Table of Contents
The current readmission metric that charges financial penalties to hospitals assumes that readmissions occur because of poor quality care. The study proposes to consider other factors such as poverty levels of patients that are readmitted, often in cases where the reason for the readmission is outside the realm of the hospital. More so, the study proposes moving away from the current readmission metric that charges financial penalties to hospitals, with the perception that readmissions result from poor quality provided by hospitals. The study intends to use a qualitative approach to understand what other socioeconomic factors are likely to affect the relationship.
Background of the problem
The national dialogue on the ability to reduce avoidable hospital readmissions is based on the understanding of what readmission signifies. Other considerations include the failures implied by readmission, and if patients are not receiving the necessary support when they are admitted. According to Epstein et al. (2011), patients leave hospital institutions vulnerable, because of many challenges linked to their recovery, despite many being unrelated to the initial diagnosis. Kangovi et al. (2015) assert that despite hospitalization not being harmless, it disrupts routine, causes stress, hinders sleep, and causes confusion, increasing chances of contracting a hospital-acquired illness. In this time and age, the traditional imperative to get patients out of the hospital no longer suffice. The understanding that hospitals have a role to play when it comes to preventing readmission has led to the emergence of new interventions to enhance transition within the care continuum (Van et al., 2010).
According to Axon and Williams (2011), Congress realized that current fee-for-service payment applications are unable to enhance improvement. Congress, therefore, included the Hospital Readmission Reduction Program through the Affordable Care Act. Congress passed a Medicare policy to penalize hospitals that recorded excess readmissions to encourage them to concentrate on care transitions. The strategy has ensured that every hospital works towards improving care coordination (Ng et al., 2007). Implementing readmission metrics has garnered mixed reactions, with hospitals, academicians and policymakers debating on the effectiveness of the readmission metrics. It is apparent that many people disagree on the value the program offers. To understand the imperative nature of the program, “Commonwealth Fund and Institute for Healthcare Improvement” convened a meeting of the leading country’s health experts to discuss measurements and improvement of hospital readmission.
The meeting participants indicated that Medicare needs to address the fragmented care, as well as the harm and confusion, which avoidable hospital readmissions cause for patients. According to Van et al. (2010), many health stakeholders have interpreted academic reviews on skirmishes associated with readmission penalty as a disagreement over whether Medicare using payment policy or any other means encourage increased coordination of patient care, as well as risk mitigation for patients with high risk of readmission. The most significant issue is how Medicare approaches the whole process, thus the need to analyze reasons as to why patients are readmitted. Joynt and Jha (2013) explain that the first developed policy has many flaws, but the significant step includes the redoubling effort to enhance measures and the incentive system. The efforts start by refocusing on the need to measure readmissions.
Axon and Williams (2011) believe that emphasis should be on targeting to improve the poorly coordinated care, which exposes many patients, as well as families, which means that they cannot access help. The patients and caregivers have no idea how to take care of themselves after discharge, hence the high risk of infections and harm, leading to readmission. Hu et al. (2014) emphasizes that the current readmission metric is an imperfect proxy, because of the inability to offer a valuable foundation to develop an enhance policy. It is necessary to build an enhanced policy, useful in sustaining improvement, ensuring accountability and relevant to the affected patients.
The most important factor to consider is the poverty levels of readmitting patients. Poverty levels of a neighborhood, of a patient living alone and across age groups determine the chances of readmission after discharge. Despite possible variation in quality of care in hospitals, with institutions that take good care of the patients, after discharge, many scenarios may cause readmission. Hu et al. (2014) established links between social factors and readmission, including marital status of patients and neighborhood poverty, showing that readmission goes beyond hospital quality. The current readmission metric using financial penalties charged to hospitals assume that readmissions occur because of poor quality care. For instance, patients living in neighborhoods with high poverty levels are 24% likely to be readmitted (Hu et al., 2014). More so, Hu (2014) adds that the process from poverty to readmission is complex and varies from one patient to another. Without doubt, things happen after a patient is discharged from hospital and sent home to the entire community, putting some at a high risk of readmission. Benbassat and Taragin (2000) indicated that it is wrong to use the readmission data to measure hospital quality.
