scholarly journals Predicting health care utilization using CIHI's Population Grouping Methodology

2018 ◽  
Vol 3 (4) ◽  
Author(s):  
Yvonne Rosehart
Keyword(s):  
Health Care ◽  
Decision Making ◽  
Health Care Utilization ◽  
Healthcare Utilization ◽  
Capacity Planning ◽  
Disease Surveillance ◽  
Health Condition ◽  
Care Utilization ◽  
Term Care ◽  
Planning Processes

IntroductionCIHI’s Population Grouping Methodology uses data from multiple sectors to create clinical profiles and to predict the entire population’s current and future morbidity burden and healthcare utilization. Outputs from the grouper can be applied to healthcare decision making and planning processes. Objectives and ApproachThe population grouping methodology starts with everyone who is eligible for healthcare, including those who haven’t interacted with the healthcare system, providing a true picture of the entire population. The grouper uses diagnosis information over a 2-year period to create health profiles and predict individuals’ future morbidity and expected use of primary care, emergency department and long-term care services. Predictive models were developed using age, sex, health conditions and the most influential health condition interactions as the predictors. These models produce predictive indicators for the concurrent period as well as one year into the future. ResultsThe power of the model lies in the user’s ability to aggregate the data by population segments and compare healthcare resource utilization by different geographic regions, health sectors and health status. The presentation will focus on how CIHI’s population grouping methodology helps client’s monitor population health and conduct disease surveillance. It assists clients with population segmentation, health profiling, predicting health care utilization patterns and explaining variation in health care resource use. It can be used for risk adjustment of populations for inter-jurisdictional analysis, for capacity planning and it can also be used as a component in funding models. Conclusion/ImplicationsCIHI’s population grouping methodology is a useful tool for profiling and predicting healthcare utilization, with key applications for health policy makers, planners and funders. The presentation will focus on how stakeholders can apply the outputs to aid in their decision-making and planning processes.

Systematic review on shared decision making for patients with lung cancer: Effects on distress and health care utilization.

2017 ◽  
Vol 35 (31_suppl) ◽  
pp. 31-31
Author(s):  
Olaf Geerse ◽  
Mariken Stegmann ◽  
Huib A.M. Kerstjens ◽  
Thijo Jeroen Nicolaas Hiltermann ◽  
Marie Bakitas ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Health Care ◽  
Decision Making ◽  
Health Care Utilization ◽  
Shared Decision Making ◽  
Healthcare Utilization ◽  
Risk Of Bias ◽  
Care Utilization ◽  
Shared Decision ◽  
The Impact

31 Background: Lung cancer is associated with significant distress, poor quality of life, and a median prognosis of less than one year. Shared decision making (SDM) has been recommended as a strategy to help guide patients facing difficult treatment trade-offs. Potential benefits of SDM include enhanced knowledge and better congruence between treatment decisions and patients’ personal values and have been described in multiple diseases. We investigated the impact of SDM on distress and healthcare utilization among patients with lung cancer. Methods: We performed a systematic literature search in the CINAHL, Cochrane, EMBASE, MEDLINE, and PsychINFO databases. Studies were eligible when conducted among patients with lung cancer, evaluated SDM, and measured distress and/or health care utilization as outcomes. Risk of bias was assessed using the Cochrane risk of bias tool. Results: A total of 11 articles were identified: two retrospective cohort studies and nine articles reporting on eight randomized controlled trials. Overall, the risk of bias of included studies was low, except for a high risk of bias concerning blinding of participants or personnel. All studies reported on a broad supportive care intervention with SDM as a component of the intervention. No beneficial effect was found in five studies measuring generic distress, while one study reported beneficial effects on depression. There was conflicting evidence regarding the effects of SDM on healthcare utilization; of the seven studies analyzing this, five studies found evidence for a reduction in healthcare utilization. Conclusions: Although relevant, only scarce evidence is currently available on the effects of SDM on distress and healthcare utilization among patients with lung cancer. Thus, additional research is needed before SDM can be recommended in the lung cancer context.

