How to Improve Data Quality in Healthcare

Published on November 25, 2021

About 20% of patients who participated in a study from the Journal of the American Medical Association found mistakes in their electronic health record (EHR) ambulatory care notes. In November 2020, The Guardian reported that missing spreadsheet data may have caused Covid-19 fatalities in the UK. A recent review of 12,000 medical malpractice claims showed that EHR documentation errors that lead to adverse consequences for patients still happen quite often. All these cases have one thing in common: poor healthcare data quality. 

The outcomes of errors in medical data vary from the increasing distrust of systems processing data to casualties. However, most of them could have been avoided, had the data quality in healthcare been set as a priority.

The good news is that we, at Demigos, have all the expertise, knowledge, and industry insights needed to develop solutions that will improve the data management in your organization. In this article, we’ll talk about why it’s important to address healthcare data quality issues and how to do it right. Let’s start with the most critical question: why is data quality important in healthcare?

Read also: Data Aggregation and Normalization in Healthcare

What does data quality mean in (and for) healthcare?

Data quality in healthcare defines the cost you pay for medical services

With the ever-increasing adoption of the Internet of Medical Things (IoMT), artificial intelligence (AI) technologies, data analytics, and data visualization tools, the importance of data quality in healthcare can’t be understated. 

In healthcare, data quality means that the data collected by a medical organization fulfills the following characteristics:

  • Accuracy. The data is accurate when every detail of patient information is correct and properly presented. 

  • Completeness. Completeness means that all information collected by the provider is properly documented and accessible.

  • Relevance. The relevance characteristic is met when the data is collected only to be used in the medical setting and for medical purposes.

  • Legality. This criterion means that the process of data collection, processing, storage, and use complies with all legal requirements and industry standards.

  • Consistency. The data is consistent when it’s continuously updated and reflects the current state of the patient’s health and medical interventions. 

  • Accessibility. The accessibility criterion is another data quality metric for healthcare. It’s met when medical staff have full access to the information they need and can use it to perform their responsibilities. 

In a nutshell, the quality of data collected through various solutions impacts decision-making processes on an individual and global level. If data lacks any of these attributes, its quality is poor, which means that its usage can lead to negative consequences for patients, hospitals, and researchers. Let’s talk about data quality problems in healthcare in more detail.

Major problems caused by poor data quality

Poor data quality in healthcare leads to making poor treatment and management decisions.

Using low-quality data can trigger many negative scenarios. How can doctors assess the patient’s condition if they can only access limited or outdated information? How high is the risk of having a dangerous drug approved to enter the market if some data on clinical trials is lost? 

We could describe numerous situations that may arise if the quality of healthcare data is insufficient. But let’s look at the big picture and discuss how data quality issues in healthcare can affect patients and hospitals in general. 

Poor patient experience and outcomes

Treatment results highly depend on data quality in healthcare information systems used by providers. 

Suppose a patient develops a severe allergic reaction to a medical component, and they report this to their doctor. The doctor prescribes another medicine but leaves this case undocumented in the patient’s EHR. Later on, the same patient is referred to a new doctor, who, being unaware of the patient's past allergic reaction, prescribes a medication that contains the allergen. The result? The patient’s health is compromised, and treatment for their original health issue is delayed. Such situations may lead the patient to distrust the whole hospital or medical practice and to avoid it in the future.

Now imagine that the patient’s EHR has this information recorded correctly. The doctor sees what kind of medications to prescribe to avoid or minimize side effects and what to prescribe to increase the efficacy of the treatment. The patient receives appropriate treatment and develops more trust in and loyalty to the care provider. 

Billing errors

To proceed with claims and review the costs for health services, insurance companies should have the full set of data on the case. Incorrect codes, missing treatment information, and misdiagnoses will affect the decision on whether the claim will be approved or not. Now imagine the patient’s frustration when, instead of the expected $30 bill, they have to pay $300 or argue with the insurer to get the bill corrected.

But it’s not only patients who suffer consequences — providers are also affected. Since insurance companies reimburse the providers based on the data they receive, healthcare data quality issues can lead to decreased cost coverage, which means they stand to lose profits. 

