--- title: Mitigating data disruptions in open claims datasets description: Discover how to navigate disruptions in open claims datasets, ensuring data integrity and analytics accuracy with HealthVerity's proactive strategies. image: https://blog.healthverity.com/hubfs/Mitigating%20data%20disruptions%20-%202.png --- Announcing HealthVerity Precision Event Alerts, a next-generation physician alerting solution. 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Some disruptions expand what you can do: new data owners entering the space, evolving collection methods and new data aggregators or suppliers. Others are the kind that can stall a project overnight, like a cyberattack across the industry or a data owner walking away from the commercial market. This discussion focuses on open claims datasets, which are uniquely susceptible to certain types of disruptions due to their structure and sourcing. While closed claims and EMR/EHR databases may also experience disruptions, they typically differ in nature. For example, EMR data is often sourced from integrated systems like health plans or provider networks, where both Rx and medical data are captured together, making isolated Rx or Mx losses less likely. Additionally, product blocking and/or unblocking is generally not a concern in closed systems. You can learn more about open, closed and integrated claims datasets in our blog: [An integrated approach for deeper insight with both open and closed claims data](https://blog.healthverity.com/an-integrated-approach-for-deeper-insight-with-both-open-and-closed-claims-data) Regardless of whether a disruption is perceived as positive or negative, it will impact your analyses and create extra work for you. Here are some common scenarios and their implications: ## Open claims data disruptions and their effects - **Rx data supplier loss/addition**: May lead to shifts in market share, longitudinal trends, temporary spikes in new to brand prescriptions (NBRx) and changes in eligible patient cohorts - **Medical claims (Mx) supplier loss/addition**: Can affect diagnosis associations, medically-administered drug volume and share, new patient starts, and first diagnosis dates - **Blocking/unblocking of products**: May alter market share visibility, longitudinal analysis capabilities, and cause artificial spikes in NBRx - **Partial losses**: Some losses or gains are partial, for example select practices drop out of a medical claim supplier or new pharmacies or volume from existing stores become commercially available suddenly. In these cases it can be more difficult to mitigate the impact particularly if the shift is sudden. These disruptions can influence key metrics such as market share, line of therapy, treatment rates, triggers and market sizing. ### Impact on key analytics - **Market share**: Variations in claims can shift market share. Changes in the medical claims sample could also disrupt the share by indication, even if the Rx volume remains unaffected. Disproportionate changes may affect both manufacturer’s and competitors - **Treatment rate/line of therapy**: The addition or loss of diagnosis, procedure or Rx claims may shift treatment start dates and therapy lines with respect to a time dimension – time to treatment initiation, time to advance line of therapy - with the impact varying based on market dominance - **NBRx/business source**: New treatment (pharmacy or medical) data can cause temporary NBRx spikes due to missing historical context when the addition is point forward only. This normalizes once history is restored. Rather than seeing an immediate spike this disruption will be reflected in a reset of trends at a new, lower, normal - **Triggers**: Claim volume changes can affect field alert counts and eligibility, requiring updated business rules to maintain accuracy - **Market sizing**: While typically based on projections rather than raw patient or claim counts, fluctuations in diagnosis, procedure, and prescription claims can still influence key metrics such as compliance and persistence. These shifts may reflect broader disruptions in the healthcare landscape, ultimately impacting the financial dynamics of market insights ## How HealthVerity supports you through disruptions At HealthVerity, we recognize that data disruptions are inevitable. Whether anticipated or unexpected, our commitment is transparency and proactive communication. The disruptions themselves may be unavoidable. Their impact on your work does not have to be. We mitigate disruptions with: - **Advance notifications and impact analyses** for known disruptions - **Timely alerts and post-event assessments** for unforeseen events - **Quality Control (QC) reports** to monitor data volume changes - **Ad hoc exclusion lists** for pharmacy-related or facility-related disruptions. - **Customizable business rules** to mitigate disruption effects, such as: - Temporarily excluding affected patients or stores - Refining NBRx definitions to ensure accuracy - Maintaining historical data integrity ## A collaborative partnership for resilience Another way to minimize disruption is through a strong partnership with your data supplier. By aligning on metrics, sharing methodologies and implementing targeted checks, your data supplier can ensure the integrity and continuity of your open claims data. ## How to leverage your data Optimize your data and minimize the negative impact of disruption by taking the following steps: - Applying the Symphony Health patient, pharmacy and physician activity panels to identify and filter data gaps - Adjusting business rules to minimise artificial trend breaks - Sharing key metrics and business rules to tailor impact analyses and reporting   HealthVerity is committed to data transparency and proactive support. Whether you are building the business case for a supplier change or responding to an unexpected event, our team will help you keep confidence in your analytics. See how HealthVerity + Symphony Health support your data strategy at[healthverity.com/symphony-health](https://healthverity.com/symphony-health/). 