An All-in-One mHealth Application: #Beats – Your health mate

Màster en Gestió de Continguts Digitals, Facultat d'Informació i Mitjans Audiovisuals, Universitat de Barcelona i UPF, curs 2019-2020. Tutor: Dr. Cristóbal Urbano. UBBy exploring the current situation of the mHealth market in Spain, and the feasibility of the open-source framework, this article looks forward to developing an all-in-one mHealth application with the concept of Mini Programs/ Instant App. It can integrate the healthcare resources and provide users with more experience of instant services without a complicated installation process. It also strengthens the protection of personal information and privacy. In the meanwhile, by applying the methodology of Rapid Prototyping, a user interface of this app, Beats, will be presented to visualize the above concepts. It may be a revolution for medical providers, doctor-patient relationships, public health care systems, and even the entire healthcare system

Challenges in medical visualization: An interactive approach to explore the effect of 3-D technology on the visualization of pain

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Pain experienced as a result of a disabling medical condition is a frequent problem in the clinical community and can often be present in any individual with this kind of health concern. Such pain is typically characterized by severe implications reflected on both a person‘s personal life, as well as on a country‘s health and economic systems. Research on pain has revealed that patients not only experience several types of pain that could prove to be challenging to address, but also that each individual can interpret the same type, location and severity of this pain in different subjective ways, making the need for more effective pain measurement methods an imperative and troublesome effort. In retrospect, the healthcare field is currently trying to enhance the available medical methods with alternatives that would be more efficient in providing accurate pain assessment. Most efforts revolve around traditional methods of measuring pain characteristics, which typically involve the 2-Dimensional (2-D) representation of the human body, often used to collect information regarding the type and location of pain. However, these 2-D pain drawings can be limited in their ability to efficiently visualize pain characteristics for diagnosis purposes. Nonetheless, patients have been shown to prefer such drawings. This research develops an alternative interactive software solution to help in addressing the aforementioned situation, by employing the capabilities that advancements in 3-Dimension (3-D) technology offer. Subsequently, in the anticipation that limitations of current 2-D pain visualization will be solved, the developed approach facilitates the measurement of pain experiences via a 3-D visualization model of the patient. To ensure that it can effectively perform in real-world medical practice, the 3-D pain drawing is evaluated in this research through real-life case studies that are carried out in designated settings. The research findings have shown that the developed approach can potentially make significant contributions to society, science/technology and healthcare provision, with patients and clinicians suggesting that 3-D technology can be a promising means in the pursuit for more effective pain measurement solutions.Brunel University, Department of Information Systems and Computing (DISC

Doctor of Philosophy

dissertationClinical decision support systems (CDSS) and electronic health records (EHR) have been widely adopted but do not support a high level of reasoning for the clinician. As a result, workflow incongruity and provider frustrations lead to more errors in reasoning. Other successful fields such as defense, aviation, and the military have used task complexity as a key factor in decision support system development. Task complexity arises during the interaction of the user and the tasks. Therefore, in this dissertation I have utilized different human factor methods to explore task complexity factors to understand their utility in health information technology system design. The first study addresses the question of generalizing complexity through a clinical complexity model. In this study, we integrated and validated a patient and task complexity model into a clinical complexity model tailored towards healthcare to serve as the initial framework for data analysis in our subsequent studies. The second study addresses the question of the coping strategies of infectious disease (ID) clinicians while dealing with complex decision tasks. The study concluded that clinicians use multiple cognitive strategies that help them to switch between automatic cognitive processes and analytical processes. The third study identified the complexity contributing factors from the transcripts of the observations conducted in the ID domain. The clinical complexity model developed in the first study guided the research for identifying the prominent complexity iv factors to recommend innovative healthcare technology system design. The fourth study, a pilot exploratory study, demonstrated the feasibility of developing a population information display from querying real complex patient information from an actual clinical database as well as identifying the ideal features of population information display. In summary, this dissertation adds to the knowledge about how clinicians adapt their information environment to deal with complexity. First, it contributes by developing a clinical complexity model that integrates both patient and task complexity. Second, it provides specific design recommendations for future innovative health information technology systems. Last, this dissertation also suggests that understanding task complexity in the healthcare team domain may help to better design of interface system

Clinical protocols enabling evidence based medicine practice in healthcare software solutions

