Assessment 2: Data Review Project Proposal Paper

Assessment 2: Data Review Project Proposal Paper

Assessment 2: Data Review Project Proposal Paper

In the past decades, two themes that have dominated mental health care are personal recovery and community living for individuals experiencing mental illness. As such, the number of individuals sent back to live in the community has substantially arisen due to deinstitutionalization, especially in developed nations (Lassemo et al., 2021). While the process has been associated with a subjective achievement of meaningful lives, problems have arisen, leading to poor health outcomes among patients with mental health issues. For example, there have been observed rates of hospital readmissions that have further impacts on the patients. Therefore, the purpose of this assignment is to select a topic for the capstone project and describe it. In addition, key performance indicators and outcomes for the proposed project and supporting sources will be explored. Besides, the data analysis framework to be applied is also explored.

Statement of the Problem

Hospital readmissions lead to adverse consequences such as higher healthcare costs, disruption of the individuals and their families, risk of healthcare-associated infections, increased length of stay, and higher mortality rates (Upadhyay et al., 2019)

Key Factors that Directly Influence the Problem

Key Factors that Directly Influence the Problem [aka Performance Indicators]

The factors that directly relate to the problem are total readmissions, total unplanned readmission, and average readmissions per unit per month (Upadhyay et al., 2019)

The factor that Directly Relates to the Problem	Precise Unit of Measurement (Days, Dollars, %, etc.)	Authoritative Source(s) for Factor and Unit of Measurement
1.      Total unplanned readmissions	The number of patients readmitted to the facility unplanned	Phillips et al.(2020)
2.      Total readmissions	The total number of patients readmitted to the psychiatry units	Phillips et al.(2020)
3.      Average readmissions per unit per month	Number of readmissions calculated as monthly averages	Phillips et al.(2020)

Value Proposition to the Organization

The project will aim at reducing the rates of readmission among patients with mental health issues by analyzing the data trends to reveal the organizational causes.

Value Proposition/Contribution to My Professional Interests/Goals

The project will help develop my data analysis skills for quality improvement in various care settings.

