Machine learning application to the reduction of ambulatory care sensitive admissions (ACSA). (13th November 2019)

• Record Type: Journal Article
• Authors: Ngo, T; Georgescu, V; Gervet, C; Laurent, A; Libourel, T; Mercier, G
• Is Part Of: European journal of public health. Volume 29(2019)Supplement 4
• Publication Date: 2019-11-13
• Subjects: Epidemiology -- Europe -- Periodicals; Public health -- Europe -- Periodicals; 362.109405
• DOI: 10.1093/eurpub/ckz187.061
• Languages: English
• ISSNs: 1101-1262

Abstract

Abstract: Background: Reducing Ambulatory Care Sensitive Admissions (ACSA) not only enhances patients' quality of life but could also save substantial costs. ACSA are avoidable admissions for chronic conditions that are associated with socio-economic status, health status, utilization and readiness of primary care service as well as environmental factors. Undoubtedly, health authorities are highly interested in enhancing the health care services in order to reduce the number of ACSA. The objective is to identify the geographic areas where the primary care workforce should be increased in order to maximize the decrease in ACSA. Methods: Using ambulatory care and inpatient claims data as well as contextual variables, we apply support vector machine regression (SVR) to select the geographic areas (fr. Bassins de vie - BVs) and the number of to-be-added primary care nurses that maximize the ACSA reduction. We also take into account the constraints related to budget and the equality of health care access. Particularly, there are three possible constraints: (1) the total number of nurses can be added in the whole region; (2) the maximum number of the nurses can be added at each area; (3) the maximum density of nurses (numbers of the nurses per 10, 000 habitants) can be reached at each area. The results are visualized using spatial maps. Preliminary results: In 2014, 27, 000 ACSA occurred in the Occitanie, France region. For a specific set of constraints values, the model identified 16 BVs (out of 201) where the addition of 30 nurses could lead to the maximum ACSA reduction in number which is 17. Conclusions: In the French Occitanie region, our SVR model was able to target a small number of geographic areas to maximize the impact of increased primary care workforce on ACSA. Our approach is applied to a single region, and it can be applied to other regions or extended at the national level as well as to other countries. Key messages: A decision support tool to help health authorities in locating primary health care resources for the maximum reduction of ambulatory care sensitive admissions. An application of machine learning in primary care services.