(HealthNewsDigest.com) – Investing in advanced analytics and machine learning tools is essential for health care organizations to identify problem areas, optimize operations and increase profit margins. According to BIS Research, health care organizations began heavily investing in financial analytical services in 2017, and the marketplace for big data is expected to grow over $68.7 billion by 2025. However, just because health care organizations are collecting data at a rapid rate doesn’t promise they’re properly internalizing and acting on their findings.
It’s easy for practices to become overwhelmed by the vast amounts of data available to them. Different datasets assist providers in care planning, reducing care costs and streamlining administrative procedures. Consequently, it’s important for organizations using big data to understand and apply the appropriate types of data analyses to corresponding KPIs for maximized return on investment.
Predictive analytics utilizes data mining, modeling and machine learning techniques to study descriptive data to answer the key question, “what could happen?” Through this analysis, organizations can address cost concerns and gaps in revenue cycle management. Here are a few ways providers can deploy and interpret predictive analytics and machine learning to increase cashflow and improve operational efficiencies.
Claim denial prediction and resolution. The transformative nature of payer guidelines makes it considerably more difficult for providers to guarantee their claims will be accepted. By applying machine learning to claims data, organizations can maximize outcomes by prioritizing claims with higher probability of payments. Also, organizations by anticipating denials can put in process to address them in a timely and effective way.
Organizations can also increase their efficiency by integrating system automation with the use of predictive analytics and machine learning. Using a combination of machine learning and automation, providers can process more claims with higher accuracy, identify underpayment trends by comparing and analyzing reimbursement, and automate appeal procedures to alleviate resources for other administrative tasks.
Predicting patient volume patterns. Whether an organization is transitioning to value-based reimbursement or has a revenue cycle based on fee-for-service, understanding patient population and providing cost-effective care is essential. Building patient profiles with predictive analytics can interpret, create and analyze datasets to assist organizations in managing patients’ chronic conditions and reduce readmission rates while lowering costs.
Analyzing EHRs allows health care organizations to identify and predict patterns for patients with chronic conditions and can generate risk assessments for patient populations. Those same patterns can be used by machine learning algorithms to identify which care results in positive patient outcomes and at what rate.
Optimizing office efficiencies. In addition to enhancing care, patient profiles can also establish guidelines for care procedures, help identify potential missed appointments and determine the best communication channels to connect with patients.
Patient profiles assist health care organizations in reducing clinical variations by studying patients’ overuse and underuse to create systematic procedures. This allows organizations to provide cost-effective care and providers to manage staffing in accordance to the specific needs of the population. Determining how to staff and manage a clinic during flu season is much different than during other yearly trends.
There is an abundance of data available to providers from sources such as EHRs, payer records and medical imaging. By utilizing predictive analytics and machine learning for the data available, health care organizations can maximize ROI of their data analytics investments, optimize workflow efficiencies and ultimately, provide better care.
Mel Gunawardena is a managing partner at SYNERGEN Health, transforming health care organizations’ revenue cycle ecosystem from end-to-end, generating the revenue essential to the viability of practitioners, enhancing patients’ experience and enabling financial success.