Healthcare Data Analytics

Healthcare Data Analytics is the discipline dedicated to integrate knowledge from Statistics, Computer Science and Health Sciences to create insights from data. Data analytics has emerged as a promising tool for solving problems in various healthcare related disciplines as: diagnostics, preventive care, health monitoring, care offer, population clustering and policy making.

It implies the use of programming skills to apply artificial intelligence methods to solve healthcare problems in a innovative way. M.A.T.H consortium team had a solid expertise in applying analytics techniques oriented towards the solution of real world problems.

Geographic Information Systems – GIS

Health Geographics covers a wide range of interdisciplinary geospatial topics in health/healthcare context. Geospatial information systems are powerful tools capable to explain why health phenomenons occur in specific locations. The use of GIS techniques to approach health issues leverage the possibilities to identify early onset of diseases, as well as social determinants that could influence health outcomes. Spatial epidemiology, spatio-temporal statistics, and even cyberspace mapping are examples of GIS use in health.

The application of GIS solutions in health can contribute to better decision making, once it helps managers to identify key predictive elements, avoiding of loss of financial resources and improving results. The integration of GIS capabilities with machine learning and Internet of Things solutions can increase the benefits of each of these tools, creating of an environment of continuous monitoring based on sentinel events.

The joint capabilities of GIS and Healthcare data analytics allow the development of solutions capable to perform epidemic surveillance, the design of spatial predictive modelling of diseases, fostering decision systems based on evidences.

Mobile Health (mHealth)

The large use of smart wearable devices is a tendency that will be consolidated in the next years. mHealth refers to the provision of health services remotely, built on technologies such as the mobile internet, smart wearable devices, electronic medical records, telehealth, cloud computing and smartphones. The impact of solutions related to the Internet of Things in healthcare will be decisive producing changes in healthcare offer. To handle the challenges linked to dissemination of mhealth solutions will demand a complex set of skills like: Big Data, Machine Learning, Data Visualization, Cloud computing and Analytical skills. The mastering of this set of knowledge is important but an inability to apply this technologies to real world problems may undermine efforts to solutions proposition. Aware of these challenges the MATH consortium aim to approach real world problems, applying leading edge technologies to produce innovative solutions.

MATH consortium team had expertise conducting studies about mhealth solutions as: impact assessment, health care monitoring, dashboards of key performance indicators and future trends related to dissemination of mhealth solutions. Our capability to integrate different solutions to deliver results create conditions to bring together the academic knowledge and real world problems.

Healthcare performance assessment

Healthcare quality monitoring is essential to promote a debate about potential improvements related to care offer process. The MATH consortium count with brazilian experts with large expertise in conducting evaluation studies oriented to health care assessment. Brazilian health information systems contain data about multiples aspects related to physical structure, work process, procedures performed, payment reward systems for all three levels of health care: primary care, ambulatory care and high complexity attention.

The availability of information allow the design of primary studies considering the following methodological approaches: population based, epidemiological surveillance, health care impact, comorbidities, cost effectiveness, user satisfaction and many others. During the next years will be available data from Electronic Health Records from all users of Public health System (SUS) opening new possibilities in terms of methodological designs and follow-up. Math consortium team are well familiar with those health systems, allowing the generation of evidences capable to support health policy discussion.

Measurement and modeling

Our team has researchers with a wide experience on developing, adapting and evaluating measurement tools and techniques. We actively work on building research capacity for different contexts by developing specific cross-cultural and population parameters for patient reported outcomes and other subjective measures. Our goal is also to increment the measurement process by advocating for the use of technology in mobile technology for subjective evaluation, computerized adaptive testings, dynamic testings and stealth assessments. Methodologically, we use cutting edge approaches to model measurement data such as: latent variable modeling, network analysis and item response theory.

Text Mining

Much of the information produced currently is in the text format, as we can see in social media posts, health records or video postings transcriptions. As such, the ability to extract good quality information from text in a reproducible format is essential for the application of analytics for health. Text mining is common name to englobe techniques to extract information from text data such as word frequency, associations, dispositions, taxonomy, clusterings or classification and apply it, for example, to predictive analytics, health monitoring or behavioral change. Several applications of these methodological approach have been proved effective in patient diagnostics, disease epidemiology surveillance, mental health assessment and other applications.
Implementation and Policy

Qualitative research

The main objective of qualitative study is to interpret an observed phenomenon, in this type of study there are no pré-conceived hypotheses, therefore they will be built after the observation. There are different ways of about conducting qualitative studies, like: ethnography, naturalistic, phenomenology , structured or semi-structured interviews. We have experienced qualitative researcher in out teams to support conducting studies that will provide information for the better development of analytical approaches as well as explore deeper understandings of specific fields in health research.