As healthcare organizations increasingly rely on data for decision-making, operational efficiency, and financial performance, the importance of data quality management in healthcare increases. For instance, a healthcare organization with inaccurate or incomplete patient data could lead to incorrect treatment decisions, risking patient health. The issue isn’t just that the healthcare organization has poor-quality data. Instead, the real problem is that these flawed data ultimately affect patient care.
This guide will explore how improving data quality can help healthcare organizations improve patient outcomes. Azulity’s healthcare data management services can provide your organization with a valuable solution and help you achieve your data quality goals.
What Is Data Quality Management?
Data Quality Management is the mature processes, tools, and in-depth understanding of data you need to make decisions or solve problems to minimize risk and impact on your organization or customers. You can have data that is of high quality and still achieve poor business outcomes. DQM is the practice of using that data to serve your purposes with flexibility and agility. To do this, you must assess what data you have today and the processes and tools that use or support data against accuracy, completeness, consistency, and timeliness measures.
DQM and MDM practices of data acquisition, use, and storage are part of everyone’s role, including vendors. This makes data a holistic asset, meaning that data is the input and output of every task and transaction your business performs. As such, the quality of your data will be improved by following best industry practices (DQM), which begin with designing a view of data flow and use (MDM).
Sound DQM and MDM practices will help overcome the following:
- Lack of trust in data causes employees to create their versions (non-standard across multiple Excel spreadsheets and data tables).
- Insufficient data underpinning bad decisions.
- The increased cost of data management and storage.
- Lack of uniformity in data use or feel, causing complicated use of applications.
- Creating levels of unacceptable risk or potential reputational damage as data is incorrectly managed.
- Deficient or misunderstood data cause communication and collaboration silos—inability to react to volatile market changes or crises (e.g., COVID-19).
- Failure to introduce digital practices prevents cross-departmental usage of non-structured data.
Simply put, data management is a holistic practice that affects every aspect of your organization. Your Master Data Management processes and model will mature as you introduce a Data Quality Management framework.
Importance of Data Quality Management in Healthcare
Data quality management in healthcare is crucial for all organizations that work with data, regardless of whether their operations involve sharing, analyzing, or managing data. However, when it comes to healthcare providers who deal with patients’ medical records that contain sensitive information daily, the importance of data quality becomes even more significant. The sensitive nature of healthcare data demands that regulations and strict security measures be put in place to ensure that patient’s privacy and confidentiality are protected.
A Centralized Data Warehouse Improves Data Quality
A centralized data warehouse in healthcare allows healthcare organizations to consolidate information from various sources, improving data accuracy, consistency, and overall quality. To truly grasp why data quality is essential in healthcare, providers must first understand the implications of accessing medical data regardless of its use. Healthcare providers must understand that obtaining a patient’s medical information, including reports and records, can significantly affect the individual and the treatment they are provided.
The Role of DQM in Patient Care
For example, the accuracy and completeness of medical data can directly impact the diagnosis’s accuracy and the treatment’s effectiveness. Poor data management practices often cause recurring errors, resulting in injuries or even death, primarily due to mistakes in recording or transcribing. Data quality in healthcare information systems is critical to improving patient outcomes.
By ensuring that healthcare data is accurate and complete, providers can make better-informed decisions, offer improved treatment options, and lower the likelihood of medical errors. Having quality data in healthcare is crucial, as it ultimately leads to increased patient satisfaction, better care, and improved patient outcomes.
Benefits of DQM in Healthcare
Data quality can provide several benefits to healthcare organizations, including:
Informed Decision-Making
The more accurate information an organization can access, the better it can make decisions and have confidence in its plans. Ensuring data quality in healthcare minimizes risks and improves care outcomes, increasing patient satisfaction overall.
Better Patient Targeting
Data accuracy in healthcare helps marketers know exactly what they are looking for instead of targeting a broad range of people. Organizations are sometimes forced to guess who their target audience might be because of inaccurate or misleading data. Your current patients are the best resource for finding new potential patients. Collecting quality data from existing people means you can identify similar potential customers and tweak your marketing campaigns and healthcare procedures accordingly.
Improved Patient-Physician Relations
Better relations with your customers (in this case, your patients) are the key to success for healthcare facilities. Gathering data from them can help you get to know your audience better and provide them with the facilities they demand. This strengthens the relations between patients and healthcare providers to a considerable extent. Several healthcare providers use telemedicine to enhance relations with their patients. It protects the patient while providing detailed medical information to the physician. Telemedicine software companies aim to develop the most updated and efficient solutions to improve patient relations.
Easier Data Implementation
High-quality data is much easier to use, increasing efficiency throughout your organization. Time is an invaluable resource, which you cannot spend fixing inconsistent or incomplete data to make it usable. Data precision in healthcare lessens the time for an organization to implement insights gathered from data analysis.
