Introduction: Adverse events are common after discharge- present in 22-28% of cases (1) – and pose a threat to patient safety, and a risk of readmission. Understanding the type and pattern of events experienced, communication habits, and timeframes of new or worsening symptoms can inform an intervention to better assist patients during the high risk post-discharge period, and potentially reduce the risk of post-discharge adverse events .
Methods: We used REDCap to collect data from patients regarding new and worsening symptoms and clinician assessment of adverse events within the 30-day post-discharge period of patients enrolled in the AHRQ-funded ePRO Transitions study. Cases eligible for our systems engineering analysis included those in which the clinician reviewers confirmed an adverse event.
Eligible cases underwent an in-depth review of all encounters documented in EPIC within the 30-days after index hospitalization. The date and type of encounter and whether it was expected at discharge, the communication method (office visit, phone call, patient message etc.), and the reason for and description of the encounter were collected for each case. Symptoms experienced by the patient, the timeframe in which these symptoms were experienced and the date that they were reported to a healthcare provider were also recorded.
Using this information, we constructed a visual care timeline in Visio for each case (Figure 1). Each timeline shows when the patient experienced and reported symptoms, when they interacted with their healthcare providers, and when the adverse event occurred.
Using Excel, the data collected for each case was graphed to illustrate when symptoms were experienced after discharge (Image 2). We used descriptive statistics to report our findings.
Results: Of 166 cases in which chart reviews were finalized, we identified 23 cases with an actual adverse event. Our analysis of these 23 cases shows that symptoms were reported to healthcare providers over the entire 30 days after discharge. On average, 1.7 symptoms (standard deviation: 0.82) were reported within 7 days, 1.8 symptoms (standard deviation: 0.79) were reported within 8-14 days, 1.89 symptoms (standard deviation: 0.78) were reported 15-30 days after discharge.
However, many patients experience symptoms that are never reported to a healthcare provider, and only were revealed in a survey administered to the patient after their 30-day study period. Of the 64 symptoms reported by patients who experienced and adverse event, 19% were not reported to a healthcare provider.
Conclusion: The Visio diagrams tell the patient’s story in a way that cannot be seen by only looking at their records. By viewing each care journey as a timeline, we can point out and learn from patient care details and begin to observe patterns. Each patient’s story is different, but by observing and discussing their intricacies as a team and defining an intervention to reach them when they need it most, we can help make their journey to wellness a safer and easier one.
We plan to use this analysis to inform the design of an intervention to prevent post-discharge AEs.
Figure 1. The timeline of a patient’s encounters and post-discharge adverse event
Figure2. The time periods after discharge in which symptoms were reported by the patient, where the vertical axis is the number of symptoms experienced, and each number on the horizontal axis identifies one patient.
1. Forster AJ, Murff HJ, Peterson JF, Gandhi TK, Bates DW. The incidence and severity of adverse events affecting patients after discharge from the hospital. Annals of Internal Medicine. 2003;138(3):161-7.
2. Tsilimingras D, Schnipper J, Duke A, Agens J, Quintero S, Bellamy G, et al. Post-Discharge Adverse Events Among Urban and Rural Patients of an Urban Community Hospital: A Prospective Cohort Study. Journal of General Internal Medicine. 2015;30(8):1164-71. doi: 3260 [pii].
3. Forster AJ, Murff HJ, Peterson JF, Gandhi TK, Bates DW. Adverse drug events occurring following hospital discharge. Journal of general internal medicine. 2005;20(4):317-23. doi: 10.1111/j.1525-1497.2005.30390.x. PubMed PMID: 15857487.