Implementing a multi-criteria decision-making approach to a new party's election campaign - A case study.

During pre-election campaigns, parties make great efforts to persuade constituents to vote for them. Usually, new parties have smaller budgets and fewer resources than veteran parties. In particular, the more heterogeneous the party's electorate, the more critical the issue of resource allocation is. This paper presents a method to allocate new party's campaign advertising resources efficiently to maximize its voters. The model developed uses Pareto principle and multi-criteria approach and integrates party's confidential data together with an official open-to-all data. The model produced clear and unbiased results, and these advantages made it effective and user-friendly for the strategic team and campaign managers. We implemented the model on a specific new party during the intensive political period before the April 2019 elections in Israel.•This paper analyses the issue of allocating new party's campaign advertising resources efficiently to maximize its voters.•Our model integrates Pareto principle with multi-criteria decision-making approach and uses the party's confidential data together with official open to all state data.•The model produced clear and unbiased results, and these advantages made it effective and user-friendly for the strategic team and campaign managers.

Identifying relevant information in medical conversations to summarize a clinician-patient encounter.

To inform the development of automated summarization of clinical conversations, this study sought to estimate the proportion of doctor-patient communication in general practice (GP) consultations used for generating a consultation summary. Two researchers with a medical degree read the transcripts of 44 GP consultations and highlighted the phrases to be used for generating a summary of the consultation. For all consultations, less than 20% of all words in the transcripts were needed for inclusion in the summary. On average, 9.1% of all words in the transcripts, 26.6% of all medical terms, and 27.3% of all speaker turns were highlighted. The results indicate that communication content used for generating a consultation summary makes up a small portion of GP consultations, and automated summarization solutions-such as digital scribes-must focus on identifying the 20% relevant information for automatically generating consultation summaries.