Research Study Critique: Preventing Readmission from Surgical Infection After Cardiac Surgery

Research Study Critique: Preventing Readmission from Surgical Infection After Cardiac Surgery

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Even though hospital readmissions after cardiac surgery are costly, health care scholars believe that they can be prevented. According to Navia (2016), early readmission after a patient has been discharged is often linked to increase in-hospital mortality as well as poor outcomes and increased expenditure in the health care system. Several programs have been introduced, including the Medicare hospital readmissions reduction program. This paper, therefore, critically evaluates the article by Gai & Pachamanova (2019) in a systemic approach to address the contents of the study.

I.   Critique of the Research Problem 

In the study “Impact of the Medicare hospital readmissions reduction program on vulnerable populations” by Yunwei Gai1 & Dessislava (2019), the researchers clearly stated the research problem and how it would solve the nursing problem at hand. In their study, the authors reveal that the Hospital readmissions Reduction Program (HRRP) was designed to reduce the excess readmissions in the health care facilities, reduce the cost of health care, and enhance patient safety and outcomes. The program targeted three conditions that included acute myocardial infarction, heart failure, and pneumonia. However, there are concerns that the HRRP can result in unintended consequences for the vulnerable patients and the health care facilities that serve them, a factor which the authors did not address in their research problem. The purpose of this study is to assess whether the observations concerning the impact of the HRRP on readmission rates apply to various types of populations, particularly the vulnerable populations.

II.  Critique of the Review of the Literature

In the literature, the authors explored various concepts with the central concept being the Hospital Readmissions Reduction Program (HRRP). Other ideas include Medicare and Medicaid administration and how they affect patient readmission rates for the targeted conditions. The authors also included terms such as socioeconomic status, high-risk patients, low-income patients, comorbidity, and mortality risk. Further, the authors ensured that they used current sources in the review of the literature. Most of the references used are less than five years old, indicating that the information included in the study is current and appropriate for the study. However, the authors have also used older references dating back from 2000 to 2009 for comparison. The literature relates to the research problem because it has evaluated previous studies on the impact of HRRP on vulnerable populations. The literature relates with the current research by focusing on the impacts of safety net programs on the rates of readmission. The literature also shows how the HRRP penalties affected the total margins of hospitals due to readmissions rates.

III.  Critique of the Theoretical / Conceptual Framework

The study clearly defined the concepts used. For instance, the authors defined the vulnerable population included in the research and how the concepts are measured. Besides, the authors defined population vulnerability in the literature in terms of quartile of patient income or the type of insurance like Medicaid to stratify the four types of the vulnerable population they listed in the research problem. The conceptual framework is based on four types of vulnerable populations. This theory draws from social statistics discipline that evaluates the demographics, socioeconomics, mortality, and comorbidity attributes of the community under the study. However, the authors did not mention any theoretical framework in the study. As a result, the neighborhood socioeconomic disadvantage theoretical framework is suitable (Karichu, 2017). 

IV. Critique of the Research Design

The study used the difference-in-difference (DD) research method, which is a quasi-experimental design. This research design method was utilized to compare the pre-HRRP difference in the rates of readmissions between treatment and control groups with the post-HRRP differences. The independent variables included Medicare acute myocardial infarction patients, heart failure patients, pneumonia patients, and gastrointestinal patients. The dependent variable was the readmission rates. However, this study had several limitations. For instance, the authors admitted that the data-imposed limitation which separates hospitals into at-risk and not-at-risk classes needed to be done year-by-year, something that is problematic. Besides, another limitation is the reliance on the secondary data that is also limited, especially when it comes to describing the clinical details of the hospitalizations and follow-up on patients. Most importantly, the study assumed that the other “shock” impacts AMI patients with Medicare and private insurance on equal terms. Besides, the study assumes that there is a similar trend between the control group and treatment group prior to the HRRP policy.

V.  Critique of the Setting and Participants

The setting for this study includes both clinical and non-clinical environments. In these settings, hospitals and physicians make efforts to minimize the rates of readmissions by connecting patients with clinics closer to them as well as working together with pharmacists for enhanced drug management, follow-up patients at their homes, and arrangement of transport to health care services. The study participants include four types of vulnerable populations aged 65 years and above. They incorporate low-income patients, patients served by hospitals that serve a more significant percentage of low-income or Medicaid patients, and high-risk patients at the highest quartile of the Elixhauser comorbidity index score. The study used a sample size of over 34 million patients discharges from 27 states, which contained data for all discharges from community hospitals from 2010 to 2014. In the study, patient attributes include age and an indicator for female, patient socioeconomic status, Elixhauser mortality index, and patient location. Further, the study utilized multiple mixtures of treatment and control groups and triple difference (DDD) methods to test the robustness of the results. The authors used probability sampling method because the sample was large, thus allowing an in-depth analysis.

