Healthcare Data Cleansing: Frequently Asked Questions
Did you know that every patient generates millions of detailed records in real-time? That’s a lot of data to collect, store, and make comprehensible. Not to mention, healthcare organizations must take special care to adhere to regulatory requirements across several different data types.
That’s where healthcare data cleansing comes in. This necessary process keeps healthcare data sets from becoming unusable which can have severe consequences. In this guide, we’ll explore data cleansing in depth by answering the following questions:
- What is healthcare data cleansing?
- What causes dirty data?
- What are the benefits of healthcare data cleansing?
- How can healthcare organizations maintain proper data hygiene?
Keep in mind that healthcare data cleansing requires a robust data platform that can either be built in-house by a team of analysts and data scientists or bought through a vendor. Whichever solution your team chooses, it will need to be scalable to keep up with an increase in data over time. With this in mind, let’s explore data cleansing in greater detail.
What is healthcare data cleansing?
Healthcare data cleansing, also called healthcare data scrubbing or cleaning, is an essential part of data hygiene and refers to the process of identifying and rectifying errors within a healthcare data set. This data set is integrated from a variety of sources such as EHRs, claims systems, lab systems, and administrative databases stored within a centralized healthcare data warehouse.
How often your organization cleans its data set is dependent on several factors, including:
- The size of your organization
- The volume of data collected
- The speed at which data is collected
- The associated regulatory and compliance requirements
- The desired outcomes of your collected data
Healthcare organizations must regularly clean their data to maintain quality standards. The frequency of data cleansing will be determined by the data quality controls put in place within your existing workflows.
What causes dirty healthcare data?
Dirty healthcare data is caused by a variety of factors that can quickly add up and cause severe system roadblocks. These factors include:
- Duplicate data: Because data is inputted from several sources, it can be easy to incur data duplications. This slows down your data reporting and analysis processes and makes it difficult to draw meaningful insights.
- Inaccurate data: Data reporting errors from patients or providers can invalidate your data set and cause lasting issues that may take significant time to resolve.
- Incomplete data: Omissions, forgotten updates, and missing data all prevent a full patient picture which could lead to workplace inefficiencies at best and inaccurate patient diagnoses and treatments at worst.
In a system as large as healthcare, data collection errors are bound to happen. To prevent them ahead of time, create standardized rules for accurate data entry and task team members to audit your database on a regular basis to locate errors.
What are the benefits of healthcare data cleansing?
A clean data set can work wonders for your organization. In fact, the benefits of healthcare data cleansing can be tracked across several key measures:
- Operational and cost efficiency: A clean data set saves both operational time and money. This means that your team will spend less time sifting through incomplete data while maximizing your resources.
- Data storage efficiency: Most data is stored within a healthcare data warehouse and must undergo substantial cleaning efforts to transform from raw data to usable data. Quality measures ensure that your organization has access to a structured, organized healthcare data warehouse.
- Data analytics accuracy: Analytics tools help your organization visualize health outcomes which include risk adjustment analysis, population health management, and patient engagement among others. Clean data keeps these analytic reports accurate and up to date.
- Improved patient outcomes: Because each data point represents an individual patient, a clean data set provides the chance to improve patient outcomes at a quicker rate. Which means providers can access the right information and the right time.
- Enhanced billing processes: Correct data streamlines the payor and patient billing process and prevents unnecessary costs. In turn, your organization can better approach financial reporting.
The bottom line: A clean healthcare data set is essential for data-backed decision-making. With comprehensive data quality measures in place, your organization can see measurable growth across major stakeholders.
How can healthcare organizations maintain proper data hygiene?
On the ground level, healthcare data cleansing can be understood as a series of steps or ordered processes. These steps include:
- Validation: Your data must be validated for accuracy, completeness, and consistency during this initial data cleansing phase. Data analysts identify and remove discrepancies and duplications to ensure data accuracy.
- Standardization: Once data discrepancies are eliminated or appropriately evaluated, data analysts must standardize data formats so that they match. For instance, an analyst must ensure that a patient with a recently changed last name is accurately represented.
- Error Correction: Data professionals must detect and correct any remaining inconsistencies. This process may include outlier detection, data profiling, and other methods to resolve inaccuracies.
- Completeness verification: Incomplete data is assessed and missing values are accounted for and properly documented using appropriate methods.
- Integration: Data is then consolidated or integrated from several sources into one data set (while adhering to privacy laws) also known as a healthcare data warehouse. Then, the usable data can be extracted for meaningful analysis.
- Review and monitoring: Data is reviewed and monitored on a regular basis to ensure quality and accuracy measures are sufficiently met. Data audits, quality assurance checks, and external data validation are all a part of this process.
Because this process can be involved, many organizations turn to healthcare data professionals to outsource their data collection, cleaning, and analysis. Often, data scientists and analysts are forced to write elaborate queries for unstable and untrustworthy databases, but data platforms like Arcadia Foundry can simplify several data collection and analysis processes.
These platforms are built for analysts by analysts and are consistently enriched with clean, quality data, so organizations don’t have to rely on their own cleansing and standardization processes to extract meaningful insights.
Maintaining an accurate, usable healthcare data set requires consistent data cleansing. If your organization decides to perform its own data cleansing be sure to follow the outlined best practices for quality assurance. If your organization decides to outsource its data cleansing to a vendor, make sure they offer comprehensive and reliable services.
About the Author: Nick Stepro
Nick Stepro is the Chief Product Officer at Arcadia, where he leads the design of the next wave of advanced healthcare analytics applications — including Arcadia Analytics, which has been praised as having one of the best user interfaces in the industry. He has worked with large health systems and payers to design and execute on innovative clinical integration and business intelligence strategies to drive improved health outcomes and reduced system costs.
Nick believes in good design and data visualization. When combined with focused expertise in analytics, healthcare and business process, the results are intuitive data-driven applications that empower users to dramatically improve the way they run their businesses. His data visualization work has been covered on NPR, U.S. News and World Report, Medical Ethics Advisor, and elsewhere. Becker’s Health IT and CIO Review recently named him one of “31 Health IT and Revenue Cycle Whiz Kids” to watch. He has spoken at Medcity CONVERGE, AMIA, and HIMSS and has been a guest lecturer on data visualization at Georgia Tech. In December 2016, he was the closing speaker at the CCO Oregon Cost of Care conference.
