Research Critique
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Literature Evaluation Table
Summary of Clinical Issue (200-250 words):
The role of healthcare organizations is to ensure the health and safety of their patients. It is for this reason that healthcare industry regulators have put in place measures to reduce hospital readmission. The increasing amount of penalty healthcare providers have to pay for having high readmission rates has made it crucial for care providers to find effective and sustainable ways of reducing patient readmission. According to Greysen (et al. 2015), reducing hospital readmission requires healthcare providers to focus on various factors that affect patient health and the interventions undertaken to overcome the health issues and challenges they are facing. These include patient education, timely appointments for outpatients, and providing patient follow up services. It is, therefore, crucial for healthcare providers to ensure that the necessary resources are availed to address the factors identified to overcome the challenge of hospital readmission.
According to Ziaeian & Fonarow 2016, heart failure is one of the leading healthcare challenges that does not only increase patient hospitalization but also their readmission. For this reason, they argue that is it imperative for healthcare providers to have the ability to monitor and predict readmission to put measures in place to prevent them from happening. Wasfy et al. (2017) argue that the passing of the Hospital Readmission Reduction Program developed by the Affordable Care Act in 2012 would have a significant effect on hospital readmission rates. Focusing on the interventions identified above provides an opportunity for healthcare providers to improve the effectiveness and efficiency of the healthcare process to reduce readmission among all patient categories. It is also imperative to point out that there are those health conditions that expose patients to higher risks of readmission, such as cancer and health disease.
Can patient specific education for heart failure and cancer help them to utilize intervention focused reduce hospital readmission rates within 30 days of discharge as opposed those patients who do not receive the specific education?
P = Patients educated and those not educated on special education on heart disease and cancer.
I = Interventions for improving post discharge health improvement and care outcomes.
C = Communication between the nurses/healthcare professionals and the patients.
O = Increased support for post discharge preventive interventions by healthcare providers
T = Evaluating readmission rates within 30 days of discharge.
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Criteria |
Article 1 |
Article 2 |
Article 3 |
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APA-Formatted Article Citation with Permalink |
Kassin, M. T., Owen, R. M., Perez, S. D., Leeds, I., Cox, J. C., Schnier, K., ... & Sweeney, J. F. (2012). Risk factors for 30-day hospital readmission among general surgery patients. Journal of the American College of Surgeons, 215(3), 322-330. Sourced from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3423490/ |
Branowicki, P. M., Vessey, J. A., Graham, D. A., McCabe, M. A., Clapp, A. L., Blaine, K., ... & Chiang, V. W. (2017). Meta-analysis of clinical trials that evaluate the effectiveness of hospital-initiated postdischarge interventions on hospital readmission. The Journal for Healthcare Quality (JHQ), 39(6), 354-366. Sourced from: https://insights.ovid.com/pubmed?pmid=27631713 |
Barnett, M. L., Hsu, J., & McWilliams, J. M. (2015). Patient characteristics and differences in hospital readmission rates. JAMA internal medicine, 175(11), 1803-1812. Sourced from: https://jamanetwork.com/journals/jamainternalmedicine/fullarticle/2434813 |
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How Does the Article Relate to the PICOT Question? |
The study investigates factors that result to readmission for patients who have undergone general surgical procedures |
To evaluate the effectiveness of hospital-initiated post discharge interventions to reduce readmission |
The study evaluates the characteristics of patients and differences the rates of hospital readmission |
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Quantitative, Qualitative (How do you know?) It is difficult to assess this without the link to the articles. |
Quantitative design- Univariate and Multivariate analysis was utilized in the identification of the risk factors. |
Qualitative design – Involves the analysis of 20 published articles on HiPDI |
Quantitative design – Involved the statistical analysis of data from 2000-2010 biennial waves |
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Purpose Statement |
The purpose of the study is to investigate factors that are associated with hospital readmission within 30 days for general surgical procedures. |
To evaluate the effectiveness of hospital-initiated post discharge interventions |
Does patient characteristics account for the difference in readmission rates among different healthcare providers |
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Research Question |
Are the 30-days risk factors the major contributors to hospital readmission for general surgery patients? |
