A Smart Digital Health Platform to Enable Monitoring of Quality of Life and Frailty in Older Patients with Cancer: A Mixed-Methods, Feasibility Study Protocol.

OBJECTIVES: LifeChamps is an EU Horizon 2020 project that aims to create a digital platform to enable monitoring of health-related quality of life and frailty in patients with cancer over the age of 65. Our primary objective is to assess feasibility, usability, acceptability, fidelity, adherence, and safety parameters when implementing LifeChamps in routine cancer care. Secondary objectives involve evaluating preliminary signals of efficacy and cost-effectiveness indicators. DATA SOURCES: This will be a mixed-methods exploratory project, involving four study sites in Greece, Spain, Sweden, and the United Kingdom. The quantitative component of LifeChamps (single-group, pre-post feasibility study) will integrate digital technologies, home-based motion sensors, self-administered questionnaires, and the electronic health record to (1) enable multimodal, real-world data collection, (2) provide patients with a coaching mobile app interface, and (3) equip healthcare professionals with an interactive, patient-monitoring dashboard. The qualitative component will determine end-user usability and acceptability via end-of-study surveys and interviews. CONCLUSION: The first patient was enrolled in the study in January 2023. Recruitment will be ongoing until the project finishes before the end of 2023. IMPLICATIONS FOR NURSING PRACTICE: LifeChamps provides a comprehensive digital health platform to enable continuous monitoring of frailty indicators and health-related quality of life determinants in geriatric cancer care. Real-world data collection will generate "big data" sets to enable development of predictive algorithms to enable patient risk classification, identification of patients in need for a comprehensive geriatric assessment, and subsequently personalized care.

Machine learning model from a Spanish cohort for prediction of SARS-COV-2 mortality risk and critical patients.

Patients affected by SARS-COV-2 have collapsed healthcare systems around the world. Consequently, different challenges arise regarding the prediction of hospital needs, optimization of resources, diagnostic triage tools and patient evolution, as well as tools that allow us to analyze which are the factors that determine the severity of patients. Currently, it is widely accepted that one of the problems since the pandemic appeared was to detect (i) who patients were about to need Intensive Care Unit (ICU) and (ii) who ones were about not overcome the disease. These critical patients collapsed Hospitals to the point that many surgeries around the world had to be cancelled. Therefore, the aim of this paper is to provide a Machine Learning (ML) model that helps us to prevent when a patient is about to be critical. Although we are in the era of data, regarding the SARS-COV-2 patients, there are currently few tools and solutions that help medical professionals to predict the evolution of patients in order to improve their treatment and the needs of critical resources at hospitals. Moreover, most of these tools have been created from small populations and/or Chinese populations, which carries a high risk of bias. In this paper, we present a model, based on ML techniques, based on 5378 Spanish patients' data from which a quality cohort of 1201 was extracted to train the model. Our model is capable of predicting the probability of death of patients with SARS-COV-2 based on age, sex and comorbidities of the patient. It also allows what-if analysis, with the inclusion of comorbidities that the patient may develop during the SARS-COV-2 infection. For the training of the model, we have followed an agnostic approach. We explored all the active comorbidities during the SARS-COV-2 infection of the patients with the objective that the model weights the effect of each comorbidity on the patient's evolution according to the data available. The model has been validated by using stratified cross-validation with k = 5 to prevent class imbalance. We obtained robust results, presenting a high hit rate, with 84.16% accuracy, 83.33% sensitivity, and an Area Under the Curve (AUC) of 0.871. The main advantage of our model, in addition to its high success rate, is that it can be used with medical records in order to predict their diagnosis, allowing the critical population to be identified in advance. Furthermore, it uses the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD 9-CM) standard. In this sense, we should also emphasize that those hospitals using other encodings can add an intermediate layer business to business (B2B) with the aim of making transformations to the same international format.

Analytical exploratory tool for healthcare professionals to monitor cancer patients' progress.

