Rational Schemas
Introduction
A document management system is an example of an information management challenges that a database can address. Record keeping is a critical component of healthcare and a foundational piece of nursing practice (NMC, 2015). Accurate documentation of all aspects of patient monitoring is critical since it is a necessary component of professional duties that involve providing quality care and managing patients. This section explains a database design for a current healthcare issue involving the Intensive Care Unit (ICU). The chosen issue of monitoring conformity with Intensive Care Unit (ICU)standards for care facilities while minimizing the risk of the audit committee and internal fines will be discussed. Additionally, this paper discusses the database’s conceptual model, named entities, identifiers, and relationships. The ER flowchart for the database shall be presented and is attached in the appendix. Finally, Three database issues will be addressed as the foundation for the theoretical framework of the database and evaluated in the conclusion.
Need for a database plan
The highlighted information management issue that a database could address is assuring an adequate quality of treatment for high blood pressure. In order to qualify for general inpatient Intensive Care Unit (ICU) level of treatment in a hospice environment, the patient must have a documented need to handle acute symptoms, as defined by the Centers for Medicare and Medicaid Services (CMS) (da Silva et al., 2020). Additional Documentation Requests (ADRs) become more likely the more prolonged Intensive Care Unit (ICU) hospitalization is, often after a week. These ADRs require the completion of the patient’s medical record to substantiate the standard of care invoiced to CMS by the hospital (Johansson et al., 2017).
Intensive Care Unit (ICU) treatment is recommended only for acute symptom management during a brief inpatient admission that cannot be delivered in another environment. In a database that monitors the number of high blood pressure patients were admitted in the previous year, the number of Intensive Care Unit (ICU) physicians had a long length of stay (LLOS) greater than a week, and sick people with long lengths of stay on four special units had recorded acute diagnosis and management can help resolve allegation dismissals by identifying sources of concerns that can be enhanced with a plan of adjustment (Johansson et al., 2017).
Ongoing surveillance of such a database could help assure optimal patient treatment and payment processing and compensation. ADR’s findings and recommendations often result in payment denials. One area of refusal that is becoming more prevalent is the use of an inadequate degree of treatment due to a lack of supporting proof (Hopia & Heikkila, 2020). CMS has clearly outlined the Intensive Care Unit (ICU) care standards, and they must be achieved. Failure to adhere to the required aspects of Intensive Care Unit (ICU) quality of care can result in huge financial fines of millions of dollars.
This type of database could ultimately result in huge financial savings for the firm. Recognizing the Intensive Care Unit (ICU) standard of care that surpasses the standard one-week length of stay may increase awareness, and the health care team does indeed require Intensive Care Unit (ICU) quality of treatment, or it may allow for a suitable adjustment in the level of care to comply with CMS guidelines (Cionni et al., 2018). Stays longer than five days at the Intensive Care Unit (ICU) are more likely to be audited by CMS contractors.
Database model
Theoretical data framework for the proposed database will include recognizing patients who had stayed longer than one week in the Intensive Care Unit (ICU) standard of care. This concept would recognize the patient, the number of days spent in Intensive Care Unit (ICU) standard of care, the time treatment began, the location of the facility and the acute illness is handled. The physician and department were selected as the objects for this system. The patient was chosen as an object as the system shall show admittance to the general inpatient level of care as specified by the sick person undergoing this treatment.
Additionally, the concept unit was adopted since the database would be used to recognize patients getting general care services at one of four inpatient care entities. Both the physician and the facilities are associated with hospital treatment at the general level. To obtain data on the general level of care provided to inpatients, the physician acts as the who, and the facility operates as the where. By gathering this data, highlighted problem zones can be resolved swiftly; additionally, tendencies and responsibilities can be resolved by pinpointing the unit. The E-R flowchart for this data model was made with the help of the website Cogent.
The layout comprises two rectangles depicting the objects physician and facility and a diamond reflecting the relationship between the two objects, which is the admittance to the Intensive Care Unit (ICU) level of care. This single E-R diagram illustrating the objects and their relationships is included in the appendix. A particular line with emphasized text connects the variables. The medical record number or MRN, the national provider identifier (NPI), the worker identifier, and the facility location were chosen for these E-R diagram components.
It will be the simplest and most effective method of collecting physician data regarding their level of care. The MRN will link patient records from registration to level of care to the location of treatment. NPI and employee id codes will provide accurate data about care workers. The four inpatient care facility locations will serve as unit identification. Each of these attributes is distinct and particular (Hoogendoorn et al., 2021). The physician and the entity are included in the relationships between objects because they must interact.
In order for Intensive Care Unit (ICU) level of care admittance to occur, there has to be a physician and a unit area. The Intensive Care Unit (ICU) level of care accepts several patients; however, each patient can only be accepted to one facility at a time. The attribute ratio is many to one, and there is only one possible relationship between two objects. Conversely, the unit can be linked to any number of other objects in a group of related objects. The character “M” in the E-R chart shows many associations, whereas the symbol “1” indicates a single connection. Straight lines indicate many-to-many relations, and arrows denote one-to-many relationships in an appendix database diagram.
Database Questions
This database poses inquiries about patients who are in Intensive Care Unit (ICU) level of care: Does blood pressure monitoring relate to clinical outcomes?
• Is blood pressure monitoring associated with clinical outcomes?
• How is adherence to medication related to blood pressure?
• How can blood pressure tracking aid in CVA (stroke) prevention?
Using these inquiries, health care will be able to collect data that will subsequently serve as a model for a report. A few points to consider when creating this database are that it should be able to produce a report with options for filtering, date ranges, and downloads.
Conclusion
In conclusion, the database will be beneficial for recording findings following each blood pressure reading. Monitoring enables the detection of a rise in blood pressure above a predetermined threshold (often 140/90mmHg), which functions as an indicator and necessitates treatment correction. Blood pressure surveillance in patients taking a particular drug enables us to determine the extent to which documented variability in pediatric groups is related to genuine changes. Variability is used to evaluate if a patient’s treatment should be adjusted.
References
Cionni, R. J., Dimalanta, R., Breen, M., & Hamilton, C. (2018). A large retrospectivedatabase analysis comparing outcomes of intraoperative aberrometry with conventional preoperative planning. Journal of Cataract & Refractive Surgery, 44(10), 1230-1235.
da Silva, R., Baptista, A., Serra, R. L., & Magalhães, D. S. (2020). Mobile application for the
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Hopia, H., & Heikkilä, J. (2020). Nursing research priorities based on CINAHL database: A
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Hoogendoorn, M. E., Brinkman, S., Bosman, R. J., Haringman, J., de Keizer, N. F., &
Spijkstra, J. J. (2021). The impact of COVID-19 on nursing workload and planning of nursing staff on the Intensive Care: A prospective descriptive multicenter study. International journal of nursing studies, 121, 104005.
Johansson, Å., Skeie, Ø. B., Sorbe, S., & Menon, C. (2017). Tax planning by multinational
firms: Firm-level evidence from a cross-country database.
Appendix
ER diagram
Relationship Entities
