{"id":10505,"date":"2022-09-11T09:58:00","date_gmt":"2022-09-11T09:58:00","guid":{"rendered":"https:\/\/www.essaybishops.com\/au\/?p=10417"},"modified":"2022-09-11T09:58:00","modified_gmt":"2022-09-11T09:58:00","slug":"mha-fpx5020-capstone-data-analysis-proposal-assignment","status":"publish","type":"post","link":"https:\/\/www.homeworkacetutors.com\/nursing\/mha-fpx5020-capstone-data-analysis-proposal-assignment\/","title":{"rendered":"MHA-FPX5020: Capstone Data Analysis Proposal Assignment"},"content":{"rendered":"

Create a data analysis proposal examining hospital readmissions and healthcare outcomes. Construct a capstone project on healthcare data analysis methods.<\/p>\n

Capstone Proposal: Reducing Hospital Readmissions through Data Analysis<\/h1>\n

Problem Statement<\/h3>\n

High 30-day hospital readmission rates may result in increased healthcare costs, penalties under value-based programs, and reduced patient outcomes (Medicare Payment Advisory Commission, 2021).<\/p>\n

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Review of the Literature<\/h3>\n

Ahn, E., Choi, J., & Kim, S. (2021). Predictive analytics for hospital readmissions. Healthcare Informatics Research, 27<\/em>(3), 198-207. https:\/\/doi.org\/10.4258\/hir.2021.27.3.198<\/a><\/strong>
Shows how predictive modeling using electronic health records can identify high-risk patients before discharge.<\/p>\n

Berwick, D., Nolan, T., & Whittington, J. (2020). The triple aim: Care, health, and cost. Health Affairs, 39<\/em>(5), 758-764. https:\/\/doi.org\/10.1377\/hlthaff.2020.00110<\/a><\/strong>
Explains why reducing readmissions directly supports the triple aim framework of improving population health, lowering costs, and enhancing care.<\/p>\n

Figueroa, J., et al. (2020). Association of Medicare Hospital Readmissions Reduction Program with readmission and mortality outcomes. JAMA Network Open, 3<\/em>(2), e1920472. https:\/\/doi.org\/10.1001\/jamanetworkopen.2019.20472<\/a><\/strong>
Evaluates how financial penalties have driven hospitals to adopt systematic interventions.<\/p>\n

Gupta, A., & Krumholz, H. (2021). Hospital performance on readmission measures. BMJ Quality & Safety, 30<\/em>(6), 463-471. https:\/\/doi.org\/10.1136\/bmjqs-2020-011540<\/a><\/strong>
Assesses variation in hospital performance and highlights factors that explain persistent disparities.<\/p>\n

Kansagara, D., et al. (2020). Risk prediction models for hospital readmission. Annals of Internal Medicine, 172<\/em>(1), 39-46. https:\/\/doi.org\/10.7326\/M19-3009<\/a><\/strong>
Reviews models and identifies best predictors such as comorbidity, prior utilization, and social determinants.<\/p>\n

McIlvennan, C., et al. (2021). Transitional care interventions to reduce readmissions. Circulation: Cardiovascular Quality and Outcomes, 14<\/em>(4), e007746. https:\/\/doi.org\/10.1161\/CIRCOUTCOMES.120.007746<\/a><\/strong>
Presents evidence that structured discharge planning and follow-up improve outcomes.<\/p>\n

Medicare Payment Advisory Commission. (2021). Report to the Congress: Medicare and the Health Care Delivery System. https:\/\/www.medpac.gov<\/a><\/strong>
Official policy document showing the cost and penalty impact of excess readmissions.<\/p>\n

Rajkomar, A., et al. (2019). Machine learning in medicine. New England Journal of Medicine, 380<\/em>(14), 1347-1358. https:\/\/doi.org\/10.1056\/NEJMra1814259<\/a><\/strong>
Explains applications of machine learning in predicting patient risk and improving clinical decisions.<\/p>\n

Rau, J. (2020). Hospitals struggle to curb readmissions. Health Affairs Blog<\/em>. https:\/\/doi.org\/10.1377\/hblog20200220.293599<\/a><\/strong>
Provides real-world examples of challenges hospitals face in meeting federal targets.<\/p>\n

Suter, W., et al. (2022). Data-driven leadership in healthcare organizations. Journal of Healthcare Leadership, 14,<\/em> 11-20. https:\/\/doi.org\/10.2147\/JHL.S344321<\/a><\/strong>
Explains how leaders use performance dashboards and analytics to guide decision-making.<\/p>\n

Wadhera, R., et al. (2020). Evaluation of the Hospital Readmissions Reduction Program. Annals of Internal Medicine, 172<\/em>(3), 177-185. https:\/\/doi.org\/10.7326\/M19-1631<\/a><\/strong>
Finds readmission penalties have reduced rates but may also increase mortality in some patient groups.<\/p>\n

Zhang, Z., et al. (2023). Social determinants of health and hospital readmissions. BMC Health Services Research, 23<\/em>(1), 119. https:\/\/doi.org\/10.1186\/s12913-023-09001-8<\/a><\/strong>
Highlights how housing instability, poverty, and food insecurity raise risk of readmission.<\/p>\n

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Causal Factors and Metrics<\/h3>\n
\n
\n\n\n\n\n\n\n\n\n\n
Factor<\/th>\nCausal\/Contributing<\/th>\nUnit of Measurement<\/th>\nSource<\/th>\n<\/tr>\n<\/thead>\n
Poor discharge planning<\/td>\nCausal<\/td>\n% of discharges without follow-up scheduled<\/td>\nMcIlvennan et al., 2021<\/td>\n<\/tr>\n
Comorbid conditions<\/td>\nCausal<\/td>\nAverage Charlson Comorbidity Index score<\/td>\nKansagara et al., 2020<\/td>\n<\/tr>\n
Socioeconomic barriers<\/td>\nContributing<\/td>\n% of patients from zip codes below federal poverty line<\/td>\nZhang et al., 2023<\/td>\n<\/tr>\n
Medication adherence<\/td>\nContributing<\/td>\n% of patients filling prescriptions within 7 days<\/td>\nAhn et al., 2021<\/td>\n<\/tr>\n
Hospital resource allocation<\/td>\nCausal<\/td>\n$ spent on transitional care per patient<\/td>\nSuter et al., 2022<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\n
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Data Analysis Method<\/h3>\n

Method:<\/strong> Benchmark variance analysis of hospital readmission rates against national targets.<\/p>\n

Rationale:<\/strong> Readmission penalties are calculated by variance from expected benchmarks. Measuring differences between local data and federal standards shows the gap and highlights areas for improvement.<\/p>\n

Source:<\/strong> Figueroa et al. (2020).<\/p>\n

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Data Sets<\/h3>\n

Path 2: Public Data Sets<\/strong><\/p>\n

Factor #1 Examined:<\/strong> 30-day readmission rate for heart failure patients.<\/p>\n