Predicting patient ‘cost blooms’ in Denmark: a longitudinal population-based study

Tamang, Suzanne; Milstein, Arnold; Sørensen, Henrik Toft; Pedersen, Lars; Mackey, Lester; Betterton, Jean-Raymond; Janson, Lucas; Shah, Nigam H.

doi:10.1136/bmjopen-2016-011580

Cited by 36 publications

(73 citation statements)

References 29 publications

(17 reference statements)

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“…In addition, top-1% patients were more likely to die compared with top-5% patients, 17 31 and persistent high-cost patients were more likely to die than episodic high-cost patients. 32 Finally, among US dual eligibles, mortality varied much across age and residence groups; nearly half of dual eligibles aged 65 years and older died. 16 …”

Section: Resultsmentioning

confidence: 99%

Systematic review of high-cost patients’ characteristics and healthcare utilisation

et al. 2018

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ObjectivesTo investigate the characteristics and healthcare utilisation of high-cost patients and to compare high-cost patients across payers and countries.DesignSystematic review.Data sourcesPubMed and Embase databases were searched until 30 October 2017.Eligibility criteria and outcomesOur 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.ResultsThe 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.ConclusionsHigh-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.

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Section: Resultsmentioning

confidence: 99%

Systematic review of high-cost patients’ characteristics and healthcare utilisation

et al. 2018

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“…We assessed overall accuracy of the predicted cost by the root-mean-squared error (RMSE), the mean absolute prediction error (MAPE), and cost accuracy (CA). These three metrics are commonly used to compare different cost-prediction models [11,21,[29][30][31]. Let y i andŷ i de-note the observed and predicted expenditure for episode i (i = 1, 2, …, n) respectively, RMSE, MAPE, and CA are defined as,…”

Section: Criteria Used For Model Comparisonmentioning

confidence: 99%

Comparison of statistical and machine learning models for healthcare cost data: a simulation study motivated by Oncology Care Model (OCM) data

Mazumdar

Lin

Zhang

et al. 2020

BMC Health Serv Res

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Background: The Oncology Care Model (OCM) was developed as a payment model to encourage participating practices to provide better-quality care for cancer patients at a lower cost. The risk-adjustment model used in OCM is a Gamma generalized linear model (Gamma GLM) with log-link. The predicted value of expense for the episodes identified for our academic medical center (AMC), based on the model fitted to the national data, did not correlate well with our observed expense. This motivated us to fit the Gamma GLM to our AMC data and compare it with two other flexible modeling methods: Random Forest (RF) and Partially Linear Additive Quantile Regression (PLAQR). We also performed a simulation study to assess comparative performance of these methods and examined the impact of non-linearity and interaction effects, two understudied aspects in the field of cost prediction. Methods: The simulation was designed with an outcome of cost generated from four distributions: Gamma, Weibull, Log-normal with a heteroscedastic error term, and heavy-tailed. Simulation parameters both similar to and different from OCM data were considered. The performance metrics considered were the root mean square error (RMSE), mean absolute prediction error (MAPE), and cost accuracy (CA). Bootstrap resampling was utilized to estimate the operating characteristics of the performance metrics, which were described by boxplots. Results: RF attained the best performance with lowest RMSE, MAPE, and highest CA for most of the scenarios. When the models were misspecified, their performance was further differentiated. Model performance differed more for non-exponential than exponential outcome distributions.

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“…Time since first hospital treatment for heart failure, previous health care expenditures, and end-of-life period have all been identified as important cost drivers [19][20][21] and were included as well. Time since first hospital treatment for heart failure, previous health care expenditures, and end-of-life period have all been identified as important cost drivers [19][20][21] and were included as well.…”

Section: Variablesmentioning

confidence: 99%

“…We used hospital claims to create dichotomous variables for heart-related admissions and surgical interventions. Time since first hospital treatment for heart failure, previous health care expenditures, and end-of-life period have all been identified as important cost drivers [19][20][21] and were included as well.…”

Section: Variablesmentioning

confidence: 99%

Characteristics and health care utilization among patients with chronic heart failure: a longitudinal claim database analysis

et al. 2019

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AimsThis study aimed to determine the characteristics of patients with heart failure and high costs (top 1% and top 2-5% highest costs in perspective of the general population) and to explore the longitudinal health care utilization and persistency of high costs. Methods and resultsLongitudinal observational study using claims data from 2006 to 2014 in the Netherlands. We identified all patients that received a hospital treatment for chronic heart failure between 1 January 2008 and 31 December 2010. Of each selected patient, all claims from the Dutch curative health system and with a starting date between 1 January 2006 and 31 December 2014 were extracted. Pharmaceutical and hospital claims were used to establish characteristics and indicators for health care utilization. Descriptive analyses and generalized estimating equation models were used to analyse characteristics, longitudinal health care utilization and to identify factors associated with high costs. Our findings revealed that the difference in costs between top 1%, top 2-5%, and bottom 95% patients with heart failure was mainly driven by hospital costs; and the top 1% group experienced a remarkable increase of mental health costs. Top 1% and top 2-5% patients with heart failure differed from lower cost patients in their higher rate of chronic conditions, excessive polypharmacy, hospital admissions, and heart-related surgeries. Heart-related surgeries contributed to the incidental high costs in 54% of top 1% patients, and the costs of the remaining top 1% patients were driven by mental health and pharmaceuticals use and rates of chronic conditions and multimorbidity. Top 1% patients were relatively young. Anaemia, dementia, diseases of arteries, veins and lymphatic vessels, influenza, and kidney failure were significantly associated with high costs. The end-of-life period was predictive of top 1% and top 5% costs. More than 90% of the population incurred at least one top 5% year during follow-up, and 31.8% incurred at least one top 1% year. Fifty-seven per cent incurred multiple top 5% years whereas only 8.6% incurred multiple top 1% years. Top 5% years were more frequently consecutive than top 1% years. Conclusions Top 1% utilization occurs predominantly incidentally and among less than a third of patients with heart failure, whereas almost all patients with heart failure experience at least one top 5% year, and more than half experience two or more top 5% years. Both medical and psychiatric/psychosocial needs contribute to high costs in heart failure patients. Comprehensive and integrated efforts are needed to further improve quality of care and reduce unnecessary costs.

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Predicting patient ‘cost blooms’ in Denmark: a longitudinal population-based study

Cited by 36 publications

References 29 publications

Systematic review of high-cost patients’ characteristics and healthcare utilisation

Systematic review of high-cost patients’ characteristics and healthcare utilisation

Comparison of statistical and machine learning models for healthcare cost data: a simulation study motivated by Oncology Care Model (OCM) data

Characteristics and health care utilization among patients with chronic heart failure: a longitudinal claim database analysis

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