Introduction: Cancer is a primary public concern in the European continent. Due to the large case numbers and survival rates, a significant population is living with cancer needs. Consequently, health professionals must deal with complex treatment decision-making processes. In this context, a large quantity of data is collected during cancer care delivery. Once collected, these data are complex for health professionals to access to support clinical decision-making and performance review. There is a need for innovative tools that make clinical data more accessible to support cancer health professionals in these activities. Methods: Following a co-creation, an interactive approach thanks to the Interactive Process Mining paradigm, and data from a tertiary hospital, we developed an exploratory tool to present cancer patients' progress over time. Results: This work aims to collect and report the process of developing an exploratory analytical Interactive Process Mining tool with clinical relevance for healthcare professionals for monitoring cancer patients' care processes in the context of the LifeChamps project together with a graphical and navigable Process Indicator in the context of prostate cancer patients. Discussion: The tool presented includes Process Mining techniques to infer actual processes and present understandable results visually and navigable, looking for different types of patients, trajectories, and behaviors.

Artificial Intelligence for Hospital Health Care: Application Cases and Answers to Challenges in European Hospitals.

The development and implementation of artificial intelligence (AI) applications in health care contexts is a concurrent research and management question. Especially for hospitals, the expectations regarding improved efficiency and effectiveness by the introduction of novel AI applications are huge. However, experiences with real-life AI use cases are still scarce. As a first step towards structuring and comparing such experiences, this paper is presenting a comparative approach from nine European hospitals and eleven different use cases with possible application areas and benefits of hospital AI technologies. This is structured as a current review and opinion article from a diverse range of researchers and health care professionals. This contributes to important improvement options also for pandemic crises challenges, e.g., the current COVID-19 situation. The expected advantages as well as challenges regarding data protection, privacy, or human acceptance are reported. Altogether, the diversity of application cases is a core characteristic of AI applications in hospitals, and this requires a specific approach for successful implementation in the health care sector. This can include specialized solutions for hospitals regarding human-computer interaction, data management, and communication in AI implementation projects.

Predicting morbidity by local similarities in multi-scale patient trajectories.

Patient Trajectories (PTs) are a method of representing the temporal evolution of patients. They can include information from different sources and be used in socio-medical or clinical domains. PTs have generally been used to generate and study the most common trajectories in, for instance, the development of a disease. On the other hand, healthcare predictive models generally rely on static snapshots of patient information. Only a few works about prediction in healthcare have been found that use PTs, and therefore benefit from their temporal dimension. All of them, however, have used PTs created from single-source information. Therefore, the use of longitudinal multi-scale data to build PTs and use them to obtain predictions about health conditions is yet to be explored. Our hypothesis is that local similarities on small chunks of PTs can identify similar patients concerning their future morbidities. The objectives of this work are (1) to develop a methodology to identify local similarities between PTs before the occurrence of morbidities to predict these on new query individuals; and (2) to validate this methodology on risk prediction of cardiovascular diseases (CVD) occurrence in patients with diabetes. We have proposed a novel formal definition of PTs based on sequences of longitudinal multi-scale data. Moreover, a dynamic programming methodology to identify local alignments on PTs for predicting future morbidities is proposed. Both the proposed methodology for PT definition and the alignment algorithm are generic to be applied on any clinical domain. We validated this solution for predicting CVD in patients with diabetes and we achieved a precision of 0.33, a recall of 0.72 and a specificity of 0.38. Therefore, the proposed solution in the diabetes use case can result of utmost utility to secondary screening.

Design of 1-year mortality forecast at hospital admission: A machine learning approach.

Palliative care is referred to a set of programs for patients that suffer life-limiting illnesses. These programs aim to maximize the quality of life (QoL) for the last stage of life. They are currently based on clinical evaluation of the risk of 1-year mortality. The main aim of this work is to develop and validate machine-learning-based models to predict the exitus of a patient within the next year using data gathered at hospital admission. Five machine-learning techniques were applied using a retrospective dataset. The evaluation was performed with five metrics computed by a resampling strategy: Accuracy, the area under the ROC curve, Specificity, Sensitivity, and the Balanced Error Rate. All models reported an AUC ROC from 0.857 to 0.91. Specifically, Gradient Boosting Classifier was the best model, producing an AUC ROC of 0.91, a sensitivity of 0.858, a specificity of 0.808, and a BER of 0.1687. Information from standard procedures at hospital admission combined with machine learning techniques produced models with competitive discriminative power. Our models reach the best results reported in the state of the art. These results demonstrate that they can be used as an accurate data-driven palliative care criteria inclusion.

