An audit of discharge summaries

Background: In the continuum of patient care, admission to the department of medicine constitutes a brief yet critical period. Subsequent patient care depends on the discharge summary (DS) and its implementation. Aim: To evaluate the department of medicine -family physician interface by a discharge summaries audit. Method: A retrospective study analyzing all admissions and discharges between a department of medicine and a primary care clinic over a period of ten months. Results: 129 DS were evaluated and compared to 97 available primary care medical charts. Most admissions were due to a medical emergency (95%), the patients were often elderly and 23% lived alone. Hospital stay averaged 4.0±2.4 days, readmission rate was 15.8%. In 73% of the DS at least one new drug was prescribed. The family physician was the one expected to continue treatment in most of the cases, but in over a third of the patients, a referral to further consultation was deemed necessary. The DS was found in 82% of the primary care charts. Median time interval between discharge and consultation with the family physician was three days (range 1-30). Home visits by physicians were documented in eight cases only. Conclusion: Most discharged patients require further evaluation and newly prescribed medications, making a timely and coordinated continuous care in the community mandatory. A high quality, rapidly available DS is therefore important for the family physician. Whether improved communication will reduce readmissions and improve patient prognosis and quality of care should be clarified by further study.peer-reviewe

Discordance between 'actual' and 'scheduled' check-in times at a heart failure clinic

<div><p>Introduction</p><p>A 2015 Institute Of Medicine statement “Transforming Health Care Scheduling and Access: Getting to Now”, has increased concerns regarding patient wait times. Although waiting times have been widely studied, little attention has been paid to the role of patient arrival times as a component of this phenomenon. To this end, we investigated patterns of patient arrival at scheduled ambulatory heart failure (HF) clinic appointments and studied its predictors. We hypothesized that patients are more likely to arrive later than scheduled, with progressively later arrivals later in the day.</p><p>Methods and results</p><p>Using a business intelligence database we identified 6,194 unique patients that visited the Cleveland Clinic Main Campus HF clinic between January, 2015 and January, 2017. This clinic served both as a tertiary referral center and a community HF clinic. Transplant and left ventricular assist device (LVAD) visits were excluded. Punctuality was defined as the difference between ‘actual’ and ‘scheduled’ check-in times, whereby negative values (i.e., early punctuality) were patients who checked-in early. Contrary to our hypothesis, we found that patients checked-in late only a minority of the time (38% of visits). Additionally, examining punctuality by appointment hour slot we found that patients scheduled after 8AM had progressively earlier check-in times as the day progressed (P < .001 for trend). In both a Random Forest-Regression framework and linear regression models the most important risk-adjusted predictors of early punctuality were: later in the day appointment hour slot, patient having previously been to the hospital, age in the early 70s, and white race.</p><p>Conclusions</p><p>Patients attending a mixed population ambulatory HF clinic check-in earlier than scheduled times, with progressive discrepant intervals throughout the day. This finding may have significant implications for provider utilization and resource planning in order to maximize clinic efficiency. The impact of elective early arrival on patient’s perceived wait times requires further study.</p></div

Partial dependence plots of adjusted-predicted punctuality as a function of the top 4 predictive variables identified by the minimal depth analysis.

<p>These plots were derived from the Random Forest-Regression (RF-R) machine learning framework, and can be interpreted as the effect on the response for a one unit change in the predictor, while averaging over the effects of all the other 20 variables (shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0187849#pone.0187849.t001" target="_blank">Table 1</a>) in the RF-R.</p

Marginal plot of predicted punctuality as a function of appointment hour slot, visit type (categories collapsed to not new versus new to hospital), age (in quartiles), and race (categories collapsed to non-white versus white race).

<p>These plots were derived from the Random Forest-Regression (RF-R) machine learning framework.</p

Predictiveness of punctuality, on basis of average minimal depth ranking of variables.

<p>Appointment hour slot was the most predictive variable, followed by visit type, and so forth.</p

Cohort characteristics.

<p>Cohort characteristics.</p

Punctuality according to appointment hour slot.

<p>Punctuality according to appointment hour slot.</p

Distribution of punctuality (differences between ‘actual’ and ‘scheduled’ check-in times).

<p>Dotted line represents a visit for which a patient checked-in exactly on time (punctuality = 0).</p