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

Survey from eHI and NextGate

Access to accurate, complete, and timely data is critical in the healthcare industry. It impacts patient care, hospital reputation, as well as government initiatives to improve public health services across the country.

Unfortunately, most healthcare facilities are dogged by poor data quality. Large backlogs of medical records are filled with inaccurate and duplicated information, which negatively impacts data accessibility and usability. But most of all, they make it impossible for staff workers and service users to trust medical authorities – which can be devastating for healthcare providers.

This post will help you to understand the role of data quality in healthcare information systems – what it is, how it benefits individuals and sectors, and how to ensure data quality organization-wide. Let’s get started.

What is data quality in healthcare?

Data quality is defined as the degree to which the data fulfills any intended purpose. In the healthcare industry, medical facilities effectively use data for multiple purposes, such as:

• Maintaining patients’ electronic health records (EHR),
• Diagnosing and treating diseases and ailments,
• Performing research and analytics on new diseases and patient histories,
• Efficiently designing medical policies and procedures, and
• Maintaining patient records for public health surveillance.

Data quality in healthcare ensures that the data housed by healthcare providers facilitates the execution of these processes. Adversely, poor data quality hinders their execution and introduces bottlenecks in system processes.

What are data quality requirements in healthcare?

The value or quality of data is evident by a number of characteristics present in data. These characteristics may differ depending on what requirements the data fulfills. But there are a number of data quality dimensions that are necessary for correct and optimal use of data across any industry – especially healthcare.

In the table below, you can see a list of these characteristics of data quality in healthcare along with their meanings and examples. This list is definitely not exhaustive – but it does establish the basic requirements of data quality in healthcare.

No.	Characteristic	Meaning	Example of health data quality requirement
1.	Availability and accessibility	Data is available when needed and is accessible to whomever needs it.	In an electronic patient record system, clinical information is readily available when needed.
2.	Accuracy	Data depicts reality and truth.	The vital signs displayed on patient monitors are accurately transcribed in the patient’s medical record.
3.	Validation	Data is present in the correct pattern and format, and belongs to the correct domain.	Vital stats such as body temperature and blood pressure fall between acceptable ranges.
4.	Completeness	Data is as comprehensive as needed.	Prescriptions contain the name of all prescribed drugs, along with the name of the prescriber, the date and time of the prescription and its expiry.
5.	Currency	Data is up-to-date or as current as possible.	Diagnosis information is updated in the patient’s EHR as soon as the diagnosis is made.
6.	Consistency	Data is the same (in terms of meaning as well as representation) across different data sources.	Patient records represent the same information – whether saved in EHR system or community health center.
7.	Identifiability	Data represents unique identities and does not contain duplicates.	Every EHR has a unique identity and no duplicate records are present for the same patient.
8.	Provenance	Data is saved with its metadata (origin and update history).	History of EHRs is well-maintained, including creation date and update history (along with modification dates and modifier identity).
9.	Usability	Data is present in a format that is understandable by the ones who intend to use it.	Manual and electronic healthcare records only contain abbreviations, codes and symbols that are approved and understandable.
10.	Security and confidentiality	Data is safe from unauthorized access and patient identity is kept secret wherever needed.	Medical staff cannot access patient records without authorization, and data that uniquely identifies patients is hidden in publicly available records.

What are the benefits of data quality in healthcare?

Now that we understand what data quality looks like in healthcare, next, we will discuss the importance of data quality in healthcare. You can read more about the benefits of data quality at this link.

1. Reliable electronic healthcare records (EHR)

S Munir defines electronic healthcare records as:

“Permanent document which holds information electronically about a patient’s lifelong, physical, mental and social state[s], disease[s] and any other abnormal condition which is detailed by healthcare professionals…”

This definition clearly states the significance of maintaining data quality of EHRs and the kind of devastating impact poor data quality in these records can have.

The most common issue with EHRs is duplication – meaning, duplicate records are present for the same patient. This implies that patient information is spread across separate records where each record does not provide a holistic view of the patient’s history. This problem is fixed by running patient matching algorithms to compute the likelihood of two records belonging to the same patient.

