The readmission risk flag: Using the electronic health record to automatically identify patients at risk for 30-day readmission

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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1651. Describing the Tuberculosis Infection Cascade of Care Based on Electronic Health Record Data

Open Forum Infectious Diseases ◽

10.1093/ofid/ofaa439.1829 ◽

2020 ◽

Vol 7 (Supplement_1) ◽

pp. S813-S814

Author(s):

Laura A Vonnahme ◽

Jonathan Todd ◽

Jon Puro ◽

Jee Oakley ◽

Matthew Jones ◽

...

Keyword(s):

At Risk ◽

Electronic Health Record ◽

Tuberculosis Infection ◽

Health Record ◽

Populations At Risk ◽

Cascade Of Care ◽

Patients At Risk ◽

Electronic Health ◽

Not At Risk ◽

Positive Results

Abstract Background Appropriate screening of individuals to detect latent tuberculosis infection (LTBI) is a critical step for achieving tuberculosis (TB) elimination in the US; >80% of TB cases are attributed to LTBI reactivation. TB infection testing and treatment must engage community health clinics where populations at risk seek care. However, there are significant data knowledge gaps in the current LTBI cascade of care (CoC) in this setting. We used an electronic health record (EHR) database from OCHIN, Inc., to characterize the LTBI CoC and identify potential future interventions. Methods We extracted a cohort of patients from 2012–2016 EHR data; we stratified by whether patients were at risk for TB based on meeting at least one of the following criteria: non-US born or non-English language preference, homelessness, encounter at correctional facility, history of close contact with a TB case, or being immunocompromised. Along each step of the LTBI CoC, we determined the proportions with a test for TB infection, with available test results, with a positive test, with an LTBI diagnosis, and with LTBI treatment prescribed. We used Χ 2 tests to compare the LTBI CoCs among patients at risk with those classified as not at risk. Results Of nearly 2.2 million patient records, 701,467 (32.0%) met criteria for being at risk for TB; 84,422 at risk (12.0%) were tested; 65,562 (77.7%) had available results, of whom 9,624 (14.7%) were positive. Among those with positive results, 6,958 (72.3%) had an LTBI diagnosis, of whom 1,732 (24.9%) were prescribed treatment. Among those classified as not at risk, fewer were tested (66,773 [4.5%], p< 0.001) and had positive results (2,500 [3.7%], p< 0.0001). Among those with positive results, 1,998 (80.0%) had an LTBI diagnosis, of whom 395 (19.8%) initiated treatment. Conclusion This study highlights gaps in the LTBI CoC, and where interventions are most needed. The largest gaps were in testing patients at risk, as 88% were not tested, and treatment, as 75% diagnosed with LTBI were not treated. Just under half (44%) of all TB tests appeared to be performed in persons with little risk for TB; this is a substantial amount of testing given very few begin treatment. Resources could be redirected to increase screening and treatment among populations at risk. Disclosures All Authors: No reported disclosures

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S3 Identification of Patients at Risk for Pancreatic Cancer in a 3-Year Timeframe Based on Machine Learning Algorithms in the Electronic Health Record

The American Journal of Gastroenterology ◽

10.14309/01.ajg.0000771992.23577.96 ◽

2021 ◽

Vol 116 (1) ◽

pp. S2-S2

Author(s):

Weicheng Zhu ◽

Mark B. Pochapin ◽

Aphinyanaphongs Yindalon ◽

Narges Razavian ◽

Tamas A. Gonda

Keyword(s):

Machine Learning ◽

Pancreatic Cancer ◽

At Risk ◽

Electronic Health Record ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Health Record ◽

Patients At Risk ◽

Electronic Health

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Identifying veterans at risk for dementia using claims‐based electronic health record data and machine learning

Alzheimer s & Dementia ◽

10.1002/alz.052636 ◽

2021 ◽

Vol 17 (S10) ◽

Author(s):

Alejandro Castellanos ◽

Otoniel Ysea‐Hill ◽

Chaohao Lin ◽

Shafiul Hasan ◽

Ou Bai ◽

...