As indicated above, the main issue includes the content of Affordable Care Act, which indicated that hospitals are to blame for increased readmission. The “Centers for Medicare and Medicaid Services” (CMS) started a program to reduce payments made to hospitals in case there were “excess” 30-day readmissions. Most studies indicate that readmission is results from numerous complex factors, with the quality of care in hospitals being just one of the many factors (Van et al., 2010; Ng et al., 2007; Kangovi et al., 2013; Hu et al., 2014). Many of the studies utilized data from many hospitals, with many difficulties of separating variable factors such as poverty from variation effects in hospital quality care for patients earning low incomes (Hu et al., 2014; Arbage et al., 2008). Other studies indicated factors linked only to hospital-specific challenges to be associated with readmission. Such factors include staffing, hospital organizational structure, discharge planning procedures and the roles the hospitals play in an integrated care system (Hu et al., 2014).
Hu (2014) examined issues making use of data from Henry Ford Hospital in Detroit to establish the effects of socioeconomic condition of patients under one fixed methodology of organizational and staffing structure, as well quality standard care patient protocols for patients depending on their financial ability. The study specifically used hospital data bank of Medicare fee-for-service patients with sixty-five years of age and above who were discharged from hospital in 2010 (Hu, 2014). The study excluded patients who died in the hospital, who were discharged against doctor’s advice and who were hospitalized for specialized ailments. In another study, Kangovi et al. (2013) used in-house data to establish the age, gender, marital status, home address, race and diagnosis. The researchers went ahead to map patients’ addresses to the census data to find their neighborhood socioeconomic factors. The socioeconomic factors used included the percentage of families that have incomes below the stated federal poverty level. Others factors included median domestic income, a percentage of population between 25 years and above and education levels (Kangovi et al., 2013). The study established that while there were many black patients, the mean age was 77. In Hu et al.’s (2014) study, only 30% of students analyzed had a high school diploma, while 17% families had income below poverty level, with median domestic income being $38,000. Philbin et al. (2001) established that married patients were less likely to be readmitted because of the social support they received at home compared to the unmarried ones. More so, older, and specifically male patients, were likely to experience readmission compared to younger and female patients (Van et al., 2010). Conventionally, discharged patients with heart conditions, acute myocardial among other special ailments were at a high risk of being readmitted than patients not suffering from such diseases.
From the findings, it is clear that social factors at patient and community level were not mystified with the variations in hospitals depending on their resources and infrastructure. It is important for new studies to contribute to ongoing debates on possible refinements of CMS readmissions measure when it comes to hospital reimbursement. According to Hu et al. (2014), it is time to reevaluate other factors that cause readmission, particularly socio-economic factors, to avoid punishing hospitals unnecessarily for poor quality, when there are other unaddressed issues in play. Whether hospitals must remain accountable for patient poverty levels, illiteracy, poor English proficiency, or poor social support with families and communities, there is need to address all the issues in a wholesome way, which should entail involving all stakeholders.
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Purpose of Study
According to Axon and Williams (2011), when establishing the relationship between social factors and readmission levels, the health sector needs to enhance poorly coordinated care. In some instances, patients and caregivers lack the resources and ability to take care of themselves after discharge, thereby leading to increased rates of readmission. The study, therefore, proposes to establish inefficiencies associated with the current readmission metric, and instigates changes that can enhance its performance. Instead of punishing hospitals for things beyond their control, Medicare stakeholders can strategize on how hospitals can coordinate with patients, caregivers and even communities after patients are released from hospital (Hu et al., 2014). The study, therefore, proposes to improve on current Medicare policy through sustainable development and enhanced accountability in the offering of the necessary care to patients based on their relevancy.
The study proposes to consider poverty levels of patients that are readmitted in hospitals often, especially in cases where it is obvious that the hospitals are not to blame for the readmissions. More so, the study proposes moving away from the current readmission metric that charges financial penalties to hospitals, with the perception that readmissions are result from poor quality provided by hospitals.