Maintaining EHR data quality can allow patients, medical professionals, administrative staff, and government bodies to trust and rely on the information reflected in these records.

2. Timely correct diagnosis

Medical professionals use a huge amount of information to reach the correct patient diagnosis, including EHRs, nurse notes, patient history notes, patient vital records, and so on. One of the biggest benefits of having quality data across various data stores (such as EHR systems, local files, or third-party applications) is reaching correct diagnosis in time to serve quality healthcare to the patients.

3. Accurate medical research and analytics

Medical records are not only used to treat patients and maintain patient information, but also for public health surveillance, medical research, and clinical trials. Detailed research and analysis are conducted to identify trends and patterns in diseases, cancers, and other ailments. Data quality enables accurate results that facilitate many areas, such as presenting evidence to support clinical decision making, finding cures for new diseases, or performing clinical trials for new medicines, etc.

4. Accurate ICD-10 classification

ICD-10 classification system (a code system that contains codes for diseases, signs and symptoms, abnormal findings, etc.) enables healthcare providers to reduce treatment errors, offer appropriate healthcare costs, ensure fair reimbursement policies, and enable global treatment. Medical professionals can only accurately classify their patients’ conditions when the collected information is correct and is free from data quality issues. Hence, good data quality helps medical facilities to correctly label diseases and injuries with accurate ICD codes.

5. Patient confidentiality

Personally identifiable information (PII) in healthcare is hidden to secure patient identity and protect confidential information. This is usually achieved by implementing data transformation techniques – that transform data to follow certain patterns and mask PII. Smooth execution of these techniques (and ensuring that they are reversible whenever needed) is possible by having quality data. Inaccurate or incomplete data will be incorrectly masked – making it impossible to reverse the transformations and uncover the hidden information if needed.

6. Reliable HL7 messaging for system interoperability

Standards like HL7 (Health Level 7) define how data should be collected, processed, and shared between healthcare institutions to enable global interoperability of health data. Medical institutions often face complications even while complying with such communication standards and the reason is poor health data quality. Reliable HL7 messaging between disparate systems is only possible with quality data.

7. Trustworthy relationships between service providers and users

One of the biggest benefits of data quality is the trust and confidence it develops between healthcare service providers and service users. Medical facilities that invest in maintaining data quality across various systems and functions are prone to offer more valuable experiences to their users – ensuring patient loyalty.

8. Compliance

Data compliance standards, such as HIPAA, compel healthcare facilities to revisit and revise their data management strategies. To comply with these standards, you must protect the personal data of your patients and ensure that data owners (the patients themselves) have the right to access, change, or erase their data.

Apart from these rights granted to data owners, the standards also hold healthcare providers responsible for following the principles of transparency, purpose limitation, data minimization, accuracy, storage limitation, security, and accountability. Healthcare facilities can only comply with these rules and principles when their data is accurate, complete, valid, and secure. And a lack of compliance can limit your business operations and make you susceptible to law suits and penalties.

9. Efficient policy and procedure making

Policies and procedures implemented in a healthcare environment are designed by analyzing large datasets gathered from past activities. This is why data quality ensures that the constructed policies and procedures are accurate and relevant, and that small errors residing across datasets are not aggregated and shown in the resulting outcomes.

10. Operational efficiency

When medical staff works with incorrect or dirty data, it badly affects their operational efficiency. At times, they have to clean the data manually every time they need to use it for their routine tasks. The most important benefit of data quality is enabling your teams, staff, and healthcare professionals to eliminate rework, enhance work efficiency, and reduce manual review of data fitness for intended purpose.

11. Fraud detection and error prevention

Poor data opens the door to fraud and costly errors. Duplicate billing, phantom claims, and false prescriptions thrive in messy data environments.

• Duplicate billing – Submitting multiple claims for the same service.
• Phantom billing – Charging for services never provided.
• False prescriptions – Issuing unnecessary prescriptions for financial gain.

High-quality data acts as a shield against fraudulent claims, exposing inconsistencies that signal fraud. It’s not just financial. In healthcare, errors can be deadly. Wrong dosages, incorrect treatments—all can stem from flawed data. By maintaining rigorous data standards and regular audits, healthcare providers can prevent these risks, ensuring both patient safety and financial integrity.

