Predictive modeling of service discontinuation in transitional age youth with recent behavioral health service use.
Health Serv Res
; 57(1): 152-158, 2022 02.
Article
in En
| MEDLINE
| ID: mdl-34396526
ABSTRACT
OBJECTIVE:
To develop and test predictive models of discontinuation of behavioral health service use within 12 months in transitional age youth with recent behavioral health service use. DATA SOURCES Administrative claims for Medicaid beneficiaries aged 15-26 years in Connecticut. STUDYDESIGN:
We compared the performance of a decision tree, random forest, and gradient boosting machine learning algorithms to logistic regression in predicting service discontinuation within 12 months among beneficiaries using behavioral health services. DATA EXTRACTION We identified 33,532 transitional age youth with ≥1 claim for a primary behavioral health diagnosis in 2016 and Medicaid enrollment of ≥11 months in 2016 and ≥11 months in 2017. PRINCIPALFINDINGS:
Classification accuracy for identifying youth who discontinued behavioral health service use was highest for gradient boosting (80%, AUC = 0.86), decision tree (79%, AUC = 0.84), and random forest (79%, AUC = 0.86), as compared with logistic regression (71%, AUC = 0.71).CONCLUSIONS:
Predictive models based on Medicaid claims can assist in identifying transitional age youth who are at risk of discontinuing from behavioral health care within 12 months, thus allowing for proactive assessment and outreach to promote continuity of care for younger persons who have behavioral health needs.Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Patient Acceptance of Health Care
/
Transitional Care
/
Mental Disorders
/
Mental Health Services
Type of study:
Diagnostic_studies
/
Prognostic_studies
/
Risk_factors_studies
Limits:
Adolescent
/
Adult
/
Humans
/
Male
Country/Region as subject:
America do norte
Language:
En
Journal:
Health Serv Res
Year:
2022
Document type:
Article
Affiliation country:
Estados Unidos