Electronic health record system is real-time patient-centered records that offer information about the patient securely and instantly to the authorized users. EHR systems have gone beyond having the standard clinical data that is offered by the health organization to enable a broader view of the patient’s care (Amponsah, 2018). The electronic health data may contain information about the patient’s medical history, medications, immunization dates, treatment plan, test results, and diagnoses. This has enabled access to evidence-based tools that allow health practitioners to make informed decisions regarding the patient’s care.
Role of Data and Technology
The world has changed radically due to digital technology (computers, tablets, and smartphones) and other web-enabled devices that have changed their daily lives and the way people communicate. Considerably, this has enabled a greater flow and seamless transfer of information within the digital healthcare infrastructure, which has been established by the electronic health records, which levels and encompasses digital processes that can alter the approach care is being delivered. Through electronic health records, information is available whenever it is needed. This has improved patient care, care coordination, patient participation, and patient outcomes.
The emergence of the Electronic health record system has brought the expectation about future uses, which include exchanging and sharing of information among the different EHR structures. A significant issue that requires attention is the accomplishment of efficient and ethical sharing of information and the development of standard data components as well as information in the EHR system. Great healthcare establishments, vendors, and corporations are developing EHR systems with much flexibility.
The ONC plays a role in promoting national health information technology infrastructure as well as oversee the development of electronic health record systems. According to the ONC, the standards have been agreed upon to allow interoperability and connection between the EHR systems. The standards apply to security of data, data format, data transport, as well as the meaning of codes. It is anticipated that every organization includes the ASTM E1384 Standard Guide on the Structure and Content of the EHR system as well as the corresponding ASTM E1633 Coded Values for an EHR system (Hammond, 2017).
The EHR standards have ensured proper description of data elements included in the system. It has also ensured the effective identification of data elements and an improved degree of granularity. The standards have also ensured that data elements have been matched across systems for improved quality care and extensive care reporting.
Types of Data, Data Formats, and Data Reporting Requirements.
EHR system records administrative and clinical data of the patient. For instance, it may have collected clinical data such as laboratory data, medical histories, medication, health patterns, care episodes. While the administrative data offers the patient identification information, eligibility information managed care encounters, and claims information. Data is stored in most common types, which include numbers, texts and images (Kopanitsa, 2017). For instance, the lab results and vital signs can be expressed through numbers while the clinical documents are presented through text. The EHR systems rely on structured and free-text formats in recording patient’s information. Due to information variability data is recorded at different levels of usage depending on the intended use of that data. For instance, the structured data is easy to represent the international standards due to the high level of detail, while the unstructured offers reality in a more accurate manner. While reporting the data into the EHR system, it has to be accessed rapidly and updated frequently. Data has to be gathered with sufficient granularity and offered in a usable format in order to answer the health issue. Relatively, the relevance of the data has to be identified in the context of uses and needs while reporting.
Data Modeling and Data Dictionaries.
Data modeling is the method of creating a data model for the clinical and administrative data to be stored in the database. The data models enable the representation of data that is required and the format to be used. This documents the way data is retrieved and stored through organizing the various elements of data. Data dictionaries are a set of rules within the EHR system that describe the type of data to be stored in the database, the structure, format, and how the data can be used (Kopanitsa, 2017). The dictionary can offer the list of definitions, names, and data elements that will be captured in the EHR system. A suitable data dictionary improves the dependability and reliability of the health record, improve documentation, lowers redundancy, and makes it easier to analyze data.
To accomplish EHR integration, the health system should have interoperability. This is the capacity of the health system to share and exchange electronic information with other systems. This is about aggregating data that the health systems and health plans generate through the electronic health record systems del Carmen (Legaz-García Et al., 2016). The EHR system should balance between data security and availability and adhere to data security regulations, privacy, and confidentiality measures through access control.
In conclusion, the EHR system standards have strengthened how the electronic health record system is implemented. Through the common standards, patient safety is guaranteed, and the clinical systems can use the integrated information structure where data is gathered and used for multiple purposes. EHR systems have enabled care providers to access full patient information and make informed decisions that have reduced patient safety risk as well as improved quality of care.
Amponsah, J. A. N. E. (2018). Assessing Managers and Staff Perceptions of the Benefits of Implementing an Electronic Health Record (EHR) System: A Case Study of the Ghana Atomic and Energy Commission Clinic (Doctoral dissertation, University of Ghana).
del Carmen Legaz-García, M., Martínez-Costa, C., Menárguez-Tortosa, M., & Fernández-Breis, J. T. (2016). A semantic web based framework for the interoperability and exploitation of clinical models and EHR data. Knowledge-Based Systems, 105, 175-189.
Hammond, W. E. (2017). Standards for Global health information systems. In Global Health Informatics (pp. 94-108). Academic Press.
Kopanitsa, G. (2017). Integration of hospital information and clinical decision support systems to enable the reuse of electronic health record data. Methods of information in medicine, 56(03), 238-247.