SHARP Project Wiki:Themes Projects

From SHARP Project Wiki
Jump to: navigation, search

Area 4: Themes & Projects

Through six steps, the Area 4 program will:

1.Standardize health data elements and ensure data integrity

Patient information can be stored using several different abbreviations and representations for the same piece of data. For example, “diabetes mellitus” (more commonly referred to as “diabetes”), can be referred to in a patient’s medical record alternately as “diabetic,” “249.00” and “DM.” The first phase of Mayo Clinic’s project, called “Clinical Data Normalization”, will work towards transforming this non-standardized patient data into one unified set terminology. In this case, “diabetes mellitus,” “diabetic,” “249.00” and “DM” would all be re-named “diabetes.”


2.Merge and standardize patient data from non-electronic forms with the EHR

Some important patient information, such as that from physician’s radiology and pathology notes, is stored in non-electronic, or “free text” form. Mayo Clinic’s project will first work to merge the patient information in free texts with that in the electronic health care record.

The next step of this project, called “Natural Language Processing” (NLP), will work towards classifying certain tags, such as “diabetic,” “DM” and “57 year old male” under specific categories, such as “disease” or “demographics.”

NLP, in addition to clinical data normalization, will help improve the efficiency of patient care by reducing inconsistencies in patient data, giving physicians more accurate and uniform information in a centralized location.


3.Seek physically observable patient traits for further study

Physically observable traits or phenotypes can include growth and development, absorption and processing of nutrients and the functioning of different tissues and organs. These traits result from interactions between a patient’s genes and environmental conditions.

Mayo Clinic will use a process called “High-Throughput Phenotyping”, which uses clinical data normalization and NLP to identify and group a particular phenotype, such as Type 2 diabetes.

This process will enhance a physician’s ability to identify and study individual phenotypes or groups of phenotypes.


4.Find processes to make clinical data normalization, NLP and high-throughput phenotyping more efficient using fewer resources

This part of the process will focus on building adequate computing resources and infrastructures to accomplish the previous steps. Called “Performance Optimization,” this system will allow for those seeking patient information to receive it quickly, increasing the efficiency of patient care.


5.Detect and reconcile inconsistent data

Mayo Clinic will utilize high-confidence services, or “data quality metrics,” to identify and optionally correct inconsistent or conflicting data.


6.Evaluate the progress and efficiency of Mayo Clinic’s project

Mayo Clinic will use an “Evaluation Framework” using the Nationwide Health Information Network (NHIN), an ONC program. NHIN is a set of standards, services and policies that enable secure health information exchange over the internet.