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Predicting Inpatient Discharge Prioritization With Electronic Health Records [article]

Anand Avati, Stephen Pfohl, Chris Lin, Thao Nguyen, Meng Zhang, Philip Hwang, Jessica Wetstone, Kenneth Jung, Andrew Ng, Nigam H. Shah
2018 arXiv   pre-print
We studied this problem using eight years of Electronic Health Records (EHR) data from Stanford Hospital. We fit models to predict 24 hour discharge across the entire inpatient population.  ...  Identifying patients who will be discharged within 24 hours can improve hospital resource management and quality of care.  ...  We approach this task by formulating a supervised learning problem, taking advantage of the abundance of patient data available in Electronic Health Records (EHR).  ... 
arXiv:1812.00371v1 fatcat:hqdbqhxhxbbqrmztumxenwx4g4

B2-2: Impact of Automated Alerts to Primary Care Providers and Staff When Patients are Discharged from the Hospital: A Randomized Trial

J. Gurwitz, T. Field, J. Ogarek, J. Tjia, S. Cutrona, L. Harrold, J. Donovan, A. Kanaan, S. Gagne, P. Preusse, L. Garber
2013 Clinical Medicine & Research  
It was integrated into the electronic health record through a web-service and pilot tested with 14 providers at 6 HealthPartners Medical Group (HPMG) clinics.  ...  Results: The decision support program for aspirin in HealthPartners Medical Group & Clinics was integrated with the electronic health record through a web-service called Cardiovascular (CV) Wizard.  ...  It was integrated into the electronic health record through a web-service and pilot tested with 14 providers at 6 HealthPartners Medical Group (HPMG) clinics.  ... 
doi:10.3121/cmr.2013.1176.b2-2 fatcat:kmz3mnb6anauzp6wld7dgk4qni

Evaluation of an arboviral syndrome query used in Maricopa County, Arizona

Kaitlyn Sykes, Rasneet S. Kumar, Melissa Kretschmer, Jessica R. White
2018 Online Journal of Public Health Informatics  
department and inpatient hospitals for chief complaint keywords and discharge diagnosis codes.  ...  The decision tree was effective at prioritizing records for further investigation.  ...  [1] Twice per week, we queried patient records from 15 Maricopa County BioSense-enrolled emergency department and inpatient hospitals for chief complaint keywords and discharge diagnosis codes.  ... 
doi:10.5210/ojphi.v10i1.8896 fatcat:2d53bixh3rejhgy6vzzjrn6kge

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

Charles A. Baillie, Christine VanZandbergen, Gordon Tait, Asaf Hanish, Brian Leas, Benjamin French, C. William Hanson, Maryam Behta, Craig A. Umscheid
2013 Journal of Hospital Medicine  
The adoption of electronic health records (EHR) may prove important to strategies designed to risk stratify patients and introduce targeted interventions.  ...  Electronic health records (EHRs) may prove to be an important component of strategies designed to risk stratify patients at the point of care.  ... 
doi:10.1002/jhm.2106 pmid:24227707 pmcid:PMC4407637 fatcat:t2yaagunmfau7kuyfg2od3ju2u

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

Ruben Amarasingham, Billy J. Moore, Ying P. Tabak, Mark H. Drazner, Christopher A. Clark, Song Zhang, W. Gary Reed, Timothy S. Swanson, Ying Ma, Ethan A. Halm
2010 Medical Care  
Data were extracted from an electronic medical record.  ...  The performance of the electronic model was compared with mortality and readmission models developed by the Center for Medicaid and Medicare Services (CMS models) and a HF mortality model derived from  ...  This study raises the possibility that such factors may be extracted from electronic medical records. This is an exploratory study with important limitations.  ... 
doi:10.1097/mlr.0b013e3181ef60d9 pmid:20940649 fatcat:6grvwwmiqzahteywkt4kqo3nlu

Development and Performance of a Clinical Decision Support Tool to Inform Resource Utilization for Elective Operations