Who benefits from data quality in healthcare?

Good data quality in a healthcare facility does not only benefit its doctors and patients, but proves to be detrimental for others as well – since healthcare is a big part of a country’s social and public welfare. Let’s take a look at a list of these individuals and sectors that benefit from quality health data.

1. Service users

These are the patients that receive medical treatment from healthcare institutions. They need quality information in their health records to be aware of their condition and make informed decisions.

2. Medical staff

These are licensed physicians or healthcare professionals that are allowed by law to provide direct treatment to patients. They require quality information to make correct diagnosis, offer optimal treatment options to their patients, and analyze past patient records to make new decisions.

3. Clinical staff

These are medical assistants, licensed practical nurses, and registered nurses that work under the supervision of licensed healthcare professionals. They need quality data – in terms of patient vitals, EHRs, and ongoing treatment information – so that they can provide good care to their patients.

4. Administrative staff

These are individuals responsible for ensuring optimal execution of the hospital’s day-to-day operations, such as ensuring required attendance of clinical and medical staff and preparing outpatient clinics. They require quality data to make optimal decisions about clinic calendars, architecting short-term and long-term organizational strategy, and ensuring compliance with government policies.

5. Social care workers

These are individuals that provide mental and emotional support to people so that they can enjoy quality living. They require quality information from healthcare facilities to ensure that they are offering services to everyone in need. For example, finding out which children that were hospitalized in the last month require follow up by a social worker.

6. Government departments

Government departments use quality information to design healthcare and social care policies, offer funding wherever needed, and audit whether institutions are complying with the enforced healthcare standards.

7. Researchers or analysts

These are individuals or institutions that use past data to interpret hidden patterns and make significant conclusions. They require quality information to identify disease causes, prevention methods, and treatment options – especially for conditions that are relatively newer.

What is data quality management in healthcare?

Data quality management in healthcare is defined as:

Implementing a systematic framework that continuously profiles data sources, verifies the quality of information, and executes a number of processes to eliminate data quality errors – in an effort to make data more accurate, correct, valid, complete, and reliable.

Health data is available in multiple formats, including electronic health records (EHRs), administrative data, claims data, patient registries, health surveys, and clinical trial information. All this data is prone to house various data quality issues and errors. Implementing a list of systematic processes that catch such errors and treat information to a data quality pipeline that fixes these issues and outputs quality information is known as data quality management.

Healthcare data standards and interoperability

High-quality data is the backbone of effective healthcare. But ensuring that data can be shared seamlessly across systems is just as critical. This is where healthcare data standards help.

While there are many data standards out there, HL7 and FHIR are two of the most important standards in healthcare today. HL7, or Health Level Seven, has been around for decades, setting the rules for how health information is shared between systems. It ensures that patient data, whether it’s lab results or medication lists, can move from one system to another without losing its meaning.

• Message formatting – HL7 standards define the structure for how healthcare information is exchanged between systems. This includes the format for messages, such as ADT (Admission, Discharge, and Transfer), ORM (Order Entry), and ORU (Observation Result), ensuring that data like patient records and test results are transmitted in a consistent and understandable way across different healthcare platforms.
• Data exchange models – HL7 uses a variety of data exchange models, including version 2.x, which relies on a text-based message structure, and version 3, which is XML-based. These models allow healthcare providers to exchange complex data sets, like clinical documents and patient demographics, while maintaining data integrity and enabling seamless integration between diverse systems.
• Interoperability and compliance: HL7 standards are crucial for achieving interoperability in healthcare IT systems. By adhering to these standards, healthcare providers ensure that their systems can communicate with others, regardless of the software or technology being used. This interoperability is essential for maintaining comprehensive and accurate patient records, reducing errors, and improving overall healthcare outcomes.

FHIR (Fast Healthcare Interoperability Resources) is a newer standard designed to keep up with the latest technology. It’s built for flexibility, allowing developers to create apps that can access and use healthcare data from different systems. FHIR makes it easier for healthcare providers to get the information they need, when they need it, and in a format that’s easy to use.

