Contemporary Social Issues

Introduction

This study highly focuses on the area of health and social care aspects that adds value to contemporary healthcare issue management. Chosen areas of contemporary healthcare issues are data safety and advanced data analysis of patient data within the nursing domain. The nursing staff is mostly processing raw data about patients. It is a basic right of patients to expect that their data is kept in secured places. All data regarding any chronic disease or a deadly one are stored in one place. It becomes important for the nursing staff to take care of the patients as well as their safety components.

The specific area within health and social care

The choice of interest in healthcare is to become a health data administrator within the healthcare domain. Healthcare in the 21st century is surrounded by technical names and it becomes the responsibility of every healthcare professional to look after patient data safety. Leaking or misplacing the data by any means is not acceptable (Manogaran et al. 2017). Currently, many misconceptions have been seen on how the covid 19 virus is going to be handled. Therefore, the focus is to manage and store data safely and use advanced analytics so that every life is saved. A better understanding of how to respond to healthcare threats and treat patients gives a spotlight on healthcare issues highly.

Discussion on the specific contemporary healthcare issue

The chosen contemporary healthcare issue here is data safety and the use of advanced data analytics in the healthcare domain. When it comes to healthcare patients are seen to have a blind trust in the doctors and nurses. Now, it becomes the responsibility of the concerned authority to look into this matter and create a secure server where only authorized personnel will get access. Moreover, there should be a data repository created that will help inpatient data management and storage easily. In order to ensure that patient data is extracted in an error-free method, there are uses of advanced analytics proposed. Uses of big data and artificial intelligence have been identified as an impactful option in healthcare and monitoring as the domain is progressing towards 100% error-free. The issues of incorrect measurement and inappropriate storage of data are leaving an impact on patient-doctor loyalty. Patients' trust in the doctor regarding the safeguarding of their private data is gradually lost with a casual attitude towards data safety and analysis by healthcare professionals in some cases (Manogaran et al. 2017). Healthcare does not leave any room for mistakes and the process is expected to minimize the risks and errors through deep analysis of available data.

The current issue in healthcare compatibility is identified as one of the major threats of the decade where the analytics tools largely depend on data-driven findings and solving this threat. This is also going to find out any inadequacies within health care that are caused due to social discrimination and interference. Healthcare analytics is in immediate need as the EHRs are one of the best possible options to rely on healthcare upgrades in the current situation. This analytics and safe data storage option is identified to manage patients even outside hospitals. It helps in managing unavoidable circumstances and implementing personalized treatments. With improved technology components the personal health records ensure that holistic health status is achieved. Insurance companies sometimes try to collect patient records without patient consent from healthcare professionals. This is a threat to data privacy as any patient history should not be revealed in front of anyone without consent. Hence, the issue has to be mitigated.

Affected people within the issue

Affected people are mostly patients. Patients belonging to different age groups are facing insecurities in healthcare domains. Their personal health information is at stake. Doctors, nurses, administration, and government bodies are identified to be the stakeholders that are affected by this issue. Improvement of patient care is associated with an enormous surge of data handling and employing prevention measures. There is no room for mistakes in medical science and any small mistake of taking readings can create a threat to the patient's life. Lack of standardization is a constant issue in healthcare when it comes to data monitoring. In remote places, many people are dying due to the inaccessibility of health care components (Manogaran et al. 2017). With advanced technology monitoring, those patients and storage of every minute heath detail are identified to be possible.

How it impacts the care delivery

The care delivery system is based on doctor and patient trust and keeping them updated regarding any issues that are occurring within the treatment plan. Now, most of the time trainees and junior doctors have been assigned to critical cases due to a shortage of staff. As the people have a lack of experience there are multiple readings taken which are wrong and need to be corrected. It is now possible with advanced data analytics systems to take appropriate readings and keep measurements error-free. Highly variable data like history, labs, Rxsensors, health, IoT socioeconomic, lifestyle behaviors are going to be associated with the data analytics components. The possible impact will be an improvement of health outcomes, reduced administrative burdens, and safeguarding patient data is observed (Cohen, and Mello, 2018). Supporting the transition from the volume of data management alongside valuing and facilitating individuals has been impacted with the use of advanced data analysis. Data safety is associated with interoperability and improving the exchange of members. Real-time and cost-effective data safety methods are achieved within valuing an aggregating data and system. EHR and clinical data should be managed on a real-time basis financial admins are associated within a development workflow to bring the value of aggregated data and systems.

Stakeholders such as doctors, patients, and nurses are having equitability within the cultural, financial, educational, demographic well-being analysis. Integrated technicalities are also helping in next-generation analysis and it is accompanying a frictionless and connected healthcare experience. Chances of data leakages are reduced from the medical professional's side. Cloud-based data storage allows both secure data components and encourages error-free data management as well. Health-related smartphone applications need to be initialized as these are going to be operated in big data and remote monitoring of patients will become much easier. Lowering the risk of healthcare discrepancies will be observed. Master patient indexes, patient portals are identified to be giving the patients enough information on the treatment process and that too within a few clicks. Lowering the increasing cost of healthcare has been done as it manages sensitive patient information to its best.

Recommendation on the way chosen issue achieves disruptive services

Healthcare analytics will be of no use if it is not centralized or systematically arranged. Patient-centric value-based health care objectives are growing every day. Practitioners are present towards the preventive treatment that helps to stave off long-term hospitalization issues and expenses. With an early diagnosis by using healthcare analytics it has become easier to manage healthcare components and predict any life threats. Healthcare analytics can be defined in different types such as:

Descriptive analysis: It uses several historical data to draw a comparison or discover the patterns. The past descriptive analytics answers questions which have occurred already. Data analytics leads to health care solutions where efficiency in all formats leads to better care (Cohen, and Mello, 2018).

Prescriptive analysis: This type of analysis makes predictions about future outcomes and machine learning has been identified as a big factor within this type of analytics. It determines the course of action and allows gaining insight into the actions that are taken to reach the actual goals.

Predictive analysis: Current and historical data are used for making predictions regarding the future. This creates answering questions that are best for the actions to be happening next. A predictive analysis identifies the exact foresight of healthcare management.

Moreover, there is predictive modeling which analyzes the infection and deterioration rates. Risk scoring, readmission, and individual patient-level analysis have been optimized within predictive modeling. Administrative applications to manage patients are done at an extremely low cost. Patient-centric health data storage like appointments and no-shows are being managed effectively. Supply chain components are decreasing fraudulent and actionable insights within data analysts. The components are identified to be collecting or mining data, examining and evaluating raw data. There is the automation of reports and predictive modeling applied to these concepts (Cohen, and Mello, 2018).

Required skills for health data analysts are indulged within structured query language, excel, and data visualization. There is the availability of statistical programming and strong communication skills that take a stand within competitive positioning. Recruiting health data analysts is highly praised through organizing health IT vendors, healthcare consulting companies, and diagnostic centers. Government health care departments are allowing digitized competency management largely which is necessary at this moment.

Conclusion

This study leaves an impact on the concepts of medical and public health research. In medical backgrounds, the importance of patient data safety and security has become highly important. There are systematic applications of data analytics and cloud discussed in this context so that the common man's health can be prioritized.

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