Project Proposal

The Centers for Medicare and Medicaid (CMS) classifies a readmission as an admission of a patient to any hospital within 30 days of being discharged. To measure organizations performance, CMS uses the observed to expected readmission ratio for six disease states acute myocardial infraction (AMI), chronic obstructive pulmonary disease, heart failure, pneumonia, coronary artery bypass graft (CABG) surgery, and elective primary total hip arthroplasty and/or total knee arthroplasty (THA/TKA). CMS also mandated that by 2014, organizations migrate from paper charts to electronic health records (EHR). Electronic health records store and keep all patient data in an electronic database, replacing the traditional paper medical record. CMS also gave organizations that the EHR have a “meaningful use”. The purpose of this study is to determine if the electronic health record can be utilized to predefine and identify patients at high risk for readmissions. I will design a wireframe for utilizing the EHR to flag or identify potential patients. The data will be analyzed to determine if there is a relationship between specific disease states and readmissions. I will also analyze the results and compare it to external research to develop targeted interventions aimed at reducing readmission and improving the overall health of the defined population of patients. The objective of this proposal is to outline a methodology to utilize the EHR to identify the characteristics of patients at high risk for readmission. The proposed research will be retrospective-prospective and would compare patients from the two disease states that the organization currently has excess readmissions for CABG and AMI. The desired end result is to develop and descriptive clinical markers and demographics for patients at high risk for readmission. Research suggests that there are very few barriers to the success of this project and the relevant stakeholders pose no threat to the success of the proposed project.

Project planning

The stakeholders for this project are the hospital and staff therein, patients, patient families, and CMS. The project research will occur with currently available resources and under the assumption that if the research is successful, the organization will utilize the data for targeted interventions. There is little to no risk involved in this potential project.

User and task analysis

The objective of the study is to determine if by utilizing the EHR, patients at high risk for readmission can be identified prior to a readmission occurring. The study will also ascertain what demographic and clinical characteristics are most common amongst this population of patients. To satisfy the objectives of this research, qualitative data will be used which will eliminate any potential concerns about violations of the Health Insurance Portability and Accountability Act (HIPPA) which prohibit the unauthorized sharing of patient information. The research approach is to begin with the two disease states for which the organization had excess readmissions for the reporting period ending in 2016 and compare it to data from 2017 and 2018. Those two disease states are coronary artery bypass graft (CABG) surgery and acute myocardial infraction (AMI). Utilizing these two specific disease states reduces the effects of confounding data and addresses the organizations’ immediate needs. Similarities between patients in each individual cohort will be drawn and subsequently comparisons made between the two respective cohorts. Given the vast amount of data on each patient, analysis will begin with looking at some fundamental demographic data such as age and sex. Analysis will then begin to look for similarities in comorbidities and disease states other than CABG and AMI. Though CMS counts readmission to any facility, the data set will be limited to patients of this organization as readmission data to other organizations is not readily available. Utilizing this method of analysis versus some existing methods will allow the organization to understand its unique patient population in this geographic area.

Readmission Quiz

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Readmission Data

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Final Project

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Email

tamika.l.chambliss@live.mercer.edu

Capstone project

Tamika Chambliss

INFM 498 SPRING 2019

Dr.Feng Liu