Search

DELIVER (DELiberative ImproVEment of oRal care quality) is a multinational project funded under the EU’s Horizon Europe program; we aim to develop a blueprint model for improving the quality of oral health care for everyone: the DELIVER Quality Improvement Model. We are a team of partners from Denmark, England, Germany, Malta, the Netherlands, Portugal, and Sweden who will use a mixed-methods research approach to create a synergistic problem-solving ecosystem to convert deliberative dialogues into meaningful support for improving the quality of oral health care. Together with citizens/patients, providers, payers, and policymakers, new quality improvement (QI) approaches will be codeveloped and coproduced in 3 phases (over 4 y): phase 1 involves defining quality of oral health care, situational analysis of the status quo of QI, and consenting of core quality indicators for a Europe-wide monitoring framework. Phase 2 involves in-depth analysis of select QI approaches: 1) QI in dental practices based on patient-reported outcome and experience measures (PROMs/PREMs), 2) community-based QI for vulnerable groups, and 3) quality-oriented commissioning of oral health services. In addition, the regulatory determinants of oral health care QI will be scrutinized. In phase 3, the knowledge gained from phases 1 and 2 will be used to develop the DELIVER Quality Toolkit, a resource containing manuals and digital tools to support context-specific implementation of oral health care QI. DELIVER is anticipated to contribute to positive changes in oral health systems.Knowledge Transfer Statement: The EU DELIVER project aims to enhance the quality of oral health care through codevelopment and coproduction of solutions together with citizens/patients, providers, and policymakers. The unique multicountry nature of the project will facilitate fast-track prototype development and testing of innovative QI approaches in select countries. Reflective learning regarding the transferability of findings between different countries and settings offers unique opportunities to drive progress toward context-specific implementation of innovative oral health care QI approaches. The collective knowledge gained from the 7 European countries involved in DELIVER can also generate knowhow for improving the quality of oral health care in other countries around the globe.

Inferior and noninferior solutions to the location set-covering problem arc differentiated and computational experience is presented which strongly suggests that noninferior solutions, from the relaxed linear programming formulation, will be fully integral with a high degree of reliability. The authors have not yet encountered a noninferior fractional solution. An explanation of this phenomenon is presented.

Cluster analysis can be performed with several models. One method is to seek those clusters for which the total flow between all within-cluster members is a maximum. This model has, until now, been viewed as mathematically difficult because of the presence of products of integer variables in the objective function. In another optimization model of cluster analysis, the p-median, a central member is found for each cluster, so that relationships of cluster members with the various central members are maximized (or minimized). This problem, although mathematically tractable, is a less realistic formulation of the general clustering problem.The formulation of the maximum interflow problem is here transformed in stages into a linear analogue which is economically solvable. Computation experience with the several transformed stages is reported and a practical example of the analysis demonstrated.

There is currently a generally accepted view that world oil resources are so limited that they are unable to sustain the growth of the industry beyond the next decade or so. This pessimistic forecast is contrasted with the somewhat earlier optimism of the oil industry concerning its future.

In region building, different models of cluster analysis conform to different theoretical spatial structures. The p-median model may be quite appropriate in nodal structures. Use of the p-median model, however, raises problems about the choice of the number of groups (p). This choice does not arise in facility location problems. An argument is made for parallel use of optimal methods and heuristics for region building. Optimal solutions are now feasible for even large problems because of the increase in computer power. Optimal methods provide one solution for each different value of p.Heuristic methods may provide different solutions for each value of p, depending on the starting position used for the heuristic. The multiple solutions from a heuristic procedure can then be interpreted as spatial competition between locations to become medians. By counting the number of times optimal and nonoptimal solutions are located by a heuristic procedure, statistics can be developed. Such statistics can then provide an objective method to select a specific number of groups over all others. Computational experience, with both heuristic and optimal methods, is presented.

Despite the long history of the multi-Weber problem and the generalised multi-Weber problem, there is no general optimal method for its solution. This paper has two aims. The first aim is a study of the shape of the objective function of the single and multi-Weber problem. The form of the objective function and the relation of a heuristic to it are critical to the decision to use a heuristic or to seek an optimal solution. The objective function of the multi-Weber problem is shown to be extremely steep in the neighbourhood of the optimal solution, indicating the importance of an optimal rather than heuristic solution. The second aim is a description of a two-step algorithm which, although it cannot guarantee optimality, will frequently terminate optimally and thus appears to be superior to other available heuristics.

Optimal p-median solutions were computed for six test problems on a network of forty-nine demand nodes and compared with solutions from two heuristic algorithms. Comparison of the optimal solutions with those from the Teitz and Bart heuristic indicates that this heuristic is very robust. Tests of the Maranzana heuristic, however, indicate that it is efficient only for small values of p(numbers of facilities) and that its robustness decreases rapidly as problem size increases.

A revised mathematical programme is presented which reduces the number of rows and columns in the Linear Programming formulation of the p-Median Problem. The revised formulation reduces both the computer space required and computational time allowing the solution of large problems while still guaranteeing optimality. Computational experience with the new formulation is presented.