The use of electronic health records (EHRs) in patient care has fully changed the way healthcare is delivered, bringing about a number of advantages. Based on my experience exercising the free cloud-based OpenEMR system, I will estimate the application of electronic health records in patient care in this project.
The electronic health record system OpenEMR has several features that intend to improve patient care and accelerate medical procedures. I added some attractive and practical enhancements to the system during my interactions with it, increasing its efficiency in patient care. The capacity to create appointment matrices is a significant feature (Agarwal & Choudhury, 2022). Healthcare professionals may effectively manage their calendars with this tool, including start and stop times, lunch breaks, and reserved periods. This feature guarantees that healthcare professionals may efficiently manage their time and maximize patient visits, improving patient access to care.
The simplicity of setting up appointments for new patients is another useful aspect of OpenEMR. Receptionists can swiftly enter patient data and make appointments using the system’s user-friendly interface. One of the providers agreed to let me schedule an appointment for Megan Casper, and I quickly added the appointment to the physician’s schedule. This function simplifies the appointment scheduling procedure, lightening the load on managers and enhancing patient satisfaction (Kapur et al., 2022). Similarly, OpenEMR makes it easier to update patient data. I was okay with updating Wanda Moore’s address information. The system enables efficient data entry and reclamation, guaranteeing current and correct patient information. This functionality is essential for preserving the accuracy of patient records and promoting treatment continuity.
A new patient intake form is also part of the system, allowing to capture of complete patient data, including insurance information. I could use the form to update Megan Casper’s insurance information. This function encourages efficient data collecting and lowers the risk of crimes or missing data. It also helps the electronic health record system seamlessly incorporate patient data (Perakslis & Stanley, 2021). The encounter summary form offered by OpenEMR gathers essential data on the patient’s health history, major complaints, issue list, and vitals. This form guarantees that medical professionals have access to crucial patient information at the moment of care, allowing them to draw knowledgeable judgments and deliver appropriate treatment.
The provision of thorough encounter summaries helps long-term care and improves patient safety. Even though OpenEMR has a lot to offer, I had to deal with many difficulties when using the system. The initial learning curve for utilizing the interface and comprehending its various features was a problem. Still, these difficulties became less severe with time and practice (Santalahti et al., 2021). Providing additional thorough training materials and support accoutrements for new users to improve user experience would be advantageous. Users might learn to navigate the system more rapidly with clear and simple documentation, video lessons, and interactive training modules.
In conclusion, using electronic health records in patient care, such as OpenEMR, provides several benefits. It improves the efficiency and precision of healthcare procedures, encourages data integrity, and aids in making well-informed decisions. The features of OpenEMR, such as appointment matrix creation, scheduling options, and the ability to update patient information, input forms, and encounter summaries, all help to improve patient care. A person learning and utilizing the system may encounter specific issues but can resolve them with proper assistance and instruction (Agarwal & Choudhury, 2022). OpenEMR is a potential example of an electronic health record system that can enhance patient outcomes, and integrating electronic health records in patient care is generally a favourable trend in healthcare technology.
References
Agarwal, K. and Choudhury, S. (2022) DeepCare: Improving patient care using Deep Learning on Electronic Health Records [Preprint]. doi:10.2172/1984684.
Kapur, K., Freidank, M. and Rebhan, M. (2022) Understanding the chronic kidney disease landscape using patient representation learning from Electronic Health Records [Preprint]. doi:10.1101/2022.10.25.22280440.
Perakslis, E.D. and Stanley, M. (2021) ‘Electronic Health Records’, Digital Health, pp. 74–84. doi:10.1093/oso/9780197503133.003.0007.
Santalahti, A. et al. (2021) ‘How GPS can recognize persistent frequent attenders at Finnish primary health care using electronic patient records’, Journal of Primary Care & Community Health, 12, p. 215013272110244. doi:10.1177/21501327211024417.
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