Review of Related Literature
Current Readmission Rates Context
According to Berwick et al. (2008), before utilizing the readmission rate as a measure of medical quality care, it is necessary to define the context upon which the indicator will be applied. It is necessary for the readmission rate to improve quality rather than just focus primarily on cost. Another important specification includes the clinical processes used in measuring the quality of care. Currently, health stakeholders use readmission rates to measure quality provided in hospitals, meaning readmission level is established by the type of care provided during the first admission in hospital. It is a wrong conception because a study conducted by Hu et al. (2014) show that the conditions to which patients are exposed to after discharge, such as patients’ ability to manage the medical condition and social networks also influence the likelihood of readmission. It is, therefore, necessary for hospitals to pay much attention to enhancing transitional care. An example includes educating patients in preparation for discharge, as well as coordinating outpatient follow-up.
Despite such a transition being essential, it is important to note that the real post-discharge period is beyond the reach of hospital institutions. For instance, readmission for chronic conditions such as heart disease is beyond any hospital’s control. Such patients are readmitted because of the severe condition of their disease, creating the need to be treated by a general practitioner, irrespective of the kind of quality care one received during the previous admission. It, therefore, means that quality of the provided care procedures indicated in readmission rates go beyond the in-hospital care. Without doubt, using readmission rates as the only quality measures needs clarity, such as rationale used to measure readmission in relation to care processes provided and the type of patients being readmitted.
Types of Readmission
American Hospital Association developed a framework after intensive consultation with clinicians to help health providers and policymakers focus on various types of readmission. Without doubt, it is possible to avoid certain readmission by offering right care during the admission. In some cases, other readmissions are unavoidable because of the type of condition a patient suffers from, hence the need to abide by a certain treatment protocol. Benbassat and Taragin (2000) assert that some readmissions occur as a planned treatment timetable, thus the need to identify both the different types and how to handle them regarding providing the best care for the patients. The framework is essential in aiding policy makers to reduce avoidable readmissions.
The type of readmissions includes planned readmission linked to initial admission, where the readmission includes an example of a series of chemotherapy treatments, reconstructive surgeries among much more. The second one includes planned readmission not related to initial admission, which includes readmission for special treatments such as removal of a tumor discovered when the patient was admitted for another condition (Benbassat & Taragin, 2000). The third type includes unplanned readmission that has no links to the first admission, such as one caused by a sustained car accident causing fractures, having been admitted for chest pains before. According to Epstein et al. (2011), the last type includes unplanned readmission linked to the initial one, such as when a patient contracts infection on a surgical area or when a patient reacts adversely to medication.
It is the last type of admissions that are related to the study, which is unplanned but linked to initial admissions. The type of admission helps in identifying targets to reduce initial admission, which AHA concentrates on, ensuring the public policy improvements help to eliminate readmissions (Joynt & Jha, 2013). Kansagara et al. (2011) believe that hospitals have no influence on the occurrence of unplanned and unrelated admissions because of their unpredictability and lack of power to prevent them. Similarly, it is not the responsibility of hospitals to eliminate planned readmission, because they form part of the entire treatment package and plans (Berwick et al., 2008). For instance, clinical guidelines on implantation of the “implantable cardiac defibrillator” (ICD) forbid implantation within forty days of a myocardial infarction to eliminate chances of cardiac arrest. It means that a patient taken in for a heart attack needs readmission for the ICD implantation. Another example includes a patient undergoing chemotherapy, with the need for readmission to undergo all chemotherapy stages. It is wrong to use such repeat admissions in measuring the quality care provided by hospitals because they are unavoidable.
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Complexity of Relationship between Readmission and Quality of Care
The AHA framework focuses on the unplanned readmission linked to the initial admission. It could show a lapse in offering the right care at the right time when the patient was admitted. Most Medicare payers, however, use readmission rates together with mortality rates as indicators of quality healthcare given to patients during admission. Conservative assumptions that higher mortality and readmission rates within the first three days after hospital discharge indicate poor quality. Nevertheless, evidence shows that mortality and readmission share an inverse relationship (Hu et al., 2014). Such findings lead to one questioning the assumption that reduced readmission rates remain desirable.
