Hospital Readmissions - What does the Research Show?
this article discusses the evidence found showing that certain tools used are not adequate enough to predict the type of patients or the illness that leads to hospital readmissionsA1.EVIDENCE BASED PRACTICE AND NURSING RESEARCH
Background- Hospitals today are often measuring healthcare providers and the quality of patient care by assessing the value of unplanned ICU readmission rates. Length of stay in an ICU can increase the risk of more complications for the patients such as hospital acquired pneumonia, or urinary tract infections. However, early discharges from the ICU can also pose a risk to those that may require higher acuity of care but are sent out to a lower acuity environment. In that low acuity environment the patient can have multiple health complications and often display early clinical warning signs of decline in their state of health. Patient discharge is a very complex process in the ICU, but there are tools to assist when evaluating a discharge. According to the study it is not clear if these materials improve the delivery of health care or have any value over clinical judgement of the provider.
Review- The literature presented was to complete a comparison of how effective decision support tools that are used while inpatient, are in "predicting early unplanned ICU readmission or death after discharge from ICU (Unplanned Readmission, 2015). The researchers presented up to date information that was gathered through use and comparison of three support tools to evaluate whether these tools were a positive predictor of unplanned ICU readmissions or unexpected death after discharge. Upon completion of reading this study by the author I was able to understand the importance of increased monitoring, the reporting of clinical values and the impact of high quality care is as on patients during their admission and the decisions being made by the health care providers in assessing readiness for discharge.
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Method- The discussion of methodology of this article is a quantitative prospective cohort study in a single tertiary hospital that involved adult patients admitted to the ICU for more than twenty four hours. The three independent variables were SWIFT (Stability and Workload Index for Transfer Score), SOFA (Sequential Organ Failure Assessment), and TISS-28 (Therapeutic Intervention Scoring System) that were calculated on the day of discharge from the ICU by researchers and not by their attending physicians. (Unplanned Readmission, 2015) After discharge the patients were followed up through interviews and review of their medical records and reported on a standardized form for each case and followed through for the first forty eight hours after discharge from the ICU.
Analysis- The data collected was done for the first forty eight hours on 1,277 patients that were discharged from the ICU. The independent variables (SWIFT, SOFA, TISS-28) values were eliminated from highest to lowest value. They estimated the odds ratios by using the receiver operating characteristic (ROC) curve. If the value was greater than 0.8 then the support tool was considered good. If the value was lower than 0.6 then the support tool was a poor predictor in performance evaluation. The researchers then compared the three different scores use the chi-squared test and proceeded to observe and predict unplanned ICU readmissions or unexpected death using the Hosmer-Lemeshow test using Stata Statistical Software Release. (Unplanned Readmission, 2015) The analysis scores only presented moderate bias in predicting unplanned readmissions.
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Conclusion- The researcher's findings in regards to the various decision support tools is unclear if their use is supportive and accurate enough to predict if a patient can be safely discharged from the ICU. The results are also unclear if readmission scores provide any pertinent value over the clinical judgement of the primary physician or are useful in improving patient outcomes during transitions from the ICU to lower acuity care units. The study had limitations in that Brazil had reported higher readmission rates in comparison to Europe and North America. So in conclusion the support tools can be utilized but only with moderate accurate prediction of unplanned ICU readmission or unexpected death in first 48 hours after discharge.
Decision support tools like SWIFT, TISS-28, and SOFA provide only a sample of information in guiding physicians in making the clinical judgement on the readiness of the patient to be discharged or transfer to a lower acuity unit or facility. They are also a small factor in predicting the rate of readmission or unexpected death within 48 hours to the ICU.
The background information in the article supports the author's conclusion by showing that the decision support tools are helpful when assessing for discharge but are unable to adequately predict readmission rates to ICU. Upon review of the literature, the tools are what they say they are, just tools to help guide. They are not the eyes and ears that are monitoring the patient on a daily basis. The tools are outlines to assist staff on presentation and points. The physical assessment conducted daily by physicians and nurses is more accurate in planning for patient discharge and supportive in evaluating the risk of readmissions to the ICU. The method of coming to the conclusion by comparing support tools, leaves room for error in those who may be utilizing them. Documentation inputted in the system would need to be precise to account for the numbers to score points. The decision support tools don't supply enough valid information to be able to predict a readmission to the ICU or eminent death. When analyzing the data collected the researchers only studied the participants for the first forty eight hours after discharge. To better understand if these support tools are predictors of an ICU readmission they should follow the patients for at least 5 days so that more data can be retrieved on why there are readmissions to the ICU or sudden death.
Patient privacy is very important in today's world. In this study researchers failed to outline if consent was obtained when measuring and collecting the data for this cohort study. They should have explained to those patients the purpose of this particular study and how it could reflect on how physicians make decisions and judgement when determining if a patient is ready for discharge. When follow up was done by interview patient confidentiality should be maintained in order to protect their rights. The study didn't state if the participants had enough information presented to them prior to collecting of any or all of the data during the admission or at discharge. Also if an unexpected death has occurred within forty eight hours the rights of the patient should be maintained and full respect given to the family at that time.
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The study presented had some limitations, such as the sample size of only 1,277 patients. If the number of patients were larger then more information and data would have been obtained that would allow for more comparison and use of the decision support tools. The other limitation was the length of time that was studied. The researchers only studied those patients for the first forty eight hours after discharge. This is a limited amount of time. Not enough data can be presented or observed in those short hours. Signs and symptoms of declining health in a patient are not always observable in forty eight hours. Some clinical signs might not appear for seventy two hours. The strength of the study is the quality of the research. The researchers gathered pertinent information from three different support tools and ultimately compared them using various step wise models and completing multiple tests to obtain their information. Also using just the data from ICU discharges and not other units within this single tertiary hospital helps to narrow down the study to a more controlled environment.
This study impacts nursing practice in a manner that it brings information to the forefront that patients are often discharged from the ICU when they are not often ready to be discharged. Daily nursing practice entails taking care of critically ill patients and assisting in getting them well again. How nurses perform from bedside nursing to their charting can aid the patient and the provider in successfully discharging from the ICU. The evidence provided here shows that reports and statistical data are important but aren't substantial enough to predict if patient is ready for discharge or if they will be readmitted in a short time. This study is informative for nurses and providers and shows monitoring and listening to our patients is of utmost importance in achieving optimal health and wellness.
I am an RN with 12 years total experience. I just finished by BSN in March of 2017. I work in the CCU.
Joined Oct '07; Posts: 1; Likes: 4.Nov 1Great article - thanks. I currently work with dialysis patients and their readmission rate is actually very high. While we (post-hospital care) don't use a scoring system, we (our neph practice) are graded on our readmission rate.
Some strategies we employ:
1. First visit to provider after any hospitalization <24 hours is to be done within 72 hours of discharge
2. Med reconciliation is to be done immediately upon return to dialysis clinic post-hospitalization
3. Social worker discusses with pt the reason for admission as well as other avenues to seek care/direction ie phone call to provider, to dialysis unit
4. Dietician reviews weights and initiates protein supplementNov 1Maybe we should treat each patient as an individual and not just a diagnosis. My unit boasts about lower admission days but I feel like a lot of their needs are not being met despite me bringing them up to the doctor, case worker, and management on my unit. It's really sad what health care is becoming.