Increased out-of-pocket obligations and rising plan costs have put payers in the hot seat like never before. Insurers are under heavy pressure to improve business efficiency, deliver high-quality service, and retain providers.
Predictive analytics holds the key to enabling payers to achieve unprecedented efficiency. Before you invest in modern predictive analytics technologies, you must identify when, where, and how to deploy the technology. Otherwise, you will run into common headaches like siloed data and a prolonged time to value.
Explore whether these seven use cases for predictive analytics could benefit your organization.
1. Claims Processing and Auto-Adjudication
Deploying predictive models into your claims processing workflows allows you to rapidly identify which claims can be adjudicated using automation tools. The technology will also flag riskier claims that require manual review.
Analyzing historical patterns of claims complexity and provider billing behavior will allow you to route claims intelligently. This can lead to drastic reductions in processing costs and accelerate payment cycles. Your claims examiners will be able to focus on truly complex cases that require human judgment, giving them a greater sense of purpose.
2. Provider Network Optimization
Predictive analytics tools allow you to pinpoint providers that are likely to leave your network. You can also identify specialties that may face capacity constraints and address network gaps before they drastically impact patients.
Use this knowledge to engage in proactive contracting tactics and target key service lines with retention efforts.
3. Broker and Employer Retention
Losing high-volume group accounts can have drastic repercussions for your bottom line. Use predictive analytics to identify accounts that are high risk of switching during renewal periods by digging into:
- Customer service interactions
- Premium trends
- Utilization patterns
- Market dynamics
Target these accounts with customized strategies and early renewal discussions. The goal is to get them back on board before you lose their business to a competitor.
4. Member Services Resource Allocation
The ability to forecast spikes in call volumes based on enrollment and claims processing cycles can drastically improve business efficiency. Predictive analytics will help you find correlations between these trends, benefit changes, and external factors so that you can maintain a high standard of customer service.
Once you identify spikes in demand, increase staffing and scale up your support capacity accordingly. You can do this with outsourcing or through seasonal hires.
Reducing wait times during predictable surge periods will boost your reputation and help promote retention at scale. You can also use analytics insights to control labor costs during lulls in demand.
5. Prior Authorization Efficiency
Use historical approval patterns and provider history to predict which prior authorization requests are likely to be approved. Requests that are flagged as potential denials can be sent back for clinical review before a patient’s care is delayed.
Fast-tracking obvious approvals and communicating with providers about denials will benefit everyone. You can strengthen your provider relationships, better serve patients, and reduce the administrative burden on your team.
6. Fraud, Waste, and Abuse Detection
Predictive analytics technology excels at detecting anomalies that may indicate:
- Abuse of loopholes
- Wasteful coding and spending
- Fraudulent activity
The best solutions can analyze entire networks of related providers and pick up on unusual referral patterns so that your team can intervene sooner. Earlier intervention allows you to intercept fraudulent claims before payment is issued rather than attempting to recover funding later.
7. Sales Cycle and Market Expansion
By identifying which prospects are most likely to jump ship, you unlock competitive intelligence that you can use to protect your bottom line. Analytics tools will score and prioritize sales opportunities so your acquisition team can generate a higher ROI and expand into prime markets.
Succeeding With Predictive Analytics
Many payers are sitting on massive sets of data, but they often face organizational silos that hinder cross-functional analytics. For example, a finance team’s data rarely talks to the clinical team’s datasets. Network management systems represent yet another silo.
To derive a strong ROI from your predictive analytics investment, you must first tear down these silos. Creating integrated environments where your key data sources are talking is the hardest part of the journey. Once you achieve data unity, applying algorithms is the easy part.
