Health Administrator Policy and Planning Health Practices
The draft research design is due ASAP, but the final version is not due until October 2. Your research design should include an introduction to the paper, the literature review that you will have already submitted, and a step by step description of how your analysis will be conducted. That means a statement of your theoretical framework, a listing of specific hypotheses, description of data sources, identification of units of analysis, operationalization of variables, and procedures used in the analysis. The operationalization of variables should include a description of precisely how the variable is to be defined and measured in the analysis. This applies to independent, dependent, and control variables. You should say how the data are to be examined once you have collected them. In short, the research design describes everything that you will need to do to complete the research. You don’t have to complete the research now, but you will in POL 689. Please do not try to summarize your conclusions. You should not have any conclusions yet since you have not conducted the analysis yet.
Health Administrator Policy and Planning Health Practices
Abstract
One of the most pressing problems the United States must solve now is how to improve healthcare delivery. This review looks at different diseases, measures of analysis, and policies concerning the mortality rate and readmission of patients in different hospitals. According to the Institute of Medicine, almost 100,000 people in the U.S die every year due to preventable medical mistakes made by medical staff. Medication mistakes continue to be a major issue in the United States. This research is based on the mortality and readmission rates.
According to Waydhas et al. (2020), mortality rates are the death rate in a particular population. Reducing avoidable hospital readmissions is promoted as a quality indicator and a cost-cutting strategy. Hospitals with 30-day readmission rates for heart attack, heart failure, or pneumonia greater than planned are subject to financial penalties. There is some speculation that a low incidence of this phenomenon in intensive care units and surgical wards indicates high-quality trauma care. We use a qualitative research approach to answer the hypothesis of how lack of medication regulation and readmission drive up hospital costs and cause drifts in planned budgets for hospitals.
Hospital outcomes like as mortality and readmission rates are widely used to evaluate and publicize the success of hospitals and individual doctors. Avoidable readmissions can be reduced, and healthcare expenses can be lowered if the reasons for readmissions are known. Variations in the process of such outcome-based quality indicators are believed to be caused by changes in the (unobservable) quality of health services. These two variables will dictate the cost of health care, the impact on the quality of care provided, and associations with the administrator’s policies and planning. The variables are mortality rates and readmission rates.
The information will be based on up to 20 research studies –( Literature review needs to reflect these 20 studies relevantly) on readmission and mortality rates in different facilities. The dependent variables are costs set, associated policies, and health administrators’ plans. The materials for the research are online access, library access, medical books, medical reports, computers, and workstations.
Literature Review
Introduction
What we mean when we talk about healthcare policies is a set of preemptive regulations established to enhance healthcare delivery. The program addresses many concerns, including healthcare financing, protracted care, mental health, and preventative medicine. One of the most pressing problems the United States must solve now is how to improve healthcare delivery. People have been able to live healthier and longer thanks significantly to Medicare’s expansion in recent decades. This study intends to investigate the policy problems facing health reform by focusing on merging related to intelligence from different areas of health inside the United States. This review looks at different diseases, analysis measures, and studies on planning and policies concerning mortality rates and readmission of patients in different hospitals.
Review
It has been suggested by Buerhaus et al. (2017) that the quality of service delivered in practice differs significantly from the quality of healthcare provided in hospitals and other medical institutions. According to the Institute of Medicine, “almost 100,000 people in the United States die every year due to preventable medical mistakes made by medical staff, lending credence to the claims made in this research.” The paper notes that many mistakes occur due to inappropriate medicine administration, inaccurate patient identification, or erroneous surgical location (Frisina and Neri 2018). Even the most minor medical blunders would be eliminated if strict protocols were strictly adhered to.
A medication policy that promotes interdisciplinary teamwork was found to be effective in reducing drug-related difficulties. Pellegrin et al. (2016) found that geriatric patients were less likely to require hospitalization due to medication-related complications when hospital pharmacists and community pharmacists worked together to manage their medications. Integrating pharmacists into primary care and other community-based healthcare teams has been proposed by Smith et al. (2016) to boost practice efficiency, care coordination, patient outcomes, and the prevention of avoidable adverse medication events. , being admitted to the hospital when they did not need to be, etc.