Background: Review of the Literature

Authoritative Source                              (APA Format)	How the Source Directly Relates to the Problem (One-Sentence Summary)
Lassemo, E., Myklebust, L. H., Salazzari, D., & Kalseth, J. (2021). Psychiatric readmission rates in a multi-level mental health care system–a descriptive population cohort study. BMC Health Services Research, 21(1), 1-15. https://doi.org/10.1186/s12913-021-06391-7	This source shows that the problem of hospital readmission exists globally and at different rates.
Phillips, M. S., Steelesmith, D. L., Campo, J. V., Pradhan, T., & Fontanella, C. A. (2020). Factors associated with multiple psychiatric readmissions for youth with mood disorders. Journal of the American Academy of Child & Adolescent Psychiatry, 59(5), 619-631. https://doi.org/10.1016/j.jaac.2019.05.024.	Explores factors connected with psychiatric readmissions and units of measurement.
Kim, B., Weatherly, C., Wolk, C. B., & Proctor, E. K. (2019). Measurement of unnecessary psychiatric readmissions: a scoping review protocol. BMJ Open, 9(7), e030696. http://dx.doi.org/10.1136/bmjopen-2019-030696	This source analyzes the ethical consideration around psychiatric readmissions, such as unnecessary readmissions.
Benjenk, I., & Chen, J. (2018). Effective mental health interventions to reduce hospital readmission rates: a systematic review. Journal of Hospital Management and Health Policy, 2. https://doi.org/10.1016/j.jaac.2019.05.024	This source highlights the existence of the problem and some strategies to reduce it.
Morel, D., Kalvin, C. Y., Liu-Ferrara, A., Caceres-Suriel, A. J., Kurtz, S. G., & Tabak, Y. P. (2020). Predicting hospital readmission in patients with mental or substance use disorders: a machine learning approach. International Journal of Medical Informatics, 139, 104136. https://doi.org/10.1016/j.ijmedinf.2020.104136	The source also indicates that the problem is common and discusses ways of predicting hospital readmissions.
Edgcomb, J. B., Sorter, M., Lorberg, B., & Zima, B. T. (2020). Psychiatric readmission of children and adolescents: a systematic review and meta-analysis. Psychiatric Services, 71(3), 269-279. https://doi.org/10.1176/appi.ps.201900234	A systematic review that explored the readmission rates of children and adolescents with mental health challenges.
Han, X., Jiang, F., Tang, Y., Needleman, J., Guo, M., Chen, Y., … & Liu, Y. (2020). Factors associated with 30-day and 1-year readmission among psychiatric inpatients in Beijing China: a retrospective, medical record-based analysis. BMC psychiatry, 20(1), 1-12. Doi: 10.1186/s12888-020-02515-1	This source underlines the fact that psychiatric readmissions have various negative impacts on patients and families in addition to increasing health care costs. The study, therefore, explored various factors connected to psychiatric readmissions. Some of them include the length of hospital stay, previous psychiatric admissions, the existence of medical comorbidities, and residing in urban areas.
Del Favero, E., Montemagni, C., Villari, V., & Rocca, P. (2020). Factors associated with 30-days and 180-days psychiatric readmissions: A snapshot of a metropolitan area. Psychiatry Research, 292, 113309. https://doi.org/10.1016/j.psychres.2020.113309	The source explored psychiatric readmission as a quality indicator in the mental health cycles. It shows that readmission rates in psychiatric settings in metropolitan areas are as high as 16%. In addition, discharging a patient to a community Mental health services is one of the nest protective factors for psychiatric readmissions.
Baeza, F. L. C., da Rocha, N. S., & de Almeida Fleck, M. P. (2018). Readmission in psychiatry inpatients within a year of discharge: the role of symptoms at discharge and post-discharge care in a Brazilian sample. General Hospital Psychiatry, 51, 63-70. https://doi.org/10.1016/j.genhosppsych.2017.11.008	This article also focuses on psychiatric readmissions and underlines that the chances of being readmitted among psychiatric patients are increased by the number of previous psychiatric admissions. It also highlights that the rates of hospital readmissions among psychiatric patients are high.
Moore, C. O., Moonie, S., & Anderson, J. (2019). Factors associated with rapid readmission among Nevada state psychiatric hospital patients. Community mental health journal, 55(5), 804-810. Doi: 10.1007/s10597-018-0316-y	This source also explores readmissions among psychiatric patients within 30 days of hospital discharge. The sources also indicate that these readmission cases are associated with high costs and financial implications. The study suggests that focusing on individuals’ history of readmissions can be key in modifying various factors to lower the rates of readmission.
Ortiz, G. (2019). Predictors of 30-day postdischarge readmission to a multistate national sample of state psychiatric hospitals. Journal for Healthcare Quality, 41(4), 228. https://dx.doi.org/10.1097%2FJHQ.0000000000000162	This source also supports the existing high prevalence rates of hospital readmissions among psychiatric patients. It also explored some of the clinical and demographic factors connected to such readmissions.

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Data Analysis Framework

The chosen framework for data analysis is the balanced scorecard framework. The reason for choosing this framework is that the proposed possible solution to the problem of psychiatric readmissions is will be like a new service line. The solution will need to be implemented in the organization to help overcome the problem. The balanced scorecard framework is key in effectively analyzing performance data with varying complexity (Psarras et al., 2020).

Presentation of the Graphics

Overview

Hospital readmissions in psychiatric health settings lead to various adverse outcomes such as higher health care spending and possible exposure to hospital-acquired infections. Various data will be used to illustrate the problem. Some of them include rates of readmission within 30-days of discharge, readmission within 100-days of discharge, total unplanned readmissions, and average monthly readmissions.

 [Graphic #1]:A graph indicating daily readmission incidences for the local health facility. The targeted year is 2021

 [Graphic #2] A line curve showing the total readmissions within 30-days of discharge and 100 days of discharge for the local health facility. The year of focus is 2021.

