What are common errors in electronic medical records?

Common Errors in Electronic Medical Records

Electronic medical records are plagued by multiple systematic errors including poor usability design, copy-paste documentation errors, incomplete data entry, patient misidentification, medication ordering mistakes, and coding inaccuracies—all of which directly threaten patient safety and care quality.

Usability and Design-Related Errors

EHR systems frequently cause errors due to poor usability rather than preventing them. The fundamental problem is that most EHRs are designed with adult patients in mind and lack pediatric-specific customizations, leading to weight-based dosing errors and other pediatric-specific mistakes 1. User errors resulting from poor usability can lead to untoward outcomes, with some errors occurring without the user even being aware of them 1.

Key usability failures include:

• Faulty computer interfaces that increase cognitive load and working memory demands 1
• Excessive alert fatigue leading to very high override rates of clinical decision support warnings 1
• Lack of adequate decision support for complex clinical decisions 1
• Miscommunication between different EHR systems when clinicians use multiple platforms 1

Documentation Errors

Copy-paste functionality represents one of the most dangerous documentation practices in modern EHRs. When entire notes are copied without proper editing, this creates factual errors that propagate from note to note, with old dates and vital signs that are never updated 1. The American College of Physicians explicitly warns that copying and pasting entire notes is inherently risky and should be avoided 1.

Documentation problems include:

• Incomplete medical record documentation of key elements such as medical history, laboratory data, and differential diagnosis in both hospital and ambulatory settings 1
• Source documents that are absent, incomplete, or contradictory due to multiple healthcare providers documenting inconsistently 1
• Template and macro misuse that standardizes away the heterogeneity making each patient encounter unique 1
• Boilerplate negative findings included to satisfy billing guidelines rather than document clinically relevant information 2

Data Quality and Completeness Errors

Missing and inaccurate data represent critical threats to patient safety that cannot be assumed to be randomly distributed. Data completeness issues arise from multiple sources including the extent to which collection is integrated with clinical care, training inconsistencies among data entry personnel, and whether data elements are required fields 1.

Specific data quality problems:

• Changing data definitions within EMRs that undermine automated coding efforts 1
• 100-fold differences in recording rates across practices, including missing diagnostic codes in patients who received treatment 1
• Inconsistent coding and lack of common data elements or inappropriate application of definitions 1
• Ambiguous data definitions and poor layout of data collection forms 1

Patient Identification Errors

Patient misidentification in EHRs can result in orders being placed in the wrong patient's chart, leading to unintended care delivery. Before implementation of verification screens with patient photographs, placement of orders in the incorrect patient's chart was the second most common cause of care being provided to the wrong patient, comprising 24% of reported errors 3. This is particularly dangerous in pediatrics where temporary names are used in newborn care 1.

Medication Ordering Errors

Computerized physician order entry (CPOE) systems can paradoxically increase medication errors rather than eliminate them. One study demonstrated that CPOE yielded a significant increase in overall medication order errors, with pre-CPOE errors at 1,741 compared to post-CPOE errors at 2,226 4. This contradicts the expectation that electronic systems would be error-free 4.

Medication-related EMR errors include:

• Typing errors and distractions during order entry 1
• Lack of knowledge about system functionality leading to incorrect orders 1
• Illegible handwriting in hybrid systems and unavailable patient information leading to ordering errors 5
• Scrambled laboratory results and equipment failures 5

Coding and Billing Errors

Nearly 40% of Evaluation and Management (E&M) claims are miscoded, with 26% upcoded and 14.5% downcoded, representing significant compliance risks. The Office of Inspector General reports that nearly half of all E&M visits for Medicare patients are coded incorrectly 2, 6. These errors can lead to accusations of billing fraud with potential for fines, exclusion from Medicare/Medicaid programs, and criminal penalties 2, 6.

Contributing factors to coding errors:

• Confusing E&M terminology such as "expanded problem focused" that makes determining service levels cognitively challenging 2
• EHRs initially designed to help physicians "right code" have not significantly reduced coding errors compared to paper records 2
• Complex format requirements that are difficult to understand and use 2

System Integration and Communication Errors

Fragmentation of documentation activities and frequent task transitions increase the load on working memory and probability of errors. The act of entering coded observations by selecting items from lists is inherently slow and awkward, with the mental activity of converting patient narratives into coded values leading to errors 1.

Critical Pitfalls to Avoid

• Never assume EHR implementation alone will reduce errors—systems must be properly monitored and customized with pediatric-specific decision support 1
• Avoid wholesale copy-paste of entire notes—if carrying forward information, verify and update it, indicating the source of pulled data 1
• Do not ignore alert fatigue—excessive alerts undermine clinical decision support effectiveness 1
• Ensure patient photographs are incorporated into order verification screens to prevent wrong-patient orders 3
• Recognize that missing data is not random—efforts to minimize missing data are critical to minimizing bias 1