Electronic Health Records in Quality Improvement
Enhancing quality health care is one fundamental goal of every health care provider. This is because it enhances patient experience and outcome. Quality improvement can be defined as systematic and continuous actions that result in improved health care services of a targeted population. This improvement should be measurable based on some set of metrics. Jackson Health System, located in Miami Florida, is a crucial health care provider for many residents (Alvarez, 2019). Since Jackson Health System provides health care to all Miami Dale county, partnership with Information technology companies will be of great help in matching their technological and innovative needs. With Jackson Health system evolving into the world’s top medical provider that stretches beyond South Florida, Electronic Health records such as Electronic medical records can improve the quality of care. This covers the patient’s medical history, immunization status, allergies, laboratory tests and results, among other critical patient information. This can be shared across different health care settings.
The purpose of storing medical records in a digital form is its benefit with the smooth transmission of patient information. This enhances patient safety and privacy by ensuring that only authorized persons access the information. Clinicians or physicians can use this information to make informed decisions through medical history or record (Cantor & Thorpe, 2018). This is important in informing an individualized treatment plan based on a thorough assessment of the signs, past laboratory results, allergies, among other critical patient information. In essence, such personalized care will improve patient outcomes. Electronic health records can improve quality care by using digitally stored data and analytics to prevent hospitalization. Jackson’s health system comprises three main campuses; Jackson Memorial Medical Centre, Jackson North Medical Centre and Jackson South Community Hospital. It also has multiple primary and specialty care centers, digital storage of patient data, and information that will significantly help (Alvarez, 2019). This allows for a smooth transfer of data from one facility to another to make an informed decision.
This quality improvement initiative will be beneficial to both the patients, clinicians and physicians alike. Essentially, high-risk patients will highly benefit from the initiative. It will help the physicians develop an effective treatment plan based on a clear understanding of a patient’s medical history. Physicians will combine multiple clinical data from the system’s health records to identify chronically ill patients (Cowie et al., 2017). This can significantly inform patients’ management systems that are highly effective and efficient.
Accuracy and legibility of patient data stored in digital form is beneficial. The paper-stored patient information could lead to increased error due to the erroneous interpretation or difficulty in reading handwriting. The errors caused by such have significantly been minimized, making it easy to track a patient’s state across time through the highly accurate data. Accessing the data in a single file and searching it directly has made work easier when it comes to extracting medical data that will be used in examining possible trends and long-term changes in a patient (Choi et al., 2017). This electronic system can be beneficial as it ensures that the replication of information and data is minimal. This is because there is only one modifiable file that is more likely to be up-to-date.
Implementing electronic medical record technology will call for collaboration with information technology experts who will help in developing a model that suits the setting (Cantor & Thorpe, 2018). They will assess the various aspects of the setting to ensure that the technology meets the needs identified. The IT experts will also help with technical support and training on how the technology’s effective use where knowledge gaps have been identified. Collaboration with other healthcare providers is critical to ensure that a smooth sharing of patient data and information is achieved to improve the quality of care and improve patient outcomes.
The cost of implementing an Electronic health record system varies based on several factors. Based on the preference, an organization can opt for cloud-based or on-premise deployment method. Both methods incur direct and indirect costs. If it is costly to get the system on-premise because of strains of independently footing the bills, a cloud-based method can be useful. A typical multi-physician practice is likely to spend roughly $ 162 000 in implementing an electronic health record system, with $ 85000 likely to go into the first year of maintenance (Cowie et al., 2017). When taking the on-premise method, you are likely to host the Electronic health record on your servers, while the cloud method stores your data on your vendor’s server, thus can be accessed through the internet. The latter can be limiting in the number of people accessing. Therefore the on-premise method is encouraged for Jackson’s Health System.
While using the electronic medical health records, the basis for evaluating its quality improvement is its efficiency and effectiveness in data sharing, improved care coordination and smooth transition in care settings. The rate at which it minimizes errors and enhances informed decision making can be essential in evaluation. Its efficiency in data sharing by improving the patient’s privacy and confidentiality is equally a necessary aspect while evaluating this improvement initiative. The extent to which it gives support in proactive decision-making can gauge its effectiveness and efficiency (Choi et al., 2017).
Reference
ALVAREZ, N. (2019). Jackson Health System EM Residency Program in Affiliation with
(Doctoral dissertation, Rosalind Franklin University of Medicine & Science).
Cantor, M. N., & Thorpe, L. (2018). Integrating data on social determinants of health into
electronic health records. Health Affairs, 37(4), 585-590.
Choi, E., Xiao, C., Stewart, W., & Sun, J. (2018). Mime: Multilevel medical embedding of
electronic health records for predictive healthcare. In Advances in Neural Information
Processing Systems (pp. 4547-4557).
Cowie, M. R., Blomster, J. I., Curtis, L. H., Duclaux, S., Ford, I., Fritz, F., … & Michel, A.
(2017). Electronic health records to facilitate clinical research. Clinical Research in
Cardiology, 106(1), 1-9.