Data Cleansing For Healthcare
Melissa AU Team | Australia, Data Cleansing, Healthcare Data Management |
A hospital or clinic’s reputation is built on how it treats patients and their caregivers. In turn, patient treatment is dependent on the data available about them. Yes, we’re talking about symptoms as well as the patient’s age, gender, lifestyle, etc.
Given that healthcare professionals collect data from hundreds of patients each day, maintaining high-quality standards is a big challenge. According to a study, by 2025, big data in healthcare is projected to have a CAGR of 36%.
In other sectors, decisions based on bad data may create temporary setbacks and financial losses but when it comes to healthcare, it could be the question of life and death. Let us understand the causes of poor data quality in this sector, the benefits to be experienced by improving data quality and the steps to do so.
Causes Of Poor Healthcare Data
As with a medical diagnosis, the cause must be identified before prescribing treatment. The most common causes of poor data in the healthcare sector are:
Incomplete Data
Researchers attempting to use Electronic Health Records data to create a risk prediction model for identifying cases of undiagnosed type 2 diabetes mellitus found that up to 90% of data were missing in some records.
Incomplete data increases the risk of misdiagnosis and cannot be used for patient care, research or any other purpose.
One of the main reasons why data fields are often left incomplete is the patient’s inability to provide the necessary information. For example, the patient may not speak the local language. Incomplete data may also result from system limitations.
Inaccurate Data Entry
Patient records are often entered in a hurry. Nurses and healthcare workers responsible for data entry are also overburdened with work and have a high risk of making errors while entering data. Hence, mistakes like misspelled names, transposed letters, etc. are common occurrences.
Miscommunication from the patient is another reason why inaccurate data may enter the system. A study found that 81% of patients lie to their doctors about their lifestyle for fear of being judged. There may also be cases where data is missed because the patient is unaware of it. For example, they may not know the medication they were prescribed earlier.
The third common cause for inaccurate data is the tendency to smoothen device data. This refers to entering generic readings rather than the exact values being displayed by medical devices.
Duplicate Entries
Inaccurate and incomplete data greatly increases the risk of creating duplicates. Duplicates may also be created when patient data is collected by different departments and at different times. For example, an individual may enter his name as Michael when he visits as an outpatient but later when he is admitted, may give his name as Michael Smith.
How to Solve Data Related Issues in Healthcare
A few simple steps can go a long way in improving data quality in healthcare.
- Data Inspection: Data must be checked before it enters the database to see that all fields are complete and accurate. In addition, data existing in the database must also be checked from time to time to identify inaccuracies and outdated information.
- Data Scrubbing And Updating: Data quality issues must be addressed as soon as possible. This means that inaccurate data must be removed, duplicates must be identified and merged, etc.
- Data Verification: Next, data must be verified against third-party databases to ensure that it is valid and conforms to your quality standards.
- Reporting: Lastly, every time data is inspected, a report on the data quality trends and progress must be generated. This will help the data governance team work on improving its policies.
Benefits Of Improving Data Quality In Healthcare
Improving the quality of data benefits healthcare service providers in many different ways.
Improved Patient Care
When doctors have access to complete, accurate, high-quality patient records, they can make a diagnosis with more confidence and prescribe the right treatment. It can even help with preventive treatment. For example, a hospital in Ohio is analyzing data to identify patients at risk of lifestyle conditions and provide timely intervention.
Cost Savings
Having reliable data can save administrative and research costs. Having correct patient addresses ensures that all marketing or transactional mail is delivered correctly. It also makes doctors, nurses and other professionals more efficient. Thus, they can achieve more with their time and hence optimize administrative costs.
Proper Billing
Access to correct and complete patient data improves the billing process and minimizes the risk of billing errors. Thus, there’s a lower chance of payment disputes. Using good data helped the Centers for Medicare and Medicaid Services prevent billing improper payments worth $210.7 million.
Easier Compliance
Good quality data reduces the risk of having staff mistakenly giving outpatient data or disrupting patient communication. It makes it easier for hospitals to protect patient records and other personal health information with regard to HIPAA compliance. This gives the brand a more trustworthy reputation.
How Can Melissa Help?
Melissa is an expert in data verification. From names to addresses and contact details, the APIs by Melissa can help automate data verification and ensure it meets the criteria required to be categorized as high-quality data. Some of the solutions that can prove helpful include:
- Global Name Parsing: Melissa parses patient names into standardized formats and flags obvious errors to improve the quality of patient records.
- Address Autocomplete: Address autocomplete at the time of patient onboarding streamlines the process and minimizes the risk of error. It also ensures that addresses are formatted correctly to minimize the risk of creating duplicates.
- Data Deduplication: Melissa can match, merge and deduplicate patient records to make the database more reliable and cut down on the risk of billing and claim errors.
- Address Updating: By comparing data in the database to third-party databases, Melissa can address outdated data and update addresses when street names, etc have changed. This ensures invoices are delivered on time. In turn, this reduces the risk of delayed payments.
Melissa’s data quality solutions have helped many healthcare providers keep their data clean and it could be just what you need too.