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Objective: In this article, we investigated the effects of external human-machine interfaces (eHMIs) on pedestrians' crossing intentions., Background: Literature suggests that the safety (i.e., not crossing when unsafe) and efficiency (i.e., crossing when safe) of pedestrians' interactions with automated vehicles could increase if automated vehicles display their intention via an eHMI., Methods: Twenty-eight participants experienced an urban road environment from a pedestrian's perspective using a head-mounted display. The behavior of approaching vehicles (yielding, nonyielding), vehicle size (small, medium, large), eHMI type (1. baseline without eHMI, 2. front brake lights, 3. Knightrider animation, 4. smiley, 5. text [WALK]), and eHMI timing (early, intermediate, late) were varied. For yielding vehicles, the eHMI changed from a nonyielding to a yielding state, and for nonyielding vehicles, the eHMI remained in its nonyielding state. Participants continuously indicated whether they felt safe to cross using a handheld button, and "feel-safe" percentages were calculated., Results: For yielding vehicles, the feel-safe percentages were higher for the front brake lights, Knightrider, smiley, and text, as compared with baseline. For nonyielding vehicles, the feel-safe percentages were equivalent regardless of the presence or type of eHMI, but larger vehicles yielded lower feel-safe percentages. The Text eHMI appeared to require no learning, contrary to the three other eHMIs., Conclusion: An eHMI increases the efficiency of pedestrian-AV interactions, and a textual display is regarded as the least ambiguous., Application: This research supports the development of automated vehicles that communicate with other road users.

Introduction: Errors in communication during handoffs are a significant source of medical error and put patients at risk. The I-PASS system was designed to systematically communicate information to the oncoming healthcare provider and has been shown to decrease the risk of communication errors. The objective of this observational quality improvement study was to determine whether the addition of a partially automated, electronic handoff tool would further decrease errors in communication during transitions of care for inpatient medical teams., Methods: We created an electronic tool to incorporate user-generated patient information in the I-PASS format with automatically compiled data derived from the electronic medical record. Numbers of errors in the printed handoff document were recorded before and after intervention., Results: The first implementation cycle demonstrated an absolute risk reduction for written errors of 45.6% (95% confidence interval [CI] 39.2-51.2%) and a number needed to treat (NNT) of three patients. The second cycle showed an absolute risk reduction of 53.3% (95% CI 39.8-63.9%; NNT 2). Aggregate data showed an absolute risk reduction of 46.6% (95% CI 41.0-51.7%, NNT 3)., Conclusions: Improving the routine task of patient handoff through the thoughtful application of technology can yield benefits in terms of decreasing documentation errors and streamlining workflow before patient handoff.

In the near future, more vehicles will have automated functions. The traffic system will be a shared space of automated and manually driven vehicles. In our study we focused on the perspective of vulnerable road users, namely pedestrians, in cooperative situations with automated vehicles. Established communication methods, such as eye-contact between pedestrians and drivers, may no longer work when automated vehicles represent the interaction partner. Therefore, we evaluated several human-machine-interfaces (HMI) in order to implement smooth and comfortable communication. We conducted a two-stage study consisting of an explorative focus group discussion with naïve pedestrians (n = 6), followed by an experimental video simulation study (n = 25) based on the results of the focus group discussion. From the focus group we sought member opinion about various HMI, upon presentation of acoustic and visual communication systems such as projections, displays and LED light strips, in addition to portable communication systems, specifically smart watches. On the basis of the focus group discussion, an evaluation criteria was derived. For the video simulation study, HMI designs were created with variations in position, type and coding of the message, and technology. These were assessed by 25 subjects according to the focus discussion derived evaluation criteria: recognizability, unambiguousness, interaction comfort and intuitive comprehensibility. The results show that direct instructions to cross the street are preferred over status information of the vehicle and that large-scale text-based messages from the vehicle to the pedestrian, deliver better results. Design recommendations for HMIs for communication between automated vehicles are derived, and the extent external HMIs may supplement informal communication strategies such as vehicle movement or braking maneuvers, is discussed., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

