Effects of the PCYC Catalyst outdoor adventure intervention program on youths' life skills, mental health, and delinquent behaviour

This study used mixed methods to examine the effects of an Australian outdoor adventure intervention on youth-at-risks' life effectiveness, mental health, and behavioural functioning. The sample consisted of 53 adolescents who completed a Catalyst program conducted by the Queensland Police-Citizens Youth Welfare Association, a non-profit organisation, in Queensland, Australia. The program involved 15 programming days over a 10–12-week period. There were small to moderate short- and longer-term improvements in life effectiveness, psychological well-being, and several aspects of behavioural conduct. There were no positive longer-term impacts on psychological distress and some aspects of behaviour. Thematic analysis of 14 participant interviews identified six major themes: overcoming challenging backgrounds, contending with adversity, personal development, social development, motivation to work for change, and a more optimistic outlook on the future. Further research utilising a comparison group, multiple sources of data, and a larger sample could help to qualify results and increase generalisability

Machine learning approaches for early DRG classification and resource allocation

Recent research has highlighted the need for upstream planning in healthcare service delivery systems, patient scheduling, and resource allocation in the hospital inpatient setting. This study examines the value of upstream planning within hospital-wide resource allocation decisions based on machine learning (ML) and mixed-integer programming (MIP), focusing on prediction of diagnosis-related groups (DRGs) and the use of these predictions for allocating scarce hospital resources. DRGs are a payment scheme employed at patients’ discharge, where the DRG and length of stay determine the revenue that the hospital obtains. We show that early and accurate DRG classification using ML methods, incorporated into an MIP-based resource allocation model, can increase the hospital’s contribution margin, the number of admitted patients, and the utilization of resources such as operating rooms and beds. We test these methods on hospital data containing more than 16,000 inpatient records and demonstrate improved DRG classification accuracy as compared to the hospital’s current approach. The largest improvements were observed at and before admission, when information such as procedures and diagnoses is typically incomplete, but performance was improved even after a substantial portion of the patient’s length of stay, and under multiple scenarios making different assumptions about the available information. Using the improved DRG predictions within our resource allocation model improves contribution margin by 2.9% and the utilization of scarce resources such as operating rooms and beds from 66.3% to 67.3% and from 70.7% to 71.7%, respectively. This enables 9.0% more nonurgent elective patients to be admitted as compared to the baseline