Satish Pala, Senior Vice President – Digital Solutions, Indium Software on the benefits and challenges of predictive analytics in healthcare which hinges on access to secure, quality data
Internet of Things (IoT) and medical-grade wearables devices have revolutionised healthcare by empowering users to have greater control over their health. The healthcare service providers are also able to provide better and more timely service to their patients by monitoring their health parameters remotely and providing on-time treatment for better patient outcomes.
Big data generated by these devices also serve as a treasure mine of training data that can be leveraged to understand disease patterns using statistical tools for predictive modeling – with a focus on improving treatment and therapy for the patient. Of course, this must be done while adhering to patient-specific data and privacy regulations.
Predictive analytics integrates machine learning with business intelligence to forecast future events from historical and real-time data and can be a big growth driver for the healthcare industry. According to Marketwatch.com, the global Healthcare Predictive Analytics market size is expected to grow from $ 2,439 million to $ 10,740 million by the end of 2026 at a CAGR of 23.4 per cent between 2021 and 2026.
Benefits of predictive analytics for healthcare industry
Leveraging big data can help the healthcare industry with greater efficiency, bettering their customer service, providing better care, anticipating a spike in disease trends, and cope with the demand for greater care as well as improve technological innovations by addressing diseases of the future.
HealthTech companies are also seeing increased access to venture capital and private equity to develop apps and device offerings using predictive analytics. These apps use real-time data to alert users as well as medical care professionals of any impending danger and access timely care.
But the role of predictive analytics goes beyond patient care. In fact, predictive analytics and intelligence are extremely handy for operations and administration. Some of the key areas where healthcare can benefit by leveraging predictive analytics include:
● Managing Operations
● Forecasting Demand
● Scheduling of Patients
● Managing the Revenue Cycle
● Planning and Scheduling of the Workforce
● Corporate Finance and Financial Planning
● Fraud Detection
● Patient Engagement
● Clinical Outcome Analysis and Management
Some of the ways in which predictive analytics can benefit the healthcare industry include:
● Finding Cures: Prognostic analytics can help doctors find cures for specific diseases by enabling accurate modeling for mortality rates for individuals. Companies are using data and intelligence to suggest customised therapies.
● Best-fit Treatment Recommendations: Even in healthcare, there is no one-treatment-fits-all. By analysing large data sets, doctors can identify correlations and hidden patterns between body types and prognosis to provide optimal treatment.
● Preventive Healthcare: Predictive analytics can help identify individuals vulnerable to chronic conditions early by creating risk scores based on the patient’s health parameters, biometric data, lab results, and so on and provide the right treatment to slow down the progression.
● Dealing with Pandemics: The spread of contagious diseases and timely intervention to prevent or at least reduce spread using predictive models.
● Preventing Patient Deterioration: During in-patient treatment, the patients are vulnerable to catching an infection or other complications. Predictive analytics can help identify potential risks and mitigate early on.
● Supply Chain Management: Hospitals can improve their spending decisions and purchasing patterns through proactive supply chain management and experience cost savings. They can also improve their price negotiation capabilities, the ordering process as well as reduce supplies variation.
● Fraud Detection: Machine learning and business intelligence can help analyse billing records and patient data to identify anomalies, prevent fraud, and use their resources more productively.
Challenges posed by predictive analytics
Without a doubt, predictive analytics can open the door for several improvements that will benefit the healthcare industry and its users. It can improve treatment delivery, outcomes, cut costs, improve efficiencies, and so on. But to achieve this, the tools alone will not be enough. Some of the factors to keep in mind would include:
Access to data
Data is the bulwark on which predictive analytics rests. Therefore, access to quality and clean data from across different functions, internal and external, would determine the effectiveness of predictive analytics efforts. Quick integration of data from disparate sources, in different formats and stored in a central repository will be critical for getting meaningful insights.
Cloud vs on-prem
Security concerns as well as legacy systems require businesses to look at hybrid clouds, raising the challenge for predictive analytics tools to work with databases from multiple environments.
Governance and security
Being a highly regulated industry, healthcare players need to ensure the security and privacy of the data they have at their disposal throughout the lifecycle.
Keeping pace with the times
Technology is changing at the speed of thought. It is essential to keep pace but it not only costs money but can also pose other challenges if not well-thought-out, planned, and implemented. Ensure the scalability of your technology investment to remain adaptable while protecting your investment.
Predicting a healthy future
The ultimate goal of every healthcare service provider is to improve treatment outcomes, provide the best treatment for their patients, prevent disease progression while keeping operational costs low. Predictive analytics can enable this provided there is access to secure, quality data.
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