Transferring Healthcare Professional's Digital Competencies to the Workplace and Patients: A Pilot Study.

The new times, marked by immediacy, globalization, and technological advances, has forced health professionals to develop new competencies to adapt to the new challenges. However, necessary skills such as using digital tools are primarily ignored by institutions, hospitals, and universities, forcing professionals to undertake training in these areas independently. This research aims to analyse if there is a transfer of what has been learned in the digital healthcare field to their professional practice and patients. To perform the study, 104 healthcare professionals, mostly nurses, who had completed online training in digital competencies answered a questionnaire with 17 questions. These questions were related to the transfer of learning to professional practice and its use for developing patient resources. Almost 60% of the professionals said that they have used what they learned in the course in their professional work, but only 16% of the participants use it daily. The main barrier to not having applied what was learned during the course, according to participants, was the situation experienced during the COVID-19 pandemic, followed by a lack of time and lack of resources. Only 23 people out of 104 developed patient resources after the course; the most created were infographics and videos. In addition, 38 people used what they learned to improve their personal productivity: searches, storage, calendars, etc. Only 11 used it for research purposes. People between 31 and 40 years old create the most patient resources and use what they learn most frequently. There is a need to improve e-learning to provide quality training that can transfer good behaviour to professional practice in the health field.

The Rising Importance of e-Health in Norway.

Drawing on three central sources of data on the development in e-health use in Norway (studies from the Norwegian Centre for e-Health Research, studies from Statistics Norway, and the Tromsø 7 Study), we describe the rising importance of e-health. Originally restricted to a limited use within the health services, in recent years the use of e-health has gained momentum both in the general population and within the traditional health services, as the Internet has offered easy access to health information as well as a range of other health-related services.

Estimating Patient Empowerment and Nurses' Use of Digital Strategies: eSurvey Study.

Patient empowerment is seen as the capability to understand health information and make decisions based on it. It is a competence that can improve self-care, adherence and overall health. The COVID-19 pandemic has increased the need for information and has also reduced the number of visits to health centers. Nurses have had to adapt in order to continue offering quality care in different environments such as the digital world, but this entails assessing the level of their patients' empowerment and adapting material and educational messages to new realities. The aim of this study is, on the one hand, to assess nurses' use of digital resources to provide reinforcing information to their patients and, on the other hand, to evaluate how they assess the level of empowerment of their patients. To perform the study, 850 nurses answered 21 questions related to their own digital literacy and patients' empowerment. The ability to make decisions is the characteristic most selected by nurses (70%) as useful in measuring patient empowerment, whereas 9.19% do not measure it in any way. Printed material is most often used by nurses to offer additional information to patients (71.93%), mobile applications are the least used option (21.58%), and elder nurses are those who most recommend digital resources. In this study, younger nurses make little or no use of technology as a resource for training and monitoring patients. In spite of some limitations concerning the study, digital health needs to be promoted as an indisputable tool in the nurse's briefcase in the future to ensure that older patients can manage electronic resources in different fields.

Design and Evaluation of a Solo-Resident Smart Home Testbed for Mobility Pattern Monitoring and Behavioural Assessment.

Aging population increase demands for solutions to help the solo-resident elderly live independently. Unobtrusive data collection in a smart home environment can monitor and assess elderly residents' health state based on changes in their mobility patterns. In this paper, a smart home system testbed setup for a solo-resident house is discussed and evaluated. We use paired Passive infra-red (PIR) sensors at each entry of a house and capture the resident's activities to model mobility patterns. We present the required testbed implementation phases, i.e., deployment, post-deployment analysis, re-deployment, and conduct behavioural data analysis to highlight the usability of collected data from a smart home. The main contribution of this work is to apply intelligence from a post-deployment process mining technique (namely, the parallel activity log inference algorithm (PALIA)) to find the best configuration for data collection in order to minimise the errors. Based on the post-deployment analysis, a re-deployment phase is performed, and results show the improvement of collected data accuracy in re-deployment phase from 81.57% to 95.53%. To complete our analysis, we apply the well-known CASAS project dataset as a reference to conduct a comparison with our collected results which shows a similar pattern. The collected data further is processed to use the level of activity of the solo-resident for a behaviour assessment.

