scholarly journals Development of a predictive model for integrated medical and long-term care resource consumption based on health behaviour: application of healthcare big data of patients with circulatory diseases

BMC Medicine ◽  
2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Tomoyuki Takura ◽  
Keiko Hirano Goto ◽  
Asao Honda
Keyword(s):  
Regression Analysis ◽  
Predictive Model ◽  
Clinical Outcomes ◽  
Long Term Care ◽  
Healthcare Resource ◽  
Resource Consumption ◽  
Coefficient Of Determination ◽  
Term Care ◽  
Adherence Score

Abstract Background Medical costs and the burden associated with cardiovascular disease are on the rise. Therefore, to improve the overall economy and quality assessment of the healthcare system, we developed a predictive model of integrated healthcare resource consumption (Adherence Score for Healthcare Resource Outcome, ASHRO) that incorporates patient health behaviours, and examined its association with clinical outcomes. Methods This study used information from a large-scale database on health insurance claims, long-term care insurance, and health check-ups. Participants comprised patients who received inpatient medical care for diseases of the circulatory system (ICD-10 codes I00-I99). The predictive model used broadly defined composite adherence as the explanatory variable and medical and long-term care costs as the objective variable. Predictive models used random forest learning (AI: artificial intelligence) to adjust for predictors, and multiple regression analysis to construct ASHRO scores. The ability of discrimination and calibration of the prediction model were evaluated using the area under the curve and the Hosmer-Lemeshow test. We compared the overall mortality of the two ASHRO 50% cut-off groups adjusted for clinical risk factors by propensity score matching over a 48-month follow-up period. Results Overall, 48,456 patients were discharged from the hospital with cardiovascular disease (mean age, 68.3 ± 9.9 years; male, 61.9%). The broad adherence score classification, adjusted as an index of the predictive model by machine learning, was an index of eight: secondary prevention, rehabilitation intensity, guidance, proportion of days covered, overlapping outpatient visits/clinical laboratory and physiological tests, medical attendance, and generic drug rate. Multiple regression analysis showed an overall coefficient of determination of 0.313 (p < 0.001). Logistic regression analysis with cut-off values of 50% and 25%/75% for medical and long-term care costs showed that the overall coefficient of determination was statistically significant (p < 0.001). The score of ASHRO was associated with the incidence of all deaths between the two 50% cut-off groups (2% vs. 7%; p < 0.001). Conclusions ASHRO accurately predicted future integrated healthcare resource consumption and was associated with clinical outcomes. It can be a valuable tool for evaluating the economic usefulness of individual adherence behaviours and optimising clinical outcomes.

scholarly journals Factors associated with long-term urinary catheterisation and its impact on urinary tract infection among older people in the community: a population-based observational study in a city in Japan

BMJ Open ◽  
2019 ◽  
Vol 9 (6) ◽  
pp. e028371
Author(s):  
Motohiko Adomi ◽  
Masao Iwagami ◽  
Takashi Kawahara ◽  
Shota Hamada ◽  
Katsuya Iijima ◽  
...  
Keyword(s):  
Urinary Tract Infection ◽  
Regression Analysis ◽  
Urinary Tract ◽  
Rate Ratio ◽  
Long Term Care ◽  
Population Based ◽  
Term Care ◽  
Factors Associated ◽  
Urinary Catheterisation

ObjectivesThis study aimed to identify factors associated with long-term urinary catheterisation (LTUC) in community-dwelling older adults and to evaluate the risk of urinary tract infection (UTI) among people with LTUC.DesignPopulation-based observational study.SettingMedical and long-term care insurance claims data from one municipality in Japan.ParticipantsPeople aged ≥75 years living at home who used medical services between October 2012 and September 2013 (n=32 617).Outcome measures(1) Use of LTUC, defined as urinary catheterisation for at least two consecutive months, to identify factors associated with LTUC and (2) the incidence of UTI, defined as a recorded diagnosis of UTI and prescription of antibiotics, in people with and without LTUC.ResultsThe 1-year prevalence of LTUC was 0.44% (143/32 617). Multivariable logistic regression analysis showed that the male sex, older age, higher comorbidity score, previous history of hospitalisation with in-hospital use of urinary catheters and high long-term care need level were independently associated with LTUC. The incidence rate of UTI was 33.8 and 4.7 per 100 person-years in people with and without LTUC, respectively. According to multivariable Poisson regression analysis, LTUC was independently associated with UTI (adjusted rate ratio 2.58, 95% CI 1.68 to 3.96). Propensity score-matched analysis yielded a similar result (rate ratio 2.41, 95% CI 1.45 to 4.00).ConclusionsWe identified several factors associated with LTUC in the community, and LTUC was independently associated with the incidence of UTI.

Prolonged tube feeding in long-term care: nutritional status and clinical outcomes.

