Increasing patient demand following health care reform has led to concerns about provider shortages, particularly in primary care and for Medicaid patients. Nurse practitioners (NPs) represent a potential solution to meeting demand. However, varying state scope of practice regulations and Medicaid reimbursement rates may limit efficient distribution of NPs. Using a national sample of 252,657 ambulatory practices, we examined the effect of state policies on NP employment in primary care and practice Medicaid acceptance. NPs had 13% higher odds of working in primary care in states with full scope of practice; those odds increased to 20% if the state also reimbursed NPs at 100% of the physician Medicaid fee-for-service rate. Furthermore, in states with 100% Medicaid reimbursement, practices with NPs had 23% higher odds of accepting Medicaid than practices without NPs. Removing scope of practice restrictions and increasing Medicaid reimbursement may increase NP participation in primary care and practice Medicaid acceptance.
Accurately predicting and reducing risk of unplanned readmissions (URs) in pediatric care remains difficult. We sought to develop a set of accurate algorithms to predict URs within 3, 7, and 30 days of discharge from inpatient admission that can be used before the patient is discharged from a current hospital stay.
METHODS:We used the Children's Hospital Association Pediatric Health Information System to identify a large retrospective cohort of 1 111 323 children with 1 321 376 admissions admitted to inpatient care at least once between January 1, 2016, and December 31, 2017. We used gradient boosting trees (XGBoost) to accommodate complex interactions between these predictors.
RESULTS:In the full cohort, 1.6% of patients had at least 1 UR in 3 days, 2.4% had at least 1 UR in 7 days, and 4.4% had at least 1 UR within 30 days. Prediction model discrimination was strongest for URs within 30 days (area under the curve [AUC] 5 0.811; 95% confidence interval [CI]: 0.808-0.814) and was nearly identical for UR risk prediction within 3 days (AUC 5 0.771; 95% CI: 0.765-0.777) and 7 days (AUC 5 0.778; 95% CI: 0.773-0.782), respectively. Using these prediction models, we developed a publicly available pediatric readmission risk scores prediction tool that can be used before or during discharge planning.CONCLUSIONS: Risk of pediatric UR can be predicted with information known before the patient's discharge and that is easily extracted in many electronic medical record systems. This information can be used to predict risk of readmission to support hospital-discharge-planning resources.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.