Critical Analysis on Prevention of CHF Readmission

Critical Analysis on Prevention of CHF Readmission.

Critical Analysis on Prevention of CHF Readmission

Abstract

The critical research review looks into a research conducted to test the effectiveness of health information technology in the prevention of readmission of congestive heart failure patients within 30 days. The critique checks on the effectiveness of the research problem, the objectives, data collection and methodological approach used and their conformity to the intentions of the study. The research proves to be effective in achieving its goals since the researcher observes the criteria of a quality research. The BG/EG Hurdle Model is expected to be effective in making predictions that can be used to draw patterns of patients with congestive heart failure. The data collection method and tools are effective in making a comprehensive and operational research.

Keywords

Health information technology (HIT)

Congestive Heart Failure (CHF)

Beta-Geometric/Erlang-2 Gamma (BG/EG) Hurdle Model

Computerized provider order entry (CPOE)

 

Critical Analysis of a Research on Prevention Congestive Heart Failure Patients Readmission

Readmission of patients with congestive heart failure within 30 days has been a major issue in the USA. In fact, preventable readmission costs the health care system over $25 billion annually (Zheng, 2015). The cost of congestive heart failure readmission is also enormous and a huge financial burden for patients with this condition. Therefore, it is important to look for means of mitigating the readmissions within the 30 days of discharge. The best way to alleviate the propensity of patient readmission is to find a model that adequately and effectively caters for the needs of the patients (Zheng, 2015).

Study Background

Here is a critical review of research that was conducted and proved effective in mitigating the rate of patient readmission within 30 days. The report is called “A Predictive Model for Readmission of Patients with Congestive Heart Failure: A Multi-hospital Perspective.” The problem statement of the research was to examine whether adoption of health information technology (HIT) is associated with a reduction in the readmission risk of CHF patients (Campbell et al., 2006). It is true that technology is fast developing and improving efficiency and output in different sectors. Therefore, the research problem developed by the research resonates with the need to look for an effective means of dealing with readmission problems for congestive heart failure patients. The research problem is feasible since technology has also been massively adopted in the healthcare system.

The researcher argues that health information technology has only been used to test efficiency in operations and output. However, the HIT can be used to counter patient readmissions in hospitals. The research is complex in nature since it involves testing a technological model that has not been tested there before. Despite being a complex model, it is within the nursing profession, and it covers the scope of aspects that are researchable. The research involves the adoption of a model called “Beta-Geometric/Erlang-2 Gamma (BG/EG) Hurdle Model” which is used to predict frequency, timing and exposure to readmission of patients diagnosed with congestive heart failure.

From the predictions, the researcher will be able to develop a preventable prediction model to mitigate readmission incidents. The researcher argues that use of other IT models such as computerized provider order entry (CPOE) have had a significant impact on the efficiency of the health system. Conversely, in another study by Campbell et al. (2006), the scientists noted that adoption of CPOE led to increased mortality rate. Therefore, it is not obvious that adoption of the Beta-Geometric/Erlang-2 Gamma (BG/EG) Hurdle Model will automatically lead to a reduction in readmission problems for CHF patients (Campbell et al., 2006). The research is not limited in scope since it involves a longitudinal panel of patients with readmission problems from various hospitals in Texas. The sample is big enough to provide reliable data. It is also a success since the data adopted is obtained from electronically stored data from various hospitals.

Objectives of the Research

The objectives of the research are clearly outlined as they clearly resonate with the research problem. Despite being clear and consistent with the research problem, the researcher deviates from the objectives by engaging in too much literature review rather than fulfilling the objectives of the research. In fact, the objective of the research is to determine how proper profiling of patients information can help in reducing preventable patient readmission.

Hypothesis

Since improved prediction is a foundational step towards mitigating future readmissions: How can Beta-Geometric/Erlang-2 Gamma (BG/EG) Hurdle Model serve as an integral component of a healthcare analytics system and predict and take preventive actions on patients with high readmission risk (Zheng, 2015)? The hypothesis is clearly stated, and it is consistent with the objectives of the research. It seeks to find how the model adopted can help in profiling of information and use the information in predicting possible patients’ readmissions by using common predictions outcomes to develop dependable preventive measures. In addition, the hypothesis helps to test how propensity, time, and frequency of readmission are related to medical practices. From that relationship, medical practitioners can adequately cater for the needs of the patients effectively.

