Why do we need to do Health data analytics?
We can use predictive analytics to leverage seemingly unrelated data to predict who is most susceptible to birth complications or chronic diseases or where and when a virulent outbreak is most likely to occur. With this information, public health officials should be able to respond before the issue manifests itself – providing the right prenatal treatments to mitigate birth complications, identifying those most likely to be exposed to lead or finding food establishments most at risk for violations. With this information, data becomes actionable. Big data and Predictive analytics has the potential to transform both how government operates and how resources are allocated, thereby improving the public’s health.
The Ways to do Health data analytics
Health care organizations are looking for more efficient and sophisticated means of collecting, managing and analyzing data and delivering medical information to physicians, clinicians and nurses. Through the application of technology, data analytics and health informatics practitioners help drive data-informed health care decisions. Professionals with a background in health informatics can develop analytical roadmaps and help others choose the right health informatics tools. Below are five examples of tools that are useful for data analytics and informatics in health care.
Machine Learning
Predictive analytics and data processing are becoming more commonplace across many industries, including health care. This trend is helping to lower the cost of the technology infrastructure, which, in turn, is creating opportunities for the application of machine learning in health informatics.The use of machine learning in imaging and diagnostics applications helps physicians determine treatments for patients and improve patient outcomes. Also, health systems are leveraging machine learning to find patterns in data to improve care pathways.
Database Management
Before the advent of EHRs, doctors’ offices were filled with rows of filing cabinets and boxes with patient files. But even as these files have become digitized, the data is often not integrated across databases, making the process of drawing insights a difficult challenge to overcome. There are plenty of opportunities for innovation and collaboration in database management. For example, open-source options such as Open Database Connectivity (ODBC), an application programming interface (API) that facilitates connections between databases, can be used to process complex health data across platforms. Health care information managers are trained to design and manage these and other database solutions. They can also support data governance and information governance to ensure data is accurate and available to physicians.
Cloud Computing
Building a reliable IT infrastructure to store collected data and enable fast and accurate processing can come at great expense. On-premise IT databases offer value and control, but health care organizations are increasingly looking for alternatives to more efficiently manage their resources. Cloud computing provides health care organizations with savings opportunities by eliminating the costs of on-premise deployments. And because cloud computing is virtual, it takes up less space.
Cloud computing enables health care organizations to keep their technology updated without investing resources in physical assets. This offers the additional benefit of scalability, allowing health care organizations to upgrade their systems to support expanded data analytics capabilities.
Predictive Analytics
Predictive analytics can strengthen current efforts to lower health care costs and improve the quality of care. Technology that enables predictive analytics typically has data-retrieval capabilities; it can extract data from sources such as EHRs, medical equipment and devices, and wearable technologies. This kind of technology also often facilitates data cleaning and risk calculation. Key steps for implementing predictive analytics for informatics in health care include the development and validation of predictive models.
The successful implementation of predictive analytics into clinical practice requires planning and collaboration from health care executives, physicians, nurses, clinicians, policy makers and patients. Health care professionals with knowledge of informatics in health care can provide leadership in efforts to leverage predictive analytics.
Data Visualization
Visual tools, such as infographics, charts and graphs, can help transform data into stories. And as data continues to grow in volume and complexity, data visualization will increasingly become more relevant in data analytics and informatics in health care. Data visualization in health care is gaining widespread adoption.
Drive Data-Informed Decisions in Health Care
Data analytics and informatics in health care are helping advance care and improve patient outcomes. An increased focus on best practices and technology platforms that collect, process and analyze data are critical to today’s health care industry, creating new opportunities for leaders with knowledge in data analytics and health informatics.
Future of Data Analytics in Healthcare
The U.S. Bureau of Labor Statistics projects the demand for health information technicians to increase by 13% between 2016 and 2026, whereas the projected average growth for all occupations is only 7%. Learn more about how Data Analytics transforms Public Health by contacting Hippalus.
Hippalus Technologies | Public Health Data Analytics