Autoimmune Diabetes Recurrence After Pancreas Transplantation: Diagnosis, Management, and Literature Review.

BACKGROUND Pancreas transplantation can be a viable treatment option for patients with type 1 diabetes mellitus (T1DM), especially for those who are candidates for kidney transplantation. T1DM may rarely recur after pancreas transplantation, causing the loss of pancreatic graft. The aim of this study was to describe the prevalence of T1DM recurrence after pancreas transplantation in our series. MATERIAL AND METHODS Eighty-one patients transplanted from 2002 to 2015 were included. Autoantibody testing (GADA and IA-2) was performed before pancreas transplantation and during the follow-up. RESULTS The series includes 48 males and 33 females, mean age 37.4±5.7 years and mean duration of diabetes 25.5±6.5 years. Patients received simultaneous pancreas kidney (SPK) transplantation. After SPK transplantation, 56 patients retained pancreatic graft, 8 patients died, and 17 patients lost their pancreatic graft. T1DM recurrence occurred in 2 of the 81 transplanted patients, yielding a prevalence of 2.5%, with an average time of appearance of 3.3 years after transplant. Pancreatic enzymes were normal in the 2 patients, ruling out pancreatic rejection. T1DM recurrence was confirmed histologically, showing selective lymphoid infiltration of the pancreatic islets. CONCLUSIONS T1DM recurrence after pancreas transplantation is infrequent; however, it is one of the causes of pancreatic graft loss that should always be ruled out. Negative autoimmunity prior to transplantation does not ensure that T1DM does not recur.

Driving Type 2 Diabetes Risk Scores into Clinical Practice: Performance Analysis in Hospital Settings.

Electronic health records and computational modelling have paved the way for the development of Type 2 Diabetes risk scores to identify subjects at high risk. Unfortunately, few risk scores have been externally validated, and their performance can be compromised when routine clinical data is used. The aim of this study was to assess the performance of well-established risk scores for Type 2 Diabetes using routinely collected clinical data and to quantify their impact on the decision making process of endocrinologists. We tested six risk models that have been validated in external cohorts, as opposed to model development, on electronic health records collected from 2008-2015 from a population of 10,730 subjects. Unavailable or missing data in electronic health records was imputed using an existing validated Bayesian Network. Risk scores were assessed on the basis of statistical performance to differentiate between subjects who developed diabetes and those who did not. Eight endocrinologists provided clinical recommendations based on the risk score output. Due to inaccuracies and discrepancies regarding the exact date of Type 2 Diabetes onset, 76 subjects from the initial population were eligible for the study. Risk scores were useful for identifying subjects who developed diabetes (Framingham risk score yielded a c-statistic of 85%), however, our findings suggest that electronic health records are not prepared to massively use this type of risk scores. Use of a Bayesian Network was key for completion of the risk estimation and did not affect the risk score calculation (p > 0.05). Risk score estimation did not have a significant effect on the clinical recommendation except for starting pharmacological treatment (p = 0.004) and dietary counselling (p = 0.039). Despite their potential use, electronic health records should be carefully analyzed before the massive use of Type 2 Diabetes risk scores for the identification of high-risk subjects, and subsequent targeting of preventive actions.

The CrowdHEALTH project and the Hollistic Health Records: Collective Wisdom Driving Public Health Policies.

INTRODUCTION: With the expansion of available Information and Communication Technology (ICT) services, a plethora of data sources provide structured and unstructured data used to detect certain health conditions or indicators of disease. Data is spread across various settings, stored and managed in different systems. Due to the lack of technology interoperability and the large amounts of health-related data, data exploitation has not reached its full potential yet. AIM: The aim of the CrowdHEALTH approach, is to introduce a new paradigm of Holistic Health Records (HHRs) that include all health determinants defining health status by using big data management mechanisms. METHODS: HHRs are transformed into HHRs clusters capturing the clinical, social and human context with the aim to benefit from the collective knowledge. The presented approach integrates big data technologies, providing Data as a Service (DaaS) to healthcare professionals and policy makers towards a "health in all policies" approach. A toolkit, on top of the DaaS, providing mechanisms for causal and risk analysis, and for the compilation of predictions is developed. RESULTS: CrowdHEALTH platform is based on three main pillars: Data & structures, Health analytics, and Policies. CONCLUSIONS: A holistic approach for capturing all health determinants in the proposed HHRs, while creating clusters of them to exploit collective knowledge with the aim of the provision of insight for different population segments according to different factors (e.g. location, occupation, medication status, emerging risks, etc) was presented. The aforementioned approach is under evaluation through different scenarios with heterogeneous data from multiple sources.