Role of Patient characteristics in Readmission
A patient’s socioeconomic conditions such as low income or ineffective social support, as well as individual features such as disability or co-morbid conditions, represent imperative factors determining readmission in hospitals. The methodology used to measure the related risk, as well as calculate readmission rates for conditions such as heart failure and diabetes should not be used as a determinant factor. It means that the new financial hospital penalties have no ability to make proper accommodation for the life of patients and the circumstances leading to readmission.
According to Rathore et al. (2006), patients suffering from co-morbidities face a high risk of readmission. Ho et al. (2014) established in their study that the higher number of chronic illness every patient suffers, the more his or her chances of readmission increases. Similarly, children patients faced high readmission rates because of increased prevalence of using assistive technology, with an example of gastronomy tube, hence the need for readmission. The most common chronic conditions leading to readmission include heart failure, renal condition, cancer, anemia and obesity. The same applies to Medicare beneficiaries suffering from an end-stage renal condition causing readmission within 30 days.
According to Hernandez et al. (2010), psychological circumstances like depression can reduce recovery rates, thereby increasing readmission likelihood. After discharge, following admission for a heart condition, a patient is likely to suffer from depression, leading to readmission within the first six months (Epstein et al., 2011). The relationship between readmission and depression does not only affect patients having cardiac conditions but other sicknesses as well.
Demographic factors such as race, age, gender, location and Medicaid coverage affects the risk of patient readmission. Many studies have lacked consensus on the most significant predictive factors, but patients with high rates of Co-morbidities Medicare face a high risk of readmission in comparison to the privately insured adult patients. Hu et al. (2014) established that racial factors, as well as ethnicity, were a major predictor for readmissions, but it has been difficult to discover how the race issue plays out. Berwick et al. (2008) established that African American patients face a high readmission risks in comparison to patients from other races. Similarly, other minority patients, apart from African Americans face a high readmission risk (Epstein et al., 2011). Arbaje et al. (2008) analyzed if readmission rate disparities are attributed to a certain race, or site of health care, because minorities care occurs in smaller hospitals. Black Medicare patients face high readmission rates compared to their white counterparts, as well as patients from minority-serving health institutions.
Another one includes language barriers that cause high readmission rates when caregivers and patients fail to comprehend the diagnosis and discharge medical guide and instructions. Hernandez et al. (2010) established that Latino and Chinese patients have a high chance of readmission compared to English speakers. More so, income status of discharged patients also plays a role, with patients that have low median income experiencing high readmission rates compared to those living in countries with high median income (Joynt & Jha, 2013).
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Based on the above literature the study formulated the following research questions: –
- Is there any empirical relationship between socioeconomic factors and readmission risks?
- Do certain hospitals offer treatment disproportionately to patients having high readmission risk?
- Should hospitals take responsibility to solve socioeconomic disparities to reduce the risk of readmission, and if so up to what level?
The researcher proposes to use a prospective design to analyze the research questions of the study. Polit and Beck (2004) assert that a prospective design affords a researcher a chance to analyze existing associations and interrelationships among the selected study variables within a specified period. Additionally, the research design shall be suitable because it measures factors before an outcome occurs. The study proposes to collect data through a qualitative study by conducting interviews with eligible participants using their medical records. The study intends to select Detroit Medical Center and Henry Ford Hospital.
The study intends to use 30-day hospital readmission, as well as the length of patient stay in the hospital as the outcome variables. Readmission to the hospital within the first month will be utilized as a dichotomous variable to establish why a person returns to the hospital through readmission. The dichotomous variable is the most effective to enhance meaningful data, in comparison to readmission frequencies. The measurement methodology shall help the researcher conduct a comparison of differences in demographics, as well as disease features.
The selected sample to be used in the study includes patients admitted for general diseases, as well as those admitted for chronic conditions. The study intends to select a sample size calculated relying on a minimum ration of observation, as well as independent variables. The independent variables were selected from the literature review.
The study instruments to be used in the qualitative study include demographic forms from Detroit Medical Center and Henry Ford Hospital, hospital depression scale and the Chronic Disease Self-Management Questionnaire. The study also intends to interview hospital management and doctors to ascertain the data collected from the instruments.
