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What are the most common data quality issues when working with medical records?

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Medical records are a valuable source of data for healthcare professionals, researchers, and policymakers. However, they are also prone to various data quality issues that can affect their accuracy, completeness, and reliability. In this article, you will learn about some of the most common data quality issues when working with medical records, and how to identify and address them.

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Calum Yacoubian, MD

Director Healthcare Strategy, Applied AI Science

1 Missing data

One of the most frequent data quality issues in medical records is missing data. Missing data can occur due to various reasons, such as human error, system failure, or intentional omission. Missing data can create gaps in the information available, and lead to biased or inaccurate analyses. To deal with missing data, you need to understand the pattern and mechanism of the missingness, and apply appropriate methods to handle it, such as imputation, deletion, or weighting.

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Calum Yacoubian, MD

Director Healthcare Strategy, Applied AI Science
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Missing data is an issue in any operational, clinical or research scenario where patient data is needed. But most frequently- we are looking at this through the lens of what is missing in the structured data. The answers often lie in the unstructured medical record - those free text components where clinicians dictate or type patient histories. Technologies like NLP are a key component to any strategy looking to fill these gaps - enabling organizations to establish what data is genuinely missing - versus what is hidden.

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Yesha Patel

Method development|qualification|method transfer | Data analysis | data evaluation |documentation|DOE|HOS|ELN |critical thinker|Trouble shooter
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Missing data can recover through back up database and cloud server. Many ways are there in importance and critical measurements of trouble shooting. Someone always know to where is data. If it is perished then system should change.

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2 Inconsistent data

Another common data quality issue in medical records is inconsistent data. Inconsistent data refers to data that does not conform to a standard format, structure, or definition. For example, different sources may use different codes, units, or labels for the same variable, or the same source may change its data collection or reporting methods over time. Inconsistent data can cause confusion, errors, and duplication, and reduce the comparability and interoperability of data. To deal with inconsistent data, you need to establish and follow data quality standards, and perform data cleaning and validation processes.

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Yesha Patel

Method development|qualification|method transfer | Data analysis | data evaluation |documentation|DOE|HOS|ELN |critical thinker|Trouble shooter
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Inconsistent data occurs when someone did mischievous act on sample database. Change mindset is important.inconsistant is also depend on negative team work and behaviour of manager to hide database.

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Calum Yacoubian, MD

Director Healthcare Strategy, Applied AI Science
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This is an issue that exists universally in healthcare, despite the widespread adoption of EMRs. A HIMSS analytics report from 2018 showed that the average hospital has 16 EMR vendors in use at its affiliate practices! There are technology solutions to this - which include bridging, ontological and semantic normalization. Whether you're a provider, payer, HIE or aggregator- if you want to get a universal view of your population - these are technologies you need to invest in

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3 Incorrect data

A third common data quality issue in medical records is incorrect data. Incorrect data refers to data that contains errors, anomalies, or outliers that do not reflect the true state of reality. For example, a medical record may contain wrong or outdated information about a patient's diagnosis, treatment, or outcome, or a data entry may be mistyped or misclassified. Incorrect data can affect the validity and reliability of data analysis, and lead to wrong decisions or actions. To deal with incorrect data, you need to verify and correct the data sources, and use robust statistical methods to detect and handle errors and outliers.

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Yesha Patel

Method development|qualification|method transfer | Data analysis | data evaluation |documentation|DOE|HOS|ELN |critical thinker|Trouble shooter
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Yes. All method analysed with 5 why data analysis.outlayers are major concern in statistics. Why it is out layer that needs to be check.

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4 Duplicate data

A fourth common data quality issue in medical records is duplicate data. Duplicate data refers to data that is repeated or redundant in the same or different sources. For example, a patient may have multiple records with different identifiers, or a record may be copied or transferred multiple times. Duplicate data can inflate the size and complexity of the data, and introduce bias or inconsistency in the data analysis. To deal with duplicate data, you need to identify and remove the duplicate records, and use unique identifiers and matching algorithms to link the records.

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Yesha Patel

Method development|qualification|method transfer | Data analysis | data evaluation |documentation|DOE|HOS|ELN |critical thinker|Trouble shooter
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Duplicate data is generated for safety purpose. Duplication done when multiple people view single database and kept a side. Non traceability occurs when team is large with toxic handling of database.

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5 Sensitive data

A fifth common data quality issue in medical records is sensitive data. Sensitive data refers to data that contains personal or confidential information about the patients, such as their name, address, medical history, or genetic profile. Sensitive data can pose ethical and legal risks, and require special protection and consent. For example, a breach of sensitive data can violate the privacy and security of the patients, or a misuse of sensitive data can harm the patients' rights or interests. To deal with sensitive data, you need to follow the ethical and legal principles and guidelines, and use encryption, anonymization, or aggregation techniques to safeguard the data.

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Calum Yacoubian, MD

Director Healthcare Strategy, Applied AI Science
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Depending on what country you are in, there are varying, well prescribed standards for handling of patient data. In my opinion- it is important to consider ALL patient data as sensitive. Fortunately - there are a number of ways that data protection standards can be applied - and there are organizations that specialize in ensuring appropriate data privacy standards are adhered to. For instance, Privacy Analytics has both AI software and expert services to enable organizations across their data privacy journey - assessing privacy risk, creating a privacy strategy, providing software and services to achieve required privacy, and expert determination. Combining tech with expertise is vital in this space

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Yesha Patel

Method development|qualification|method transfer | Data analysis | data evaluation |documentation|DOE|HOS|ELN |critical thinker|Trouble shooter
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Loyalty and strong work ethic is required against sensitive database.database is secure when cloning is not done.security also kept strong by shut mouth.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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What are the most common data quality issues when working with medical records?

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2

3

4

5

6

1 Missing data

2 Inconsistent data

3 Incorrect data

4 Duplicate data

5 Sensitive data

6 Here’s what else to consider

Analytical Skills

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What are the most common data quality issues when working with medical records?

1

2

3

4

5

6

1 Missing data

2 Inconsistent data

3 Incorrect data

4 Duplicate data

5 Sensitive data

6 Here’s what else to consider

Analytical Skills

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