Healthcare providers and lawmakers are daily faced with the task of making the best possible decisions for patients and the healthcare industry. From choosing the best treatments, determining the most effective ways to unitize resources, or keeping up with physical technology, leaders are making crucial decisions every day that impact health outcomes and costs. With all of this information to consider, it’s no wonder that big data analytics tools have played an increasingly significant role in healthcare decision-making.
So what is healthcare data analytics? Data analytics shine a light on important healthcare data like trends in patient care and improve the patient experience by providing more insight to health care providers and lawmakers. The analytics that can result from this have significant potential to drive and improve value-based care for patients, the use of assets, the utilization of resources, and ultimately, affect revenue. Let’s dive deeper into what data analytics is and how it is the future in healthcare.
Importance of data analytics in healthcare
Data analytics can provide patient health tracking to predict problems and crises before they become severe. Identifying patient health problems before they become severe can be an important step for healthcare organizations to become even more productive and effective. More than that, though, when healthcare providers can better understand the needs of their patients, they can better manage their own resources and plan for the future.
Benefits of data analytics in healthcare
The usage of big data analytics in healthcare brings many positive and life-saving patient outcomes. Leveraging data analytics in healthcare brings in many benefits including:
- Improved patient care
- Predicting high-risk patients quicker and more efficiently
- Easing patient diagnostics with electronic health record systems (EHRs)
- Reducing health costs
- Delivering greater insights into patient cohorts
- Generating real-time alerting
Data tools of data analytics in healthcare
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.
- Machine learning – In image and diagnostics applications, the use of machine learning helps healthcare professionals determine treatments for patients and improve patient outcomes. They also help to find patterns in data to improve care pathways.
- Database management – Database management is designed to manage database solutions. They can also support data governance and information governance to ensure data is accurate and available to physicians.
- Cloud computing – 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.
- Data visualization – This tool helps reveal patterns hidden in large volumes of raw data. 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 healthcare.
Impact of data analytics in healthcare
The impact of data analytics in healthcare shows through an advanced patient experience, allowing for lower healthcare costs and a more personalized experience. Data analytics gives healthcare companies the information needed to streamline customer service processes that personalize healthcare and create best practices for working with consumers or patients. This also can help policymakers make more informed healthcare decisions, contributing to better public and population health.
An example of data analytics in healthcare
The COVID-19 pandemic is perhaps the most notable example of organizations leveraging big data analytics models to inform decision-making. When COVID-19 hit the U.S., Community Health Network in Indianapolis, IN needed to provide caregivers with more predictive modeling, like how much personal protective equipment or how many ventilators would be needed in the future. Through Moser Consulting’s Honeycomb data analytics solution, custom, real-time dashboards were created quickly, empowering providers to respond rapidly to the evolving pandemic situation.
Moser Consulting’s Honeycomb for Healthcare
Moser Consulting provides data management, infrastructure, and data and analytics services. Since 1996, Moser has consistently delivered top-tier business and technology solutions. The desire to support health systems to achieve these data analytic goals is what drove Moser to create the Honeycomb solution. Honeycomb centralizes all of a health system’s data into one proven platform that brings actionable analytics to the doorstep. Honeycomb was designed specifically to decrease the time to value for clients who are more concerned with consumable data than with platforms and integrations. Contact us with questions or to receive support.