How data warehouses can speed identification of rare diseases

Because of the rapidly increasing number of rare diseases, a database that contains phenotypic and genomic data can accelerate the decision making process for treatment.

There are more than 7,000 rare diseases for physicians to consider when diagnosing a patient. For even the most experienced physician, this is a daunting task. This is further compounded by the fact that one in ten people have one of these diseases.

Recognizing and sifting through a plethora of signs and symptoms takes an average of seven years and seven doctors, leaving patients suffering for years from rare diseases and a lack of answers. It takes an emotional and financial toll on patients and their families as they wait, hoping for a positive health outcome.

The future demands a new approach, where phenotypic and genomic data are captured and stored in a way that is easily understood and used to make more informed decisions during patient evaluations, improving the diagnostic process of rare genetic disorders.

Phenotypic information is the clinical description of the patient as they really are, such as having a broad neck or cognitive delay. This information is complementary to genotype information for the clinical reality of the patient is often a result of their genetic code. Without precise and comprehensive phenotypic information, it is nearly impossible to identify which of the patient’s genes are being expressed and may be responsible for the disease symptoms. New technologies have made it possible to analyze phenotypic information to highlight the clinically relevant disease-causing genetic variants, in turn, increasing the chances of honing in on the correct diagnosis.

Like any data-driven process, having phenotypic and genotypic information stored in a secure yet accessible way is critical. Hospitals need a system that can sift through the enormous amount of clinical and genetic information quickly and accurately, to accelerate the diagnostic process in a scalable way.

Achieving a diagnosis sooner gives patients the answers they are looking for and enables healthcare providers to offer treatments and solutions that will improve their overall health. With the guidance of their providers, patients are able to make more informed decisions, in regards to prognosis, managed care, therapeutics options and family planning. Furthermore, access to this growing database will support new diagnostically relevant findings to assist in the diagnosis of future patients.

Many hospitals express desperation for a solution to capture and organize this data in an objective and useful way. Of primary concern is choosing a capable technology that can capture and analyze this data in a way that improves decision-making, workflow and quality of life for rare disease patients. Implementation of a phenotypic and genotypic data warehouse can add this new dimension of objective data that may help clinicians make progress on cases that they otherwise could not have.

With hospitals using electronic medical records (EMR) systems for patient information, they would appear to be the obvious choice as a point of collection of this data. However, most EMRs are not designed to support the specific details of genetics and phenotyping in a structured way. A data warehouse for genetic disorders, such as Face2Gene, must be used to capture and store the incredibly complex information. Through integration to EMRs, the phenotypic and genotypic information and insights become accessible to physicians through their normal workflow. This delivers faster turnaround time for genetic testing and supports more insightful research by clustering patient cohorts.

Genetics and phenotyping represent a key to understanding disease states, not only for genetic disorders but also for areas like neurology, oncology, musculoskeletal and psychiatry. With the promise of big data unfolding the genetic cues for many other medical conditions, this data will play a key role. The future demands this new approach, using a functional phenotypic and genotypic data warehouse to make more informed decisions.

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