Data Management Analytics to Predict Patient Propensity to Pay and Cut Bad Debts

Healthcare delivery networks unite tens of hospitals and clinics, rehabilitation locations and pharmacies, provide work to tens of thousands of staff, and assist hundreds of thousands of patients. Not all of those patients possess health insurance. Some patients are underinsured, i.e., they have to face significant out-of-pocket costs, as their insurance does not cover enough. Many people have health insurance plans with high deductibles and co-payments, forcing them to self-pay for many services. According to the latest statistics, around 100 million people in the U.S. are burdened with medical debts in the amount of under $500 up to $5,000. Consequently, healthcare networks define self-pay accounts as the main source of bad debts.

Data management analytics is used by providers to benefit from its embedded predictive models that allow for the analysis of patients and support a propensity to pay strategy. Companies that provide healthcare solution development services to data analytics vendors offer customized solutions that assist in cutting bad debt expenses. In this post, experts from the Belitsoft software development company share their competent opinions about healthcare data management analytics, its benefits, and its functionality.

Challenges of Predicting Propensity to Pay

Before 2018, U.S. hospitals reported bad debt as the difference between the billed amount and the amount that was actually paid by a patient, regardless of the reasons. In the current situation, only the amount that a patient cannot pay due to bankruptcy or unemployment can be reported as bad debt. Statistics say that in 2019, the total U.S. medical debt accounted for $195 billion. Therefore, hospitals need to adjust their collection practices and revenue cycle management to comply with the new standard. It involves more precise assessments of a patient’s ability to pay and potentially offers more financial assistance or payment plans to reduce uncollected amounts. Here are the challenges healthcare organizations have to overcome:

• Necessity to develop a reliable and accurate propensity to pay predictive model.
• Difficulties with finding a competent vendor.
• Shortage of an informed strategy to foresee the probability of self-payments and suggest relevant follow-up collection actions to bring the most success.
• Absence of a well-designed approach to gathering and interpreting complex medical data that would allow for developing a valid predictive model.
• Inability to identify and focus precisely on those patients with outstanding balances who were likely to pay their bills, even in spite of well-organized reaching out to all patients. 
• Lack of a relevant analytics tool that would analyze the types of accounts and patients.
• Low-efficient methods of collecting payments, such as personalized cold calls.

Features of Data Management Analytics

Healthcare networks partner with healthtech vendors to successfully implement solutions that help to analyze payers’ accounts. Such tools are used for segmenting payers into several categories and further usage of those categories for proper follow-up collection strategies. The segments are validated by a team of subject matter experts (SMEs) from such domains as medical, financial, and analytical areas. 

Integrated machine learning (ML) and artificial intelligence (AI) use hundreds of thousands of training cases to determine whether a patient will pay their bill in the target period of time.

Embedded decision tree algorithms select the variables that should be included in the final model to calculate the probability of a patient’s payments. The segments and follow-up collection strategies may be the following:

• Patients who are highly likely to cover their self-pay costs. It is not recommended to spend resources on calling these patients, as they will pay their bills without additional contact with financial specialists.
• Patients who are less likely to pay their bills. Financial representatives can call such patients to notify them that the outstanding balance will be sent to bad debt soon. Besides, financial experts might offer a special payment plan to such patients to help them pay their bills in a way that aligns with their payment plan guidelines, even if they haven’t officially signed up for a formal plan. This approach helps prevent these patients from falling behind on their payments and potentially having their debts sent to collections. 
• Patients who are unlikely to pay their bills. Financial experts may not lose their resources to collect payments if such patients have previously shown that they are not able to pay. Specialists might call these patients to notify them that the outstanding balance will be transferred to collections.
• Patients who receive charity care. Healthcare representatives usually contact such patients to inform them about possible medical assistance and to check if any help is required.

The information from the predictive model is exchanged with such sources as electronic medical records (EMRs) and a patient statement platform, allowing for automated patient contact and assisting financial experts in selecting a relevant self-pay collection strategy.

Data management analytics software enables organizations to concentrate collection efforts on patients with a higher probability of paying, rather than wasting resources on those who will pay without notifications or not pay at all. Under these conditions, organizations allocate their resources effectively and increase payments for their services. 

Benefits of Implementing Analytics for Minimizing Bad Debts

Healthcare networks that have already implemented analytics into the process of identifying, prioritizing, and interacting with patients who have self-pay accounts report the following improvements in their performance:

• A few millions of dollars in overall collections in about a year.
• Hundreds of thousands collected over the phone in the initial couple of months following the deployment of the ML algorithms.
• Significant relative improvements in the quantity of outbound and inbound calls.
• Relevant patient segmentation and subsequent effective methods of reaching self-pay accounts.
• Further integrating self-payment cases into machine learning algorithms to refine propensity to pay models.

The Role of a Healthcare Software Development Company

Healthcare data analytics companies cooperate with healthcare software development companies like Belitsoft to build and customize data analytics applications and platforms. Data operating systems and integrated analytics platforms become valuable assistants to healthcare organizations in analyzing their performance, looking for the inefficiencies in their workflows, and improving processes.

Healthcare software development companies create integrated data platforms that are used for collecting, storing, processing, and analyzing large datasets from such sources as electronic medical records (EMRs), clinic management systems, laboratory systems, billing systems, etc. Those platforms provide the following functionality:

• Workflow automation (cleansing, standardization, and normalization)
• Setting up scalable data warehouses
• Implementing analytical tools for designing dashboards, reports, and data visualizations
• High level of data security and compliance with healthcare regulations such as HIPAA
• Integration of machine learning and AI into analytics.

Healthcare software development companies develop specialized analytical applications like Data Management Analytics for:

• Data integration from various sources.
• Integrated model performance metrics.
• Embedded tools for patient segmentation.

Technological solutions allow healthcare organizations to reach better outcomes for patients and manage costs effectively.

Companies seeking expert assistance in developing and integrating data analytics, data infrastructure, data platforms, HL7 interfaces, workflow engineering, and cloud development (AWS, Azure, Google Cloud), as well as hybrid or on-premises environments, can turn to a custom healthcare software development company like Belitsoft for support.