Evaluation of a Care Management Program for Pediatric Epilepsy Patients

Impact of Race in Missed Appointments in Pediatric Neurology Resident Clinic at a Large Tertiary Medical Center

Missed medical appointments are a common problem across specialties. The discontinuity of care leads to unplanned health care utilization, increased costs, and poor health outcomes. Previous studies evaluating pediatric epilepsy have shown significant socioeconomic barriers to care. In several specialties, resident clinic no-show rates are higher than faculty clinics because of socioeconomic barriers. We sought to understand the relationship between race, socioeconomic factors, and missed appointments in a pediatric neurology resident clinic at a large tertiary care hospital. Resident clinic encounters for 1 year were extracted and analyzed for missed appointments, socioeconomic factors, and health care utilization. We found that missed appointments occur for 1 in 5 patients and correlate with socioeconomic factors (eg, income and insurance) and race. Race was a more significant factor than socioeconomic factors for missed appointments. These results provide areas to target and track interventions to improve health outcomes in children in pediatric neurology clinics.

Short-stay urgent hospital admissions of children with convulsions: A mixed methods exploratory study to inform out of hospital care pathways

OBJECTIVE

To inform interventions focused on safely reducing urgent paediatric short stay admissions (SSAs) for convulsions.

METHODS

Routinely acquired administrative data from hospital admissions in Scotland between 2015-2017 investigated characteristics of unscheduled SSAs (an urgent admission where admission and discharge occur on the same day) for a diagnosis of febrile and/or afebrile convulsions. Semi-structured interviews to explore perspectives of health professionals (n = 19) making referral or admission decisions about convulsions were undertaken. Interpretation of mixed methods findings was complemented by interviews with four parents with experience of unscheduled SSAs of children with convulsion.

RESULTS

Most SSAs for convulsions present initially at hospital emergency departments (ED). In a subset of 10,588 (11%) of all cause SSAs with linked general practice data available, 72 (37%) children with a convulsion contacted both the GP and ED pre-admission. Within 30 days of discharge, 10% (n = 141) of children admitted with afebrile convulsions had been readmitted to hospital with a further convulsion. Interview data suggest that panic and anxiety, through fear that the situation is life threatening, was a primary factor driving hospital attendance and admission. Lengthy waits to speak to appropriate professionals exacerbate parental anxiety and can trigger direct attendance at ED, whereas some children with complex needs had direct access to convulsion professionals.

CONCLUSIONS

SSAs for convulsions are different to SSAs for other conditions and our findings could inform new efficient convulsion-specific pre and post hospital pathways designed to improve family experiences and reduce admissions and readmissions.

Predicting Patient No-Shows in an Academic Pediatric Neurology Clinic

Background: No-shows can negatively affect patient care. Efforts to predict high-risk patients are needed. Previously, our epilepsy clinic identified patients with 2 or more no-shows or late cancelations in the past 18 months as being at high risk for no-shows. Our objective was to develop a model to accurately predict the risk of no-shows among patients with epilepsy seen at our neurology clinic. Methods: Using electronic health record data, we developed a least absolute shrinkage and selection operator (LASSO)-regularized logistic regression model to predict no-shows and compared its performance with our neurology clinic's above-mentioned ad hoc rule. Results: The ad hoc rule identified 13% of patients seen at our neurology clinic as high-risk patients for no-shows and resulted in a positive predictive value of 38%. In comparison, our LASSO model resulted in a positive predictive value of 48%. Our LASSO model identified that lack of private insurance, inactive Epic MyChart, greater past no-show rates, fewer appointment changes before the appointment date, and follow-up appointments were more likely to result in no-shows. Conclusions: Our LASSO model outperformed the ad hoc rule used by our neurology clinic in predicting patients at high risk for no-shows. Social workers can use the no-show risk scores generated by our LASSO model to prioritize high-risk patients for targeted intervention to reduce no-shows at our neurology clinic.