Higher Profitability
Improved patient relations, more informed decision-making, and better advertisement campaigns lead to higher profitability. Data quality in healthcare also leads to greater efficiency levels by preventing resource wastage. This means your operations become smooth and cost-effective, contributing to higher profits.
Azulity: Your Experts in Healthcare Master Data Management
Azulity specializes in healthcare master data management, bringing proven expertise in implementing healthcare data solutions and credentialing across the US. Our comprehensive platform ensures consistent patient, provider, location, and claims data synchronization across all systems and departments. Key features include healthcare MDM, provider MDM, reference data management, credentialing, and provider enrollment. We serve healthcare technology leaders – from CIOs and CDOs to VPs of data platforms and credentialing – helping them eliminate the costly problems of fragmented data systems. Book a call to learn more about our healthcare master data management services today!
Related Reading
How To Ensure Data Quality Management In Healthcare in 11 Easy Steps
1. Equipping Data Quality Management Tools Like Azulity
Azulity is a leader in healthcare master data management. Their expertise in implementing healthcare data solutions and credentialing helps organizations eliminate the costly problems of fragmented data systems. Azulity’s comprehensive platform consistently synchronizes patient, provider, location, and claims data across all systems and departments.
Key features include healthcare MDM, provider MDM, reference data management, credentialing, and provider enrollment. The platform serves healthcare technology leaders from CIOs to VPs of data platforms and credentialing. With proven capabilities for improving data quality, Azulity will help your organization eliminate the costly problems of fragmented data systems.
2. Incorporate Data Validation Checks
Validation checks significantly improve data quality in healthcare. The data processing software must only process records that follow predetermined rules, promptly flagging errors or outliers that do not meet acceptable ranges. By doing so, healthcare providers can ensure that their data is reliable, accurate, and complete, avoiding potentially life-threatening mistakes. For instance, Cleveland Clinic implemented a data validation system that checks patient records for real-time completeness and accuracy. This system flags data points that don’t match expected parameters, helping quickly identify errors and minimizing the risk of incorrect treatments.
3. Convert Data into a Standardized Format
Standardization is critical for consistent healthcare data. Healthcare organizations can achieve data standardization by adopting well-established frameworks, such as SNOMED-CT, FHIR, or ICD-10, allowing smooth data exchange among internal and external systems. Following these standards promotes transparency and openness in data sharing. Using standard file formats, forms, and templates also helps promote uniformity in data entry across different systems, facilitating collaboration efforts among healthcare providers. For example, Kaiser Permanente adopted the SNOMED-CT and ICD-10 coding systems to standardize patient information across all their facilities. By implementing these standards, they streamlined data sharing between locations and ensured each practitioner could interpret data accurately.
4. Establish a Data Governance Framework
A solid data governance framework improves data quality management in healthcare. Create a data governance team that includes stakeholders from various departments, such as IT, clinical, and administrative staff. Define roles, responsibilities, and accountabilities for data quality management, ensuring clear communication and collaboration among team members. For instance, the Mayo Clinic created a data governance council that includes members from IT, clinical operations, administration, and data science. This council sets data quality standards and ensures all teams follow them.
5. Develop Data Quality Policies and Standards
Data quality policies and standards ensure consistency in DQM efforts. Clearly defined objectives, policies, and standards specific to your organization’s needs and healthcare regulatory requirements will ensure teams are aligned in improving data quality. These policies should be well-documented, communicated, and easily accessible to all team members. For example, the University of California, San Francisco (UCSF) Health created a comprehensive data quality manual covering data entry standards, regulatory compliance, and error handling. The policies were made accessible to all departments, ensuring every staff member had a consistent understanding of data handling practices.
6. Implement Data Quality Improvement Initiatives
Data quality improvement initiatives kickstart enhancing the accuracy of healthcare data. Based on the assessment results, prioritize and implement data quality improvement initiatives. These may include data cleansing, enrichment, validation, and standardization, among other activities. For example, Mount Sinai Health System implemented a series of data quality improvement projects, such as automated data cleansing, enrichment, and validation. The initiatives included the automatic removal of duplicates and real-time data validation, significantly reducing the time spent on manual data checks and improving overall accuracy in patient records.
7. Perform Routine Data Audits
Routine data audits allow healthcare providers to ensure that their records are accurate and up-to-date. Regularly reviewing data helps identify discrepancies or errors and take corrective action before they lead to adverse outcomes. Moreover, these audits help facilities identify areas for improvement, resulting in better patient outcomes. For example, the UK’s National Health Service (NHS) performs routine audits to monitor the quality of patient records. By regularly reviewing records, NHS staff identify inaccuracies or incomplete data, allowing them to correct information before it leads to clinical errors.
