While a seismic shift in the administration, receipt, and payment for healthcare in the U.S. Quality has become a hot topic of discussion among stakeholders, change has been slow. The definitions of quality and value are rarely simple to quantify, and waste and error remain ubiquitous.
The U.S. healthcare system’s information technology infrastructure is a house of cards when it comes to interoperability and clinical data usability. Efficiently sharing patient and member information among clinicians, practices, payers, and patients is an ongoing challenge and this house of cards often collapses due to complex interoperability and data quality issues.
Payers typically have no shortage of clinical data at their disposal, with information from health screenings, health assessments, electronic health records (EHRs), clinician notes, test results, and more. But the quality of the data is suspect. In our experience, only about 40% of the clinical data our clients receive from providers or health information exchanges are usable, without need for curation and enrichment. The remaining 60% are inaccurate, incomplete, duplicative, or unstandardized. It requires various levels of curation and enrichment to make data standardized, accurate, and complete.
Black men face many risk factors for being diagnosed with prostate cancer, including a lack of access to healthcare, racial bias that can cause black men not to seek treatment, socioeconomic status, and delayed care. Yet if prostate cancer is detected at the early stages of development, the survival rate is 99%.4 The study's conclusion is that PSA screenings are not only more effective than scientists thought for the general population but benefit black men more than other races. Due to prostate cancer's prevalence in black men, they should be screened earlier and more frequently to monitor and mitigate their risk.
The 21st Century Cures Act was designed to give consumers greater access to their own clinical data and further advance interoperability. Yet, while these two goals are laudable, the Act does not make it any easier to get meaningful insights from payer data. This is because so much of the growing mountain of ingested data is unusable that it must be curated and enriched before it can yield anything of value.
Incomplete, inaccurate, or delayed data analyses can all negatively impact clinical decision-making, case management, and disease management interventions. Payers looking to identify gaps in care often utilize claims data that can take many months to receive face challenges obtaining accurate high-quality clinical data from their contracted providers. Data curation services, which acquire, standardize, and normalize clinical data, can present health plans with a complete picture of member encounters.
The promise of precision medicine is growing as fast as the volumes of data required for its application. Precision medicine structures a person’s disease prevention and treatment in relation to their genomics, environment, and lifestyle.
The demand for accurate and timely insights from a fractured and siloed health ecosystem, spanning payer, provider, and healthcare organization information infrastructures, has never been greater. New technologies and innovations have proven to be a burden on the healthcare system, including new payment models where patient outcomes are being rewarded more than the quantity of services provided. Learn more from this white paper about what you can do to solve the problem.
Transforming raw data into actionable insights is essential for Fast healthcare Interoperability Resources (FHIR) standards and improving HEDIS scores. Learn more from this roadmap on how to achieve scalable efficiencies for managing clinical data at the enterprise level. The strategic value of clinical data is growing significantly for payers, but finding the right types of clinical data and gaining access to it remain challenges in the current healthcare ecosystem.
Verinovum, Inc. recently utilized marketing partner Envision Health to execute a third-party market research study of Medicare Advantage (MA) programs in seven payer organizations of varying sizes and demographics. The goal of the study was to better understand the role and structure of MA programs within different kinds of payer organizations and how these payers and programs obtain and use clinical data for their most pressing needs.