Interview Questions for EHR Review

Retrospective and prospective EHR reviews differ fundamentally in their timing and purpose. A retrospective review analyzes past patient data already recorded in the EHR. Think of it like looking back at a completed exam – you’re evaluating what happened. It’s often used for quality improvement, research, or auditing purposes. For example, we might retrospectively review patient charts to assess the effectiveness of a new treatment protocol.

In contrast, a prospective review focuses on current or future patient care. It’s like having a checklist before you begin an exam. It might involve monitoring data in real-time or reviewing charts during active treatment to ensure care aligns with guidelines and best practices. An example is using prospective review to immediately identify and address potential medication errors or adverse events as they emerge.

The key difference lies in the timeline. Retrospective reviews assess past events, while prospective reviews influence ongoing and future care.

My experience spans several leading EHR systems. I’ve extensively worked with Epic, navigating its robust functionalities for data extraction, reporting, and quality metrics. I’m proficient in using its various modules, from inpatient to outpatient, and am comfortable using its query tools for complex data analysis. For instance, I utilized Epic’s reporting capabilities to analyze readmission rates for heart failure patients.

I also possess considerable experience with Cerner, focusing on its comprehensive patient portal capabilities and its strengths in managing clinical documentation. I’ve been involved in projects using Cerner’s Millennium system, specifically extracting data for clinical research related to diabetes management. This included navigating the nuances of Cerner’s reporting tools to gather precise patient data.

My familiarity extends to Meditech, where I’ve worked on projects requiring data migration and system integration. I understand the system’s specific data structures and have experience troubleshooting data inconsistencies within the Meditech environment. A recent project involved consolidating data from multiple Meditech instances to create a unified view of patient outcomes.

Ensuring data integrity during an EHR review is paramount. My approach involves a multi-pronged strategy:

• Data Validation: I systematically verify data accuracy by comparing information from multiple sources within the EHR (e.g., comparing lab results with physician notes). I also cross-reference data with external sources when appropriate, such as comparing medication lists with pharmacy records.
• Audit Trails: I thoroughly examine audit trails to track data modifications, identifying any unauthorized changes or inconsistencies. This helps pinpoint potential data integrity issues and understand the context of any alterations.
• Data Governance Policies: I adhere to the specific data governance policies established by the healthcare organization, ensuring compliance with data access protocols and data security measures. This includes strict adherence to HIPAA regulations.
• Documentation: I meticulously document all findings, including any inconsistencies or anomalies detected during the review process. This detailed documentation ensures transparency and traceability.
• Data De-identification: For research or auditing purposes, I employ robust de-identification techniques to protect patient privacy and adhere to regulations like HIPAA.

These steps, taken in conjunction, create a robust system to ensure the trustworthiness of the EHR data under review.

Several key regulatory compliance requirements govern EHR data. HIPAA (Health Insurance Portability and Accountability Act) is central, dictating stringent rules for protecting patient privacy and the security of electronic health information. This includes requirements for data encryption, access control, and breach notification.

Meaningful Use regulations (now largely replaced by the Promoting Interoperability Program) previously incentivized the adoption and use of EHRs, setting standards for data exchange and interoperability. While the focus has shifted, the underlying need for data integrity and appropriate use remains crucial.

FDA regulations play a role concerning EHR systems used in clinical trials or for managing data related to medical devices. Compliance requires adherence to specific data validation procedures and documentation requirements. Other regulations, like those related to state licensing and accreditation, also impact EHR data handling and management.

Ultimately, compliance requires a comprehensive understanding and implementation of these regulatory frameworks to ensure ethical and legal handling of sensitive patient data.

Identifying and addressing discrepancies in EHR data requires a systematic and thorough approach:

• Define Discrepancy Types: First, I classify discrepancies into categories such as missing data, conflicting data (e.g., different diagnoses from different providers), or data entry errors.
• Source Verification: I meticulously trace the source of the discrepancy. For example, a conflicting diagnosis might require reviewing physician notes, consultation reports, and lab results to ascertain the most accurate information.
• Data Reconciliation: I attempt to reconcile conflicting data points by prioritizing information from reliable sources and establishing a clear rationale for chosen data points. If reconciliation is not possible, I clearly document the discrepancy and its potential implications.
• Clarification with Clinicians: If I’m uncertain about the accuracy of a particular data point, I directly communicate with the relevant clinicians to clarify and obtain the most accurate information.
• Documentation of Resolution: Any changes made or resolutions reached concerning discrepancies are meticulously documented with a clear explanation of the process and decision-making.

