽

10.1055/s-0037-1615787 ◽

2018 ◽

Vol 09 (01) ◽

pp. 037-045 ◽

Cited By ~ 9

Author(s):

Allan Simpao ◽

Luis Ahumada ◽

Beatriz Larru Martinez ◽

Ana Cardenas ◽

Talene Metjian ◽

...

Keyword(s):

Electronic Health Record ◽

Visual Analytics ◽

Prescribing Patterns ◽

Empiric Antibiotic Therapy ◽

Health Record ◽

Pediatric Hospital ◽

National Guidelines ◽

Monthly Basis ◽

Susceptibility Data ◽

Electronic Health

Background Hospitals use antibiograms to guide optimal empiric antibiotic therapy, reduce inappropriate antibiotic usage, and identify areas requiring intervention by antimicrobial stewardship programs. Creating a hospital antibiogram is a time-consuming manual process that is typically performed annually. Objective We aimed to apply visual analytics software to electronic health record (EHR) data to build an automated, electronic antibiogram (“e-antibiogram”) that adheres to national guidelines and contains filters for patient characteristics, thereby providing access to detailed, clinically relevant, and up-to-date antibiotic susceptibility data. Methods We used visual analytics software to develop a secure, EHR-linked, condition- and patient-specific e-antibiogram that supplies susceptibility maps for organisms and antibiotics in a comprehensive report that is updated on a monthly basis. Antimicrobial susceptibility data were grouped into nine clinical scenarios according to the specimen source, hospital unit, and infection type. We implemented the e-antibiogram within the EHR system at Children's Hospital of Philadelphia, a tertiary pediatric hospital and analyzed e-antibiogram access sessions from March 2016 to March 2017. Results The e-antibiogram was implemented in the EHR with over 6,000 inpatient, 4,500 outpatient, and 3,900 emergency department isolates. The e-antibiogram provides access to rolling 12-month pathogen and susceptibility data that is updated on a monthly basis. E-antibiogram access sessions increased from an average of 261 sessions per month during the first 3 months of the study to 345 sessions per month during the final 3 months. Conclusion An e-antibiogram that was built and is updated using EHR data and adheres to national guidelines is a feasible replacement for an annual, static, manually compiled antibiogram. Future research will examine the impact of the e-antibiogram on antibiotic prescribing patterns.

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Sentiment Measured in Hospital Discharge Notes Is Associated with Readmission and Mortality Risk: An Electronic Health Record Study

PLoS ONE ◽

10.1371/journal.pone.0136341 ◽

2015 ◽

Vol 10 (8) ◽

pp. e0136341 ◽

Cited By ~ 26

Author(s):

Thomas H. McCoy ◽

Victor M. Castro ◽

Andrew Cagan ◽

Ashlee M. Roberson ◽

Isaac S. Kohane ◽

...

Keyword(s):

Electronic Health Record ◽

Hospital Discharge ◽

Mortality Risk ◽

Health Record ◽

Electronic Health

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Business: The Electronic Health Record: Front Line Experience

Perspectives on Administration and Supervision ◽

10.1044/aas21.1.18 ◽

2011 ◽

Vol 21 (1) ◽

pp. 18-22

Author(s):

Rosemary Griffin

Keyword(s):

Health Care ◽

Information Technology ◽

Electronic Health Record ◽

Financial Incentives ◽

Best Practice ◽

Health Care Systems ◽

The United States ◽

Health Record ◽

Health Trends ◽

Electronic Health

National legislation is in place to facilitate reform of the United States health care industry. The Health Care Information Technology and Clinical Health Act (HITECH) offers financial incentives to hospitals, physicians, and individual providers to establish an electronic health record that ultimately will link with the health information technology of other health care systems and providers. The information collected will facilitate patient safety, promote best practice, and track health trends such as smoking and childhood obesity.

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