Conceptual Framework

Moreover, the conceptual framework used in the research is well-grounded, and it encompasses different aspects to make the research complete and to ensure conformity to the research problem. An incisive introduction and background information on the study are provided to give a better understanding of the research and its objectives. Literature review, data collection methods, empirical analysis, development of the model and the findings and conclusions are well arranged. The development of the model helps to provide insight into how the model works to provide data needed for making predictions that can help to mitigate the incidents of readmissions. Despite being a new model, the researcher remains within the nursing scope to explain and to provide relevant accounts on how the model works.

Literature Review

The research is based on a new study that has not been researched by many scientists previously. For that reason, the literature review is premised on the overall impact of health information technology on the operational and productivity efficiency in the healthcare system (Hagland, 2011). That means that the findings of the research cannot be measured directly against other previous research on the same subject. Consequently, the effectiveness of the research is questionable since no other similar research on the impact of HIT on preventive patient readmission has ever been conducted (Hagland, 2011). Despite that, the quality of the research is high, and it helps to fill the gap in missing literature review on the same subject. The literature review is arranged in chronological order, and it provides a good flow towards achieving the objectives of the research.

Methodological Aspects

The methodological aspects used in the research are model development, empirical analysis, and review of existing literature. The model development method is sufficient in its role since it uses the gaps to explain the core of the issue, which is baseline model to develop the BG/EG Hurdle Model. Both the model development and empirical analysis work best in getting the intended results.

Data Collection Methods

The data collection method used was a longitudinal panel of congestive heart failure patients and the tool used was a master patient index where data of patients with cardiovascular and congestive heart failure was collected and analyzed from different hospitals in Texas. It was an effective method since it provided a large sample, thus enhancing the credibility of the results. Privacy of patients’ information was one of the ethical issues in the data collection process.

Effectiveness of the Model and Research

The BG/EG Hurdle Model was appropriate in data analysis as it helped to display a set of variables that other models could not reflect. In fact, the model can provide the frequency of readmission, time and likelihood of readmission and that information helps in making vital estimations and predictions. The model also incorporates patients’ demographics and their readmission risks.

Analysis of the Research

The model is successful in making predictive analytics that helps in understanding a patient’s readmission patterns. The predictive results of the BG/EG Hurdle Model are more reliable when compared to all other health care programs in the country. When medics can predict the propensity of a patient’s readmission, they can give the best health attention depending on needs of the patient (Hagland, 2011).

Conclusion

The research proves that BG/EG Hurdle Model is effective in providing important predictions about the patient readmission. In fact, it is possible to tackle and prevent issues that lead to readmission by analyzing the profile of a patient. Evidently, use of health information technology can solve readmission problems within 30 days of discharge with predictive prevention model.


 

References

Campbell, E.M, Sittig, J., Ash, K., Guappone, R., & Dykstra, H. (2006).Types of unintended consequences related to computerized provider order entry. Journal of the American Medical Informatics Association 13(5), 541-554.

Hagland, M. (2011). Mastering readmissions: Laying the foundation for change. Healthcare Informatics 28(4), 14.

Zheng, E. Z. (2015). A predictive model for readmission of patients with congestive heart failure: a multi-hospital perspective. Health Information Position 12(3), 1-39.

Figure 1.

Effectiveness of the B/EG readmission prediction

 

 

 

Length of stay (below 15 days)                                    Length of stay (in 15 days)

 

 

 

 

Effectiveness of the Readmission prediction

Figure 2.

Prediction Models against Effectiveness on a Scale of 5

Blue: BG/EG hurdle model     pink: Baseline model              Green: manual prediction

Category 1- below 1-month prediction

Category 2- over 1-month prediction

Category 3- over 2 month’s prediction

Category 4- over 3 month’s prediction

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Critical Analysis on Prevention of CHF Readmission

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