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
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 putting together a solid data improvement plan.c
Drawing from the activities of the past 6 weeks, use the outline below to develop your plan:
- Introduction section (no more than 2 pages):
- Highlights the benefits of data for a healthcare organization.
- Addresses the data related pain points in your organization (data breach, privacy issues, etc.).
- Identifies the purpose statement.
- Data Privacy and Security Plan (no more than 2 pages): Addresses how your organization is handling:
- HIPAA Regulations
- Privacy Rule
- Security Rule
- Administrative Safeguards
- Physical Safeguards
- HITECH Act
- Data Use Agreement
- HIPAA Regulations
- Needs Assessment section that addresses:
- The approach you are taking to learn more about your organization's data needs
- The stakeholders you reached out to
- Identify key types of healthcare data you will be collecting and your rationale for doing so (no more 3 pages), including:
- Clinical
- Operational
- Financial
- Benchmarks
- Incorporate the table you developed in Week 5 into your plan
- Data use (no more than 2 pages)
- Deidentification
- Data use agreement
- Breach notification and Research
2
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/
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