Are hospital-initiated post discharge interventions effective enough in reducing readmission rates? |
The aim of the study to investigate the extent to which patient characteristics influence readmission among various care providers. |
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Outcome |
Most common reason for readmission includes: 1. Surgical infection 2. Gastrointestinal problem 3. Malnutrition Surgical procedures with higher rates of readmission: 1. Liver resection 2. Pancreatomy and 3. Colectomy Occurrences leading to increased risk of readmission: 1. Post-operative pulmonary complications 2. Blood transfusion 3. Wound Complication
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Standardized protocol was used to identify articles to be reviewed. 503 abstracts identified from the search. 20 articles chosen for meta-analysis. |
1. Study included 33 158 index admissions from 2000 to 2012 for 8767 beneficiaries of Medicare 2. There were similar associations between patients’ characteristics and hospital readmission |
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Setting (Where did the study take place?) |
Within healthcare setting focusing on general surgery |
Systematic review of clinical trials published in the past 25 years. |
The study was conducted in a healthcare setting focusing on patients enrolled in Medicare. |
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Sample |
1442 general surgery patients |
The articles reviewed those from January 1, 1990–June 30, 2014 |
Study included HRS survey respondents The sample was limited to only those participants who were hospitalized within the specific period of focus. |
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Method |
Utilized the National Surgical Quality Improvement Project Protocol |
Meta-analysis of aggregated data utilizing randomized controlled trials. |
Patient admission analyzed between two subsequent years. |
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Key Findings of the Study |
1. 11.3% of the patients readmitted within 30 days. 2. Post-operative complications are the most significant risk factors for readmission |
Most common interventions: 1. Home visits 2. Follow up calls HiPDI interventions reduce readmission rates |
1. Many patient characteristics are not included in the adjustment of patient readmission rates. This could potentially increase the rate for many care providers. 2. Clinical and social predictors can influence readmission rates especially for those providers who are publicly known for having higher readmission rates. |
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Recommendations of the Researcher |
Focusing on interventions to reduce post-operative complications will reduce the associated readmission rates. |
1. Healthcare providers should utilize HiPDI interventions to reduce the likelihood of readmission for their discharged patients. 2. Investing in quality improvement for the healthcare professionals responsible for the post discharge care |
Medicare should not penalize healthcare providers on readmission rates based on the patients severed but rather focusing on the specific characteristics of the patients and the health condition. |
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Criteria |
Article 4 |
Article 5 |
Article 6 |
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APA-Formatted Article Citation with Permalink |
Krumholz, H. M., Wang, K., Lin, Z., Dharmarajan, K., Horwitz, L. I., Ross, J. S., ... & Normand, S. L. T. (2017). Hospital-readmission risk—isolating hospital effects from patient effects. New England Journal of Medicine, 377(11), 1055-1064. Soured from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5671772/ |
Aman, M. W., Stem, M., Schweitzer, M. A., Magnuson, T. H., & Lidor, A. O. (2016). Early hospital readmission after bariatric surgery. Surgical endoscopy, 30(6), 2231-2238. Sourced from: https://link.springer.com/article/10.1007%2Fs00464-015-4483-4
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Nuckols, T. K., Keeler, E., Morton, S., Anderson, L., Doyle, B. J., Pevnick, J., ... & Shekelle, P. (2017). Economic evaluation of quality improvement interventions designed to prevent hospital readmission: a systematic review and meta-analysis. JAMA internal medicine, 177(7), 975-985. Sourced from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5710454/ |
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How Does the Article Relate to the PICOT Question? |
It focuses on isolating hospital and patient effects that promote readmission among healthcare providers. |
The study is focused on the impact of reducing early readmission rates on the success of healthcare organization. |
The study evaluates the effectiveness of quality improvement interventions in reducing hospital readmission. |
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Quantitative, Qualitative (How do you know?) Hard to assess this without the link to the articles. |
Quantitative design – Performance quartiles were utilized to examine readmission rates. |
Quantitative design – A multi-variable logic regression analysis method was utilized to evaluate the relationship between readmission rates and patient factors. |
