A look ahead with predictive analytics
The healthcare industry is one of the fastest growing industries in the world, having a direct impact on a patient's quality of life. Global healthcare systems are struggling to manage and treat chronic diseases associated with an aging population. Diabetes, chronic heart disease, cancer and Alzheimer's are just some of the illnesses placing a strain on financial resources of both the private and public health sectors, creating added stress and having a detrimental effect on patient outcomes.
As pressure mounts, an increasing number of healthcare service providers are looking for innovative, cost-effective methods to deliver technology-centric services. Predictive analytics is standing out as a solution of choice. According to Grand View Research Inc., the global healthcare analytics market is forecast to reach $53 billion worldwide by 2025. Driven by the need to achieve cost-effective solutions and adapt to a population with a longer life expectancy, predictive analytics promises to use data and machine learning algorithms to deliver efficient and accurate personalized care.
Several key factors are contributing to a transformation within healthcare.
The digitalization of healthcare has resulted in the creation of an enormous amount of new data, which is transmitted to healthcare service providers on a daily basis. These massive quantities of "big data" have the potential to support a wide range of medical and healthcare functions, including clinical decision-making, disease monitoring and health management.
As "value-based care" has emerged, focusing on healthcare quality over quantity, predictive analytics is already playing a substantial role.
Predictive analytics examines historical data in order to predict future outcomes. These analytics have come a long way from their initial introduction to the healthcare system. Today's AI systems are highly improved, predicting likely patterns by merging technology with statistical methods and machine learning algorithms. The ability to predict such patterns provides invaluable support to physicians during the decision-making process, thus improving the accuracy and speed of diagnoses.
How predictive analytics changes healthcare?
Shifting from volume-based healthcare to value-based healthcare is almost impossible to achieve without the use of predictive analytics, which requires data warehousing and integration from all available sources. Once fully implemented, predictive analytics offers foreseeable benefits.
Prevention: Preventive management is a valued advantage resulting from the implementation of predictive analytics because of its capacity to identify patients with potential, high-risk health issues. As lifestyle diseases are reaching endemic proportions, a more critical focus on prevention is mandatory.
Predictive analytics plays an important role in helping physicians detect and predict a wide variety of disorders and diseases, often years before a patient becomes symptomatic.
By pairing a patient's medical history and symptoms with an elaborate databank that also conducts MRI, CT and X-Ray analysis, diagnoses can be accurately identified. Those patients suffering from serious illnesses such as cancer, heart disease and Alzheimer's will greatly benefit from the use of predictive analysis technology, which offers earlier and more targeted treatments, helping eliminate unnecessary medications and enhance a patient's quality of life.
Operational efficiency: Predictive analytics has the potential to significantly improve operational efficiency in a healthcare setting. For instance, hospitals can predict a cold or flu season by using their own historical data and the use of external sources such as weather forecasts and social media, helping to prepare resources, inventory and improve staffing levels. Analytics are especially valuable in emergencies, where speed is of the essence, because of their ability to quickly gather medical data and inspect test results. In these types of situations, AI can move at speeds that physicians alone simply cannot match.
Improved patient care: When patient care is improved, hospital admissions, readmissions and overall costs are reduced. By applying predictive analytics in tandem with clinical expertise, healthcare systems can develop predictive models that will drastically reduce avoidable readmissions. Such models can enable physicians to make pre-discharge decisions and determine if a patient is at low, medium or high risk for readmission. These analytics assess the variables and have demonstrated how often flags such as socioeconomic factors, follow-up calls and appointments can drastically affect readmission rates. For instance, if a patient doesn't progress as expected, an analytics flag can schedule a follow-up appointment long before any significant deterioration occurs.
Personalized care: Personalized treatment plans are at the center of attention in healthcare. By specifying genetic and lifestyle information and integrating it with big data, physicians can have clearer, more detailed analysis of their patients, allowing them to choose the best possible treatment and develop a customized care plan. This type of precision-based, personalized medicine can suggest alternatives otherwise overlooked, thereby minimizing side effects and avoiding unnecessary costs for both the patient and the facility. Focusing on highly individualized care not only improves patient outcomes, but also improves consumer engagement and enhances the patient experience.
Predictive analytics' ability to turn massive amounts of data into actionable insights will help healthcare practitioners identify needs, improve services, predict and prevent crises, and improve patient care.
Dongmin Kim, PhD, is the CTO/Director of the AI R&D Center for JLK Inspection a Seoul, South Korea-based medical solutions provider specializing in AI-based technology. JKL's universal AI platform is created by a combination of big data, experts, and our own unique engines and algorithms, providing on-site/real-time service, seamlessly connected to all systems.
