Week 5 Assignment: Data Collection Effort For Information

94202013week 5 Assignment Plan Data Collectioneffort For Inform

There are times when you do not have internal data to inform your organizational growth and performance. In such cases, you will have to either collect your own data or look externally for data. These data can serve as a baseline for your strategic goals and objectives.

Part 1: To ensure that your organization builds a solid data program, you have decided to provide the implementation team with a plan for the type of data needed. Based on your reading for the week, populate the table below with information that you deem most appropriate.

As the table shows, highlight two clinical, two operational, two financial, and two benchmarking data that your stakeholders are interested in capturing at this time. Table 2. Template for Informed Decision Making Data

Category | Strategic measures | Stakeholders | Source of data | Type of data (units) | Provide examples of measures that align with the data categories: Players or actors interested in this measure (e.g., board, leadership, providers, patients, regulatory agencies, etc.)

Clinical | | | | |

Operational | | | | |

Financial | | | | |

Benchmarking | | | | |

Part 2: Narrative of Data Collection Process and Rationale

Provide a brief narrative of the process you took to get the information you used to populate the completed table. Also, provide your rationale for the source of data and type of data you identified in the table. Length: 1-3 pages, including 1 table, not including title and reference pages. References: Include a minimum of 4 scholarly resources. The completed assignment should address all of the assignment requirements, exhibit evidence of concept knowledge, and demonstrate thoughtful consideration of the content presented in the course. The writing should integrate scholarly resources, reflect academic expectations and current APA standards.

Paper For Above instruction

In the pursuit of effective organizational decision-making, especially within healthcare settings, it is imperative to gather relevant and accurate data. When internal data is insufficient, external data sources become vital for establishing baselines and informing strategic growth. This paper details the process undertaken to populate a data collection plan for an organization, focusing on clinical, operational, financial, and benchmarking data, along with the rationale behind these choices.

To begin, I conducted a comprehensive review of current literature on data-driven decision-making in healthcare to understand the types of data most relevant to organizational success (McGinnis & Foege, 1993; Dubowitz et al., 2015). I prioritized selecting data that align with broader strategic objectives, stakeholder interests, and quality improvement initiatives. The process involved identifying potential data sources—both internal and external—evaluating their reliability, accessibility, and relevance to the specific measures needed.

In populating the table, I selected two clinical measures: infection rates and patient readmission rates. For infection rates, stakeholders such as infection control teams and regulatory agencies like the CDC are interested, as these measures affect patient safety and compliance with standards (magill et al., 2014). Readmission rates are critical for healthcare leaders and providers, reflecting care quality and informing resource allocation (Jencks et al., 2009). The sources for these data include hospital electronic health records (EHRs), national databases, and quality reporting organizations. The data are quantitative, expressed in percentages or rates, providing clear benchmarks for improvement.

Operational data selected included patient wait times and staff turnover rates. Operational leaders and front-line staff are stakeholders interested in these measures, as they directly impact patient experience and staff efficiency (Peters et al., 2015). Data sources such as patient surveys and HR records were chosen for their accessibility and direct relevance. These measures are expressed in units of time or percentages, offering straightforward indicators for operational efficiency.

Financial data involved hospital operating margins and cost per patient. Finance executives, board members, and regulatory bodies are key stakeholders concerned with financial health and sustainability. Data collection came from financial statements, billing systems, and government reimbursements data (Higgins et al., 2013). The data are numerical and expressed in monetary units, facilitating analysis of financial performance over time.

Benchmarking data focused on comparing hospital performance with national standards, including average length of stay and patient satisfaction scores. These measures are useful for strategic positioning and quality enhancement (Sandler et al., 2010). External sources such as national registries and standardized surveys provide these data. They are expressed as rates, scores, or percentages, enabling comparative analysis across organizations (Burns & Naylor, 2017).

The rationale for these choices lies in the need to construct a comprehensive view of organizational performance, integrating clinical outcomes, operational efficiency, financial health, and external competitiveness. Utilizing varied sources ensures data reliability and offers multi-faceted insights. Emphasizing quantitative measures allows for precise tracking of progress and targeted interventions, aligning with evidence-based management principles (Swayne et al., 2013).

In summary, this process involved identifying critical performance areas, selecting stakeholder-relevant measures, evaluating credible data sources, and choosing appropriate data types. The structured approach ensures that the organization can develop a robust data program, supporting informed decision-making and continuous improvement.

References

  • Burns, L. R., & Naylor, M. (2017). Benchmarking in healthcare: concepts and practices. Healthcare Management Review, 42(3), 176-184.
  • Dubowitz, T., Eibner, C., & Bavly, K. (2015). The use of big data in healthcare. Journal of Healthcare Management, 60(2), 102-113.
  • Higgins, T., Greer, S. J., & Willard, M. (2013). Financial management in healthcare organizations. Journal of Healthcare Finance, 40(4), 36-46.
  • Jencks, S. F., Williams, M. V., & Coleman, E. A. (2009). Rehospitalizations among patients in Medicare fee-for-service. New England Journal of Medicine, 360(14), 1418-1428.
  • Magill, S. S., et al. (2014). Multistate point-prevalence survey of healthcare-associated infections. New England Journal of Medicine, 370(13), 1198-1208.
  • McGinnis, J. M., & Foege, W. H. (1993). Actual causes of death in the United States. JAMA, 270(18), 2207-2210.
  • Peters, B., et al. (2015). Improving patient flow: An operational perspective. Journal of Hospital Administration, 4(1), 32-41.
  • Sandler, J. et al. (2010). Benchmarking hospital performance: methodology and application. Quality Management in Healthcare, 19(4), 215-223.
  • Swayne, L. E., et al. (2013). Managing health organizations. Jossey-Bass.

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