Data Collection Procedure
The researcher intends to meet the Head Nurse daily during the time of the study, where the Head Nurse shall identify patients that qualify to become potential participants. The researcher shall then approach the patients after explaining the aim and benefits of the study.
Recommendations and Summary
The reviewed literature indicates that readmission results from numerous complex factors, with the quality of care in hospitals being just one of the many factors (Van et al., 2010; Ng et al., 2007; Kangovi et al., 2013; Hu et al., 2014). Many of the studies utilized data from many hospitals, with many difficulties of separating variable factors such as poverty from variation effects in hospital quality care for patients earning low incomes (Hu et al., 2014; Arbage et al., 2008). Other studies indicated factors only linked to hospital-specific challenges to be associated with readmission. Such factors include staffing, hospital organizational structure, discharge planning procedures and the roles the hospitals play in an integrated care system (Hu et al., 2014). This study, however, intends to establish the relationship between socio-economic factors and readmission through a qualitative study. The study intends to interview the head nurses at Detroit Medical Center and Henry Ford Hospital.
The study shall exclude patients who died in the hospital, patients who were discharged against doctor’s advice, and patients hospitalized for specialized ailments. More so, the study shall use similar variables as those used in the study of Kangovi et al. (2013) that used in-house data to establish the age, gender, marital status, home address, race and diagnosis. The researcher intends to map patients’ addresses to the census data to find their neighborhood socioeconomic factors. The socioeconomic factors used include the percentage of families that have incomes below the stated federal poverty level. It is related to the problem statement where the study intends to establish that readmission factors go beyond hospitals quality care to include other factors such as chronic conditions, demographic factors and depression.
The medical stakeholders should target to improve the poorly coordinated care, which exposes many patients, as well as families, meaning they cannot access help. The patients and caregivers have no idea how to take care of themselves after discharge, hence the high risk of infections and harm, leading to readmission. The current readmission metric is an imperfect proxy, because of the inability to offer a valuable foundation to develop an enhance policy. It is necessary to build an enhanced policy useful in sustaining improvement to ensure accountability and relevant to the affected patients.
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Hu, J., Gonsahn, M. D., & Nerenz, D. R. (2014). Socioeconomic status and readmissions: evidence from an urban teaching hospital. Health Affairs, 33(5), 778-785.
The article is essential for the research because the authors indicate clearly that Medicaid Services concentrate on readmissions within 30 days and the risk leading to such readmissions, which is the emphasis of the current study. The article focused on elements of individual features and socioeconomic status that affect readmission of patients as an important factor in the study. The study proposes to improve on current Medicare policy through sustainable development and enhanced accountability, in offering the necessary care to patients based on their relevancy, making the above article relevant to the study.
Rathore, S. S., Masoudi, F. A., Wang, Y., Curtis, J. P., Foody, J. M., Havranek, E. P., &Krumholz, H. M. (2006). Socioeconomic status, treatment, and outcomes among elderly patients hospitalised with heart failure: findings from the National Heart Failure Project. American heart journal, 152(2), 371-378.
The above article indicates that older, and specifically male patients, were likely to experience a readmission compared to young and women patients. Older patients are likely to suffer from a heart condition, and men lack ability to take good care of themselves compared to women. Such an establishment is relevant to the study because one of the variables in the study include demographic factors, where gender appears, hence necessary in helping the researcher establish that fact.
Epstein, A. M., Jha, A. K., &Orav, E. J. (2011). The relationship between hospital admission rates and rehospitalizations. New England Journal of Medicine, 365(24), 2287-2295.
Hospital strategies to reduce hospital readmission have for the longest time concentrated on enhancing transitional care. However, variation in admission rates reflects variation in readmission rates concerning quality care during and after discharge. The above is the main focus of the article, a factor that resonates well with the study. The article is, therefore, essential in helping the researcher establish a gap between this article and the study to ensure patients receive the best transactional care.
Polit, D. F., & Beck, C. T. (2004). Nursing research: Principles and methods. Lippincott Williams & Wilkins.
