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A predictive model for decreasing clinical no-show rates in a primary care setting

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A challenging obstacle to primary health care in the United States (US) is patient no-shows or missed appointments. The no-show rate can vary from 5.5% to 50%. A no-show may contribute to increased health risks, poor continuity of care, and loss of revenue. In this study, we develop and test a predictive model of patient visits. Retrospective regression analyzed patient visits in 2014 and 2015. Dependent variables were month, day, age, gender, race, ethnicity, insurance type, visit type, and the number of previous no-shows. A threshold for classifying no-shows was determined. The model was prospectively tested on patient visits in 2016. Significant variables included age, visit type, insurance, and number of previous no-shows. The model performed at 47% sensitivity and 79% specificity. The receiver operating characteristic (ROC) area under curve (AUC) was 0.72 (95% CI, 0.69–0.76) for the model and 0.70 (95% CI, 0.65–0.74) for prospective analysis. Simulated overbooking with the model resulted in 3.67 vs. 6.87 unused appointments, P < 0.000 (mean diff 3.2, 95% CI, 2.9–3.5). It is feasible to develop and implement a predictive model for single physician practices and implementation may improve practice efficiency.
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International Journal of Healthcare Management
ISSN: 2047-9700 (Print) 2047-9719 (Online) Journal homepage: https://www.tandfonline.com/loi/yjhm20
A predictive model for decreasing clinical no-show
rates in a primary care setting
M. Usman Ahmad, Angie Zhang & Rahul Mhaskar
To cite this article: M. Usman Ahmad, Angie Zhang & Rahul Mhaskar (2019): A predictive model
for decreasing clinical no-show rates in a primary care setting, International Journal of Healthcare
Management, DOI: 10.1080/20479700.2019.1698864
To link to this article: https://doi.org/10.1080/20479700.2019.1698864
Published online: 09 Dec 2019.
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A predictive model for decreasing clinical no-show rates in a primary care
setting
M. Usman Ahmad
a
, Angie Zhang
a
and Rahul Mhaskar
b
a
Medical Education, University of South Florida (USF) Morsani College of Medicine (MCOM), Tampa, FL, USA;
b
Internal Medicine, USF MCOM,
Tampa, FL, USA
ABSTRACT
A challenging obstacle to primary health care in the United States (US) is patient no-shows or
missed appointments. The no-show rate can vary from 5.5% to 50%. A no-show may contribute
to increased health risks, poor continuity of care, and loss of revenue. In this study, we develop
and test a predictive model of patient visits. Retrospective regression analyzed patient visits in
2014 and 2015. Dependent variables were month, day, age, gender, race, ethnicity, insurance
type, visit type, and the number of previous no-shows. A threshold for classifying no-shows
was determined. The model was prospectively tested on patient visits in 2016. Signicant
variables included age, visit type, insurance, and number of previous no-shows. The model
performed at 47% sensitivity and 79% specicity. The receiver operating characteristic (ROC)
area under curve (AUC) was 0.72 (95% CI, 0.690.76) for the model and 0.70 (95% CI, 0.65
0.74) for prospective analysis. Simulated overbooking with the model resulted in 3.67 vs. 6.87
unused appointments, P< 0.000 (mean di3.2, 95% CI, 2.93.5). It is feasible to develop and
implement a predictive model for single physician practices and implementation may
improve practice eciency.
ARTICLE HISTORY
Received 11 April 2019
Accepted 29 October 2019
KEYWORDS
Appointments and schedules;
regression analysis; machine
learning; family practice;
population health
management
Introduction
One of the most challenging obstacles to primary
health care delivery in the United States (US) is patient
no-shows or missed appointments. In 2011 in the US,
the majority of physicians and surgeons have group
practice sizes with less than 50 members with approxi-
mately 42% in groups of less than 10 members [1]. A
no-show bears the potential consequences for
increased health risks for the patient, poor continuity
of care, and potential loss of multiple streams of rev-
enue originating from decreased ecacy and capacity
to provide services. In one study, the loss to revenue
from a no-show rate of 5.5% was equivalent to the sal-
ary of three nursing sta[2]. This can be especially
economically devastating for small group practices.
The no-show rate in outpatient medical settings can
vary from 5.5% to 50% in multiple studies [27]. In
US primary care practices, Lasser et al. showed a
range of no-show rates from 6% to 21.5% across 16
dierent primary care practice settings in New England
[6]. Thus, individual practice settings are an important
contributor to the rate and type of patient no-shows.