Bucknall et al. (2019) find that patients’ preferences on their medication management should be assessed at the time of hospital admission, even though patients’ perspectives on their involvement in medication management differ. According to research, comprehensive medication management (CMM) has been shown to help clinical pharmacists become more self-aware of how they might influence drug outcomes (Walker et al., 2015). Despite the fact that numerous studies have demonstrated the critical nature of drug management, medication mistakes continue to be a significant issue in the United States. (Jones and Treiber, 2018).
Medication safety can be enhanced in several ways; one such way is through increased collaboration between healthcare providers, patients, and community and hospital physicians. Medication management is only one of many processes that can benefit from applying the Lean Six Sigma methodology (Nayar et al., 2016). It can be used to implement solutions such as identifying and implementing the Nurses’ Rights of medicines (at the right dose, Right treatment, Right patient, via the correct route, and during the right time) to formalize as well as legitimize caregiver control over through the administration process and to establish a fair culture concerning medication surroundings (Jones & Treiber, 2018).
Measures of analysis
This research is based on the mortality and readmission rates in measuring the hospital administration’s success and failures of policies and procedures. According to Waydhas et al. (2020), mortality rates are the death rate in a particular population. Readmission rates are the rate of people being readmitted to a facility with the same illness, lack of comprehensive care, and not fully recovering based on services offered. These two analysis measures can help determine the effectiveness of administration planning and policies in providing health services to such a population.
According to Upadhyay et Al. (2019), a study follows 98 hospitals in Washington State from 2012 to 2014 to see if the publicly available readmission statistics on Hospital Compare affect the hospitals’ bottom lines. The AMI, PN, and HF readmission rates were compared to revenue per patient, costs per patient, and operating margin. An examination of 276 hospital-year observations using hospital-level fixed effects regression showed a positive correlation between reduced AMI readmission rates and operational revenues. The cost of running a hospital also rises when readmission rates are lowered. There may be a little rise in operating margin due to more significant operating revenues attributable to higher PN readmission rates. Nonetheless, as readmissions persist, the potential for rising costs comes with greater resource use, which might eventually eat into profits (Allen et Al., 2015).
Reducing avoidable hospital readmissions is promoted as a quality indicator and a cost-cutting strategy (O’Connor et al., 2021). The Hospital Readmission Reduction Program (HRRP) was launched in 2012 as part of the Affordable Care Act (ACA). Hospitals with 30-day readmission rates for heart attack, heart failure, or pneumonia more significant than planned are subject to financial penalties under this program (Upadhyay et al., 2019).
Although some trauma patients survive hospitalization, most of those who die do so while receiving emergency or intensive care (ICU). Yet many people do not make it past the initial hospital stay, even after being released from the intensive care unit. Initial impressions can label these incidents as “failure to rescue” victims who could have been saved. There is some speculation that a low incidence of this phenomenon in intensive care units and surgical wards indicates high-quality trauma care.
Therefore, the first step in recognizing inefficiencies of this kind and developing corrective policies is the identification and measurement of inappropriate treatment (Park et al., 2017). Consider the importance of assigning a monetary value to the health benefits of a treatment to ascertain whether or not the action in question is suitable and cost-effective.
Upadhyay et al. (2019) showed that a medical condition’s mortality and readmission rates varied, indicating that the quality of care also varied. Admin policy and practices play a crucial role in ensuring that the utmost is gained from the decisions made, not resulting in increased mortality and readmission rates. Improving care safety and health outcomes for patients requires looking at how we treat them while they are in the hospital, preparing them for life after discharge, and supporting them in the community once they return home.
Methodology
Research Design
For this study, a qualitative research design was employed. To answer the hypothesis, how lack of medication regulation and readmission drive up hospital costs and cause drifts in planned budgets for hospitals. Non-numerical information is typically gathered using qualitative research techniques. In order to get to the bottom of people’s motivations, worldviews, and interpretations, this method is employed. Empirical studies have progressed under the qualitative research methodology to question the traditional notions of evidence and truth while still adhering to the essential principles of acknowledging the subjects being investigated as empirical (Powner, 2015).