This methodical approach minimizes the risk of inaccurate or misleading data interpretations while maintaining data integrity.

Chart abstraction and data extraction are core aspects of my work. Chart abstraction involves manually reviewing patient charts to extract specific clinical information, while data extraction uses more automated tools to obtain information directly from the EHR system. My experience covers both methods.

I’ve extensively used both manual abstraction for complex cases needing in-depth qualitative analysis and automated extraction techniques for large-scale studies or quality improvement initiatives. For instance, I’ve manually abstracted data on patient experiences from free-text physician notes for a patient satisfaction study, and used automated extraction to compile comprehensive medication lists for a medication safety review involving thousands of patients. The choice of method depends on the project’s scope, data complexity, and research objectives.

I am familiar with various data extraction tools and techniques, including structured query language (SQL) to directly query the EHR database, and the use of APIs (Application Programming Interfaces) for seamless data transfer.

Verifying the accuracy of patient information involves a combination of techniques:

• Cross-referencing: I systematically cross-reference information from multiple sources within the EHR. For example, I compare medication lists from pharmacy records with physician orders and patient self-reported medications.
• Data Validation Rules: I leverage pre-defined data validation rules to identify potential inconsistencies or errors. For instance, a rule could flag discrepancies between reported age and date of birth.
• Clinical Context Review: I consider the clinical context of the information to assess its plausibility. For example, laboratory results should be assessed alongside clinical symptoms and diagnoses.
• Statistical Analysis: For large datasets, I might employ statistical methods to detect outliers or anomalies that could indicate data entry errors.
• External Data Sources: When appropriate and permitted, I might cross-reference information with external sources, such as external lab reports or imaging results, to confirm accuracy.

The use of these methods, along with maintaining a critical and questioning mindset, ensures the reliability of the patient information utilized.

Incomplete or missing data in an EHR is a common challenge. My approach involves a multi-step process. First, I carefully document the missing data, noting the specific field(s) affected and the potential impact on the overall review. This documentation is crucial for transparency and auditability. Then, I investigate the reason for the missing data. Was it a data entry error? A system glitch? Or simply an omission? Understanding the cause helps determine the best course of action.

Depending on the context and the nature of the missing data, I might employ several strategies. For example, if the missing data is non-critical to the review objectives, I may proceed with the analysis based on the available information, explicitly noting the limitations in my report. However, if the missing data is crucial for a specific analysis, I might attempt to recover it through various means: contacting the data source (e.g., the healthcare provider), reviewing related documentation, or using data imputation techniques (e.g., using the average value for a similar patient group, but only if statistically sound and appropriate).

Finally, I always ensure that any assumptions made due to missing data are clearly stated in the final report, to maintain transparency and prevent misinterpretations. For example, I might state: “Analysis of patient X’s medication adherence was limited due to missing prescription fill data for the period of January 1st to March 15th.” This approach maintains the integrity of the review while acknowledging the data limitations.

HIPAA, the Health Insurance Portability and Accountability Act, is the cornerstone of patient data privacy and security in the United States. In EHR review, understanding and adhering to HIPAA is paramount. It dictates stringent rules around the access, use, and disclosure of Protected Health Information (PHI). PHI includes any individually identifiable health information, such as medical records, billing data, and even seemingly innocuous information like dates of service. It’s not just about protecting patient privacy; HIPAA violations can result in significant financial penalties and legal repercussions for the organization conducting the review.

During an EHR review, my adherence to HIPAA involves several key steps: ensuring all personnel involved are properly trained in HIPAA compliance, utilizing secure data access protocols (e.g., VPN, encrypted connections), implementing robust data encryption and anonymization techniques where feasible, and meticulously documenting all access and use of PHI. Even seemingly insignificant details matter. For example, if we need to print a report containing PHI, we ensure that it’s printed securely and disposed of properly using HIPAA-compliant shredding methods. This is crucial to ensure we maintain the confidentiality and integrity of the data throughout the entire review process.

Prioritizing tasks in a large-scale EHR review requires a structured approach. I typically start by defining clear objectives and scope of work. Then, I break down the project into smaller, manageable tasks. This helps in assigning priorities based on several factors, including: urgency (regulatory deadlines), criticality (impact on overall conclusions), and resource availability.