As the desire for deploying automated ("driverless") vehicles increases, there is a need to understand how they might communicate with other road users in a mixed traffic, urban, setting. In the absence of an active and responsible human controller in the driving seat, who might currently communicate with other road users in uncertain/conflicting situations, in the future, understanding a driverless car's behaviour and intentions will need to be relayed via easily comprehensible, intuitive and universally intelligible means, perhaps presented externally via new vehicle interfaces. This paper reports on the results of a questionnaire-based study, delivered to 664 participants, recruited during live demonstrations of an Automated Road Transport Systems (ARTS; SAE Level 4), in three European cities. The questionnaire sought the views of pedestrians and cyclists, focussing on whether respondents felt safe interacting with ARTS in shared space, and also what externally presented travel behaviour information from the ARTS was important to them. Results showed that most pedestrians felt safer when the ARTS were travelling in designated lanes, rather than in shared space, and the majority believed they had priority over the ARTS, in the absence of such infrastructure. Regardless of lane demarcations, all respondents highlighted the importance of receiving some communication information about the behaviour of the ARTS, with acknowledgement of their detection by the vehicle being the most important message. There were no clear patterns across the respondents, regarding preference of modality for these external messages, with cultural and infrastructural differences thought to govern responses. Generally, however, conventional signals (lights and beeps) were preferred to text-based messages and spoken words. The results suggest that until these driverless vehicles are able to provide universally comprehensible externally presented information or messages during interaction with other road users, they are likely to contribute to confusing and conflicting interactions between these actors, especially in a shared space setting, which may, therefore, reduce efficient traffic flow., (Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Automated conversational agents built with medical applications in mind, have the potential to reduce healthcare readmissions and improve accessibility to medical knowledge. In this work, we demonstrate the development and evaluation of an automated chatbot for triage and conditions assessment, based on user inputs in natural language. The implemented bot engages patients in conversation about symptoms experienced and provides a personalized pre-synopsis based on their symptoms and profile. Our chatbot system was able to predict user conditions correctly based on two sets of patient test cases with an average precision of 0.82. Our implementation demonstrates that a medical chatbot can help with automatic triage and pre-assessment of patients with simple symptom analysis and a conversational approach without the use of cumbersome form-based data entry.

Treating a human mind like a machine is an essential component of dehumanization, whereas attributing a humanlike mind to a machine is an essential component of anthropomorphism. Here we tested how a cue closely connected to a person's actual mental experience-a humanlike voice-affects the likelihood of mistaking a person for a machine, or a machine for a person. We predicted that paralinguistic cues in speech are particularly likely to convey the presence of a humanlike mind, such that removing voice from communication (leaving only text) would increase the likelihood of mistaking the text's creator for a machine. Conversely, adding voice to a computer-generated script (resulting in speech) would increase the likelihood of mistaking the text's creator for a human. Four experiments confirmed these hypotheses, demonstrating that people are more likely to infer a human (vs. computer) creator when they hear a voice expressing thoughts than when they read the same thoughts in text. Adding human visual cues to text (i.e., seeing a person perform a script in a subtitled video clip), did not increase the likelihood of inferring a human creator compared with only reading text, suggesting that defining features of personhood may be conveyed more clearly in speech (Experiments 1 and 2). Removing the naturalistic paralinguistic cues that convey humanlike capacity for thinking and feeling, such as varied pace and intonation, eliminates the humanizing effect of speech (Experiment 4). We discuss implications for dehumanizing others through text-based media, and for anthropomorphizing machines through speech-based media. (PsycINFO Database Record, ((c) 2016 APA, all rights reserved).)

Social interactions with other road users are an essential component of the driving activity and may prove critical in view of future automation systems; still up to now they have received only limited attention in the scientific literature. In this paper, it is argued that drivers base their anticipations about the traffic scene to a large extent on observations of social behaviour of other 'animate human-vehicles'. It is further argued that in cases of uncertainty, drivers seek to establish a mutual situational awareness through deliberate communicative interactions. A linguistic model is proposed for modelling these communicative interactions. Empirical evidence from on-road observations and analysis of concurrent running commentary by 25 experienced drivers support the proposed model. It is suggested that the integration of a social interactions layer based on illocutionary acts in future driving support and automation systems will improve their performance towards matching human driver's expectations. Practitioner Summary: Interactions between drivers on the road may play a significant role in traffic coordination. On-road observations and running commentaries are presented as empirical evidence to support a model of such interactions; incorporation of drivers' interactions in future driving support and automation systems may improve their performance towards matching driver's expectations.

