Using Data Analytics to Predict Outcomes in Healthcare
Predictive analytic tools are being used more and more in many industries, including healthcare. The vast amount of healthcare data that is now digitized has created massive new data sets available from sources such as electronic health record systems, health claims data, radiology images, and lab results.
By utilizing data from these sources, predictive analytics can be used to seek new solutions for providers for medical diagnosis, modeling health risks, and precision medicine. This tool can help organizations shift their focus from reactionary care delivery to a proactive approach. Predictive analytics can help to better inform and guide care decisions with real-time patient data, streamline care delivery models with risk notifications, identify patient behavior patterns, account for social determinants of health and address healthcare disparities, and improve operational efficiency to reduce staff burnout and increase focus on care.
What is Predictive Analytics?
Predictive analytics are a type of advanced analytics that can be used to make predictions about future outcomes, such as health outcomes, using historical data combined with statistical modeling, data mining techniques, and machine learning. Based on logic drawn from theories to fit a hypothesis or prediction, predictive analytics can also seek patterns and structure in the data and cluster them into groups or insights.
Organizations can use predictive analytics to find patterns in data to identify risks, such as using data to detect and manage the care of chronically ill patients. Predictive analytics can be used at the individual level to help providers deliver the right care to the right patient at the right time. This tool can help health systems identify and understand larger trends, such as strategies that can be used for improving population health.
The benefits of using predictive analytics in healthcare include improving efficiencies for operational management of healthcare operations, accuracy of diagnosis and treatment in personal medicine, and insights to enhance treatment.
Predictive analytics are changing health outcomes through personalized care delivery, proactive risk identification, and improved operational outcomes. Analytics allow for providing personalized care to patients by tracking individual progress toward health goals and giving healthcare professionals evidence-based information to use for clinical decision making. Analytics also are useful in proactive risk identification because they provide the ability to track health outcomes and see trends and patterns in health outcomes for different demographics.
Predictive analytics are beneficial for improved operational outcomes through the ability to track measures related to efficiency, productivity, safety, and quality.
Tools for Predicting Outcomes
There are different types of predictive analytics models designed to assess historical data, discover patterns, observe trends, and use the information to predict future trends. Some of the most common predictive analytics models are classification models, clustering models, and time series models.
Classification models are used regularly in healthcare to make decisions about how to enhance patient health, how to provide health care services at a lower cost, and how to predict fraud in health insurance. Cluster models provide the ability to assess and profile individuals on the basis of characteristics such as age, inpatient admissions, risk of emergency hospital admission in the next 12 months, etc. Time series models can plot a sequence of observations made over time, such as monthly admissions to an emergency department or annual expenditures on health care.
Regardless of which model is used, predictive analytics can lead to better operational efficiencies, improved patient safety, and improved patient outcomes by helping organizations anticipate when, where and how care should be provided.
Predictive analytics have been used by health systems to create platforms for predicting and preventing the most common and costly diagnoses, provide enhanced accuracy in predicting length of stay, and help providers personalize treatments. For individual patients, there are many tools that can give medical providers valuable information to enhance patient care, such as medical monitors, patient wearable devices, fitness trackers, and health apps. Data from these sources can be used in predictive analytic tools and analyzed to provide more personalized care.
Technology has provided healthcare organizations and providers with many advances that have changed how healthcare is delivered. The advent of technology and digitization of healthcare records has brought an influx of data that can be utilized to improve health outcomes. Predictive analytics provide healthcare organizations and providers with the ability to analyze data and use the information to identify trends and provide patients with personalized care.
Lesley Clack, ScD, CPH, is an associate professor and department chair of health sciences at Florida Gulf Coast University.