Healthcare Data Collection and Plan for Analysis

Introduction

The practice change intervention in the East Orange Hospital Clinic is dedicated to influencing clients’ behavior toward their follow-up appointments. The majority of patients represent the impoverished population, and the preceptor noticed the tendency to skip additional visits among them. The lack of attendance at follow-ups is a serious issue because it influences clients’ outcomes and decreases the quality of healthcare services; thus, the practice change was initiated (Ofei-Dodoo et al., 2019). The project included the evidence-gathering, planning, and execution stages and was developed to impact patients through education, notification, and external factors addressing. Several months have passed since the beginning of the practice change, and the team accumulated a measurable scope of information necessary to evaluate the intervention’s efficiency. This paper aims to describe the data collection and analysis planning and discuss the developed scorecard and dashboard’s functionality.

Data Collection

A correct data collection strategy is essential for the intervention to change patients’ behavior toward follow-up appointments with their doctor. Gathering information became a part of an action plan for all participants; every stakeholder was encouraged to log feedback, observe their practices’ outcomes, and keep a quantitative journal about appointments. The team also created a template for data collection to make it less complicated for the participants to identify what to gather (Amer et al., 2022). Consequently, a massive scope of data was collected, the most valuable of which was patients’ feedback, their responsiveness to notifications, and the number of attended follow-up appointments.

The three main parts of intervention were patient education, communication, and notification, and physicians, practitioners, and administrators were involved in addressing these aspects, respectively. Every stakeholder was encouraged to interact with the impoverished clients, register them to track if they attended the follow-up visit, and gather data about their experience with healthcare services. Furthermore, physicians took notes about patients’ awareness of the outcomes of skipping appointments, practitioners explored if external obstacles to accessing medical care exist, and administrators counted how many individuals responded to the notification. Although the gathered data was diverse, it was sufficient to develop a framework for the scorecard and compare the current situation to the pre-intervention one (Bohm et al., 2021). As each impoverished patient interacted with a physician, practitioner, and administrator, the different information collected by stakeholders could be assembled into a client’s profile. Merging and sorting the data helped the team recognize patterns, identify the need for assistance from executives and policy-makers, and adjust the initial intervention program to increase effectiveness.

Analysis Plan

The scope of data collected during the intervention has been registered as a database by the preceptor and is being continuously updated. Every weekly meeting has a feedback gathering part where all stakeholders forward the information and discuss the new and uncommon patterns or details they noticed. The practice is useful for the intervention because it helps keep the scoreboard up to date and timely address the problems or obstacles in achieving the goals (Bohm et al., 2021). To assess the efficiency of intervention developed to influence the impoverished patients’ attitude toward attending follow-up visits, data analysis must contain qualitative and quantitative aspects. Indeed, the former was retrieved from conversations with clients, which became a valuable source of information, and the latter was collected by checking their appointments and responses to notifications.

Data analysis should reveal if the changes positively impacted patients’ attendance at the follow-up visits and display the decreased number of skipped visits. For a successful statistical comparison, the team identified the impoverished clients and collected information about their appointments within six months before the intervention (Amer et al., 2022). The difficulty in assessing the intervention is related to the unstable stream of clients, which depends on the season, chronic conditions’ improvement or worsening, and the need for follow-up visits. Consequently, the statistics were compared based on the number of patients, 200 before and 200 after the practice change implementation, rather than on a time-related basis. The qualitative data were analyzed by identifying the patterns in patients’ behaviors by identifying the prevalent behaviors and assumptions about healthcare services (Coller, 2018). The effectiveness of the notification practice was evaluated by calculating the percentage of clients who responded to the administrator and then visited the follow-up appointment.

Scorecard and Dashboard Functionality

The scorecard table was developed to evaluate the intervention’s current and final results and contains the columns with the initial objectives, such as decreasing the number of skipped follow-ups by 75% and improving patient education practices. It also included the financial, learning, client-based, and internal operations-related factors considered during the intervention and required optimization to address the issues. The scorecard’s main aim in the practice change initiative is to demonstrate the efficiency of the selected approaches and to determine weak points which require additional attention (Coller, 2018). Preceptor also developed a brief version to present to the executives to propose external financial and policy-making support. Dashboard’s data was divided into three aspects of initiative – patient education, communication, and notification to make the results visible and identifiable for each section (Victor & Farooq, 2021). These management and assessment tools were integrated to measure the effectiveness of the intervention, and they were functional because of the team’s accuracy and continuous data collection.

Conclusion

Data collection is essential for successful practice change, and the intervention to influence patients’ attendance on follow-up visits demonstrated the importance of gathering information throughout the entire project. Analysis was performed based on the three main aspects – education, communication, and notification, and then the results were represented qualitatively and quantitatively. A scorecard and dashboard with the intervention’s objectives, recourses, and timeline helped the team measure the strategies’ efficiency and timely notice the weak points to address.

References

Amer, F., Hammoud, S., Khatatbeh, H., Lohner, S., Boncz, I., & Endrei, D. (2022). The deployment of a balanced scorecard in health care organizations: is it beneficial? A systematic review. BMC Health Services Research, 22(1), 1-14, Web.

Bohm, V., Lacaille, D., Spencer, N., & Barber, C. E. H. (2021). Scoping review of balanced scorecards for use in healthcare settings: Development and implementation. BMJ Open Quality, 10(3), e001293. Web.

Coller, B. S. (2018). Expand the scorecard for healthcare reform to achieve a better result and enhance clinical and translational science. Journal of Clinical and Translational Science, 2(5), 276-279, Web.

Ofei-Dodoo, S., Kellerman, R., Hartpence, C., Mills, K., & Manlove, E. (2019). Why patients miss scheduled outpatient appointments at urban academic residency clinics: A qualitative evaluation. Kansas Journal of Medicine, 12(3), 57, Web.

Victor, S., & Farooq, A. (2021). Dashboard visualisation for healthcare performance management: Balanced scorecard method. Asia Pacific Journal of Health Management, 16(2), Web.

Cite this paper

Select style

Reference

NursingBird. (2024, November 26). Healthcare Data Collection and Plan for Analysis. https://nursingbird.com/healthcare-data-collection-and-plan-for-analysis/

Work Cited

"Healthcare Data Collection and Plan for Analysis." NursingBird, 26 Nov. 2024, nursingbird.com/healthcare-data-collection-and-plan-for-analysis/.

References

NursingBird. (2024) 'Healthcare Data Collection and Plan for Analysis'. 26 November.

References

NursingBird. 2024. "Healthcare Data Collection and Plan for Analysis." November 26, 2024. https://nursingbird.com/healthcare-data-collection-and-plan-for-analysis/.

1. NursingBird. "Healthcare Data Collection and Plan for Analysis." November 26, 2024. https://nursingbird.com/healthcare-data-collection-and-plan-for-analysis/.


Bibliography


NursingBird. "Healthcare Data Collection and Plan for Analysis." November 26, 2024. https://nursingbird.com/healthcare-data-collection-and-plan-for-analysis/.