Continuity of Primary Care and Preventable Hospitalization for Acute Conditions: A Machine Learning-Based Record...

Reducing potentially preventable hospitalization (PPH), also known as ambulatory care-senstive conditions, is a global concern. This study linked data from Sax Institute's 45 and Up Study on individuals aged 45 years and older from New South Wales, Australia, with Australian Medicare claims data to establish a causal relationship between continuity of care and acute PPH using a double machine learning model.

We utilized 11 years of linked data (2007-2017) to analyze the impact of continuity of care on acute PPH, controlling for key patient characteristics (ie, age, multimorbidity status, cultural diversity, sex, education level, psychological status, physical limitation, smoking status, socioeconomic deciles). Estimation was done using a double machine learning technique with 4 algorithms (ie, least absolute shrinkage and selection operator, random forest, extreme gradient boosting, artificial neural network) to ensure robustness.

Among 54,376 participants, 27,634 individuals (50.8%) experienced at least 1 acute PPH episode during the 11-year study period. Our findings indicate that even a slight improvement in continuity of care can reduce the incidence of acute PPH compared with non-acute PPH. For example, the reduction in the probability of acute PPH compared with non-acute PPH ranges from 9.8% (95% CI, 1.1%-17.8%) to 23.5% (95% CI, 14.1%-32.4%) across 4 models when continuity of care increases from the 45th percentile (0.274) to the 50th percentile (0.301).

Continuity of care at the primary level plays a key role in reducing acute PPH. Policies focused on person-centered or integrated care should include initiatives to promote continuity of care and support general practitioners in improving continuity of care.The authors of this article have provided Hindi and Vietnamese translations of the abstract.