Estágio realizado na ALERT Life Sciences Computing, S. A.Tese de mestrado integrado. Engenharia Informática e Computação. Faculdade de Engenharia. Universidade do Porto. 200

Enhanced Depression Screening in the Cardiac Rehabilitation Setting An Evidence-Based Project

Depression is common amongst patients with cardiovascular disease. This evidence-based project was designed to evaluate the impact of a practice improvement intervention for screening and referrals in cardiac patients with depression. The American Heart Association Advisory Council recommends depression screening, treatment, and support to provide adequate diagnosis, treatment, and follow-up to patients. Cardiacrehabilitation (CR) is a coordinated effort of structured treatments with physical, mental, and social components, to favorably influence cardiac healing and resume optimal functioning. The problem is current depression screening and referrals are not being documented electronically in the cardiac rehabilitation (CR) setting limiting real-time communication amongst the healthcare team. Descriptive data (number of patients screened, positive screens documented, and number of appropriate referrals completed electronically) will be collected and evaluated. This project successfully implemented a new electronic Patient Health Questionnaire 2-9 (PHQ2-9) depression and referral screening document flowsheet. A 30-day post intervention chart audit revealed a 97% documentation improvement rate for PHQ2-9 surveys. 11% of the patients screened positive for moderate depression. 27% of those patients received a documented referral. Improving the current charting system to an electronic charting system should enhance communication between members of cardiac multidisciplinary team to improve patient care. Further research and staff education need to occur to align patients with moderate to severe depression with a referral. The overall goal of this project was to provide the best evidence for practice improvement in depression care for patients with cardiac disease

Structured and unstructured data integration with electronic medical records

In recent years there has been a great population and technological evolution all over the world. At the same time, more areas beyond technology and information technology have also developed, namely medicine, which has led to an increase in average life expectancy which in turn, leads to a greater need for healthcare. In order to provide the best possible treatments and healthcare services, nowadays the hospitals store large amounts of data regarding patients and diseases (in the form of electronic medical records) or the logistics of some departments in their storage systems. Therefore, computer science techniques such as data mining and natural language processing have been used to extract knowledge and value from these information-rich sources in order not only to develop, for example, new models for disease prediction, as well as improving existing processes in healthcare centres and hospitals. This data storage can be done in one of three ways: structured, unstructured or semi-structured. In this paper, the author tested the integration of structured and unstructured data from two different departments of the same Portuguese hospital, in order to extract knowledge and improve hospital processes. Aiming to reduce the value loss of loading data that is not used in the healthcare providers systems.Nos últimos anos tem-se assistido a uma grande evolução populacional e tecnológica por todo o mundo. Paralelamente, mais áreas para além da tecnologia e informática têm-se também desenvolvido, nomeadamente a área da medicina, o que tem permitido um aumento na esperança média de vida que por sua vez leva a uma maior necessidade de cuidados de saúde. Com o intuito de fornecer os melhores serviços de saúde possíveis, nos dias que hoje os hospitais guardam nos seus sistemas informáticos grandes quantidades de dados relativamente aos pacientes e doenças (sobre a forma de registos médicos eletrónicos) ou relativos à logística de alguns departamentos dos hospitais, etc. Por conseguinte, a estes dados têm vindo a ser utilizadas técnicas da área das ciências da computação como o data mining e o processamento da língua natural para extrair conhecimento e valor dessas fontes ricas em informação com o intuito não só de desenvolver, por exemplo, novos modelos de predição de doenças, como também de melhorar processos já existentes em centros de saúde e hospitais. Este armazenamento de dados pode ser feito em uma de três formas: de forma estruturada, não estruturada ou semi-estruturada. Neste trabalho o autor testou a integração de dados estruturados e não estruturados de dois departamentos diferentes do mesmo hospital português, com o intuito de extrair conhecimento e melhorar os processos do hospital. Com o intuito de reduzir a perda do armazenamento de dados que não são utilizados

Review of software for space-time disease surveillance

Disease surveillance makes use of information technology at almost every stage of the process, from data collection and collation, through to analysis and dissemination. Automated data collection systems enable near-real time analysis of incoming data. This context places a heavy burden on software used for space-time surveillance. In this paper, we review software programs capable of space-time disease surveillance analysis, and outline some of their salient features, shortcomings, and usability. Programs with space-time methods were selected for inclusion, limiting our review to ClusterSeer, SaTScan, GeoSurveillance and the Surveillance package for R. We structure the review around stages of analysis: preprocessing, analysis, technical issues, and output. Simulated data were used to review each of the software packages. SaTScan was found to be the best equipped package for use in an automated surveillance system. ClusterSeer is more suited to data exploration, and learning about the different methods of statistical surveillance