In previous literature reasons outlined for patient
no-shows included race, age, insurance type, income,
psychiatric co-morbidities, prior attendance, and
appointment lead time [35,810]. However, there is
a lack of consensus amongst studies with these vari-
ables and their eect on patient no-shows. In a recent
systematic review, signicant variables which may
aect no-shows with moderate concordance (>70% of
published studies) include lead time, prior no-show
history, medical history, number of previously sched-
uled visits, use of medication, telephone number
recorded, residence, and year of appointment [7]. Vari-
ables which may not aect no-shows with moderate
concordance (>70% of published studies) include gen-
der, educational level, characteristics of clinic, weather,
and religion [7]. Other variables had an unclear agree-
ment amongst studies (signicant or nonsignicant in
3070% of published studies). Patient-reported reasons
for missing an appointment included forgetting, mis-
communication, transportation problems, and time
owork [3,4].
In order to reduce the rate of clinical no-shows or
missed appointments, various strategies have been pro-
posed. A systematic review from 2012 compared the
utility of telephone, mail, text message, e-mail, and
open-access scheduling to reduce no-shows. Of the
options, text messaging was cost-eective and provided
a net nancial benet but had limited applicability [11].
Other research has studied methods of reducing no-
shows, including: statelephone reminders [12], auto-
mated telephone reminders [13], text messages [13,14],
exit interviews [15], missed appointment fees [16],
overbooking [1719], predictive modeling [2025],
and predictive modeling with overbooking [2635].
© 2019 Informa UK Limited, trading as Taylor & Francis Group
CONTACT M. Usman Ahmad musman.ahmad@cuanschutz.edu Medical Education, University of South Florida (USF) Morsani College of Medicine
(MCOM), Tampa, FL, USA
INTERNATIONAL JOURNAL OF HEALTHCARE MANAGEMENT
https://doi.org/10.1080/20479700.2019.1698864
There is variation in eectiveness and disadvantages
associated with each management strategy. The most
eective strategy may be closely tailored to a clinics
no-show population and minimize lost productivity
by overbooking. In previous studies, predictive model-
ing and overbooking have been investigated in the
endoscopy suite aliated with a large academic medi-
cal center [3335], a VeteransAairs Medical Center
[27], a large mental health care center [26], a general
pediatrics clinic aliated with a large academic medical
center [29,30], a multispecialty outpatient primary care
facility aliated with a large academic medical center
[32], and simulation models [28,31]. Predictive model-
ing has been used to forecast emergency department
patient arrivals in a private hospital in Turkey [36].
However, evidence about this topic regarding a private
family medicine practice with a single physician has
been lacking.
Objectives
The present paper describes the development of a pre-
dictive model for patient no-shows or missed appoint-
ments in single physician family medicine practice. It
was our aim to (a) develop a predictive model for
patient no-shows and (b) test the performance of the
model on subsequent patient visits.
Methods
The present study was approved by the Institutional
Review Board (IRB). All study procedures were per-
formed in compliance with the World Medical Associ-
ation Declaration of Helsinki on Ethical Principles for
Medical Research Involving Human Subjects. The
study was conducted in a single physician family medi-
cine practice with retrospective analysis of patient data
for model development in Phase 1 and prospective data
collection for testing of the model in Phase 2. Phase 1
included process ow mapping, development of a
model, and selecting the diagnostic threshold. Phase
2 included collection of prospective data and measur-
ing the performance of the predictive model.
Process ow map
Stainterviews were conducted in order to determine
the current process by which patients move through
the clinic system. The operating model for the practice
was mapped and plotted using Microsoft Visio (Micro-
soft Inc., Redmond, WA, USA) with concepts adopted
from the Lean and Lean Six Sigma methodology [37].
Predictive model
Patient visit information was collected from the elec-
tronic health record system, eClinicalWorks
(eClinicalWorks, Westborough, MA, USA), from Janu-
ary 2014 to December 2016. Each unique patient visit
was matched to a patient identication number,
month, day, age, gender, race, ethnicity, insurance
type, visit type, and a number of previous no-shows
Microsoft Excel (Microsoft Inc., Redmond, WA,
USA). Stata 13 (StataCorp LLC, College Station, TX,
USA) was used to run a probit regression with the
dependent variable, no-show, and independent vari-
ables described. The result of the regression was used
to generate a line equation.
Diagnostic threshold
Using Microsoft Excel, the output of this equation was
calculated for each patient visit and plotted on a histo-
gram as a no-show or show visit with the frequency of
an output on the yaxis and the output of the equation
on the xaxis. A threshold for classifying show vs. no-
show was chosen using this chart to minimize the
sum of Type I and Type II error.
Predictive model performance
Patient visit information was collected from the elec-
tronic health record system, eClinicalWorks, from Jan-
uary 2016 to December 2016. Patient visits were
matched to visit status and other variables as described
previously using Microsoft Excel. Patients missing one
or more demographic variable were excluded from our
dataset. The predictive model was used to calculate an
output for each patient visit using the previously cho-
sen threshold and designated a show or no-show
visit. This was compared to actual outcomes of visit sta-
tus, and sensitivity and specicity were calculated for
the model. Receiver operating characteristic (ROC)
was generated for the model and tested data with
area under curve (AUC) calculated. A simulation of
predictive model use with overbooking was conducted
by summarizing patient visits, no-shows, predicted no-
shows, and overbooked visits for each day in 2016. A
two-tailed test of signicance was conducted on a num-
ber of unused appointments with and without the
model.