• Modular data structure: FHIR (Fast Healthcare Interoperability Resources) is designed around modular components called “resources.” Each resource represents a specific healthcare concept, such as a patient, a medication, or an observation. These resources can be easily combined and extended, allowing developers to build flexible and customizable healthcare applications that can handle various types of clinical data with ease.
• RESTful API Support: FHIR is built on modern web technologies, specifically using RESTful APIs. This means that healthcare applications can interact with FHIR servers through standard HTTP methods like GET, POST, PUT, and DELETE. This web-based approach makes it easier for healthcare systems to integrate and exchange data in real-time, improving the speed and efficiency of data access across different platforms.
• Interoperability and Flexibility: FHIR is designed to work with existing healthcare standards like HL7 v2 and CDA, but it also offers greater flexibility by supporting multiple data formats, including JSON and XML. This flexibility ensures that FHIR can be adopted across various healthcare settings, from large hospital networks to small clinics, enabling seamless data exchange and improved patient care coordination across the entire healthcare ecosystem.

These standards make healthcare data accurate and accessible. Without them, the best data quality practices won’t matter if systems can’t talk to each other. Integrating standards like HL7 and FHIR into your data quality strategy will help you with compliance as well as delivering better care.

How to ensure data quality in healthcare?

In this section, we will see the different types of data quality processes that are useful for catching and fixing data quality issues present in various forms of health data. Note that these systematic processes will help you to fix quality errors present in the data stored. For establishing a consistent data quality improvement plan, you need to implement an end-to-end data quality framework.

1. Profile sources that store health data

Data profiling means assessing the current state of data and uncovering hidden details about its structure and contents. A data profiling algorithm analyzes data and identifies potential data cleansing opportunities. A data profiling algorithm finds answers to questions such as which data is:

• Missing,
• Duplicate / non-unique,
• Following incorrect pattern or format,
• Falling outside of acceptable value domain,
• Recorded using incorrect unit of measurement, and so on.

2. Add missing information

Once you have a list of missing information (from the generated data profile report), you need to fetch it and fill it out. In some cases, you can find the incomplete data from other datasets or by contacting relevant staff members or patients.

3. Clean and standardize data values

Data cleansing and standardization is the process of eliminating incorrect and invalid information present in a dataset to achieve a consistent and usable view across all data sources. Some common data cleansing and standardization activities include:

• Remove and replace empty values,
• Parse aggregated or longer columns,
• Transform letter cases,
• Merge same or similar columns together,
• Transform values of a column to follow the correct pattern and format,
• Perform operations (flag, replace, delete) on the most repetitive words in a column to remove noise in bulk.

4. Match duplicate patient records

Patient data matching (also known as record linkage and entity resolution) is the process of comparing two or more patient records and identifying whether they belong to the same patient. In the presence of unique identifiers, you can use exact matches to determine whether they belong to the same entity. But in the absence of unique identifiers, you may need to use complex fuzzy matching algorithms to compute the likelihood of two records belonging to the same patient.

5. Deduplicate matching entities

Data deduplication is the process of eliminating multiple records that belong to the same entity. This process helps you to preserve the correct information and eliminate duplicate records.

6. Merge records and retain information

Data merge and survivorship is the process of building rules that merge duplicate records together through conditional selection and overwriting. This helps you to prevent data loss and retain maximum information from duplicates.

8 Challenges in maintaining data quality in healthcare

Data in healthcare organizations is usually handled by healthcare providers like doctors and nurses. They collect and use patient data for diagnosis and treatment. Other professionals, such as data analysts, IT professionals, and compliance officers also gain access to this data to analyze and secure it and ensure compliance. With so many people creating, processing, and retrieving healthcare data –often in different systems – there are bound to be challenges in maintaining quality. The following are eight challenges in maintaining data quality in healthcare and how to overcome them. 

1. Inconsistent data entry

Inconsistent data entry is a silent threat in healthcare. Small variations in how information is recorded can lead to big problems. Data entry errors like a misspelled name, a swapped number, or a forgotten field, all add up and results in poor quality data. They create confusion, lead to incorrect diagnoses, and make it harder to provide the right care. In a system that relies on precision, even minor mistakes can have serious consequences. Consistency in data entry is vital to ensure that every piece of information is accurate and reliable.

