Data quality for healthcare
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How bad data affects healthcare?
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
Customer Stories
See what healthcare providers are saying...
DataMatch Enterprise™ was much easier to use than the other solutions we looked at. Being able to automate data cleaning and matching has saved us hundreds of person-hours each year.
We liked the ability of the product to categorize the data in the way that we need it, and its versatility in doing that.
The idea of linking two groups of records was overwhelming for the research department. The process would be very time-consuming and threaten the timeliness and process of the research activitie'
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
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Building a case for data quality: What is it and why is it important
According to an IDC study, 30-50% of organizations encounter a gap between their data expectations and reality. A deeper look at this statistic shows that:
Data quality API: Functions, architecture, and benefits
While surveying 1900 data teams, more than 60% cited too many data sources and inconsistent data as the biggest data quality challenge they encounter. But
Building a case for data quality: What is it and why is it important
According to an IDC study, 30-50% of organizations encounter a gap between their data expectations and reality. A deeper look at this statistic shows that:
Data quality API: Functions, architecture, and benefits
While surveying 1900 data teams, more than 60% cited too many data sources and inconsistent data as the biggest data quality challenge they encounter. But
Batch processing versus real-time data quality validation
A recent survey shows that 24% of data teams use tools to find data quality issues, but they are typically left unresolved. This means that
