Data Architecture and Design Plan Research paper | Homework Help

Any organization’s critical component is data. Its amount and significance are continuously increasing. There is a high demand for it, which is continually expanding. Many firms face enormous demands from competitors, consumers, and regulatory bodies. Healthcare companies are complicated ecosystems with a lot of data but not much information. Many healthcare companies are adept at gathering and obtaining data but fail to realize its true worth. The cost of living is rising. Data is not always available in the manner or pace necessary. Duplicate data abounds everywhere, and many times the organization is unable to discern whether data is reliable. Information is a critical resource within a healthcare business that must be trusted, confirmed, and preserved. Accessing information stored on various data storage systems in a timely way will be critical to helping enterprises manage their data as the amount and demands for data grows.

Selected Environment or Scenario

The number of aged individuals in society is increasing globally, and this phenomenon, identified as humanity’s ageing, has significant consequences for healthcare services, particularly in reference to costs. In such a case, depending on traditional methods might decrease living quality for millions of individuals. A slew of healthcare systems has been devised in an attempt to address this issue. Their fundamental premise is to regularly communicate medical data including blood pressure, heart rate, and body temperature to an computerized system intended to monitor patients’ status in real-time. The number of data sources in the healthcare business has recently increased significantly due to the prevalent use of mobile and wearable sensor technologies, which has inundated the healthcare industry with a massive quantity of information (Degele et al., 2017). As a result, doing healthcare data analysis using standard approaches that are unsuitable for dealing with a large number of diverse medical data becomes difficult.

Questions and Problems That Will Be Addressed With the Data.

It attempts to explain why specific events happened and what variables caused them. Diagnostic analysis, for instance, uses approaches such as clustering and decision trees to identify why certain patients are readmitted frequently. Secondly, Even though the nursing process is structured to collect data in the EHR, the issue of missing data persists. For example, as part of the nursing workflow, each time a drug was delivered, the details on drug administration should be entered in the medication administration records, and the system is intended to collect everything. Numerous medical sources in healthcare create high amounts of data, including biomedical photographs, lab test results, doctor written notes, and health state metrics enabling real-time patient health observation (Benhlima, 2018). In addition to the massive amount and variety, healthcare data moves at a breakneck pace. Consequently, data architecture methods provide significant prospects for improving the efficiency of healthcare systems.

Stakeholders

Patients, nurses, and physicians are all critical stakeholders in the scenario. An expanded use case would include the pharmaceutical sector, research, government, more healthcare providers, and insurance stockholders. Data records may be arranged by source, chronologically, according to a predetermined protocol structure, or by allocating all information to specific issues. Every use case presented by interested party comprises of a data source, collection, storage, processing, and serving procedure. The specific process determines which data formats are required (Degele et al., 2017). The observed use case’s data sources include smart gadgets such as smartphones that provide various fitness data types.

Data sources

Some physical data sources are required for this attempt, such as age, sex, blood pressure, pulse, body weight and height, (prior) illnesses, and limitations. However, most of these information artifacts are only cooperative when combined with additional data. The body mass may provide beneficial information, but only if the body height is also known. Age and sex may also be used to make suitable remarks. Moreover, such a categorization requires more than just physical information. Extra information such as probable inherited disorders, dietary practices, sports undertakings, alcohol and drug intake, workplace, or whether or not the client smokes may assist health insurance companies in gaining a complete picture of the consumer (Benhlima, 2018). To match a client to the most appropriate policy framework, specific criteria must be developed from accessible data to enable client categorization. Furthermore, the insights provided by the patient’s fitness data allow the insurer to recommend personalized dietary plans or exercise programs. In the event of life-threatening ailments such as heart disease, the continuous use of fitness trackers enables the detection of anomalies. Furthermore, the volume of data gathered from various insurers allows pattern-matching, which leads to better diagnostics.

Needs analysis

Based on the previous assignment, the needs analysis performed on Health Ford is based on creating an enterprise-wide risk-based security program that protects IT resources, reputation, and details via proactive alignment with Health Ford’s business plan. The fundamental goal of this department is to “defend the trust,” which is divided into three categories: infrastructure security, clinical information and distribution system defence, and Internet Protocol (IP) and data protection. Network security and server safety, anti-virus applications, monitoring, and efficient vulnerability analysis are all aims of the needs analysis performed in the previous assignment. The major goal should be to ensure that new systems offer the organization’s project managers all of the data needed to make informed decisions precisely, responsive, and substantial.

Proposed Data Architecture Design

Personal information such as name, address, phone number, and a unique patient ID are recorded for each insured. The insured may have a tracking device, such as a smartphone that serves as a data source. The monitored data is delivered to the central data collector given by the insurer, which converts the data into a standardized format and provides it to the processing organization. If severe anomalies are discovered, a doctor will be notified and instructed to call the insured. After analysis, the data is saved as a fitness profile. This profile contains the corresponding patient, the monitored raw data, an analysis of the frequency of training to assess the covered person’s fitness state, and personalized health recommendations. These ideas may include suggestions for health-improving actions based on the policy holder’s current tastes. Still, they may also have cautions or reminders if a professional check-up visit seems to be essential. A digital portal allows the patient to view his fitness profile. The health profiles of the insured are also utilized by the financial application in the back end to compute reductions on insurance premiums depending on the relevant health status.

Conclusion

The issue of relevant data is not straightforward since it is heavily reliant on the use case under consideration. The more data an insurance company has, the more relationships between behaviour patterns and insurance claims may be discovered and anticipated. The structure of the acquired data, use cases, and procedures provided by stakeholders establish functional requirements. Data governance and management and national and international health and insurance regulations determine further criteria. An assessment of the current state of practice in different health insurance companies might give additional insights into the validity and completeness of the proposed data architecture in future work.

 

 

References

Benhlima, L. (2018). Big data management for healthcare systems: architecture, requirements, and implementation. Advances in bioinformatics2018.

Degele, J., Hain, J., Kinitzki, V., Krauß, S., Kühfuß, P., & Sigle, N. (2017). Data architecture for digital health insurances (pp. 107-116). Gesellschaft für Informatik.

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Data Architecture and Design Plan Research paper | Homework Help . (2022, June 23). Essay Writing . Retrieved June 29, 2022, from https://www.essay-writing.com/samples/data-architecture-and-design-plan/
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