Mixed-initiative Personal Assistants

Specification and implementation of flexible human-computer dialogs is challenging because of the complexity involved in rendering the dialog responsive to a vast number of varied paths through which users might desire to complete the dialog. To address this problem, we developed a toolkit for modeling and implementing task-based, mixed-initiative dialogs based on metaphors from lambda calculus. Our toolkit can automatically operationalize a dialog that involves multiple prompts and/or sub-dialogs, given a high-level dialog specification of it. Our current research entails incorporating the use of natural language to make the flexibility in communicating user utterances commensurate with that in dialog completion paths

Modeling and Operationalizing Flexible Human-Computer Dialogs

We demonstrate a tool for rapidly prototyping dialog-based systems for interactive use. The tool enables a dialog designer to evaluate a variety of dialogs without having to program each individual dialog, and provides a proof-of-concept for our approach to mixed-initiative dialog modeling and implementation. Applications of our Our tool can be applied to human-computer dialogs common in automated teller machines (ATMs), kiosks, personal assistants, and online forms including course scheduling.https://ecommons.udayton.edu/stander_posters/1783/thumbnail.jp

Natural Language, Mixed-Initiative Personal Assistant Agents

The increasing popularity and use of personal voice assistant technologies, such as Siri and Google Now, is driving and expanding progress toward the long-term and lofty goal of using artificial intelligence to build human-computer dialog systems capable of understanding natural language. While dialog-based systems such as Siri support utterances communicated through natural language, they are limited in the flexibility they afford to the user in interacting with the system and, thus, support primarily action-requesting and information-seeking tasks. Mixed-initiative interaction, on the other hand, is a flexible interaction technique where the user and the system act as equal participants in an activity, and is often exhibited in human-human conversations. In this paper, we study user support for mixed-initiative interaction with dialog-based systems through natural language using a bag-of-words model and k-nearest-neighbor classifier. We study this problem in the context of a toolkit we developed for automated, mixed-initiative dialog system construction, involving a dialog authoring notation and management engine based on lambda calculus, for specifying and implementing task-based, mixed-initiative dialogs. We use ordering at Subway through natural language, human-computer dialogs as a case study. Our results demonstrate that the dialogs authored with our toolkit support the end user\u27s completion of a natural language, human-computer dialog in a mixed-initiative fashion. The use of natural language in the resulting mixed-initiative dialogs afford the user the ability to experience multiple self-directed paths through the dialog and makes the flexibility in communicating user utterances commensurate with that in dialog completion paths---an aspect missing from commercial assistants like Siri

This Time It's Personal: from PIM to the Perfect Digital Assistant

Interacting with digital PIM tools like calendars, to-do lists, address books, bookmarks and so on, is a highly manual, often repetitive and frequently tedious process. Despite increases in memory and processor power over the past two decades of personal computing, not much has changed in the way we engage with such applications. We must still manually decompose frequently performed tasks into multiple smaller, data specific processes if we want to be able to recall or reuse the information in some meaningful way. "Meeting with Yves at 5 in Stata about blah" breaks down into rigid, fixed semantics in separate applications: data to be recorded in calendar fields, address book fields and, as for the blah, something that does not necessarily exist as a PIM application data structure. We argue that a reason Personal Information Management tools may be so manual, and so effectively fragmented, is that they are not personal enough. If our information systems were more personal, that is, if they knew in a manner similar to the way a personal assistant would know us and support us, then our tools would be more helpful: an assistive PIM tool would gather together the necessary material in support of our meeting with Yves. We, therefore, have been investigating the possible paths towards PIM tools as tools that work for us, rather than tools that seemingly make us work for them. To that end, in the following sections we consider how we may develop a framework for PIM tools as "perfect digital assistants" (PDA). Our impetus has been to explore how, by considering the affordances of a Real World personal assistant, we can conceptualize a design framework, and from there a development program for a digital simulacrum of such an assistant that is not for some far off future, but for the much nearer term

Design and evaluation of acceleration strategies for speeding up the development of dialog applications

