Assessment of the Problem
Patient readmission to hospitals negatively affects the healthcare system. They drain facility resources and destructively influence government budgets. On the extreme, these readmissions reduce the quality of life for those involved patients. Nonetheless, readmission of patients with chronic conditions is expected in the contemporary world. In Western countries, chronic illnesses are becoming increasingly prevalent due to a lack of proper treatment for underlying conditions and population ageing. Once a patient has been hospitalized, the chances of being readmitted increase drastically (Brunner-La Rocca et al., 2020). With chronic disease being on the rise, healthcare givers can take various steps to ensure that patients with chronic conditions are kept out of hospitals. These steps begin with properly planning how patients should be discharged and following up on them on their recovery journey. In this part, we will examine how the readmission of an ethnically diverse population with chronic conditions influences patient safety, quality of care, and costs incurred by these patients and the system.
Readmission of patients with chronic illnesses poses a substantial burden to healthcare systems and those affected since they contribute to increased morbidity rates. Morbidity is not only crucial to well-being but also has a significant economic influence in connection with the cost associated with hospitalization of these patients. Hence, the treatment of chronic diseases should be focused on preventing readmissions (Brunner-La Rocca et al., 2020). Various chronic diseases have different readmission risks, not attributed to treatment but other underlying conditions in a patient. Many patients diagnosed with chronic disease often have other underlying issues that increase their risk of being readmitted. These underlying illnesses tend to influence each other, and treating one disease greatly impacts the other. Additionally, the hospital’s healthcare quality also adversely affects the readmission rate.
Moreover, patient readmission also has a negative influence on healthcare systems. Hospitals must have at least two incentives to mitigate the readmission rates (Upadhyay et al., 2019). These incentive policies were imposed to encourage hospitals to minimize readmission cases. For instance, transparency through the provision of public reports helps reduce readmission rates since hospitals tend to avoid “shaming.” These policies have led healthcare institutions at high speeds to discourage patients from choosing them. As a result, hospitals are under constant pressure to maintain a positive public opinion. There are instances where hospitals face repercussions for constant increases in readmissions. For example, hospitals under the Centers for Medicare and Medicaid Services (CMS) regulations are penalized for high readmission rates (Upadhyay et al., 2019). Therefore, to avoid being penalized, hospitals are being more accountable by informing the administrations on how they can decrease the rate of readmissions. One possible way to contribute to positive outcomes is by avoiding the early discharge of patients.
The person assessed during this practicum is John, a chronic diabetic patient whose condition adversely affected him and his family. The patient’s family has experienced several safety concerns due to frequent hospital visits and incorrectly prescribed medication in the past month. During this period, John has experienced heart attacks on two different occasions, and his mental health has been greatly compromised. In all these events, the family has been necessitated to rush the patient to St. Peter’s community hospital emergency department resulting in his admission on all three occasions. John’s family stated that they are required to visit the hospital’s emergency department twice a month on average. During these visits, the patient is offered medication such as Insulin, Metformin, and Invokana to help manage the condition. The patient is covered by Life-watch Insurance Company which covers about eighty percent of the total cost associated with treatment and medication.
Nonetheless, the family indicated that they are struggling to pay the insurance company since the frequent patient attacks, and the company has termed John, a high-risk patient. The re-occurrence of the attacks resulted in the family paying vast amounts of money to the insurance company. The family also stated they must pay a lot to cover the remaining twenty percent. They are sometimes forced to depend on friends and extended family for donations. In managing the condition, there are also required therapies that the insurance company does not cover. Moreover, the patient indicated that although the medications help manage the disease, they have side effects. John explained that he experiences diarrhea, vomiting, and nausea after taking Metformin.
Experience of the Practicum on the Readmission of Patients with Chronic Diseases
After engaging with John and his family in a practicum meeting, they revealed some vital information that had not been considered. The family, which consists of ethnically diverse members, has been through continuous readmissions to the hospital, which has put a strain on their already stressful life. John’s diagnosis of chronic diabetes caused his family to adapt to a new way of life that forced them to invest money and time into managing the disease. This family has been forced to live in a financially unstable household due to the community’s poor quality of health services. Moreover, they are also met with risk factors such as socioeconomic and extremes in age. These problems have contributed to each family member’s mental health issues since they directly impact them.
There are several resources on the impact of hospital readmissions. The article “Correction to Hospital Readmission of Patients with Diabetes” (Rubin, 2018) acknowledges that reducing hospital readmissions requires high priority where cost reduction target and quality measure is concerned. The rise of rehospitalized diabetic patients has caused an affliction in costs and the quality of services provided. Both the hospital and patients are victims of the readmission problem, which costs them safety, quality services, and finances. By fostering a way of reducing readmissions, hospitals can reduce the risk factors tremendously while improving the quality of care. The article projected a high potential for diabetic patients experiencing readmission than those without diabetes. Since this potential target group has been established, a study on interventions for these readmission rates for diabetic patients is necessary. To address the safety concerns of these diabetes patients and the increased economic costs of hospitals, a conducive strategy should be implemented in these hospitals.