Employee distrust of new technology

The information received from software tools and solutions plays a critical role in the medical decision-making process, especially when it comes to an emergency. If the information is sometimes wrong, medical staff might lose trust in the technology.

Think about the data generated by implanted devices, AgeTech apps, or sensor-based tools used for the treatment of bedridden patients. What would happen if the doctor gets incorrect data for interpretation? The wrong treatment paths might be followed, with possibly serious consequences for the patients. If attention is not paid to this issue, while other practices will be reaping the benefits of medical IoT and connected devices, your practice’s standards will look like a replication of healthcare from the early 1920s. All because of incorrect data.

Low-quality healthcare regulations 

Statistics play a leading role when policymakers are developing healthcare laws, policies, and guidelines. If data is insufficient or incorrect, the lawmakers will be misled as to what’s happening in the medical industry. As a result, they may draw incorrect conclusions, which, in turn, may lead to poor decisions being made.

However, armed with the correct data, policymakers can make necessary changes to fix major issues in a healthcare system. In addressing the opioid crisis and OxyContin prescriptions, the US Centers for Disease Control and Prevention relied on large datasets collected between 1999-2019, which from 2014 onwards categorized synthetic opioids (other than methadone) separately among the overall 500,000 opioid overdose deaths in the US between 1999-2019.

Decrease in administrative efficiency

When an organization uses outdated or incomplete data, its processes become ineffective. Mistakes and decisions based on incorrect information create bottlenecks, and it usually takes a long time to resolve these. Besides, if medical personnel know that they cannot fully rely on the data, they have to manually check every piece of information, slowing down the workflow and decreasing hospital productivity. 

These problems can be avoided if medical organizations choose to apply solutions for healthcare data quality management. So, let’s talk about how to improve data quality in healthcare in more detail.

The top 6 methods to improve data quality in healthcare

Quality audits, professional consultations, and team training help to improve data quality.

Proper data organization, classification, and distribution lie at the core of improving data quality in healthcare. Let’s see how it can be done. 

Do data management audits

If you want to know where your organization is at risk of lagging in the data management domain, audits are the way to go. You can delegate this to a third-party auditing agency or assign the task to an in-house analyst. 

Alternatively, you can ask a development team to analyze your situation and recommend solutions you can implement based on the capabilities of current technologies and the needs of your business. They may offer ready-made solutions that can help you streamline your data gathering process or develop a custom analytics tool for you. Either way, you’ll identify and close data management gaps and improve your performance.

Maintain and update data in a correct format

The data format is crucial for software solutions to interpret data sets properly. In general, data formats have to correspond to national standards to assure:

  • Integration of data across all used systems

  • Factual decision-making

  • Improved compliance and compatibility

  • Reliable claims and billing processing

Most healthcare data regulations govern terminology, content, privacy and security, and data exchange. 

The wrong format can mess up the delivery of healthcare services. For instance, if an EHR system has old insurance code names for disease classification and isn’t updated when the new codes are applied, the billing process may be drawn out, and claims may be denied or rejected because of incorrect information. 

This is why when you hire developers to create solutions for improving data quality, you need to make sure they plan the development with industry standards in mind. 

Implement integrated data analytics

Having terabytes of data means nothing unless it’s organized, classified, and prepared for further usage. Some data management solutions are designed for collecting, storing, and classifying the information, while others also offer integration of analytical solutions on top. Let’s explain why we, at Demigos, choose to apply an integrated approach at the core of our healthtech data software development.

When integrated into a continuously updated database, analytics allows for quick monitoring of critical metrics. Analytical tools also provide decision-makers with reliable data to help them develop effective strategies and make informed decisions. Besides, they ensure that the analyzed data can be used and accessed by various stakeholders, from nurses to the hospital’s top management. 

Read also: How data visualization helps in medical management decisions.