One common thread to health care reform in the United States is an emphasis on effectively managing the care of patients with chronic conditions. A landmark study by McGlynn et al demonstrated that patients receive about 55% of the treatment they need. While technological advances allow automated means for identifying and reaching out to patients in need of treatment, few studies have evaluated their impact. The purpose of this study is to measure how an automated outreach program can be used to improve the quality of care for patients with diabetes and hypertension. Billing and electronic medical records data from a large health system in Wisconsin were studied, identifying patients with a history of diabetes and hypertension but no visits recorded in billing data related to their condition in the past 6 months. The outcomes of interest were the occurrence of a chronic care-related visit and a necessary test within 6 months of the nonadherence date. Diabetes patients who were successfully contacted were significantly more likely to have both a chronic care-related visit and an HbA1c test (odds ratio [OR] = 4.61, 95% confidence interval [CI] 3.87-5.49) than their counterparts who were not contacted. As well, hypertension patients were significantly more likely to have both a chronic care-related visit and a systolic blood pressure reading recorded in an electronic medical record (OR = 3.18, 95% CI 2.90-3.48). An automated patient identification and outreach program can be an effective means to supplement existing practice patterns to ensure that patients with chronic conditions in need of care receive the necessary treatment.

We present: (1) an automated telephone check-in system to capture emotional health, based on automatic emotion classification, crowd-sourcing, and the experience sampling method; (2) a method that combines acoustic-based and perception-based emotion classifiers to maximize the likelihood of correctly identifying the emotion in a speech recording; (3) an evidence-based toolkit to measure and assess emotional health; and (4) the results of three experimental trials held in 2010 and 2011: (a) English speaking members of Alcoholics Anonymous, (b) English and French speaking general population, and (c) English speaking Opioid addicts undergoing Suboxone maintenance treatment. Emotional health can be defined as the ability to express emotions, identify one's own emotions, relate to other people's emotions, and to live life with predominantly positive emotions. Emotional health plays a major role in addiction treatment and Cognitive Behavioral Therapy (CBT).

System disturbances are likely to be a key factor affecting the acceptance and safety of future automation. Since hardware and software are rarely totally reliable, humans are always required in socio-technical command and control environments such as air traffic management (ATM). Unfortunately, human-automation interaction is known to be problematic, particularly when the human assumes a monitoring or back-up role. Hence an understanding of how humans manage system disturbances is required, together with a method of looking at the problem for new systems. In this paper we outline a contextual framework of the process by which people recover from system disturbances, together with literature data and findings from 31 interviews with ATM personnel. The framework describes the context and causes of a problem, the problem itself, the effect and exposure, the recovery process, and the outcome. The framework, together with the research findings and operational experience, is also the basis for a performance prediction tool called the Recovery from Automation Failure Tool (RAFT).

A tool and a method for scenario-based workload assessment and performance validation in complex socio-technical systems design, such as command and control rooms of military vessels, are described. We assess workload in terms of communication and the task load that each agent is able to handle. The method employs subjective task and communication estimates used to calculate the workload of human operators, using static and scenario-based analyses. This enables the identification of bottlenecks to be addressed by the designer with the appropriate allocation of function between humans and smart technology. This task is supported by the functional allocation adviser tool. A case study demonstrating the use of the tool for the design of the command and control room of a military vessel is presented.

The control of the right application of medical protocols is a key issue in hospital environments. For the automated monitoring of medical protocols, we need a domain-independent language for their representation and a fully, or semi, autonomous system that understands the protocols and supervises their application. In this paper we describe a specification language and a multi-agent system architecture for monitoring medical protocols. We model medical services in hospital environments as specialized domain agents and interpret a medical protocol as a negotiation process between agents. A medical service can be involved in multiple medical protocols, and so specialized domain agents are independent of negotiation processes and autonomous system agents perform monitoring tasks. We present the detailed architecture of the system agents and of an important domain agent, the database broker agent, that is responsible of obtaining relevant information about the clinical history of patients. We also describe how we tackle the problems of privacy, integrity and authentication during the process of exchanging information between agents.

The present study investigated whether older and younger adults use a schema to organize and remember spoken reminder messages for taking medication. Previous research has shown that older and younger adults share preferences for organizing printed instructions for taking medication, suggesting a shared schema. Older and younger participants in Experiment 1 of the present study used a similar schema to organize medication reminder messages. This finding suggests that the medication schema generalizes across communication purpose (to remind or to instruct) as well as across patient age. Medication reminder messages were better understood and remembered when organized to match this schema, whether the reminders were presented as automated telephone messages (Experiment 2) or in printed form (Experiment 3). Schema-compatible organization especially helped people draw inferences from the messages, suggesting that organization helps older and younger adults construct a situation model of the medication-taking task from the messages. Potential applications of organized messages include increasing the impact of automated systems for delivering health services.

