The hierarchical medical treatment system is an efficient way to solve the problem of insufficient and unbalanced medical resources in China. Essentially, classifying the different degrees of diseases according to the doctor’s diagnosis is a key step in pushing forward the hierarchical medical treatment system. This paper proposes a framework to solve the problem where diagnosis values are given as picture fuzzy numbers (PFNs). Point operators can reduce the uncertainty of doctor’s diagnosis and get intensive information in the process of decision making, and the Choquet integral operator can consider correlations among symptoms. In order to take full advantage of these two kinds of operators, in this paper, we firstly define some point operators under the picture fuzzy environment, and further propose a new class of picture fuzzy point–Choquet integral aggregation operators. Moreover, some desirable properties of these operators are also investigated in detail. Then, a novel approach based on these operators for multiattribute decision-making problems in the picture fuzzy context is introduced. Finally, we give an example to illustrate the applicability of the new approach in assisting hierarchical medical treatment system. This is of great significance for integrating the medical resources of the whole society and improving the service efficiency of the medical service system.

China has made many achievements in health care industry since the establishment of hierarchical medical treatment system, but also exposed a lot of problems, such as the problem of ‘easy to upward-referral, hard to downward-referral’, which occurred in the implementation process of two-way referral. Recently, the appearance of ‘hard to downward-referral’ have gotten more and more attention from scholars. The key step in solving the problem of ‘downward-referral’ is to classify hospitals at different levels evaluated by the professional doctors. This paper proposes a framework to solve the problem where evaluation values of the hospital are given as q-rung orthopair fuzzy uncertain linguistic variables (q-ROFULVs). Considering Schweizer-Sklar t-conorm and t-norm (SSTT) can make the information aggregation process more flexible,and Hamy Mean operator can consider the correlation between hospital indicators. In order to take full advantage of these two kinds of operators, firstly, we extend SSTT to q-ROFULVs and define Schweizer-Sklar operational rules of q-ROFULVs. Secondly, we combine the Hamy Mean operator with Schweizer-Sklar t-conorm and t-norm, and propose the q-rung orthopair fuzzy uncertain linguistic Schweizer-Sklar Hamy mean aggregation operators and q-Rung orthopair fuzzy uncertain linguistic Schweizer-Sklar weighted Hamy Mean respectively. Afterwards, q-rung orthopair fuzzy uncertain linguistic Schweizer-Sklar dual Hamy mean operator and its weighted form are also developed. Then, we have studied in detail some of the ideal properties of these operators. Furthermore, a novel multi-attribute group decision-making approach based on proposed operators is introduced. Finally, we apply the new approach to solve the problem about the downward conversion in the hierarchical medical treatment system. This is of great significance for integrating the medical resources of the whole society and improving the service efficiency of the medical service system.

Dual hesitant fuzzy information aggregation plays an important role in dual hesitant fuzzy set theory, which has received more and more attention in recent years. In this paper, we first define a new concept of hesitancy degree under dual hesitant fu