Primary care visits and ambulatory care sensitive diabetes hospitalizations among adult Alabama Medicaid beneficiaries

2021 ◽  
Author(s):  
Janet M. Bronstein ◽  
Lei Huang ◽  
John P. Shelley ◽  
Emily B. Levitan ◽  
Caroline A. Presley ◽  
...  
Keyword(s):  
Primary Care ◽  
Ambulatory Care ◽  
Medicaid Beneficiaries ◽  
Ambulatory Care Sensitive

1655-P: Primary Care Visits and Ambulatory Care Sensitive Diabetes Hospitalizations among Adult Alabama Medicaid Beneficiaries

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 1655-P
Author(s):  
JANET M. BRONSTEIN ◽  
LEI HUANG ◽  
APRIL A. AGNE ◽  
FAVEL L. MONDESIR ◽  
CAROLINE A. PRESLEY ◽  
...  
Keyword(s):  
Primary Care ◽  
Ambulatory Care ◽  
Medicaid Beneficiaries ◽  
Ambulatory Care Sensitive

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

scholarly journals Hospitalization of older adults due to ambulatory care sensitive conditions

2014 ◽  
Vol 48 (5) ◽  
pp. 817-826 ◽  
Author(s):  
Aline Pinto Marques ◽  
Dalia Elena Romero Montilla ◽  
Wanessa da Silva de Almeida ◽  
Carla Lourenço Tavares de Andrade
Keyword(s):  
Older Adults ◽  
Primary Care ◽  
Information System ◽  
Ambulatory Care ◽  
Rio De Janeiro ◽  
The State ◽  
Ambulatory Care Sensitive Conditions ◽  
Global Rate ◽  
Ambulatory Care Sensitive ◽  
Sensitive Conditions

OBJECTIVE To analyze the temporal evolution of the hospitalization of older adults due to ambulatory care sensitive conditions according to their structure, magnitude and causes. METHODS Cross-sectional study based on data from the Hospital Information System of the Brazilian Unified Health System and from the Primary Care Information System, referring to people aged 60 to 74 years living in the state of Rio de Janeiro, Souhteastern Brazil. The proportion and rate of hospitalizations due to ambulatory care sensitive conditions were calculated, both the global rate and, according to diagnoses, the most prevalent ones. The coverage of the Family Health Strategy and the number of medical consultations attended by older adults in primary care were estimated. To analyze the indicators’ impact on hospitalizations, a linear correlation test was used. RESULTS We found an intense reduction in hospitalizations due to ambulatory care sensitive conditions for all causes and age groups. Heart failure, cerebrovascular diseases and chronic obstructive pulmonary diseases concentrated 50.0% of the hospitalizations. Adults older than 69 years had a higher risk of hospitalization due to one of these causes. We observed a higher risk of hospitalization among men. A negative correlation was found between the hospitalizations and the indicators of access to primary care. CONCLUSIONS Primary healthcare in the state of Rio de Janeiro has been significantly impacting the hospital morbidity of the older population. Studies of hospitalizations due to ambulatory care sensitive conditions can aid the identification of the main causes that are sensitive to the intervention of the health services, in order to indicate which actions are more effective to reduce hospitalizations and to increase the population’s quality of life.

scholarly journals Impact of a national primary care pay-for-performance scheme on ambulatory care sensitive hospital admissions: a small-area analysis in England

BMJ Open ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. e036046
Author(s):  
Christos Grigoroglou ◽  
Luke Munford ◽  
Roger Webb ◽  
Navneet Kapur ◽  
Tim Doran ◽  
...  
Keyword(s):  
Primary Care ◽  
Ambulatory Care ◽  
Small Area ◽  
Negative Binomial ◽  
Hospital Admissions ◽  
Pay For Performance ◽  
Administrative Dataset ◽  
Practice Performance ◽  
Quality And Outcomes ◽  
Ambulatory Care Sensitive

