Data standardization is one of the most important aspects of using electronic health records (EHRs) in health care. Without consistent and reliable standards, data within EHRs and between systems cannot be effectively shared or used. Torous et al. (2019) study states that using data standard integration strategies ensures that data is effectively shared and utilized across systems and applications. This ensures that patient’s medical records can be retrieved even remotely, regarding the right protocols, such as authorized access, are granted. Healthcare organizations must recognize these standardization benefits and the methods for integrating data standards as electronic health records (EHRs) gain prominence to enhance patient care and outcomes. This article will discuss the value of data modeling and dictionaries and methods for implementing consistent data standards in various software while integrating them into the provided case study. The case study is about someone visiting a different country or state suddenly becoming ill and unresponsive. It thus cannot provide their medical history to the medical attendants.
EHR Standards and Organizations Developing Standards
SDOs (Standard development organizations) establish and maintain the standards that govern EHRs. These standards help ensure effective EHRs implementation and accuracy and data interoperability. Some of the set standards include,
- Vocabulary and content standards ensure that specific structured terms describe certain clinical concepts. This standard applies when a medical code is useful in data retrieval, such as the E08.52 code for diabetes mellitus (Hempel, 2018).
- Transport standards contain protocols for sending or receiving health information messages. They include HL7 (Health Level 7 and FHIR (Fast Healthcare Interoperability Resources). This code applies to our case study patient, where FHIR can be used for faster transportation of patient data in this emergency case, regardless of their location.
- Privacy and security standards contain protocols and measures to be followed to ensure EHRs information confidentiality, availability, and integration. They include HIPAA (Health Insurance Portability and Accountability Act) and NIST (National Institute of Standards and Technology cybersecurity framework.
- CQMS (Clinical Quality Measure Standards) defines the computing and reporting of healthcare quality criteria. They include eCQMs (electronic Clinical Quality Measures).
One of the most vital standards is the vocabulary standard, which guarantees that all systems and applications use the same terms and definitions. The specific vocabulary standard addressed in the case study mentioned above is the C-CDA (Consolidated Clinical Document Architecture), which is structured with clinical summaries and patient progress notes on an ongoing chronic condition, medications, or allergies. This document requires that it be accessed from the client’s side, which might cause issues with accessibility, such as delays in accessing the patient information. This issue can be addressed by ensuring that the information can also be accessed via the facility’s side so that the attending physicians can collaborate with the primary care facility for the patient to obtain their medical information.
Data Formats vs. Data Types vs. Data Reporting Requirements
Data types refer to the different formats in which a given system can store information. Personal details, lab results, and prescriptions are examples of patient information. Information storage mediums like databases and spreadsheets are examples of data formats. Data reporting requirements refer to the laws and rules that dictate what data must be reported and in what format. The HIPAA regulations and data protection laws are two good examples of such regulations and laws (Özkan et al., 2022). They share similarities; they are all important EHR components that ensure efficient and accurate patient information communication. They are also different in that data formats and types focus on data structuring and storage patterns while reporting requirements focus on standards to be adhered to when communicating the data. In the case study provided above, for instance, there is a need to understand the format of their data, either as HL7 or XML, then identify the data type as chronic illness, lists of medications or allergies, and finally, ensure that they adhere to HIPAA protocols such as confidentiality in reporting about the patient’s medical information (Özkan et al., 2022).
Purpose of data modeling and data dictionaries
Data modeling and dictionaries are essential for any EHR system that values data integrity and reliability. Data modeling ensures uniform and efficient data organization, while data dictionaries explain the various forms of data in use. In addition, data modeling and data dictionaries can help guarantee usable, high-quality information. In addressing the data structure, the case study ensures an appropriate data design and plan by carefully selecting and implementing accurate databases, such as allergies records, from the patient’s medical history data warehouses, then uses metadata to assess the quality, meaning, and context of the patient data. Data quality and usability ensure accurate medical insights, better decision-making, and reduced risks of patient complications.
Application of Data Standard Integration Strategies
SDFs (Standardized Data Formats), which ensure that patient data is captured consistently for easy sharing across various healthcare systems. This strategy allows quick accessibility of critical information about the patient in emergency states, such as in our case study. The most applicable strategy, in this case, was FHIR and HIE, which I also highly recommend because they provide accurate and effective patient information sharing across various systems regardless of location. In this case, the patient has traveled to a different location. SDFs were not very effective in this case because the patient was in a different location and there were better strategies to employ in this case.
Conclusion
Generally, data standardization within EHR systems is critical for ensuring data security, regulation compliance, and high quality and usability. Standards development organizations that govern EHRs, data modeling, data dictionaries, and strategies for integrating data standards among applications are crucial to ensuring efficient data sharing and utilization. One of the most efficient methods for healthcare providers to increase patient engagement is to inform and educate patients about the advantages of electronic health records. (EHRs). Once educated, the patients will comply and use these records for their benefit and better health outcomes.
References
Hempel, R. (2018). No Digital Divide in this Rural Kentucky Practice | HealthIT.gov. Healthit.gov. https://www.healthit.gov/case-study/no-digital-divide-rural-kentucky-practice
Özkan, Ö., Şahinol, M., Aydinoglu, A. U., & Aydin Son, Y. (2022). Reflections on Turkish Personal Data Protection Law and Genetic Data in Focus Group Discussions. NanoEthics, 1-16.
Torous, J., Andersson, G., Bertagnoli, A., Christensen, H., Cuijpers, P., Firth, J., … & Arean, P. A. (2019). Towards a consensus around standards for smartphone apps and digital mental health. World Psychiatry, 18(1), 97.
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