Not surprisingly, many health plans are rapidly updating their models to improve inaccuracies in provider data, enable members to find the right care, and avoid hefty fines for non-compliant data.and ghost network Amid recent headlines and inaccurate data impacting patient access, health plans across the country need to move quickly to rethink their current data solutions to improve the member experience and save costs through automation.
In 2017, the Centers for Medicare and Medicaid Services (CMS) conducted a study of online provider directories and found that 46% of exam location information is inaccurate, preventing access to care. Although inaccurate data is a major culprit in patient access to care, patients still trust health plan Provide accurate information to help them identify the right provider, according to the JD Power 2021 U.S. Business Member Health Plan Study.
So what does this mean for health plans? Health plans have an opportunity to innovate their current data infrastructure to not only update provider data in real time, but also improve the provider search experience to increase member retention and satisfaction, and ultimately help patients find better care.
The way to achieve this ultimate goal is to consume data. In the case of wellness programs, consumerizing data will involve repositioning current products and services to simulate a similar consumer experience for members, such as shopping online. In short, a health plan’s provider network should display insightful information about providers, allowing each user to personalize their care decisions. In this article, I explore several ways that health plans can return to this solution using their existing data. Done right, this approach can help health plans provide more client-centric services that help patients find differentiated, high-quality care that meets their specific needs.
Single source of truth for data from multiple sources
More data doesn’t necessarily mean accurate data or a better patient experience. What health plans do with this data drives member retention and acquisition. Health plans should view gaps in their current data as driving points for improving their current infrastructure and absorbing diverse data beyond traditional provider sources. This can help health plans position their provider search platform as a single source of truth for provider information across various data points. To do this, health plans must leverage a rich network of external data to extract disparate provider data beyond phone numbers, emails, and addresses.
because Consumers know that health plan provider directories are not always accurate, they seek information from many other sources, such as search engines, doctor-rating sites, and social media. The rise of ghost networks and outdated provider data has created a frustrating experience for members, which can lead to health plan failures if members travel to providers who are not actually in the network and receive unnecessary and expensive procedures and services. Additional charges. Instead, data should be validated, synthesized, and distributed from payers to members in a clear format to help members make confident decisions about their care providers.
The rise of telehealth is a relatively new but key factor to consider. Virtual care visits are cost-effective for members, providers and health plans alike.One McKinsey & Company analysis, May 2020 Suggesting virtual urgent care assistance could reduce emergency room visits by 20 percent. Including information such as virtual care availability and improving the overall comprehensiveness of the health plan provider directory can position the directory as a single source of truth. This reduces the friction and frustration that members experience when they are forced to search multiple sources to determine whether a doctor actually buys their insurance or actually treats knee pain rather than back pain.
Leveraging machine learning to drive actionable data
When consuming existing data, machine learning can be a health plan’s secret weapon. Health plans can improve their information about providers by acquiring data from outside sources—and there are clear opportunities for plans to use this data to offer consumers more personalized provider options.
In addition to phone numbers and addresses, health plans should expand their provider data to include considerations such as cultural background, language used, majors and areas of focus, and quality metrics. Health plans should also look at optimizing their current provider search experience to allow patients to drive their provider searches using personalized filters that include ratings and social/cultural parameters.
For example, if I’m looking for a provider that specializes in joint pain, is located in Idaho, has 4.5 star reviews from other patients, and speaks Spanish, I might have trouble finding this data through my health plan’s current provider . However, if this health plan leveraged the data provided by machine learning, I might experience a more seamless and accurate search experience.
A machine learning model would sift through thousands of search results and predict that Dr. Camila Velasquez, who practices at 41 Broadway, is the same provider as Dr. Camila Velasquez-Smith, who practices at 41 W Broadway and no longer practices at 66 Main Street. Instead of seeing 20 similar results for the same person, I was presented with an accurate and comprehensive list with the information I was looking for. Machine learning will also identify the numerous terms and professional focus areas into which “joint pain” can be categorized, and extract those results to make sure I see all the options available.
The results I get from my machine learning-powered provider search are personalized, focused, and filtered to match my quality expectations. As a patient, this saves me time and frustration and ensures that I am directed to a provider that fits my specific needs.
Enrich existing provider data to enable members to make personalized care decisions
Data is not enough unless it is personalized. Health plans can increase member trustworthiness and trust by offering personalized provider searches (like the example I shared above). With rich provider data, health plans can provide members with more personal information relevant to their care searches, thereby increasing member retention and increasing their market share through an enhanced member experience.
The opportunity to innovate here will only increase wellness programs.according to JD Power 2021 U.S. Business Member Health Plan Studyonly 22% of members said their wellness plan was “innovative” and mentioned the need to improve digital channels to increase customer engagement.
Research shows that when members see providers who share the same cultural background, speak the same language, or reflect their experiences, their Improved healthcare outcomes. Some states have taken a proactive approach to developing health plans to collect this information and share it with members.
Colorado, in particular, enacted the Colorado Option Rule on March 2, 2022, which requires health plans to collect demographic information, such as race, ethnicity, disability status, sexual orientation, and gender identity, from health professionals and enrollees, To improve membership and supplier matching. Such information is critical to enabling consumerized data and providing patients with a quality-criteria-centric experience to identify the right provider.
in conclusion
Ultimately, health plans and members share the same goal: to seamlessly navigate to the right in-network provider that best meets a member’s needs.
The current structure of health plan provider searches is not necessarily standard. Rapid consumerization in other industries provides health plans with a model for leveraging today’s technology to personalize the patient experience. Patients are the ultimate winners, as health plans aim to make this shift. For the first time, we can begin to see the network effects of enabling patients to have access to accurate, rich provider data to facilitate more convenient, cost-effective, and high-quality healthcare decisions.
