Harnessing the Electronic Health Record and Computerized Provider Order Entry Data for Resource Management During the COVID-19 Pandemic: Development of a Decision Tree
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has resulted in shortages of diagnostic tests, personal protective equipment, hospital beds, and other critical resources. OBJECTIVE: We sought to improve the management of scarce resources by leveraging electronic health record (EHR) functionality, computerized provider order entry, clinical decision support (CDS), and data analytics. METHODS: Due to the complex eligibility criteria for COVID-19 tests and the EHR implementation-related challenges of ordering these tests, care providers have faced obstacles in selecting the appropriate test modality. As test choice is dependent upon specific patient criteria, we built a decision tree within the EHR to automate the test selection process by using a branching series of questions that linked clinical criteria to the appropriate SARS-CoV-2 test and triggered an EHR flag for patients who met our institutional persons under investigation criteria. RESULTS: The percentage of tests that had to be canceled and reordered due to errors in selecting the correct testing modality was 3.8% (23/608) before CDS implementation and 1% (262/26,643) after CDS implementation (P<.001). Patients for whom multiple tests were ordered during a 24-hour period accounted for 0.8% (5/608) and 0.3% (76/26,643) of pre- and post-CDS implementation orders, respectively (P=.03). Nasopharyngeal molecular assay results were positive in 3.4% (826/24,170) of patients who were classified as asymptomatic and 10.9% (1421/13,074) of symptomatic patients (P<.001). Positive tests were more frequent among asymptomatic patients with a history of exposure to COVID-19 (36/283, 12.7%) than among asymptomatic patients without such a history (790/23,887, 3.3%; P<.001). CONCLUSIONS: The leveraging of EHRs and our CDS algorithm resulted in a decreased incidence of order entry errors and the appropriate flagging of persons under investigation. These interventions optimized reagent and personal protective equipment usage. Data regarding symptoms and COVID-19 exposure status that were collected by using the decision tree correlated with the likelihood of positive test results, suggesting that clinicians appropriately used the questions in the decision tree algorithm.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".