Systems Offer Centralized Mechanism for Evaluating the Completeness and Accuracy of Patient Files in Medical Databases

Technology #11503

Key Points

  • Systems for determining the completeness, accuracy, and consistency of data in patient electronic medical records (EMRs)
  • Provides quantitative assessments using novel scoring algorithms and statistical techniques
  • Can be used to analyze one or more databases and then compare results across databases


Researchers at the University of Central Florida have developed technologies for quantitatively evaluating, tracking and managing patient medical records. With unique methodologies and statistical analyses, the technologies provide tools for assessing the strength, completeness, consistency, and accuracy of patient electronic medical records (EMRs) in one or more databases. One of the inventions also allows organizations to track chronic condition diagnoses and determine plans for staging and managing the conditions. By helping organizations identify the strengths and shortcomings of their record-keeping procedures, the UCF technologies provide the healthcare industry with a clearer path to ensuring the highest standard of care.

Technical Details

Patent ID 33510 and 34394, Method and System for Managing Healthcare Patient Record Data: These inventions provide methods and systems for managing patient EMRs in terms of completeness, consistency, and accuracy relative to established guidelines. One tool that the systems use to assess patient EMRs is the Data Completeness Analysis Package (DCAP). DCAP analyzes a patient’s medical records holistically to identify the lack of pertinent and necessary patient data. Once implemented, DCAP uses scoring algorithms and robust statistical analysis techniques to determine the completeness of individual patient records as well as aggregate patient records across health care centers and subpopulations. The system can also verify individual fields for completeness across an entire database.

Additionally, technology 34394 enables organizations to assess and compare two or more EMR databases as well as comparing the strength of a selected data field across the databases. The invention’s graphical user interface can provide remote access to a database over a network.

In one example implementation, DCAP generates visual representations (concept maps) of data in conjunction with statistical analysis. The concept mapping method works as a schema to represent stored data that users can uniformly examine regardless of the platform that originally held the data or other health care protocols that may make cross-examinations of data sets more difficult. Once developed through DCAP, the system converts the concept maps to standard data format CSV (comma-separated values) files. The CSV files are analyzed through a parser that allows the user to determine the strength of individual patient records and record-keeping throughout a particular database.

The result is a Record Score Strength (RSS) that is based upon the care provider’s input of Importance Weights (IW), along with a Patient Database Score (PDS) that defines the overall strength of the records. Using database segmentation techniques, DCAP also generates a Patient Subgroup Score (PSS) to compare subpopulations of patient records. It attains the PSS by averaging the RSS scores of the patients of interest by age, race, gender, and insurance status.

Patent ID 34565, Method and System for Managing Chronic Illness Health Care Records: The invention is a system and methods for identifying inconsistencies in the EMRs of patients with chronic illnesses. The system includes a scoring algorithm called Chronic Condition Mapping Package (CCMP) which can track chronic condition diagnoses in terms of completeness, consistency, and accuracy relative to established guidelines.

In one example use, the system first converts native EMR data into a standardized comma-separated values (CSV) data format using concept mapping to identify how data is stored. It then extracts data relevant to chronic condition diagnoses, including encounter level data, subjective narrative data, and objective data. The encounter level data can identify the stage of a chronic illness at each encounter to determine if and how the chronic illness has changed. Afterward, it analyzes the data relative to data translators, such as an ICD-10 chronic condition identifier. Once it processes the data, the system uses a complex scoring algorithm to identify and compare scores that incorporate the algorithm's aims. The scoring algorithm enables organizations to compare patient records, subpopulations, and databases, both at a chronic condition level and across chronic conditions. Scores form the basis for improvement in chronic condition data capturing and improving healthcare delivery for patients. The system also generates alerts to inform users when a pre-existing chronic illness is not subsequently identified or if a subsequent diagnosis indicates that the condition has changed by some predetermined amount.

Partnering Opportunity

The research team is looking for partners to develop the technology further for commercialization.

Stage of Development

Working prototypes available via test environment.


  • Strengthens patient data quality while reducing costs
  • Improves record-keeping procedures
  • Enables proper staging and management of health conditions
  • Easy to implement

Market Application

  • Hospitals
  • Health care centers
  • Medical clinics
  • Private practices and medical groups