Predicting hospitalisations related to ambulatory care sensitive conditions with machine learning for population health planning: derivation and validation cohort study

Abstract

ObjectiveTo predict older adults’ risk of avoidable hospitalisation related to ambulatory care sensitive conditions (ACSC) using machine learning applied to administrative health data of Ontario, Canada.Design, setting and participantsA retrospective cohort study was conducted on a large cohort of all residents covered under a single-payer system in Ontario, Canada over the period of 10 years (2008–2017). The study included 1.85 million Ontario residents between 65 and 74 years old at any time throughout the study period.Data sourcesAdministrative health data from Ontario, Canada obtained from the (ICES formely known as the Institute for Clinical Evaluative Sciences Data Repository.Main outcome measuresRisk of hospitalisations due to ACSCs 1 year after the observation period.ResultsThe study used a total of 1 854 116 patients, split into train, validation and test sets. The ACSC incidence rates among the data points were 1.1% for all sets. The final XGBoost model achieved an area under the receiver operating curve of 80.5% and an area under precision–recall curve of 0.093 on the test set, and the predictions were well calibrated, including in key subgroups. When ranking the model predictions, those at the top 5% of risk as predicted by the model captured 37.4% of those presented with an ACSC-related hospitalisation. A variety of features such as the previous number of ambulatory care visits, presence of ACSC-related hospitalisations during the observation window, age, rural residence and prescription of certain medications were contributors to the prediction. Our model was also able to capture the geospatial heterogeneity of ACSC risk in Ontario, and especially the elevated risk in rural and marginalised regions.ConclusionsThis study aimed to predict the 1-year risk of hospitalisation from ambulatory-care sensitive conditions in seniors aged 65–74 years old with a single, large-scale machine learning model. The model shows the potential to inform population health planning and interventions to reduce the burden of ACSC-related hospitalisations.

Big Data Analytics to Reduce Preventable Hospitalizations—Using Real-World Data to Predict Ambulatory Care-Sensitive Conditions

“…See Sundmacher et al for the full list of ICD-10 codes of ambulatory-care-sensitive conditions used for this study [ 25 ]. At the least, the core list includes chronic diseases that are also commonly included in definitions of ACSC in the context of other countries [ 10 ].…”

“…The outcome variable was defined similar to prior studies focusing on ACSH prediction [ 10 , 20 , 21 ]. It is the event of an individual being hospitalized with an ACSC in the prediction year.…”

“…One field of specific political interest is the analysis and reduction of ambulatory care-sensitive hospitalizations (ACSH), i.e., inpatient hospital cases that are at least partly considered avoidable with improved care in the outpatient sector in the context of nursing homes or through prevention achieved, e.g., by public health activities [ 7 , 8 , 9 ]. Reductions in ACSH can both improve the patient experience and avoid an unnecessary usage of health system resources so that the ACSH-rate is also used as a measure of healthcare quality [ 10 , 11 ]. A study analyzing the cost associated with ACSH in the German health insurance system estimated a cost of EUR 3.5 billion per year (increasing per year by 0.9%) based on the mean costs of such hospital cases from the German Diagnosis Related Group (DRG) system [ 12 ].…”

“…This is comparable to an approach of the Veterans Health Administration providing patient-specific care assessment need scores based on data from the corporate data warehouse that can be accessed by healthcare providers and population health managers [ 13 ]. Several studies exist predicting (re-)hospitalizations in general [ 7 , 14 , 15 , 16 , 17 , 18 , 19 ], but only a few specifically predict ACSH in the context of the health systems of the USA, Canada and Italy [ 10 , 20 , 21 , 22 ]. While the methodologies are comparable to a certain extent, this study extends the context to Germany, which is on the one hand valuable since ACSH definitions are most often adapted to the specific health system characteristics and therefore models and results from other contexts cannot be directly transferred or put into practice.…”

Priorities for successful use of artificial intelligence by public health organizations: a literature review

“…Population health assessment involves understanding the health of communities, population sub-groups and the determinants of health to improve health policies, services and research to identify effective public health interventions [ 4 ]. Gradient boosting decision trees have been used to predict the incidence of preventable hospitalizations for population health planning purposes [ 28 ]. A deep learning algorithm has been shown to reduce the spread of tuberculosis in India by informing the use of limited resources [ 29 ].…”