BackgroundThe personalization of conversational agents with natural language user interfaces is seeing increasing use in health care applications, shaping the content, structure, or purpose of the dialogue between humans and conversational agents.ObjectiveThe goal of this systematic review was to understand the ways in which personalization has been used with conversational agents in health care and characterize the methods of its implementation.MethodsWe searched on PubMed, Embase, CINAHL, PsycInfo, and ACM Digital Library using a predefined search strategy. The studies were included if they: (1) were primary research studies that focused on consumers, caregivers, or health care professionals; (2) involved a conversational agent with an unconstrained natural language interface; (3) tested the system with human subjects; and (4) implemented personalization features.ResultsThe search found 1958 publications. After abstract and full-text screening, 13 studies were included in the review. Common examples of personalized content included feedback, daily health reports, alerts, warnings, and recommendations. The personalization features were implemented without a theoretical framework of customization and with limited evaluation of its impact. While conversational agents with personalization features were reported to improve user satisfaction, user engagement and dialogue quality, the role of personalization in improving health outcomes was not assessed directly.ConclusionsMost of the studies in our review implemented the personalization features without theoretical or evidence-based support for them and did not leverage the recent developments in other domains of personalization. Future research could incorporate personalization as a distinct design factor with a more careful consideration of its impact on health outcomes and its implications on patient safety, privacy, and decision-making.

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IntroductionNonattendance at scheduled outpatient appointments for primary care is a major health care problem worldwide. Our aim was to estimate the prevalence of nonattendance at scheduled appointments for outpatients seeking primary care, to identify associated factors and build a model that predicts nonattendance at scheduled appointments.MethodsA cohort study of adult patients, who had a scheduled outpatient appointment for primary care, was conducted between January 2010 and July 2011, at the Italian Hospital of Buenos Aires. We evaluated the history and characteristics of these patients, and their scheduling and attendance at appointments. Patients were divided into two groups: those who attended their scheduled appointments, and those who did not. We estimated the odds ratios (OR) and corresponding 95% confidence intervals (95% CI), and generated a predictive model for nonattendance, with logistic regression, using factors associated with lack of attendance, and those considered clinically relevant. Alternative models were compared using Akaike’s Information Criterion. A generation cohort and a validation cohort were assigned randomly.ResultsOf 113,716 appointments included in the study, 25,687 were missed (22.7%; 95% CI: 22.34%–22.83%). We found a statistically significant association between nonattendance and age (OR: 0.99; 95% CI: 0.99–0.99), number of issues in the personal health record (OR: 0.98; 95% CI: 0.98–0.99), time between the request for and date of appointment (OR: 1; 95% CI: 1–1), history of nonattendance (OR: 1.07; 95% CI: 1.07–1.07), appointment scheduled later than 4 pm (OR: 1.30; 95% CI: 1.24–1.35), and specific days of the week (OR: 1.00; 95% CI: 1.06–1.1). The predictive model for nonattendance included characteristics of the patient requesting the appointment, the appointment request, and the actual appointment date. The area under the receiver operating characteristic curve of the predictive model in the generation cohort was 0.892 (95% CI: 0.890–0.894).ConclusionEvidence related to patient characteristics, and the identification of appointments with a higher likelihood of nonattendance, should promote guided strategies to reduce the rate of nonattendance, as well as to future research on this topic. The use of predictive models could further guide management strategies to reduce the rate of nonattendance.

Summary Background Nonattendance to scheduled appointments in outpatient clinics is a frequent problem in ambulatory medicine with an impact on health systems and patients' health. The characterization of nonattendance is fundamental for the design of appropriate strategies for its management. Aims To identify causes of nonattendance of scheduled ambulatory medical appointments by adult patients. Methods Case and two controls study nested in a prospective cohort. A telephone‐administered questionnaire was applied within the first 72 hours to identify the causes of attendance, nonattendance, or cancellation in patients who had a scheduled appointment to which they had been present, absent, or cancelled. Results A total of 150 absences (cases), 176 attendances, and 147 cancellations (controls) in a prospective cohort of 160 146 scheduled appointments (2012/2013) were included. According to self‐reports in telephone interviews, the most frequent causes of nonattendance were forgetting 44% (66), unexpected competing events 15.3% (23), illness or unwellness 12% (18), work‐related inconvenience 5.3% (8), transport‐related difficulties 4.7% (4), and cause that motivated appointment scheduling already resolved 4.7% (4). Discussion The main cause of nonattendance is forgetting the scheduled appointment, but there is a proportion of different causes that do not respond to reminders but could respond to different strategies.

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.