Results
Process ow
A process ow diagram was generated using Microsoft
Visio depicting the process of a patients movement
through the clinic (Figure 1). Based on process map-
ping, a no-showwas dened as a case where a patient
has a scheduled appointment, does not cancel, and
does not attend. Oce starelated that patients do
call in and cancel appointments prior to the appoint-
ment time. These were not categorized as no-show in
2M. U. AHMAD ET AL.
the health record. Thus, the actual number of missed
appointments may be understated.
Patient visit types were dened as follows: CU =
Check Up, INJ = Injection, OV = Oce Visit, and NP
= New Patient, Physical = Physical Exam. Check Up
visits were described by staas follow-up visits from
a hospital admission or maintenance visits for chronic
illness. Injection visits were for immunizations. Oce
Visits were sick visits for existing patients. New
patients were patients new to the oce. Physical
Exam visits were those required for sports, work, or
other periodic reasons for a physical exam.
Predictive model
In total, 6758 patient visits were analyzed by Stata 13
using a probit regression analysis. Linear regression
requires that the data have (a) a linear relationship
between independent and dependent variables, (b)
variables are multivariate normal, and (c) no multicol-
linearity. The likelihood ratio chi-square was 152.25
with a P-value of 0.000 showing good t of the
model. The variables were all transformed into binary
data with a reference category under a probit
regression. Ethnicity was dropped as a variable due to
a high level of collinearity. The demographics of
patients analyzed are shown in Table 1. The equation
of the line was as follows:
ln r
(1 r)

=−4.47 +
b
month +
b
day
+
b
gender +
b
race
+
b
insurance +
b
visittype
+
b
previousnoshow (1)
The output of the probit regression analysis is reported
in Table 2. Variables with a signicant eect on
increasing the likelihood of a no-show include 1825
years of age, 3639 years of age, check up visits, and
two previous no-show visits. It can be inferred that
uninsured patients also increased the likelihood of a
no-show based on similar decreased risk with Medicare
and private insurance.
Diagnostic threshold
A histogram based on data from the predictive model
was generated with frequency on the yaxis and the
equation output on the xaxis (Figure 2). The no-
show patients showed a bimodal distribution com-
pared to patients who did not miss appointments.
The threshold was chosen at the minimum of
Figure 1. A process ow diagram constructed with Microsoft Visio for the single physician oce with typical patient ow.
Table 1. Demographics.
Characteristics
Phase 1 (n=
2946)
Phase 2 (n=
2209)
Age, years: mean (SD) 51.6 (18.6) 53.2 (18.9)
Sex, male: N(%) 1744 (59.2%) 1317 (59.6%)
Race, N(%)
White 2677 (90.9%) 1989 (90.0%)
Black 165 (5.6%) 133 (6.0%)
Hispanic 48 (1.6%) 39 (1.8%)
Asian 27 (0.9%) 24 (1.1%)
Other 29 (1.0%) 24 (1.1%)
Ethnicity, Hispanic or Latino: N
(%)
65 (2.2%) 48 (2.2%)
Insurance, N(%)
Medicare 601 (20.4%) 499 (22.6%)
Private insurance 2213 (75.1%) 1634 (74.0%)
Uninsured 132 (4.5%) 76 (3.4%)
INTERNATIONAL JOURNAL OF HEALTHCARE MANAGEMENT 3
intersecting lines between the two patient populations
at 0.160 (95% CI, 0.1490.151).
Predictive model performance
For model training data, the model performed at 59%
sensitivity and 70% specicity. The model predicted
outcomes with 47% sensitivity and 79% specicity on
new data. The ROC AUC was 0.72 (95% CI, 0.69
0.76) for the model (Figure 2) and 0.70 (95% CI,
0.650.74) for predicted data (Figure 2). Simulated
overbooking resulted in 3.67 vs. 6.87 unused appoint-
ments, P< 0.000 (mean di3.2, 95% CI, 2.93.5).
There were 43 days with 115 visits above capacity
with mean visits above capacity 0.46 (95% CI, 0.29
0.62). Visit utilization increased from 69% to 82%
using the model (Figure 2).
Discussion
Our team set out to predict patient no-shows with a tai-
lored model due to variability on applicability of exist-
ing models in the literature [7]. Age, visit type,
insurance status, and two previous no-show visits
were signicant in our model. However, more than
two no-shows were not signicant. In our study popu-
lation, we also found an association between check up
visits for chronic illness and/or hospital admissions
with patient no-shows. Our model performed with
high specicity, but moderate sensitivity. Simulated
overbooking using our model produced increased
visit utilization with mean visits above capacity per
day at less than 0.5 across the study period.
