scholarly journals Seeking out SARI: an automated search of electronic health records

2018 ◽  
Vol 146 (8) ◽  
pp. 1065-1069
Author(s):  
John C. O'Horo ◽  
Mikhail Dziadzko ◽  
Amra Sakusic ◽  
Rashid Ali ◽  
M. Rizwan Sohail ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Electronic Health Records ◽  
Search Algorithm ◽  
Temporal Trends ◽  
Epidemiologic Study ◽  
Health Records ◽  
Severe Acute Respiratory Infection ◽  
Electronic Health ◽  
Automated Search

AbstractThe definition of severe acute respiratory infection (SARI) – a respiratory illness with fever and cough, occurring within the past 10 days and requiring hospital admission – has not been evaluated for critically ill patients. Using integrated electronic health records data, we developed an automated search algorithm to identify SARI cases in a large cohort of critical care patients and evaluate patient outcomes. We conducted a retrospective cohort study of all admissions to a medical intensive care unit from August 2009 through March 2016. Subsets were randomly selected for deriving and validating a search algorithm, which was compared with temporal trends in laboratory-confirmed influenza to ensure that SARI was correlated with influenza. The algorithm was applied to the cohort to identify clinical differences for patients with and without SARI. For identifying SARI, the algorithm (sensitivity, 86.9%; specificity, 95.6%) outperformed billing-based searching (sensitivity, 73.8%; specificity, 78.8%). Automated searching correlated with peaks in laboratory-confirmed influenza. Adjusted for severity of illness, SARI was associated with more hospital, intensive care unit and ventilator days but not with death or dismissal to home. The search algorithm accurately identified SARI for epidemiologic study and surveillance.

Delirium in Postoperative Patients Admitted to the Intensive Care Unit

2018 ◽  
Vol 84 (6) ◽  
pp. 875-880
Author(s):  
Timothy R. Romanauski ◽  
Erin E. Martin ◽  
Juraj Sprung ◽  
David P. Martin ◽  
Darrell R. Schroeder ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Electronic Health Records ◽  
Assessment Method ◽  
Chronic Health Evaluation ◽  
Surgical Patients ◽  
Health Evaluation ◽  
Health Records ◽  
Chronic Health ◽  
Electronic Health

Postoperative delirium (POD) is common among surgical patients admitted to the intensive care unit (ICU) and is associated with increased resource utilization, morbidity, and death. Our primary aim was to compare rates of POD using administrative International Classification of Diseases, Ninth Revision, records and automated interrogation of electronic health records from Confusion Assessment Method for the ICU (CAM-ICU) screening. The secondary aim was to assess POD risk associated with patient and perioperative characteristics. Electronic health records of surgical patients admitted to the ICU during 2011 through 2014 were abstracted for POD assessment by CAM-ICU and by administrative codes, Charlson comorbidity index, surgical characteristics, and Acute Physiology, Age, Chronic Health Evaluation III scores. Of 6338 patients, CAM-ICU identified 606 (9.6%) and administrative records identified 55 (0.9%) POD cases, with agreement on 50 cases. In multivariable logistic regression based on POD identified with CAM-ICU, preexisting dementia had the strongest association with POD (odds ratio [95% confidence interval], 6.47 [3.68–11.37]; P < 0.001). Other associations found were older age, congestive heart failure, chronic pulmonary disease, increased surgical duration, emergency cases, blood transfusions, postoperative ventilation, and higher Acute Physiology, Age, Chronic Health Evaluation III scores (all P ≤ 0.01). POD cases had lengthier ICU and hospital stays and a higher mortality rate (all P < 0.001). CAM-ICU scores identified higher rates of POD than a search for POD based on administrative codes. Preoperative presence of dementia and major comorbidities were associated with POD. Delirium in surgical patients is associated with worse outcomes.

Investigating the Impact of Intensive Care Unit Interruptions on Patient Safety Events and Electronic Health Records Use

2019 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Saif Khairat ◽  
Stevan Whitt ◽  
Catherine K. Craven ◽  
Youngju Pak ◽  
Chi-Ren Shyu ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Patient Safety ◽  
Intensive Care ◽  
Electronic Health Records ◽  
Health Records ◽  
Electronic Health ◽  
The Impact

scholarly journals Deep Multi-Modal Transfer Learning for Augmented Patient Acuity Assessment in the Intelligent ICU

2021 ◽  
Vol 3 ◽  
Author(s):  
Benjamin Shickel ◽  
Anis Davoudi ◽  
Tezcan Ozrazgat-Baslanti ◽  
Matthew Ruppert ◽  
Azra Bihorac ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Electronic Health Records ◽  
Transfer Learning ◽  
Care Delivery ◽  
Small Sample ◽  
Patient Acuity ◽  
Health Records ◽  
Patient Health ◽  
Electronic Health

Accurate prediction and monitoring of patient health in the intensive care unit can inform shared decisions regarding appropriateness of care delivery, risk-reduction strategies, and intensive care resource use. Traditionally, algorithmic solutions for patient outcome prediction rely solely on data available from electronic health records (EHR). In this pilot study, we explore the benefits of augmenting existing EHR data with novel measurements from wrist-worn activity sensors as part of a clinical environment known as the Intelligent ICU. We implemented temporal deep learning models based on two distinct sources of patient data: (1) routinely measured vital signs from electronic health records, and (2) activity data collected from wearable sensors. As a proxy for illness severity, our models predicted whether patients leaving the intensive care unit would be successfully or unsuccessfully discharged from the hospital. We overcome the challenge of small sample size in our prospective cohort by applying deep transfer learning using EHR data from a much larger cohort of traditional ICU patients. Our experiments quantify added utility of non-traditional measurements for predicting patient health, especially when applying a transfer learning procedure to small novel Intelligent ICU cohorts of critically ill patients.

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