Dynamic Models Supporting Personalised Chronic Disease Management through Healthcare Sensors with Interactive Process Mining.

Rich streams of continuous data are available through Smart Sensors representing a unique opportunity to develop and analyse risk models in healthcare and extract knowledge from data. There is a niche for developing new algorithms, and visualisation and decision support tools to assist health professionals in chronic disease management incorporating data generated through smart sensors in a more precise and personalised manner. However, current understanding of risk models relies on static snapshots of health variables or measures, rather than ongoing and dynamic feedback loops of behaviour, considering changes and different states of patients and diseases. The rationale of this work is to introduce a new method for discovering dynamic risk models for chronic diseases, based on patients' dynamic behaviour provided by health sensors, using Process Mining techniques. Results show the viability of this method, three dynamic models have been discovered for the chronic diseases hypertension, obesity, and diabetes, based on the dynamic behaviour of metabolic risk factors associated. This information would support health professionals to translate a one-fits-all current approach to treatments and care, to a personalised medicine strategy, that fits treatments built on patients' unique behaviour thanks to dynamic risk modelling taking advantage of the amount data generated by smart sensors.

Machine Learning-Based Identification of Obesity from Positive and Unlabelled Electronic Health Records.

INTRODUCTION: Prevalence of overweight and obesity are increas- ing in the last decades, and with them, diseases and health conditions such as diabetes, hypertension or cardiovascular diseases. However, hos- pital databases usually do not record such conditions in adults, neither anthropomorfic measures that facilitate their identification. METHODS: We implemented a machine learning method based on PU (Positive and Unlabelled) Learning to identify obese patients without a diagnose code of obesity in the health records. RESULTS: The algorithm presented a high sensitivity (98%) and predicted that around 18% of the patients without a diagnosis were obese. This result is consistent with the report of the WHO.

Temporal variability analysis reveals biases in electronic health records due to hospital process reengineering interventions over seven years.

OBJECTIVE: To evaluate the effects of Process-Reengineering interventions on the Electronic Health Records (EHR) of a hospital over 7 years. MATERIALS AND METHODS: Temporal Variability Assessment (TVA) based on probabilistic data quality assessment was applied to the historic monthly-batched admission data of Hospital La Fe Valencia, Spain from 2010 to 2016. Routine healthcare data with a complete EHR was expanded by processed variables such as the Charlson Comorbidity Index. RESULTS: Four Process-Reengineering interventions were detected by quantifiable effects on the EHR: (1) the hospital relocation in 2011 involved progressive reduction of admissions during the next four months, (2) the hospital services re-configuration incremented the number of inter-services transfers, (3) the care-services re-distribution led to transfers between facilities (4) the assignment to the hospital of a new area with 80,000 patients in 2015 inspired the discharge to home for follow up and the update of the pre-surgery planned admissions protocol that produced a significant decrease of the patient length of stay. DISCUSSION: TVA provides an indicator of the effect of process re-engineering interventions on healthcare practice. Evaluating the effect of facilities' relocation and increment of citizens (findings 1, 3-4), the impact of strategies (findings 2-3), and gradual changes in protocols (finding 4) may help on the hospital management by optimizing interventions based on their effect on EHRs or on data reuse. CONCLUSIONS: The effects on hospitals EHR due to process re-engineering interventions can be evaluated using the TVA methodology. Being aware of conditioned variations in EHR is of the utmost importance for the reliable reuse of routine hospitalization data.

A Web-Based Telemanagement System for Patients With Complex Inflammatory Bowel Disease: Protocol for a Randomized Controlled Clinical Trial.