1992 ◽  
Vol 11 (3) ◽  
pp. 309-325 ◽  
Author(s):  
C T Henderson ◽  
L S Trumbore ◽  
S Mobarhan ◽  
R Benya ◽  
T P Miles
Keyword(s):  
Nutritional Status ◽  
Clinical Outcomes ◽  
Long Term Care ◽  
Tube Feeding ◽  
Term Care

scholarly journals Long-Term Care Market Trend and Patterns of Caregiving in the United States

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 84-85
Author(s):  
Rashmita Basu
Keyword(s):  
Regression Analysis ◽  
Long Term Care ◽  
Unmet Need ◽  
Adult Child ◽  
Primary Source ◽  
The United States ◽  
Community Dwelling ◽  
Formal Care ◽  
Term Care

Abstract Objectives: The current study aims to: 1) identify patterns of the use of long-term care services and supports (LTSS) among community-dwelling individuals; 2) examine if the changes in supply of formal care predict the use of informal care (IC). Methods: Linking the market supply of formal LTSS to individual level Health and Retirement Survey data from (N=7,781), descriptive and regression analysis were performed. Results: Supply of formal home and residential LTSS indicates a stronger upward trend. More than 90% of people used IC and 40% received both informal and formal help. People aged under 60 years, IC from spouse dominates but then falls sharply and an adult child becomes the primary source. More than 20% reported unmet needs. Regression analysis indicates that the formal home care supply significantly predicts the likelihood of using IC. But the rate and intensity of unpaid IC use among individuals aged 85 or older is low and that of paid formal care use are highest. We find that about 20% of care recipients experienced at least one unmet need with ADL assistance in our sample. The prevalence of an unmet need sharply decreases as individuals receive care from multiple caregivers (including paid professionals) but receiving care from too many caregivers contributes to higher unmet ADL needs. Discussion: The findings suggest opportunities to create a holistic system of care for people needing LTSS.

Implementing an Incontinence Management Protocol in Long-Term Care: Clinical Outcomes and Costs

2003 ◽  
Vol 29 (8) ◽  
pp. 46-53 ◽  
Author(s):  
Rita A Frantz ◽  
George C Xakellis ◽  
Pam C Harvey ◽  
Anne R Lewis
Keyword(s):  
Clinical Outcomes ◽  
Long Term Care ◽  
Term Care ◽  
Management Protocol

scholarly journals Acinetobacter Infections and Outcomes at an Academic Medical Center: A Disease of Long-Term Care

2015 ◽  
Vol 2 (1) ◽  
Author(s):  
Jennifer Townsend ◽  
An Na Park ◽  
Rita Gander ◽  
Kathleen Orr ◽  
Doramarie Arocha ◽  
...  
Keyword(s):  
Clinical Outcomes ◽  
Long Term Care ◽  
Medical Center ◽  
Academic Medical Center ◽  
Clinical Isolates ◽  
University Hospital ◽  
Term Care ◽  
Academic Medical ◽  
Resistance Patterns

Abstract Background.  Our study aims to describe the epidemiology, microbial resistance patterns, and clinical outcomes of Acinetobacter infections at an academic university hospital. This retrospective study analyzed all inpatient clinical isolates of Acinetobacter collected at an academic medical center over 4 years. The data were obtained from an Academic tertiary referral center between January 2008 and December 2011. All consecutive inpatients during the study period who had a clinical culture positive for Acinetobacter were included in the study. Patients without medical records available for review or less than 18 years of age were excluded. Methods.  Records were reviewed to determine source of isolation, risk factors for acquisition, drug resistance patterns, and clinical outcomes. Repetitive sequence-based polymerase chain reaction of selected banked isolates was used to determine patterns of clonal spread in and among institutions during periods of higher infection rates. Results.  Four hundred eighty-seven clinical isolates of Acinetobacter were found in 212 patients (in 252 admissions). Patients with Acinetobacter infections were frequently admitted from healthcare facilities (HCFs) (59%). One hundred eighty-three of 248 (76%) initial isolates tested were resistant to meropenem. One hundred ninety-eight of 249 (79.5%) initial isolates were multidrug resistant (MDR). Factors associated with mortality included bacteremia (odds ratio [OR] = 1.93, P = .024), concomitant steroid use (OR = 2.87, P &lt; .001), admission from a HCF (OR = 6.34, P = .004), and chronic obstructive pulmonary disease (OR = 3.17, P &lt; .001). Conclusions.  Acinetobacter isolates at our institution are frequently MDR and are more common among those who reside in HCFs. Our findings underline the need for new strategies to prevent and treat this pathogen, including stewardship efforts in long-term care settings.

scholarly journals Development and validation of gradient boosting decision tree models for predicting care needs using a long-term care database in Japan

2021 ◽  
Author(s):  
Huei-Ru Lin ◽  
Koki Fujiwara ◽  
Minoru Sasaki ◽  
Ko Ishiyama ◽  
Shino Ikeda-Sonoda ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Predictive Model ◽  
Long Term Care ◽  
Confusion Matrix ◽  
Claim Data ◽  
Gradient Boosting ◽  
Care Needs ◽  
Term Care

AbstractObjectiveThe purpose of the study was to develop machine learning models using data from long-term care (LTC) insurance claims and care needs certifications to predict the individualized future care needs of each older adult.MethodsWe collected LTC insurance-related data in the form of claims and care needs certification surveys from a municipality of Kanagawa Prefecture from 2009 to 2018. We used care needs certificate applications for model generation and the validation sample to build gradient boosting decision tree (GBDT) models to classify if 1) the insured’s care needs either remained stable or decreased or 2) the insured’s care needs increased after three years. The predictive model was trained and evaluated via k-fold cross-validation. The performance of the predictive model was observed in its accuracy, precision, recall, F1 score, area under the receiver-operator curve, and confusion matrix.ResultsLong-term care certificate applications and claim data from 2009–2018 were associated with 92,239 insureds with a mean age of 86.1 years old at the time of application, of whom 67% were female. The classifications of increase in care needs after three years were predicted with AUC of 0.80.ConclusionsMachine learning is a valuable tool for predicting care needs increases in Japan’s LTC insurance system, which can be used to develop more targeted and efficient interventions to proactively reduce or prevent further functional deterioration, thereby helping older adults maintain a better quality of life.

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