Data Quality In Healthcare

Introduction to Data Quality

Data consistency and usefulness should  be considered when determining data quality. An essential step in the management of data quality is its  evaluation. Data quality metrics are determined by data traits and successful  business outcomes from   insights. If there is a discrepancy in the data stream, we ought to be able to recognize the specific faulty data. The next step is to locate data inaccuracies that need to be fixed and determine whether the data systems is suitable for the intended use. Many initiatives could be doomed by issues with data quality, which could result in extra costs, lost sales opportunities, or fines for inappropriate financial or regulatory compliance reporting in any industry.

Today's organizations rely on data for all their decisions and view it as a crucial corporate asset. Data quality is becoming more important in company data strategy as business analysts and data scientists seek to find reliable data to power the  solutions.

Impact of Poor data quality

People in medical facilities employ database platforms and systems to acquire comprehensive perspectives of their operations, patient profiles, and overall results. With the amount of information that healthcare systems gather, track, and use, it is simple to see how poor data quality in these systems could cause doctors to draw incorrect conclusions or negatively impact their ability to make decisions.

Hence, Data from these sources must be accurate, complete, trustworthy, and correct to be valuable. Errors in decision-making, fatal mistakes in patient treatment, biased research results, and other serious issues are all caused by flawed data. While many healthcare facilities have gathered patient data, they have not yet created modern systems to uphold the caliber of the services offered. In Healthcare industry – Poor data quality and consistency would impact the following

    • Patient Experience

Healthcare data frequently have problems with poor data integrity. When information is input erroneously or insufficiently, it needs to be corrected or manually checked for the right records. Patients who require urgent care may experience delays and frustration because of this.

    • Employee Productivity

With numerous diverse systems storing vast amounts of healthcare data from various digital sources, it would be expensive, challenging, and time consuming to locate the right records leading to employee productivity

    • Poor Policy Decisions 

Since many business decisions are based on aggregated and modified information, any initial data entry mistakes cause issues when handled by other users or systems downstream over time.

How to ensure data quality in healthcare?

Here, we’ll look at different data quality techniques that can be used to identify and correct problems with various types of health data. Keep in mind that these organized procedures will assist you in correcting data quality issues that may be present. You must build an end-to-end data quality strategy to create a consistent plan for improving data quality.

    • Profile sources that store health data

Data profiling is the process of evaluating the present condition of data and revealing obscure aspects of its composition and organization. An algorithm for data profiling examines data and spots potential chances for data cleaning.

    • Add missing information

Once you have a list of the missing data, you must collect and complete  it (from the created data profile report). In some cases, you can find the missing data by looking at other datasets or getting in touch with the necessary patients or staff.

    • Clean and standardize data values

To achieve a uniform and useful picture across all data sources, data cleansing and standardization is the act of removing inaccurate and erroneous information that is included in a dataset.

    • Match duplicate patient records

The process of comparing two or more patient records to determine whether they belong to the same patient is known as patient data matching, also known as record linkage and entity resolution.

    • Deduplicate matching entities

Deduplicating data involves removing duplicate records that pertain to the same entity. This procedure aids in maintaining accurate data and removing duplicate records.

    • Merge records and retain information

Building rules that combine duplicate records via conditional selection and overwriting is known as data merging and survival. This aids in data loss prevention and redundant information retention.

    • Conduct routine audits for health data quality

One method of proactively identifying the issues that exist in the datasets of a health institution is to carry out audits to evaluate the quality of the data. These audits are scheduled in advance, and a summary of the audit's aims and objectives is provided. To quickly assess the present status of data quality, several auditors in the healthcare industry use self-service data quality tools on a subset of data.

    • Implement systematic data quality management

Executing data quality approaches sporadically may produce benefits but won't guarantee consistent outcomes. Put in place a data quality management system  so that new and upcoming data is batch processed for data quality checking and repair.

    • Deploy  data quality officers

The adoption of better data management techniques that reduce data loss and maximize data quality is the responsibility of on-site data quality officers. They are responsible for maintaining or supervising healthcare data.

    • Perform root-cause analysis for health data errors

Long-term error elimination can be aided by addressing the root causes of data quality problems. By taking a proactive approach, you may help our teams spend less time correcting data quality issues.

Our solution to problems with data quality

The C_CDA Scorecard's comprehensive scoring system, which enables implementers to improve data quality, and a few machine learning techniques applied for patient record matching, which is the process of identifying and linking medical records for the same patient across different data sources whether this is being done internally by a provider or through HIE among different providers, are among the solutions that the Xyram team has extensive experience implementing. possesses a wealth of experience managing data across systems as well.


Xyram Software Solutions has gained expertise over the years in building technology solution to ensure Data Quality in Healthcare domain. We have successfully implemented our proprietary knowledge, frameworks and tools in this area to impact positive outcomes for out customers. Please reach out  for further details to  help you in your data quality  journey

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