MHA FP 5020 Assessment 4 Data Review Project Report and Presentation

Sample Answer for MHA FP 5020 Assessment 4 Data Review Project Report and Presentation Included After Question

Write a data review project report and record a client presentation. There are no page or slide limits for this assessment.

Overview and Preparation

In this assessment you will submit your project report and presentation, which are based on the work you have completed in the previous assessments.

In this assessment, you will write your final report and develop a presentation suitable for executive leaders.

This assessment is in two parts:

Part 1: Project Report. This report should be succinct, substantive, and written for a hypothetical executive leadership team. It is not a lengthy academic paper.

Part 2: Project Report Presentation. This recorded presentation is an overview of the project, also intended for an executive leadership team.

A Sample Answer For the Assignment: MHA FP 5020 Assessment 4 Data Review Project Report and Presentation

Title: MHA FP 5020 Assessment 4 Data Review Project Report and Presentation 

EXECUTIVE SUMMARY

Hospital readmission is one of the major problems experienced in the healthcare sector. This problem is even more common among patients who experience or living with mental illnesses. Such high rates of readmissions have been shown to have various negative impacts on patients and healthcare facilities. For example, while the patients end up spending more on care and become more exposed to healthcare-acquired infections, hospitals usually end up having a tainted image.  With reference to patients experiencing mental illnesses,  hospital readmissions lead to adverse consequences such as higher healthcare costs, disruption of the individuals and their families, risk of healthcare-associated infections, increasedlength of stay, and higher mortality rates (Upadhyay et al., 2019).

The key factors directly influencing the problem include average readmissions per unit per month, total unplanned readmissions, and total readmissions. As such, this project focuses on lowering the rates of readmission among patients experiencing mental illnesses. Such a feat is to be achieved through analyzing the data trends to reveal the organizational causes. The implication is that the organization’s data on readmissions have to be studied and necessary interventions applied.  The key performance indicators will be measured as the number of patients readmitted to the facility unplanned, the total number of patients readmitted to the psychiatry units, and the number of readmissions as monthly averages. Through the reduction of the readmission rates, this project aims to add value to the identified organization. For instance, it is hoped that the organizations will have lower spending related to hospital readmissions. In addition, patient satisfaction is expected to improve hence having a long-term positive impact on the hospital’s revenue.

Introduction

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

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 ProblemPrecise Unit of Measurement (Days, Dollars, %, etc.)Authoritative Source(s) for Factor and Unit of Measurement
Total unplanned readmissionsThe number of patients readmitted to the facility unplannedPhillips et al.(2020)
Total readmissionsThe total number of patients readmitted to the psychiatry unitsPhillips et al.(2020)
Average readmissions per unit per monthNumber of readmissions calculated as monthly averagesPhillips 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 Research21(1), 1-15. https://doi.org/10.1186/s12913-021-06391-7This 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 Psychiatry59(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 Open9(7), e030696. http://dx.doi.org/10.1136/bmjopen-2019-030696This 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 Policy2. https://doi.org/10.1016/j.jaac.2019.05.024This 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 Informatics139, 104136. https://doi.org/10.1016/j.ijmedinf.2020.104136The 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 Services71(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 psychiatry20(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 Research292, 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 Psychiatry51, 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 journal55(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 Quality41(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.

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 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)

BusinessFinanceCustomerOrganizational 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.  

Analysis of the Data

The presented graphs are relevant and important to the project and the objectives. Graphic one shows daily readmission incidences for the year 2021. The graph indicates the monthly readmission rates for the psychiatric unit. While the month with the lowest tally is October, May had the highest incidence at 28%. The second graph, graphic 2, indicates the readmission rates within 30 days and 100 days of patient discharge for the year 2021. While July had the highest readmission within 30 days of discharge, the highest rate of readmission within 100 days of discharge was observed in April.

Evidence-Based Recommendations

As earlier indicated, hospital readmissions among patients with mental illnesses have various negative impacts. As such, there have been various evidence interventions used in attempts to contain the problem.

  1. One of such evidence-based recommendations is the use of a comprehensive education initiative during patient discharge. As part of the initiative, the patients and their family members are taught about various aspects such as correct medication use, the importance of medication adherence, and effective symptom management (Benjenk & Chen, 2018).
  2. The next intervention is physical health telemonitoring, which enables the healthcare professionals to remotely monitor the patients and make appropriate therapeutic decisions that can help in preventing readmissions (Benjenk & Chen, 2018).
  3. Groups and individual psychotherapy sessions for discharged patients can help in reducing the chances of readmissions (Benjenk & Chen, 2018).

From the evidence-based recommendations mentioned, it is key that the organization implements realistic strategies. In addition, the chosen strategy should well be within the financial capability of the organization to avoid putting pressure on the already available limited resources.

Conclusion

The problems experienced in clinical health settings usually put patient safety at risk hence calling for effective interventions that can be used to solve such problems. Therefore, this project has focused on hospital readmission among patients with mental illnesses. Hospital readmissions have been shown to lead to various problems, such as increased healthcare spending. Therefore, applying an intervention was to hopefully reduce such spending by at least fifty percent. Hospital readmissions are a widespread problem, both in the USA and the world. The implication is that the healthcare industry is heavily and negatively impacted by the problem, calling for better interventions that can be applied to ensure that the problem is solved.

In most cases, various facilities resort to multidisciplinary staff who can come together and use their expertise to perform a proper evaluation and come up with possible solutions (Morris et al., 2018). While some measures have been used to control the hospital-based factors, some have been focused on the factors connected to where patients are living back in their homes to ensure that whatever may enhance the increased rates of readmissions are eliminated. It is important to continue with efforts to control hospital readmissions. Future studies should focus on other strategies for preventing readmissions. For example, real-time monitoring through data mining presents a real opportunity of fighting 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 post-discharge care in a Brazilian sample. General Hospital Psychiatry51, 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 Policy2. 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 Research292, 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 Services71(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 psychiatry20(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 Open9(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 Research21(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 Journal55(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 Informatics139, 104136. https://doi.org/10.1016/j.ijmedinf.2020.104136.

Morris, D. W., Ghose, S., Williams, E., Brown, K., & Khan, F. (2018). Evaluating psychiatric readmissions in the emergency department of a large public hospital. Neuropsychiatric Disease and Treatment14, 671. https://dx.doi.org/10.2147%2FNDT.S143004

Ortiz, G. (2019). Predictors of 30-day postdischarge readmission to a multistate national sample of state psychiatric hospitals. Journal for Healthcare Quality41(4), 228. https://dx.doi.org/10.1097%2FJHQ.0000000000000162

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 Sciences10(4), 102. https://doi.org/10.3390/admsci10040102

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 Psychiatry59(5), 619-631. https://doi.org/10.1016/j.jaac.2019.05.024.

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 Financing56, 0046958019860386. https://dx.doi.org/10.1177%2F0046958019860386.