BACKGROUND: Telemedicine has been successfully used to provide inflammatory bowel disease (IBD) patients with health care services remotely via the implementation of information and communications technology, which uses safe and feasible apps that have been well accepted by patients in remission. However, the design of telemedicine apps in this setting involves difficulties that hinder the adherence of patients to the follow-up plans and the efficacy of these systems to improve disease activity and quality of life. OBJECTIVE: This study aimed to evaluate the development of a Web platform, Telemonitoring of Crohn Disease and Ulcerative Colitis (TECCU), for remote monitoring of patients with complex IBD and the design of a clinical trial involving IBD patients who received standard care (G_Control), nurse-assisted telephone care (G_NT), or care based on distance monitoring (G_TECCU). METHODS: We describe the development of a remote monitoring system and the difficulties encountered in designing the platform. A 3-arm randomized controlled trial was designed to evaluate the effectiveness of this Web platform in disease management compared with G_NT and G_Control. RESULTS: According to the schedules established for the medical treatment initiated (corticosteroids, immunosuppressants, or biological agents), a total of 63 patients (21 patients from each group) answered periodic questionnaires regarding disease activity, quality of life, therapeutic adherence, adverse effects, satisfaction, work productivity, and social activities. Blood and stool analyses (fecal calprotectin) were performed periodically. On the basis of the results of these tests in G_TECCU, alerts were generated in a Web platform with adapted action plans, including changes in medication and frequency of follow-up. The main issues found were the development of an easy-to-use Web platform, the selection of validated clinical scores and objective biomarkers for remote monitoring, and the design of a clinical trial to compare the 3 main follow-up methods evaluated to date in IBD. CONCLUSIONS: The development of a Web-based remote management program for safe and adequate control of IBD proved challenging. The results of this clinical trial will advance knowledge regarding the effectiveness of TECCU Web platform for improvement of disease activity, quality of life, and use of health care resources in complex IBD patients. TRIAL REGISTRATION: ClinicalTrials.gov NCT02943538; https://clinicaltrials.gov/ct2/show/NCT02943538 (Archived by WebCite at http://www.webcitation.org/6y4DQdmt8). INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR1-10.2196/9639.

A Web-Based Telemanagement System for Improving Disease Activity and Quality of Life in Patients With Complex Inflammatory Bowel Disease: Pilot Randomized Controlled Trial.

BACKGROUND: The reported efficacy of telemedicine in patients with inflammatory bowel disease (IBD) is inconsistent among studies, and data for complex IBD are lacking. OBJECTIVE: We aimed to evaluate the impact of remote monitoring using a Web system-Telemonitorización de la Enfermedad de Crohn y Colitis Ulcerosa or Telemonitoring of Crohn's Disease and Ulcerative Colitis (TECCU)-as compared to standard care and telephone care on health outcomes and health care in patients with complex IBD. METHODS: We performed a 3-arm randomized controlled trial. Adult patients with IBD who received immunosuppressants and biological agents were recruited from the IBD Unit of a tertiary university hospital. The patients were randomized into groups to receive remote monitoring (G_TECCU), nurse-assisted telephone care (G_NT), or standard care with in-person visits (G_control). All patients completed the study visits at baseline and at 12 and 24 weeks in addition to each type of intervention. The primary outcome was the percentage of patients in remission at 24 weeks. Secondary health outcomes were quality of life, medication adherence, adverse effects, satisfaction, and social activities. Data on the number of outpatient visits and telephone calls, emergency visits, hospitalizations, IBD-related surgeries, and corticosteroid courses were also collected. RESULTS: A total of 63 patients were selected (21 patients in each group). During the study, 90.5% (19/21) of patients in G_control, 95.2% (20/21) in G_NT, and 85.7% (18/21) in G_TECCU were compliant to the intervention. After 24 weeks, the percentage of patients in remission was higher in G_TECCU (17/21, 81%) than in G_NT (14/21, 66.7%) and G_control (15/21, 71.4%). A higher improvement in disease activity was observed in G_TECCU than in G_control in terms of the Harvey-Bradshaw/Mayo (odds ratio=0.12, 95% CI=0.003-2.162, P=.19) and Harvey-Bradshaw/Walmsley (odds ratio=0.11, 95% CI=0.004-1.55, P=.13) indexes. Improvement in disease activity was associated with a larger reduction in fecal calprotectin values in G_TECCU compared to G_control (estimated intervention effect: odds ratio=-0.90; 95% CI=-1.96 to 0.16, P=.11). All completers adhered to treatment in G_TECCU. In addition, the quality of life, social activities, and satisfaction improved in all 3 groups. Although the number of outpatient visits and telephone calls was lower in G_TECCU than in G_NT and G_control, the safety profile was similar in all 3 groups. CONCLUSIONS: This pilot clinical trial suggests that the TECCU Web-based system is a safe strategy for improving health outcomes in patients with complex IBD and reducing the use of health care resources. TRIAL REGISTRATION: ClinicalTrials.gov NCT02943538; https://clinicaltrials.gov/ct2/show/NCT02943538 (Archived by WebCite at http://www.webcitation.org/746CRRtDN).