8. Upskill Healthcare Team Members
Enhancing the skills of healthcare data management teams will improve data quality. Digital literacy programs and encouraging data ownership help staff understand the importance of accurate data. When team members feel ownership of the data they collect and analyze, they’re more likely to take responsibility for ensuring its accuracy and completeness. Along with digital literacy, staff should receive HIPAA training to ensure compliance with regulations for handling sensitive data. For instance, Johns Hopkins Hospital has developed digital literacy and HIPAA compliance training programs for all staff involved in data handling.
9. Enhance Data Capture and Entry Processes
Streamlining data capture and entry processes minimizes errors and inconsistencies. Standardizing these processes helps ensure accuracy and that data handling teams follow the same procedures. For example, St. Jude Children’s Research Hospital implemented streamlined data entry guidelines and a standardized template for patient intake forms. By standardizing these forms and training data entry staff, St. Jude minimized errors in data capture, leading to more consistent patient records and improved data quality.
10. Continuously Improve Data Quality Processes
Regularly review and update your data quality policies, standards, and processes to keep pace with the evolving healthcare landscape and your organization’s needs. Use lessons from previous data quality initiatives to refine and enhance your data quality management approach. For instance, the Centers for Disease Control and Prevention (CDC) conducts periodic reviews of its data quality processes and updates policies based on recent findings. These continual improvements ensure that data quality practices stay relevant and practical.
11. Invest in Data Quality Tools and Technologies
Utilize data quality tools and technologies to automate data validation, cleansing, and monitoring processes. Implement data integration platforms to facilitate smooth data exchange and ensure data consistency across systems. For example, Johns Hopkins Medicine invested in data integration tools like Informatica and Talend to automate data validation and cleansing. These tools help manage large data sets by automatically flagging errors, cleansing data, and maintaining consistency across platforms. This investment has allowed Johns Hopkins to manage patient data across various systems efficiently, enhancing data accuracy and availability for clinical decision-making.
Dimensions of Data Quality In Healthcare
Safeguarding Sensitive Healthcare Data
Improving data quality must be accompanied by safeguarding patient privacy and data security. Healthcare organizations must implement strong data encryption, access controls, and monitoring systems to protect sensitive healthcare data from unauthorized access, breaches, and misuse.
Tracing Data Origins and Changes
Understanding your data’s source, history, and transformations is essential for establishing trust in the data. Healthcare organizations should maintain clear documentation of data lineage and provenance to ensure transparency and traceability in data quality efforts.
Integrating Data From Multiple Sources
Data quality management in healthcare is further complicated by the need to integrate data from various sources, such as EHR, lab, imaging, and billing systems. Ensuring data quality across these sources can be challenging, so healthcare organizations should invest in data integration platforms that facilitate smooth data exchange and maintain consistency across systems.
Ensuring Real-Time Data Quality
Healthcare organizations increasingly leverage real-time data for decision-making, patient monitoring, and early intervention. Ensuring that data quality efforts extend to real-time data streams and batch and historical data is critical for improving patient outcomes.
Validating Patient-Generated Data
With the increasing adoption of remote patient monitoring and wearable devices, patient-generated data is becoming more crucial for healthcare organizations. Developing processes to validate, clean, and standardize this data will ensure its quality and usability.
Addressing Data Quality in AI and Machine Learning
Healthcare organizations increasingly use AI and machine learning for various applications, such as predictive analytics, diagnosis support, and personalized care. Data quality management must support these advanced analytics initiatives, as data quality can lead to accurate predictions, biased results, and suboptimal care.
Continuous Data Quality Improvement
Data quality management should be ongoing, with regular assessments, monitoring, and improvement initiatives. Healthcare organizations should develop a feedback loop to learn from past data quality issues and refine processes to prevent future occurrences.
Planning for Change Management
Implementing data quality initiatives in healthcare organizations can involve significant cultural and operational changes. Developing a comprehensive change management plan will engage stakeholders, address resistance, and facilitate the successful adoption of data quality practices.
Who Benefits From Data Quality in Healthcare?
Service Users: The Patients Who Depend on Quality Healthcare Data
Individuals who seek medical assistance from healthcare providers need quality information about their health to make informed decisions about their treatment. Accurate data in their electronic health records, or EHRs, allows patients to understand their health conditions better, track progress over time, and make choices about their care.
For example, suppose a patient knows he has high blood pressure. In that case, he can research the condition, learn how to manage it and make decisions about his treatment, including consulting with his doctor on specific areas of concern he discovers during his research. Quality data also helps patients avoid redundant or unnecessary treatments that can adversely affect their health and lead to medical errors.
Medical Staff: The Physicians Who Treat Patients
Doctors and licensed healthcare professionals rely on quality data to make informed patient treatment decisions. Accurate information in a patient’s health records helps clinicians make correct diagnoses, identify optimal treatment options, and analyze records to make decisions about ongoing care.
