Engagement overview

A leading hospital group operating a number of major healthcare facilities across South Africa sought to improve its success rate with facilities claims submitted to medical aids for payment. In many cases, claims are rejected due to inconsistencies between the patient’s claim and the prescribed benefits stipulated in the patient’s medical aid plan.

Rejected claims are handed over to a team of debtor controllers for processing and resolution. The claims’ landscape is complex with roughly 140 medical aid providers, each with their own rejection reasons, adding to a backlog of rejected claims at various stages of workflow. With thousands of claims rejected daily, the task of prioritising and resolving claims is complex and time-consuming. The net result is millions of Rands in ‘lost’ revenue due to unprocessed, stale or prescribed debt.

BSG proposed a collaborative approach, using a team made up of generalist and specialist skillsets, to help the hospital group leverage its existing data to optimise their debt collections process. Through targeted collections the Group was able to unlock millions of Rands in untapped revenue and make significant improvements in their debtors’ days.

The insight-led solution was built around two requirements:

  • producing a list of claims, prioritised based on significance and resolution probability, updated daily
  • classifying rejected claims into categories, aligned to consistent workflows

The BSG team opted for a lean, agile approach, creating a minimal viable product (MVP) to show benefit early. The machine learning model developed could accurately prioritise incoming claims, based on significance (value, age, type, etc.) and the probability of resolution.

Timing of claims emerged as a firm driver of priority. In many cases, claims in a suspended state in workflow due to lead times in the process, were incorrectly actioned by debtor controllers, resulting in time being spent on cases that could not be resolved. By appropriately prioritising cases, the model directed debtor controllers to spend time on cases with a higher likelihood of resolution (i.e., effort was prioritised where it could most benefit the bottom line).

A second model was built to classify rejected claims into categories aligned to specific workflows. This provided clarity to all debtor controllers on the procedures to follow when actioning claims.

During the data-discovery phase, it emerged that debtor controllers relied on reports drawn from several separate systems to investigate and resolve claims. A significant amount of time was spent switching between systems to extract these reports. Consolidating all the necessary information into one dashboard, resulted in a 50% improvement in efficiency of debt collections, directly impacting revenue collected.

The combined effect of the model and dashboard was to provide debtor controllers with a prioritised list of claims each day, as well as all the information they needed, and a recommended action to resolve the claims.

Making a difference

Making a difference in communities

Through implementation of the debt management dashboard and prioritisation model, the hospital group was able to significantly reduce its bad debt. The efficiencies introduced by the model resulted in significant time savings across the debtors’ team – an effective 232 full-time equivalent (FTE) days per month, as well as a 50% improvement in efficiency of debt collections.

By adopting a lean, agile approach, using a multi-skilled delivery team, all of this was accomplished by a 4-person team over the course of 16 weeks (8, two-week sprints)

Chief Executive Officer

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