Integration of Distributed Services and Hybrid Models Based on Process Choreography to Predict and Detect Type 2 Diabetes.

Life expectancy is increasing and, so, the years that patients have to live with chronic diseases and co-morbidities. Type 2 diabetes is one of the most prevalent chronic diseases, specifically linked to being overweight and ages over sixty. Recent studies have demonstrated the effectiveness of new strategies to delay and even prevent the onset of type 2 diabetes by a combination of active and healthy lifestyle on cohorts of mid to high risk subjects. Prospective research has been driven on large groups of the population to build risk scores that aim to obtain a rule for the classification of patients according to the odds for developing the disease. Currently, there are more than two hundred models and risk scores for doing this, but a few have been properly evaluated in external groups and integrated into a clinical application for decision support. In this paper, we present a novel system architecture based on service choreography and hybrid modeling, which enables a distributed integration of clinical databases, statistical and mathematical engines and web interfaces to be deployed in a clinical setting. The system was assessed during an eight-week continuous period with eight endocrinologists of a hospital who evaluated up to 8080 patients with seven different type 2 diabetes risk models implemented in two mathematical engines. Throughput was assessed as a matter of technical key performance indicators, confirming the reliability and efficiency of the proposed architecture to integrate hybrid artificial intelligence tools into daily clinical routine to identify high risk subjects.

CrowdHEALTH: Holistic Health Records and Big Data Analytics for Health Policy Making and Personalized Health.

Today's rich digital information environment is characterized by the multitude of data sources providing information that has not yet reached its full potential in eHealth. The aim of the presented approach, namely CrowdHEALTH, is to introduce a new paradigm of Holistic Health Records (HHRs) that include all health determinants. HHRs are transformed into HHRs clusters capturing the clinical, social and human context of population segments and as a result collective knowledge for different factors. The proposed approach also seamlessly integrates big data technologies across the complete data path, providing of Data as a Service (DaaS) to the health ecosystem stakeholders, as well as to policy makers towards a "health in all policies" approach. Cross-domain co-creation of policies is feasible through a rich toolkit, being provided on top of the DaaS, incorporating mechanisms for causal and risk analysis, and for the compilation of predictions.

Evaluating the Social Media Performance of Hospitals in Spain: A Longitudinal and Comparative Study.

BACKGROUND: Social media is changing the way in which citizens and health professionals communicate. Previous studies have assessed the use of Health 2.0 by hospitals, showing clear evidence of growth in recent years. In order to understand if this happens in Spain, it is necessary to assess the performance of health care institutions on the Internet social media using quantitative indicators. OBJECTIVES: The study aimed to analyze how hospitals in Spain perform on the Internet and social media networks by determining quantitative indicators in 3 different dimensions: presence, use, and impact and assess these indicators on the 3 most commonly used social media - Facebook, Twitter, YouTube. Further, we aimed to find out if there was a difference between private and public hospitals in their use of the aforementioned social networks. METHODS: The evolution of presence, use, and impact metrics is studied over the period 2011- 2015. The population studied accounts for all the hospitals listed in the National Hospitals Catalog (NHC). The percentage of hospitals having Facebook, Twitter, and YouTube profiles has been used to show the presence and evolution of hospitals on social media during this time. Usage was assessed by analyzing the content published on each social network. Impact evaluation was measured by analyzing the trend of subscribers for each social network. Statistical analysis was performed using a lognormal transformation and also using a nonparametric distribution, with the aim of comparing t student and Wilcoxon independence tests for the observed variables. RESULTS: From the 787 hospitals identified, 69.9% (550/787) had an institutional webpage and 34.2% (269/787) had at least one profile in one of the social networks (Facebook, Twitter, and YouTube) in December 2015. Hospitals' Internet presence has increased by more than 450.0% (787/172) and social media presence has increased ten times since 2011. Twitter is the preferred social network for public hospitals, whereas private hospitals showed better performance on Facebook and YouTube. The two-sided Wilcoxon test and t student test at a CI of 95% show that the use of Twitter distribution is higher (P<.001) for private and public hospitals in Spain, whereas other variables show a nonsignificant different distribution. CONCLUSIONS: The Internet presence of Spanish hospitals is high; however, their presence on the 3 main social networks is still not as high compared to that of hospitals in the United States and Western Europe. Public hospitals are found to be more active on Twitter, whereas private hospitals show better performance on Facebook and YouTube. This study suggests that hospitals, both public and private, should devote more effort to and be more aware of social media, with a clear strategy as to how they can foment new relationships with patients and citizens.