The effect of telehealth, telephone support or usual care on quality of life, mortality and healthcare utilization in elderly high-risk patients with multiple chronic conditions. A prospective study / Telecuidados, apoyo telefónico o cuidados habituales: impacto sobre la calidad de vida, mortalidad y uso de recursos sanitarios en pacientes ancianos de alto riesgo con múltiples condiciones crónicas. Un estudio prospectivo

Background and objective: To assess the effect of home based telehealth or structured telephone support interventions with respect to usual care on quality of life, mortality and healthcare utilization in elderly high-risk multiple chronic condition patients. Patients and methods: 472 elderly high-risk patients with plurimorbidity in the region of Valencia (Spain) were recruited between June 2012 and May 2013, and followed for 12 months from recruitment. Patients were allocated to either: (a) a structured telephone intervention, a nurse-led case management program with telephone follow up every 15 days; (b) telehealth, which adds technology for remote self-management and the exchange of clinical data; or (c) usual care. Main outcome measures was quality of life measured by the EuroQol (EQ-5D) instrument, cognitive impairment, functional status, mortality and healthcare resource use. Inadequate randomization process led us to used propensity scores for adjusted analyses to control for imbalances between groups at baseline. Results: EQ-5D score was significantly higher in the telehealth group compared to usual care (diff: 0.19, 0.08-0.30), but was not different to telephone support (diff: 0.04, −0.05 to 0.14). In adjusted analyses, inclusion in the telehealth group was associated with an additional 0.18 points in the EQ-5D score compared to usual care at 12 months (p<0.001), and with a gain of 0.13 points for the telephone support group (p<0.001). No differences in mortality or utilization were found, except for a borderline significant increase in General Practitioner visits. Conclusions: Telehealth was associated with better quality of life. Important limitations of the study and similarity of effects to telephone intervention call for careful endorsement of telemedicine. Clinicaltrials.gov (identifier: NCT02447562)

Fundamento y objetivo: Evaluar el impacto de un programa de telecuidados domiciliarios o de apoyo telefónico con respecto a cuidados habituales sobre la calidad de vida, la mortalidad y el uso de recursos en ancianos de alto riesgo pluripatológicos. Pacientes y métodos: Se reclutaron 472 pacientes ancianos con plurimorbilidad en la región de Valencia entre junio de 2012 y mayo de 2013 y se les siguió durante 12 meses. Los pacientes fueron asignados a: a) una intervención de apoyo telefónico estructurado, con recordatorios y seguimiento por enfermería cada 15 días; b) telecuidados, que añade tecnología para el automanejo y la transmisión remotos de información clínica; o c) cuidados habituales. Las medidas de resultado fueron calidad de vida medida con el instrumento EuroQoL-5D (EQ-5D), afectación cognitiva, estatus funcional, mortalidad y uso de recursos sanitarios. Debido a fallos en el proceso de aleatorización, se ajustó los análisis mediante propensity scores para controlar las diferencias basales entre grupos. Resultados: La puntuación EQ-5D fue significativamente mayor en el grupo de telecuidados frente a cuidados habituales (dif. 0,19, 0,08 a 0,30), pero no frente a apoyo telefónico (dif. 0,04, −0,05 a 0,14). En análisis ajustados, la inclusión en el grupo de telecuidados se asoció con la obtención de 0,18 puntos adicionales en la escala EQ-5D comparado con cuidados habituales a 12 meses (p<0,001), y con 0,13 puntos en el caso de apoyo telefónico (p<0,001). No se hallaron diferencias en mortalidad o uso de recursos, salvo un incremento marginal en visitas al médico de AP. Conclusiones: Los telecuidados se asociaron con una mayor calidad de vida. Limitaciones importantes del estudio y la similitud de los efectos con la intervención de apoyo telefónico llaman a un apoyo ponderado de las tecnologías e-health. Clinicaltrials.gov (identifier: NCT02447562)

