Pricing and Reimbursement Pathways of New Orphan Drugs in South Korea: A Longitudinal Comparison

Lee, Jong Hyuk

doi:10.3390/healthcare9030296

Cited by 10 publications

(7 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The governments pledged hundreds of millions of dollars to support the local digital health industry. The prediction of individual health insurance costs is an essential task in the healthcare system, particularly for people with orphan diseases [2][3][4]: prevention and medical insurance help to decrease the cost of treatment.…”

Section: Introductionmentioning

confidence: 99%

An Ensemble Methods for Medical Insurance Costs Prediction Task

Shakhovska¹,

Melnykova²,

Chopiyak³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

The paper reports three new ensembles of supervised learning predictors for managing medical insurance costs. The open dataset is used for data analysis methods development. The usage of artificial intelligence in the management of financial risks will facilitate economic wear time and money and protect patients' health. Machine learning is associated with many expectations, but its quality is determined by choosing a good algorithm and the proper steps to plan, develop, and implement the model. The paper aims to develop three new ensembles for individual insurance costs prediction to provide high prediction accuracy. Pierson coefficient and Boruta algorithm are used for feature selection. The boosting, stacking, and bagging ensembles are built. A comparison with existing machine learning algorithms is given. Boosting modes based on regression tree and stochastic gradient descent is built. Bagged CART and Random Forest algorithms are proposed. The boosting and stacking ensembles shown better accuracy than bagging. The tuning parameters for boosting do not allow to decrease the RMSE too. So, bagging shows its weakness in generalizing the prediction. The stacking is developed using K Nearest Neighbors (KNN), Support Vector Machine (SVM), Regression Tree, Linear Regression, Stochastic Gradient Boosting. The random forest (RF) algorithm is used to combine the predictions. One hundred trees are built for RF. Root Mean Square Error (RMSE) has lifted the to 3173.213 in comparison with other predictors. The quality of the developed ensemble for Root Mean Squared Error metric is 1.47 better than for the best weak predictor (SVR).

show abstract

Section: Introductionmentioning

confidence: 99%

An Ensemble Methods for Medical Insurance Costs Prediction Task

Shakhovska¹,

Melnykova²,

Chopiyak³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…Hence, the Korean government has given leeway with a wide range of incremental cost-effectiveness ratio (ICER) thresholds [maximum 50 million/quality-adjusted life year (QALY)], particulars of RSA, and exemption rules for CEA and pharmacoeconomic (PE) waivers to improve patients' access to innovative new drugs (52)(53)(54)(55). For the cases of orphan drugs, anticancer drugs, and severe life-threatening diseases, the CEA can be exempted, and the expenditure-capped RSA becomes effective and the prices of A7 countries are referred to as reference prices (Table 4).…”

Section: Risk Sharing Agreementmentioning

confidence: 99%

Cell and gene therapy regulatory, pricing, and reimbursement framework: With a focus on South Korea and the EU

Lee

2023

Front. Public Health

Self Cite

View full text Add to dashboard Cite

Ever since relevant bioengineering technologies have sufficiently matured to the platformizable commercialization stage, a slew of money has flocked to the cell and gene therapy market over the last few years, resulting in an abundance of clinical studies in the field. Newer modalities have brought up a string of regulatory and legislative tasks, such as developing guidelines and legislative rules to systematically regulate newer pharmaceutical products. Accordingly, another layer of legislation and guidelines tailored for cell and gene therapies has been introduced and is expected to evolve on par with technological progress. Furthermore, authorities have shifted to pricing and reimbursement policies that can share risks for cost and outcome among stakeholders altogether, such as developers and the government, while expanding the accessibility of patients to innovative cell and gene therapies. This review attempts to capture the salient regulatory features of the cell and gene therapy market in the context of South Korea and the European Union and points out where two sovereign entities currently stand on each policy element and how each tackles regulatory challenges. We can observe the converging trend where regulatory, pricing and reimbursement rules of adjoining countries in the supranational union or member countries of a consortium are getting more aligned. Evidently, concerted efforts to share regulatory science knowledge and embrace reference pricing have played their parts. The authors argue that policy priorities should be placed on initiatives to harmonize with other medical authorities to better the rights of patients and clear out the uncertainties of developers, ultimately to share and advance regulatory science and layout forward-looking policies at opportune times.

show abstract

“…Recently, the Korean government planned to enhance the application of MEAs in order to facilitate access to high priced medicines ( Ministry of Health and Welfare MOHW, 2017 ). This built on previous studies that had investigated the positive impact on patient care with increased access to new treatments, particularly those for cancer and orphan diseases ( Kim et al, 2017 ; Yoo et al, 2019 ; Kim et al, 2020 ; Lee, 2021 ). Yoo et al (2019) reported that the introduction of MEAs and cost-effectiveness analysis waiver track, alongside increasing patient’s co-payments where there were concerns with their cost-effectiveness, contributed to improving patient access to new treatments.…”

Section: Introductionmentioning

confidence: 99%

Introduction of managed entry agreements in Korea: Problem, policy, and politics

et al. 2023

View full text Add to dashboard Cite

Objectives: This study aimed to understand Managed Entry Agreements (MEAs) in Korea through the framework of three streams of the policy window model and its practical management and impact on pricing and reimbursement scheme.Methods: An extensive literature review based on Kingdon’s model was conducted. We also performed descriptive analyses of MEA implementation using data on medicines listed in Korea and compared its MEA scheme with four different countries.Results: As per problem streams, patients with rare disease or cancers have considerable difficulties in affording their medicines and this has challenged the drug benefit system and raised an issue of patient’s access. Policy streams highlighted that MEAs were introduced as a benefit enhancement plan for four major diseases since January 2014. MEAs have also been strengthened as a bypass mechanism to expand the insurance coverage especially for new premium-priced medicines under Moon Care (Listing all non-listed services). In descriptive analysis of MEAs, a total of 48 medicines were contracted as MEAs from January 2014 to December 2020, accounting for 73.4% of listed medicines for cancer or rare diseases and 97.9% of the cases were finance-based contracts. Meanwhile, outcome-based contracts such as CED accounted for only 2.1%. The application of MEAs differs across countries, resulting in a kappa coefficient of 0.00–0.14 (United Kingdom 0.03, Italy 0.00, Australia 0.14), indicating a lack of consistency compared to South Korea.Conclusion: MEAs, which were introduced as a bypass mechanism, have now superseded the standard process for anticancer agents or orphan drugs. Further studies are needed to evaluate the impact of the confidential agreements and effectiveness of new high-priced medicines with limited clinical data at launch.

show abstract

Pricing and Reimbursement Pathways of New Orphan Drugs in South Korea: A Longitudinal Comparison

Cited by 10 publications

References 33 publications

An Ensemble Methods for Medical Insurance Costs Prediction Task

An Ensemble Methods for Medical Insurance Costs Prediction Task

Cell and gene therapy regulatory, pricing, and reimbursement framework: With a focus on South Korea and the EU

Introduction of managed entry agreements in Korea: Problem, policy, and politics

Contact Info

Product

Resources

About