Set data metrics

Data metrics in healthcare are valuable sources of information. Depending on the organization profile, you may need to collect both qualitative and quantitative metrics that operate within various data. These are the most common in the healthcare environment:

  • Length of average hospital stay

  • Average readmission rate

  • Patient care and drug cost-per-stay

  • Patient satisfaction rate

  • Rate of missed/cancelled appointments

  • Bed availability

  • Any other metrics that your organization deems suitable

When you hire the developers to build a data management solution, make sure you know what metrics you need to monitor, so they can set up the usage of particular data for analytical needs. 

Ensure data interoperability 

The World Health Organization emphasized the importance of data interoperability in its Global Strategy on Digital Health 2020-2025. According to the document, the key goal of the global health strategy is to create a safe and interoperable digital health ecosystem that allows secure data processing and its secondary use. Comparing the data and research across the nations would give scientists and research institutes access to more factual material that they can use for research. 

On a local level, interoperability also benefits the hospitals, as it ensures that the doctors have full access to complete patient information. This empowers them with more data for leveraging risks and benefits and making fact-based decisions. Health data interoperability is empowered by HL7 standards that create a universal medical IT language for most countries in the world.

Provide training on how to use data processing solutions

Having top-notch data processing solutions is not sufficient by itself. It’s equally important to provide proper staff training. It’s not enough to show employees where to insert the codes or numbers — it’s important that they know why they should do so. As you implement new solutions, organize training sessions for your employees, so they know how to fill out the forms, update information, report issues, and use analytical tools. In this way, your organization will always have quality, up-to-date data for further use. 

In general, methods to improve data quality in healthcare may vary, but you most certainly need a developer team with deep technical expertise. So, before you start your data quality revolution, team up with a reliable vendor that is not only technically savvy but also understands the industry’s needs and requirements. 

Now that you know the basics of improving clinical data quality, let’s see what awaits the industry in the future. 

The future of data quality in healthcare

High quality of data in healthcare is impossible without cutting-edge technologies!

The speed of IT solution adoption in healthcare is incredibly fast. This creates plenty of trends that constantly change each other, creating continuous progress and improvement. Let’s talk about tendencies that will most surely affect data quality management in the near future. 

  • Migration to the cloud. Data interoperability may only be possible through a connected network, which is hard to imagine without migrating healthcare computing and data to the cloud. Cloud infrastructure offers the industry such benefits as better accessibility at a reduced cost, more reliable data recovery management, and a bigger storage capacity.

  • Focus on machine learning algorithm-based data automation. As more medical data gets digitized, there’s a growing need for solutions that will allow for its classification and analysis. This is one of the directions current ML-based data analytics is going in. For example, hospitals are already testing computer-assisted coding (CAC) to automate medical coding decisions, algorithms that interpret X-rays tests on par with trained specialists, and software that predicts and potentially reduces the readmission rate. 

  • Stricter electronic data processing policies. With patient information going online, policymakers will continue to push healthcare data security requirements further to ensure the appropriate protection of patient data.

New IT solutions allow for the collection and processing of high-quality healthcare data that lead to advancing healthcare data management. Operating with quality data expands the ability of healthcare providers to predict and avoid situations that may lead to poor patient outcomes. It also allows providers to improve hospital management and staff administration

Improve the quality of your data management with Demigos

Healthcare data is generated by various tools and systems used in healthcare. Since this data influences the decision-making processes, it’s important to maintain its quality at all times. Ensuring the data quality will allow providers to overcome administrative challenges, improve patient outcomes, and avoid billing errors. 

You can increase the quality of your healthcare data by auditing data management practices, sticking to industry-approved standards, integrating analytics, ensuring data interoperability, choosing relevant analytical metrics, and teaching staff how to use the data management solutions on a daily basis. The thing is, this can’t be done without a reliable tech partner. 

Want to discuss how to improve data quality for healthcare projects in more detail? Our team is ready to put our expertise to work and choose the methods and solutions that will benefit your practice the most. Contact us today to learn more!

Ivan Dunskiy
Ivan has been working in the tech industry for more than 10 years as a Quality Assurance Engineer, Mobile Software Developer, and Product Manager. Co-founder of 2 startups.