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

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.

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

PEDIATRICS ◽  
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

scholarly journals Electronic health record-based clinical decision support alert for severe sepsis: a randomised evaluation

2019 ◽  
Vol 28 (9) ◽  
pp. 762-768 ◽  
Author(s):  
Norman Lance Downing ◽  
Joshua Rolnick ◽  
Sarah F Poole ◽  
Evan Hall ◽  
Alexander J Wessels ◽  
...  
Keyword(s):  
Severe Sepsis ◽  
Decision Support ◽  
Electronic Health Record ◽  
Clinical Decision Support ◽  
Tertiary Care ◽  
Clinical Decision ◽  
Health Record ◽  
Hospitalised Patients ◽  
Significant Difference ◽  
Electronic Health

BackgroundSepsis remains the top cause of morbidity and mortality of hospitalised patients despite concerted efforts. Clinical decision support for sepsis has shown mixed results reflecting heterogeneous populations, methodologies and interventions.ObjectivesTo determine whether the addition of a real-time electronic health record (EHR)-based clinical decision support alert improves adherence to treatment guidelines and clinical outcomes in hospitalised patients with suspected severe sepsis.DesignPatient-level randomisation, single blinded.SettingMedical and surgical inpatient units of an academic, tertiary care medical centre.Patients1123 adults over the age of 18 admitted to inpatient wards (intensive care units (ICU) excluded) at an academic teaching hospital between November 2014 and March 2015.InterventionsPatients were randomised to either usual care or the addition of an EHR-generated alert in response to a set of modified severe sepsis criteria that included vital signs, laboratory values and physician orders.Measurements and main resultsThere was no significant difference between the intervention and control groups in primary outcome of the percentage of patients with new antibiotic orders at 3 hours after the alert (35% vs 37%, p=0.53). There was no difference in secondary outcomes of in-hospital mortality at 30 days, length of stay greater than 72 hours, rate of transfer to ICU within 48 hours of alert, or proportion of patients receiving at least 30 mL/kg of intravenous fluids.ConclusionsAn EHR-based severe sepsis alert did not result in a statistically significant improvement in several sepsis treatment performance measures.

scholarly journals COMPARISON OF A FRAILTY RISK SCORE AND COMORBIDITY FOR EARLY REHOSPITALIZATION USING ELECTRONIC HEALTH RECORD DATA

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S906-S906
Author(s):  
Deborah A Lekan ◽  
Thomas P McCoy ◽  
Marjorie Jenkins ◽  
Somya Mohanty ◽  
Prashanti Manda
Keyword(s):  
Electronic Health Record ◽  
Average Length ◽  
Laboratory Data ◽  
Hospital Setting ◽  
Adverse Outcomes ◽  
Risk Scores ◽  
Unplanned Readmission ◽  
Health Record ◽  
Electronic Health Record Data ◽  
Electronic Health

Abstract Frailty is a clinical syndrome of impaired homeostasis and decreased physiologic reserve and resilience resulting in diminished ability to recover from stressors. In the hospital setting, barriers to adoption of popular frailty assessments make them impractical for widespread use. Improving quality and costs associated with hospitalization has motivated using data from the electronic health record (EHR) to identify patients at risk for adverse outcomes such as early readmission. Patient-level factors such as frailty and comorbidity may signal high readmission risk. In this retrospective study and secondary analysis of EHR data, we investigated Frailty Risk Scores (FRS) in models that included sociodemographic, comorbidity, and laboratory data for early 3-, 7-, and 30-day unplanned readmission. Study data were collected from a health system in the Southeastern U.S. on adults >50 years with an inpatient stay of >24 hours, 2013-2017. Exclusions included planned readmission and in-hospital mortality. The FRS was constructed using ICD-10-CM codes mapped for symptoms, syndromes, and laboratory values. Cox and logistic regression were conducted to examine associations with readmission. Area under the receiver operating characteristic curve (AUC) quantified accuracy. The sample was 53% female and 73% non-Hispanic White (N=55,778). About one-third took at least 7 prescribed medications (34%) and average length of stay was 4.3 days (max=103.6). FRS was a significant predictor of readmission for almost all models, independently of three comorbidity indices (range AUC=.850-.854 for 3-day, .809-.813 for 7-day, and .757 to .768 for 30-day). Frailty and comorbidity are independently associated with early rehospitalization.

scholarly journals Are specific elements of electronic health record use associated with clinician burnout more than others?

2020 ◽  
Vol 27 (9) ◽  
pp. 1401-1410
Author(s):  
Ross W Hilliard ◽  
Jacqueline Haskell ◽  
Rebekah L Gardner
Keyword(s):  
Confidence Interval ◽  
Electronic Health Record ◽  
Odds Ratio ◽  
Adjusted Odds Ratio ◽  
Well Being ◽  
Health Record ◽  
Face To Face ◽  
Efficiency Measures ◽  
Electronic Health ◽  
Copy And Paste