For instance, if a patient is not responding to treatment for a particular condition, the medical staff must have access to accurate data about the patient’s history and any other underlying issues contributing to his lack of progress. This may help avoid dangerous complications and ensure the patient receives the appropriate care as quickly as possible.
Clinical Staff: The Caregivers Delivering Patient Care
Clinical staff, such as nurses and medical assistants, depend on quality healthcare data to perform their jobs effectively. These individuals work under the supervision of licensed physicians and rely on accurate medical information to provide safe patient care. For example, clinical staff use data on patient vitals, electronic health records, and ongoing treatment information to help them deliver quality care. If the data they reference needs to be updated or corrected, it could pose serious risks to the patient.
Administrative Staff: The Backbone of Healthcare Organizations
Healthcare administrators are responsible for ensuring that operations run smoothly and efficiently. They depend on quality data to make crucial organizational strategy and compliance decisions. Accurate information helps healthcare administrators optimize clinic schedules, develop short- and long-term goals for the organization, and meet government regulations.
Social Care Workers: The Unsung Heroes of Patient Recovery
Social workers are critical in helping patients recover from illness and injury. These professionals help patients, and their families cope with emotional and mental health issues that arise due to medical conditions and adverse events. Social care workers need quality information from healthcare facilities to ensure they offer services to everyone in need. For example, data may reveal which children hospitalized in the last month require follow-up by a social worker.
Government Departments: The Watchdogs of Healthcare
Government departments use quality information to design healthcare and social care policies, offer funding wherever needed, and audit whether institutions comply with the enforced healthcare standards.
Researchers and Analysts: The Data Experts
Researchers and analysts 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 relatively newer conditions.
Related Reading
- Healthcare Provider Data Management
- MDM Implementation
- Reference Data Management
- Healthcare Data Integration
- Healthcare IT Consultants
Book a Call to Learn More About Our Healthcare Master Data Management Services
Master data management in healthcare allows organizations to create and maintain an accurate, consistent, and up-to-date collection of critical data. This process helps organizations eliminate unnecessary duplication, reduce inconsistency, and improve overall data quality. In healthcare, master data typically includes information about patients, providers, locations, and payers. The more innovative and more automated the MDM processes are, the better. Healthcare organizations can implement robust MDM solutions to manage their master data more effectively and deal with the inevitable changes and updates over time.
Provider Master Data Management
Provider master data management (MDM) is a subset of healthcare MDM that focuses on improving healthcare providers’ data quality. It helps organizations manage and automate the processes for collecting, organizing, and maintaining accurate provider data. Provider data can include information about individual practitioners, such as their credentials, education, and work history, as well as details about the organizations and facilities where they provide care. This data is critical for various healthcare operations, including revenue cycle management, quality improvement initiatives, regulatory compliance, and patient safety.
Reference Data Management
Reference data management (RDM) is managing data that categorizes other critical data in healthcare. This includes data sets that help define and organize master data, such as those that describe healthcare providers, patients, payers, and locations. RDM also involves managing codes and classifications used by various healthcare organizations and regulatory agencies to ensure data consistency and compliance. For example, nearly every healthcare organization uses the International Classification of Diseases (ICD) codes for billing and reporting. Proper RDM helps organizations mitigate operational risks associated with fragmented data and improve overall data quality.
Credentialing
Credentialing is verifying and evaluating the qualifications of medical staff and healthcare providers. This process involves collecting and organizing extensive data about practitioners, such as their education, training, professional experience, and disciplinary actions. Credentialing helps healthcare organizations hire and grant qualified providers privileges to deliver safe and effective patient care.
This process can take months to complete, and during this time, errors, omissions, and outdated information in provider data can lead to patient safety risks and costly disruptions in healthcare operations. Master data management can help organizations streamline credentialing processes by improving provider data quality and automating time-consuming tasks.
Provider Enrollment
Provider enrollment involves adding a newly credentialed provider to health plans’ networks. This process requires healthcare organizations to submit detailed data about freshly qualified practitioners to various insurance companies and government agencies to ensure they can bill for services upon delivery. Similar to credentialing, provider enrollment can take several months to complete and is fraught with risks associated with poor-quality data. Implementing a robust master data management solution can help healthcare organizations improve provider data quality before enrollment, streamline the process, and reduce the time it takes to complete.
Azulity: Your Experts in Healthcare Master Data Management
Azulity specializes in healthcare master data management. We help healthcare organizations eliminate the costly problems of fragmented or siloed data. Our healthcare MDM solutions create a trusted, single source of truth for patient, provider, location, and claims data. With our comprehensive platform, you’ll ensure consistent synchronization across all systems and departments.
Key features of our healthcare master data management platform include provider data management, reference data management, credentialing, and provider enrollment. We work with healthcare technology leaders—from CIOs and CDOs to VPs of data platforms and credentialing—to implement our solutions and improve healthcare operations and outcomes.