Our Services

Anolinx™ specializes in the innovative use of real-world healthcare data (RWD) to generate real-world evidence (RWE) in support of clinical research, drug development and improving health outcomes.

Pharmacoepidemiology

Health Economics

Outcomes Research

Protocol Feasibility & Optimization

Analysis of Protocol Competitiveness

Data-Driven Site Identification

Observational Research

Systems Analysis

Natural Language Processing (NLP)

Virtual Registries

Multi-Site Studies

Validation Studies

Biomedical Informatics

As a specialized clinical research organization (CRO), Anolinx™ helps you make more informed decisions as you strive to improve health outcomes and develop new medicines. We do this by analyzing identified and de-identified electronic health records (EHR) for a variety of clinical research activities.

Utilizing our own proprietary tools, including advanced informatics technologies & methodologies, we are able to identify patient populations in our data sources that can’t typically be found in other administrative claims and health-care data. Most organizations are limited to using billing codes (such as ICD-9/ICD-10) to identify patients; however, our clients are often interested in studying patients with a unique medical condition, a sub-type of a given medical condition or patients with other qualifiers (e.g., moderate-severe disease). Anolinx™ is able to search the clinical documents available in EHR/EMR data to accurately identify these patients and related outcomes of interests.

Leveraging our real-world healthcare data, we help our clients generate novel evidence, improve health outcomes and make more informed decisions as they strive to develop new medicines. We identify, explore, describe and quantify patient populations; and carefully study treatment and referral patterns, compare patient populations, determine unmet medical needs and define baseline rates of outcomes of interest, including potential adverse events.

Natural Language Processing

The majority of electronic clinical documentation is stored as “free text” rather than as structured, coded data. An advantage of free text is that it gives clinical authors autonomy in expressing their thoughts. The variety of ways used to express information in text means that although this data is rich and descriptive, it is locked away, unable to be used in computerized research and decision support.

Anolinx™ overcomes this hurdle by employing a variety of natural language processing (NLP) methods to extract concepts, context, and relationships found in narrative text. In our projects, specific NLP tools are developed, tested and optimized for accuracy and reliability, according to the definitions for the cohort criteria and/or outcomes of interest.

While NLP is not a “solved” science, there are many tasks that NLP can do very reliably including extracting concepts (symptoms, diseases, medications) and values (ejection fracture values, lab values, vital signs) that are stored in the text. More complex tasks, such as determining what caused an event of interest or why a patient discontinued a medication can be extracted to answer specific study questions.

Use of NLP in our projects often includes:

  • Defining each clinical concept in detail by qualified clinician specialists and NLP experts
  • Developing NLP algorithms & tools based on the detailed definitions
  • Iteratively training the NLP tools using information from the clinical notes
  • Testing the NLP tools for accuracy & reliability against a manually annotated sample of the clinical notes

Anolinx™ utilizes manual chart review, the gold standard for validation of the NLP tools. This process results in a validated tool to accurately and reliably identify patients and outcomes of interest.