Balanced Scorecard

Organization’s Directional Strategy: (Growth, Reduction, Quality Leader, et cetera)

Business	Finance	Customer	Organizational Learning/Growth
Key Performance Indicator and Metric Reduce the rates of psychiatric hospital readmission	Reduce healthcare spending related to hospital readmissions by 60%	Improve the patients’ satisfaction scores related to mental health by at least 20%.	-Improvement of communication between the care teams to help reduce readmission rates. -Offering clear discharge direction and education to the patients and their families to boost chances of staying healthy at home. -Equipping the psychiatric staff with adequate screening knowledge to help them accurately identify patients at risk of readmission.

 

Conclusion

Among the most common problem encountered in managing mental health patients is hospital readmissions. While some of the causes result from the patients, some of the causes are related to how the patients are handled in the care environment by the healthcare professionals, especially during discharge. Therefore, having identified the key performance indicators for the improvement of the situation, this write-up has explored various aspects such as the data analysis framework and more authoritative sources that support the existence of the problem.

References

Baeza, F. L. C., da Rocha, N. S., & de Almeida Fleck, M. P. (2018). Readmission in psychiatry inpatients within a year of discharge: the role of symptoms at discharge and postdischarge care in a Brazilian sample. General Hospital Psychiatry, 51, 63-70. https://doi.org/10.1016/j.genhosppsych.2017.11.008

Benjenk, I., & Chen, J. (2018). Effective mental health interventions to reduce hospital readmission rates: a systematic review. Journal of Hospital Management and Health Policy, 2. https://doi.org/10.1016/j.jaac.2019.05.024.

Del Favero, E., Montemagni, C., Villari, V., & Rocca, P. (2020). Factors associated with 30-days and 180-days psychiatric readmissions: A snapshot of a metropolitan area. Psychiatry Research, 292, 113309. https://doi.org/10.1016/j.psychres.2020.113309

Edgcomb, J. B., Sorter, M., Lorberg, B., & Zima, B. T. (2020). Psychiatric readmission of children and adolescents: a systematic review and meta-analysis. Psychiatric Services, 71(3), 269-279. https://doi.org/10.1176/appi.ps.201900234.

Han, X., Jiang, F., Tang, Y., Needleman, J., Guo, M., Chen, Y., … & Liu, Y. (2020). Factors associated with 30-day and 1-year readmission among psychiatric inpatients in Beijing China: a retrospective, medical record-based analysis. BMC Psychiatry, 20(1), 1-12. Doi: 10.1186/s12888-020-02515-1

Kim, B., Weatherly, C., Wolk, C. B., & Proctor, E. K. (2019). Measurement of unnecessary psychiatric readmissions: a scoping review protocol. BMJ Open, 9(7), e030696. http://dx.doi.org/10.1136/bmjopen-2019-030696

Lassemo, E., Myklebust, L. H., Salazzari, D., & Kalseth, J. (2021). Psychiatric readmission rates in a multi-level mental health care system–a descriptive population cohort study. BMC Health Services Research, 21(1), 1-15. https://doi.org/10.1186/s12913-021-06391-7.

Moore, C. O., Moonie, S., & Anderson, J. (2019). Factors associated with rapid readmission among Nevada state psychiatric hospital patients. Community mental health journal, 55(5), 804-810. Doi: 10.1007/s10597-018-0316-y

Morel, D., Kalvin, C. Y., Liu-Ferrara, A., Caceres-Suriel, A. J., Kurtz, S. G., & Tabak, Y. P. (2020). Predicting hospital readmission in patients with mental or substance use disorders: a machine learning approach. International Journal of Medical Informatics, 139, 104136. https://doi.org/10.1016/j.ijmedinf.2020.104136.

Ortiz, G. (2019). Predictors of 30-day postdischarge readmission to a multistate national sample of state psychiatric hospitals. Journal for Healthcare Quality, 41(4), 228. https://dx.doi.org/10.1097%2FJHQ.0000000000000162

Phillips, M. S., Steelesmith, D. L., Campo, J. V., Pradhan, T., & Fontanella, C. A. (2020). Factors associated with multiple psychiatric readmissions for youth with mood disorders. Journal of the American Academy of Child & Adolescent Psychiatry, 59(5), 619-631. https://doi.org/10.1016/j.jaac.2019.05.024.

