Overhaul Collections Performance in Healthcare with predictive analytics and machine learning | HCLTech

Overhaul Collections Performance in Healthcare with predictive analytics and machine learning

Overhaul Collections Performance in Healthcare with predictive analytics and machine learning
November 18, 2021

The ever-evolving billing code and systems combined with the recent shift to patient-dependent revenue cycles is forcing hospitals and care providers to alter their collection strategies. Add to that the sweeping regulatory changes made to address the pandemic-induced surge. This means care providers need to prioritize increasing their margins. In this climate, predictive analytics and machine learning hold great potential in changing the plot for healthcare collections.

The ever-evolving billing code and systems combined with the recent shift to patient-dependent revenue cycles is forcing hospitals and care providers to alter their collection strategies.

Overhaul

Figure 1 RECE prediction for three completed months. RE: “Resolution Effectiveness” and CE: “Cost-Effectiveness”

Recently, HCLTech partnered with a US-based durable medical device company executing massive changes across their inventory mix to improve their collection rates. HCLTech implemented a machine learning-based forecasting model that runs on a 95% accuracy rate for predicted collections.

The improvements in the achieved resolution and cost-effectiveness were not happenstance. Depth of domain expertise and attention to detail in training the system determines success in such interventions. The HCLTech machine learning-based model was trained on three million datasets for over a year across multiple touchpoints – amount open, settled, adjusted, write-off, and performance indicators like payor, last action taken, last denial reason, number of times worked on the invoice, etc. Consequently, the system was able to deploy predictive analytics to provide weekly cash collection forecasts and timely recommendations to increase the cash flow.

A high-impact lever to identify the high yields accounts, increase collections, and drive an empathetic experience is segregating the collection inventory. At the invoice level, the HCLTech model applied ‘propensity-to-pay’ scoring.  

And the result? The medical device company experienced significant improvement in operational performances as compared to historical levels. HCLTech’s machine learning and predictive analytics based solution also delivered a clear march over the competition.

cost effective

Client Testimonial

I want to take a moment to express my appreciation for the hard work done by HCLTech over the past few months. We were struggling to meet queue expectations for quite some time. You were committed to getting the numbers in line and have followed through. Please share my gratitude with your team. Let’s keep it going!

-- Director, Billing Operations

US-based Durable Medical Device Company

 

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