The effect of telehealth, telephone support or usual care on quality of life, mortality and healthcare utilization in elderly high-risk patients with multiple chronic conditions. A prospective study.

BACKGROUND AND OBJECTIVE: To assess the effect of home based telehealth or structured telephone support interventions with respect to usual care on quality of life, mortality and healthcare utilization in elderly high-risk multiple chronic condition patients. PATIENTS AND METHODS: 472 elderly high-risk patients with plurimorbidity in the region of Valencia (Spain) were recruited between June 2012 and May 2013, and followed for 12 months from recruitment. Patients were allocated to either: (a) a structured telephone intervention, a nurse-led case management program with telephone follow up every 15 days; (b) telehealth, which adds technology for remote self-management and the exchange of clinical data; or (c) usual care. Main outcome measures was quality of life measured by the EuroQol (EQ-5D) instrument, cognitive impairment, functional status, mortality and healthcare resource use. Inadequate randomization process led us to used propensity scores for adjusted analyses to control for imbalances between groups at baseline. RESULTS: EQ-5D score was significantly higher in the telehealth group compared to usual care (diff: 0.19, 0.08-0.30), but was not different to telephone support (diff: 0.04, -0.05 to 0.14). In adjusted analyses, inclusion in the telehealth group was associated with an additional 0.18 points in the EQ-5D score compared to usual care at 12 months (p<0.001), and with a gain of 0.13 points for the telephone support group (p<0.001). No differences in mortality or utilization were found, except for a borderline significant increase in General Practitioner visits. CONCLUSIONS: Telehealth was associated with better quality of life. Important limitations of the study and similarity of effects to telephone intervention call for careful endorsement of telemedicine. Clinicaltrials.gov (identifier: NCT02447562).

Telemedicina en el manejo de pacientes con enfermedad inflamatoria intestinal / Telemedicine in the treatment of patients with inflammatory bowel disease

La enfermedad inflamatoria intestinal (EII) es una dolencia crónica y recidivante con un importante impacto sanitario, social y económico. Los pacientes con EII requieren un seguimiento continuo y el uso de recursos sanitarios en este contexto está aumentando progresivamente. En la última década, la telemedicina ha transformado el manejo de enfermedades crónicas como la EII mediante la aplicación de atención sanitaria a distancia a través de las tecnologías de la información y la comunicación. Las diferentes aplicaciones de la telemedicina (telemonitorización, teleconsulta y teleeducación) favorecen un seguimiento más estrecho, además de promover la autonomía del paciente y mejorar el conocimiento de su enfermedad, lo que permite optimizar el tratamiento en cada momento evolutivo de la enfermedad. En esta revisión se describe el impacto de las aplicaciones de la telemedicina sobre los resultados en salud de los pacientes con EII, así como las limitaciones para su implantación en la vida real (AU)

Inflammatory bowel disease (IBD) is a chronic and relapsing disorder with significant medical, social and financial impacts. IBD patients require continuous follow-up, and healthcare resource use in this context increases over time. In the last decade, telemedicine has influenced the treatment of chronic diseases like IBD via the application of information and communication technologies to provide healthcare services remotely. Telemedicine and its various applications (telemanagement, teleconsulting and tele-education) enable closer follow-up and provide education resources that promote patient empowerment, encouraging treatment optimisation over the entire course of the disease. We describe the impact of using telemedicine on IBD health outcomes and discuss the limitations of implementing these systems in the real-life management of IBD patients (AU)

Telemedicine in the treatment of patients with inflammatory bowel disease. / Telemedicina en el manejo de pacientes con enfermedad inflamatoria intestinal.