Multi-sensor Framework for Heart Rate and Blood Oxygen Saturation Monitoring of Human Body

Cardiovascular diseases have been the cause of death for millions of people. Some of these deaths could be avoided if there was a signi cant increase of diagnosis for the detection of such diseases. This diagnosis, in turn, could be realized with the increased availability of robust and low-cost medical diagnostic devices. Integrated technology sensors available on wearable devices have been commonly used to read physiological data in users (patients). Particularly the pulse oximetry sensors, o ers a unique, non-invasive method that can be used to detect the severity of such diseases. This evaluation of the physical condition of the patient for certain diseases is possible due to non-invasive measurement through photoplethysmography, which allows the extraction of heart rate and oxygen saturation in the blood. Since some diseases diagnoses require simultaneous monitoring of blood oxygen saturation values at various sites in the body, a project has been developed to perform such reading of physiological data. This thesis presents the development of a systems platform based on the use of multiple pulse oximetry sensors connected to an application developed for a mobile device though a wireless connection. The purpose of this platform is to provide an easy-to-read experience of health data that can be analyzed to diagnose cardiovascular disease symptoms, aiding in an early diagnosis. The complete structure as well as the aspects of the analysis and implementation of the systems related to the proposed architecture are described in this dissertation

Improving Routine Human Immunodeficiency Virus Screening in a Primary Care Setting

According to the Centers for Disease Control and Prevention, in 2017, over 38,700 people receive an human immunodeficiency virus (HIV) diagnosis in the US. The United States Preventive Services Task Force (USPSTF) published recommendations in 2013 for routine HIV screening of patients ages 15 to 65 years old. Primary care providers who offer routine HIV screening can identify patients with a positive result and promptly connect them to care to decrease transmission of HIV. This process improvement project targeted health care providers and staff, using evidence-based interventions, 2013 USPSTF recommendations, and the Chronic Care Model, to improve HIV screening at a primary care site. Information sessions were held with health care providers and staff pre- and post-intervention. Participants were given a pre-survey (n=28) and post-survey (n=25) questionnaires, information on the electronic medical record screening reminder and educational materials about routine HIV screening. Monthly visits were made to the clinic by the primary investigator who conducted semi-structured interviews with participants. A retrospective chart review evaluated HIV screening data during the months of September, October, and November for 2017, (baseline year), compared to September - November 2018, intervention months. The pre- and post-intervention surveys were confidential and paired by the number assigned to each provider participant (n=6). The results were analyzed using descriptive statistics and paired t-tests to determine if perspectives on HIV screening changed from pre- to post-survey. There were no statistically significant findings from the survey questionnaire results, however, the mean Likert scores improved in the post-survey in most topics. Twenty-five percent of encounters during the 2017 baseline months and 2018 intervention months had an HIV test ordered. During the 2018 intervention year, September had a 3.5% increase and October had a 1.0% increase in percentage of tests ordered when compared to 2017; however, November 2018 had a 5.8% decrease from November 2017. This project piloted interventions to increase provider and clinic staff’s knowledge on routine HIV screening practices to help further reduce HIV transmissions among patients with an unknown serostatus. Further work is needed to identify ways to improve screening rates, such as clinic staff-initiated screening and rapid screening.Doctor of Nursing Practic

Usability analysis of contending electronic health record systems

In this paper, we report measured usability of two leading EHR systems during procurement. A total of 18 users participated in paired-usability testing of three scenarios: ordering and managing medications by an outpatient physician, medicine administration by an inpatient nurse and scheduling of appointments by nursing staff. Data for audio, screen capture, satisfaction rating, task success and errors made was collected during testing. We found a clear difference between the systems for percentage of successfully completed tasks, two different satisfaction measures and perceived learnability when looking at the results over all scenarios. We conclude that usability should be evaluated during procurement and the difference in usability between systems could be revealed even with fewer measures than were used in our study. © 2019 American Psychological Association Inc. All rights reserved.Peer reviewe