Using Acuity to Predict Oncology Infusion Center Daily Nurse Staffing and Outcomes.
BACKGROUND: Outpatient oncology infusion centers (OICs) use acuity to quantify the complexity and intensity of care to improve staffing levels and equitable patient assignments. OIC interviews revealed inconsistent measurement of acuity and a mixture of use cases. No publications measured objective...
| Publicado en: | Clinical Journal of Oncology Nursing Vol. 28; no. 2; pp. 181 - 188 |
|---|---|
| Autores principales: | , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Oncology Nursing Society
Apr2024
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=176165396&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 176165396 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10921095 GNN jtl: Clinical Journal of Oncology Nursing issn: 10921095 maglogo: N pubinfo: dt: Apr2024 vid: 28 iid: 2 pid: 12496 pub: Oncology Nursing Society place: Pittsburgh, Pennsylvania artinfo: ui: 176165396 176165396 176165396 10.1188/24.CJON.181-187 176165396 ppf: 181 ppct: 7 formats: fmt: @attributes: type: P tig: atl: Using Acuity to Predict Oncology Infusion Center Daily Nurse Staffing and Outcomes. aug: au: Tobias, Pamela F. Oliver, Zachary Yue Huang Bayne, Christopher Fidyk, Lisa affil: Director of customer success, LeanTaaS in Santa Clara, CA sug: subj: Oncology Care Units Infusions, Intravenous Ambulatory Care Facilities Personnel Staffing and Scheduling Standards Nursing Care Needs Assessment Models, Theoretical Prediction Models Nursing Practice, Evidence-Based Human Waiting Lists Machine Learning Algorithms Work Assignments Automation Semi-Structured Interview Pennsylvania Workflow Logistic Regression Patient Admission Statistics and Numerical Data McNemar's Test Surveys Questionnaires ab: BACKGROUND: Outpatient oncology infusion centers (OICs) use acuity to quantify the complexity and intensity of care to improve staffing levels and equitable patient assignments. OIC interviews revealed inconsistent measurement of acuity and a mixture of use cases. No publications measured objective operational benefits beyond surveyed nurse satisfaction or compared different models of acuity. OBJECTIVES: This study assessed three acuity models across multiple centers to determine whether acuity was superior to patient volumes or patient hours in predicting the number of nurses needed to care for scheduled patients in an OIC, as well as the effect on objective metrics of missed nurse lunch breaks and patient wait times. A secondary end point was used to identify a superior model. METHODS: Classification machine learning models were built to assess the predictive value of three acuity models compared to patient hours and patient visits. FINDINGS: None of the tested acuity models were found to have statistically significant improvement to the prediction of needed OIC nurse staffing, patient wait times, or missed nurse lunch breaks. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|