I frequently use a combination of project management tools and techniques, such as work breakdown structures (WBS) and Gantt charts, to visualize the tasks, dependencies, and timelines. I’d also hold regular meetings with the team to track progress, discuss challenges, and re-prioritize tasks as needed.

For example, if a regulatory deadline is approaching for a specific data analysis component, I’d prioritize that task over others, even if they appear equally important in isolation. This pragmatic approach ensures project completion on time while maintaining focus on critical objectives. We may also employ a risk assessment matrix to assess and assign weights to the potential impact of delays or issues that might be encountered with different tasks, allowing us to prioritize according to risk exposure.

My experience with data analysis tools for EHR review is extensive. I’m proficient in using various software packages, including SQL, R, Python, and commercial tools such as SAS and SPSS. I leverage these tools to extract, clean, transform, and analyze large datasets from EHR systems. This includes tasks such as creating custom reports, performing statistical analyses, and visualizing data trends.

For instance, I recently used SQL to query a large EHR database to identify patients with a specific diagnosis and then used R to conduct statistical analysis on their treatment outcomes, creating visualizations to present to the client. The ability to write efficient SQL queries is essential to pull out relevant data swiftly and accurately from the often massive datasets within an EHR system. This reduces the time required for data preparation and allows for more thorough analysis within reasonable timeframes.

Furthermore, I’m adept at using tools to create custom dashboards and reports for visualizing key findings from the review. This empowers stakeholders to understand complex information quickly and efficiently. Visualisations that include charts, graphs, and summary statistics are critical in presenting review findings concisely and compellingly.

Several common challenges emerge during EHR reviews. One significant challenge is data quality. EHR data can be inconsistent, incomplete, or inaccurate due to various reasons including errors in data entry, system glitches, or variations in documentation practices across different providers. Addressing data quality issues often involves significant data cleaning and validation efforts before any meaningful analysis can be performed.

Another challenge is the sheer volume and complexity of data contained within EHR systems. Navigating these systems efficiently and extracting relevant data requires strong technical expertise and the ability to use specialized software tools. Furthermore, differing EHR system architectures across providers (or even within a single organization) can add to the complexity, necessitating a flexible and adaptable approach.

Finally, regulatory compliance, especially adherence to HIPAA guidelines, adds another layer of complexity. Ensuring patient privacy and data security throughout the entire review process requires careful planning, meticulous execution, and a deep understanding of relevant regulations. The lack of standardized data formats and terminologies across different EHR systems can also be an impediment, as this increases the complexity of data analysis and data integration.

Ensuring the confidentiality and security of patient data during an EHR review is my utmost priority. This involves a layered security approach that starts with securing access to the data itself. We utilize secure access protocols, like VPNs, and restrict access to the data only to authorized personnel on a need-to-know basis. This limits exposure to a minimum and reduces the risk of unauthorized access. Furthermore, all systems and software we use are regularly patched and updated to minimize vulnerabilities and maintain the overall integrity of the security posture.

Data encryption is crucial. We encrypt the data both in transit and at rest using strong encryption algorithms. Data anonymization techniques, where legally and ethically permissible, are employed to remove or mask identifying information, thereby protecting patient privacy. Regular security audits and vulnerability assessments help to identify and remediate potential security gaps.

Finally, robust data governance policies, which address all aspects of data handling from storage to access to disposal, are meticulously followed. All activities are carefully documented to maintain a detailed audit trail of data access and use. This complete and rigorous approach demonstrates our dedication to patient data privacy and security in line with applicable regulations.

Reporting findings from an EHR review requires a clear, concise, and well-structured approach. My reports typically include an executive summary that highlights the key findings and conclusions, followed by a detailed methodology section that describes the review process, data sources, and analytical methods used. Transparency is key; all assumptions and limitations are clearly stated to maintain the integrity and reliability of the findings.

The findings section presents the results using a combination of tables, charts, and graphs to provide a clear and visually engaging summary of the data. I tailor the level of detail to the audience; for instance, a high-level executive summary might suffice for senior management, while a more detailed technical appendix would be appropriate for technical stakeholders.

For example, if we are reviewing medication adherence, our report might include tables summarizing rates of adherence, charts showing trends over time, and a discussion of the implications of our findings. Finally, the report incorporates clear, actionable recommendations based on the identified strengths, weaknesses, and areas for improvement.