Abstract Objective The study sought to examine the association between clinician burnout and measures of electronic health record (EHR) workload and efficiency, using vendor-derived EHR action log data. Materials and Methods We combined data from a statewide clinician survey on burnout with Epic EHR data from the ambulatory sites of 2 large health systems; the combined dataset included 422 clinicians. We examined whether specific EHR workload and efficiency measures were independently associated with burnout symptoms, using multivariable logistic regression and controlling for clinician characteristics. Results Clinicians with the highest volume of patient call messages had almost 4 times the odds of burnout compared with clinicians with the fewest (adjusted odds ratio, 3.81; 95% confidence interval, 1.44-10.14; P = .007). No other workload measures were significantly associated with burnout. No efficiency variables were significantly associated with burnout in the main analysis; however, in a subset of clinicians for whom note entry data were available, clinicians in the top quartile of copy and paste use were significantly less likely to report burnout, with an adjusted odds ratio of 0.22 (95% confidence interval, 0.05-0.93; P = .039). Discussion High volumes of patient call messages were significantly associated with clinician burnout, even when accounting for other measures of workload and efficiency. In the EHR, “patient calls” encompass many of the inbox tasks occurring outside of face-to-face visits and likely represent an important target for improving clinician well-being. Conclusions Our results suggest that increased workload is associated with burnout and that EHR efficiency tools are not likely to reduce burnout symptoms, with the exception of copy and paste.

scholarly journals Promoting Quality Face-to-Face Communication during Ophthalmology Encounters in the Electronic Health Record Era

2020 ◽  
Vol 11 (01) ◽  
pp. 130-141
Author(s):  
Sally L. Baxter ◽  
Helena E. Gali ◽  
Michael F. Chiang ◽  
Michelle R. Hribar ◽  
Lucila Ohno-Machado ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
High Volume ◽  
Health Record ◽  
Patient Centeredness ◽  
Face To Face ◽  
Single Sign On ◽  
Timing Data ◽  
After Hours ◽  
Electronic Health ◽  
Physician Patient

Abstract Objective To evaluate informatics-enabled quality improvement (QI) strategies for promoting time spent on face-to-face communication between ophthalmologists and patients. Methods This prospective study involved deploying QI strategies during implementation of an enterprise-wide vendor electronic health record (EHR) in an outpatient academic ophthalmology department. Strategies included developing single sign-on capabilities, activating mobile- and tablet-based applications, EHR personalization training, creating novel workflows for team-based orders, and promoting problem-based charting to reduce documentation burden. Timing data were collected during 648 outpatient encounters. Outcomes included total time spent by the attending ophthalmologist on the patient, time spent on documentation, time spent on examination, and time spent talking with the patient. Metrics related to documentation efficiency, use of personalization features, use of team-based orders, and note length were also measured from the EHR efficiency portal and compared with averages for ophthalmologists nationwide using the same EHR. Results Time spent on exclusive face-to-face communication with patients initially decreased with EHR implementation (2.9 to 2.3 minutes, p = 0.005) but returned to the paper baseline by 6 months (2.8 minutes, p = 0.99). Observed participants outperformed national averages of ophthalmologists using the same vendor system on documentation time per appointment, number of customized note templates, number of customized order lists, utilization of team-based orders, note length, and time spent after-hours on EHR use. Conclusion Informatics-enabled QI interventions can promote patient-centeredness and face-to-face communication in high-volume outpatient ophthalmology encounters. By employing an array of interventions, time spent exclusively talking with the patient returned to levels equivalent to paper charts by 6 months after EHR implementation. This was achieved without requiring EHR redesign, use of scribes, or excessive after-hours work. Documentation efficiency can be achieved using interventions promoting personalization and team-based workflows. Given their efficacy in preserving face-to-face physician–patient interactions, these strategies may help alleviate risk of physician burnout.

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

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.

scholarly journals Implementation and Continuous Monitoring of an Electronic Health Record Embedded Readmissions Clinical Decision Support Tool

2020 ◽  
Vol 10 (3) ◽  
pp. 103
Author(s):  
David Gallagher ◽  
Congwen Zhao ◽  
Amanda Brucker ◽  
Jennifer Massengill ◽  
Patricia Kramer ◽  
...  
Keyword(s):  
Decision Support ◽  
Electronic Health Record ◽  
Clinical Decision Support ◽  
Hospital Readmissions ◽  
General Medicine ◽  
Clinical Decision ◽  
Unplanned Readmission ◽  
Health Record ◽  
Support Tool ◽  
Electronic Health

Unplanned hospital readmissions represent a significant health care value problem with high costs and poor quality of care. A significant percentage of readmissions could be prevented if clinical inpatient teams were better able to predict which patients were at higher risk for readmission. Many of the current clinical decision support models that predict readmissions are not configured to integrate closely with the electronic health record or alert providers in real-time prior to discharge about a patient’s risk for readmission. We report on the implementation and monitoring of the Epic electronic health record—“Unplanned readmission model version 1”—over 2 years from 1/1/2018–12/31/2019. For patients discharged during this time, the predictive capability to discern high risk discharges was reflected in an AUC/C-statistic at our three hospitals of 0.716–0.760 for all patients and 0.676–0.695 for general medicine patients. The model had a positive predictive value ranging from 0.217–0.248 for all patients. We also present our methods in monitoring the model over time for trend changes, as well as common readmissions reduction strategies triggered by the score.

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

PLoS ONE ◽  
2015 ◽  
Vol 10 (8) ◽  
pp. e0136341 ◽  
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

Business: The Electronic Health Record: Front Line Experience

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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