How can the health insurance catalog adjustment be assured of finding "value for money"? Establishing a "management closed loop" is key.

Text . . . Tao Libo's reference and application to the evaluation data of medical economics (including cost effect analysis CEA and budget impact analysis BIA, etc.) are deepening in the adjustment and access work of China's medical insurance catalogue.
the introduction of these research data is of far-reaching significance to enhance the evidence-based and objectivity of catalog adjustment and the scientific nature of decision-making.
, however, because of the complexity of medical economics evaluations, it often requires a lot of data collection and computation, and contains estimates of the unpredictable future.
changes in the value of some of these details could have a big impact on the end result, with the so-called "devils in the details."
the stability and reliability of the evaluation results of medical economics has been questioned.
, policymakers are skeptical about the entanglements and potential risks of their business interests.
the fact that medical economics evaluations (including CEA and BIA) are based on pre-decision historical experience, and the use of mathematical tools to predict the future after decision-making has a lot of uncertainty that is difficult to eliminate.
, it is not realistic to demand the correctness and reliability of evaluation results in decision-making.
has not been a good solution to this dilemma, which has been evaluated by the Chinese people in medical economics.
, even if the relevant technical guidelines are issued, it is difficult to regulate the details of the study.
The author believes that one of the root causes of this dilemma is that when we apply the evaluation data of medical economics, we tend to pay attention only to the quality of the data when making decisions, but do not observe the real situation that follows, whether it is consistent with the original analysis.
, just guess the beginning, but don't pay attention to the results.
, after applying economic evaluation data in decision-making, we should continue to pay attention to the follow-up development and establish a "management closed loop".
Specifically, the term "management closed loop" here refers to the reference to pharmaceutical economics evaluation data at the time of catalog access to support access and pricing decisions, and, if pharmaceutical products are allowed to enter, to review the evaluation reports used in decision-making based on real data generated during this period after a period of time (e.g. 2 years), and to evaluate and review the evaluation reports used in the next step of the review, and to update the medical economics evaluation model to guide the next step of catalog adjustment, renewal, pricing, etc.
this, a complete management closed loop is formed.
Cost Effect Analysis (CEA) in the evaluation of pharmaceutical economics is a comprehensive display of the clinical efficacy, health output and health cost of pharmaceutical products, which reflects the value of pharmaceutical products and is an important reference for decision-making when adjusting the medical insurance catalogue.
CEA calculations, clinical efficacy data often come from multi-center clinical studies, long-term health output is often based on the experience of the development process of past diseases and modeling derivation, cost data from the past real-world data and long-term model derivation, a wide range of content, data sources.
because of the large number of complex data calculation process in CEA, it is difficult to judge its true reliability.
even with "expert checks and balances" and one expert reviewing another expert's research report, it is difficult to draw clear conclusions.
However, insiders know that the setting of some of the basic parameters in the CEA process can have a substantial impact on the results of the study, such as the rate of occurrence of a critical medical event, the quality of life factor of a particular critical disease state, and so on, and the value of these basic parameters can be verified in a short period of time.
For example, when a new tumor drug enters the health insurance directory for a period of time, it is desirable to look retrospectively at the rate of deterioration of the patients it treats and then compared with the corresponding parameter values in the drug's CIA report.
In the Oncology CEA study, this parameter value often has a significant impact on the formation of the final result, and if this value in the real world clearly does not match the CEA report, it suggests that the quality of the CEA report is questionable and will intentionally or unintentionally mislead the decision."
this method of verification and judgment is obviously much more meaningful than looking at whether the researchers are big names and whether the score of the published journals is high.
Impact Analysis (BIA) in the evaluation of pharmaceutical economics is an estimate of the possible impact of the fund budget after pharmaceutical products enter the medical insurance catalogue, and is also an important reference for decision-making.
BIA calculations also involve a large amount of data, including the price of related pharmaceutical products, market share of similar pharmaceutical products and their changes, the probability and cost of related medical events.
, BIA is a prediction of the future of pharmaceutical products when they enter the health insurance catalog, but there are so many variables in the future that it is difficult to predict clearly.
, for example, whether other similar pharmaceutical products will enter the belt collection in the future, how much the price will fall after harvesting, how will the market share distribution change, and so on.
these future changes are difficult or even unpredictable, and if they happen, they will have a huge impact on health-care spending.