Collaboration during an EHR review is crucial for its success. It’s not just about analyzing data; it’s about understanding the clinical context and ensuring the review’s findings are actionable and relevant. I approach this by establishing clear communication channels and roles from the outset. This includes regular meetings with clinicians—physicians, nurses, and other relevant medical staff—to discuss the review’s scope, methodology, and preliminary findings. I actively seek their input on data interpretation, ensuring we’re all on the same page regarding the meaning and significance of the information extracted.

For example, during a medication reconciliation review, direct feedback from pharmacists on medication coding practices and potential discrepancies is invaluable. Similarly, conversations with nurses can clarify unusual entries in patient notes. Beyond clinicians, I actively engage with IT staff to understand system limitations, data sources, and potential technical challenges. This collaborative approach ensures the review is thorough, accurate, and addresses the real-world needs of the healthcare organization.

Furthermore, I utilize project management tools to track progress, share documents, and facilitate communication among all stakeholders. This ensures transparency and allows for efficient resolution of any issues that may arise.

Staying current in the ever-evolving landscape of EHR technology and regulations requires a multifaceted approach. I subscribe to industry publications such as journals focusing on health informatics and HIMSS (Healthcare Information and Management Systems Society) resources. These provide updates on new EHR systems, functionalities, and compliance requirements. I also actively participate in professional development opportunities such as webinars, conferences, and workshops offered by organizations like AHIMA (American Health Information Management Association). This keeps me abreast of the latest best practices, emerging technologies, and changes in regulatory standards like HIPAA and meaningful use criteria.

Additionally, I maintain a network of colleagues and experts in the field. We share insights, discuss challenges, and learn from each other’s experiences. This peer-to-peer learning complements my formal education and keeps me grounded in the realities of the industry. Finally, I regularly review relevant government websites and regulatory updates to ensure I’m always compliant with the latest legal and regulatory changes affecting EHR data handling and review.

My experience encompasses a wide range of EHR audits, including:

• Compliance Audits: These focus on ensuring adherence to regulatory requirements such as HIPAA, meaningful use, and other relevant legal frameworks. For example, I’ve conducted audits to verify proper patient data encryption and access controls.
• Security Audits: These assess the security posture of the EHR system and identify vulnerabilities. I’ve participated in audits analyzing login attempts, access privileges, and system logs to identify potential security breaches.
• Functional Audits: These evaluate the functionality of the EHR system and ensure it’s meeting its intended purpose. For example, I’ve reviewed the completeness and accuracy of patient data, workflow efficiency, and the usability of the system’s features.
• Performance Audits: These evaluate the system’s efficiency and responsiveness, identifying areas for improvement in terms of speed, reliability, and resource utilization. I’ve used performance monitoring tools to analyze system response times and identify bottlenecks.

The approach to each type of audit varies based on its objectives and scope, but all share the common goal of ensuring data integrity and system effectiveness.

Audit trails are essential for tracking user activities within an EHR system. They act as a comprehensive record of data access, modifications, and other events. My experience includes using audit trails for various purposes, including:

• Identifying data breaches: Analyzing audit trails helps pinpoint unauthorized access attempts or data modifications, enabling swift response and mitigation strategies.
• Investigating discrepancies: In cases of data inconsistencies or errors, audit trails provide a timeline of events leading to the problem, enabling efficient problem-solving.
• Ensuring compliance: Audit trails serve as evidence of compliance with regulations. For example, they can demonstrate adherence to HIPAA’s access control and audit requirements.
• Improving workflow: Analyzing usage patterns revealed in audit trails can help identify bottlenecks or inefficiencies in clinical workflows, enabling process optimization.

I’m proficient in using the different query and reporting tools within various EHR systems to extract and analyze audit trail data, ensuring data integrity and security.

Ensuring the quality and consistency of EHR review work relies on a combination of rigorous methodologies and quality control measures. I adhere to established review protocols and checklists tailored to the specific audit type and objectives. These protocols ensure consistency and standardization across all my reviews. I meticulously document every step of the review process, including data extraction techniques, analysis methods, and findings. This detailed documentation allows for easy replication and verification of my work.

Regular peer review is a crucial component of quality control. I routinely have my work reviewed by colleagues with similar expertise to catch potential errors or biases. This fosters continuous improvement and ensures the accuracy of my findings. Additionally, I regularly calibrate my methods against industry best practices and updates in regulatory guidance to stay aligned with the latest standards. Finally, I utilize data validation techniques (as explained in the next question) to confirm the accuracy and reliability of the data being reviewed. This multi-pronged approach ensures both the quality and consistency of my EHR review work.