The effects of an automated system on team processes and performance were assessed in a laboratory simulation. Two-person crews were required to fly a complex emergency-response scenario under conditions of low and high workload. These flights were completed with or without the aid of an autopilot. The results indicated that the autopilot was effective in reducing subjective workload. However, the automation was associated with improved performance on only 1 of 4 performance measures. Furthermore, it was observed that problem-solving performance was worse in the autopilot condition during the high-workload flights. Investigation of crew process data indicated that workload savings afforded by the autopilot might have been invested in more explicit coordination. The results are discussed in terms of their implications for military aviators' performance, system design, and team training.

Research and operational experience have shown that one of the major problems with pilot-automation interaction is a lack of mode awareness (i.e., the current and future status and behavior of the automation). As a result, pilots sometimes experience so-called automation surprises when the automation takes an unexpected action or fails to behave as anticipated. A lack of mode awareness and automation surprises can he viewed as symptoms of a mismatch between human and machine properties and capabilities. Changes in automation design can therefore he expected to affect the likelihood and nature of problems encountered by pilots. Previous studies have focused exclusively on early generation "glass cockpit" aircraft that were designed based on a similar automation philosophy. To find out whether similar difficulties with maintaining mode awareness are encountered on more advanced aircraft, a corpus of automation surprises was gathered from pilots of the Airbus A-320, an aircraft characterized by high levels of autonomy, authority, and complexity. To understand the underlying reasons for reported breakdowns in human-automation coordination, we also asked pilots about their monitoring strategies and their experiences with and attitude toward the unique design of flight controls on this aircraft.

Increasing levels of automation are being introduced into the cockpit. Yet, it is difficult to predict the impact of these automatic systems on other elements of flight, such as crew communication and the ability to arrive at an effective decision. This study attempted to clarify the relation among these variables. Forty-eight pilots were assigned to two-person crews and asked to fly a simulated mission in either automated or manual conditions using a low-fidelity simulator. The scenario was designed to require crewmembers to arrive at a collective decision based on information obtained about an evolving simulated disaster. The results indicated that the introduction of automation was not associated with better performance. However, several significant differences were observed in the communications of crews flying in the automated versus manual conditions. The results are discussed in terms of their implications for communications training for advanced technology aircraft.

This study assessed the impact of automated telephone reminders in a population of 2,008 patients scheduled for appointments in a public health tuberculosis clinic. Overall, remainders increased appointment attendance from 52% to 62%. Reminders were more effective for some applications than others, but the effectiveness of reminders did not differ significantly across patient age, sex, or ethnicity. Counter to theoretical predictions, neither attribution of the reminder message to an authority nor a statement stressing the importance of the appointment significantly increased the effectiveness of the reminder above the level obtained without these enhancements.

Much of what white collar workers do in offices is communication-related. White collar workers make up the majority of the labor force in the United States today and the majority of current labor costs. Because office automation represents more productive structured techniques for handling both written and oral communication, office automation therefore offers the potential to make organizations more productive by improving organizational communication. This article: (1) defines communication, (2) identifies the potential benefits to be realized from implementing office automation, and (3) offers caveats related to the implementation of office automation systems. Realization of the benefits of office automation depends upon the degree to which new modes of communication may be successfully substituted for traditional modes.

The objectives, place and advantages of automation in blood-banking services are outlined with reference to the perennial need of blood services to achieve the optimum balance between fluctuating requirements for and supplies of blood while allowing for the brevity of most blood products' useful life-span. Regional, national and international integration of automated systems in blood services is then shown to depend upon mutual agreement concerning data definitions, formats and representations and the development of an internationally accepted standard blood data communication record is proposed.

