Using phenotypic data from the Electronic Health Record (EHR) to predict discharge
Abstract Background Timely discharge to post-acute care (PAC) settings, such as skilled nursing facilities, requires early identification of eligible patients. We sought to develop and internally validate a model which predicts a patient’s likelihood of requiring PAC based on information obtained in...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-07-01
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| Series: | BMC Geriatrics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12877-023-04147-y |
| _version_ | 1797778729558081536 |
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| author | Monisha C. Bhatia Jonathan P. Wanderer Gen Li Jesse M. Ehrenfeld Eduard E. Vasilevskis |
| author_facet | Monisha C. Bhatia Jonathan P. Wanderer Gen Li Jesse M. Ehrenfeld Eduard E. Vasilevskis |
| author_sort | Monisha C. Bhatia |
| collection | DOAJ |
| description | Abstract Background Timely discharge to post-acute care (PAC) settings, such as skilled nursing facilities, requires early identification of eligible patients. We sought to develop and internally validate a model which predicts a patient’s likelihood of requiring PAC based on information obtained in the first 24 h of hospitalization. Methods This was a retrospective observational cohort study. We collected clinical data and commonly used nursing assessments from the electronic health record (EHR) for all adult inpatient admissions at our academic tertiary care center from September 1, 2017 to August 1, 2018. We performed a multivariable logistic regression to develop the model from the derivation cohort of the available records. We then evaluated the capability of the model to predict discharge destination on an internal validation cohort. Results Age (adjusted odds ratio [AOR], 1.04 [per year]; 95% Confidence Interval [CI], 1.03 to 1.04), admission to the intensive care unit (AOR, 1.51; 95% CI, 1.27 to 1.79), admission from the emergency department (AOR, 1.53; 95% CI, 1.31 to 1.78), more home medication prescriptions (AOR, 1.06 [per medication count increase]; 95% CI 1.05 to 1.07), and higher Morse fall risk scores at admission (AOR, 1.03 [per unit increase]; 95% CI 1.02 to 1.03) were independently associated with higher likelihood of being discharged to PAC facility. The c-statistic of the model derived from the primary analysis was 0.875, and the model predicted the correct discharge destination in 81.2% of the validation cases. Conclusions A model that utilizes baseline clinical factors and risk assessments has excellent model performance in predicting discharge to a PAC facility. |
| first_indexed | 2024-03-12T23:21:42Z |
| format | Article |
| id | doaj.art-d133bb14b2694d2790875392f790805c |
| institution | Directory Open Access Journal |
| issn | 1471-2318 |
| language | English |
| last_indexed | 2024-03-12T23:21:42Z |
| publishDate | 2023-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Geriatrics |
| spelling | doaj.art-d133bb14b2694d2790875392f790805c2023-07-16T11:27:24ZengBMCBMC Geriatrics1471-23182023-07-012311910.1186/s12877-023-04147-yUsing phenotypic data from the Electronic Health Record (EHR) to predict dischargeMonisha C. Bhatia0Jonathan P. Wanderer1Gen Li2Jesse M. Ehrenfeld3Eduard E. Vasilevskis4Vanderbilt University School of MedicineDepartment of Anesthesiology, Vanderbilt University Medical CenterDepartment of Surgery, Vanderbilt University School of MedicineDepartment of Anesthesiology, Vanderbilt University Medical CenterCurrent Address: Medical College of WisconsinAbstract Background Timely discharge to post-acute care (PAC) settings, such as skilled nursing facilities, requires early identification of eligible patients. We sought to develop and internally validate a model which predicts a patient’s likelihood of requiring PAC based on information obtained in the first 24 h of hospitalization. Methods This was a retrospective observational cohort study. We collected clinical data and commonly used nursing assessments from the electronic health record (EHR) for all adult inpatient admissions at our academic tertiary care center from September 1, 2017 to August 1, 2018. We performed a multivariable logistic regression to develop the model from the derivation cohort of the available records. We then evaluated the capability of the model to predict discharge destination on an internal validation cohort. Results Age (adjusted odds ratio [AOR], 1.04 [per year]; 95% Confidence Interval [CI], 1.03 to 1.04), admission to the intensive care unit (AOR, 1.51; 95% CI, 1.27 to 1.79), admission from the emergency department (AOR, 1.53; 95% CI, 1.31 to 1.78), more home medication prescriptions (AOR, 1.06 [per medication count increase]; 95% CI 1.05 to 1.07), and higher Morse fall risk scores at admission (AOR, 1.03 [per unit increase]; 95% CI 1.02 to 1.03) were independently associated with higher likelihood of being discharged to PAC facility. The c-statistic of the model derived from the primary analysis was 0.875, and the model predicted the correct discharge destination in 81.2% of the validation cases. Conclusions A model that utilizes baseline clinical factors and risk assessments has excellent model performance in predicting discharge to a PAC facility.https://doi.org/10.1186/s12877-023-04147-yPost-acute carePrediction modelsFrailtyFunctional statusHealth systems |
| spellingShingle | Monisha C. Bhatia Jonathan P. Wanderer Gen Li Jesse M. Ehrenfeld Eduard E. Vasilevskis Using phenotypic data from the Electronic Health Record (EHR) to predict discharge BMC Geriatrics Post-acute care Prediction models Frailty Functional status Health systems |
| title | Using phenotypic data from the Electronic Health Record (EHR) to predict discharge |
| title_full | Using phenotypic data from the Electronic Health Record (EHR) to predict discharge |
| title_fullStr | Using phenotypic data from the Electronic Health Record (EHR) to predict discharge |
| title_full_unstemmed | Using phenotypic data from the Electronic Health Record (EHR) to predict discharge |
| title_short | Using phenotypic data from the Electronic Health Record (EHR) to predict discharge |
| title_sort | using phenotypic data from the electronic health record ehr to predict discharge |
| topic | Post-acute care Prediction models Frailty Functional status Health systems |
| url | https://doi.org/10.1186/s12877-023-04147-y |
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