Data validation techniques are crucial for ensuring the accuracy and reliability of the data used in EHR reviews. I employ several techniques, including:

• Range checks: Verifying that values fall within expected ranges (e.g., age, lab results).
• Consistency checks: Ensuring that data across different fields or records are consistent (e.g., verifying that a patient’s date of birth is consistent across all records).
• Completeness checks: Verifying that all required data fields are populated.
• Cross-referencing: Comparing data from different sources to confirm its accuracy. For example, verifying medication information against pharmacy records.
• Data profiling: Examining the characteristics of the data to identify anomalies or patterns that may indicate errors or inaccuracies.
• Data scrubbing: Cleaning and standardizing data to improve its quality and consistency.

The specific techniques used depend on the type of data and the objectives of the review. For instance, when reviewing billing data, range checks and consistency checks are critical to ensure accurate billing practices. When reviewing clinical data, data profiling and cross-referencing are essential for identifying potential inconsistencies or errors in diagnosis and treatment.

EHRs use a variety of data formats, and understanding these is fundamental to effective review. Common formats include:

• HL7 (Health Level Seven): This is a widely used standard for exchanging electronic health information. It’s often used for transmitting data between different healthcare systems. Understanding HL7 message structures is crucial for interpreting data exchanged between different EHR systems.
• DICOM (Digital Imaging and Communications in Medicine): This is a standard for handling, storing, printing, and transmitting medical images. Expertise in DICOM is vital when reviewing radiology and imaging data.
• XML (Extensible Markup Language): This is a flexible format used for representing data in a structured way. Many EHR systems use XML to store and exchange data. Understanding XML structure is critical for extracting information from EHR databases.
• CSV (Comma-Separated Values): A simple, text-based format frequently used for exporting data from EHR systems. While simple, understanding the structure and potential limitations of CSV is important for data import and analysis.
• Relational Databases (SQL): EHR data is often stored in relational databases using SQL. Proficiency in SQL is essential for querying and extracting data directly from the database.

My experience encompasses working with various data formats, and I employ appropriate techniques to extract, transform, and load (ETL) data for analysis. This ensures that I can effectively analyze data regardless of its underlying format. Proficiency in different programming languages (like Python or R) can aid in data manipulation and analysis across diverse data formats.

Documenting findings from an EHR review is crucial for ensuring accuracy, traceability, and facilitating effective communication. My approach involves a structured system combining qualitative and quantitative data.

Firstly, I utilize a standardized template or reporting format, often customized for the specific audit’s objectives. This ensures consistency and allows for easy comparison across multiple reviews. The template includes sections for identifying the specific record, the type of issue found (e.g., missing data, inconsistencies, coding errors), the severity of the issue, and supporting evidence (screenshots, relevant data excerpts).

Secondly, I use a robust system for organizing evidence. This often involves creating a digital repository with clearly labeled folders and files corresponding to each finding. For instance, I might have a folder for each individual patient record reviewed, containing screenshots of any identified issues. Each finding is meticulously documented with a unique identifier, allowing for easy cross-referencing and tracking. This method makes it easy to review the evidence during reporting and provides an audit trail.

Finally, the report itself presents the findings concisely and clearly. It summarizes the overall quality of the EHR data, highlighting key issues and their potential impact. The report uses data visualization to illustrate trends and patterns, making it easier for stakeholders to understand the implications of the findings. This often includes tables summarizing the frequency of various issues, and charts showing trends over time.

Data visualization is indispensable for presenting complex EHR review findings in a clear, understandable manner. I’m proficient in using several tools, including Tableau and Power BI, to create insightful dashboards and reports.

For example, if I’m analyzing medication reconciliation errors, I might use a bar chart to compare the frequency of errors across different departments or medication types. This visually highlights areas requiring immediate attention. Similarly, a heatmap could illustrate the geographic distribution of patient records with missing data, allowing for targeted improvement efforts.

Beyond these tools, I also believe in the power of simple charts such as pie charts to show proportions (e.g., percentage of records with complete vs. incomplete documentation) and line graphs to display trends over time (e.g., improvement in data quality following an intervention). The key is to select the visualization that best communicates the specific finding to the intended audience, tailoring the complexity to their technical understanding. My experience ensures I choose the most impactful and easily understood method of data representation.

