Tessa Quinn
Electronic health records (EHRs) are used for a variety of purposes, including managing individual patient care, organisational management, and medical and health services research. However, there are a number of possible sources of bias in EHRs that can affect the quality of the data and the validity of research conclusions. These biases can arise from the data collection process, environmental influences, and the original purpose for which the data was recorded. For example, self-reported data by patients may be subject to social desirability bias, where patients underreport behaviours such as substance abuse and smoking. Other types of biases include diagnostic suspicion bias, selection bias, and measurement bias due to inconsistencies in data collection practices. Addressing these biases is critical for ensuring equitable healthcare and the accurate reuse of EHR data for research purposes.
| Characteristics | Values |
|---|---|
| Purpose of data collection | Managing individual patient care, management of organizations, medical and health services research |
| Data quality issues | Inconsistencies in data recording, missing data, data harmonization issues, measurement errors, self-reporting bias |
| Biases | Diagnostic suspicion bias, migration bias, selection bias, information bias, implicit bias, social desirability bias |
| Impact of biases | Affect patient outcomes, influence research conclusions, impact quality of healthcare |
| Mitigation strategies | Identify sources of bias, validate self-reporting instruments, use statistical adjustment methods |
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What You'll Learn
Inconsistencies in self-reported data
One study compared self-reported medical conditions to electronic medical records in a large US military cohort and found near-perfect negative agreement and moderate positive agreement for 38 diagnoses. This indicates that while self-reports may accurately identify the absence of certain conditions, there is a higher likelihood of false positives or over-reporting.
Another study focused on inconsistencies in self-reported recommendations for follow-up after abnormal Pap tests in rural Appalachia. It found inconsistencies ranging from 15.0% for repeat Pap tests to 35.3% for gynecologist referrals. Inconsistencies were more common among women with a history of abnormal Pap tests and those with more severe initial results.
The accuracy of self-reported data can be influenced by social desirability bias, where individuals underreport or misrepresent certain behaviours due to social norms and expectations. For example, patients are more likely to underreport substance abuse and smoking behaviours if self-reported rather than directly observed.
Additionally, the design of data collection instruments and filters used to select patients for research can introduce bias. Different EHR systems may have incompatible interfaces, leading to systematic missing values and challenges in data harmonization. Furthermore, certain filters applied to patient data may introduce biases depending on the known characteristics of the patients.
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Biases in healthcare data analytics
Electronic health records (EHRs) are an invaluable source of data for healthcare providers, researchers, and clinicians. However, it is important to acknowledge that biases exist in these records and can impact the quality and validity of the data. These biases can arise from various sources and have significant implications for patient care and outcomes.
Sources of Bias
One of the primary sources of bias in EHRs is patient self-reporting. Patients may underreport or misreport certain behaviours, such as alcohol consumption, smoking, or substance abuse, due to social desirability bias. This can lead to inaccuracies in research and clinical decision-making. Additionally, patients may interact with multiple healthcare systems or providers, resulting in incomplete medical histories at any one hospital. This can create a biased view of a patient's health, particularly if they are managing their health across different facilities.
Another source of bias is the healthcare system itself. The location of a laboratory or vital measurement can impact its value and analysis. For example, measurements taken in an emergency department (ED) may differ from those taken in an outpatient setting due to selection effects. Additionally, staffing levels and acute crises can lead to delays in data recording, resulting in gaps or inconsistencies in patient records.
Impact of Bias
Biases in EHR data can have significant implications for healthcare data analytics and patient outcomes. They can lead to misdiagnoses, inappropriate treatments, and inequitable healthcare. For example, diagnostic suspicion bias, or over-diagnosis bias, occurs when symptomatic or high-risk patients are more likely to be screened, leading to a higher likelihood of diagnosis and treatment. This can result in an inflated prevalence rate for a particular condition.
Furthermore, biases can impact the generalizability of research findings. For instance, sampling from specific geographic locations may not reflect the typical healthcare setting, limiting the applicability of the results.
Addressing Bias
Identifying and addressing biases in EHRs is critical for improving data quality and patient care. Researchers and clinicians must be aware of the data collection process and potential environmental influences. To mitigate biases, EHRs should aim to meet specific criteria, including representative coverage of the population, standardized data collection and recording, and data harmonization to ensure compatibility between different EHR systems.
Additionally, bias adjustment methods can be employed, such as simulation-extrapolation, regression calibration, and the instrumental variable approach. Validating self-reporting instruments through internal or external methods can also reduce social desirability bias. By addressing these biases, healthcare data analytics can become more accurate, reliable, and effective in improving patient care.