Automation is a defining feature of today's societies—not only since ChatGPT and generative artificial intelligence (AI) have accomplished to produce yet another wave of hype. This essay introduces a special issue on automation and communication in the digital society. It aims to study how subjectivity, agency, and empowerment become defined and reconfigured in novel human–machine encounters and, more broadly, in societies which in large parts are kept going and sustained by complex digital infrastructures. The issue includes contributions from a wide array of disciplines and perspectives and engages with conditions, contexts, and consequences of automation in very different settings ranging from journalism to self-service hotels, and from social movements in Hong Kong to the Russian Invasion to the Ukraine. The articles offer critical perspectives on the transition of human activity into machine operations, and back, as well on the social dynamics changing and emerging in increasingly digitized and datafied societies. [ABSTRACT FROM AUTHOR]

The Government of India offers various schemes for various classes of citizens. Most of the application forms of schemes to be filled are in English and it is observed that monolingual individuals find it difficult to access and fill the forms. This paper addresses the challenges faced by monolingual individuals in India, particularly the elderly, people with impairments, and those from marginalized communities. The proposed work is to create an interactive system called "Dhvani" voicebot, specifically designed for the Kannada language. It helps users in identifying suitable government schemes and fills forms in English. The proposed system is developed using the RASA chatbot framework and NLP techniques to comprehend user utterances. RNN and SVM algorithms are employed to ensure smooth conversation flow and interaction with the users. To enhance scheme suggestion accuracy, a knowledge graph is created, containing relevant data on government schemes. The intent classification model achieves an accuracy of 97%, indicating its ability to accurately understand user intentions. The integration of a knowledge graph improves the accuracy of scheme identification and suggestion to users. The system automates the process of filling out government scheme forms based on user inputs. Dhvani voicebot system presents a practical solution to address the challenges faced by monolingual individuals in accessing government schemes in India. The high accuracy of intent classification and the use of a knowledge graph contribute to the system's effectiveness. The study suggests that this system can be extended to other languages. An automated tool called "Dhvani" will solve the problem of aged, illiterate and physically challenged persons filling forms in post offices and banks. Most of the schemes, pension funds, cash withdrawal, cash deposit is through these organizations. So. the tool makes the process easier for the above mention persons without the help of others. An intent recognition and interactive tool developed in Kannada Language which is widely spoken in Karnataka, India. The digital resources available in Kannada Language is very sparce. Use of technology like interactive tool, Knowledge graph, RNN and SVM are used in the development of the tool. Government scheme recommendation interactively makes the users to choose the scheme faster in an interactive way. The form is filled automatically and can be edited to rectify mistakes. [ABSTRACT FROM AUTHOR]

The relevance of the study is due to the growing need to use chatbots to optimize business processes. The purpose is to form a theoretical basis and practical tools for increasing the efficiency of using chatbots in business processes. The theoretical basis involves substantiating the theoretical foundations of forming a conditional chatbot profile for an optimization system. The practical toolkit includes chatbot components that depend on the complexity of tasks, the type of services, the specifics of customers, financial conditions, and other features of business processes. The result is the formation of a system profile of the chatbot, which would allow increasing the efficiency of its use in business processes. The key system components of the chatbot are substantiated: the technologies used, types of users, optimal areas of application, application algorithms, basic tools, and limitations in application. By varying the parameters of system components, one can choose their optimal values to increase the efficiency of using chatbots in business processes. It is advisable to use the specified system in business processes when determining the demand for products and their sales. The use of chatbots allows to reduce the time to complete business processes, personnel costs, and resources related to their implementation. AcknowledgmentThe paper was prepared in the framework of the research projects “Fundamental grounds for Ukraine’s transition to a digital economy based on the implementation of Industries 3.0; 4.0; 5.0” (No. 0124U000576) and “Digital transformations to ensure civil protection and post-war economic recovery in the face of environmental and social challenges” (No. 0124U000549).

Purpose: Augmentative and alternative communication (AAC) technology innovation is urgently needed to improve outcomes for children on the autism spectrum who are minimally verbal. One potential technology innovation is applying artificial intelligence (AI) to automate strategies such as augmented input to increase language learning opportunities while mitigating communication partner time and learning barriers. Innovation in AAC research and design methodology is also needed to empirically explore this and other applications of AI to AAC. The purpose of this report was to describe (a) the development of an AAC prototype using a design methodology new to AAC research and (b) a preliminary investigation of the efficacy of this potential new AAC capability. Method: The prototype was developed using a Wizard-of-Oz prototyping approach that allows for initial exploration of a new technology capability without the time and effort required for full-scale development. The preliminary investigation with three children on the autism spectrum who were minimally verbal used an adapted alternating treatment design to compare the effects of a Wizard-of-Oz prototype that provided automated augmented input (i.e., pairing color photos with speech) to a standard topic display (i.e., a grid display with line drawings) on visual attention, linguistic participation, and (for one participant) word learning during a circle activity. Results: Preliminary investigation results were variable, but overall participants increased visual attention and linguistic participation when using the prototype. Conclusions: Wizard-of-Oz prototyping could be a valuable approach to spur much needed innovation in AAC. Further research into efficacy, reliability, validity, and attitudes is required to more comprehensively evaluate the use of AI to automate augmented input in AAC. [ABSTRACT FROM AUTHOR]

