What Is a Data Cleansing Guide and Why Is It Important In Healthcare Industry

Data cleansing is becoming an increasingly important aspect of a company's business intelligence strategy as it strives to become more data-driven.

The proportional cost of resolving a data quality problem increases exponentially over time, according to Validity's 1-10-100 quality principle. It costs $1 to identify faulty data at the beginning of the process, $10 to remedy existing errors in the middle, and $100 to fix a problem once it has caused a failure later on. Bad and filthy data continues to be the most significant impediment to firms' efforts to become data-driven. As a result, data cleansing is the most important aspect of a company's business intelligence strategy.

What is data cleansing?

Data cleansing is a wide word for the act of standardizing and altering data so that it can be utilized for a variety of reasons. In general, data cleansing entails:

1. Correct any inaccuracies in the data so that it reflects reality.
2. Validate information by utilizing the appropriate data types and formats.
3. Fill in any missing or blank values.
4. To represent separate entities, deduplicate data.
5. To create a single source of truth, combine facts from several sources.

When data is acquired using several apps, it is stored with numerous messes and dangerous errors. Data cleansing processes and approaches can aid in ensuring data quality so that it can be used for its intended purpose.

To ensure data quality, measure data cleanliness.

You should check your data against the following six dimensions to see how clean it is:

1. Accuracy - How accurate is your data in representing reality?
2. Completeness - Do you have the needed attributes in your data?
3. Consistency - Do the same matching records appear in different data sources?
4. Validity - Does your data exist in the right format, data type, and within acceptable range?
5. Timeliness - Is your data up to date to your satisfaction?
6. Uniqueness - Do you have any duplicate records in your database?

A dataset that adheres to these six data quality guidelines is considered clean and ready to be used.

Data cleansing workflow

Data must go through a variety of processes to correspond to the six quality dimensions described above:

1. Data profiling: Create data profiles to identify any variables in your dataset that are misspelled, missing, erroneous, or duplicated, as well as potential data cleaning possibilities.
2. Data standardization: Transform data values to represent correct, legitimate, and complete dataset properties using the data profile generated above.
3. Data matching: Implement sophisticated, proprietary data matching algorithms for phonetic, numeric, domain-specific, and fuzzy matching scenarios to identify numerous occurrences of the same thing.
4. Data deduplication: Decide which records should be removed or merged together based on the estimated data match findings to create a single source of truth.

Activities for data cleansing

Several data cleansing processes take performed as part of the above-mentioned workflow execution. The following are some of these activities:

1. To see the volume of different values within a column, create histograms against your datasets.
2. Accurate representations, patterns, and data formats are used to replace partial or inaccurate values.
3. Running data values through a library or dictionary of terms, such as first names, last names, or addresses, to determine which components should be kept and which should be removed.
4. Identifying data patterns to ensure that all values of a given attribute follow the same pattern. This may entail recognizing common patterns such as email addresses and phone numbers, or defining proprietary patterns with a regular expression logic builder.
5. To make the dataset more understandable or usable for an intended purpose, data properties are parsed into several columns or various fields are merged together.
6. Using data matching algorithms that have been fine-tuned to ensure maximum match accuracy and the fewest possible false positives.
7. Building transformational logic based on the matched findings to choose which records to combine or purge in order to achieve the golden record.

Best methods for keeping data clean

Let's take a look at some of the best practices for keeping your data clean and standardized, as well as reducing time spent on data inspection and cleansing.

Put validation checks on data entry: The majority of data is collected through front-end forms in the digital age. Validation controls on these forms can save you a lot of time and work in terms of error screening and correction. This entails implementing specialized controls to limit data input; for example, date type fields should only allow the user to enter dates using a calendar. Similarly, value patterns should be validated; if only work email addresses are permitted, the field should be blacklisted for all other free mail domains, and so on.

Use data quality tools that are automated: There are several data quality management tools on the market, but only a few will provide a complete solution at a reasonable price. An automated software solution will eliminate the time and effort required to manually screen millions of data records and create uniform cleaning logic that can be applied to any dataset.

Get buy-in from management: The management of a company is generally aware of the necessity of data for business intelligence, but there is a gap in their understanding of the importance of clean data for extracting accurate, useful insights. It is critical for data analysts to link business goals to data cleansing operations in order to gain management support. Because data is involved in every company operation, each employee must make a concerted effort to keep data clean.

Create a data glossary and associated meta data: At your firm, data is generated, stored, and processed for a variety of purposes. This is why a single, uniform definition of all data-related terminology is required for everyone. This will assist your staff in comprehending why particular data is being collected, the appropriate data type, format, and range for these data values, as well as where the data is used.

Define and monitor data quality indicators: Establish a data quality framework that identifies all roles at your company that impact data quality, filters data for quality issues, and delivers key metrics including data consistency, validity, and completeness. These indicators will assist you in tracking data in your business and ensuring that future data meets your expectations.

Importance In Healthcare System

Data quality is vital in a variety of businesses, but it is especially important in healthcare. Physicians are being urged to use electronic health records (EHR) systems that can eliminate human errors and help doctors extract vital patient information as they try to comply with the Health Information Technology for Economic and Clinical Health (HITECH) Act.

However, providers must be aware that data sets must be maintained on a regular basis to ensure that they are meeting their objectives. A good data cleanse can assist healthcare institutions improve billing procedures, eliminate mistakes, and lower operating costs by verifying valid information and identifying faulty sets.

Streamline billing procedures

The primary purpose of healthcare providers is to deliver high-quality care, but this may not be possible if they are not running lucrative companies. According to Pulse, the billing process is an important aspect of a practice's continuity, and it might be threatened if there are issues with patient data. Although data cleansing may appear to be a time-consuming operation, it can assist keep the billing pipeline clean, preventing doctors from later discovering that much of their revenue has been lost due to erroneous names, obsolete addresses, and expired insurance information.

According to the source, a good cleansing can help facilities remove out the names of patients who have stopped receiving treatment from the provider, moved away, or died.

Author: Himanshu Sharma

#datacleansing #datacleansinginhealthcare #datacleansingprocess #healthandfitness #databasecleansing #useofdatacleansing 

References:

Javeria, Gauhar, 2021. Data Cleansing Guide: What Is It and Why Is It Important. [online] RTI Insights. Available at: <https://www.rtinsights.com/data-cleansing-guide-what-is-it-and-why-is-it-important/> [Accessed 2 March 2022].

Rachel, Wheeler, 2018. A good data cleanse can clean up some healthcare practices. [online] Experian. Available at: <https://www.edq.com/blog/a-good-data-cleanse-can-clean-up-some-healthcare-practices/#:~:text=A%20good%20data%20cleanse%2C%20which,mistakes%20and%20reduce%20operating%20expenses.> [Accessed 2 March 2022].

Drew, Lopez, 2019. How healthcare data will make or break healthcare AI. [online] Accenture. Available at: <https://www.accenture.com/us-en/blogs/insight-driven-health/how-healthcare-data-will-make-or-break-healthcare-ai> [Accessed 3 March 2022].