Essay on Type of System Patterns Needs Identified for a Hospital Readmission

Visible Patterns that are easily measured Inadequate medical follow-up with patients after discharge

Inadequate or poor communication with patients, caregivers, or both about medications, tests, and sing or symptoms of a deteriorating health condition

Patterns that are only partially visible or invisible, bus easily measured A weak or fragmented discharge plan

Miscommunication or failure to communicate important information at the time of transition

Inadequate preparation of patients for discharge or self-management

Patterns that are invisible and not easily measured Inadequate communication about any patient social conditions (transportation, lack of money for medication, lack of housing) to contribute to the possibility of a readmission

Projects Return on Investment

The project seeks to reduce the readmissions by 20%. Notably, the current cost of readmission is $2.6 billion per year. A reduction by 20% equals to $2.08 billion per year. Therefore, the projects return will be $0.52 billion for the first year. Therefore, the ROI will be (0.52/2.6) 0.2.

Root Cause Analysis

The root cause analysis (RCA) is the data collection technique used to collect hospital discharge data in Hialeah Hospital. On the other hand, the needs assessment seeks to analyze the aspects that lead to readmission at the hospital and prove that with the right methods, the readmissions can be limited. Notably, the RCA reflects the purpose of the needs assessment significantly because it allows the attainment of an excellent analysis of evidence. Moreover, it applies three principle concepts, namely, focusing on the cause of a problem, why it happens, and ways to prevent it from occurring again.

Feedback from Hialeah Hospital

The hospital readmission team conducted research to determine the root causes of the high readmission rate on beneficiaries in Medicares traditional fee-for-service plan of aged 65 years and above. According to this investigation, the majority of the patients were women, approximately 62.5%, of Hispanic origin (85.1%) of which 65.7% were living with someone before their acute care hospitalization. Around half of the re-hospitalized participants were readmitted within 11 days after discharge. Among those discharged home, 78% went home without a sufficient transitional care program or any other type of nursing follow up. However, those discharged to SNF, ALF and Long Term Care Facilities, approximately 84%, their principal problem was poor communication between the hospital and the late discharge without properly handout reports within the hospital.

Measure of Reliability and Validity of Quantitative Data

Reliability refers to the consistency of the quantitative data. Researchers estimate the reliability of quantitative data through test or retest and internal consistency (Suen & Ary, 2014). Test/retest requires that the researcher implements the measurement tool at two separate times for each participant, computes the correlation between the different measures, and assumes that there is no variation in the underlying condition between both tests. On the other hand, internal consistency estimates reliability by grouping questions in a questionnaire that a similar measure concept and administer it to two groups and then computing a correlation between the two groups to establish the reliability of the data. On the other hand, validity is the strength of the conclusions or inferences drawn from the gathered quantitative data. The validity is measured by establishing if the data is accurate and if it regards what it is intended (Zohrabi, 2013). In particular, the hospitals QI team performed rapid-cycle experiments to measure the reliability and validity of the data and identified needs similar to those outlined by the hospital readmission team.

Measure of Trustworthiness in Qualitative Data

According to Shenton (2004), researchers can use four constructs to gauge the trustworthiness in qualitative data. First, the credibility of the data that demonstrates that an accurate picture of the situation under study is being presented. Second, they use transferability, which refers to the ability of application of the findings to another situation. Additionally, researchers use dependability that allows future researchers to repeat the study to measure the trustworthiness of the data. Finally, to attain confirmability, researchers proof that the findings arising from the gathered data instead of personal predispositions.

References

Shenton, A. K. (2004). Strategies for ensuring trustworthiness in qualitative research projects. Education for information, 22(2), 63-75.

Suen, H. K., & Ary, D. (2014). Analyzing quantitative behavioral observation data. Psychology Press.

Zohrabi, M. (2013). Mixed method research: Instruments, validity, reliability and reporting findings. Theory and Practice in Language Studies, 3(2), 254.