Health informatics systems are evolving with an aim to revolutionize health and healthcare programs worldwide. However, turning this hope-filled vision into a reality will take enormous effort from thousands of designers, analysts, software engineers, usability specialists, and medical professionals. Albeit challenging, the role of information visualization and visual analytics processes is gaining importance. These algorithms, interactive designs, and analytic processes support exploration, monitoring, insight discovery, professional collaboration, and comprehensible presentations to patients, clinicians, policy makers, and the general public.

Data visualization is the art of representing data in a pictorial or graphical format. Analyzing patterns and trends from large data sets can be a herculean process. Data visualization helps in simplifying this process and allows decision-makers to derive analytical results from information presented visually. Patterns, trends and correlations that might go undetected in text-based data can be exposed and recognised easier with data visualization.

Critical building blocks of healthcare analytics are data analytics and visual analytics. Both consist of systematic approaches, techniques and tools, clinical and business resources, used to interpret and analyze high volumes of dynamic patient data, clinical records, financial and other business data, and present findings to various audiences from the clinicians, hospital administration and management, to government and the public. Visual analysis refers to “the science of analytical reasoning facilitated by interactive visual interfaces”. Healthcare providers increasingly explore various visual and interactive techniques in generating and examining large graphs and charts, interactive visualizations, and 2D/3D visualization of discrete event simulation (DES) to understand complex and large datasets, identify and connections and trends, model and simulate healthcare events, and communicate and interpret the findings. Expected outcomes include more efficient and effective clinical performance monitoring and improvement, patient flow modeling and management, better patient care quality, safety and efficiency, better support for clinical costing and resource coordination, better planned growth and competitive advantage.

Conventional visualization methods often require significant processing time, which limits high-throughput analysis. Interactive visualization systems maintain a closed loop between the user and the system and, thus, need to be very fast. Building such a system requires the development of new visualization methods; and there exists the need to design new and effective interaction techniques which are being developed by researchers. One of such techniques is ‘R’ that is being extensively used for statistical computing and graphics. Informatics for Integrating Biology and the Bedside (i2b2), an initiative sponsored by the NIH Roadmap National Centers for Biomedical Computing is another such program that provides a query tool that supplies aggregate counts and basic analyses of patient populations from clinical data warehouses (CDWs). i2b2 (i2b2 to Tableau) is effective at estimating patient cohort sizes and has an extendable architecture where plug-ins with additional features can be developed. Similarly, Hadoop is an open source program; which, according to researchers, is the most significant data processing platform for big data analytics in healthcare. It supports the processing and storage of extremely large data sets in a distributed computing environment, and will open up entire new research domains for discovery. To add to this list is Python, which is yet another solid and open scientific computing and visualization framework. Python is often known as the Swiss Army knife of programming languages–it can, among many things, support machine learning, web development, web scraping, desktop applications. While R is more narrowly focused on statistics compared to Python, it is also great at several things. First, it offers well-documented algorithms and tools for whatever you want to do in statistics. Second, it has fantastic visualization software (better than Python, it could be argued), and thirdly it is great at professional document generation for both reports and data education. Furthermore, recent developments in the R language, and in particular the Shiny package for R, have allowed R programmers to interactively show the output for R programs to Web browsers. The R and Shiny package reactively updates output in response to changing input by means of widgets (such as sliders and radio buttons) but owing to the flexible R language and combination with extension packages, the pharmacometrician has control in coding all elements of a population model, the appearance of the application’s user interface, and generated output.

Today, data visualization solutions can be found everywhere in healthcare systems from hospital operations monitoring and patient profiling to demand projection and capacity planning. However, recent technological developments have made it easier to work with data than it has ever been in the past in case of business intelligence as well.Business Intelligence (BI) can be described as “a set of techniques and tools for the acquisition and transformation of raw data into meaningful and useful information for business analysis purpose”.

Now, it’s not necessary to work with complex data sets that take a long time to sort and make sense of. Contemporary data dashboards allow users to access data in a visual manner that allows for faster and more accurate business decisions to be made. The payoff from joining the big-data and advanced-analytics management revolution is no longer in doubt.

India also is witnessing healthcare analytics being rapidly adopted. Data coming from variety of sources are being recorded in different forms like patient records, patient satisfaction surveys, patient complaint registers, quality of care assessments, etc. Researchers in India feel that effective analysis of these large amounts of organizational data can lead to better decision making.

Additionally, social media are opening remarkable possibilities for health and healthcare researchers. For the first time in history much of what we do is recorded online, and for the first time in history we have the tools to capture, analyze, and visualize these data. Moreover, health informatics databases and networks have amplified benefits with information visualization as it dramatically expands the capacity of patients, clinicians, and public health policy makers to make better decisions.

To put them together in one sentence, “Visualizing business data (data visualization), the right way helps to find meaningful patterns in data (analytics) to analyze the business and make decisions (business intelligence) accordingly”.

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One Response to How Data Visualization is Helping in Healthcare Decision Making?
  1. Thanks for sharing the great blog. I agree Data visualization techniques are widely applied in all kinds of organizations, turning tables of numbers into visualizations for discovery, information communication, and knowledge sharing. Data visualization solutions can be found everywhere in healthcare systems from hospital operations monitoring and patient profiling to demand projection and capacity planning. Healthcare visualizations are similar to today’s popular infographics except the data they show is specific to healthcare. With their easy-to-understand graphical representation of complex data, healthcare visualizations can be used by anyone involved in healthcare improvements. In specific, their format can be used as a spark to help the leaders of health systems move from a passive understanding of the data to active support of data-driven quality improvement recommendations.


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