Lessons learnt from a MOOC about social media for digital health literacy.

Nowadays, the Internet and social media represent prime channels for health information seeking and peer support. However, benefits of health social media can be reduced by low digital health literacy. We designed a massive open online course (MOOC) course about health social media to increase the students' digital health literacy. In this course, we wanted to explore the difficulties confronted by the MOOC users in relation to accessing quality online health information and to propose methods to overcome the issues. An online survey was carried out to assess the students' digital health literacy. This survey was one of the activities for the enrolled learners in an online course entitled "Social Media in Health Care" on "FutureLearn", one of the popular MOOC platforms. The course was hosted by Taipei Medical University, Taiwan. Data from a total of 300 respondents were collected through the online survey from 14 December 2015 to 10 January 2016. Most participants (61%) considered finding online health information is easy or very easy, while 39% were unsure or found it difficult to retrieve online health information. Most (63%) were not sure about judging whether available information can be used for making health decisions. This study indicates a demand for more training to increase skills to improve the capability of health consumers to identify trustworthy, useful health information. More research to understand the health information seeking process will be crucial in identifying the skillsets that need to be further developed. MOOCs about digital health can be a great source of knowledge when it comes to studying patients' needs.

A domain-based approach for retrieving trustworthy health videos from YouTube.

Health information retrieval and YouTube can be used as powerful tools to improve user's health knowledge. However, YouTube videos must be carefully analysed in order to avoid misleading, inaccurate, obsolete and incorrect health content. We present an approach for re-ranking health videos obtained from YouTube, called Domain-based ranking. Our system automatically identifies videos coming from trusted sources (channels), such as hospitals and health organizations, and re-ranks YouTube results so that such videos are presented first in the ranking list. Video and channel metadata are used to automatically determine if a video is provided by a trusted source. The approach is tested and results show that the amount of relevant and reliable videos ranked within top-10 increase when using Domain-based ranking, compared with the original YouTube ranking.

Use of social media by Spanish hospitals: perceptions, difficulties, and success factors.

This exploratory study has two aims: (1) to find out if and how social media (SM) applications are used by hospitals in Spain and (2) to assess hospital managers' perception of these applications in terms of their evaluation of them, reasons for use, success factors, and difficulties encountered during their implementation. A cross-sectional survey has been carried out using Spanish hospitals as the unit of analysis. Geographical differences in the use of SM were found. Social networks are used most often by larger hospitals (30% by medium-size, 28% by large-size). They are also more frequently used by public hospitals (19%, p<0.01) than by private ones. Respondents with a negative perception of SM felt that there is a chance they may be abused by healthcare professionals, whereas those with a positive perception believed that they can be used to improve communication both within and outside the hospital. Reasons for the use of SM include the idea of maximizing exposure of the hospital. The results show that Spanish hospitals are only just beginning to use SM applications and that hospital type can influence their use. The perceptions, reasons for use, success factors, and difficulties encountered during the implementation of SM mean that it is very important for healthcare professionals to use SM correctly and adequately.