How To Clean Your Healthcare Contact Database

Cleaning up healthcare contact databases is so important to take full advantage of healthcare big data. All the benefits of healthcare big data, namely improved care, increased revenue, and better decision making will be realized only if the quality of data is good.

Quality of data refers to the data that are completely free from duplicate info, omission, misleading data, data that is not integrated properly, and data that are simply wrong with the files.

And for this, the healthcare contact database needs time-to-time cleaning. The amount of data collected in healthcare is bigger than in any other industry; it requires more frequent cleaning. This ensures information continues to be precise and reliable despite changes that can occur in address, email, and other key decisions.

The quality of data determines the growth of your healthcare business. And that’s why Ampliz ensures you 98%  accuracy of the data it provides.

For this, Ampliz data solution cleans the data and gets them verified before offering them to customers. Ampliz healthcare data cleaning process involves:

  • Collects the data from 500+ credible sources
  • Addressing data inconsistencies and errors
  • Transform the clean data into useful data through ML 
  • Prospect intelligence adding and quality check
  • Check accuracy via telephone and email verification process

  Now let’s see how the quality of data impacts the healthcare industry.

Importance of Data Quality Across Healthcare

The healthcare contact database is nothing but the set of information that lets you stay connected with your patients as well as your target audience. An updated quality of the database helps strategists to analyze the database with confidence to draw accurate conclusions.

 Good quality data helps you in many ways like reducing costs and streamlining the internal process, anticipating new trends, providing more effective patient care, eliminating mistakes in patient care, etc.

Let’s see how important it is to maintain a good quality of data.

1) Good Quality of Data Helps to Maintain Accuracy

Frequent cleaning and maintaining a good quality database will help you to get rid of any duplicated or expired information of your patients.

This lets you stay connected with them, even offering them online consultations in case of any adverse situation. This helps you to prevent losing any of the patients and provide timely care.

2) Help in Enhancing Patient Billing Address

To provide the best possible patient care, healthcare organizations have to invest in required services and products and have to pay them regularly to continue the best possible service.

And for that, they have to make patients clear their bills as soon as they recover. For that, hospitals should have the correct set of information like phone numbers, and email addresses to let patients know their bill amount and urge them to pay earliest.

3) Helps in Reducing Cost

Maintaining a good quality healthcare contact database helps in removing the guesswork around how, when, and through what channel is best to reach individuals and saving your time and money.

Besides this, data quality solutions help billing department to:

Having understood the importance of the quality of healthcare contact databases, let’s understand what it means by healthcare data cleaning.

  • Include additional contact information to make a more comprehensive patient contact record
  • Ensure accurate contact data information despite growing numbers of contacts in the list
  • Keep contact information up to date despite changes in phone numbers or mail ids.

What is Healthcare Data Cleaning

As its name suggests, data cleaning refers to the cleaning of inaccurate, incomplete, or duplicate data and replacing it with current and accurate figures.

How often you should clean your data depends upon many things like:

  • The size of your business
  • The amount of data collected
  • The speed at which data has been collected
  • The quality of data governance and management

By cleaning your data at regular intervals you can ensure portability, accessibility, and interoperability of information that helps healthcare’s ability to boost digital transformation.

Along with the regular cleaning of databases, it’s also important to know the causes of dirty data that helps you to maintain the data quality by avoiding certain actions.

Let’s see what are some key causes of dirty data.

Key Causes of Healthcare Dirty Data

The main causes of healthcare dirty data are an inaccuracy, duplicacy, and incomplete data which is caused due to storing of collected data in different databases and then merging them together.

For example hospitals, dental clinics, diagnostic centers, and physicians store their patient details in different databases. This data is then spread among a number of applications like revenue cycle management, decision support systems, Electronic Health Records, etc.

Thus it’s hard to trace mistakes or inaccuracies in data collected from different sources. Now let’s understand in brief how duplicacy, inaccuracy, and complete data actually happened.

1) Duplicate data

Duplication of data is the biggest cause of dirty data. As per one research, Hospital’s EHR consists of 5-10% of duplicate data. This number may expand to a rate of 20% if the hospital entity has many locations.

Finding duplicate data is hard because of mismatched patient details. For example, sometimes the same sets of information are stored under two different names of the same person. This causes duplication of data.

