Healthcare Systems- Decision-Making

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Healthcare Systems, Decision-Making

Words: 1235

Subject: Healthcare Research

Table of Contents Data-Driven Decision Making in Complex Systems Data Collection to Measure Outcomes Case Application Data-Driven Decision-Making Conclusion References Data-Driven Decision Making in Complex Systems Healthcare systems deal with a vast amount of various data regarding patient outcomes, diagnoses, prescriptions, etc. Managing this information is important for determining outcomes of healthcare systems because it helps identify the areas of improvement and influence the decision-making process of developing more effective practices (Sonğur et al., 2017). Data collected in healthcare systems have a significant role in promoting healthcare quality, safety, and efficiency since a lack of information leads to poor clinical and administrative decisions in healthcare (Sonğur et al., 2017). Sufficient data, on the contrary, reduce decision errors, thus reducing patient risks and improving safety, quality, and efficiency (Sonğur et al., 2017). This paper will review one specific data point used as a quality metric in healthcare, namely, hospital readmission rates. First, the purpose of collecting this data and its benchmark will be discussed. Further, the impact of unsatisfactory readmission rates will be explored. In the final section, the role of this data point in decision making will be reviewed, and an action plan for improving healthcare outcomes will be proposed. The conclusion will summarize the importance of outcome assessment for promoting quality, safety, and efficiency.

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Data Collection to Measure Outcomes The selected data point for this paper is hospital readmission rates. Readmission is a patient’s unplanned admission to a hospital within 30 days after discharge from a healthcare setting. Readmission rates are measured for such conditions as stroke, pneumonia, heart failure, chronic obstructive pulmonary disease, CABG, heart attack, and hip/knee surgery (Seabold et al., 2020). Collecting data about readmission rates provides information on patient care delivery and outcomes (Seabold et al., 2020). The purpose of the collection of this data point within the healthcare system is to identify the share of patients whose readmission could have been prevented by providing better treatment and care transition at discharge (Edgerton et al., 2018). Thus, readmission rates are a significant indicator of hospital performance. Outcomes surrounding readmission rates contribute to the assessment of organizational performance regarding healthcare quality and efficiency. While readmission rates depend on patient and community characteristics, such as age, income, insurance, the prevalence of chronic illness, and geographic location, this data point may also indicate organizational outcomes (Basu et al., 2016). Data about readmission rates can reveal issues with care delivery processes, including care coordination, appropriate length of stay, use of services, and equitable care (Basu et al., 2016). In addition, readmission rates may disclose problems with care structure, such as an inadequate number of hospital beds, and the process of care transition after discharge (Basu et al., 2016). Apart from the mentioned quality issues, readmission rates indicate how successful hospitals are in providing efficient health care. Readmissions significantly increase the cost of health care, and it has been estimated that about 20% of Medicare beneficiaries are readmitted to hospitals within 30 days after discharge (Holt, 2017). It results in approximately $17 billion costs, which could be prevented if patients received adequate health care and avoided readmission (Holt, 2017). Thus, readmission rates are an important data point for evaluating healthcare quality and efficiency. For a particular data point to indicate an organization’s success or failure, it should undergo a process of benchmarking. Benchmarking allows for comparison among different organizations’ performance or patient outcomes and helps to identify areas of improvement (Seabold et al., 2020). A standard benchmark used for assessing hospitals’ success regarding readmission rates is the excess readmission ratio (Goldberg et al., 2017). This ratio measures a hospital’s readmission rates on a particular disease and compares it to readmission rates of other hospitals serving patients with similar comorbidities and demographic characteristics (Goldberg et al., 2017). The excess readmission ration of a hospital having more unplanned readmissions than other similar hospitals will be greater than 1.00 for a particular condition (Goldberg et al., 2017). It is an external benchmark since it involves a comparison of the performance of an organization with that of outside organizations rather than the performance of different internal units of the same organization (Seabold et al., 2020). Meeting this benchmark is important for ensuring the quality and efficiency of health care. Case Application This section will describe the impact of failing to meet the identified benchmark on organizational quality and efficiency, as well as stakeholders. Suppose a hospital treating patients with conditions used in the readmission rate measurement scored the excess readmission ratio greater than 1.00. It would mean that other hospitals treating similar patients turned out to be more effective in providing high-quality and efficient patient care. A failure to meet the identified benchmark would negatively impact organizational quality since it would signify such quality issues as early patient discharge, poor patient assessment and consulting, and absence of home care plans (Choudhury & Greene, 2018). Furthermore, since it has been stated that high rates of unplanned readmissions lead to avoidable expenditures, a failure to meet the benchmark would mean that the hospital’s efficiency needs improvement. The key stakeholders affected by the poor outcomes are patients, hospitals, and the community. The consequences of high unplanned readmission rates for patients include poor health outcomes due to the provision of inadequate health care. As a rule, if a patient is readmitted to a hospital soon after the discharge, it means that the hospital failed to provide the right care and assessment and ensure adequate care transition. Hospitals are negatively affected by the high readmission rate because it leads to increased costs. Moreover, hospitals are penalized for high readmission rates by the US health care system (Holt, 2017). Finally, increased costs of healthcare due to readmissions affects the public at large since the money spent on avoidable readmissions could have been allocated to meet other essential community needs. Data-Driven Decision-Making The data indicating high readmission rates in the case scenario can be used to inform strategic decision-making to improve quality and efficiency outcomes. Information about readmission rates is significant for financial planning and quality assessment (Edgerton et al., 2018). Since high readmission rates may indicate issues with patient assessment and care delivery and transition, these outcomes encourage organizational decision-makers to consider strategies for improvements in the identified areas.