Previous research has shown moderate concor-
dance with history of no-shows; however, data are
mixed on age and insurance status [7,8]. Interest-
ingly, our study population did not show an associ-
ation between no-shows and race or gender which
conicts some previous research [24,25]. Race and
gender eects may have been dierent in our study
due to dierences in the local environment [6]. In
a recent systematic review many studies reported
gender as nonsignicant (22.2% of studies showed
signicance), while race had mixed data (56.7% of
studies showed signicance) [7]. This is an important
nding as implicit bias in healthcare with regard to
race and gender is a validated issue and may aect
patient access to healthcare [38].
Our data also showed increased rates of no-shows
for check upvisits or those patients following up for
chronic medical issues or hospital admissions as in pre-
vious research [39]. Overbooking may present clinic
operations as ecient but may overlook these chronic
care patients. Appropriate follow-up and referral of
these patients should be considered with any
implementation of predictive modeling and overbook-
ing to maintain quality health for the population. Ten
quality factors are associated with high-quality referrals
in health systems including safe, timely, eective,
ecient, patient centered, equitable, structured referral
letters, dissemination of referral guidelines, centralized
computer systems, and inclusion criteria of a referred
patient which can be adapted to target and follow-up
for these patients within the local healthcare infrastruc-
ture [40].
Simulated overbooking resulted in an improvement
in visit utilization when compared to the clinics cur-
rent practice similar to prior research [26,27,33,34].
However, we did not compare predictive overbooking
with at overbooking as other studies to show relative
improvement [29,35]. Future research may study the
work eort required by stain overbooking with a pre-
dictive model vs. at overbooking methods in a pri-
mary care setting. Future research may also be useful
in prospectively overbooking patient visits with a
Table 2. Results of probit regression analysis.
Month Beta coecient (95% CI) Signicance
January 0.02 (0.24 to 0.29) 0.857
February 0.04 (0.23 to 0.31) 0.773
March 0.01 (0.26 to 0.27) 0.965
April 0.07 (0.19 to 0.32) 0.621
May 0.02 (0.3 to 0.26) 0.886
June 0.08 (0.34 to 0.19) 0.581
July 0.19 (0.47 to 0.1) 0.203
August 0.01 (0.26 to 0.28) 0.953
September 0.08 (0.19 to 0.35) 0.563
October 0.21 (0.48 to 0.07) 0.145
November 0.03 (0.31 to 0.25) 0.837
December 0 (0 to 0) Reference
Day
Monday 3.08 (211.67 to 217.84) 0.978
Tuesday 2.95 (211.81 to 217.71) 0.979
Wednesday 2.97 (211.79 to 217.72) 0.978
Thursday 3 (211.76 to 217.76) 0.978
Friday 2.74 (212.02 to 217.5) 0.98
Age
<12 0.19 (0.71 to 0.34) 0.487
1317 0.3 (0.08 to 0.68) 0.126
1825 0.31 (0.05 to 0.57) 0.019*
2635 0.01 (0.24 to 0.26) 0.95
3649 0.26 (0.06 to 0.45) 0.012*
5064 0.01 (0.19 to 0.17) 0.915
> 65 0 (0 to 0) Reference
Gender
Female 0.01 (0.12 to 0.1) 0.893
Male 0 (0 to 0) Reference
No Gender Selected 0 (0 to 0) Reference
Race
White 0.48 (0.97 to 0.02) 0.058
Black 0.04 (0.47 to 0.55) 0.873
Asian 0.56 (1.35 to 0.23) 0.162
Hispanic 0.17 (0.74 to 0.41) 0.568
Insurance
Medicare 0.3 (0.6 to 0.01) 0.046*
Private Insurance 0.27 (0.51 to 0.03) 0.030*
Uninsured 0 (0 to 0) Reference
Visit Type
CU 0.39 (0.23 to 0.56) 0.000*
INJ 0.11 (0.19 to 0.4) 0.489
OV 0.07 (0.08 to 0.21) 0.382
NP 0.17 (0.38 to 0.04) 0.110
Physical 0 (0 to 0) Reference
Previous no-shows
None 0.3 (0.26 to 0.85) 0.299
Once 0.44 (0.13 to 1.02) 0.131
Twice 0.94 (0.3 to 1.58) 0.004*
Three or More 0 (0 to 0) Reference
Note: CU = Check Up, INJ = Injection, OV = Oce Visit, NP = New Patient,
Physical = Physical Exam, * = signicant at P< 0.05.
4M. U. AHMAD ET AL.
predictive model rather than simulation in a single
physician practice.
While developing a threshold for our predictive
model, a bimodal distribution was found for those
patients that had no-show visits. Thus, there may be
masked variables that may continue to improve the
sensitivity of our predictive model. Further research
should focus on additional variables which may not
have been analyzed in our model based on the avail-
ability of information in retrospective data analysis
for model development, but have been shown to be sig-
nicant across studies in a recent systematic review [7].
