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Nontraditional data such as geolocation, credit card transactions, web usage, app analytics, receipts, satellite images or obscure records that capture a more robust profile of who a de-identified patient is and can be used to determine commonality across demographically diverse groups.

A typical, regularly occurring disorder or condition that frequently appears in the population (e.g. diabetes). Therapies or standard-of-care treatments are more likely to exist for common diseases and to be pervasive in the market than for rare or uncommon diseases, which affect a much lower percentage of patients.

Any of nine values that divide data into ten equal parts.

First-party data is audience or customer data obtained directly by a company.

All members that make up the health care community including the patient, provider and stakeholders, and extending to the life sciences and healthcare industry, regulators and payers into the system.

A classification system used by US HCPs to code all diagnoses, symptoms and procedures recorded in conjunction with hospital care.

A cohort comprised of diagnosed and/or treated patients, undiagnosed or misdiagnosed patients, patients ready to switch therapies or patients about to progress or relapse that can benefit from medical intervention.

Uncovering those who look and act like the members of a target audience.

A unique, anonymized identifier for a health care provider used in compliance with HIPAA law.

The number of patients treated or evaluated usually in a set time period, such as a hospital shift.

Precision medicine factors in a patient's genetics, lifestyle and environment to determine the best treatment specifically tailored to them.

A tendency towards behavior or action.

Data derived from a number of real world sources such as patient surveys, electronic health records (EHRs), claims and billing activities, product and disease registries or biometric monitoring devices.

The impact of socioeconomic factors on health, including access to healthcare and treatment outcomes.

A technology platform that facilitates gathering, organizing, transforming, consuming, and analyzing diverse sets of data with statistical modeling tools to detect patterns, report on past events, predict outcomes with a high degree of confidence, apply business rules and policies and provide actionable intelligence.

A concept that utilizes Machine Learning (ML) and Artificial Intelligence (AI) in conjunction with a curated pool of unified data sets to enhance decision-making.

An excess of data that can't be captured or analyzed, often incomplete or a violation of privacy.

A drug administered at an earlier interval in a disease progression that may stop or slow a condition.

An individual with a specialized education who is directly engaged with the provision of health services such as disease diagnosis, care and treatment.

A federal law that largely protects patient privacy and prevents the disclosure of health information without explicit patient consent or knowledge.

An ICD-9 is defined by a shorter code than an ICD-10 and conveys less specificity.

A type of research design involving the repeated monitoring of study subjects over the short or long term.

A consulting HCP with extensive industry experience that establishes and maintains relationships with leading physicians, researchers, and clinicians at academic institutions, hospitals or practices.

A rare disease affecting fewer than 200,000 people or a common disease that is prevalent in the developing world (such as malaria, cholera and tuberculosis).

The entire sequence of events that a patient experiences within a given healthcare system or across providers, from scheduling an appointment and being diagnosed to receiving treatment for an illness or injury.

Embedding privacy into operations via privacy-by-design and meeting HIPAA/NAI compliance requirements by concealing patient identity through tokenization.

While there is no universal definition, a disease is defined as rare in the United States if it affects less than 200,000 people (620 patients per million). Today, there are nearly 7,000 known rare diseases affecting 30 million people in the US. While each disease affects a small patient population, as a category, rare diseases have a significant impact on patients, their caregivers, and the healthcare system. Rare diseases are devastating to children and families as 95% have no pharmacological treatment options, exacerbating misdiagnosis and resulting in significant healthcare spend that does not improve patient outcomes. Many of these diseases are chronic, debilitating, or fatal. In the small number of cases where treatments exist, they are often complex, costly, and patients may wait years for a correct diagnosis. Rare diseases are challenging to diagnose because patients, families, and physicians have little awareness of the disease and its often complex symptomatology.

Evidence obtained from the analysis of real world data (RWD).

Largely caused by lifestyle factors, specialty diseases are typically of polygenic origin, meaning that the disease mechanism involves the combined action of multiple genes. With specialty diseases, a standard of care is generally well-established and patients usually have access to multiple treatment options that are approved and available. While there are exceptions, rare cancers are generally much better studied and characterized than non-oncological rare diseases.

The process of turning an identifiable piece of data such as an account number into a random string of characters called a "token" that has no meaningful value if breached — tokens serve as a reference to the original data but conceal actual values.