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Nonadherence to therapy negatively impacts mortality and quality of life and results in suboptimal efficacy of treatment regimens, threats to patient safety, and increased healthcare costs for disease management. Mobile health solutions can offer users instruments that can promote therapeutic adherence. The objective of this review is to investigate the impact mobile health systems have on therapeutic adherence. Specifically, we want to map the main systems used, the functions implemented, and the different methods of adherence detection used. For this purpose, a scoping review was conducted. The following databases were consulted: PubMed, Cochrane Library, EBSCO (including APA PsycINFO, CINAHL Plus with Full Text, ERIC), including English-language studies published in the last 10 years (2012–2022). The main mobile health systems used are as follows: applications, automated messaging, interactive voice response, and mobile video games. The main features implemented to support medication management were as follows: reminders, self-monitoring instruments, educational support, and caregiver involvement. In conclusion, the use of interactive mobile health instruments intended for use by the patient and/or caregiver can improve objectively and subjectively detected therapeutic adherence. The use of these systems in the therapeutic pathway of users, with a special focus on people with comorbidities and in polypharmacy treatment, represents a challenge to improve caregiver health. No abstract available Computer-based technologies have been widely used in nursing education, although the best educational modality to improve documentation and nursing diagnostic accuracy using electronic health records is still under investigation. It is important to address this gap and seek an effective way to address increased accuracy around nursing diagnoses identification. Nursing diagnoses are judgments that represent a synthesis of data collected by the nurse and used to guide interventions and to achieve desirable patients' outcomes. This current investigation is aimed at comparing the nursing diagnostic accuracy, satisfaction, and usability of a computerized system versus a traditional paper-based approach. A total of 66 nursing students solved three validated clinical scenarios using the NANDA-International terminologies traditional paper-based approach and then the computer-based Clinical Decision Support System. Study findings indicated a significantly higher nursing diagnostic accuracy (P Care coordination is a crucial component of healthcare systems. However, little is known about data needs and uses in ambulatory care coordination practice. Therefore, the purpose of this study was to identify information gathered and used to support care coordination in ambulatory settings. Survey respondents (33) provided their demographics and practice patterns, including use of electronic health records, as well as data gathered and used. Most of the respondents were nurses, and they described varying practice settings and patterns. Although most described at least partial use of electronic health records, two respondents described paper documentation systems. More than 25% of respondents gathered and used most of the 72 data elements, with collection and use often occurring in multiple locations and contexts. This early study demonstrates significant heterogeneity in ambulatory care coordination data usage. Additional research is necessary to identify common data elements to support knowledge development in the context of a learning health system. Delirium is a common disorder for patients after cardiac surgery. Its manifestation and care can be examined through EHRs. The aim of this retrospective, comparative, and descriptive patient record study was to describe the documentation of delirium symptoms in the EHRs of patients who have undergone cardiac surgery and to explore how the documentation evolved between two periods (2005-2009 and 2015-2020). Randomly selected care episodes were annotated with a template, including delirium symptoms, treatment methods, and adverse events. The patients were then manually classified into two groups: nondelirious (n = 257) and possibly delirious (n = 172). The data were analyzed quantitatively and descriptively. According to the data, the documentation of symptoms such as disorientation, memory problems, motoric behavior, and disorganized thinking improved between periods. Yet, the key symptoms of delirium, inattention, and awareness were seldom documented. The professionals did not systematically document the possibility of delirium. Particularly, the way nurses recorded structural information did not facilitate an overall understanding of a patient's condition with respect to delirium. Information about delirium or proposed care was seldom documented in the discharge summaries. Advanced machine learning techniques can augment instruments that facilitate early detection, care planning, and transferring information to follow-up care. No abstract available 
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