Data quality in healthcare can directly affect patient outcomes, physicians’ decision-making abilities and more. Unfortunately, there are many examples of data quality issues running rampant in healthcare. Improving healthcare data quality is a crucial and ongoing process. Here’s a closer look at how data quality impacts healthcare.
- How does data quality integrate with healthcare?
- The impact of poor data quality in healthcare
- Current problems caused by insufficient data quality
- Possible resolutions to improve healthcare data quality
- How will you improve data quality in healthcare?
How does data quality integrate with healthcare?
People at medical facilities use database systems and platforms to get holistic views of their patient profiles, operations and general outcomes. Collected healthcare information can reveal information like:
- The average amount of time doctors spend with patients
- The likelihood of an admitted patient getting readmitted in a year
- The money spent per department, physician or researcher
- The success of programs that let ill people recover at home
With as much data as healthcare systems collect, track and utilize, it’s easy to imagine how substandard data quality in these healthcare information systems could lead providers to the wrong conclusions or negatively affect their decision-making capabilities.
SEE: Healthcare data literacy course (Coursera)
On the other hand, high-quality data can show providers what’s working well within a medical organization and where room for improvement exists. It can help people react quickly and correctly to problems before those issues get out of hand and lead to everything from HIPAA noncompliance to medical malpractice.
The impact of poor data quality in healthcare
People usually define data quality as the extent to which data meets a user’s requirements. Ripple effects occur when an organization falls short of these expectations and needs.
Sometimes, the issues seem rather small but have far-reaching consequences. Consider a case where someone mistakenly records a patient’s lab test incorrectly or fails to note that they take a certain medication that causes well-known side effects. Such instances could mean a doctor misdiagnoses the person or incorrectly rules out the cause of symptoms. The doctor could also prescribe a medication that proves fatal for that patient.
Operational inefficiencies can also hurt medical facilities that don’t have well-developed plans for maintaining better data quality. Those organizations may lose marketplace competitiveness, particularly as patients look for better options. They may also frequently go over budget, making it more challenging to provide the necessary standards of care.
Current problems caused by insufficient data quality
The impact of poor data quality in healthcare can be so substantial that an entire organization becomes less productive while patient outcomes suffer. One reason is that people usually consult multiple medical professionals when seeking treatment for an illness. They might first visit their general practitioner, who refers them to a laboratory for blood draws and a specialist for a specific type of scan.
SEE: How to address poor internal and external data quality for your business (TechRepublic)
An organization that engages in effective data quality management will have all those interactions properly recorded in secure digital systems, and relevant parties can access them as needed. However, data quality errors may mean the patient’s journey through a health system is improperly recorded in a way that negatively impacts the care they receive.
Data security and privacy issues in telehealth
The increased popularization of services like telemedicine can also spell trouble for healthcare data quality. The Blue Cross Blue Shield Association found that over 60% of its members wanted to participate in telemedicine in 2020. Despite the many benefits telehealth can have for patients, it can also result in poor data protection and storage because of the digital platform. Many digital platforms that healthcare networks choose to use do not have the necessary native security and healthcare compliance protocols built into their systems. These platforms require expert data teams to secure and maintain data privacy over time.
Errors and inefficiencies with medical health records
Complications can also result when people visit multiple facilities in different states or if some of their health records are still in a paper format. This health record data sprawl can lead to poor communication and poor care. Fortunately, medical data is becoming increasingly accessible to everyone who needs it when healthcare decision-makers utilize integrations. There are fewer slowdowns, and physicians can quickly act on updated information.
Medical record duplication and sprawl harms healthcare data quality. It could mean people get too much or not enough prescribed medication, lab tests are unnecessarily repeated or incorrect vital signs are listed in patient records.
Misrepresented minority patient data
Researchers have also pointed out that having correct information available is not necessarily an indicator of high-quality data in healthcare. Much of the current information in databases fails to adequately represent minority populations and their healthcare experiences. That’s why some projects center on working with organizations that primarily serve minority groups. These targeted efforts should make healthcare information more representative of all patients.
Possible resolutions to improve healthcare data quality
When people start exploring how they can improve data quality in healthcare, they quickly realize there’s no one-size-fits-all solution to address every issue. However, awareness is a great first step. Healthcare decision-makers must recognize their data quality shortcomings before they can begin to tackle them.
Invest in data management technologies and resources
Next, they can start researching technology and resources to help bring about the desired results. A growing number of healthcare providers are investing in solutions that rely on patient biometrics to address duplicate data issues. Meeting with consultants and technology vendors about how to ensure data quality in healthcare is another excellent option, which enables healthcare systems to use customized data quality solutions.
Use preventive safeguards and notifications in all data systems
Another possibility is to utilize preventive measures and system safeguards to prevent data entry errors and maintain data integrity. Consider a case where someone uses a database to document that a patient received a certain kind of blood test twice a day. Ideally, something in the system would flag the second instance and ask to verify its accuracy.
Offer data and compliance training to all providers
Having relevant staff members complete periodic training about data quality and healthcare data quality management is also helpful. Many people don’t realize how certain mistakes could ultimately reduce the reliability of the available information.
It’s easier to take more ownership of data quality and recognize that improving it is a team effort once all providers become more aware of how errors occur and what effects they could have. On top of the regular HIPAA training that your staff is likely already doing, consider investing in a healthcare data literacy training course as well.
How will you improve data quality in healthcare?
This overview of how poor data quality can negatively impact healthcare should be enough to get you thinking about the future. Which strategies seem most relevant to your organization, and how might you implement them? What’s your budget for any relevant technologies or consultancy services?
SEE: Top data quality tools (TechRepublic)
Answering these questions will put you on the right track for identifying data quality issues in a healthcare organization. Then, you can start developing a strategy for improvement and choose relevant metrics to track your progress. Your healthcare providers and patients alike will benefit from the additional work that goes into data quality upkeep efforts.
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