2) Incomplete data

Incomplete data greatly impacts the hospital’s ability to serve patients. When patients don’t fill in all the necessary details, improving patient care is difficult, and even hospitals can’t use this data for their marketing strategies.

The reason for incomplete data is either the patient’s inability to give all the required information or the system’s limitations.

With effective healthcare contact database cleansing, you can overcome this hindrance and can fill in the missing information.

3) Inaccuracy

Inaccuracy in data is the main cause of all the errors in data. Misspellings, transposed letters, and incorrect spacing lead to bad data. This limits hospitals from using these records for better insights and treatment plans.

4) Incorrect mailing address

Email is the most preferred means of communication in the healthcare industry. 

Any minor mistake in the spelling of the mail ID will make you pay higher prices. An invalid mail ID will prevent you from connecting your present customers as well as prospects.

Thus, dirty data is the biggest roadblock in your journey of serving patients and getting new customers. That’s why it’s important to have frequent cleaning of data  to have accurate and updated data.

Now let’s see, how to clean your healthcare contact database.

How to Clean Your Healthcare Database

There are basically five steps of cleaning your healthcare data.

1) Standardized 

The very first step in data cleaning is to standardize your data in one place. Storing the data in a different place and then merging them in one sheet is the cause of many inaccuracies in data.

That’s why it’s always better to have some standardized data rules and define a cross-organizational structure. The manual process is quite time consuming and requires more people.

 But with the help of automated solutions, you can easily scale rapid data entry. Standardizing your data will help you to transform data points to more relevant formats from where you can easily drive more insights and values.

2) Cleaning and Updating

During this step the organization uses several procedures for data scrubbing to either modify or clean its data. This process removes all the inaccuracies and duplicity from the database and updates all the information.

3) Validate Your Data

Validate your data for its authenticity, accuracy, reliability, and its prescribed quality guidelines and standards. Validated data gives you more confidence for its performance.

 However, data validation is a costly and time-consuming process. But by automating validation, you will have more accuracy and it saves you cost and time.

4) Duplicate data

Duplicate data increases the chances of inconsistencies between datasets, reduces the data quality, and increases your data storage needs. 

For these reasons, it’s vital to remove such duplicated data from the lists. With the help of an automated solution, you can remove duplicate data from the database which saves you time and money to write code.

5) Analyze your data quality

Once you have followed the above steps of standardizing the data, validating it, and removing duplicated data from it, it’s very crucial to analyze the data at a regular time period. 

You need to analyze the data for accuracy, completeness, and originality of data. You should check whether the data needs cleaning. Unless and until you can’t recognize the reasons for which your data needs cleansing you can’t follow this step precisely.

Advantages of Data Cleaning

Data cleaning improves the effectiveness of big data. By cleaning duplicate, incomplete and inaccurate data from the datasets, we can make the best of collecting big data in healthcare.

Data cleaning benefited to the business in many ways, let’s see some of them:

1) Reduce Administrative Costs

Incomplete or inaccurate data makes it hard for hospital staff to find the details of any patients. It consumes a lot of time which results in delays and a lack of efficiency that can add to unnecessary costs.

This can be avoided by healthcare contact database cleaning. Data cleaning keeps your data accurate and updated and makes it easier to find any patient’s details easily.

2) Avoid email failure

Data cleaning verifies the email address and ensures whether it is working or not. Sending an informational or promotional email to an invalid mail id will not serve any purpose. They will just increase your email failure rate and prevent you from reaching your patients and potential customers.

By frequently cleaning your data, you can ensure the accuracy of email addresses.

3) Maintain the brand image

A minor change in either a phone number or email address might cause leakage of important data about your patients. And because of that, your patients may lose trust in you.

But data cleaning ensures the accuracy of all the information of your patients and prevents data leakage.

Conclusion

Thus, time-to-time cleaning of healthcare data is a must to maintain good quality data. This helps healthcare organizations to get the right insights and provide the best patient care.

Ampliz healthcare data solutions ensure 98% data accuracy as it cleans and verifies data before offering it to customers.

Ampliz healthcare contact database cleaning process involves 

  • Collecting the data from 500+ credible sources
  • Addressing data inconsistencies and errors
  • Transforming the clean data into useful data through ML 
  • Prospect intelligence adding and quality check
  • Checking accuracy via telephone and email verification process