This approach may be fully or partially unstructured. The findings from this kind of study tend to be more descriptive than prescriptive. This paves the way for the researcher to conclude that the hypothesis or theory under scrutiny is correct. Much empirical study has looked for causes for the wide range of hospital readmission rates seen in high-income nations. Avoidable readmissions can be reduced, and healthcare expenses can be lowered if the reasons for readmissions are known. Indicators of hospital outcomes like as mortality and readmission rates, are widely used to evaluate and publicize the success of hospitals and individual doctors.
Qualitative studies often use a limited sample size because of constraints such as time and money. Its purpose is to provide readers with additional background or perspective on the issue at hand. Interviews, experiments, and focus groups are some of the most common research approaches. Variations in the process of such outcome-based quality indicators are believed to be caused by changes in the (unobservable) quality of health services, as reflected in the procedures of medical service and care organization, according to the reasoning behind outcome-based evaluation criteria like mortality or readmission rates. It is known, for instance, that the likelihood of readmission can be affected by the availability of suitable rehabilitation treatments for patients who have suffered a fall and fracture.
In the qualitative empirical study, two variables have been considered to examine the impact of health administrator policy and planning on health readmission and mortality rates. The variables are mortality rates and readmission rates. These two variables will dictate the cost of health care impacts. The dependent variables are costs set, associated policies, and health administrators’ plans.
The research will be based on historical records and research conducted on different health facilities and situations. The participants in this document review are patients, health care professionals, and administrators. The information will be based on up to 20 research studies on readmission and mortality rates in different facilities. The inclusion and exclusion criteria employed are the research must be peer-reviewed by scholars and conducted by reputable scholars and professionals in the medical field. This will validate the sources of the information, ensuring unparalleled and highly accurate data.
The materials for the research are online access, library access, medical books, medical reports, computers, an internet connection, and working stations. The process of research being a qualitative research study will involve interviews, questionaries’ and research documents of previously researched matter. This is to obtain a perception of researchers of different scholarly papers, medical professionals, and medical reports. The selected participants are asked to answer a questionnaire or schedule an interview online or in person to better understand the data in the research documents.
In data analysis, the dependent variables were table and the impact on the independent variables placed along them. This allowed easy comparison of different data and reasons for their effect. These results indicate whether the data is based on planning, policies, and costs.
Reference
Alper, E., O’Malley, T. A., Greenwald, J., Aronson, M. D., & Park, L. (2017). Hospital discharge and readmission. Up-to-date. Waltham, MA: Up-to-date.
American College of Clinical Pharmacy, McBane, S. E., Dopp, A. L., Abe, A., Benavides, S., Chester, E. A., Dixon, D. L., Dunn, M., Johnson, M. D., Nigro, S. J., Rothrock-Christian, T., Schwartz, A. H., Thrasher, K., & Walker, S. (2015). Collaborative drug therapy management and comprehensive medication management―2015. Pharmacotherapy: The Journal of Human Pharmacology and Drug Therapy, 35(4), e39-e50. https://doi.org/10.1002/phar.1563
Bucknall, T., Digby, R., Fossum, M., Hutchinson, A. M., Considine, J., Dunning, T., Hughes, L., Weir-Phyland, J., & Manias, E. (2019). Exploring patient preferences for involvement in medication management in hospitals. Journal of Advanced Nursing, 75(10), 21892199. https://doi.org/10.1111/jan.14087
Buerhaus, P. I., Skinner, L. E., Auerbach, D. I., & Staiger, D. O. (2017). Four challenges facing the nursing workforce in the United States. Journal of Nursing Regulation, 8(2), 40-46.
Frisina Doetter, L., & Neri, S. (2018). Redefining the state in health care policy in Italy and the United States. European Policy Analysis, 4(2), 234-254.
Cylus, J., Papanicolas, I., Smith, P. C., & World Health Organization. (2016). Health system efficiency: how to make measurement matter for policy and management. World Health Organization. Regional Office for Europe.
Dwenger, A. T., Fox, E. R., Macdonald, E. A., & Edvalson, B. J. (2019). Implement hyperlinks to medication management policies and guidelines in the electronic health record. American Journal of Health-System Pharmacy, 76(Supplement_3), S69-S73. https://doi.org/10.1093/ajhp/zxz122
Gupta, A., & Fonarow, G. C. (2018). The Hospital Readmissions Reduction Program—learning from the failure of a healthcare policy. European journal of heart failure, 20(8), 1169-1174.
