| Abstract |
Introduction: The purpose of clinical microbiology laboratories is to provide laboratory
results that are affordable, accurate and timely. The delays between patient admission,
sampling and provision of test results are associated with late diagnosis and, hence,
problems in patient care, unnecessary hospitalisation, empirical use of antibiotics,
nosocomial infections and waste of resources. To avoid such delays, a growing number of
rapid diagnostic tests have been developed. Most of these tests are based on immunochromatographic
or agglutination assays. Point of care (POC) tests are performed near the
patient, aiming at an immediate diagnosis, a more effective treatment and a shorter length
of stay (LOS) in the hospital. POC tests are already used to detect Influenza viruses,
Legionella pneumophila, Group B Streptococcus, Streptococcus pneumoniae, Rotavirus,
Respiratory Syncytial Virus (RSV), Human Immunodeficiency Virus (HIV), etc. In the
past decade, new molecular techniques and laboratory automation have significantly
advanced clinical microbiology enabling rapid detection of pathogens. In the future, new
threats from infectious microorganisms will necessitate an even more rapid diagnosis.
Economic constraints exert pressures on many national healthcare systems, influencing
hospital budgets, resource availability and quality of services. In Crete, Greece, the time
to laboratory diagnosis of infectious diseases in public hospitals is quite long. Herein we
present a retrospective study involving outpatients and hospitalised patients, to investigate
time, accuracy and cost factors and the potential benefits of quick diagnosis on LOS. The
final scope is to investigate whether financing the pilot implementation of rapid diagnosis
techniques holds any promise for the patients and the healthcare system as well. The
study mainly focused on the impact assessment of time to laboratory diagnosis reduction
on total LOS, bed occupancy, targeted therapy and utilization of other hospital resources.
Methods: Laboratory data over a period from October 2011 to September 2016 were
anonymously recorded at five public hospitals in Crete, Greece, denoted hA, hB, hC, hD
and hE. The data relate to specific tests performed at the clinical microbiology laboratories
for the detection of 18 agents, which are also detectable by rapid tests. We identified the inpatients with positive laboratory tests and appended their clinical course data to
generate sojourn time records of all officially hospitalised patients whose sole reason for
hospitalisation was an infection by the examined pathogens. Therefore, the study is
divided into several stages. Inpatients are an important subject of this study. The length of
hospital stay was partitioned into pre- and post-laboratory diagnosis stages. The following
four time points were recorded for each hospitalized patient:
A: admission date
B: sampling date (B ≥ A)
C: date of reporting of laboratory analysis results (C ≥ B)
D: discharge date
We studied three distinct patient groups g:
g = 0: patients for whom the laboratory results were reported on the very day of arrival
A, i.e., AC = 0;
g = 1: the subset of remaining patients who stayed in the hospital at least one day after
the laboratory diagnosis, i.e., AC ≥ 1 and CD ≥ 1;
g = 2: patients with AC ≥ 1 and CD ≤ 0, who left the hospital not later than day C.
The first objective was to assess how the patient sojourn times could have changed had
they received quick diagnostic tests right upon admission. We have that LOSg =
ACg+CDg for groups g = 0, 1. For group 2 patients we made a conservative assumption
that rapid diagnosis would not lead to any LOS reduction. Then we performed regression
analyses to investigate the effect of rapid diagnosis on LOS1 for group 1, to which we
appended group 0 patients to improve the accuracy of the statistical estimates, although
LOS0 could not have been reduced by rapid testing. After testing different statistical
models and various combinations of predictor variables, we adopted the linear regression
model
18
g 0 1 g j j
j 1
CD a a AC b I error
=
= + +Σ + , g = 0, 1,
where Ij is a binary function indicating the presence (Ij = 1) or absence (Ij = 0) of
infectious agent j, j = 1, …, 18, and the coefficients a0, a1 and bj are estimated by
minimizing the sum of squared errors. The corresponding savings in average occupancy
and resource use are computed using Little’s law from queuing theory: (average occupancy) = (average rate of admissions)×LOS
Regression analysis, elementary queueing theory and other more sophisticated models
were applied to estimate the impact of quick diagnosis on the mean length of stay and the
utilization of healthcare resources.
Results: Current average LOS for all groups equals 6.97 days. However, a LOS reduction
up to 34% could be achieved through a systematic use of immediate diagnosis. A
reduction in the mean LOS causes an equal relative reduction in the use of various
healthcare resources. To see this, take for example the mean number of occupied beds (in
bed days) before and after the hypothesis of introduction rapid diagnosis, denoted N and
Nq, respectively. Let λ be the inpatient admissions rate. Application of Little's formula
yields N = λ LOS and Nq = λ LOSq, which imply that the ratios Nq/N and LOSq/LOS are
equal. Furthermore, upon subtraction we obtain
N – Nq = λ(LOS – LOSq)
For the specific data set studied herein we have that λ = 441 patients in three years and an
estimate for LOS – LOSq ranging from 0.70 to 2.37 days. Therefore, quick diagnosis
could free up an average of 1045 hospital bed days for these patients (out of a total of
3072 bed days), and result in a reduction of clinicians and paramedical personnel working
hours and other important resources associated with hospitalisation such as antibiotics
receiving, medical tests’ ordering, etc..
Discussion & Conclusions: For infectious diseases, the early delivery of laboratory
results gives clinicians the opportunity to change the administered antibiotic therapy from
empirical, broad spectrum treatment to a directed treatment against the pathogen of
interest. In addition, the early detection of some pathogens such as Legionella species, B.
pertussis, Influenza viruses, etc., may improve public health indicators.
Our analysis indicates that the longer the time to diagnosis AC the longer the after
diagnosis hospitalisation CD and, consequently, the longer the total LOS. Therefore, had
all test results been reached upon admission, the total LOS would have been reduced and
a fraction of the patients would have been discharged earlier. The inclusion of additional
numerical and categorical variables such as inpatient's age, gender and hospital attended
was not found to be statistically significant. The main conclusion of this study is that the
application of rapid tests upon admission could decrease mean LOS, free up beds and save medical and nursing staff working hours as well as other important resources. The
main contribution of this research is that it does not require a pilot operation of quick tests
at the hospital to see how the adoption of rapid tests would affect the various performance
indicators but it estimates these savings using information from patients hospitalized
under the present laboratory testing procedures.
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