To overcome this challenge, implement strict data governance frameworks and use tools like Data Ladder that help tackle the challenge of inconsistent data entry by automating data cleansing and standardization processes. They identify and correct errors, such as misspellings and formatting inconsistencies, across large datasets. These tools also enable data validation and profiling, ensuring that data is consistent and reliable. By doing this, healthcare providers can maintain high data quality, reducing the risks associated with inconsistent data entry and improving overall data accuracy and reliability in patient care.

2. Data fragmentation

Data fragmentation is a hidden danger in healthcare. Patient information is often scattered across different systems — EHRs, lab results, billing records — making it hard to get a complete picture. This fragmentation leads to gaps, duplicates, and inconsistencies in patient data. The result? Delayed treatments, missed diagnoses, and potential harm. To provide the best care, all pieces of a patient’s data puzzle need to fit together seamlessly. Eliminating data fragmentation is key to ensuring accurate, comprehensive patient records.

To overcome data fragmentation, implement integrated data systems that unify information across all departments. Adopt standardized data formats like HL7 and FHIR for consistent data exchange between systems. Perform regular data audits and use data integration tools to consolidate fragmented records. This way, you will get a complete and accurate view of patient information. Additionally, invest in data management solutions like Data Ladder to further mitigate fragmentation and enhance data quality.

3. Duplicate records

Duplicate records are very common in healthcare. When the same patient has multiple records, it creates confusion and risks. Critical information can be split across these duplicates, leading to incomplete histories and potential treatment errors. Identifying and merging duplicate records is essential to ensure that all relevant data is in one place. This improves the accuracy of patient care and streamlines operations. A single, unified record for each patient is key to maintaining high-quality healthcare data.

Tools like Data Ladder helps eliminate duplicate records by using advanced data matching and deduplication techniques. The software identifies similar or identical records across different systems, even when there are slight variations in the data. Once identified, these duplicates can be merged into a single, accurate record, ensuring that all patient information is consolidated and accessible in one place. This process reduces the risk of errors, improves patient care, and enhances the overall efficiency of healthcare data management.

4. Data integration issues

In healthcare, lab systems, EHRs, and billing often operate in silos, leading to disconnected data. When these systems don’t communicate effectively, data becomes fragmented, inconsistent, and hard to manage. This disconnect can result in incomplete patient records and errors in care delivery. To solve this, healthcare organizations need robust integration strategies that connect all systems, ensuring that data flows seamlessly across platforms. Effective integration is key to maintaining accurate and unified patient data.

You can overcome data integration issues by implementing standardized data formats like HL7 and FHIR, which facilitate seamless data exchange across different systems. Also, invest in interoperable healthcare IT solutions that connect disparate systems. Using data integration tools that aggregate and harmonize data from multiple sources can help create a unified view of patient information. Regular audits and validation processes ensure that the integrated data remains accurate and consistent across all platforms, reducing the risk of errors and improving patient care.

5. Lack of standardization

Lack of standardization in healthcare data is a major barrier to accurate and consistent patient records. Different systems often use varied formats, codes, and terminologies, making data integration difficult. This inconsistency leads to errors, misinterpretations, and inefficiencies in care delivery. Without standardization, achieving seamless data exchange and interoperability across systems is nearly impossible. To overcome this challenge, healthcare organizations should adopt industry standards like HL7 and FHIR, ensuring that all systems speak the same language and data is consistent across platforms.

6. Timeliness

In healthcare, having accurate and timely data is critical. Delayed data entry or updates can lead to outdated information, resulting in incorrect diagnoses, ineffective treatments, and compromised patient care. Real-time access to accurate data is essential for making informed decisions quickly. To address this issue, healthcare organizations should implement automated data entry systems and ensure that all updates are processed promptly. Regular monitoring and audits can also help maintain the timeliness of data, ensuring that healthcare providers always have the most current information.

7. Security and privacy concerns

Healthcare data is highly sensitive and requires strict protection. Security breaches or lapses in privacy can lead to severe consequences, including patient identity theft and legal penalties. Protecting patient data while ensuring it is accessible to authorized personnel is a delicate balance. To overcome these concerns, healthcare organizations must implement encryption methods, access controls, and regular security audits. Adhering to regulations like HIPAA and continuously updating security protocols can help safeguard patient data against unauthorized access and breaches.