Disagreements regarding EHR data accuracy are common and require a diplomatic yet firm approach. My strategy focuses on collaborative problem-solving rather than confrontation.

First, I meticulously document the discrepancy, including the specific data points in question, the source of my information, and the clinician’s perspective. I avoid accusatory language and focus on presenting the facts objectively.

Next, I initiate a discussion with the clinician, emphasizing our shared goal of accurate data. I present my evidence clearly and respectfully, inviting the clinician to explain their perspective. Often, these disagreements stem from different interpretations of the data or documentation practices.

If the disagreement persists, I may involve a neutral third party, such as a medical informatics specialist or compliance officer, to facilitate a resolution. The goal is not to ‘win’ the argument, but to achieve a shared understanding and correct any inaccuracies in the EHR. Sometimes, a simple clarification or a modification to the documentation process will resolve the issue. If a systemic problem is identified, it needs to be addressed formally through a change control process.

Identifying potential risks and compliance issues during an EHR review requires a systematic and multi-faceted approach. My strategy combines automated checks with manual review.

Firstly, I leverage automated tools that can flag potential problems like missing data fields, inconsistencies in coding, or violations of data privacy regulations. These tools often generate reports that highlight areas needing closer scrutiny.

Secondly, I conduct a manual review, focusing on areas of high risk, such as medication administration, patient allergies, and diagnostic test results. I check for completeness, accuracy, and compliance with relevant regulations (e.g., HIPAA, Meaningful Use). I also pay close attention to documentation related to high-risk procedures or patients with complex medical histories.

Thirdly, I use a checklist based on regulatory requirements and industry best practices. This checklist ensures that I systematically examine all relevant areas of the EHR, leaving no stone unturned. A final step involves a detailed review of audit trails to identify any unauthorized access or data modifications.

Prioritizing errors and discrepancies is crucial for efficient remediation. My approach involves a risk-based prioritization scheme, considering the potential impact on patient safety and regulatory compliance.

I use a matrix to categorize issues based on severity (e.g., high, medium, low) and likelihood of impact (e.g., high, medium, low). High-severity, high-likelihood issues (e.g., incorrect medication dosages) receive immediate attention.

For example, a missing allergy entry for a patient with a severe allergy would be considered high severity and high likelihood, and therefore top priority. Conversely, a minor typographical error might be classified as low severity and low likelihood and addressed later. This matrix helps to focus resources on the most critical issues first while still ensuring that all discrepancies are addressed. This is a key factor in minimizing risks and maximizing efficiency.

Root cause analysis (RCA) is a vital component of EHR data issue resolution. My approach follows a structured methodology, often utilizing the ‘5 Whys’ technique or a fishbone diagram.

For instance, if I uncover frequent discrepancies in medication reconciliation, I wouldn’t just document the errors; I’d investigate the underlying causes. Using the ‘5 Whys’ technique, I might ask: Why are the discrepancies occurring? (Lack of communication). Why is communication lacking? (Insufficient system integration). Why is there insufficient system integration? (Lack of budget for software upgrades). Why is there a lack of budget? (Prioritization of other projects). Why were other projects prioritized? (Lack of awareness of medication reconciliation errors and their impact). This process helps pinpoint the root cause, enabling targeted solutions.

The fishbone diagram provides a visual representation of potential contributing factors. The ‘head’ of the fish represents the problem (e.g., medication errors), and the ‘bones’ represent contributing factors (e.g., staffing issues, workflow inefficiencies, inadequate training). This process helps to identify a broad range of contributory factors and makes RCA more collaborative and intuitive.

My contribution to EHR process improvement extends beyond simply identifying problems; it involves providing actionable recommendations and actively participating in the implementation of solutions.

Following an EHR review, I prepare a comprehensive report outlining my findings, including specific recommendations for improvement. These recommendations are tailored to the organization’s context, considering factors such as budget, staff expertise, and technical infrastructure.

For instance, if I discover recurring data entry errors related to a specific field, I might recommend implementing automated data validation rules or providing targeted training to staff. If a systemic issue is detected, a formal change management process might be necessary, involving discussions with IT and clinical leadership.

Moreover, I actively participate in process improvement initiatives, offering my expertise to optimize EHR workflows and data quality. This includes participating in design discussions for new EHR systems or features, suggesting changes to existing procedures, and collaborating with stakeholders to implement changes effectively. My approach focuses on sustainable improvement, not just immediate fixes.