The Impacts of Long-Term Care Insurance on Health Care Utilization and Expenditure: Evidence From China

2022 ◽  
Author(s):  
He Chen ◽  
Jing Ning
Keyword(s):  
Health Care ◽  
Health Care Utilization ◽  
Treatment Effect ◽  
Health Expenditure ◽  
Long Term Care ◽  
Care Utilization ◽  
Ageing Population ◽  
Term Care ◽  
Long Term Care Insurance

Abstract Long-term care insurance (LTCI) is one of the important institutional responses to the growing care needs of the ageing population. Although previous studies have evaluated the impacts of LTCI on health care utilization and expenditure in developed countries, whether such impacts exist in developing countries is unknown. The Chinese government has initiated policy experimentation on LTCI to cope with the growing and unmet need for aged care. Employing a quasi-experiment design, this study aims to examine the policy treatment effect of LTCI on health care utilization and out-of-pocket health expenditure in China. The Propensity Score Matching with Difference-in-difference approach was used to analyse the data obtained from four waves of China Health and Retirement Longitudinal Study (CHARLS). Our findings indicated that, in the aspect of health care utilization, the introduction of LTCI significantly reduced the number of outpatient visits by 0.322 times (p<0.05), the number of hospitalizations by 0.158 times (p<0.01), and the length of inpatient stay during last year by 1.441 days (p<0.01). In the aspect of out-of-pocket health expenditure, we found that LTCI significantly reduced the inpatient out-of-pocket health expenditure during last year by 533.47 yuan (p<0.01), but it did not exhibit an impact on the outpatient out-of-pocket health expenditure during last year. LTCI also had a significantly negative impact on the total out-of-pocket health expenditure by 512.56 yuan. These results are stable in the robustness tests. Considering the evident policy treatment effect of LTCI on health care utilization and out-of-pocket health expenditure, the expansion of LTCI could help reduce the needs for health care services and contain the increases in out-of-pocket health care expenditure in China.

Trends in health care utilization, cost, and aggressive care at end of life among older cancer patients in the Deep South.

2015 ◽  
Vol 33 (29_suppl) ◽  
pp. 155-155
Author(s):  
Elizabeth Ann Kvale ◽  
Gabrielle Rocque ◽  
Kerri S. Bevis ◽  
Aras Acemgil ◽  
Richard A. Taylor ◽  
...  
Keyword(s):  
Health Care ◽  
Health Care Utilization ◽  
End Of Life ◽  
Healthcare Utilization ◽  
Linear Models ◽  
Care Utilization ◽  
Deep South ◽  
Medicare Beneficiaries ◽  
Aggressive Care ◽  
The Impact

155 Background: Healthcare utilization and costs escalate near diagnosis and in the final months of life. There is a national trend toward aggressive care at end of life (EOL). We examined patterns in utilization and cost across the trajectory of care and during the last two weeks of life during implementation of a lay navigation intervention. Methods: Claims data were obtained for Medicare beneficiaries ≥ 65 years old with cancer in the UAB Health System Cancer Community Network (UAB CCN). For 10 quarters from January 2012 -June 2014, we examined healthcare utilization for the population at large, navigated patients, and decedents. All analyses included ER visits, hospitalizations, and ICU admissions and use of chemotherapy in the last 2 weeks of life, and hospice utilization (admission or less than 3 days of hospice) in the quarter of death for decedents. Descriptive analyses and linear regression were used to test trends over time; general linear models evaluated changes in health care utilization and cost. Results: Across the population reduction of 13.4% to 11% for hospitalization (18% decrease, p < 0.01), 8.0% to 7.1% for ER visits (12% decrease, p < 0.01), 2.9% to 2.5% for ICU admissions (14% decrease, p = 0.04) and an increase of 3.9% to 4.3% for hospice (9.2% increase p = 0.37) were found. Among 5,861 decedents, in the last 2 weeks of life, there were decreases in ICU admissions (14.6% decrease, p = 0.11), from 39.2% to 32.0%, ER visits (18.4% decrease, p = 0.03), and chemotherapy, from 4.7% to 3.5% (25.5% decrease, p = 0.11).Over the 10 quarters, hospice enrollment increased from 70.7% to 77.4% (9.48% increase; p = 0.06), and the proportion of patients on hospice for less than 3 days changed from 7.8% to 7.5% (3.85% decrease, p = 0.30). Costs decreased about $158 per quarter per beneficiary. A significant pre-post decrease of $952 per beneficiary (p < 0.01) led to an estimated reduction in Medicare costs of $18,406,920 for the 19,335 beneficiaries in the UAB CCN for the five quarters post-implementation. Conclusions: We observed decreased healthcare utilization and cost and trends toward decreased aggressive care at EOL in the UAB CCN. Further work is needed to determine the impact of navigation on utilization trends.

scholarly journals An Econometric Analysis Of Health Care Utilization In Kenya

2016 ◽  
Vol 12 (16) ◽  
pp. 443 ◽  
Author(s):  
Diana N. Kimani ◽  
Mercy G. Mugo ◽  
Urbanus M. Kioko
Keyword(s):  
Public Health ◽  
Health Care ◽  
Health Care Utilization ◽  
Healthcare Utilization ◽  
Household Head ◽  
Econometric Analysis ◽  
Health Facilities ◽  
Public Health Care ◽  
Care Utilization