Keyword(s):

Machine Learning ◽

At Risk ◽

Electronic Health Record ◽

Health Record ◽

Electronic Health Record Data ◽

Record Data ◽

Electronic Health

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Electronic health record cue identifies epilepsy patients at risk for obstructive sleep apnea

Neurology Clinical Practice ◽

10.1212/cpj.0000000000000502 ◽

2018 ◽

Vol 8 (6) ◽

pp. 468-471 ◽

Cited By ~ 1

Author(s):

Martha A. Mulvey ◽

Aravindhan Veerapandiyan ◽

David A. Marks ◽

Xue Ming

Keyword(s):

Risk Factors ◽

At Risk ◽

Obstructive Sleep Apnea ◽

Sleep Apnea ◽

Electronic Health Record ◽

Seizure Control ◽

Health Record ◽

Obstructive Sleep ◽

Patients At Risk ◽

Patients With Epilepsy

BackgroundPrior studies have reported that patients with epilepsy have a higher prevalence of obstructive sleep apnea (OSA) that contributes to poor seizure control. Detection and treatment of OSA can improve seizure control in some patients with epilepsy. In this study, we sought to develop, implement, and evaluate the effectiveness of an electronic health record (EHR) alert to screen for OSA in patients with epilepsy.MethodsA 3-month retrospective chart review was conducted of all patients with epilepsy >18 years of age who were evaluated in our epilepsy clinics prior to the intervention. An assessment for obstructive sleep apnea (AOSA) consisting of 12 recognized risk factors for OSA was subsequently developed and embedded in the EHR. The AOSA was utilized for a 3-month period. Patients identified with 2 or more risk factors were referred for polysomnography. A comparison was made to determine if there was a difference in the number of patients at risk for OSA detected and referred for polysomnography with and without an EHR alert to screen for OSA.ResultsThere was a significant increase in OSA patient recognition. Prior to the EHR alert, 25/346 (7.23%) patients with epilepsy were referred for a polysomnography. Postintervention, 405/414 patients were screened using an EHR alert for AOSA and 134/405 (33.1%) were referred for polysomnography (p < 0.001).ConclusionAn intervention with AOSA cued in the EHR demonstrated markedly improved identification of epilepsy patients at risk for OSA and referral for polysomnography.

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Using Electronic Health Record Alerts to Increase Safety Planning with Youth At-Risk for Suicide: A Non-randomized Trial

Child & Youth Care Forum ◽

10.1007/s10566-018-9435-4 ◽

2018 ◽

Vol 47 (3) ◽

pp. 391-402 ◽

Cited By ~ 1

Author(s):

Jazmin A. Reyes-Portillo ◽

Erica M. Chin ◽

Josefina Toso-Salman ◽

J. Blake Turner ◽

David Vawdrey ◽

...

Keyword(s):

At Risk ◽

Electronic Health Record ◽

Randomized Trial ◽

Health Record ◽

Safety Planning ◽

Youth At Risk ◽

Electronic Health

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Using REDCap and Apple ResearchKit to integrate patient questionnaires and clinical decision support into the electronic health record to improve sexually transmitted infection testing in the emergency department

Journal of the American Medical Informatics Association ◽

10.1093/jamia/ocz182 ◽

2019 ◽

Vol 27 (2) ◽

pp. 265-273 ◽

Cited By ~ 3

Author(s):

Fahd A Ahmad ◽

Philip R O Payne ◽

Ian Lackey ◽

Rachel Komeshak ◽

Kenneth Kenney ◽

...

Keyword(s):

At Risk ◽

Emergency Department ◽

Decision Support ◽

Electronic Health Record ◽

Clinical Decision Support ◽

Clinical Decision ◽

Computer Assisted ◽

Health Record ◽

Sexually Transmitted ◽

Electronic Health

Abstract Objective Audio-enhanced computer-assisted self-interviews (ACASIs) are useful adjuncts for clinical care but are rarely integrated into the electronic health record (EHR). We created a flexible framework for integrating an ACASIs with clinical decision support (CDS) into the EHR. We used this program to identify adolescents at risk for sexually transmitted infections (STIs) in the emergency department (ED). We provide an overview of the software platform and qualitative user acceptance. Materials and Methods We created an ACASI with a CDS algorithm to identify adolescents in need of STI testing. We offered it to 15- to 21-year-old patients in our ED, regardless of ED complaint. We collected user feedback via the ACASI. These were programmed into REDCap (Research Electronic Data Capture), and an iOS application utilizing Apple ResearchKit generated a tablet compatible representation of the ACASI for patients. A custom software program created an HL7 (Health Level Seven) message containing a summary of responses, CDS recommendations, and STI test orders, which were transmitted to the EHR. Results In the first year, 1788 of 6227 (28.7%) eligible adolescents completed the survey. Technical issues led to decreased use for several months. Patients rated the system favorably, with 1583 of 1787 (88.9%) indicating that they were “somewhat” or “very comfortable” answering questions electronically and 1291 of 1787 (72.2%) preferring this format over face-to-face interviews or paper questionnaires. Conclusions We present a novel use for REDCap to combine patient-answered questionnaires and CDS to improve care for adolescents at risk for STIs. Our program was well received and the platform can be used across disparate patients, topics, and information technology infrastructures.

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