Applying the Analytic Hierarchy Process in healthcare research: A systematic literature review and evaluation of reporting

Background: The Analytic Hierarchy Process (AHP), developed by Saaty in the late 1970s, is one of the methods for multi-criteria decision making. The AHP disaggregates a complex decision problem into different hierarchical levels. The weight for each criterion and alternative are judged in pairwise comparisons and priorities are calculated by the Eigenvector method. The slowly increasing application of the AHP was the motivation for this study to explore the current state of its methodology in the healthcare context. Methods: A systematic literature review was conducted by searching the Pubmed and Web of Science databases for articles with the following keywords in their titles or abstracts: "Analytic Hierarchy Process," "Analytical Hierarchy Process," "multi-criteria decision analysis," "multiple criteria decision," "stated preference," and "pairwise comparison." In addition, we developed reporting criteria to indicate whether the authors reported important aspects and evaluated the resulting studies' reporting. Results: The systematic review resulted in 121 articles. The number of studies applying AHP has increased since 2005. Most studies were from Asia (almost 30 %), followed by the US (25.6 %). On average, the studies used 19.64 criteria throughout their hierarchical levels. Furthermore, we restricted a detailed analysis to those articles published within the last 5 years (n = 69). The mean of participants in these studies were 109, whereas we identified major differences in how the surveys were conducted. The evaluation of reporting showed that the mean of reported elements was about 6.75 out of 10. Thus, 12 out of 69 studies reported less than half of the criteria. Conclusion: The AHP has been applied inconsistently in healthcare research. A minority of studies described all the relevant aspects. Thus, the statements in this review may be biased, as they are restricted to the information available in the papers. Hence, further research is required to discover who should be interviewed and how, how inconsistent answers should be dealt with, and how the outcome and stability of the results should be presented. In addition, we need new insights to determine which target group can best handle the challenges of the AHP.CHER

Comparison of different approaches applied in Analytic Hierarchy Process - An example of information needs of patients with rare diseases

Background: The Analytic Hierarchy Process (AHP) is increasingly used to measure patient priorities. Studies have shown that there are several different approaches to data acquisition and data aggregation. The aim of this study was to measure the information needs of patients having a rare disease and to analyze the effects of these different AHP approaches. The ranking of information needs is then used to display information categories on a web-based information portal about rare diseases according to the patient's priorities. Methods: The information needs of patients suffering from rare diseases were identified by an Internet research study and a preliminary qualitative study. Hence, we designed a three-level hierarchy containing 13 criteria. For data acquisition, the differences in outcomes were investigated using individual versus group judgements separately. Furthermore, we analyzed the different effects when using the median and arithmetic and geometric means for data aggregation. A consistency ratio ≤0.2 was determined to represent an acceptable consistency level. Results: Forty individual and three group judgements were collected from patients suffering from a rare disease and their close relatives. The consistency ratio of 31 individual and three group judgements was acceptable and thus these judgements were included in the study. To a large extent, the local ranks for individual and group judgements were similar. Interestingly, group judgements were in a significantly smaller range than individual judgements. According to our data, the ranks of the criteria differed slightly according to the data aggregation method used. Conclusions: It is important to explain and justify the choice of an appropriate method for data acquisition because response behaviors differ according to the method. We conclude that researchers should select a suitable method based on the thematic perspective or investigated topics in the study. Because the arithmetic mean is very vulnerable to outliers, the geometric mean and the median seem to be acceptable alternatives for data aggregation. Overall, using the AHP to identify patient priorities and enhance the user-friendliness of information websites offers an important contribution to medical informatics.BMB

Therapy preferences of patients with lung and colon cancer: A discrete choice experiment

Objectives: There is increasing interest in studies that examine patient preferences to measure health-related outcomes. Understanding patients’ preferences can improve the treatment process and is particularly relevant for oncology. In this study, we aimed to identify the subgroup-specific treatment preferences of German patients with lung cancer (LC) or colorectal cancer (CRC). Methods: Six discrete choice experiment (DCE) attributes were established on the basis of a systematic literature review and qualitative interviews. The DCE analyses comprised generalized linear mixed-effects model and latent class mixed logit model. Results: The study cohort comprised 310 patients (194 with LC, 108 with CRC, 8 with both types of cancer) with a median age of 63 (SD =10.66) years. The generalized linear mixed-effects model showed a significant (P<0.05) degree of association for all of the tested attributes. “Strongly increased life expectancy” was the attribute given the greatest weight by all patient groups. Using latent class mixed logit model analysis, we identified three classes of patients. Patients who were better informed tended to prefer a more balanced relationship between length and health-related quality of life (HRQoL) than those who were less informed. Class 2 (LC patients with low HRQoL who had undergone surgery) gave a very strong weighting to increased length of life. We deduced from Class 3 patients that those with a relatively good life expectancy (CRC compared with LC) gave a greater weight to moderate effects on HRQoL than to a longer life. Conclusion: Overall survival was the most important attribute of therapy for patients with LC or CRC. Differences in treatment preferences between subgroups should be considered in regard to treatment and development of guidelines. Patients’ preferences were not affected by sex or age, but were affected by the cancer type, HRQoL, surgery status, and the main source of information on the disease

