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Systematic review of high-cost patients' characteristics and healthcare utilisation.

ABSTRACT: OBJECTIVES:To investigate the characteristics and healthcare utilisation of high-cost patients and to compare high-cost patients across payers and countries. DESIGN:Systematic review. DATA SOURCES:PubMed and Embase databases were searched until 30 October 2017. ELIGIBILITY CRITERIA AND OUTCOMES:Our final search was built on three themes: 'high-cost', 'patients', and 'cost' and 'cost analysis'. We included articles that reported characteristics and utilisation of the top-X% (eg, top-5% and top-10%) patients of costs of a given population. Analyses were limited to studies that covered a broad range of services, across the continuum of care. Andersen's behavioural model was used to categorise characteristics and determinants into predisposing, enabling and need characteristics. RESULTS:The studies pointed to a high prevalence of multiple (chronic) conditions to explain high-cost patients' utilisation. Besides, we found a high prevalence of mental illness across all studies and a prevalence higher than 30% in US Medicaid and total population studies. Furthermore, we found that high costs were associated with increasing age but that still more than halve of high-cost patients were younger than 65 years. High costs were associated with higher incomes in the USA but with lower incomes elsewhere. Preventable spending was estimated at maximally 10% of spending. The top-10%, top-5% and top-1% high-cost patients accounted for respectively 68%, 55% and 24% of costs within a given year. Spending persistency varied between 24% and 48%. Finally, we found that no more than 30% of high-cost patients are in their last year of life. CONCLUSIONS:High-cost patients make up the sickest and most complex populations, and their high utilisation is primarily explained by high levels of chronic and mental illness. High-cost patients are diverse populations and vary across payer types and countries. Tailored interventions are needed to meet the needs of high-cost patients and to avoid waste of scarce resources.

SUBMITTER: Wammes JJG

PROVIDER: S-EPMC6129088 | biostudies-other | 2018 Sep

REPOSITORIES: biostudies-other

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Systematic review of high-cost patients' characteristics and healthcare utilisation.

Wammes Joost Johan Godert JJG van der Wees Philip J PJ Tanke Marit A C MAC Westert Gert P GP Jeurissen Patrick P T PPT

BMJ open 20180908 9

<h4>Objectives</h4>To investigate the characteristics and healthcare utilisation of high-cost patients and to compare high-cost patients across payers and countries.<h4>Design</h4>Systematic review.<h4>Data sources</h4>PubMed and Embase databases were searched until 30 October 2017.<h4>Eligibility criteria and outcomes</h4>Our final search was built on three themes: 'high-cost', 'patients', and 'cost' and 'cost analysis'. We included articles that reported characteristics and utilisation of the ...[more]

PMID: 30196269

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Project description:ObjectiveTo determine medical needs, demographic characteristics and healthcare utilisation patterns of the top 1% and top 2%-5% high-cost beneficiaries in the Netherlands.DesignCross-sectional study using 1 year claims data. We broke down high-cost beneficiaries by demographics, the most cost-incurring condition per beneficiary and expensive treatment use.SettingDutch curative health system, a health system with universal coverage.Participants4.5 million beneficiaries of one health insurer.MeasuresAnnual total costs through hospital, intensive care unit use, expensive drugs, other pharmaceuticals, mental care and others; demographics; most cost-incurring and secondary conditions; inpatient stay; number of morbidities; costs per ICD10-chapter (International Statistical Classification of Diseases, 10th revision); and expensive treatment use (including dialysis, transplant surgery, expensive drugs, intensive care unit and diagnosis-related groups >€30 000).ResultsThe top 1% and top 2%-5% beneficiaries accounted for 23% and 26% of total expenditures, respectively. Among top 1% beneficiaries, hospital care represented 76% of spending, of which, respectively, 9.0% and 9.1% were spent on expensive drugs and ICU care. We found that 54% of top 1% beneficiaries were aged 65 years or younger and that average costs sharply decreased with higher age within the top 1% group. Expensive treatments contributed to high costs in one-third of top 1% beneficiaries and in less than 10% of top 2%-5% beneficiaries. The average number of conditions was 5.5 and 4.0 for top 1% and top 2%-5% beneficiaries, respectively. 53% of top 1% beneficiaries were treated for circulatory disorders but for only 22% of top 1% beneficiaries this was their most cost-incurring condition.ConclusionsExpensive treatments, most cost-incurring condition and age proved to be informative variables for studying this heterogeneous population. Expensive treatments play a substantial role in high-costs beneficiaries. Interventions need to be aimed at beneficiaries of all ages; a sole focus on the elderly would leave many high-cost beneficiaries unaddressed. Tailored interventions are needed to meet the needs of high-cost beneficiaries and to avoid waste of scarce resources.

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Project description:OBJECTIVE:To assess the effect of cannabis legalisation on health effects and healthcare utilisation in Colorado (CO), the first state to legalise recreational cannabis, when compared with two control states, New York (NY) and Oklahoma (OK). DESIGN:We used the 2010 to 2014 Healthcare Cost and Utilisation Project (HCUP) inpatient databases to compare changes in rates of healthcare utilisation and diagnoses in CO versus NY and OK. SETTING:Population-based, inpatient. PARTICIPANTS:HCUP state-wide data comprising over 28?million individuals and over 16?million hospitalisations across three states. MAIN OUTCOME MEASURES:We used International Classification of Diseases-Ninth Edition codes to assess changes in healthcare utilisation specific to various medical diagnoses potentially treated by or exacerbated by cannabis. Diagnoses were classified based on weight of evidence from the National Academy of Science (NAS). Negative binomial models were used to compare rates of admissions between states. RESULTS:In CO compared with NY and OK, respectively, cannabis abuse hospitalisations increased (risk ratio (RR) 1.27, 95% CI 1.26 to 1.28 and RR 1.16, 95%?CI 1.15 to 1.17; both p<0.0005) post-legalisation. In CO, there was a reduction in total admissions but only when compared with OK (RR 0.97, 95%?CI 0.96 to 0.98, p<0.0005). Length of stay and costs did not change significantly in CO compared with NY or OK. Post-legalisation changes most consistent with NAS included an increase in motor vehicle accidents, alcohol abuse, overdose injury and a reduction in chronic pain admissions (all p<0.05 compared with each control state). CONCLUSIONS:Recreational cannabis legalisation is associated with neutral effects on healthcare utilisation. In line with previous evidence, cannabis liberalisation is linked to an increase in motor vehicle accidents, alcohol abuse, overdose injuries and a decrease in chronic pain admissions. Such population-level effects may help guide future decisions regarding cannabis use, prescription and policy.

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Dataset Information

Systematic review of high-cost patients' characteristics and healthcare utilisation.

Publications

Systematic review of high-cost patients' characteristics and healthcare utilisation.

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