Data Quality in Healthcare: Ways To Improve Data
What is healthcare data quality? How can it be measured? What parameters does it require? What are the strategies to improve the process and increase healthcare data quality levels?
The healthcare industry is turning into a developed and complex marketplace with tough competition. Data collection tools and methods appear to be crucial sources for generating information about patients. This information is supposed to improve the overall quality healthcare approach, and deliver care according to patient needs.
The more complex systems organizations use, however, the more problems they may face. Poor data quality in healthcare is the number one issue that requires major improvement. The lack of reliable security measures and strategies results in inaccurate information without appropriate protection. Complete and accurate data is a vital component of the complex system. Otherwise, organizations will not be able to deliver high-quality care to their patients.
How to Improve Data Quality in Healthcare
Though the healthcare industry is a complex platform, it hardly differs from other industries that use different tools to generate necessary data. Moreover, they may actually implement the same methods - the only thing that differs is the information they generate. Like in any other industry, healthcare organizations are organized with a variety of interlinked standard processes and systems.
Once there is a need to change the system, you’ll need to focus on a single process. The idea is to identify the weak link and improve it, in order to deal with the impending challenges. According to current research, we may need to change only 20% of all processes in order affect 80% of the entire system, along with improving healthcare data quality issues. This challenge does not seem too difficult, does it?
So, how do we define that 20% of processes? What are the main characteristics of data quality in healthcare?
Challenges and Solutions for Healthcare Data Quality
Before we start looking for a way to improve data quality, we need to define the main challenges, which should not be a problem if you have a clear understanding of the importance of data quality in healthcare.
Challenge #1- Documentation and Observation Value Proposition
The challenge here is to establish and provide the value proposition for document creators and observers. Once an organization hires a new clinical member, it should ensure this employee is trained appropriately on documentation and observation.
Develop a detailed case describing the process of collecting data;
Explain how that data is important for the patient and organization; and,
Demonstrate the role of data quality when improving patient care (risk evaluation, remote consultations, ease of payment, etc.).
Challenge #2 - Data Interoperability
Non-standard data that lacks the required standardization elements is of low value. For this reason, enterprises have established some common terms and definitions in order to ensure ease of information processing.
Make the data compliant by implementing comparability and standardization;
Leverage the transactions, if possible; and,
Compare your data with other entities and check how your enterprise looks from the outside.
Challenge #3 - Data Sharing and Monitoring
Bringing data up to a single standard will not guarantee higher data quality. It needs to be constantly monitored and tracked and, moreover, organizations need to evaluate it regularly, using special metrics and ensuring pattern and data visibility.
Have a clear picture of what others can see in your data, and use special warehouses accessible to other employees or entities;
Always explore your data to learn more about it;
Establish a single standard report system based on data quality metrics and coding; and,
Work with other responsible clinicians when collecting and processing data.
The above-mentioned solutions and strategies have proved to be efficient; however, they will never guarantee success unless you consider some additional methods to improve data quality.
Integrated Data Analytics
Every time you need to fill in possible healthcare data quality gaps, you will also need to understand the importance of integrated analytics. Imagine that you need to evaluate the efficiency of your marketing campaign. You would probably use different analytic tools and metrics. The same thing applies in the healthcare industry.
As we have already said, the marketplace hardly differs from other fields when it comes to generating data and implementing modern technologies. The dataset should include the following three points:
Capture. At this stage, staff or automated systems deliver necessary data to EHR. The data confirms that a particular event has happened (returned lab results or an encounter). Clinicians enter all results accurately.
Structure. This is the process of storing captured information. Keep in mind that all data should be kept in a proper format. Make sure it is delivered on time to the right place, and keep an eye on the correct values, as well as EHR platform configurations and settings.
Transfer. At this stage, you need to extract data and deliver it from storage to a back-end database connection. In other words, you need to make a conclusion based on previous records. Here, clinicians should use key factors, in addition to an efficient transport mechanism, that will ensure the quality of outgoing data.
Measure Only Measurable Data
If there are no tools or metrics to measure a particular piece of data, you will never be able to improve it. Only data-driven quality improvement will be a success. In other words, a healthcare organization without quality data is nothing but dead in the water when trying to compete with other enterprises that are using advanced methods to generate data.
On the other hand, not all data can result in improved patient care. At the same time, we should not be 100% dependent on the information collected across various sources. We should consider the fact that some data simply cannot be measured.
Manage the Process, Not the Staff
Whenever you need to improve care quality, you need to manage the process; you don’t, necessarily, need to manage the nurses and physicians. We have already witnessed the rise of the care management movement back in the 90s. It was followed by an incorrect approach when enterprises tried to tell their staff what to do, and how.
In reality, a more effective method is to engage clinicians in the process of collecting and evaluating healthcare data. Organizations need to provide education programs to inform staff on how to use specific tools, methods and techniques that appear in the healthcare marketplace.
On-Time Data Delivery in the Right Format
If an enterprise wants to its manage care delivery process, it definitely needs accurate and visible data. Moreover, that data should be delivered on time in the proper format, and to the right location. The key to success is to get the information into the right hands. In other words, it should be processed by responsible clinicians ready to handle the task.
Smart Cogs in Healthcare
Smart cogs are clinicians who find themselves on the frontline. They are actually the ones responsible for the process of delivering care to their patients. These individuals should not only be intelligent and experienced, but also highly educated and committed. Clinicians are supposed to be ready and willing to change, otherwise, organizations will never be able to improve the quality of their healthcare data. The question is, are they really willing to change?
Are Physicians Ready for Change?
The latest surveys prove that the majority of clinicians are eager to change. 84% of them have a clear understanding of the role that new technologies and data collection tools play in today's healthcare industry. The only thing they need is a stronger argument or encouragement to change. This is where organizations should think of stimulation issues to help train their staff.
The main problem for enterprises is the inability to properly inform their physicians and empower them to change. They call for engagement and inspiration. It is actually the same as launching a mobile app startup. You need to define purpose, including why you actually want to develop the application. The same applies within the healthcare industry.
Clinicians should set clear targets and understand the purpose of better quality healthcare data. If the challenge is too hard for your organization, contact us. We represent a top software building company, and can boast a huge portfolio featuring some of our most successful projects. We develop success, and we mean it! Contact us now: email@example.com.