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Three Trends are Driving AI Adoption in MedTech

The med tech industry is undergoing a significant transformation, with competition from new players, disconnected patients, and new trends in population health requiring companies to increase their efficiency. As med tech companies strive to optimize their revenue operations and discover new revenue streams, they’ll be challenged to get maximum value out of their data.

To meet this challenge, med tech companies can take advantage of emerging capabilities in artificial intelligence and machine learning (AI/ML). In this article, we’ll explore three key trends in med tech AI that companies should be aware of and how to use them to optimize revenue. In combination with widely used systems, AI is ready to become a key technological advantage for your company, allowing you to maximize your revenue potential.

Harnessing Healthcare Data for Revenue Insights

One of the most significant advantages of AI is its ability to process and analyze vast amounts of data at lightning speeds, uncovering trends that previously would have required hours or even days of analysis to see. For med tech companies, this can mean quicker insights on patient behavior, treatment outcomes, and revenue patterns.

Of course, AI is like most other analytics capabilities—to get it to work, you’ll need a strong data foundation, with robust capabilities for collecting, organizing, and analyzing healthcare data from various sources, including electronic health records (EHRs), claims data, and patient-generated data in your CRM. The more accurate and complete the data in your CRM is, the more value AI analytics will be able to deliver—and by using a system that has HIPAA compliance built in, you’ll have fewer compliance headaches later.
When you bring AI into the picture, it’s helpful to start with technology that integrates easily into your CRM. Using med tech AI tools alongside your CRM, med tech companies should focus on:

  • Identifying revenue opportunities. Many companies have found AI/ML analytics especially effective at identifying new opportunities that look like past successful ones. Teach your AI tools what a good opportunity looks like, and they’ll be able to spot them quickly and accurately, leading to new sources of revenue.
  • Detecting anomalies. On the same principle, when AI knows what normal looks like, it can spot abnormal faster than any analyst. We recommend setting up your AI analytics system to spot anomalies for fraud detection and prevention. AI algorithms can monitor billing and payment transactions for suspicious activities or fraudulent claims, mitigating financial risks and ensuring compliance with regulatory requirements.
  • Making data-driven decisions. Imagine using the data from your medical devices to predict the likelihood of a patient or patient populations developing specific diseases or conditions. Algorithms analyzing patient data can make these predictions, enabling healthcare professionals the ability to intervene earlier, develop personalized treatment plans, and improve patient satisfaction and outcomes—leading to more loyal relationships and improving revenue.

To make sure these analytics solutions get used, they should be accessible through existing systems, with defined policies, procedures, and governance, so your people, processes, and technologies all create a seamless flow of data.

One med tech company specializing in medical devices recently used AI-powered analytics to analyze device usage patterns, patient outcomes, and reimbursement rates. By identifying trends and correlations, the company could optimize its pricing strategies, better target high-value customers, and improve its product offerings to maximize revenue.

Predictive Modeling for Revenue Maximization

Predictive modeling has become another major use case for AI. By reviewing previous periods, AI models can forecast sales and revenue numbers more accurately than human analysts—not to mention much faster. Med tech companies can optimize their pricing strategies and make predictions regarding other factors that will impact future revenue, such as patient demographics, disease prevalence, and market trends.

Getting started with AI-powered predictive modeling can be as simple as custom prediction models using CRM data, which doesn’t require extensive data science expertise. These models can help identify high-value customers, predict the likelihood of contract renewals, and optimize pricing strategies based on market demands.

Integrating predictive modeling capabilities into your CRM environment will ensure that the models are tailored to your specific business needs and data sources. For example, predictive AI models can be trained to surface account health metrics and then display those metrics alongside other account information. This should include an account health score, with insights as to why that account is doing well or what could be improved, easily visible on each account page so your reps can use it quickly to strengthen relationships. By leveraging predictive insights, med tech companies can proactively adjust their revenue strategies, identify potential risks, and capitalize on emerging opportunities.

In one example, a MedTech company that provides diagnostic services can use predictive modeling to forecast demand for specific tests based on seasonal trends, patient demographics, and disease outbreaks. By understanding this, companies can plan ahead, optimizing resource allocation and pricing strategies to ensure efficient, profitable service delivery.

Streamlining Billing Processes through Med Tech AI Analytics

Efficient billing processes are crucial for med tech companies. Timely reimbursements mean stronger cash flows and a steadier revenue stream. However, the complexity of healthcare billing, with its numerous codes, regulations, and payer requirements, can lead to errors, delays, and revenue leakage. AI-driven analytics can help streamline billing processes by automating coding, identifying errors, and optimizing claims management.

Again, a centralized CRM platform should be your first step towards putting AI to work. By feeding patient data including billing information and insurance details through AI-powered tools such as natural language processing (NLP) and machine learning, med tech companies can automate the extraction of relevant billing information from unstructured data sources, such as clinical notes and diagnostic reports.

AI-driven systems can automate and expedite the prior authorization process by analyzing patient eligibility criteria, medical necessity, and insurance coverage in real-time. Once companies integrate AI-driven billing analytics solutions into their CRM environments, they can identify billing discrepancies, suggest accurate coding, and predict the likelihood of claim denials, enabling them to optimize their billing processes and minimize revenue leakage.

For pricing and contract negotiation, AI also has uses. Med tech companies should take advantage of AI to analyze historical billing data, payer contracts, and market trends to optimize pricing strategies, negotiate favorable contract terms, and identify opportunities for revenue enhancement. With patients, AI can also provide personalized cost estimates, payment plans, and financial assistance options based on individual patient profiles. Companies should automate patient billing communications and reminders to streamline the billing process, improve revenue collection rates, and optimize patient financial engagement. By building this functionality companies can contribute to improved financial performance and patient loyalty.

We recently saw a med tech company that provides remote patient monitoring services leverage AI-driven analytics to automatically extract billing codes from patient data, validate the accuracy of claims, and predict the likelihood of reimbursement based on payer policies. By streamlining its billing processes, the company reduced administrative costs, accelerated revenue collection, and improved its bottom line.

Adopting Med Tech AI has Clear Benefits

AI is poised to transform the ways companies do business, and revenue optimization is no exception. By harnessing healthcare data for insights, leveraging predictive modeling for forecasting, and streamlining billing processes through AI-driven analytics, today’s early AI adopting MedTech companies can unlock new revenue opportunities and improve their financial performance in a highly competitive landscape.

As the healthcare industry continues to evolve, embracing AI as part of a complete tech stack will help MedTech companies stay competitive and thrive in the market. Spaulding Ridge uses Salesforce’s robust platform combined with several AI tools to help med tech companies unlock these benefits. If your company is interested in how these technologies can unlock profitability, we’d love to talk to you.