Data Analytics and Informatics in Health Care: 5 Tools Professionals Should Know
Health informatics is “a science that defines how health information is technically captured, transmitted and utilized,” according to the American Health Information Management Association (AHIMA). There are a wide range of tools for data analytics and informatics in health care, with clinical and operational applications to help organizations capture health data for advancing medical care.
But there are challenges. Health data is collected from a variety of systems and devices, such as online patient portals, electronic medical records, glucometers, health tracking devices, diagnostic systems and genomics. As a result, data exists in different formats, from clinical notes to medical images such as CT scans, and at times, the data is unstructured.
How can health care organizations make the most use of this data? In clinical settings, health informatics tools provide clinicians with key insights to make informed decisions on the delivery of care and create individualized treatment plans. Health informatics offers operational and managerial benefits as well, such as helping nursing, clinical and operational teams improve time management and resource management in hospitals.
There is a growing demand for professionals with knowledge of health informatics to help address the challenges of working with data and close the gaps often found between technology and processes. These professionals include directors of clinical operations, clinical data analysts, nurse informaticists, pharmacy informatics specialists, EMR trainers and health care application analysts.
For those interested in pursuing a career in health informatics, an advanced education can help put them on the path to success. For example, the University of Illinois at Chicago’s Master of Science in Health Informatics program equips students with the technical knowledge and leadership skills to develop and implement health informatics strategies to use data to advance care.
Going Beyond Electronic Health Records
Health care institutions and hospitals have amassed volumes of data collected by electronic health records (EHR) systems. This is largely because the passage of the Health Information Technology for Economic and Clinical Health Act (HITECH) — which became law as part of the larger American Recovery and Reinvestment Act (ARRA) of 2009 — incentivized the use of EHRs. The data collected by these systems provides new opportunities to enhance patient care.
In turn, 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.
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.
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. As a Brookings Institute report explains, “Health care data is split among different entities and have different formats such that building an insightful, granular database is next to impossible.”
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.
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 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. According to a research article published in Health Affairs, “The use of predictive modeling for real-time clinical decision making is increasingly recognized as a way to achieve the Triple Aim of improving outcomes, enhancing patients’ experiences and reducing health care costs.”
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.
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. In one example, the National Center for Health Statistics presents dashboards with data on a wide range of health-related subjects, from leading causes of death to teen birth rates. In another example, the Robert Wood Johnson Foundation and the University of Michigan Center for Health Communications Research share health care risk information through data visualizations. This tool also helps reveal patterns hidden in large volumes of raw data.
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.
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 a Master of Science in Health Informatics from the University of Illinois at Chicago can get you started down the path to becoming a leader in this promising and growing field.