Subsequently, another article proposed ways that this problem could be combated. In “Strategies to Reduce Hospital Readmission Rates in a Non-Medicaid-Expansion State” (J. Warchol et al., 2019), a multiple case study was conducted to evaluate transformational leaders’ organizational strategies used to reduce rates of readmissions in hospitals. The study involved fifteen hospital heads across five metropolitan heads who were asked for their opinions on which strategies would work to reduce hospital readmission rates. One of the critical suggestions they gave was using predictive analysis differentiated by patient population to reduce avoidable readmission to the hospital (Warchol et al., 2019). This could be done by thoroughly leveraging data from electronic health records and identifying the patients at risk.
Further, these leaders proposed the need to address social determinants that affect the population’s health needs, such as proper housing, healthy foods, and reliable transportation. At times, these readmission cases are not only caused by hospitals. Some patients that find themselves back in the hospital may be because of their daily living standards. For strong health, medicine needs to be accompanied by proper nutrition. The living and mobility services must be favorable enough to accommodate comfort and emergency cases. These social constraints could sometimes be the causes of readmission of patients in hospitals.
While engaging with John’s family, I encountered some problems. First, there was a communication barrier since English was not the family’s first language since the members were Persians and Latinos. I had to adjust the communication strategy accordingly. Some members needed a complete translation offered by those conversant with English. Secondly, there was not enough time for the research since most of the time was used doing the translations. Thirdly, some respondents were skeptical about offering their personal information. There was a back-and-forth discussion of whether the information provided will not compromise their safety. Nonetheless, the cooperative family members offered important information that was enough to conduct this study.
The family agrees that there has been a problem with how the hospital has handled the readmission problem since they have first-hand experience with the hardships they have faced in seeking treatment for John. They showed concern for the assessment because they believed that this problem affected the minority group of people more harshly. Although there was still skepticism from some of the family members, the change management imposed created a flowed discussion. These discussions required more intentional representation to ensure they felt free to speak on sensitive issues that may contribute to a significant change in the future. I used a transitional change strategy of open-ended questions to give them room to share what they deemed fit without any limitations to regroup the thinking of the family.
Based on this assessment, there are several ways that the rate of hospital readmissions can reduce for diabetic patients. They include proper discharge instructions, post-discharge support, better inpatient self-care education, and predictive analysis to determine high-risk patients. By offering clear and well-projected instructions to outpatients, they would not need to frequent their hospital visits. Before a patient is released from the hospital, a thorough examination should be done to ensure that the patient can take care of themselves. The hospitals should also constantly evaluate the patients’ records to prioritize risky conditions. A literature review on “Application of Internet of Things in Predictive Health Analysis” by Poornima and Karani (2020) has affirmed that smart devices in healthcare have made it easier to analyze patient data. It explains the convenience brought by the use of technology into the health care systems.
These smart devices can track the patients’ progress in real-time, making data analysis for prediction purposes more convenient than before. This technology has eliminated the need for patients to visit the hospital for checkups frequently. This communication technology has enabled hospitals to have mobile services that attend to patients and maintain records within a connected network (Rghioui et al., 2020). Hospitals will reduce economic costs by investing in monitoring devices that periodically monitor blood sugar levels. A diabetic patient is given an automatic schedule that will dictate an alarm time for taking prescribed medication. Portable monitoring devices are becoming popular as they improve the quality of life of diabetic patients whose hospital visits are reduced. Predicting analysis, therefore, could be carried out in real-time, generating accurate data for proper decision making.
While all these are feasible strategies that would reap significant effects, the problem must be addressed from the beginning. I firmly believe that the leading cause for readmission is the negligence of hospital staff in giving quality care and instructions to patients before they are discharged. Further, more diligence is required when assessing and evaluating the patient’s previous records to ensure admission or readmission is done only when necessary. Imposing strict penalties on increased rates of readmission for hospitals can be problematic in the long run. Hospitals may avoid admitting patients in critical conditions for fear of being penalized. The most viable strategy is using smart devices and remote services to monitor the patient’s health without them visiting the hospitals. From this perspective and suggestions, hospitals would be able to regulate the number of admissions and improve the quality of service offered. In return, there will be minimized costs and time for the patients and the hospital.
References
Brunner-La Rocca, H., Peden, C., Soong, J., Holman, P., Bogdanovskaya, M., & Barclay, L. (2020). Reasons for readmission after hospital discharge in patients with chronic diseases—Information from an international dataset. PLOS ONE, 15(6), e0233457.
Poornima K, G., & Prasad Karani, K. (2020). Application of IoT in Predictive Health Analysis-A review of literature. International Journal of Management, Technology, And Social Sciences (IJMTS), 5(1).
Rghioui, A., Lloret, J., Sendra, S., & Oumnad, A. (2020). A smart architecture for diabetic patient monitoring using machine learning algorithms. Healthcare, 8(3), 348.
Rubin, D. (2018). Correction to: Hospital readmission of patients with diabetes. Current Diabetes Reports, 18(4).
Warchol, S. J., Monestime, J. P., Mayer, R. W., & Chien, W. (2019). Strategies to reduce hospital readmission rates in a non-Medicaid-expansion state. Perspective in Health Information Management, 16. Web.
Upadhyay, S., Stephenson, A., & Smith, D. (2019). Readmission rates and their impact on hospital financial performance: A study of Washington Hospitals. INQUIRY: The Journal of Health Care Organization, Provision, and Financing, 56, 004695801986038.