Health Administrator Policy And Planning For Health Practice

Health Administrator Policy And Planning Health Practices The draft Res

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.

Paper For Above instruction

Introduction

Healthcare policy and planning serve as foundational elements in the continual effort to improve the quality, safety, and efficiency of health services. An effective research design tailored to evaluate how policies influence hospital outcomes, particularly mortality and readmission rates, can substantially inform administrative strategies and policy development. This research aims to delineate the relationships among hospital policies, medication management, mortality rates, and readmission rates, underpinning the operational and strategic decisions of health administrators.

Theoretical Framework

This study employs a quasi-experimental, contextual framework grounded in health services research theory. Central to this framework is the Donabedian model, which conceptualizes healthcare quality through three interconnected components: structure, process, and outcomes (Donabedian, 2003). Here, hospital policies and medication management practices form the structural element, influencing processes such as patient care and medication safety. These processes ultimately impact patient outcomes, reflected in mortality and readmission rates. Additionally, the Policy Implementation Framework guides understanding how administrative decisions translate into practice and influence clinical outcomes (Sabatier & Mazmanian, 1980).

Hypotheses

  • H1: Strict medication regulation policies are associated with lower mortality rates in hospitals.
  • H2: Comprehensive medication management practices decrease 30-day readmission rates.
  • H3: Hospitals with policies emphasizing interdisciplinary teamwork in medication management exhibit reduced healthcare costs.
  • H4: Variations in hospital policies significantly predict differences in patient outcomes, including mortality and readmission rates.

Data Sources

The research will analyze secondary data obtained from peer-reviewed journal articles, hospital administrative records, and publicly available healthcare quality databases such as CMS Hospital Compare. These sources provide quantitative data on mortality, readmission rates, hospital policies, and financial metrics across diverse healthcare facilities. Additionally, literature encompassing up to 20 relevant studies will form the qualitative basis, providing contextual insights into policy implementation and its effects.

Units of Analysis

The primary units of analysis encompass individual hospitals, healthcare facilities, and departments within hospitals such as Intensive Care Units (ICUs) and surgical wards. The patient-level analysis will be incorporated indirectly through aggregated mortality and readmission rates, which reflect patient outcomes at the institutional level. The temporal scope spans data collected over at least three years to account for policy changes and their lagged effects.

Operationalization of Variables

  • Independent Variables:
    • Hospital Policy Stringency: Operationalized as a composite score derived from the presence of formal medication safety policies, interdisciplinary collaboration protocols, and adherence to national standards. Measured using a coding scheme based on policy documentation and interviews with administrative personnel.
    • Medication Management Practices: Assessed through staff surveys and audits, capturing the extent of pharmacist involvement, use of medication reconciliation, and electronic health record (EHR) integration. Quantified on a Likert scale from 1 (minimal integration) to 5 (comprehensive integration).
  • Dependent Variables:
    • Mortality Rate: Percentage of patients who die within 30 days of hospital admission for specified conditions, obtained from hospital records, operationalized as a ratio of deaths to total admissions for targeted diagnoses.
    • Readmission Rate: Percentage of patients readmitted within 30 days post-discharge, operationalized similarly as a ratio of readmissions to total discharges for conditions like heart failure, pneumonia, etc.
  • Control Variables:
    • Hospital size (number of beds), measured via administrative data.
    • Patient demographic characteristics, including age, gender, and comorbidities, obtained from electronic health records.
    • Type of hospital (public vs. private), identified through organizational reports.

Analysis Procedures

The collected data will undergo descriptive statistical analysis to identify baseline characteristics and distributions. Inferential analysis will involve multiple regression models to examine associations between hospital policies and outcomes, controlling for hospital size, patient demographics, and hospital type. Hierarchical linear modeling (HLM) may be employed to account for nested data structures—patients within hospitals. Effect sizes will be estimated to determine the practical significance of policy variables, accompanied by confidence intervals to assess statistical significance.

Procedures for Data Examination

Data will be reviewed for completeness, accuracy, and consistency. Missing data will be addressed through multiple imputation techniques. Assumptions of statistical tests, such as normality and homoscedasticity, will be tested beforehand. Sensitivity analyses will be conducted to validate findings across different model specifications. Trend analyses over the period of study will identify temporal effects linked to policy implementations.

Conclusion

The research design outlined provides a comprehensive plan to investigate the impact of hospital policies and medication management practices on critical healthcare outcomes. Through rigorous operationalization and analysis, it aims to generate evidence that can guide health administrators toward policies that enhance patient safety, reduce costs, and improve overall quality of care.

References

  • Donabedian, A. (2003). An introduction to quality assurance in health care. Oxford University Press.
  • Sabatier, P. A., & Mazmanian, D. A. (1980). The implementation of policy: A framework for analysis. Policy Studies Journal, 8(4), 538-560.
  • Waydhas, C., Hamsen, U., Drotleff, N., Lefering, R., Gerksemeyer, J., & Schildhauer, T. A. (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., & 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).
  • Jones, J. H., & Treiber, L. A. (2018). Nurses' rights of medication administration: including authority with accountability and responsibility. Nursing Forum, 53(3), 342-352.
  • Kä¶berlein-Neu, J., Mennemann, H., Hamacher, S., Waltering, I., Jaehde, U., & Schaffert, C. (2016). Interprofessional medication management in patients with multiple morbidities: a cluster-randomized trial (the WestGem Study). Deutsches Ärzteblatt international, 113(44), 741-747.
  • Laudicella, M., Donni, P. L., & Smith, P. C. (2013). Hospital readmission rates: signal of failure or success? Journal of health economics, 32(5), 1075-1090.
  • 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., & Nguyen, A. T. (2016). Applying Lean Six Sigma to improve medication management. International Journal of Health Care Quality Assurance, 29(1), 16-23.
  • 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, 330.

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