Benefits and Challenges of Electronic Health Records

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The healthcare system has improved significantly over the years, as institutions adopt novel mechanisms of patient-care, documentation, and payment systems among others. Such innovations have made the medical facilities to be efficient. One of the most common technology introduced in the medical centers is the Electronic Health Records (EHR), which was adopted in the U.S. medical system in 2015. The system was implemented to enhance precision as well as enhance documentation (Gartee & Richard). Healthcare stakeholders are therefore expected to aid in the evaluation of current medical records and optimize proper methodologies for capturing data in digital format to achieve high efficacy at low cost (Gartee & Richard).


The system has enhanced operations in the healthcare system, particularly in documentation. Effective application of the EHR often improves operating competences in health care units. Moreover, the introduction of this technology equally improved the medical era and pharmaceutical precision at a relatively reduced cost (Amendola & Sara, 148). Due to the implementation of the EHR, predictive data analytics have been enabled, which assist in diagnosis of various diseases and infections that significantly reduce mortality rates. An excellent example to this would be in the case of congestive heart failure, which is continuously increasing in the US. The earlier the ailment is diagnosed, the easier it can be treated, and resultant complications prevented (Gartee & Richard). Georgia technologies clarified that the machine learning algorithms, for data analytics and generation of EHR data often look at more adverse factors on both providers’ and patients’ charts (Amendola & Sara, 150). It generates additional learning features that substantially improve the general ability to identify and easily detect congestive heart failure patients.


The general analyzation and populating of large data amounts using a standardized format has not yet been efficiently achieved due to delay in maturing of protocols and data sources. Therefore, it is at times difficult to recognize the importance of digitized data application in attaining proper patient care.

Although the EHR system has improved the healthcare system, accidents still happen. For instance, there was a moment a Hospital gave a patient 38 times his normal dosage due to insufficient data integrity within the facility. As such, without proper data integrity, it is difficult for practitioners to keep patient data more accurate and secure, hence, destabilizing their medical routines (Gartee & Richard). Accidents related to such incidences include pasting patients’ data in a mistaken medical record with

information not relevant to a particular patient’s condition due to issues of system inflexibility and data delivery.

Recommendations for healthcare improvement

Achievement of high medical precision will call for a broad step of old and new data integration into validated data. Achieving this procedure will, therefore, involve the conversion of this data into information that can be applied directly to treatment, diagnosis or prognosis (Tsai & Chun, 20). Development of integrated environmental knowledge for information capturing, growth, accumulation, organization and institutionalization of new data is critical for easy accessibility by health care providers (Amendola & Sara, 146). In turn, all accumulated information either from clinical data within the EMRs will lead to the discovery of better novel therapeutic methods and medical precision application.

Also, the medical practitioners have to be continuously trained to make them competent regarding the utilization of the various EHR features. It is paramount that professionals in the medical field are well-acclimatized with the technology in order to provide sufficient care. As such, this can only be achieved through training.

Works Cited

Amendola, Sara, "RFID technology for IoT-based personal healthcare in smart spaces." IEEE Internet of things journal 1.2 (2014): 144-152.

Gartee, Richard. Electronic health records: understanding and using computerized medical records. Prentice Hall, 2016.

Tsai, Chun-Wei, "Big data analytics." Big Data Technologies and Applications. Springer, Cham, 2016. 13-52.

September 11, 2023


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