scholarly journals Using ambulatory care sensitive hospitalisations to analyse the effectiveness of primary care services in Mexico

2015 ◽  
Vol 144 ◽  
pp. 59-68 ◽  
Author(s):  
David G. Lugo-Palacios ◽  
John Cairns
Keyword(s):  
Primary Care ◽  
Ambulatory Care ◽  
Care Services ◽  
Primary Care Services ◽  
Ambulatory Care Sensitive

scholarly journals Machine learning application to the reduction of ambulatory care sensitive admissions (ACSA)

2019 ◽  
Vol 29 (Supplement_4) ◽  
Author(s):  
T Ngo ◽  
V Georgescu ◽  
C Gervet ◽  
A Laurent ◽  
T Libourel ◽  
...  
Keyword(s):  
Machine Learning ◽  
Primary Care ◽  
Health Care ◽  
Ambulatory Care ◽  
Support Vector ◽  
Care Services ◽  
Health Authorities ◽  
Primary Care Workforce ◽  
The Impact ◽  
Ambulatory Care Sensitive

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.

scholarly journals Screening Neurocognitive Disorders in Primary Care Services: The Quick Mild Cognitive Impairment Approach

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 158-158
Author(s):  
Constanca Paul ◽  
Susana Sousa ◽  
Pedro Santos ◽  
Rónán O’Caoimh ◽  
William Molloy
Keyword(s):  
Mental Health ◽  
Primary Care ◽  
Cognitive Impairment ◽  
Community Sample ◽  
Neurocognitive Disorders ◽  
Cognitive Status ◽  
Health Concerns ◽  
Care Services ◽  
Primary Care Services ◽  
Mental Health Concerns

Abstract Neurocognitive Disorders (NCD) is an increasingly common condition in the community. The General Practitioner (GP) in Primary Care Services (PCS), have a crucial role in early detection of NCD and is usually the first professional to detect the signs of MCI. The objective of this study was to test the feasibility and utility of the cognitive screening instrument QMCI in Primary Care. A community sample of 436 people 65+ living in the community was randomly selected from a larger group of old people with mental health concerns (N=2734), referred by their GPs. The mean age of the sample was 75.2 years (sd 7.2), with 41.3% men and 58.7% women; 60.4% married followed by 28.7% widows. The education level was low with 21% illiterate and 69,8% people with 4 years education. The QMCI mean was 37.1/100 (sd 16.2). The amount of people screening positive for cognitive impairment QMCI (<62/100) was 94.2%. In the distribution of people with cognitive impairment by Global Deterioration Scale (GDS) three recoded categories, of the 286 people 76,1% where classified as having very mild or mild impairment, 19,4% moderate or moderately serious and 4,5% severe or very severe impairment. These results confirm the perception of GPs about their clients having mental health concerns and the ability of QMCI accurately discriminate MCI. The QMCI is very brief (3-5mins) fitting the short time of GPs to assess cognitive status and timely refer clients to nonpharmacological interventions that could postpone NCD symptoms.

scholarly journals Identifying priorities for primary care investment in Ireland through a population-based analysis of avoidable hospital admissions for ambulatory care sensitive conditions (ACSC)

BMJ Open ◽  
2019 ◽  
Vol 9 (11) ◽  
pp. e028744 ◽  
Author(s):  
Geraldine McDarby ◽  
Breda Smyth
Keyword(s):  
Primary Care ◽  
Ambulatory Care ◽  
Integrated Care ◽  
Hospital Admissions ◽  
Acute Hospital ◽  
Ambulatory Care Sensitive Conditions ◽  
Hospital System ◽  
Bed Days ◽  
Ambulatory Care Sensitive ◽  
Sensitive Conditions

BackgroundIn 2016, the Irish acute hospital system operated well above internationally recommended occupancy targets. Investment in primary care can prevent hospital admissions of ambulatory care sensitive conditions (ACSCs).ObjectiveTo measure the impact of ACSCs on acute hospital capacity in the Irish public system and identify specific care areas for enhanced primary care provision.DesignNational Hospital In-patient Enquiry System data were used to calculate 2011–2016 standardised bed day rates for selected ACSC conditions. A prioritisation exercise was undertaken to identify the most significant contributors to bed days within our hospital system. Poisson regression was used to determine change over time using incidence rate ratios (IRR).ResultsIn 2016 ACSCs accounted for almost 20% of acute public hospital beds (n=871 328 bed days) with adults over 65 representing 69.1% (n=602 392) of these. Vaccine preventable conditions represented 39.1% of ACSCs. Influenza and pneumonia were responsible for 99.8% of these, increasing by 8.2% (IRR: 1.02; 95% CI 1.02 to 1.03) from 2011 to 2016. Pyelonephritis represented 47.6% of acute ACSC bed days, increasing by 46.5% (IRR: 1.07; 95% CI 1.06 to 1.08) over the 5 years examined.ConclusionsPrioritisation for targeted investment in integrated care programmes is enabled through analysis of ACSC’s in terms of acute hospital bed days. This analysis demonstrates that primary care investment in integrated care programmes for respiratory ACSC’s from prevention to rehabilitation at scale could assist with bed capacity in acute hospitals in Ireland. In adults 65 years and over, including chronic obstructive pulmonary disease patients, the current analysis supports targeting community based pulmonary rehabilitation including pneumococcal and influenza vaccination programmes in order to reduce the burden of infection and hospitalisations. Further exploration of pyelonephritis is necessary in order to ascertain patient profile and appropriateness of admissions.

Primary-Care Visits and Hospitalizations for Ambulatory-Care–Sensitive Conditions in an Inner-City Health Care System

2003 ◽  
Vol 3 (6) ◽  
pp. 324-328 ◽  
Author(s):  
John F. Steiner ◽  
Patricia A. Braun ◽  
Paul Melinkovich ◽  
Judith E. Glazner ◽  
Vijayalaxmi Chandramouli ◽  
...  
Keyword(s):  
Primary Care ◽  
Health Care ◽  
Inner City ◽  
Ambulatory Care ◽  
Health Care System ◽  
Ambulatory Care Sensitive Conditions ◽  
Care System ◽  
Ambulatory Care Sensitive ◽  
Sensitive Conditions
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