PRISMA Analysis of 30 Day Readmissions to a Tertiary Cancer Hospital

Abstract

Background: Hospital readmissions are increasingly used as a quality indicator. Patients with cancer have an increased risk of readmission. The purpose of this study was to develop an in depth understanding of the causes of readmissions in patients undergoing cancer treatment using PRISMA methodology and was subsequently used to identify any potentially preventable causes of readmission in this cohort. Methods: 50 consecutive 30 day readmissions from the 1st November 2014 to the medical admissions unit (MAU) at a specialist tertiary cancer hospital in the Northwest of England were analysed retrospectively. Results: 25(50%) of the patients were male with a median age of 59 years (range 19-81). PRISMA analysis showed that active (human) factors contributed to the readmission of 4 (8%) of the readmissions, which may have been potentially preventable. All of the readmissions were driven by a medical condition related to the patient’s underlying cancer and ongoing cancer treatment. Conclusion: The majority of readmissions of patients undergoing cancer treatment appear to be related to the underlying condition and, as such, are predictable but not preventable. This suggests that hospital readmission is not a good quality indicator in this cohort of patients.

Hospital Readmissions – Independent Predictors of 30-day Readmissions derived from a 10 year Database

“…In previous work, many risk factors associated with 30 day readmissions were non-modifiable disease-related factors, such as the natural progression of underlying disease or unavoidable recurrence of disease. [18][19][20][21][22] The risk factors identified in our cohort were mostly disease-related but also patient-elated (e.g.,alcohol) the latter being potentially modifiable.…”

Root Cause Analysis Using the Prevention and Recovery Information System for Monitoring and Analysis Method in Healthcare Facilities: A Systematic Literature Review

“…This is in line with our results; in the studies where the patient file was the only data source, the majority of the articles had a human factor as most common root cause 16,18,23,24,37 in contrast to other data sources. Multiple studies 14,17,18,23,29,37 suggested that besides the patient file or a report, an interview with those involved at a local level can lead to a deeper understanding. The interview depends on the recall of the reporters, and UEs should be discussed within a few days.…”

“…In the 2 other studies, patients were included from 3 allied healthcare disciplines (physical therapy, occupational therapy, and Cesar-Mensendieck exercise therapy) 16 and from a blood and transfusion center. 20 The majority of studies (n = 16) limited the study population to one department or specialty, 14,15,[17][18][19]21,22,[24][25][26]28,29,32,33,35,36 whereas others included cases from various hospital departments. 19,29,33,[36][37][38][39] Data Collection and Implementation of PRISMA…”

“…In 3 articles, it is unknown who performed the PRISMA. 14,20,25 Furthermore, interobserver agreement on root causes and ECM classification was studied in 10 of the studies, 16,24,[27][28][29][30][31][32][33]37 with scores ranging from "moderate" to "good. "…”

“…In 6 studies, events without deviations were analyzed. 14,15,[17][18][19][20] The study objects in these articles were event reports in transfusion medicine, 20 readmissions, 14,17,19 unplanned intensive care department admissions, 18 and an emergency department (ED) length of stay >6 hours. 15 In 19 studies, events with deviations were analyzed, including incidents, 16,24,26,[30][31][32]34,36 UEs, 22,27,28,33,35 errors, 12,21,25 and adverse events.…”