How health plans can improve member experience through consumerized data

It’s no surprise that many health plans are rapidly changing their models to improve provider errors, enable members to get the right care, and avoid costly fees for inconsistent data. With ghost networks recently in the headlines and inaccurate data affecting patients’ ability to access care, health plans across the country need to take immediate action to rethink their current strategies to improve member experience and save money through automation.

In 2017, The Centers for Medicare & Medicaid Services (CMS) conducted a survey of online health care providers and found that 46% of the sites reviewed had incorrect information that resulted in them not receiving care. While inaccurate data is a factor in patient access, patients still trust health plans to provide accurate information to help them determine who is the right fit, according to the JD Power 2021 US Commercial Member Health Plan Study.

So what does this mean for health plans? Health plans have the opportunity to develop their technology tools to not only change their offerings in real time but to improve service provider research to increase retention and member satisfaction, and ultimately help patients get better care.

The way to achieve this goal is to use data. Purchase data, in the case of health plans, may include redesigning products and services available to meet the needs of consumers, including online shopping, for members. In short, health plan provider networks need to display service information that allows each user to choose how to care for their needs. In this article, I explore a number of health strategies that can be re-introduced into this solution and those that already exist. This process, if done correctly, can help health plans to provide customer-oriented services that help patients find different, high-quality care that meets their needs.

A single source of truth for data from multiple sources

More information does not necessarily mean more accurate data or better patient experiences. It’s what health plans do with that data that drives member retention and access. Health plans need to see gaps in their knowledge as a way to manage their resources and capture information that isn’t coming from providers. This can help health plans position their search platforms as a single source of truth for provider information on a variety of issues. In order to do this, health plans must use rich, external networks to enter a variety of data outside of phone numbers, email, and addresses.

Because consumers know that health advice is not always accurate, they seek information from many other sources such as search engines, doctor’s offices, and social networks. The rise of ghost networks and long-term provider data has led to member frustration and can result in additional costs for health plans if a member goes to an out-of-network provider, making unnecessary and expensive procedures. Instead, data must be verified, compiled, and shared from payer to member in a clear way to help members make informed decisions about their care provider.

The rise of telehealth is a new and important thing to consider. Health care visits are affordable for members, providers, and health plans. An analysis by McKinsey & Company in May 2020 showed that providing urgent care can reduce emergency room visits by 20%. Including information such as the availability of real care and improving the clarity of the directory of health care providers can position the directory as a single source of truth. This reduces the confusion and frustration members face when they are forced to search multiple locations to find out if a doctor accepts their insurance or treats knee pain, not back pain.

Using data processing systems for action

Machine learning can be a key tool for health plans when it comes to leveraging existing data. Health plans can improve their information on providers by pulling data from external sources – there is also a clear opportunity for plans to use this data to help consumers with more personalized choices.

In addition to phone numbers and addresses, health plans should expand their sponsor information to include things like culture, languages ​​spoken, special skills and areas of expertise, and quality indicators. Health plans should also look to optimize the search results of their current providers to allow patients to manage their search with personalized filters that include ratings and social/cultural categories.

For example, if I am looking for a therapist who treats mainly joint pain, who is located in Idaho, has 4.5 star reviews from other patients, and can speak Spanish, I may have trouble finding this except through my health plan. data. However, if this healthy system were to support data informed by machine learning, I would be able to experience better and more accurate searches.

Machine learning can analyze thousands of results and predict which Dr. Camila Velasquez who performs at 41 Broadway is the same assistant as Dr. Camila Velasquez-Smith is currently performing at 41 W Broadway and is no longer performing at 66 Main Street. Instead of seeing 20 identical results for the same person, I’m presented with an accurate and integrated list that contains exactly what I’m looking for. Machine learning also recognizes many terms and specific areas that “intermediate pain” can be classified into, and pulls the following to make sure I see all the options available.

The results I get from searches powered by machine learning are standardized, averaged, and filtered to match my expectations. As a patient, this saves me time, frustration, and ensures that I am directed to the right provider for my specific needs.

Leveraging existing information to enable more personalized care decisions