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EHR Database and Data Management

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EHR Database and Data Management

Introduction

According to medical studies in regards to cancer treatment, Prostate cancer is regarded as the most common illness among men (Hernandez-Boussard, Blayney & Brooks, 2020). Recently diagnosed men encounter complex therapy choices, each with various risks of obtained patient-centered outcomes such as urinary and erectile dysfunction. In these present times, care providers and patients find it difficult to contrast the trade-offs among patient-centered results across various treatments since the experimental evidence about these trade-offs does not exist (Hernandez-Boussard, Blayney & Brooks, 2020). According to experts, this is because patient-centered outcomes are not consistently recorded in computable formats.  For healthcare institutions to enhance cancer care and accuracy of data, evidence recorded in computable forms should be placed in the hands of the clinicians and patients through a Web-based tool (Hernandez-Boussard, Blayney & Brooks, 2020).  There are three significant innovative measures proposed in this article concerning the management of cancer patient’s data.

The first proposed approach endorses the development of an EHR prostate cancer database that will create an opportunity for clinical information to be analyzed alongside diagnostic details (Hernandez-Boussard, Blayney & Brooks, 2020).  The second approach creates new ontological representations of quality metrics that are public and reliable across the EHR programs. The third proposed approach involves gathering a robust data information mining workflow that expands on modern methods by centering on ontology-based dictionaries to interpret the free text (Hernandez-Boussard, Blayney & Brooks, 2020). Combining these three innovative approaches will uniquely allow both clinicians and patients to use current EHRs to understand the trade-offs among patient-centered outcomes across various treatments adequately.

Utilizing EHR to measure and enhance prostate cancer treatment is essential since it creates room for healthcare facilities to share necessary information (Hernandez-Boussard, Blayney & Brooks, 2020).  Through the EHR, it easy to develop the building blocks desirable to recognize quality metric information in EHRs. It is of the essence to create an EHR database, map quality metrics to medical vocabularies, and develop electronic quality metric phenotypes. The EHR program creates a web-based tool that integrates the empirical evidence and clinical characteristics to evaluate patient personalized risk prediction that assists care providers and patients in selecting a treatment option (Hernandez-Boussard, Blayney & Brooks, 2020). These options provide the best-anticipated quality of care given the significance they assign to each patient-centered outcome. Making use of EHR will assist in addressing a crucial gap in evidence for prostate cancer therapy and research by offering care providers and patients practical evidence desirable to contrast the trade-offs among patient-centered outcomes across various treatments.

Prostate cancer is a complex illness, and existing therapies have associated risks of patient-centered outcomes, although no definite evidence exists on the variation of hazards across treatments (Hernandez-Boussard, Blayney & Brooks, 2020). Therefore, this article’s proposal develops measures to gather patient-centered outcomes recorded in EHRs and a risk evaluation tool to estimate the personalized hazards of issues across therapies. Such data will assist policymakers and healthcare staff in enhancing a patient’s healthcare experience and results (Hernandez-Boussard, Blayney & Brooks, 2020). Moreover, Electronic Health Records helps clinicians by making it easy to gather and analyze information regarding patients in a meaning full manner that keeps track of patients over time and recognize trends associated with cancer.

According to studies concerning cancer diagnosis and therapy, using EHRs is associated with enormously higher healthcare quality for cancer (all types of cancer) (Hernandez-Boussard, Blayney & Brooks, 2020). The EHRs are essential since it provides information without difficulty keeps records of treatment and enables individuals to assess knowledge better.

In the current healthcare organization, no one is engaged in any unit of healthcare delivery, or planning can fail to recognize immense changes in the perspective of data management (Magyar,2017). This article reviews examples of healthcare databases used in healthcare organizations to enhance patient care concerning cancer therapy (Magyar,2017). For one to comprehend the range of EHRs that the healthcare department might access and why there might be a concern in regards to the protection of individual data, care providers are obliged to consider various factors of EHRs data management such as comprehensiveness (Magyar,2017). According to healthcare studies, comprehensiveness portrays the completeness of data of patient’s healthcare experiences and data pertinent to an individual patient (Magyar,2017). Comprehensiveness incorporates the amount of data care providers have regarding patients both for each personal experience with the healthcare department as well as treatment procedures.