The above study intends to take a qualitative approach and particularly use a prospective design to analyze the aim and research questions of the study. The book, therefore, is relevant and shall help the researcher develop evidence of practice and enhance reliability and validity of the study.
Kangovi, S., Barg, F. K., Carter, T., Long, J. A., Shannon, R., & Grande, D. (2013). Understanding why patients of low socioeconomic status prefer hospitals over ambulatory care. Health Affairs, 32(7), 1196-1203
The research is about the relationship between socio-economic factors and readmission of patients, making this article necessary for the study. The article shall help the researcher in reviewing why low-earning patients face increased rates of readmission, and how policy initiatives developed by health officials is relevant.
- Axon, R. N., & Williams, M. V. (2011). Hospital readmission as an accountability measure. Jama, 305(5), 504-505.
- Arbaje, A. I., Wolff, J. L., Yu, Q., Powe, N. R., Anderson, G. F., &Boult, C. (2008). Postdischarge environmental and socioeconomic factors and the likelihood of early hospital readmission among community-dwelling Medicare beneficiaries. The Gerontologist, 48(4), 495-504.
- Benbassat, J., &Taragin, M. (2000). Hospital readmissions as a measure of quality of health care: advantages and limitations. Archives of internal medicine, 160(8), 1074-1081.
- Berwick, D. M., Nolan, T. W., & Whittington, J. (2008). The triple aim: care, health, and cost. Health Affairs, 27(3), 759-769.
- Epstein, A. M., Jha, A. K., &Orav, E. J. (2011). The relationship between hospital admission rates and rehospitalizations. New England Journal of Medicine, 365(24), 2287-2295.
- Hernandez, A. F., Greiner, M. A., Fonarow, G. C., Hammill, B. G., Heidenreich, P. A., Yancy, C. W., … & Curtis, L. H. (2010). Relationship between early physician follow-up and 30-day readmission among Medicare beneficiaries hospitalized for heart failure. Jama, 303(17), 1716-1722.
- Hu, J., Gonsahn, M. D., &Nerenz, D. R. (2014). Socioeconomic status and readmissions: evidence from an urban teaching hospital. Health Affairs, 33(5), 778-785.
- Joynt, K. E., &Jha, A. K. (2013). A path forward on Medicare readmissions. New England Journal of Medicine, 368(13), 1175-1177.
- Kangovi, S., Barg, F. K., Carter, T., Long, J. A., Shannon, R., & Grande, D. (2013). Understanding why patients of low socioeconomic status prefer hospitals over ambulatory care. Health Affairs, 32(7), 1196-1203.
- Kansagara, D., Englander, H., Salanitro, A., Kagen, D., Theobald, C., Freeman, M., &Kripalani, S. (2011). Risk prediction models for hospital readmission: a systematic review. Jama, 306(15), 1688-1698.
- Philbin, E. F., Dec, G. W., Jenkins, P. L., &DiSalvo, T. G. (2001). Socioeconomic status as an independent risk factor for hospital readmission for heart failure. The American journal of cardiology, 87(12), 1367-1371.
- Polit, D. F., & Beck, C. T. (2004). Nursing research: Principles and methods. Lippincott Williams & Wilkins.
- Ng, T. P., Niti, M., Tan, W. C., Cao, Z., Ong, K. C., &Eng, P. (2007). Depressive symptoms and chronic obstructive pulmonary disease: effect on mortality, hospital readmission, symptom burden, functional status, and quality of life. Archives of internal medicine, 167(1), 60-67.
- Rathore, S. S., Masoudi, F. A., Wang, Y., Curtis, J. P., Foody, J. M., Havranek, E. P., &Krumholz, H. M. (2006). Socioeconomic status, treatment, and outcomes among elderly patients hospitalized with heart failure: findings from the National Heart Failure Project. American heart journal, 152(2), 371-378.
- Van Walraven, C., Dhalla, I. A., Bell, C., Etchells, E., Stiell, I. G., Zarnke, K., …& Forster, A. J. (2010). Derivation and validation of an index to predict early death or unplanned readmission after discharge from hospital to the community. Canadian Medical Association Journal, 182(6), 551-557.