Hamsen, U., Drotleff, N., Lefering, R., Gerstmeyer, J., Schildhauer, T. A., & Waydhas, C. (2020). Mortality in severely injured patients: nearly one of five non-survivors have already been discharged alive from ICU. BMC anesthesiology, 20(1), 1-8.
Hunt‐O’Connor, C., Moore, Z., Patton, D., Nugent, L., Avsar, P., & O’Connor, T. (2021). The effect of discharge planning on length of stay and readmission rates of older adults in acute hospitals: A systematic review and meta‐analysis of systematic reviews. Journal of Nursing Management, 29(8), 2697-2706.
Jones, J. H., & Treiber, L. A. (2018, July). Nurses’ rights of medication administration: Including authority with accountability and responsibility. In Nursing Forum (Vol. 53, No. 3, pp. 299-303). https://doi.org/10.1111/nuf.12252
Köberlein-Neu, J., Mennemann, H., Hamacher, S., Waltering, I., Jaehde, U., Schaffert, C., & Rose, O. (2016). Interprofessional medication management in patients with multiple morbidities: a cluster-randomized trial (the WestGem Study). Deutsches Ärzteblatt international, 113(44), 741.
Laudicella, M., Donni, P. L., & Smith, P. C. (2013). Hospital readmission rates: signal of failure or success? Journal of health economics, 32(5), 909-921.
McIlvennan, C. K., Eapen, Z. J., & Allen, L. A. (2015). Hospital readmissions reduction program. Circulation, 131(20), 1796-1803.
Nayar, P., Ojha, D., Fetrick, A. and Nguyen, A.T. (2016), “Applying Lean Six Sigma to improve medication management,” International Journal of Health Care Quality Assurance, Vol. 29 No. 1, pp. 16-23. https://doi.org/10.1108/IJHCQA-02-2015-0020
Pellegrin, K. L., Krenk, L., Oakes, S. J., Ciarleglio, A., Lynn, J., McInnis, T., Bairos, A. W., Gomez, L., McCary, M. B., Hanlon, A. L., & Miyamura, J. (2017). A quasi-experimental study is a reduction in medication‐related hospitalizations in older adults with medication management by hospital and community pharmacists. Journal of the American Geriatrics Society, 65(1), 212-219. https://doi.org/10.1111/jgs.14518
Ridwan, E. S., Hadi, H., Wu, Y. L., & Tsai, P. S. (2019). Effects of transitional care on hospital readmission and mortality rate in subjects with COPD: a systematic review and meta-analysis. Respiratory Care, 64(9), 1146-1156.
Smith, M. A., Spiggle, S., & McConnell, B. (2017). Strategies for community-based medication management services in value-based health plans. Research in Social and Administrative Pharmacy, 13(1), 48-62. https://doi.org/10.1016/j.sapharm.2016.01.005
Upadhyay, S., Stephenson, A. L., & Smith, D. G. (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, 0046958019860386.
Vollam, S., Dutton, S., Lamb, S., Petrinic, T., Young, J. D., & Watkinson, P. (2018). A systematic review and meta-analysis is an out-of-hours discharge from intensive care, in-hospital mortality, and intensive care readmission rates. Intensive care medicine, 44(7), 1115-1129.
Wong, E. L., Cheung, A. W., Leung, M., Yam, C. H., Chan, F. W., Wong, F. Y., & Yeoh, E. K. (2011). Unplanned readmission rates, length of hospital stay, mortality, and medical costs of ten common medical conditions: a retrospective analysis of Hong Kong hospital data. BMC health services research, 11(1), 1-8.
Wong, E. L., Cheung, A. W., Leung, M., Yam, C. H., Chan, F. W., Wong, F. Y., & Yeoh, E. K. (2011). Unplanned readmission rates, length of hospital stay, mortality, and medical costs of ten common medical conditions: a retrospective analysis of Hong Kong hospital data. BMC health services research, 11(1), 1-8.