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.

Causes of appointment attendance, nonattendance, and cancellation in outpatient consultations at a university hospital.

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.

The Personalization of Conversational Agents in Health Care: Systematic Review.

BACKGROUND: The 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. OBJECTIVE: The 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. METHODS: We 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. RESULTS: The 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. CONCLUSIONS: Most 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.

Epidemiology of chronic disease related to arsenic in Argentina: A systematic review.

Four million people in Argentina are exposed to arsenic contamination from drinking waters of several center-northern provinces. A systematic review to examine the geographical distribution of arsenic-related diseases in Argentina was conducted, searching electronic databases and gray literature up to November 2013. Key informants were also contacted. Of the 430 references identified, 47 (mostly cross-sectional and ecological designs) referred to arsenic concentration in water and its relationship with the incidence and mortality of cancer, dermatological diseases and genetic disorders. A high percentage of the water samples had arsenic concentrations above the WHO threshold value of 10µg/L, especially in the province of Buenos Aires. The median prevalence of arsenicosis was 2.6% in exposed areas. The proportion of skin cancer in patients with arsenicosis reached 88% in case-series from the Buenos Aires province. We found higher incidence rate ratios per 100µg/L increment in inorganic arsenic concentration for colorectal, lung, breast, prostate and skin cancer, for both genders. Liver and skin cancer mortality risk ratios were higher in regions with medium/high concentrations than in those with low concentrations. The relative risk of mortality by skin cancer associated to arsenic exposure in the province of Buenos Aires ranged from 2.5 to 5.2. In the north of this province, high levels of arsenic in drinking water were reported; however, removal interventions were scarcely documented. Arsenic contamination in Argentina is associated with an increased risk of serious chronic diseases, including cancer, showing the need for adequate and timely actions.

Health Information Systems: evaluation and performance of a Help Desk.

UNLABELLED: A Help Desk (HD) is crucial in a computerized hospital. OBJECTIVE: to describe the performance of a HD. DESIGN: retrospective cohort study. RESULTS: the sociodemographic characteristics of users, as well as their relationship with the institution influence behaviour when requesting support to a HD. Also we observed a relationship between the flow of users request and the functioning of hospital services. CONCLUSIONS: complexity of HD process realizes the need to identify and define standards to ensure quality of service.

Factors associated with nonattendance at clinical medicine scheduled outpatient appointments in a university general hospital.

INTRODUCTION: Nonattendance 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. METHODS: A 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. RESULTS: Of 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). CONCLUSION: Evidence 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.