In this paper, we describe a complete development platform that features different innovative acceleration strategies, not included in any other current platform, that simplify and speed up the definition of the different elements required to design a spoken dialog service. The proposed accelerations are mainly based on using the information from the backend database schema and contents, as well as cumulative information produced throughout the different steps in the design. Thanks to these accelerations, the interaction between the designer and the platform is improved, and in most cases the design is reduced to simple confirmations of the “proposals” that the platform dynamically provides at each step. In addition, the platform provides several other accelerations such as configurable templates that can be used to define the different tasks in the service or the dialogs to obtain or show information to the user, automatic proposals for the best way to request slot contents from the user (i.e. using mixed-initiative forms or directed forms), an assistant that offers the set of more probable actions required to complete the definition of the different tasks in the application, or another assistant for solving specific modality details such as confirmations of user answers or how to present them the lists of retrieved results after querying the backend database. Additionally, the platform also allows the creation of speech grammars and prompts, database access functions, and the possibility of using mixed initiative and over-answering dialogs. In the paper we also describe in detail each assistant in the platform, emphasizing the different kind of methodologies followed to facilitate the design process at each one. Finally, we describe the results obtained in both a subjective and an objective evaluation with different designers that confirm the viability, usefulness, and functionality of the proposed accelerations. Thanks to the accelerations, the design time is reduced in more than 56% and the number of keystrokes by 84%

Using patient-reported measures to drive change in healthcare: the experience of the digital, continuous and systematic PREMs observatory in Italy

BACKGROUND: The use of Patient Reported Experience Measures (PREMs) has great potential in healthcare service improvement, but a limited use. This paper presents an empirical case of PREMs innovation in Italy, to foster patient data use up to the ward level, by keeping strengths and addressing weaknesses of previous PREMs survey experiences. The paper reports key lessons learned in this ongoing experience of action research, directly involving practitioners. METHODS: The aim of this paper is to present the results of an ongoing action research, encompassing the innovation of PREMs collection, reporting and use, currently adopted by 21 hospitals of two Italian regions. The continuous and systematic PREMs collection has been implemented between 2017 and 2019 and includes: a continuous web-based administration, using web-services; an augmented and positive questionnaire matching standard closed-ended questions with narrative sections; the inclusion and benchmarking of patient data within a shared performance evaluation system; public disclosure of aggregated anonymized data; a multi-level and real-time web-platform for reporting PREMs to professionals. The action research was carried out with practitioners in a real-life and complex context. The authors used multiple data sources and methods: observations, feedback of practitioners, collected during several workshops and meetings, and analysis of preliminary data on the survey implementation. RESULTS: A continuous and systematic PREMs observatory was developed and adopted in two Italian regions. PREMs participation and response rates tend to increase over time, reaching stable percentages after the first months. Narrative feedback provide a 'positive narration' of episodes and behaviours that made the difference to patients and can inform quality improvement actions. Real-time reporting of quantitative and qualitative data is enabling a gratifying process of service improvement and people management at all the hospitals' levels. CONCLUSIONS: The PREMs presented in this paper has been recognized by healthcare professionals and managers as a strategic and positive tool for improving an actual use of PREMs at system and ward levels, by measuring and highlighting positive deviances, such as compassionate behaviours

Are providers prepared for genomic medicine: interpretation of Direct-to-Consumer genetic testing (DTC-GT) results and genetic self-efficacy by medical professionals.

BACKGROUND:Precision medicine is set to deliver a rich new data set of genomic information. However, the number of certified specialists in the United States is small, with only 4244 genetic counselors and 1302 clinical geneticists. We conducted a national survey of 264 medical professionals to evaluate how they interpret genetic test results, determine their confidence and self-efficacy of interpreting genetic test results with patients, and capture their opinions and experiences with direct-to-consumer genetic tests (DTC-GT). METHODS:Participants were grouped into two categories, genetic specialists (genetic counselors and clinical geneticists) and medical providers (primary care, internists, physicians assistants, advanced nurse practitioners, etc.). The survey (full instrument can be found in the Additional file 1) presented three genetic test report scenarios for interpretation: a genetic risk for diabetes, genomic sequencing for symptoms report implicating a potential HMN7B: distal hereditary motor neuropathy VIIB diagnosis, and a statin-induced myopathy risk. Participants were also asked about their opinions on DTC-GT results and rank their own perceived level of preparedness to review genetic test results with patients. RESULTS:The rates of correctly interpreting results were relatively high (74.4% for the providers compared to the specialist's 83.4%) and age, prior genetic test consultation experience, and level of trust assigned to the reports were associated with higher correct interpretation rates. The self-selected efficacy and the level of preparedness to consult on a patient's genetic results were higher for the specialists than the provider group. CONCLUSION:Specialists remain the best group to assist patients with DTC-GT, however, primary care providers may still provide accurate interpretation of test results when specialists are unavailable