Create a Data Improvement Plan, for your organization. The purpose of this assignment is to demonstrate your competency in discussing key elements that are essential for putti

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Week 5 Assignment: Plan Data Collection Effort for Informed Decision Making

Shermaine M. Stuckey

DHA-7012: Data-Driven Decision Making

Northcentral University

Dr. Linda Mast

January 28, 2024

Data Category	Strategic measure	Stakeholders	Source of data	Type of data (units)
Clinical	Patient Satisfaction Score	Healthcare Providers, Patients	Surveys, Interviews	Percentage
Clinical	Readmission Rate	Healthcare Providers, Regulatory Agencies	Internal Records, Government Databases	Rate
Operational	Appointment Wait Times	Leadership, Patients	Internal Records	Time (minutes)
Operational	Staff Productivity	Management, Staff	Time and Task Tracking Systems	Percentage
Financial	Cost per Patient Encounter	CFO, Finance Team	Internal Financial Records	Monetary Value
Financial	Revenue Growth Rate	Leadership, Investors	Financial Reports	Percentage
Benchmarking	Hospital Bed Utilization	Operations, Competitor Analysis	Industry Reports, Competitor Data	Percentage
Benchmarking	Physician Productivity Compared to Industry Standards	Medical Directors, Competitor Analysis	Industry Benchmarks, Competitor Data	Rate

Process Narrative

The data collection efforts began with detailed initiatives in stakeholder involvement, such as consultations on healthcare providers, leaderships patient population regulatory agencies and financial teams. By this manner of collaboration, it was possible to guarantee that the data program would be related to strategic purposes of organization. This was then followed by a robust collaboration with the data team, involving disparate stakeholders to determine relevant descriptive categories and measures that were practically viable. Enrichment of decision making was achieved by conducting a literature review that focused on industry regulations and current trends in health care (Jo & Gebru, 2020).

Using the internal data, patient records analysis along with financial reports and operational metrics for strengths and opportunities were implemented following ideas gotten from research (Comfort, Kapucu, Ko, Menoni, & Siciliano, 2020). At the same time, external data exploration used government databases and industry reports to supplement their insights. This bifocal approach was designed to provide a complete picture of the healthcare landscape.

Rationale for Data Sources and Types

In healthcare settings, different data sources can be used from various records of clinical or evaluations of operational research collected from other sources (Kwok, Muntean, Mallen, & Borovac, 2022). When using the selected data, it is important to understand the purpose for collecting the data. The extent of the data collected, and its completeness can become questionable, if not obtained correctly. Examples of data sources:

Patient Satisfaction Score: Surveys and interviews were selected to provide patients with a chance to voice subjective data directly about how they feel.

Readmission Rate: Internal records and government databases were used to determine readmission rates, allowing a sufficient calibration of the efficacy in clinical care and compliance.

Appointment Wait Times: Using internal records, real-time data was obtained for better operational efficiency and to improve patient experience.

Staff Productivity: To gain precise insights, the time and task tracking systems were chosen to ensure proper resource distribution allocation optimization.

Cost per Patient Encounter: Resource utilization efficiency at a detailed level was achieved through internal financial reports.

Revenue Growth Rate: Financial reports provided a detailed overview of the company’s financial performance.

Hospital Bed Utilization: Strategic resource allocation conformed to industry reports and competitor data in standards.

Physician Productivity: During the evaluation phase, then industry norms and competitor data assessed physician productivity compared to competing organizations baseline performances.

Conclusion

Using data for informed decision making helps the stakeholders make the best decision for the organization. By utilizing the data in the table, there is potential for positive impact on the organization, the employees, and the patients (Batko & Slezak, 2022). It allows the organization to figure out what is working best and what can be better improved for the future.

References Batko, K., & Slezak, A. (2022). The use of Big Data Analytics in healthcare. Journal of Big Data. doi:https://doi.org/10.1186/s40537-021-00553- Comfort, L. K., Kapucu, N., Ko, K., Menoni, S., & Siciliano, M. (2020). Crisis Decision-Making on a Global Scale: Transition from Cognition to Collective Action under Threat of COVID-19. Retrieved from https://onlinelibrary.wiley.com/doi/10.1111/puar.13252 Jo, E. S., & Gebru, T. (2020). Lessons from archives: strategies for collecting sociocultural data in machine learning. Conference on Fairness, Accountability, and Transparency, 306-316. Retrieved from https://dl.acm.org/doi/10.1145/3351095.3372829 Kwok, C. S., Muntean, E.-A., Mallen, C. D., & Borovac, J. A. (2022). Data Collection Theory in Healthcare Research: The Minimum Dataset in Quantitative Studies. Clinics and Practice, 832-844. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9680355/