, like CEA, it is difficult to determine the true reliability of BIA calculations.
But relatively speaking, the BIA's calculation time is shorter (usually five years, but now it is easier to verify because it is re-contracted two years after the entry of the innovative drug negotiations, so the three-year BIA calculation is sufficient).
Some time after the drug enters the medical insurance catalog, the manager can verify the data of the BIA report on the medical price, market share change, medical incident rate, medical insurance expenditure and so on, based on the real clinical results and medical insurance reimbursement amount data, to judge the reliability of the report results and update the calculation model.
the above-mentioned medical economics evaluation data application and verification of the "management closed loop" can be established, then the relevant management procedures can be straightened out.
First of all, with the means of after-the-fact verification, managers can be more assured in the access and pricing of medical insurance catalog reference medical economics evaluation data, to judge the value of new drugs and budgetary impact, so as to make evidence-based decisions.
a period of time after the new drug enters the catalog, the evaluation results used in the original decision can be verified based on the clinical efficacy and health costs that have occurred.
If it is proven that the results of the original medical economics evaluation were significantly overestimated, or that the budget impact was significantly underestimated, it would indicate poor research quality, misleading decision-making and costing patients and health care."
at this time, management may punish the reporting vendor accordingly and make adjustments in terms of the price paid, the scope of use, the amount of reimbursement (the specific content may be agreed upon at the time of the entry contract).
further, managers can update the model of medical economics analysis to provide reference for the next management work, thus forming a complete specification for the application of medical economics evaluation data to the management of medical insurance catalogues.
other hand, the verification of the evaluation results of medical economics can also be applied to the evaluation and credit of relevant researchers.
evaluation reports submitted by pharmaceutical manufacturers, usually in cooperation with academic institutions and research experts.
the difficulty and complexity of research, people often need to rely on the qualifications and visibility of research institutions and researchers to help judge the quality and credibility of research.
because of the huge commercial interests involved in the evaluation of medical economics, the entanglements are difficult to distinguish.
, if an ex post-verification method is established, the ability and credibility of researchers can be judged to some extent, thus more effectively regulating research in this field.
another issue worth exploring is that China's health insurance has been exploring and manufacturers to sign therapeutic effects or financial risk sharing of innovative contracts.
common risk-sharing approach is to act accordingly based on events that occur in the process, such as paying based on patient outcomes or adjusting payments based on the use of Health Care funds.
because china's medical insurance system is large, multi-level, management is more complex, based on process events to adjust payment, operation is very difficult.
if we look at the evaluation of medical economics, CEA is an estimate of individual efficacy and health outcomes, and BIA is an estimate of the financial risks of the fund.
companies submit such studies to managers can be seen as a commitment to the efficacy of their products and the level of health care spending, while rewards and penalties based on the above-mentioned closed-loop management can be seen as a risk-sharing based on real results.
such a retrospective risk sharing based on real results, than the immediate risk sharing based on process events, may be more operational in the specific situation of our country, this exploration is also worth carrying out.
, it is an inherent requirement to strengthen scientific evidence-based decision-making to apply the evaluation data of medical economics to the adjustment of medical insurance catalogue.
cost effect analysis CEA can effectively expound the value of new drugs, budget impact analysis BIA can effectively explain the impact on health insurance funds, are important decision-making reference content, further can also be applied to the risk sharing with manufacturers.
because of the complexity of the evaluation of medical economics, it is very difficult to judge the quality of research clearly in decision-making.
this, it is one of the ideas to solve the problem by using the data that happened in the later period, verifying the original economic evaluation data, making appropriate rewards and punishments, and updating the calculation model to guide the future work.
In fact, strict requirements for data quality in directory adjustment are "pre-approval", while review and verification based on actual results and reward and punishment are the idea of "after-the-fact supervision".
strengthen the supervision after the event is also one of the directions of the reform of the management mechanism of our government.
For the application of medical insurance catalogue adjustment chinese medicine economic evaluation data, if we can establish appropriate closed-loop management, it will help to improve the scientific and objective of catalog adjustment, but also promote the high-quality development of China's medical economics evaluation work, the policy design and institutional arrangements in this regard, it is worth in-depth thinking.
title: The evaluation of applied medical economics in the adjustment of medical insurance catalogue needs to establish "management closed loop"