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[READ MORE](https://blog.healthverity.com/mitigating-data-disruptions-in-open-claims-datasets) #### Who we serve - [Life Sciences](https://healthverity.com/life-sciences/) - [Government](https://healthverity.com/government/) - [Insurance](https://healthverity.com/insurance/) - [Channel Partners](https://healthverity.com/channel-partners/) - [Data Ecosystem Partners](https://healthverity.com/data-ecosystem-partners/) #### How we help - [HealthVerity Marketplace](https://healthverity.com/marketplace/) - [HealthVerity Mastersets](https://healthverity.com/mastersets/) - [HealthVerity FLOW](https://healthverity.com/flow/) - [The IPGE Approach](https://healthverity.com/ipge-approach/) #### Resources - [HealthVerity Blog](https://blog.healthverity.com/) - [News & Events](https://healthverity.com/resources/news-and-events/) - [Resource Library](https://healthverity.com/resources/resource-library/) - [Publications](https://healthverity.com/resources/publications/) #### Our company - [About Us](https://healthverity.com/about-us/) - [Leadership](https://healthverity.com/about-us/#leadership) - [Board of Directors](https://healthverity.com/about-us/#board) - [Careers](https://healthverity.com/careers/) - [Contact](https://healthverity.com/contact/) ©2026 HealthVerity, Inc.    |    [Terms & Conditions](https://healthverity.com/terms-and-conditions/)    |    [Privacy Policy](https://healthverity.com/privacy-policy/) ```json { "@context" : "https://schema.org", "@type" : "BlogPosting", "author" : { "@type" : "Person", "name" : "Michael Eckrote" }, "datePublished" : "2026-07-30", "description" : "Learn how healthcare data disruptions impact open claims datasets. Discover strategies for mitigating issues like supplier losses, product blocking, and cyberattacks to maintain accurate analytics.", "headline" : "Mitigating data disruptions in open claims datasets", "keywords" : [ "open claims datasets", "healthcare data disruptions", "medical claims", "data transparency", "real-world data", "RWD", "Symphony Health", "market share analytics", "claims data mitigation" ], "mainEntityOfPage" : { "@id" : "https://blog.healthverity.com/mitigating-data-disruptions-in-open-claims-datasets", "@type" : "WebPage" }, "publisher" : { "@type" : "Organization", "logo" : { "@type" : "ImageObject", "url" : "https://healthverity.com/wp-content/uploads/2023/08/healthverity-logo.png" }, "name" : "HealthVerity" } } ``` ```json { "@context" : "https://schema.org", "@type" : "FAQPage", "mainEntity" : [ { "@type" : "Question", "acceptedAnswer" : { "@type" : "Answer", "text" : "Open claims datasets are uniquely susceptible to disruptions because of their structure and how they are sourced from multiple, broad-based healthcare providers and clearinghouses. Unlike closed claims (which are contained within a single payer network), open claims rely on various data owners, making them vulnerable if a specific supplier leaves the commercial market, changes collection methods, or experiences a cyberattack." }, "name" : "Why are open claims datasets susceptible to data disruptions?" }, { "@type" : "Question", "acceptedAnswer" : { "@type" : "Answer", "text" : "Common disruptions include the loss or addition of medical claims suppliers, the blocking or unblocking of specific products, and partial losses (such as specific pharmacies or practices dropping out of a dataset). These can also include sudden industry-wide events like cyberattacks." }, "name" : "What are common types of disruptions in healthcare open claims data?" }, { "@type" : "Question", "acceptedAnswer" : { "@type" : "Answer", "text" : "Disruptions can severely impact key analytics such as market share, line of therapy, treatment rates, and market sizing. 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They provide advance notifications for known disruptions, Quality Control (QC) reports to monitor data volume changes, and customizable business rules (such as applying Symphony Health activity panels) to help clients maintain confidence and integrity in their analytics." }, "name" : "How does HealthVerity handle healthcare data disruptions?" } ] } ``` ```json { "@context" : "https://schema.org", "@type" : "BreadcrumbList", "itemListElement" : [ { "@type" : "ListItem", "item" : "https://healthverity.com/", "name" : "Home", "position" : 1 }, { "@type" : "ListItem", "item" : "https://blog.healthverity.com/", "name" : "Blog", "position" : 2 }, { "@type" : "ListItem", "item" : "https://blog.healthverity.com/mitigating-data-disruptions-in-open-claims-datasets", "name" : "Mitigating data disruptions in open claims datasets", "position" : 3 } ] } ```