Our methodology used the existing healthcare elec-
tronic health record and relatively inexpensive software
and data analysis tools. From a practical standpoint,
implementation requires consideration of infrastruc-
ture changes, human resources, managerial issues,
and cost. Health information technology that captures
data for decision making was found to be available in
hospitals in Canada in an urban location and with a
larger size; however, teaching status and stasize
were not signicant [41]. Thus, existing infrastructure
may not be limited to large, academic practices. Train-
ing and skill development may help improve sta
acceptance of data-driven tools, while older and longer
term stamay require more development [42]. The
cost of data keeping technology in the US on healthcare
delivery has remained relatively stable from 2006 to
Figure 2. Results and performance of predictive model and simulated overbooking. (a) A histogram plotted using Microsoft Excel
with the output of the regression equation for 6758 patient visits from 2014 to 2015. Visit status by show and no-show is separated
to highlight distributional dierences. A threshold of 0.160 (95% CI, 0.1490.151) was chosen to classify a visit as a show or no-show
for model deployment. (b) In total, 3571 patient visits in 2016 were analyzed as 251 visit days. One patient day with one visit was
excluded as an outlier due to holiday scheduling. The remaining visits, no-shows, and overbooked appointments with predictive
model were compared to maximum capacity for each visit day. Visit days were converted into visit weeks and maximum capacity,
total visits, and visits with model displayed. Holiday weeks 23, 28, 29, 37, 48, and 53 were excluded in this image to simplify visu-
alization of maximum capacity. Simulated predictive overbooking resulted in 3.67 vs. 6.87 unused appointments, P< 0.000 (mean
di3.2, 95% CI, 2.93.5). Visit utilization increased from 69% with normal scheduling to 82% with predictive overbooking. (c) The
threshold value of 0.16 is marked on the ROC for the training data in Microsoft Excel. The AUC is 0.72 (95% CI, 0.690.76). (d) The
threshold value of 0.16 is marked on the ROC for the predicted data. The AUC is 0.70 (95% CI, 0.650.74) in Microsoft Excel.
INTERNATIONAL JOURNAL OF HEALTHCARE MANAGEMENT 5
2016; however, 81% of patients believed it improved
service delivery [43]. Thus, xed and variable costs
have the potential to remain at, but, patient knowl-
edge of utilization of technology may improve patient
satisfaction scores.
Limitations
This project was conducted in a single physician family
medicine practice and may not be broadly applicable to
other clinical settings. Although prospective in data
collection, the model was tested in simulation to
avoid the Hawthorne eect and not as an intervention
in clinical practice. Other variables which may be sig-
nicant predictors may not have been included in
our model. These factors limit the ndings of our
study.
Conclusions
Prediction of no-shows or missed appointments
depends on practice-specic issues. It is possible
to develop a predictive model to prospectively pre-
dict no-shows for clinics as small as single phys-
ician practices with 47% sensitivity and 79%
specicity. Overbooking using predictive modeling
may improve a clinics ability to schedule unused
visit slots.
Implications for healthcare management
Predictive modeling and overbooking is a cost-eective
and reliable method to decrease the rate of underutili-
zation. A central aim of implementing the model is
focused on maximizing eciency while reducing the
risk of scheduling visits above capacity which may be
an issue with other methods. Eective innovation in
the context of healthcare requires a diligent evaluation
of both managerial and organizational issues [44].
However, human resource related issues including
stawellness to prevent burnout should be integral
to any implementation of this tool. In a systematic
review, burnout for stais related to low job control,
workplace demands, low support in the workplace,
high workloads, low reward or compensation, and
job insecurity [45]. In another review, Rothenberger
from the Department of Surgery at the University of
Minnesota had similar ndings with additional issues
related to a culture of blame and conicting values in
patient care [46]. Koussa et al. found that there were
dierences in high-income and low-income countries
for issues related to stawellness in the healthcare sec-
tor. Although all factors were relevant in both settings
there was greater value placed on autonomy, pro-
fessional work environment, and workload in high-
income countries [47]. In low-income countries greater
value was placed on infrastructure, career
development, and nancial incentives [47]. Linzer
et al. provide an excellent framework for managerial
changes related to communication, workow, and tar-
geted quality improvement in randomized trial in the
primary care setting in the US that reduced burnout
for sta[48].
In addition, certain sub-populations that are not
accessing healthcare should not be overlooked in
order to minimize potential negative eects on popu-
lation-based health. Our study found that check up
visits for chronic diseases and patients discharged
from the hospital had a higher rate of no shows.
For our population there is a risk that these patients
may be overlooked if the model is implemented with-
out special consideration. In addition to improving
eciency in the clinic, follow-up may reduce the
rate of hospital readmissions. The literature rec-
ommends a partnership with inpatient and outpatient
settings for both surgical and medical patients being
discharged from the hospital [49,50]. In addition to
the 10 quality factors for high-quality referrals [40],
an outpatient practice may benet from direct strat-
egies. Although systematic literature reviews have
mixed results due to quality of methods and study
design, a primary care nurse-based program of call-
ing at-risk patients within 72 h of hospital discharge
may be a useful tool [5153]. A recent high-quality
trial of this intervention improved appointment
attendance rates with successful phone contact vs.
missed contact (60.1% vs. 38.5% attendance, P=
0.004) [54].