Data that is comprehensive incorporates demographic information, organizational data, health risks, and conditions, patient therapeutic history, existing management of health status, and result statistics (Magyar,2017).

  • Demographic information

Demographic information consists of statistics including age, race, gender, national origin, marital standing, address of dwelling, names of direct relatives and other information concerning immediate relatives, and alternative data (Magyar,2017). Moreover, demographic data in the EHR also include information regarding employment status and employers, education level, and some indicator of socioeconomic rank.

  • Administrative Information

Administrative data incorporates information about health insurance such as membership and eligibility, dual coverage, and required copayments and deductibles for a provided benefits package (Fox, Aggarwal, Whelton & Johnson, 2018). Administrative data commonly recognize care providers with an exclusive identifier and probably offer extra specific data. These may include the kind of practitioner, physician specialty, and culture of the institution.

  • Health risks and Health status data

Health risks data reflects the lifestyle and characters of an individual. For instance, in cases of cancer patients, the care provider might ask if the individual uses tobacco products or regularly engages in strenuous activities (Magyar,2017). Health risk data also includes information about genetic factors and family history, such as whether a person has first-degree relatives with a significant class of cancer.

Health status information is generally and often reported by individuals themselves. Health condition data reflects factors of health such as physical status, emotional and mental actuality, intellectual functioning, communal and role functioning, and observations of an individual’s health in the past, current, and future and contrasted with that of an individual’s peers. Health conditions and quality of life measures are commonly considered outcomes of healthcare (Fox et al., 2018). Still, evaluators and researchers also require such information to record their analysis of the mix of patients and the range of severity of health status.

 

 

  • Patient Therapeutic History

Patient medicinal history incorporates information on previous health check encounters, including hospital admissions, surgical processes, pregnancies, and live births (Fox et al., 2018). It contains data on past medical issues and probably the family history of events such as intoxication or parental separation (Magyar,2017). Additionally, although such information is essential for quality care, they may be vital for case-mix and severity alteration.

  • Existing Medical Management

Existing medical management includes information in regards to the gratification of experience procedures and parts of the patient file (Magyar,2017). Such data might replicate health screening, existing health issues, and diagnosis, treatment processes conducted, laboratory tests performed, and counseling offered.

  • Outcomes information

Outcomes data includes a range of choices of procedures of the effects of health care and the outcome of different health issues across the spectrum, from mortality to increased stages of performance and wellbeing (Fox et al., 2018).  Outcomes data reflect healthcare occasions such as readmission to healthcare institutions or unplanned difficulties and side effects of care. Consequently, outcome data often incorporates measures of satisfaction with patient care.  Results evaluated weeks or months after therapy procedures, and by information straight from individuals or immediate relatives, are desirable. However, such data appear to be the least commonly found in the secondary record (Magyar,2017). The EHRs program manages and presents the historical and existing test result in suitable healthcare providers. Through this, healthcare professions can review the patient’s information with the ability to filter and compare the outcome. Additionally, the EHR system allows physicians to manage patient records electronically and store them for future references.

Conclusion

According to medical studies, the more inclusive the EHR is, the more present and probably more sensitive data regarding patients is likely to be. The comprehensiveness of the Electronic Health Records has a significant correlation with concerns regarding confidentiality and privacy. One of the most significant approaches to ensure individuals have complete advantage of the benefits of EHRs and enhance quality care, preventive cancer care, and patient outcome is to attain meaningful use. By healthcare institutions achieving meaningful use, they can obtain benefits beyond monetary enticements. Over the past years, approximately every significant healthcare institute invested majorly in computerization. These technological advances, such as EHRs, are allowing care providers to present a faster and more efficient patient outcome.

 

 

 

 

 

 

 

 

 

 

 

References

Magyar, G. (2017). Blockchain: Solving the privacy and research availability tradeoff for EHR data: A new disruptive technology in health data management. In 2017 IEEE 30th Neumann Colloquium (NC) (pp. 000135-000140). IEEE.

Hernandez-Boussard, T., Blayney, D. W., & Brooks, J. D. (2020). Leveraging digital data to inform and improve quality cancer care.

Fox, F., Aggarwal, V. R., Whelton, H., & Johnson, O. (2018, June). A data quality framework for process mining of electronic health record data. In 2018 IEEE International Conference on Healthcare Informatics (ICHI) (pp. 12-21). IEEE.

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