In recent years, healthcare organizations have begun to acknowledge data quality challenges in the industry. Our team has worked closely with private healthcare businesses, government agencies, and local hospitals to improve their data quality to achieve targets such as organizational efficiency, patient care and treatment, medical research and advancement, public health monitoring and surveillance, health and policy decision making among a broad range of applications. 

Today, as healthcare organizations are preparing to use electronic health records (EHR) extensively to improve the delivery of care, data quality remains a top priority. 

In this article, we share our experience and insights on priorities that when implemented strategically helped healthcare organizations improve efficiency as well as effective decision-making. 

Here goes. 


Priority #1:  Identifying Reasons Behind Poor Data Quality 

healthcare data quality

Data quality problems affect downstream processes in the form of flawed insights and misleading information. In the case of healthcare data, it could be lethal medical errors, poor patient diagnosis and care, or inadequate treatment planning. When this happens, organizations tend to focus on solving the problems at face value. They seldom do a deep-dive into understanding the underlying causes of poor data quality. 

For example, a client we worked with discovered their software design did not have proper validation mechanisms in place, causing the system to accept erroneous or incompatible entries. This resulted in medication records with incorrect dosages, misspelled drug names, or conflicting instructions. Such flawed data can lead to medication errors, potentially causing patient harm or adverse drug interactions.

Software systems become difficult and costly due to increasing size and complexity. Additionally, software improvements and upgrades also cause disruptions to the data collection and entry process, leading to flawed data. This is also rampant in web-based forms and user interfaces where the lack of user input validation causes significant errors such as typos, transposition errors, duplicates, and incomplete data. 

Sometimes, the cause of poor data is a software, hardware, or a technical problem, and not necessarily a process problem. Identifying the cause enables better data quality management! 


Priority # 2: Standardizing Multisource Data Inconsistencies 

Data sharing among multiple sites is becoming increasingly important in healthcare especially since healthcare data is used at government level to study public health at a large scale. However, the usefulness of these shared data repositories depends on the quality of the data they contain. Ensuring high-quality data in these repositories is a challenge for healthcare professionals and researchers who need access to accurate and complete information.

There are several reasons why the quality of data in these repositories can be a problem. Differences in how data is collected, managed, and the policies surrounding healthcare can lead to variations in the data. Errors can also occur during the input and management of data, both by accident and on purpose. These differences and errors can cause variability in the data over time and between different sources.

This variability in data can cause issues. It can lead to inaccurate and unreliable results, making it difficult to draw meaningful conclusions from the data. When data from multiple sources or over time is used together, it is important to assume that the data is consistent. However, if there are differences in the data distributions, this assumption may not hold true. These differences can make it challenging to reuse data from these repositories for research, clinical trials, or other analyses. They can introduce biases and weaken the validity of any conclusions drawn from the data.

To resolve this specific challenge, organizations must prioritize the implementation of standardized data governance practices. Establishing clear guidelines, protocols, and frameworks for data collection, management, and sharing is essential to ensure consistency and integrity across different data sources. By promoting standardized processes and enforcing data quality standards, healthcare organizations can minimize variability, reduce errors, and enhance the reliability and usability of shared data. 


Priority # 3: Resolving Common Data Defect Type 

Data quality by itself means, data that is fit for use. Therefore, data that has a large number of defects such as: 

healthcare data defects
Types of data defects by Yili Zhang and Güneş Koru in the paper, “Understanding and detecting defects in healthcare administration data: Toward higher data quality to better support healthcare operations and decisions.”


❌Incorrectness: misspellings, implausible values, misfielded values, distorted values 

❌Incompleteness: required value missing, dummy entry, incomplete fields 

❌ Invalid syntax: when values do not match required syntax. For example “OO” instead of “00”

❌Inconsistent semantics: when the value does not indicate if it is within the range set. For example, is MD interpreted as “medical doctor” or “maryland”

❌Duplication: when two or more entities have the same primary keys or when they have the same information referred to in different ways, often caused by spelling issues, data entry violations, and poor user input validation. For example John Smith vs Johnny Smith. 

Healthcare organizations need to prioritize cleaning, standardizing, and resolving for common data defect types before moving on to adopting a wider and broader data governance strategy. 

Automated data cleaning solutions like WinPure can easily facilitate this step through a WYSIWYG, no-code interface. 

Case Study: See how WinPure helped Centura Health improve efficiency with codeless data cleaning and deduplication. 


Priority # 4: Using a Top-Down Approach to Resolve Healthcare Data Quality Challenges

In an Oracle study, C-level executives from healthcare organizations are invested in data collection, however, 47% of the respondents admit that they are unable to interpret and translate the data into actionable insights – meaning, for example, they are unable to prove the success rate of their organization in treating heart diseases. For these executives, data means advancing financial standing, generating efficiencies, and having access to accurate business intelligence. 

Yet, none of the executives recognized data quality as a consistent challenge.

leadership team diagram

Data quality needs to be an organizational effort instead of a departmental effort. Executives and stakeholders must recognize, address, and implement solutions to resolve challenges. 

In a top down strategy, the decision-making and implementation processes originate from the highest level of authority within an organization and flow downward to lower levels. In the context of resolving data quality challenges, a top-down strategy entails establishing overarching policies, setting out employee training guidelines, and frameworks at the organizational level to address data quality issues comprehensively.

In this strategy, the organization’s leadership, such as executives or management, takes the initiative to define the vision, goals, and objectives related to data quality. They set the direction for data governance and establish clear expectations regarding data quality standards and practices. These directives are then communicated down the hierarchy to all relevant stakeholders, including department heads, managers, and frontline staff.

A top-down strategy encompasses employees, third-party sources, vendors, and suppliers to the same standards, preventing the issues that come with variability, and inconsistent standards. 

Priority # 5: Preparing for Electronic Health Records (EHR) 

electronic health records

The top-down strategy will also tie into healthcare organizations’ need to embrace electronic health records (EHR) for the purpose of improving patient care in real time. However, EHR depends on quality data! 

Healthcare organizations will have to invest in data quality training programs for their staff – from nurses to technicians, every one must be aware of the basics of data quality. Most hospital staff simply enter data, but do not understand concepts of data accuracy and reliability. The term “data” itself feels alienated and restricted to just the IT department. 

To overcome this lack of knowledge, organizations must educate employees on data quality best practices, EHR usage, and proper data entry procedures. Emphasize the importance of accurate, complete, and consistent data, and provide ongoing training to keep staff updated on data quality requirements. Engage clinicians and frontline staff in the training process, as they play a critical role in data entry and are well-positioned to identify and address data quality issues in real-time.


Data Quality is a Vital Concern for Healthcare Organizations 

As healthcare organizations maintain patient, financial, and research data in large-scale software and systems, data quality becomes a vital concern. 

To resolve these challenges, and to be better prepared for EHR, healthcare businesses and organizations must implement policies, regularly monitor the data for defects, and prioritize key actions such as data cleaning, data deduplication, data standardization and data consolidation. 

The question is, are healthcare organizations ready to address data quality concerns even at the most basic level? 

Want to know about how WinPure can help with healthcare data quality management? Watch this quick video by our solution expert, Matthew Trumbull.


Written by Farah Kim

Farah Kim is a human-centric product marketer and specializes in simplifying complex information into actionable insights for the WinPure audience. She holds a BS degree in Computer Science, followed by two post-grad degrees specializing in Linguistics and Media Communications. She works with the WinPure team to create awareness on a no-code solution for solving complex tasks like data matching, entity resolution and Master Data Management.

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