In order to successfully implement the model,
improve healthcare outcomes for at-risk patients, and
maintain/improve stawellness we recommend the
following steps:
1. Institute managerial changes in practice setting that
include communication, workow, and quality
improvement [48].
2. Partner with local health system including inpatient
settings, urgent care, emergency room, and other
medical settings to ensure high-quality referrals
[40,49,50].
3. Assign a quality improvement team to development
and implementation of the model using the
methods in this study.
4. Identify special populations by analyzing model
data and signicance.
5. Develop a phone outreach program as needed if a
sub-population of patients is identied that requires
additional follow-up [5154].
6. Implement the model and apply continuous
improvement at pre-dened intervals to update
the algorithm as new data become available.
Geolocation information, WOEID 2503863
6M. U. AHMAD ET AL.
Acknowledgements
This work has been supported by mentorship by the faculty
in the SELECT MD program at the University of South Flor-
ida Morsani College of Medicine. This work was used to
fulll the requirements of capstone in the SELECT MD pro-
gram. We are also thankful for the team at the private medi-
cal practice of Dr Michael Reilly in St. Petersburg, Florida,
USA, for their support and assistance with this research
project.
Disclosure statement
No potential conict of interest was reported by the authors.
Ethics The study was approved by Institutional Review
Board (IRB). All study procedures with performed in com-
pliance with the World Medical Association Declaration of
Helsinki on Ethical Principles for Medical Research Invol-
ving Human Subjects.
Notes on contributors
M. Usman Ahmad is currently an intern in the Department
of Surgery at the University of Colorado.
Angie Zhang is currently a resident in Neurological Surgery
at the University of California Irvine.
Dr. Rahul Mhaskar is the director of the Oce of Research
and Associate Professor of Internal Medicine in the Univer-
sity of South Florida Morsani College of Medicine and
Associate Professor of Global Health in the University of
South Florida College of Public Health.
ORCID
M. Usman Ahmad http://orcid.org/0000-0001-9797-7106
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8M. U. AHMAD ET AL.
... Earlier investigations have established the significance of integrating trust in understanding users' intentions to use online technology (Ahmad et al., 2019;Nguyen et al., 2019;Xie et al., 2020). Gefen et al. (2003) dissected the pertinence of trust in understanding intention to use online technology, including OHIS, to that trust is as significant as other developments in web-based use. ...
... Correspondingly, Zahedi and Song (2008) dissected trust development toward wellbeing by incorporating social conduct, financial aspects, and individual points of view of buyer activities. Contrasted with numerous information innovation frameworks, many studies reported direct linkages between trust and users' OHIS intention (Ahmad et al., 2019;Nguyen et al., 2019;Xie et al., 2020). Thus, the following hypothesis is advanced: ...
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The information and communication technologies, health information seeking, and citizens' behavioral intention has been established as a promising area of attention. However, recent growth in digital health information surveillance has dynamics of information-seeking behavior entirely. The paper, therefore, aims to develop a digital literacy conceptual framework to understand the dynamics of Online Health Information Seeking (hereafter OHIS) and Behavioral Intention. Drawing an analogy from past health behavior and information-seeking theories, this conceptual framework provides guidelines for future quantitative studies. The study employed the literature review method and extracted the past empirical and review studies from the most reliable databases using the search words. In total, 10 years dataset of the published work has been used as a sample published from 2010 to 2020. The study analyzed the published work and advanced the body of knowledge by proposing the understudied tradeoff between information quality, perceived value, perceived personal health value, and trust in OHIS intention. Thereby, the theoretical foundation offered in this study supports the further investigations taping antecedents and consequences of OHIS intention in Asia. The research also identified the Hospital's corporate image as a moderating variable that possibly intensifies the influence of the antecedents as mentioned above on OHIS intention. The empirical validation of this model could assist policymakers and Malaysian and Indonesian citizens to understand the health information seeking and behavioral patterns of people in a digital age.
... In these studies, AUCs of 0.81 and 0.886 were reported, respectively. Another approach that has been considered to predict no-show is probit regression in Ahmad et al. [40], obtaining an AUC of 0.7. Other research works used LR in 2019, like those of Chua and Chow [41] and Dantas et al. [42]. ...
... This limitation makes it very difficult to predict missing attendance on the first visit. Different authors have addressed this problem by means of different techniques such as not including in the analysis patients who do not have a certain number of previous visits [5,28], not including the first appointment in the study [7,15,16,21,30,44,53], or including a variable that indicates whether the appointment corresponds to the first visit [14,17,20,23,27,29,32,34,[40][41][42][43]48,[50][51][52]56,[58][59][60]. ...
