Healthcare professionals manage a myriad of forms and information—patient records, provider info, payment, and billing data—through a vast range of disparate systems.
The magnitude of data and a variety of intricate processes, if managed traditionally, can result in slow, ineffective, and expensive patient care, making it difficult for healthcare providers to scale operations effectively.
The healthcare industry collectively spends a staggering $2.1B every year on manual tasks related to provider data alone. Similarly, insurance companies spend between $6M to $24M every year to fix the data quality.
Moreover, US healthcare particularly faces issues managing menial administrative tasks such as filling out forms, assigning medical records to providers, and classifying insurance bills, which add to operational costs.
A recent study estimates that the US spends $1.1T on healthcare administration, and $504B of that amount is excess.
As the healthcare sector strives to solve these challenges related to high costs and operational inefficiency through digitization, Robotic Process Automation (RPA) emerges as a key enabler. RPA not only helps healthcare providers in optimizing costs, improving productivity, and increasing operational efficiency but also enables them to enhance patient interaction and outcomes rapidly at scale.
The pandemic has further intensified the need to optimize costs and improve patient outcomes resulting in the accelerated adoption of RPA in the healthcare industry.
According to Gartner, a leading analyst, half of the United States healthcare providers will invest in RPA in the next three years, up from 5% today.
RPA uses software Bots to perform work by emulating human actions through the user interface of any system. Multiple systems involved in each business process can be automated without any need for integration or changes to the underlying system. This avoids the most significant cost element of many automation projects.
Healthcare providers can leverage RPA to automate a range of tasks such as patient onboarding, verifying insurance eligibility, claims submissions, data cleansing, claims reconciliations, inventory management, customer complaints management, auditing processes, etc.
The patient appointment booking involves a range of labor-intensive tasks – from collecting patient’s personal information and diagnosis details to insurance policy and eligibility details. Also, managing appointments as per the availability of the doctors can be highly ineffective if done manually.
Missed appointments or patient no-shows cost the U.S. healthcare system over $150B a year.
How can patient scheduling and communication be improved? Automation is the answer. RPA when combined with Artificial Intelligence (AI) can help healthcare professionals improve patient appointment scheduling and other interactions without the need to hire more staff.
Estimates suggest that by 2023, 20% of all patient interactions will involve AI enablement within clinical or non-clinical processes.
The Nividous team has built an effective solution for an eyecare group utilizing voice and chat-enabled AI-powered Nividous RPA Bots to serve the patients in appointment scheduling. Patients can interact through a voice or chat Bot to locate the nearest store or to book an appointment quickly. The Bots interact on existing legacy system interfaces to:
Healthcare claim processing costs can be reduced by almost 75% by automating manual processes such as data collection, claims submissions, and reconciliation.
There are billions of medical claims filled each year in the US alone. Health insurers can use RPA to automate these manual processes and improve their bottom line significantly. Healthcare providers can use RPA to automate several rule-based and transactional tasks involved in claims processing rapidly and at one hundred percent accuracy. Hospitals can use RPA automation for processing their health plans and reduce the claims backlog.
A Nividous customer who has arrangements with more than 40 insurance carriers offering hundreds of different insurance plans has used Nividous RPA to automate claims management and achieves over 80% claim payment cycle efficiency. Read the complete case study.
The coding of medical diagnosis and treatment has always been a challenge. The process of manually analyzing clinical documentation and determining which codes are relevant to a specific case is highly tedious.
It is difficult for any person to learn and keep up with all of the changes to the 70,000+ codes that are used for medical billing. Even when the process is completely outsourced, it is expensive, and management and oversight can be a significant drain on your staff. When done manually, this process is not just time-consuming but is highly error-prone due to the complexity and the sheer number of codes and changes.
According to the Centers for Medicare & Medicaid Services (CMS), errors resulted in $16.7B in improper payments in FY2019.
Machine Learning and Natural Language Processing are emerging applications in the field of AI and when applied in combination with RPA Bots, can dramatically reduce the standard work hours and human errors required for the coding process.
Nividous’ customer Kane Wound Care successfully automates its medical coding and billing process using AI-powered Nividous RPA Bots, achieving a 90% improvement in coding accuracy and 85% improved process turnaround time. Read the complete case study.
Effective management of the healthcare revenue cycle is highly crucial for the long-term profitability of healthcare providers. RPA can help in optimizing the revenue cycles by reducing the overall time involved between patient service delivery and payments. With the automation, healthcare providers can significantly reduce claim denials and claim resolution timelines by eliminating manual errors and accelerating the processing of transactions.
Nividous’ customer uses Nividous RPA as a standardized platform to revolutionize its revenue cycle management by automating a series of cross-functional operations involving doctors and administrative staff. Read the complete case study.
Healthcare providers need to conduct frequent audit checks. The process involves periodic report generation and complying with several norms. RPA can help streamline the auditing process by automating a range of manual tasks, including data management, and report generation, while auditors are freed up to focus on more decision-driven tasks. Timely reports help administrative staff to take corrective measures quickly. The automation can also help in detecting the pattern or source of non-compliance.
Today, healthcare providers are not limiting the use of RPA and AI-enabled automation to only effectively optimizing costs and improving operational efficiency but are driving Innovative ideas and pushing the envelope as hard and as fast as they can. If you are interested to learn more about RPA use cases, do check out several success stories of Nividous RPA in healthcare.