ObjectiveWe aimed to spatially describe hospital admissions for ambulatory care sensitive conditions (ACSC) in England at small-area geographical level and assess whether recorded practice performance under one of the world’s largest primary care pay-for-performance schemes led to reductions in these potentially avoidable hospitalisations for chronic conditions incentivised in the scheme.SettingWe obtained numbers of ACSC hospital admissions from the Hospital Episode Statistics database and information on recorded practice performance from the Quality and Outcomes Framework (QOF) administrative dataset for 2015/2016. We fitted three sets of negative binomial models to examine ecological associations between incentivised ACSC admissions, general practice performance, deprivation, urbanity and other sociodemographic characteristics.ResultsHospital admissions for QOF incentivised ACSCs varied within and between regions, with clusters of high numbers of hospital admissions for incentivised ACSCs identified across England. Our models indicated a very small effect of the QOF on reducing admissions for incentivised ACSCs (0.993, 95% CI 0.990 to 0.995), however, other factors, such as deprivation (1.021, 95% CI 1.020 to 1.021) and urbanicity (0.875, 95% CI 0.862 to 0.887), were far more important in explaining variations in admissions for ACSCs. People in deprived areas had a higher risk of being admitted in hospital for an incentivised ACSC condition.ConclusionSpatial analysis based on routinely collected data can be used to identify areas with high rates of potentially avoidable hospital admissions, providing valuable information for targeting resources and evaluating public health interventions. Our findings suggest that the QOF had a very small effect on reducing avoidable hospitalisation for incentivised conditions. Material deprivation and urbanicity were the strongest predictors of the variation in ACSC rates for all QOF incentivised conditions across England.

scholarly journals Admissions for ambulatory care sensitive conditions on rural islands and their association with patient experience: a multicentred prospective cohort study

BMJ Open ◽  
2019 ◽  
Vol 9 (12) ◽  
pp. e030101 ◽  
Author(s):  
Makoto Kaneko ◽  
Takuya Aoki ◽  
Masafumi Funato ◽  
Keita Yamashiro ◽  
Kaku Kuroda ◽  
...  
Keyword(s):  
Heart Failure ◽  
Primary Care ◽  
Cohort Study ◽  
Ambulatory Care ◽  
Patient Experience ◽  
Prospective Cohort Study ◽  
Rural Areas ◽  
Prospective Cohort ◽  
Ambulatory Care Sensitive Conditions ◽  
Ambulatory Care Sensitive

ObjectivesThe rate of admissions for ambulatory care sensitive conditions (ACSCs) is a key outcome indicator for primary care, and patient experience (PX) is a crucial process indicator. Studies have reported higher rates of admission for ACSCs in rural areas than in urban areas. Whether there is an association between admissions for ACSCs and PX in rural areas has not been examined. This study aimed to document admissions for ACSCs on Japanese rural islands, and assess whether there was an association between the rate of admissions for ACSCs and PX.DesignMulticentred, prospective, cohort studySettingThis study was conducted on five rural islands in Okinawa, Japan.ParticipantsThe study participants were all island inhabitants aged 65 years or older.Primary outcome measuresThis study examined the association between ACSCs and PX assessed by a questionnaire, the Japanese Version of Primary Care Assessment Tool. ACSCs were classified using the International Classification of Diseases, Tenth Revision, and the rate of admissions for ACSCs in 1 year.ResultsOf 1258 residents, 740 completed the questionnaire. This study documented 38 admissions for ACSCs (29 patients, males/females: 15/14, median age 81.9) that included congestive heart failure (11), pneumonia (7) and influenza (5). After adjusting for covariates and geographical clustering, admissions for ACSCs had a significant positive association with each patient’s PX scores (OR per 1 SD increase=1.62, 95% CI 1.02–2.61).ConclusionsPhysicians serving rural areas need to stress the importance of preventive interventions for heart failure, pneumonia and influenza to reduce the number of admissions for ACSCs. Contrary to previous studies, our findings might be explained by close patient–doctor relationships on the rural islands.

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.

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