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Nowadays, across the most important problems faced by health centers are those caused by the existence of patients who do not attend their appointments. Among others, these patients cause loss of revenue to the health centers and increase the patients’ waiting list. In order to tackle these problems, several scheduling systems have been developed. Many of them require predicting whether a patient will show up for an appointment. However, obtaining these estimates accurately is currently a challenging problem. In this work, a systematic review of the literature on predicting patient no-shows is conducted aiming at establishing the current state-of-the-art. Based on a systematic review following the PRISMA methodology, 50 articles were found and analyzed. Of these articles, 82% were published in the last 10 years and the most used technique was logistic regression. In addition, there is significant growth in the size of the databases used to build the classifiers. An important finding is that only two studies achieved an accuracy higher than the show rate. Moreover, a single study attained an area under the curve greater than the 0.9 value. These facts indicate the difficulty of this problem and the need for further research.
... The systematic review conducted by Carreras et al. showed that at least half of the studies on no-show prediction identified age, gender, distance from home to the healthcare center, weekday, visit time, lead time, and history of previous attendance as predictors of non-attendance; marital status and visit type (first or successive) were also frequently used [14]. Our findings were mostly in line with the results reported by Carreras et al., although we did not find an association between gender and non-attendance, as reported elsewhere [18,19]. Other studies described that nonattendance was associated with the number of previous appointments [20,21], the status of the last appointment [22,23], and the treatment category (e.g., surgery) [24], which was also consistent with our results. ...
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Background Non-attendance to scheduled hospital outpatient appointments may compromise healthcare resource planning, which ultimately reduces the quality of healthcare provision by delaying assessments and increasing waiting lists. We developed a model for predicting non-attendance and assessed the effectiveness of an intervention for reducing non-attendance based on the model. Methods The study was conducted in three stages: (1) model development, (2) prospective validation of the model with new data, and (3) a clinical assessment with a pilot study that included the model as a stratification tool to select the patients in the intervention. Candidate models were built using retrospective data from appointments scheduled between January 1, 2015, and November 30, 2018, in the dermatology and pneumology outpatient services of the Hospital Municipal de Badalona (Spain). The predictive capacity of the selected model was then validated prospectively with appointments scheduled between January 7 and February 8, 2019. The effectiveness of selective phone call reminders to patients at high risk of non-attendance according to the model was assessed on all consecutive patients with at least one appointment scheduled between February 25 and April 19, 2019. We finally conducted a pilot study in which all patients identified by the model as high risk of non-attendance were randomly assigned to either a control (no intervention) or intervention group, the last receiving phone call reminders one week before the appointment. Results Decision trees were selected for model development. Models were trained and selected using 33,329 appointments in the dermatology service and 21,050 in the pneumology service. Specificity, sensitivity, and accuracy for the prediction of non-attendance were 79.90%, 67.09%, and 73.49% for dermatology, and 71.38%, 57.84%, and 64.61% for pneumology outpatient services. The prospective validation showed a specificity of 78.34% (95%CI 71.07, 84.51) and balanced accuracy of 70.45% for dermatology; and 69.83% (95%CI 60.61, 78.00) for pneumology, respectively. The effectiveness of the intervention was assessed on 1,311 individuals identified as high risk of non-attendance according to the selected model. Overall, the intervention resulted in a significant reduction in the non-attendance rate to both the dermatology and pneumology services, with a decrease of 50.61% ( p <0.001) and 39.33% ( p =0.048), respectively. Conclusions The risk of non-attendance can be adequately estimated using patient information stored in medical records. The patient stratification according to the non-attendance risk allows prioritizing interventions, such as phone call reminders, to effectively reduce non-attendance rates.
... 11,12 Beyond reasons for missing appointments, research has also found that being of younger age, lack of insurance, scheduling a check-up visit, and 2 or more previous no show visits increase the likelihood of missing appointments. 13 Further, only 1 purely qualitative study in the U.S has explored the contextual factors that influence primary care appointment attendance behavior. Lacy et al 14 found that not understanding the scheduling system, emotions related to the appointment, and perceived disrespect from staff are key factors in non-attendance. ...
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Introduction While evidence has been established on the impact of medical appointment non-attendance on the healthcare system and patient health, previous research has not focused on how poverty and rurality may influence patient experiences with non-attendance. This paper explores patient perceptions of non-attendance among those experiencing poverty in a rural U.S county to better inform providers to the context in which their patients make attendance-related decisions. Methods Using a grounded theory approach, we conducted semi-structured interviews with 32 U.S. low-income adults in the rural Western U.S. who recurrently missed primary care appointments. We also used a questionnaire to assess individual characteristics related to health, resiliency, personal mastery, medical mistrust, life chaos, and adverse childhood experiences. Results Participants identified 3 barriers to attending appointments: appointment disinterest, competing demands, and insufficient systems. Appointment disinterest stemmed from physical and mental health issues, misalignment between needs and treatment, and comfort with the provider. Competing demands included family responsibilities, employment, and relationships. Finally, participants reported that current scheduling and transportation systems were helpful but insufficient. To provide further context, participants also reported low overall health, moderate levels of medical mistrust, life chaos, and mastery, moderate to low resilience, and very a high number of adverse childhood experiences. Conclusions Results point to the need for modified structures that allow low-income patients more control over their personal health and highlight opportunities for clinics to address patients’ lack of interest and fear in the medical encounter.
