Nursing Practice Problem and PICOT Question

Research Critiques and PICOT Statement Final Draft
Name
Institution
Nursing Practice Problem and PICOT Question
The use of electronic health records plays a significant role in enhancing diagnosis and patient outcomes. As a health worker, proper documentation improves diagnosis, reduces medical errors, and facilitates positive patient outcomes. Hospitals rely on medical information and patient's personal information to deliver treatment. Lack of proper sharing and recording information would lead to medical errors, prompting patient readmission. PICOT question: In a population of patients treated in hospitals (P) with Electronic Health Records (HER) (I) compared to non-HER hospitals (C), what is the prevalence of readmission rates (O) measured over one year period (T).
Quantitative Studies
Background
The first study of the quantitative research appraisal, A Public-Private Partnership Develops and Externally Validates A 30-Day Hospital Readmission Risk Prediction Model. In the Online Journal of Public Health Informatics, the researchers assessed the efficiency of electronic health records in predicting readmission risk. The research was carried out by advocate health care in Chicago and Cerner. The researchers conducted a systematic review to evaluate the performance of prediction models used to assess hospital readmission risk. The problem to be solved was the poor performance of prediction models for hospital readmission risk (Chaudhry, Li, Davis, Erdmann, Sikka & Sutariya, 2013). The research focused on establishing effective hospital readmission risk prediction models for admission and before discharge.
Moreover, the study also aimed at assessing the performance of prediction models using key metrics. The research question was, How efficient are the prediction models used in hospital readmission risk? (Chaudhry et al., 2013). The research question is significant to nursing since it helps identify gaps within the management of population risk. Integration of effective prediction models in EHR's is a step towards ensuring low readmission rates.
In the second quantitative study, Relationship Between Hospital Performance Measures and 30-Day Readmission Rates, the research focused on "Examining the relationship between computerized physician order entry scores medication reconciliation scores and 30-day readmission rates" (Carter, 2016). Therefore, the research aimed to investigate whether the use of EHR impacted medication reconciliation and readmission rates. The research problem was the occurrence of medical errors during prescription, which lead to patient readmission within 30 days of discharge. The research question was, "What is the relationship between computerized physician order entry scores, medication reconciliation scores, and 30-day readmission rates? (Carter, 2016)" The research question related to the study's problem was essential to help hospital leaders improve business performance and mitigate financial losses.
Article Support
In the first article, the quantitative study relates to the PICOT question. It applies readmission models to identify patients eligible for interventional measures; hence reducing readmission rates proves that EHRs are useful. In the second article, the quantitative study relates to the PICOT question. It uses information from physician order entry scores and medication reconciliation scores obtained via EHRs to determine the prevalence of readmission rates.
Methods of Study
The first quantitative study applied a mixed-method approach toward evaluating risk factors. Moreover, the research applied inclusion and exclusion techniques on cohorts entailing patients, split for derivation and validation. Furthermore, stepwise logistic regression was used to develop a predictive model for admission and another for discharge. An assessment was further carried out to determine discrimination ability performance and calibration. One benefit of using multiple regression is that it helps achieve the relative influence of one or more predictable variables. Despite its advantages, the result could be erroneous due to the use of incomplete data.
In the second quantitative study, multiple linear regression designs were incorporated to examine the relationship between various predictor variables and the independent variable. Moreover, the research applied correlation designs that enabled the researchers to draw connections between variables. Descriptive statistics were incorporated to provide evidence during the study to reject or accept the research hypothesis. Data collection included sampling through stratification. Upon violation of normality and homoscedasticity, bootstrapping was used to generate a bootstrapping 95% confidence intervals. Bootstrapping is essential in eliminating parametric assumptions; however, it is not effective when dealing with small samples since it applies the sampling technique with replacement. However, the methods used in quantitative studies are related to the PICOT question and the research questions.
Qualitative Studies
Background
In the first qualitative study of the research appraisal, Preventing hospital readmissions: healthcare providers' perspectives on "impactibility" beyond EHR 30-day readmission risk prediction. Journal of General Internal Medicine, the researchers explored healthcare providers' perspectives about patient characteristics eligible for readmission prevention programs (Flaks-Manov, Srulovici, Yahalom, Perry-Mezre, Balicer, and Shadmi, 2020). By identifying patients' characteristics, the researchers determined whether the subjects were eligible for inclusion in readmission pr...