How to improve data quality in healthcare?

Archer's Blog - How to improve data quality in healthcare?
Data collection tools and methods generate information about patients that is supposed to improve the quality of medical services, treatment, and care according to the patient's needs. The quality of the collected data ensures the competitive advantage of the medical facility.

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Contrary to the common idea the more complex software solutions are used by the healthcare organizations the more problems they may have to face in terms of data quality they gather. And healthcare is among the industries where poor data quality is the number one issue that requires major improvement as the stakes are too high.

The lack of reliable data security measures and strategies results in inaccurate information without appropriate protection. Complete and accurate data is a vital component of the complex healthcare system. Otherwise, organizations will not be able to deliver high-quality care to their patients.

Challenges and solutions for healthcare data quality


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 has been trained appropriately on documentation and observation.

  • Develop a detailed case describing the process of collecting data
  • Explain in what way that data is important for the patient and organization;
  • Demonstrate the role of data quality when improving patient care (risk evaluation, remote consultations, ease of payment, etc.).

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;
  • Compare your data with other entities and check how your enterprise looks from the outside.

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 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 data storages 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;
  • Work with other responsible clinicians when collecting and processing data.

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 data analytics.

The medical data set should include the following three points:

  1. Capture. At this stage, staff or automated systems deliver necessary data to electronic health record (EHR). The data confirms that a particular event has happened (returned lab results or an encounter). Clinicians enter all results accurately.
  2. 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.
  3. 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 health records. Here, clinicians should use key factors, in addition to an efficient transport mechanism, that will ensure the quality of outgoing data.
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  • Use tools to qualify and quantify collected data

If there are no tools or metrics to measure a particular set of data, you will never be able to improve it. There are advanced methods to generate, store, and evaluate the data in a healthcare organization, and using them could be beneficial for the patient care, but only a structured, comprehensive and holistic approach to the health data collected across various sources would ensure the better service.

  • 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. 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 the medical facility wants to 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 health 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 on the front-line responsible for the process of delivering care to the patients. These individuals should not only be intelligent and experienced but also highly educated. Clinicians are supposed to be committed to always improve the quality of their inputs to the healthcare data.

The majority of clinicians have a clear understanding of the role that new technologies and data collection tools play in today's healthcare industry. They need to consider the importance of a data-driven approach for the whole digital health system. The only thing they need is the right support, training and other ways to learn about the possibilities they have and the tools they can use to advance own professionalism together with the level of the provided service. This is where organizations could and should help their staff on a regular basis.

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