Six Practical Applications of Real-Time Analytics in Healthcare

 Real-time analytics can deliver operational, financial, and clinical improvements to healthcare providers and organizations worldwide. Post the COVID pandemic, organizations are looking to improve patient engagements quickly with real-time decision making, modernize electronic health records system, and accelerate the process of saving lives by digitizing critical business processes. Most recent forecasts indicate that health-related analytics, its applications, and the overall market will grow to $28 billion by 2025--and it's easy to see why. Data and analytics combined with modern innovations in AI and ML can help organizations focus on improving patient care, build smart clinical solutions with real-time actionable insights, and better safeguard patient records.  Here we share six pragmatic applications of real-time analytics in the healthcare industry:

1. Forecasting patient load and utilization pattern

Similar tohow analytics is being widely used in the transport industry to predict passenger load at certain times of the day, analytics can help predict how many patients are likely to visit each department at any specific time. Real-time insights can further help remove operational bottlenecks thus ensuring faster and quick treatment for patients, improved patient experience, and better care. It helps healthcare organizations ensure optimal staffing levels, manage resources well, and reduce unnecessary costs.  

Thus using analytics and AI topredict utilization rates and visualize patient flow patterns using historical data and ML can assist healthcare professionals and management in course correcting schedules and adjusting workflows to ensure staff availability and faster patient care.   Along with faster care, it helps reduce staff burden by even distribution of workloads and by optimizing time and costs.

2. Healthcare fraud detection and risk management

Healthcare fraud is estimated to cost the US healthcare system around $68 billion annually. Organizations need to plan for detecting fraud and take preventive measures to mitigate associated risks. Along with fraud, organizations also need to assess risk from mistakes and errors in manual processes.  Real-time analytics provides data insights to help organizations predict and expose fraud and automation to help trigger corrective measures. An example of fraud detection is automatically detecting duplicate billing, upcoding, and phantom billing by healthcare providers by using real-time insights from billing data and using automation to filter such transactions. Another example is analysing claim patterns across different insurers and insurance policies to discern attempts to file reimbursement claims for services that were never provided.

3. Predicting the risk of chronic diseases

Real-time analytics can generate disease risk scores based on claims data, biometric information, laboratory testing, and patient-generated health data, among other factors. This can help healthcare providers identify, stratify, and manage high-risk patients to improve quality care, ensure timely treatment, and control cost outcomes. It also allows physicians to provide early intervention and prevent long-term complications for patients at high-risk of developing chronic conditions. 

4. Forecasting the need to optimize patient appointments

Appointment no-shows do not just throw off the workflow of clinicians—they can also have serious financial ramifications, especially for smaller practices. With real-time analytics, it is now possible to identify which patients are more likely to skip appointments. A good example is a patient who lives far away coupled with high probability of heavy rains near the patient's home on the day of the appointment is a good indication that the patient might miss an appointment.  With real-time insights organizations can fine-tune such practices and then decide to either send additional reminders to those patients or suggest alternative dates and times, whichever is  more convenient for the patient This decreases revenue losses of the overall business while allowing empty appointment slots to be utilized by other patients thus, improving access to health care. 

5. Ensuring healthcare data privacy with analytics

Real-time analytics can also minimize data security threats by continuously monitoring for suspicious activities and network changes to detect potential cyberattacks. It can observe data sharing, utilization, and access, providing early warnings when something changes.

In addition, artificial intelligence and machine learning can calculate the real-time risk levels of specific transactions and databases. The system can then use this risk score to grant easy access for low-risk events and similarly block or enable multi-factor authentication for higher-risk events. This strategy is particularly effective in preventing ransomware from infiltrating healthcare databases. 

6. Maximize healthcare efficiency with optimised supply chain

Large healthcare organizations and hospitals are turning to real-time analytics to reduce variation in supply chain operations with critical actionable insights into healthcare supply utilization and ordering patterns.. According to Global Healthcare Exchange, hospital executives ranked supply chain analytics as a top 'wish list' item in 2018. Using new age data analytics solutions can help hospitals save more than $10 million annually by allowing them to make proactive data-based decisions about pricing negotiations and ordering process optimization.  

 Interested in implementing real-time data analytics solutions to increase the efficiency, security and help deliver better and faster patient care in your healthcare organization?

Talk to InfoCepts, a global data analytics firm that recently won a Data Break Through Award for its Real-Time Analytics Solution.