Rapid-Cycle Improvement Plan

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Describe a plan for a short study. Timeframe, number of subjects, financial implications, and intervention methodology discussed. Discussion includes specific strategies to meet objectives listed. Includes current organizational culture issues to be addressed, what internal or external resources currently exist that could be utilized; potential internal/external resource acquisition may be required to accomplish the objectives, and strategies for utilizing these resources appropriately.
In the study section
An analysis plan to evaluate the results of your brief intervention is presented. Include specific outcome measures that will be examined. How will you determine if your objective was met? Include a discussion of appropriate statistical analysis or research studies that should be utilized in this determination. Provide a brief summary of any financial implications involved in this phase. Discuss specific stakeholders that should be involved in the action plan, who will contact them and how, as well as timelines for th

 

Sample Solution

In addition to helping your practice/hospital/health centers overcome any staff resistance to workflow and workforce changes, continually implementing improvements will enable you to better serve your patients, achieve your business goals, realize the benefits of EHRs, and improve quality.Rapid-cycle improvement is a “quality improvement method that identifies, implements and measures changes made to improve a process or a system.”1 Rapid-cycle improvement implies that changes are made and tested over periods of three or months or less, rather than the standard eight to twelve months.Rapid-cycle improvement is an important part of electronic health record (EHR) implementation because it enables your practice/hospital/health center to continually improve how you use EHR technology.

The crude mortality rate for a given age for any given year is the probability that a person at age x dies that year. Crude mortality rates are usually calculated by simply dividing the relevant number of deaths by the number of life-years that were exposed to the risk of death over that period. The crude mortality rates for each plan year 1990, 1995, 2000 and 2005 were developed accordingly.

 

Description of Female Pension Data
Pension data is considered to be of the form of number of deaths and number of living pensioners who are exposed to death which are in cells by year of death and age at death. The study focus on the occurrence of death for a year which gives a count (discrete) variable outcome. A total of 424 deaths occurred within the five year interval period from age 55 to 80 years. The data was cleaned by discarding all pensioners who are over 80 years since much record was not recorded. The R software was then used to analyse the data by finding the descriptive statistics for each cohort group. The result from the output which shows there were excess of zeros with large variation was used to propose the model to be used for the data. The following models were proposed to model the data; zero inflated negative poisson and negative binomial. Before discussing them let’s consider poisson regression model and the zero inflated model. The response variable is the number of death that occurred in the year and is represented by y and the predictor variable is the age at which death occurred and is represented by x.

Models
Pension data consist of count variable outcome interest which might contain too many zeros. And with this count data the expected number of occurrence of death is the dependent variable and the age is the predictor variable. Different models were proposed to fit count data with too many zeros than expected: Lambert (1992) described the zero-inflated Poisson regression models with an application to defects in manufacturing; Hall (2000) also described the zero-inflated binomial regression model and incorporated random effects into ZIP and ZIB models.

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