Data quality for healthcare

Identify patient data across multiple EHR records and databases. Clean and standardize inconsistent EHR fields, reconcile unresolved patient identities, and achieve a single patient view across your data ecosystem.

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How bad data affects healthcare?

100%

Patient matching remains a critical challenge

38 percent of U.S. healthcare providers have incurred an adverse event within the last two years due to a patient matching issue.

Absence of patient identifier

Healthcare providers lack a unique patient ID with which to accurately link records across thousands of records.

Duplicate medical records

Multiple name variations, varied data formats, and data entry errors can increase the complexity of existing datasets.

Incorrect diagnosis

Poor patient matching can lead to patients diagnosed with wrong prescription drugs or cause delays in treatment.

Higher operating costs

Duplicate records and denied claims due to complex data silos can cost hospitals thousands of dollars per patient.

ICD-10 classification issues

Healthcare providers are required to accurately map 14000+ diagnostic codes with their clinical practices for compliance purposes.

Inconsistent data standards

Lack of data governance standardization rules can lead to varied data formats, preventing a single patient view.

Solution

DataMatch Enterprise – A robust patient matching software

DataMatch Enterprise is Data Ladder’s enterprise-grade matching engine designed to deliver precise patient matches across billions of EHR records through batch scheduling or real-time API flows. Its seamless user interface and turnkey matching and cleansing options make improving patient data an effortless task.

Customer Stories

See what healthcare providers are saying...

Business Benefits

What’s in it for you?

Enhanced interoperability

A single patient view across internal and external systems can optimize data sharing among relevant stakeholders as required.

Reduce healthcare costs

Lack of duplicate medical records and inconsistencies can help avoid unnecessary treatment equipment and medical staff expenses.

Effective patient care

Efficient matching can ensure a patient’s history is correctly linked with the right diagnosis and treatment, resulting in improved patient satisfaction.

Rapid ICD-10 classification

Save hundreds of man-hours in mapping the sheer number of ICD-10 diagnostic codes with medical procedures and practices.

Better visibility

Consistent data standards and lack of data silos can improve tracking of patients across multiple hospital visits, medical tests, and treatment procedures.

Lower patient waiting times

Real-time API integration and data cleansing flows can lower the time-to-insight and find matches in minutes, reducing treatment delays.

Want to know more?

Check out DME resources

Merging Data from Multiple Sources – Challenges and Solutions