Hospital Readmissions Evidence-Based Care Project

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Analysis of Evidence

The search identified 408 records, though, after the full-text screening of 200 studies, only 70 met the inclusion criteria. In total, these studies enrolled 1,437 participants with the median of the study sample size being equal to 198. Approximately 96% of the articles included emergency departments, long-term care settings, and intensive care units in the US and Europe. Participants in these studies were patients with at least one chronic condition aged 55 and older. The risk of bias was assessed using Cochrane’s Risk of Bias tool. 3 records out of 70 had serious limitations and were thus excluded from the analysis.

The number of hospital readmissions within 50 days to any hospital after index hospitalization discharge was considered a dependent variable, and the kind of intervention was an independent variable. The main groups of interventions identified in the selected studies are as follows: in-home visit, brief telephone needs assessment, drug counseling, relaxation training, and cognitive behavioral therapy (Dubard, Vann, & Jackson, 2015). Age, gender, ethnicity, and health condition of patients were also considered independent variables. The network meta-analysis was conducted in STATA to calculate the relative effects and effectiveness ranking for interventions that could guarantee lower readmission rates among incessantly sick patients.

Pairwise meta-analysis and network meta-analysis were performed in R (Rstudio) where tau2 and I2 measures were determined, and forest plots were built. To calculate the effectiveness of interventions, their SUCRA values were calculated. The analysis results suggested that telephone needs assessment and the weekly home visit had the highest probability to be the most effective interventions with the SUCRA value being equal to 80.24% and 77.83% correspondingly.

In turn, the SUCRA value of drug counseling is equal to 68.70%. Considering the high certainty of the evidence, these findings can be regarded as reliable. As compared to standard procedures, there is moderate evidence that cognitive-behavioral therapy and relaxation training may reduce hospital readmission rates among chronically sick patients.

Analysis of Interviews

30 nurses participated in an interview across 5 medical institutions (6 nurses from each site). They answered questions about the standard hospital discharge of a patient with chronic conditions. Then, nurses were asked to compare and contrast this procedure with interventions that were identified as effective in lowering hospital readmission rates (Verhaegh et al., 2014). Responses were grouped into two domains: awareness of standard hospital discharge and awareness of current practices to reduce the number of hospital readmissions. To reinforce qualitative research through naturalistic observations, nurses, together with interviewers, identified current procedures employed by healthcare professionals.

Most nurses had a good general understanding of standard hospital discharge procedures and described them in sufficient detail. When told about effective strategies discovered in the course of the systematic analysis, participants said that they were not common practices at their work. It was also asserted by healthcare specialists that the utilization of some of the discussed procedures could be successfully implemented in their healthcare settings yet required additional staff training. After a series of interviews, it was discovered that new interventions need a knowledgeable practice scholar to adapt the procedure for a particular environment and lead a change.

Discussion of Information Obtained During the Systematic Review and Interviews

Possible interventions that could change patient outcomes for the best include weekly home visits, post-discharge telephone counseling, drug counseling, and a set of cognitive approaches. These simple transitional care procedures may be associated with lower 50-day rehospitalization rates among chronically ill patients (Mihailoff, Deb, Lee, & Lynn, 2017).

Based on the results of meta-analysis and interviews with nurses, it can be concluded that despite the multitude of interventions with statistically significant positive effects, they have yet to be incorporated in healthcare settings. This means that the majority of healthcare establishments lack an appropriate level of knowledge translation that would allow for improving outcomes of patients.

Ramifications of the above-described transitional considerations for incessantly sick patients include a decrease in rehospitalization rates, improved health outcomes, and better disease management. Recommendations for further practice should focus on the elaboration of knowledge translation tools that would enable to implementation of effective interventions in healthcare facilities. The study of investment strategies to reduce preventable readmissions of people with chronic conditions could be an area of future research.

Project Limitations

Despite strengths, such as robust study design and credibility of peer-reviewed articles, several limitations should be mentioned. Firstly, co-interventions of included studies were disregarded since some of them were not fully described by researchers. Secondly, the “standard hospital discharge” that was a reference in network meta-analysis may be considered a potential source of bias that could have affected the calculation of relative effects of interventions. Thirdly, three studies that had a zero effect were excluded from analysis as they could not have been appraised using STATA.


The ever-present uncertainty in care transitions for patients with chronic illness coupled with a small number of evidence-based practices being employed by medical institutions account for poorly executed care services. This, in turn, leads to an increased rate of rehospitalization and the use of the emergency unit. Being at particular risk of recurrent hospitalization, incessantly sick patients need to be offered specific interventions. Among the most effective ones, a post-discharge telephone-based needs assessment, weekly home visits, medication counseling, relaxation training, and cognitive-behavioral therapy have been recognized.


Dubard, C. A., Vann, J. C., & Jackson, C. T. (2015). Conflicting readmission rate trends in a high-risk population: Implications for performance measurement. Population Health Management, 18(5), 351-357.

Mihailoff, M., Deb, S., Lee, J. A., & Lynn, J. (2017). The effects of multiple chronic conditions on adult patient readmissions and hospital finances: A management case study. INQUIRY: The Journal of Health Care Organization, Provision, and Financing, 54(1), 1-6.

Verhaegh, K. J., Macneil-Vroomen, J. L., Eslami, S., Geerlings, S. E., Rooij, S. E., & Buurman, B. M. (2014). Transitional care interventions prevent hospital readmissions for adults with chronic illnesses. Health Affairs, 33(9), 1531-1539.

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NursingBird. (2021, June 23). Hospital Readmissions Evidence-Based Care Project. Retrieved from


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"Hospital Readmissions Evidence-Based Care Project." NursingBird, 23 June 2021,


NursingBird. (2021) 'Hospital Readmissions Evidence-Based Care Project'. 23 June.


NursingBird. 2021. "Hospital Readmissions Evidence-Based Care Project." June 23, 2021.

1. NursingBird. "Hospital Readmissions Evidence-Based Care Project." June 23, 2021.


NursingBird. "Hospital Readmissions Evidence-Based Care Project." June 23, 2021.