Single-shot temporal characterization of XUV pulses with duration from ~10 fs to ~350 fs at FLASH

Ultra-short extreme ultraviolet pulses from the free-electron laser FLASH are characterized using terahertz-field driven streaking. Measurements at different ultra-short extreme ultraviolet wavelengths and pulse durations as well as numerical simulations were performed to explore the application range and accuracy of the method. For the simulation of streaking, a standard classical approach is used which is compared to quantum mechanical theory, based on strong field approximation. Various factors limiting the temporal resolution of the presented terahertz streaking setup are investigated and discussed. Special attention is paid to the cases of very short (similar to 10 fs) and long (up to similar to 350 fs) pulses.We want to acknowledge the work of the scientific and technical team at FLASH. NMK acknowledges the hospitality and financial support from DESY and from the theory group in cooperation with the SQS research group of the European XFEL (Hamburg). KW and MD acknowledge support by the SFB925-A1. UF and AD acknowledge support by the excellence cluster `The Hamburg Center for Ultrafast Imaging-Structure, Dynamics and Control of Matter at the Atomic Scale' (DFG)-EXC 1074 project ID 194651731. SW acknowledges support by the DFG Forschergruppe FOR 1789. Editoria

Practitioner’s Section: Integrated Resource Efficiency Analysis for Reducing Climate Impacts in the Chemical Industry

Reducing greenhouse gas emissions of the material-intensive chemical industry requires an integrated analysis and optimization of the complex production systems including raw material and energy use, resulting costs and environmental and climate impacts. To meet this challenge, the research project InReff (Integrated Resource Efficiency Analysis for Reducing Climate Impacts in the Chemical Industry) has been established. It aims at the development of an IT-supported modeling and evaluation framework which is able to comprehensively address issues of resource efficiency and climate change within the chemical industry, e.g. the minimization of material and energy intensity and consequently greenhouse gas emissions, without compromising on production performance. The paper presents background information on resource efficiency and the research project, an ideal-typical decision model for resource efficiency analysis, the conceptual approach for an IT-based integration platform as well as the case study design at the industrial project partners’ sites. These first results are linked to future activities and further research questions are highlighted in the concluding section

Herz- und Lungenerkrankungen: Cardiopulmonary Institute (CPI)

Herz- und Lungenerkrankungen sind weltweit eine der häufigsten Todesursachen. Das Cardio-Pulmonary Institute (CPI) widmet sich der Erforschung dieser Krankheiten auf molekularer Ebene, um innovative Behandlungsmethoden für Patient*innen zu entwickeln. Als interdisziplinäres Forschungsinstitut der Goethe-Universität Frankfurt, der Justus-Liebig-Universität Gießen und des Max-Planck-Instituts für Herz- und Lungenforschung in Bad Nauheim ist das CPI ein einzigartiges Zentrum

Cardio-Pulmonary Institute : "Keine Angst vor Technologie oder Datenwissenschaft!"

Herz- und Lungenkrankheiten sind weltweit die häufigsten Todesursachen. Das Cardio-Pulmonary Institute (CPI) besteht aus grundlagenorientierten, klinischen und translationalen Forscher*innen und Expert*innen, die sich zusammengeschlossen haben, um Herz- und Lungenerkrankungen zu verstehen und neue Therapieansätze zu finden. Das Konsortium der Universitäten Frankfurt (GU) und Gießen (JLU) sowie des Max-Planck-Instituts für Herz- und Lungenforschung (MPIHLR) wird im Rahmen der Exzellenzstrategie der Deutschen Forschungsgemeinschaft gefördert

Cardio Pulmonary Institute : Herzgesundheit im Fokus: Nachwuchsforschung am Cardio-Pulmonary Institute

Herz- und Lungenkrankheiten sind weltweit die häufigsten Todesursachen. Das Cardio-Pulmonary Institute (CPI) besteht aus grundlagenorientierten, klinischen und translationalen Forscher*innen und Expert*innen, die sich zusammengeschlossen haben, um Herz- und Lungenerkrankungen zu verstehen und neue Therapieansätze zu finden. Das Konsortium der Universitäten Frankfurt (GU) und Gießen (JLU) sowie des Max-Planck-Instituts für Herz- und Lungenforschung (MPI-HLR) wird im Rahmen der Exzellenzstrategie der Deutschen Forschungsgemeinschaft gefördert

ExStra – die Exzellenzstrategie des Bundes und der Länder

Für die anstehende Runde der Exzellenzstrategie des Bundes und der Länder bewirbt sich die Goethe-Universität Frankfurt mit vier neuen Clustern zu den Forschungsthemen Vertrauen im Konflikt (CONTRUST), Infektion und Entzündung (EMTHERA), Ursprung der Schweren Elemente (ELEMENTS) und zelluläre Architekturen (SCALE). Die Anträge vereinen die Kompetenzen und zukunftsweisenden Ideen der Goethe-Universität mit denen der Kolleg:innen des Verbunds der Rhein-Main-Universitäten (RMU) und weiterer Partner der vier großen Organisationen der außeruniversitären Forschung. Der seit 2019 bestehende Exzellenzcluster Cardiopulmonary Institute (CPI) wird im kommenden Jahr direkt einen Vollantrag einreichen