Efficiency of electronic health records in predicting readmission risk

Rough Draft Quantitative Research Critique and Ethical Considerations
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Background of Study
Without sufficient knowledge of medication, physicians may fail to administer the right prescription. Errors arising from the treatment could result in readmission within 30 days of discharge. Moreover, the mitigation of patient readmission is considered a way of reducing cost implications in the health sector. The hospitals' responsibility is to ensure improvement in the quality of care and decrease instances of unnecessary readmissions. For example, inpatients at Medicare readmitted within 30 days of dismissal account for 17 billion dollars Expenditure at Medicare (Shulan, Gao and Moore, 2013). Furthermore, Medicare experiences losses of up to 1% of the annual investment for every readmission within 30 days of the same illness. Moreover, certain prediction models are employed to establish the risk of readmission. Electronic health records are essential in formulating these records. Furthermore, EHR helps in healthcare management by generating patient data. Moreover, these systems provide a link between questions and doctors while improving data access efficiency among employees within an organization. By using electronic health records, physicians can identify patients at high risk for readmission and provide health care solutions to these patients. Lawmakers within the United States health subject have suggested using electronic health records as a method to reduce hospital readmission, mitigate the cost, and improve the quality of health care. However, certain actions have been taken as correspondence to the increasing health care costs. For instance, lawmakers in the United States introduced financial penalties for health institutions recording high readmission rates. However, these regulation methods negatively influence productivity and profitability among health care institutions(McCormack et al.,2013). Readmission in hospitals is a useful tool in analyzing the performance of a health care facility. Electronic health records to establish a 30-day readmission rate could be essential in improving healthcare quality and mitigating financial losses. Therefore, the research is significant to nursing since it provides information on the efficiency of EHR and its usage towards improving health care.
Chaudhry et al. (2013) conducted quantitative research to investigate the efficiency of electronic health records in predicting readmission risk. The study included various prediction models for hospital readmission. The primary focus was to establish the efficiency of these models in the determination of readmission risk. Variability in readmission rates regarding prediction models provides information on the prevalence of readmission rates in emergency departments with EHRs compared to non-EHR. On the other hand, Carter (2016) analyzed numerical data to make inferences on the correlation between scores generated by electronic health records and readmission rates. Moreover, this research was intended to represent the general findings of a larger population. Carter employed data from electronic health records and conducted a statistical analysis to establish the relationship between the variables. Both authors provide af...