Psarras, A., Anagnostopoulos, T., Tsotsolas, N., Salmon, I., & Vryzidis, L. (2020). Applying the balanced scorecard and predictive analytics in the administration of a European funding program. Administrative Sciences, 10(4), 102. https://doi.org/10.3390/admsci10040102

Upadhyay, S., Stephenson, A. L., & Smith, D. G. (2019). Readmission rates and their impact on hospital financial performance: a study of Washington hospitals. INQUIRY: The Journal of Health Care Organization, Provision, and Financing, 56, 0046958019860386. https://dx.doi.org/10.1177%2F0046958019860386.

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Construct a draft proposal for your project, and integrate it with the organizational balanced scorecard model. There is no page limit for this assessment.

Introduction
Note: Each assessment of your capstone project is built on the work you have completed in previous assessments. Therefore, you must complete the assessments in this course in the order in which they are presented.

Writing a project proposal is an essential skill for leaders. Presenting a proposal to senior executives requires that you identify the problem, the value to the organization of solving the problem, the relevant aspects of the problem, and your recommendations for action. Your recommendations should be conveyed in a concise but thorough manner, retaining the essential information needed by the decision makers.

This assessment provides an opportunity for you to draft a proposal for your capstone project, which you will have to present to the prospective client.

Overview and Preparation
Note: This assessment incorporates and builds on the work you completed in Assessment 1.

To prepare for this assessment, complete the following:

Expand on your list of peer-reviewed or authoritative sources from Assessment 1 that substantiate your approach and method for the data review, such as accrediting body standards, accounting principles, federal laws, or Medicare conditions of participation.
Add a minimum of five relevant, authoritative sources.
Limit commercial website references to no more than two entries.
Format your citations and references using APA style.
Note: While an annotated bibliography is not a part of the graded assessment, you will need to integrate your sources into the proposal.
Review the Balanced Scorecard Example [DOCX]. You will construct a balanced scorecard table in this assessment to convey the value of your project to the organization.
Review this resource, which provides information on the steps to creating a balanced scorecard:
Balanced Scorecard Institute. (n.d.). Building and implementing a balanced scorecard: Nine steps to success. http://balancedscorecard.org/Resources/The-Nine-Steps-to-Success
Download and review the Assessment 2 Proposal Template [DOCX], which you will use to complete this assessment.
Requirements
Using the Assessment 2 Proposal Template [DOCX], draft a proposal for your data review project. After your proposal has been graded, integrate faculty feedback prior to sharing your proposal with the prospective client.

Supporting Evidence
Add a minimum of five relevant, authoritative sources, cited within the outline, in addition to the original citations from Assessment 1. Limit commercial website references to no more than two entries. Format your citations and references using APA style.

Proposal
Note: The requirements outlined below correspond to the grading criteria in the scoring guide. Be sure that your proposal addresses each point, at a minimum. You may also want to read the assessment scoring guide to better understand how each criterion will be assessed.

Choose a framework appropriate for examining the type of data under investigation. Look at the following for examples, and explain the basis for your choice.
For a compliance project, use the regulations.
For an accrediting assessment, use the standards and site survey criteria.
For the implementation of a new service line, consider using the balanced scorecard framework.
Develop proposed structures (for example, pie chart, graph, spreadsheet, process map) for the visual display of summarized raw data.
Reflect on what type of visual display structure will best fit your selected topic. Although you are just beginning to collect data, you will want to consider how to display it.
Ensure that your data displays are clear and easily interpreted.
Ensure that the proposed title includes the focus of the data, units of measurement, the organization’s name, and time frame.
Explain how a project addresses and adds value in each of the four areas of an organizational balanced scorecard. (The four areas are business operations, finance, customer service, and organizational learning and growth.)
Create a balanced scorecard table similar to the one presented in the Balanced Scorecard Example [DOCX].
Consider a strategic systems perspective as you contemplate value to the organization and how the project aligns with the organizational mission, vision, and strategy.
Find evidence to support your assertions and conclusions.
Determine what additional information would strengthen your value proposition.
Combine clear, coherent, and original writing, in APA style, with relevant and credible evidence from the scholarly and professional literature.
Apply correct APA formatting to your source citations.
Consider how or why a particular piece of evidence supports your main points, claims, or conclusions.
Make sure your supporting evidence is clear and explicit.