Benjamin A. Goldstein, Marcelo Cerullo, Vijay Krishnamoorthy, Jeanna Blitz, Leila Mureebe, Wendy Webster, Felicia Dunston, Andrew Stirling, Jennifer Gagnon, Charles D. Scales
2020 JAMA Network Open  
In this prognostic study, predictive modeling was used on retrospective electronic health records data from a large academic health system comprising 1 tertiary care hospital and 2 community hospitals  ...  Data from the electronic health records of 42 199 patients from 3 hospitals were abstracted for analysis.  ...  Since 2014, the DUHS has used a common electronic health record (EHR) for inpatient and outpatient reviewing of medical records; the ordering of laboratory tests, medications, and radiology studies; and  ... 
doi:10.1001/jamanetworkopen.2020.23547 pmid:33136133 pmcid:PMC7607444 fatcat:xiwz42olgzh6foyu7jlyvcdvnm

An Electronic Medical Record–Based Discharge Disposition Tool Gets Bundle Busted: Decaying Relevance of Clinical Data Accuracy in Machine Learning

Alexander S. Greenstein, Jack Teitel, David J. Mitten, Benjamin F. Ricciardi, Thomas G. Myers
2020 Arthroplasty Today  
This is the first prediction tool using an electronic medical record-integrated ANN to predict discharge disposition after TJA based on locally generated data.  ...  This model serves as a proof of concept for developing an ML prediction tool using a relatively small data set and subsequent integration into the electronic medical record.  ...  such a prediction tool into our electronic medical record (EMR).  ... 
doi:10.1016/j.artd.2020.08.007 pmid:33088883 pmcid:PMC7567055 fatcat:bpcirz6q5rcm7auadr2fx52peu

Discharge Clinical Characteristics and Post-Discharge Events in Patients with Severe COVID-19: A Descriptive Case Series

Faysal G. Saab, Jeffrey N. Chiang, Rachel Brook, Paul C. Adamson, Jennifer A. Fulcher, Eran Halperin, Vladimir Manuel, David Goodman-Meza
2021 Journal of general internal medicine  
Design Retrospective chart reviews were performed to record laboratory values, temperature, and oxygen requirements of 99 adult inpatients with COVID-19.  ...  Those variables were used to predict emergency department (ED) visit or readmission within 30 days post-discharge.  ...  at UCLA or another facility whose electronic health record communicated with the UCLA Health System.  ... 
doi:10.1007/s11606-020-06494-7 pmid:33532963 pmcid:PMC7853705 fatcat:7jiakagthnb67n4bye47ov4ydu

Association of Self-reported Hospital Discharge Handoffs With 30-Day Readmissions

Ibironke Oduyebo, Christoph U. Lehmann, Craig Evan Pollack, Nowella Durkin, Jason D. Miller, Steven Mandell, Margaret Ardolino, Amy Deutschendorf, Daniel J. Brotman
2013 JAMA Internal Medicine  
Interventions: Self-reported communication was captured from a mandatory electronic discharge worksheet field.  ...  Design: We conducted a single-center prospective study of self-reported communication patterns by discharging health care providers on inpatient medical services from September 2010 to December 2011 at  ...  INSTITUTIONAL OPERATIONAL BACKGROUND At Johns Hopkins Hospital, health care providers are required to complete an electronic discharge worksheet in the inpatient electronic medical record.  ... 
doi:10.1001/jamainternmed.2013.3746 pmid:23529278 pmcid:PMC3692004 fatcat:thfy4zuv6bgfrkygsrojmkfsqe

Implementing a Pharmacist-Led Medication Management Pilot to Improve Care Transitions

Rachel Root, Pamela Phelps, Amanda Brummel, Craig Else
2012 INNOVATIONS in Pharmacy  
Adult patients with a predicted diagnosis-related group (DRG) of congestive heart failure or chronic obstructive pulmonary disease admitted to the medical-surgical and intensive care units who utilized  ...  The number of patients receiving follow-up care varied with 10 (25%) receiving MTM follow-up, 26 (65%) completing a primary care visit after their first hospital discharge, and 23 (58%) receiving a home  ...  Once discharge orders were written by the inpatient provider, the decentralized pharmacist received notification in the inpatient electronic health record (Sunrise Clinical Manager™ Eclipsys b ).  ... 
doi:10.24926/iip.v3i2.258 fatcat:jdajvzsemvfh7l2ft27lhva4vi

