Emergency Room Readmissions: A Case Study

The overall process of discharging a patient from a hospital and the transition back home or to a care facility are critical advancements in the overall course of both acute and long-term care. It is important that the hospitals releasing these patients have ensured the proper overall course of care from beginning to end. The lack of consistency with both the discharge process and the quality of discharge planning has led to many avoidable readmissions. To reduce the amount of hospital readmissions, it is imperative that hospitals recognize the need for focused patient care and that programs are being implemented to assist in the care transition.

hospitals, psychiatric hospitals, and hospitals claiming “other specialty”. The criteria for the study were previous suicide attempts, drug abuse, and being admitted to a hospital for suicidality. Whether or not the hospitals conducted a mental health assessment was not a requirement for participation in the study, but this factor was considered.

Research and psychological studies show that suicidal behavior stems from at least one or more mental disorders that are treatable. Individuals with suicidal behaviors often feel hopeless which contributes to these behaviors and can lead to suicide attempts or succession. Recognizing these behaviors can save someone’s life, being compassionate, empathetic, and proactive can greatly reduce an individual’s suicide behavior. The goal is to recognize these behaviors and get help for these individuals quickly.

The National Institute of Mental Health (NIMH) has published a fact sheet of statistics on suicide in the United States. In 2007, it is reported that suicide was the tenth leading cause of death. Furthermore, for every suicide committed, eleven were attempted. A total of 34,598 deaths occurred from suicide with an overall rate of 11.3 suicide deaths per 100,000 people. (NIMH, 2010). Risk factors were also noted on this report and listed “depression and other mental disorders, or a substance abuse disorder (often in combination with other mental disorders). More than ninety percent of people who die by suicide have these risk factors (NIMH, 2010).”

The Affordable Care Act was enacted to improve health care and to lower health care cost in America. The ACA developed different strategies to meet these goals called the “pay for performance” programs. These strategies are aimed at the different providers to improve quality care. The strategy that I selected is the “Hospital Readmissions Reduction Program” this program/strategy is also known as the HRRP and was begun in October of 2012. HRRP is aimed at hospitals and penalizes hospitals that have a high 30 day readmission rate. The penalties are assessed and based on a number of comparisons, those such as, performance, patient demographics, comorbidities and frailty.

Readmission is defined as “an admission to subsection hospital within 30 days of a discharge from the same or another subsection hospital” for the same diagnosis or for a complication related to the initial diagnosis (American College of Emergency Physicians, 2015). According to the Robert Wood Johnson Foundation (2014) report, the annual cost of hospital readmission in the nation is about $41 billion. More importantly, hospital readmission disrupts patient’s life and decreases the quality of life (Padhukasahasram, Reddy, Li, & Lanfear, 2015). Consequently, due to the human and economic burden of hospital readmission, the Center for Medicare and Medicaid (CMS), in 2012 implemented penalty on hospitals that exhibit a high readmission for diagnoses of Heart Failure, Pneumonia and Septicemia. This action has resulted in the reduction of readmissions to hospitals specifically associated with

The good intentions to improve the quality of health care sparked financial initiatives such as pay for performance and quality bonus payments. These financially influenced methodologies do not come without unintended and ethical consequences.

A suicide assessment is essential for clients who have suicidal behavior (Ribeiro, Bodell, Hames, Hagan, & Joiner, 2013). A suicide risk assessment can be a vital step for prevention of suicidal behavior (Ribeiro, Bodell, Hames, Hagan, & Joiner, 2013). Suicide risk assessment is a crucial element in working with saddened clients and is the initial step in treating suicidal clients (Huh et al., 2012; Regehr, LeBlanc, Bogo, Paterson, & Birze, 2015). The risk of attempting or finalizing a suicide increases with the more signs or symptoms a client disclose (Kanel, 2015). Individuals usually give a sign or a warning before they commit suicide, but they may not openly say what their plan is or how they are feeling (Kanel, 2015).

Silverstein et al. in [24] were to develop and validate predictors of 30 day hospital readmission using a dataset of more than 29,000 patient’s record over the age of 65 and to compare prediction models that used alternate comorbidity classifications. In these paper they were capable to identify the risk factors of hospital readmission and calculated the risk of all the attributes by using prediction model. An important limitation of their study was that it did not directly include information on patients’ abilities to perform activities of daily living or other measures of physical function.

Referring to a study conducted by IBM, over $30 billion are spent in US each year on unnecessary hospital admissions. Clinical and behaviour data can be used to identify patients at high-risk and lead healthcare providers to develop strategies to care for patients before it is too late. The prediction of patient’s problems can reducing the number of unnecessary hospitalizations and the related costs.

Care coordination and patient-centered care is on the rise, and the use of this advanced technology is helping to make it happen on a larger scale. This technique is also beneficial in computing the risk of a pool of patients carried by insurance companies in order to best manage that risk. Insurance companies employ actuaries who perform calculations in order to determine risk and therefore policy eligibility and premium rate to help balance the risk involved in carrying that

Kim, K, Changhyiok, L. O’Leary, K. Rosenauer, S. Mehrotra, 2014. Predicting Patient Volumes in Hospital Medicine. Retrieved November 1, 2017 from

The results of data analytics may be used to identify areas of key risk, fraud, errors or misuse; improve business efficiencies; verify process effectiveness; and influence business decisions. The success of data analytics cannot be achieved if it isn’t aligned to the company’s business objectives, if risk is not properly managed, or the overall process is not planned, designed, implemented, tested and governed effectively. When implementing data analytics, organizational leaders must consider how they can maximize ROI, how it complies with project budgets, how they can manage false positives, and how to ensure the protection and confidentiality of their data sources and results. The process of securing high-quality data can become overwhelming, costly and time consuming. Another issue that makes data analytics time consuming is being able to find useful data, access it and understand how it affects your business. The healthcare industry is flooded with information from numerous sources. This information is spread among hospitals, primary care providers, researchers, health insurers, and state and federal governments, etc. This is where IT reliability and efficiency comes into play. Management and analysts need to be able to rely on the availability, accuracy, accessibility,

Secondly, predictive analytics not only use statistical algorithms and forecasts on historical data but also anticipate likely situations to plan ahead. The means of these predictive models differs depending on their behaviour and future events but the aim is to go beyond reporting what has happened in providing the best assessment of what will happen. Data mining is an important component that tends to identify trends, patterns and relationship. They largely depend on statistical models and multivariate analysis techniques. Thereby decision making can be streamlined and new insights can be revealed leading to better decision making. Organisations are adapting to predictive analytics for competitive advantage. They are used in identifying trends, understanding customers, enhance business performance, predict behaviour and drive decision making. (SAS and Nyce)

There is a lot of hype recently around big data and its potential. When leveraged correctly big data can provide many great benefits for organizations in almost any industry. Some organizations currently have a lot of big data they have gathered throughout their life but have no current way of leveraging this data. One of the ways companies are able to leverage all of their big data is through predictive analytics.