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Home healthcare services play a crucial role in supporting patients with chronic illnesses or those recovering from acute conditions. Monitoring the outcomes of home healthcare services is essential for assessing the effectiveness of care delivery, optimizing patient outcomes, and ensuring patient safety. By tracking relevant metrics and trends, healthcare providers can identify areas for improvement and tailor interventions to meet the individual needs of patients receiving home healthcare services.

Preparation: For this project, you will describe how and why you are monitoring the outcomes of home healthcare services to improve care. You will begin by creating a spreadsheet that contains pertinent data categories and illustrates data trending over time.

Create a Spreadsheet:

1. Identify the Quality Outcome: Select a specific quality outcome related to home healthcare services. For example, you may choose to focus on patient adherence to treatment plans, incidence of hospital readmissions, or patient satisfaction with care received.

2. Identify Benchmarks: Determine benchmarks associated with the chosen outcome and how often they are measured. Benchmarks may include national standards, quality improvement targets, or organizational goals.

3. Data Categories: Determine the categories of data to measure in support of ongoing development of home healthcare services. This may include patient demographics, clinical assessments, interventions provided, patient-reported outcomes, and caregiver feedback.

4. Create the Spreadsheet: Develop a spreadsheet that includes baseline data for each category and additional data illustrating trending over time. Utilize appropriate visualization tools such as charts, graphs, or tables to present the data effectively.

Create a Video Recording:

1. Data Analysis: Analyze the collected data to identify trends, patterns, and areas for improvement in home healthcare services.

2. Conclusions and Recommendations: Present your conclusions and recommendations to administrators in a recorded video presentation.

· Describe the outcomes you are supporting and the benchmarks related to those outcomes.

· Explain your data collection methods and rationale for selecting specific data categories.

· Evaluate the data measures and trends related to the chosen quality outcome.

· Support your interpretation with relevant outside sources and demonstrate how you used the data to reach your conclusions.

Competencies Measured:

1. Apply Data Management Techniques: Analyze the what, why, and how to measure specific quality outcomes related to home healthcare services.

2. Create Data Representation Methods: Evaluate data measures and trends to effectively report on the quality of home healthcare services.

3. Articulate Strategies for Data Analysis: Create a data spreadsheet illustrating trending data for home healthcare services and articulate strategies for querying and generating reports from health information system databases.

4. Communicate Technical Standards: Communicate your findings and recommendations effectively in a video presentation, demonstrating proficiency in technical standards related to healthcare informatics.

Video Presentation and Spreadsheet

Instructions Resources Attempt 1 available Attempt 2Attempt 3

InstructionsResourcesAttempt 1 availableAttempt 2Attempt 3

Create a spreadsheet that illustrates data trending over time related to a particular line of service that you are monitoring. Then, record a 5-7 minute professional video presentation in which you describe how and why you are monitoring the outcomes for a particular service line in order to improve care.

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Introduction

To develop strategies for service line development, leaders need to know how to monitor outcomes that provide pertinent information in support of strategic development. Leaders then need to be skilled at communicating their analysis of those outcomes to other leaders in the organization in order to create awareness and build support for any proposed strategies arising from the analysis.

Preparation

For this assessment, you will record a video presentation in which you describe how and why you are monitoring the outcomes for a particular service line in order to improve care. You will begin by creating a spreadsheet that contains the pertinent data categories and illustrates data trending over time.

Create a spreadsheet:

· Analyze the what, why, and how to measure for a specific quality outcome related to a service line in a practice setting or organization.

· Chose a specific quality outcome related to a service line in a practice setting or organization.

· Identify the benchmarks associated with that outcome and how often the benchmarks are measured.

· Determine the categories of data you will be measuring in support of the ongoing development of the service line.

· Create a spreadsheet that includes baseline data for each category and additional data that illustrates the trending over time for that category.

· Be ready to explain why you chose these particular data sets and how you intend to use the data to improve outcomes.

Create a video recording:

· Analyze your data and present your conclusions and recommendations to administrators in a recorded video presentation.

· Describe the outcomes you are supporting and the benchmarks related to that outcome.

· Describe your data collection methods and rational.

· Evaluate the data measures and data trending for the specific quality outcome related to the service line.

· Share your interpretation of the data related to the benchmarks of the outcome.

· Support your interpretation with any relevant outside sources.

· Demonstrate to the administrators how you used the data in the spreadsheet to reach your conclusions.

Additional Requirements:

Video Recording:

· Your video recording should be between 3-7 minutes in length. You must appear in professional attire and with a professional demeanor, as if you are presenting to the administration of your organization.