Inflammatory bowel disease (IBD) is a chronic and relapsing disorder with significant medical, social and financial impacts. IBD patients require continuous follow-up, and healthcare resource use in this context increases over time. In the last decade, telemedicine has influenced the treatment of chronic diseases like IBD via the application of information and communication technologies to provide healthcare services remotely. Telemedicine and its various applications (telemanagement, teleconsulting and tele-education) enable closer follow-up and provide education resources that promote patient empowerment, encouraging treatment optimisation over the entire course of the disease. We describe the impact of using telemedicine on IBD health outcomes and discuss the limitations of implementing these systems in the real-life management of IBD patients.

Using process mining for automatic support of clinical pathways design.

The creation of tools supporting the automatization of the standardization and continuous control of healthcare processes can become a significant helping tool for clinical experts and healthcare systems willing to reduce variability in clinical practice. The reduction in the complexity of design and deployment of standard Clinical Pathways can enhance the possibilities for effective usage of computer assisted guidance systems for professionals and assure the quality of the provided care. Several technologies have been used in the past for trying to support these activities but they have not been able to generate the disruptive change required to foster the general adoption of standardization in this domain due to the high volume of work, resources, and knowledge required to adequately create practical protocols that can be used in practice. This chapter proposes the use of the PALIA algorithm, based in Activity-Based process mining techniques, as a new technology to infer the actual processes from the real execution logs to be used in the design and quality control of healthcare processes.

Telemedicine in inflammatory bowel disease: opportunities and approaches.

This review article summarizes the evidence about telemedicine applications (e.g., telemonitoring, teleconsulting, and tele-education) in the management of patients with inflammatory bowel disease (IBD), and we aim to give an overview of the acceptance and impact of these interventions on health outcomes. Based on the literature search on "inflammatory bowel disease," "Crohn's disease" and "ulcerative colitis" in combination with "e-health," "telemedicine," and "telemanagement," we selected 58 titles and abstracts published up to June 2014 and searched in PubMed, EMBASE, MEDLINE, Cochrane Database, Web of Science and Conference Proceedings. Titles and abstracts were screened for a set of inclusion criteria: e-health intervention, IBD as the main disease, and a primary study performed. Finally, 16 were included for full reading, data extraction, and critical appraisal of the evaluation. Most studies use telemonitoring (home telemanagement system or web portal) and telecare (real-time telephone and image) as telemedicine applications and assessed the feasibility and acceptance of these systems, adherence to treatment, quality of life, and patient knowledge, particularly in patients with ulcerative colitis. Furthermore, some of these studies evaluated the patients' empowerment, health care costs, and safety of telemonitoring in IBD. In conclusion, the health outcomes of telemedicine applications in IBD suggest that these could be implemented in clinical practice because they are safe and feasible applications that are well accepted by the patient and improve adherence, quality of life, and disease knowledge. Further studies with large sample sizes and complex diseases are needed to confirm these results.

Accessing to electronic medical history using a mobility intra hospital system.

The aim of this paper is to describe the solution that has been developed in Valencia Region (Spain) to provide health professionals (physicians and nurses) access to all the functionalities of a Hospital Information System (HIS) already available at fixed clients workstations. These functionalities are adapted to the care process carried out at patient bedside. In this way, professionals will have access to treatment and administration, recording of vital signs, nursing assessment, scales, care plan, extractions, medical records, progress notes so that they have all necessary information at the bedside, and record swiftly changes that occur in-situ. In addition, clinical safety is reinforced, including RFID patient identification mechanisms and barcode readers for blood samples or unidosis medication.

e_Disease Management. A system for the management of the chronic conditions.

Disease Management (DM) is a system of coordinated healthcare intervention and communications for populations with conditions in which patient self-care efforts are significant. e-DM makes reference to processes of DM based on clinical guidelines sustained in the scientific medical evidence and supported by the intervention of Information and Telecommunication Technology (ICT) in all levels where these plans are developed. This paper discusses the design and implementation of a e-DM system which meets the requirements for the integrated chronic disease management following the recommendations of the Disease Management Association and the American Heart Association.