Electronic Health Records and Hospital Readmission Rates: A PICOT Analysis of Prediction Models and Clinical Outcomes
Overview
This paper analyzes the role of electronic health records (EHRs) in reducing hospital readmission rates through a structured PICOT framework. The author critiques two quantitative studies examining EHR efficiency in predicting readmission risk and the relationship between physician order entry systems and medication reconciliation on patient outcomes.
What this coursework covers
- Nursing Practice Problem and PICOT Question: Establishes the clinical problem of readmission rates and formulates a PICOT question comparing EHR and non-EHR hospitals
- Quantitative Studies Background: Reviews two quantitative research studies on readmission prediction models and hospital performance measures
- Article Support: Analyzes how the reviewed studies relate to the PICOT question and support the use of EHRs in reducing readmissions
Preview
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 a proper system of 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
In the first study of the quantitative research appraisal, A public-private partnership develops and externally validates a 30-day hospital readmission risk prediction model. 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.