· Kaltura is the preferred tool for creating your video. Any tools other than Kaltura should be cleared with your instructor prior to using.

Competencies Measured

By successfully completing this assessment, you will demonstrate your proficiency in the following course competencies and assessment criteria:

· Competency 1: Apply data management techniques to decision making in nursing practice.

· Analyze the what, why, and how to measure for a specific quality outcome related to a service line in a practice setting or organization.

· Competency 2: Create various data representation methods for reporting and professional communications.

· Evaluate data measures and data trending for a specific quality outcome related to a service line in a practice setting or organization.

· Competency 3: Articulate strategies for querying and generating reports from health information system databases.

· Create a data spreadsheet that illustrates trending data for the service line.

· Competency 4: Communicate technical standards as they relate to various informatics technologies.

· Develop a scholarly video presentation of the measures and data for the service line.

Use the resources linked below to help complete this assessment.

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Producing Applications in Practice With Data

· McGonigle, D., & Mastrian, K. (2022).  Nursing informatics and the foundation of knowledge (5th ed.). Jones & Bartlett.  Available in the courseroom via the VitalSource Bookshelf link.

· Chapter 23, "Translational Research: Generating Evidence for Practice."

· Kurniati, A. P., Rojas, E., Hogg, D., Hall, G., & Johnson, O. A. (2018, November 29).  The assessment of data quality issues for process mining in healthcare using Medical Information Mart for Intensive Care III, a freely available e-health record database .  Health Informatics Journal, 1–16.

· Shameer, K., Perez-Rodriguez, M. M., Bachar, R., Li, L., Johnson, A., Johnson, K. W., Glicksberg, B. S., Smith, M. R., Readhead, B., Scarpa, J., Jebakaran, J., Kovatch, P., Lim, S., Goodman, W., Reich, D., Kasarskis, A., Tatonetti, N. P., & Dudley, J. T. (2018). (2018).  Pharmacological risk factors associated with hospital readmission rates in a psychiatric cohort identified using prescriptome data mining .  BMC Medical Informatics and Decision Making,  18(Suppl. 3), 1–19.

· Vial, G. (2019).  Understanding digital transformation: A review and a research agenda .  The Journal of Strategic Information Systems,  28(2), 118–144.

Evidence-Based Development

· Ansaripour, A., Zendehdel, K., Tadayon, N., Sadeghi, F., Uyl-de Groot, C. A., & Redekop, W. K. (2018).  Use of data-mining to support real-world cost analyses: An example using HER2-positive breast cancer in Iran .  PloS One,  13(10), 1-16.

· Bjarnadottir, R. I., Bockting, W., Yoon, S., & Dowding, D. W. (2019).  Nurse documentation of sexual orientation and gender identity in home healthcare: A text mining study .  CIN: Computers, Informatics, Nursing,  37(4), 213-221.

· Ghorbani, R., & Ghousi, R. (2019).  Predictive data mining approaches in medical diagnosis: A review of some diseases prediction .  International Journal of Data and Network Science,  3(2), 47-70.

· Shi, J., Zheng, M., Yao, L., & Ge, Y. (2018).  Developing a healthcare Dataset Information Resource (DIR) based on semantic web .  BMC Medical Genomics,  11(Suppl. 5), 1-14.

Producing Applications in Practice With Evidence-Based Information

· Ahmed, S., Seddawy, A. I. E., & Nasr, M. (2019).  A proposed framework for detecting and predicting diseases through business intelligence applications .  International Journal of Advanced Networking and Applications,  10(4), 3951-3957.

· Ismail, W. N., Hassan, M. M., Alsalamah, H. A., & Fortino, G. (2018, August 1).  Mining productive-periodic frequent patterns in tele-health systems .  Journal of Network and Computer Applications,  115, 33-47.

· Jones, M. (2019).  What we talk about when we talk about (big) data .  Journal of Strategic Information Systems,  28(1), 3-16.

· Soleimani-Roozbahani, F., Ghatari, A. R., & Radfar, R. (2019).  Knowledge discovery from a more than a decade studies on healthcare big data systems: A scientometrics study .  Journal of Big Data,  6(1), 1-15.

· Yoo, S., Kim, S., Kim, E., Jung, E., Lee, K., & Hwang, H. (2018).  Real-time location system-based asset tracking in the healthcare field: Lessons learned from a feasibility study .  BMC Medical Informatics and Decision Making,  18(80), 1-10.

Scope and Standards of Practice

· American Nurses Association. (2015).  Nursing informatics:   Scope and standards of practice  (2nd ed.) . Author.