Background: Increasing access to health care has been a policy concern for many governments, Kenya included. The Kenyan government introduced and implemented a number of initiatives in a bid to address the healthcare utilization challenge. These initiatives include 10/20 policy, exemptions for user fees for some specific health services (treatment of children less than five years, maternity services in dispensaries and health centers, Tuberculosis treatment in public health facilities), and increase in the number of health facilities and health workforce. These initiatives notwithstanding, healthcare utilization in Kenya remains a challenge. The Kenya Household Health Expenditure and Utilization Survey of 2007 found that 17 percent of those who needed health care services could not access the services from both government and private health facilities largely due to financial constraints. This paper employed econometric analysis to examine what could be constraining health care utilization in Kenya despite all the efforts employed. Methods: Using the 2007 Kenya Household Health Expenditures and Utilization Survey (KHHEUS) data (n = 8414), this paper investigates the factors that affect health care utilization in Kenya by estimating a count data negative binomial model. The model was also applied to public and private health facilities to better understand the specificities of poverty in these two facility types. Common estimation problems of endogeneity, heterogeneity, multicollinearity and heteroskedasticity are addressed. Findings: The econometric analysis reveals that out-of-pocket expenditures, waiting time, distance, household size, income, chronic illness area of residence and working status of the household head are significant factors affecting health care utilization in Kenya. While income and distance are significant factors affecting public health care utilization they are not significant in explaining healthcare utilization in private facilities. In addition, working status of the household head, insurance cover and education are significant in explaining private and not public health care utilization. A striking finding is the positive relationship between distance and health care utilization implying that people will travel long distances to obtain treatment. This is perhaps associated with expectations of higher quality of care at far away higher level facilities, especially in rural areas. Conclusion: The paper confirms the existing evidence of the negative effects of Out-of-Pocket (OOP) expenditures and other determinants of health care utilization. With a better understanding of why people use or do not use health services, health care organizations can seek to improve the quality of human life. The bypassing of health facilities for higher level far away facilities implies that it is not so much about availing health facilities, but the quality of the services offered in those facilities. The government should therefore assure quality to increase utilization of the lower level facilities, especially in the rural areas.

Hospital Diagnosis of Malnutrition: A Call for Action

2009 ◽  
Vol 70 (1) ◽  
pp. 37-41 ◽  
Author(s):  
Mary Ann Bocock ◽  
Heather H. Keller
Keyword(s):  
Older Adults ◽  
Health Care ◽  
Health Information ◽  
Hospital Admissions ◽  
Care Facility ◽  
International Classification Of Diseases ◽  
Health Condition ◽  
Care Utilization ◽  
Discharge Abstract Database ◽  
Term Care

The Canadian Institute for Health Information (CIHI) provides accurate health information needed to establish sound health care policies. The CIHI mandate is to develop and coordinate a uniform approach to health care information in Canada. The institute uses the International Classification of Diseases (ICD) system to record the most responsible diagnosis for each hospital admission. This investigation was conducted to determine if six ICD protein-calorie malnutrition (PCM) codes could be used for health care utilization analyses. Aggregate data (1996 to 2000) from the CIHI discharge abstract database were used. The data analyzed were the most responsible diagnoses data for the six PCM codes and a single summary statistic for all other “non-malnutrition” diagnoses for all long-term care facility residents aged 65 or older who were transferred to an acute care facility. In this population, fewer than five hospital admissions per year were assigned a PCM code. There were too few PCM cases to do trend analyses for morbidity or mortality. This study suggests a lack of recognition and documentation of PCM as a specific health condition in older adults. Lack of tracking of this diagnosis prevents documentation that could lead to policy changes to support older adults’ nutrition.

scholarly journals Effects of Shared Decision Making on Distress and Health Care Utilization Among Patients With Lung Cancer: A Systematic Review

2018 ◽  
Vol 56 (6) ◽  
pp. 975-987.e5 ◽  
Author(s):  
Olaf P. Geerse ◽  
Mariken E. Stegmann ◽  
Huib A.M. Kerstjens ◽  
Thijo Jeroen N. Hiltermann ◽  
Marie Bakitas ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Systematic Review ◽  
Health Care ◽  
Decision Making ◽  
Health Care Utilization ◽  
Shared Decision Making ◽  
Care Utilization ◽  
Shared Decision
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