Objective: The purpose of this study was to implement a 2-phase approach to rapidly increase the number of annual wellness visits (AWVs) and build a sustainable model at 3 study units (Mayo Clinic in Rochester, Minnesota, and clinics in 2 regions of Mayo Clinic Health System), which collectively serve approximately 80 000 patients who qualify for an AWV annually. Methods: In the rapid improvement phase, beginning in July 2022, goals at the facilities were reoriented to prioritize AWVs, educate staff on existing AWV resources, and create low-effort workflows so that AWVs could be incorporated into existing patient appointments. Staff at all 3 study units worked independently and iterated quickly. In the second phase, all study units collaborated to design and implement a best-practice solution while they leveraged the engagement and lessons learned from the first phase and invested in additional system elements and change management to codify long-term success. Results: The number of AWVs completed monthly increased in each study unit. In the rapid improvement phase, the number of AWVs increased but then plateaued (or decreased at some study units). In April 2023, the final scheduled outreach automation and visit tools were implemented, and the number of AWVs was sustained or increased, while outreach and scheduling times were decreased. The number of completed AWVs increased from 1148 across all study units in the first 6 months of 2022 to 14 061 during the first 6 months of 2023. Conclusions: The lessons learned from this project can be applied to other health systems that want to provide more patients with AWVs while improving operational efficiency. The keys are to have a clear vision of a successful outcome, engage all stakeholders, and iterate quickly to find what works best for the organization. [ABSTRACT FROM AUTHOR]

Fundus fluorescein angiography (FFA) is a crucial diagnostic tool for chorioretinal diseases, but its interpretation requires significant expertise and time. Prior studies have used Artificial Intelligence (AI)-based systems to assist FFA interpretation, but these systems lack user interaction and comprehensive evaluation by ophthalmologists. Here, we used large language models (LLMs) to develop an automated interpretation pipeline for both report generation and medical question-answering (QA) for FFA images. The pipeline comprises two parts: an image-text alignment module (Bootstrapping Language-Image Pre-training) for report generation and an LLM (Llama 2) for interactive QA. The model was developed using 654,343 FFA images with 9392 reports. It was evaluated both automatically, using language-based and classification-based metrics, and manually by three experienced ophthalmologists. The automatic evaluation of the generated reports demonstrated that the system can generate coherent and comprehensible free-text reports, achieving a BERTScore of 0.70 and F1 scores ranging from 0.64 to 0.82 for detecting top-5 retinal conditions. The manual evaluation revealed acceptable accuracy (68.3%, Kappa 0.746) and completeness (62.3%, Kappa 0.739) of the generated reports. The generated free-form answers were evaluated manually, with the majority meeting the ophthalmologists' criteria (error-free: 70.7%, complete: 84.0%, harmless: 93.7%, satisfied: 65.3%, Kappa: 0.762–0.834). This study introduces an innovative framework that combines multi-modal transformers and LLMs, enhancing ophthalmic image interpretation, and facilitating interactive communications during medical consultation. [ABSTRACT FROM AUTHOR]

This article describes an organizational continuous process improvement project using Appreciative Inquiry to evaluate staff members’ perception of their unreasonable workload, as indicated in the Federal Employee Viewpoint Survey. The goal for approaching the management-directed study was to use AI to frame the conversation around the lifegiving forces aspects AI brings to the organization rather than a traditional continuous process improvement approach in which we would investigate a problem, identify issues, and propose solutions to correct it. [ABSTRACT FROM AUTHOR]

Reliably processing and interlinking medical information has been recognized as a critical foundation to the digital transformation of medical workflows, and despite the development of medical ontologies, the optimization of these has been a major bottleneck to digital medicine. The advent of large language models has brought great excitement, and maybe a solution to the medicines' 'communication problem' is in sight, but how can the known weaknesses of these models, such as hallucination and non-determinism, be tempered? Retrieval Augmented Generation, particularly through knowledge graphs, is an automated approach that can deliver structured reasoning and a model of truth alongside LLMs, relevant to information structuring and therefore also to decision support. [ABSTRACT FROM AUTHOR]