Qualitative design – Risk differences and nets costs were calculated from the data sources. |
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Purpose Statement |
To examine readmission outcomes for patients with multiple readmission for common health illnesses and diagnosis at more than one hospital within a single year. |
The purpose of the study is identification of incidents, risk factors and reasons for early hospital readmission. |
The aim of the study is to systematically review the economic evaluation of quality improvement initiatives and interventions focused on reducing hospital readmission. |
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Research Question |
Do risk standardized hospital readmission rates affect readmission outcomes for patients with the same diagnosis in the same hospital? |
Do the incidents, reasons and risk factors of early hospital readmission for bariatric surgery increase readmission rates? |
Can economic evaluations of quality improvement interventions provide insights for reducing hospital readmission rates? |
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Outcome |
Various hospitals with common diagnoses experienced varied readmission rates. Consistency in readmission rates observed among patients with common health diagnosis |
The American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) data set was used in the analysis utilizing the data set of 2012-2013. |
50 studies out of the 5,205 articles were eligible. 25 studies were focused on health issues associated with heart failure. |
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Setting (Where did the study take place?) |
The study was conducted in a healthcare setting. |
The study was conducted within a healthcare setting with a particular focus on the patients of batriatric surgical procedures. |
The study focused on the review of past articles and studies to provide answers to the research questions. The various databases searched include, Econlit, PubMed, the Centre for Reviews & Dissemination Economic Evaluations, Worldcat and New York Academy of Medicine's Grey Literature Report (January 2004 to July 2016) |
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Sample |
Study sample included 37,508 patients. A total of 4272 hospitals were covered |
36042 patients were identified within the healthcare organization for the study. |
The study included 10445 participants from the general public. 15 studies lasted up to 30 days. Others lasted 24 months. |
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Method |
Risk standardized readmission within 30 days was calculated. Hospital performance divided into quartiles
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Identification of patient with particular diagnosis e.g. diabetes Defining at least one hospitalization for patients within 30 days. |
The study involved a dual review of selected English language studies and reporting the findings relating to hospital readmission and the related costs. |
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Key Findings of the Study |
Readmission rates were higher for patients seeking care services in healthcare providers in the worst performing quartile. Significant difference observed between the readmission rates for patients in the best performing and the worst performing quartiles. |
1. Overall early hospital readmission rate was 4.70%. 2. The rate decreased from 2012-2013 (5.15 vs. 4.32 %, p < 0.001) 3. The median age was 44 years 4. Median BMI was 44.7 kg/m2. 5. Nausea and vomiting were the most common reason for early readmission. |
1. Readmission declined by 12.01% among patients with heart failure 2. Mean net saving to the healthcare system per patient was $972 for heart failure patients. 3. Greater net savings were realized for interventions that focused on engagement between patients and caregivers. |
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Recommendations of the Researcher |
Healthcare providers should strive to increase the quality of their healthcare services as a means of improving their performance thus realizing the desired positive healthcare outcomes while reducing readmission rates within 30 days. |
Standard protocols should be implemented for outpatients in batriatric centers and surgeries to enable the monitoring of patients to identify potential risks for readmission. |
Multiple quality improvement interventions should be utilized to reduce hospital readmission among heart failure patients. Greater value in the form of positive outcomes and reduced costs can be realized by increased patient-healthcare professional engagement. |
References
Greysen, S. R., Cenzer, I. S., Auerbach, A. D., & Covinsky, K. E. (2015). Functional impairment and hospital readmission in Medicare seniors. JAMA internal medicine, 175(4), 559-565.
Wasfy, J. H., Zigler, C. M., Choirat, C., Wang, Y., Dominici, F., & Yeh, R. W. (2017). Readmission rates after passage of the hospital readmissions reduction program: a pre–post analysis. Annals of internal medicine, 166(5), 324-331.
Ziaeian, B., & Fonarow, G. C. (2016). The prevention of hospital readmissions in heart failure. Progress in cardiovascular diseases, 58(4), 379-385.
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