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EHR system incompatibility
Electronic Health Records (EHRs) are well-known for their issues with system interoperability and compatibility in most high-income countries. EHR compatibility refers to the ability of a single EHR software to communicate and share data with other EHR software and medical systems. When a patient is discharged from the hospital, they receive a health summary, often in PDF format, which can be shared with another EHR system. However, this transfer of data between systems is not always seamless.
EHR systems may face compatibility issues when attempting to communicate with each other. This is due to competing software products that do not adhere to common standards and frameworks. For example, not every EHR software exports a CCD that can be read by another system. This can result in important data being omitted or incorrect values being imported, such as crucial medicine dosages.
Furthermore, EHR systems may not have compatible interfaces, leading to systematic missing values. This can be caused by hardware, syntax, and system usability issues. In addition, different EHR software may have different capabilities and functionalities, causing further problems when attempting to transfer data. For instance, one EHR system may be web-based, while another is not, creating difficulties in transferring files.
To address these challenges, healthcare organizations can follow best practices for EHR interoperability. This includes adhering to commonly used standards and frameworks, such as FHIR and HL7, to ensure compatibility between systems. Additionally, leveraging a standard terminology and coding system, such as SNOMED CT or LOINC, can facilitate data consistency across different EHR platforms. Moving to the cloud can also enable seamless sharing of clinical data with any external system, while also allowing for easy scaling of data volumes.
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Diagnostic suspicion bias
Another example of diagnostic suspicion bias is when individuals exposed to a carcinogen present to a medical facility sooner or are more likely to attend screening than a non-exposed population. Medical staff might more readily suspect cancer in these individuals due to their knowledge of their exposure, influencing which tests are ordered and how quickly.
To address diagnostic suspicion bias, prospective studies with consecutive patient recruitment and uniform assessment and measurement throughout are recommended. Retrospective studies should carefully consider diagnostic procedures and make adjustments for any disparities identified.
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Migration bias
One example of migration bias is when individuals move from one country to another and their health data is not transferred or integrated into the new country's health information system. This can result in a lack of continuity of care, as the new healthcare providers may not have access to the individual's complete medical history. It can also lead to challenges in disease control and prevention, as mobile and migrant populations can be difficult to reach with treatment and prevention measures.
Another example of migration bias is when individuals move within a country, but their health data is not updated or transferred to their new location. This can occur when individuals change healthcare providers or insurance plans, or when they move to a different state or province with a separate healthcare system. This can lead to gaps in their medical history and make it difficult to track their health outcomes over time.
In addition, migration bias can also occur when individuals are not accurately identified as migrants in health records. This can be due to difficulties in ascertaining migrant status or a lack of migration variables within health information systems. For example, individuals on shorter-term visas may have lower linkage rates to healthcare identifiers, leading to a higher risk of bias in studies of these groups.
The impact of migration bias can be mitigated through diligent patient follow-up and tracking, as well as the use of unique identifiers that can be interconnected across various national registers. Additionally, the inclusion of migration variables within routine health information systems can improve the disaggregation of health outcomes by migrant status, enabling more evidence-based decision-making.
Overall, migration bias is a significant consideration in healthcare data and can have important implications for research, policy, and clinical practice. By addressing this bias, healthcare systems can better understand the healthcare needs of migrant populations and improve the accuracy and completeness of health data.
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Frequently asked questions
Measurement bias is any kind of "contamination" that occurs during the data collection process. This can refer to the approach taken to obtain or confirm measurements, the context of the encounter, or the location of the laboratory or vital measurement.
There are several sources of bias in hospital records, including self-reporting bias, diagnostic suspicion bias, migration bias, and selection bias. Self-reporting bias occurs when patients underreport behaviours such as substance abuse and smoking. Diagnostic suspicion bias occurs when symptomatic or high-risk patients are more likely to be screened and diagnosed. Migration bias occurs when patients are lost during follow-up. Selection bias occurs when the EHR system does not capture the entire target population.
Measurement bias can affect the validity of health research and the quality of data. It can also impact patient outcomes and contribute to disparities in healthcare.
There are several ways to address measurement bias in hospital records. These include validating self-reporting instruments, using statistical adjustment methods, and ensuring that EHR systems meet certain criteria, such as coverage of the entire population and standardized recording of measures.
One challenge is that there may be missing data or incompatible EHR systems that make it difficult to obtain a complete medical history of a patient. Another challenge is that the same measurements may not be conducted at regular intervals for all patients, and there may be delays in data recording due to low staffing levels or acute crises.