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An action plan designed to correct the poor outcomes regarding readmission rates should include certain strategies for improving the quality of care and reducing costs. The most effective strategies for enhancing the quality and efficiency outcomes related to readmission rates are patient education, collaboration, telephone follow-up, home visits, and discharge planning (Kash et al., 2018). Various stakeholders, such as nurses, physicians, and pharmacists, should be involved in the implementation of the action plan. These healthcare professionals should collaborate to establish care plans for discharge, monitor patients’ conditions after discharge via telephone or home visits, and deliver patient education on disease treatment and self-care (Kash et al., 2018). The success of the action plan will be influenced by patients’ willingness to collaborate with the medical personnel (Kash et al., 2018). Therefore, an emphasis should be put on encouraging patients’ involvement in treating their conditions. Conclusion Information management and outcome assessment play a significant role in informing decision-making within healthcare systems. Collecting health care data, such as readmission rates described in this paper, allows for assessing the quality, safety, and efficiency of health care and identify areas of improvement. Continuous monitoring of organizational outcomes helps decision-makers gain an insight into the current performance and develop plans to sustain or improve the quality, safety, and efficiency within healthcare systems. References Basu, J., Avila, R., & Ricciardi, R. (2015). Hospital readmission rates in U.S. States: Are readmissions higher where more patients with multiple chronic conditions cluster? Health Services Research, 51(3), 1135-1151. Web. Choudhury, A., & Greene, D. C. M. (2018). Evaluating patient readmission risk: A predictive analytics approach. American Journal of Engineering and Applied Sciences, 11(4), 1320-1331. Web. Edgerton, J. R., Herbert, M. A., Hamman, B. L., & Ring, W. S. (2018). Can use of an administrative database improve accuracy of hospital-reported readmission rates? The Journal of Thoracic and Cardiovascular Surgery, 155(5), 2043-2047. Goldberg, E. M., Morphis, B., Youssef, R., & Gardner, R. (2017). An analysis of diagnoses that drive readmission: What can we learn from the hospitals in Southern New England with the highest and lowest readmission performance? Rhode Island Medical Journal, 100(8), 23-28. Holt, H. D. (2017). The readmission difference: Examining the negative impact of hospital readmissions on financial performance. International Journal of Health Sciences, 5(3), 31-41. Web.

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Kash, B. A., Baek, J., Cheon, O., Coleman, N. E., & Jones, S. L. (2018). Successful hospital readmission reduction initiatives: Top five strategies to consider implementing today. Journal of Hospital Administration, 7(6), 16-23. Web. Seabold, K., Kaufmann, M., & McNett, M. (2020). Quality and benchmarking data in health systems. In M. McNett (Ed.), Data for nurses: Understanding and using data to optimize care delivery in hospitals and health systems (pp. 13-29). Academic Press. Sonğur, C., Özer, Ö., Gün, Ç., & Top, M. (2017). Patient safety culture, evidence-based practice and performance in nursing. Systemic Practice and Action Research, 31(4), 359-374. Web.

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