8. Inadequate data governance

Inadequate data governance leads to chaotic data management and quality issues. Without clear policies and procedures, data can become inconsistent, inaccurate, and unreliable. Effective data governance ensures that data is managed systematically across the organization, with well-defined roles, responsibilities, and standards. To address this, healthcare organizations should establish a comprehensive data governance framework that includes policies on data collection, storage, sharing, and quality control. Regular training and audits can reinforce these practices, ensuring data integrity and reliability across the organization.

How to consistently improve data quality in healthcare?

In addition to executing data quality processes, it is best to perform consistent efforts that enable quality data across all sources. This is achieved by designing a data quality improvement plan that implements the best data quality practices. Let’s take a look at a few of these practices below.

1. Conduct routine audits for health data quality

Conducting audits to assess data quality is one way to proactively identify the challenges present in a health institution’s datasets. These audits are planned beforehand where a list of goals and objectives of the audit are described. Some auditors run self-service data quality tools in healthcare on a subset of data to get a quick overview of current state of data quality.

An in-depth audit will help you to list the strengths, weaknesses, threats, and opportunities present in the data. The audits are usually finalized by sharing recommendations and suggestions to make data quality better.

2. Implement systematic data quality management in healthcare institutions

On-off execution of data quality techniques gets you results for today but would not ensure consistent data quality results in the future. This is where you need to implement a data quality management system – especially through automated workflows so that new and upcoming data is batch processed for data quality checking and fixing, before being stored in the destination source.

3. Involve healthcare leadership and management

Making organization-wide changes is only possible when you have buy-in or approval of the institution’s leaders and higher management. Many healthcare facilities hire on-premise data quality officers – these roles are responsible for adopting better data management practices that minimize data loss and maximize data quality. They are considered to be the caretakers or overseers of healthcare data.

4. Perform root-cause analysis for health data errors

Getting to the core of data quality issues can help eliminate errors in the longer term. You do not always have to work in a reactive approach and keep fixing errors as they arise. With a proactive approach, you can allow your teams to minimize their efforts on fixing data quality errors. A root-cause analysis for data quality errors can involve getting the latest data profile report and collaborating with your team to find answers to questions like:

• What data quality errors were encountered?
• Where did they originate from?
• When did they originate?
• Why did they end up in the system despite all data quality validation checks? Did we miss something?
• How can we prevent such errors from ending up in the system again?

5. Train and educate healthcare teams

The ability to correctly and accurately read, understand, and analyze data across all levels empowers your medical and clinical staff to make the right decisions. It also ensures their operational efficiency and reduces mistakes while communicating matters involving data.

You can educate your staff about data by creating data literacy plans and designing courses that introduce them to healthcare data and explain:

• What it contains,
• What each data attribute means,
• What are the acceptability criteria for its quality,
• What is the wrong and right way for entering/manipulating data?
• What data to use to achieve a given outcome?

6. Utilize technology to sustain health data quality

Utilizing technology for attaining a sustainable data quality management lifecycle is at the core of improving data quality in healthcare facilities. No process is promised to perform well, and give the best ROI – if it is not automated and optimized using technology. Invest in adopting a technological system that comes with all functionalities that you need to ensure data quality across datasets.

Conclusion

To be useful, data must be correct, complete, reliable, and accurate. Flawed data leads to errors in decision-making, lethal mistakes in patient care (such as making a wrong diagnosis, or making a correct diagnosis on the wrong patient), skewed numbers in research, and other critical problems.

While many healthcare facilities have collected data on patients, they have yet to develop up-to-date systems to maintain the quality of services provided. A self-service data quality tool as DataMatch Enterprise empowers authorized users to prepare data for its multiple uses without having to rely on IT or any SQL expertise.

More importantly, it gives organizations a head-start into the data improvement journey. Once the organization understands the problems affecting data quality, they can be in a better position to make necessary amendments, coming up with a more robust data management plan.

Download our free trial or book a demo today to see how you can clean and link your organization’s records the easy, code-free way.