Comparison of Approaches for Heart Failure Case Identification From Electronic Health Record Data

Saul Blecker, Stuart D. Katz, Leora I. Horwitz, Gilad Kuperman, Hannah Park, Alex Gold, David Sontag
2016 JAMA cardiology  
The high predictive accuracy of machine learning using free text demonstrates that support of such analytics in future electronic health record systems can improve cohort identification.  ...  From a random 75% sample of hospitalizations, we developed 5 algorithms for heart failure identification using electronic health record data: (1) heart failure on problem list; (2) presence of at least  ...  A ccurate, real-time identification of the diseases or conditions of a hospitalized patient is important for direct patient care, quality improvement, in-hospital registries, and electronic health record  ... 
doi:10.1001/jamacardio.2016.3236 pmid:27706470 pmcid:PMC5289894 fatcat:obuaqt7n3neg5d3tmmoyozjfia

Real-time prediction of inpatient length of stay for discharge prioritization

Sean Barnes, Eric Hamrock, Matthew Toerper, Sauleh Siddiqui, Scott Levin
2015 JAMIA Journal of the American Medical Informatics Association  
the most parsimonious model with high accuracy that utilizes data that can be automatically extracted from electronic medical record systems.  ...  Our objective for this study is to support automation of the RTDC process by producing daily predictions of patient discharge times for a single inpatient medical unit.  ... 
doi:10.1093/jamia/ocv106 pmid:26253131 pmcid:PMC4954620 fatcat:tbnsxzlcrjdshncgu2etxpybdm

Barriers to Implementing an Artificial Intelligence Model for Unplanned Readmissions

Sally L Baxter, Jeremy S Bass, Amy M Sitapati
2020 ACI Open  
Electronic health record (EHR) vendors now offer "off-the-shelf" artificial intelligence (AI) models to client organizations.  ...  care resource utilization due to potentially over-referring discharged patients to home health services).  ...  Acknowledgments The authors wish to thank the UCSD Health AI Committee for input and feedback throughout the stakeholder engagement process described in this case study.  ... 
doi:10.1055/s-0040-1716748 pmid:33274314 pmcid:PMC7710326 fatcat:n7kkb35v25c6rflcskfsqviof4

Development and Validation of a Machine Learning Model to Aid Discharge Processes for Inpatient Surgical Care

Kyan C. Safavi, Taghi Khaniyev, Martin Copenhaver, Mark Seelen, Ana Cecilia Zenteno Langle, Jonathan Zanger, Bethany Daily, Retsef Levi, Peter Dunn
2019 JAMA Network Open  
The model's performance was compared with a baseline model using historical procedure median length of stay to predict discharges.  ...  If patients were not discharged when predicted, the causes of delay were recorded. The primary outcome was the out-of-sample area under the receiver operating characteristic curve of the model.  ...  predict inpatient surgical care discharges.  ... 
doi:10.1001/jamanetworkopen.2019.17221 pmid:31825503 pmcid:PMC6991195 fatcat:r6p6gsazondptpny6arpvn4ali

Engaging hospitalized patients in clinical care: Study protocol for a pragmatic randomized controlled trial

Ruth Masterson Creber, Jennifer Prey, Beatriz Ryan, Irma Alarcon, Min Qian, Suzanne Bakken, Steven Feiner, George Hripcsak, Fernanda Polubriaginof, Susan Restaino, Rebecca Schnall, Philip Strong (+1 others)
2016 Contemporary Clinical Trials  
Background-Patients who are better informed and more engaged in their health care have higher satisfaction with health care and better health outcomes.  ...  general internet access, or 3) iPad with access to the personalized inpatient portal.  ...  Acknowledgments Funding support We would like to acknowledge Jungmi Han, the computer programmer of the personalized inpatient portal, and Niurka Suero-Tejeda for her assistance with recruiting patients  ... 
doi:10.1016/j.cct.2016.01.005 pmid:26795675 pmcid:PMC4818160 fatcat:cbawp4wm7rfvjfvesn4w4gzd3y
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