VI. Critique of the Data Collection Instruments

The researchers used a Quasi-experiment to collect data from secondary data sources. The secondary data sources include the Nationwide Readmissions Database. The authors were able to compare the treatment group and control group in the readmission rates with their post-HRRP difference. As a result, the authors to identify two control or comparison groups to be used for the study. This instrument was appropriate for the study.

VII. Critique of the Reliability and Validity

In their study, the researcher used researcher-designed research instruments, which enabled them to compare the pre-HRRP and post-HRRP of the treatment and control groups. The researchers created the Elichausr mortality index score for each patient’s zip code of residence. The authors identified reliability and validity levels of the instruments prior to use by carrying out several robustness checks. 

VIII. Approvals

This study did not require any form of approval, and as a result, did not go through IRB approval. On the issue of informed consent, the researchers collected data from the Healthcare Cost and Utilization Project (HCUP) database, which had already deleted all information that could be used to identify the research subjects. The deletion was done to protect the privacy of the participants and address the concept of informed consent. 

XI. Critique of the Data Collection

In the study, the authors collected data by searching through Nationwide Readmissions Database from the period 2010 to 2014, before and after the launch of HRRP. In this database, the authors included dataset regarding all discharges from community hospitals in the selected 27 states. However, the authors excluded rehabilitation or long-term acute care health care facilities. 

X. Critique of the Data Analysis

The authors used Stat 15 MP software during the analysis. The authors primarily use descriptive and trend analyses to analyze the collected data. In this study, all the standard errors were robust and clustered at the hospital level for every year. The results were displayed in the form of charts and tables.

 XI. Critique of the Integrity/Human Subjects Protection    

In the study, the authors ensured the protection of human subjects by ensuring that the data obtained from the Healthcare Cost and Utilization Project (HCUP) had no identifying information for all those included in the study to protect their privacy. The authors also took a step to maintain the anonymity and confidentiality of the subjects by removing all identifying information of patients, physicians, and hospitals that participated in the study.             

XII. Critique of the Implications, Recommendations, and Conclusions

The findings from the study have several implications. For instance, it is noted that there is a possibility for hospitals situated in high-income regions to serve a more significant percentage of low-income patients. Besides, based on the results of the analysis, the study sheds light on the impact of HRRP across various hospitals and patient populations, especially considering the sizeable national dataset and the differences in outcomes for the multiple hospitals or patients when it comes to measures of socioeconomic and health status. Besides, this study has implications for both future research and public health policy because, for future research, improved sources of data can provide further tests and enhance the conclusions. Besides, the HRRP addresses a significant issue in the healthcare sector by incorporating Pay-for-Performance principles considering that it hardly negatively affects vulnerable populations. 

Based on the study findings, it is recommended policymakers should analyze why HRRP works better for some conditions and population groups and not others for better outcomes. Besides, adjustment is recommended when it comes to long-term implementation of the HRRP to prevent adverse consequences for vulnerable populations. Overall, the findings in this study indicated a decline in the rates of readmissions across the board, which is a good sign that HRRP is a useful tool for preventing readmission from surgical infection after cardiac surgery. Besides, the study conforms with the previous studies suggesting that HRRP plays the role it was designed to perform. However, the researcher cannot generalize the findings of the study to the target population due to a difference in patients’ attributes. These findings are significant to nursing because they can help policymakers enact policies that may positively impact the health status of the vulnerable population and improve outcomes.

References

Gai, Y., & Pachamanova, D. (2019). Impact of the Medicare hospital readmissions reduction program on vulnerable populations. BMC health services research, 19(1), 837.

Karichu, J. K. (2017). Assessment of Variability in Hospital Readmissions Among Medicare Beneficiaries in the United States (Doctoral dissertation, Kent State University).

Navia, D. (2016). 30-day readmission score after cardiac surgery. Clinical Trials and Regulatory Science in Cardiology, 20, 1-5.

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