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No-show appointments in healthcare is a problem faced by medical centers around the world, and understand the factors associated with the no-show behavior is essential. In the last decades, artificial intelligence took place in the medical field and machine learning algorithms can work as a efficient tool to understand the patients behavior and to achieve better medical appointment allocation in scheduling systems. In this work, we provide a systematic literature review (SLR) of machine learning techniques applied to no-show appointments aiming at establishing the current state-of-the-art. Based on a SLR following the Kitchenham methodology, 24 articles were found and analyzed, in which the characteristics of the database, algorithms and performance metrics of each studies were synthesized. Results regarding which factors have a higher impact on missed appointment rates were analyzed too. The results indicate that the most appropriate algorithms for building the models are decision tree algorithms. Furthermore, the most significant determinants of no-show were related to the patients age, whether the patient missed a previous appointment, and the distance between the appointment and the patients scheduling.
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No-show behavior, a distinct form of absenteeism, is often problematic, particularly in the service industry. Given the growing relevance of the phenomenon in the industry, an increasing number of researchers from such different disciplines as educational economics, healthcare management research, and sports economics have begun examining both the potential antecedents and measures to counteract no-show behavior, recently. Although the continuously growing body of literature on no-show behavior has been reviewed before, most such previous attempts were neither interdisciplinary nor systematic, sometimes lacking both completeness and thoroughness. In this article, we address this gap in the still-emerging literature by providing a systematic, interdisciplinary review of the literature. We observe that, although the potential antecedents are manifold and not always neatly comparable across the different industries, there still exist commonalities, most of which are related to product characteristics and the opportunity costs arising from attendance. In contrast, although we document a promising effect of exploiting reminders in reducing no-show behavior, our understanding of the effectiveness of potential countermeasures is less pronounced.
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Background: Non-attendance to scheduled hospital outpatient appointments may compromise healthcare resource planning, which ultimately reduces the quality of healthcare provision by delaying assessments and increasing waiting lists. We developed a model for predicting non-attendance and assessed the effectiveness of an intervention for reducing non-attendance based on the model. Methods: Candidate models were built using retrospective data from appointments scheduled between January 1, 2015, and November 30, 2018, in the dermatology and pneumology outpatient services of the Hospital Municipal de Badalona (Spain). The predictive capacity of the selected model was then validated prospectively with appointments scheduled between January 7 and February 8, 2019. The effectiveness of selective phone call reminders to patients at high risk of non-attendance according to the model was assessed on all consecutive patients with at least one appointment scheduled between February 25 and April 19, 2019. Patients identified by the model as high risk of non-attendance were randomly assigned to either a control (no intervention) or intervention group, the last receiving phone call reminders one week before the appointment. Results: Models were trained and selected using 33,329 appointments in the dermatology service and 21,050 in the pneumology service. Average results for specificity and balanced accuracy for the prediction of non-attendance were 79.90% and 73.49% for dermatology, and 71.38% and 64.61% for pneumology outpatient services. The prospective validation showed a specificity of 78.34% (95%CI 71.07, 84.51) and balanced accuracy of 70.45% for dermatology; and 69.83% (95%CI 60.61, 78.00) for pneumology, respectively. The effectiveness of the intervention was assessed on 1,311 individuals identified as high risk of non-attendance according to the selected model. Overall, the intervention resulted in a significant reduction in the non-attendance rate to both the dermatology and pneumology services, with a decrease of 50.61% (p<0.001) and 39.33% (p=0.048), respectively. Conclusions: The risk of non-attendance can be adequately estimated using patient information stored in medical records. The patient stratification according to the non-attendance risk allows prioritizing interventions, such as phone call reminders, to effectively reduce non-attendance rates.
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Artificial Intelligence is revolutionizing the world. In the last decades, it is applied in almost all fields especially in medical prediction. Researchers in artificial intelligence have exploited predictive approaches in the medical sector for its vital importance in the process of decision making. Medical prediction aims to estimate the probability of developing a disease, to predict survivability and the spread of a disease in an area. Prediction is at the core of modern evidence-based medicine, and healthcare is one of the largest and most rapidly growing segments of AI. Application of technologies such as genomics, biotechnology, wearable sensors, and AI allows to: • increase availability of healthcare data and rapid progress of analytics techniques and make the foundation of precision medicine; • progress in detecting pathologies and avoid subjecting patients to intrusive examinations; • make an adapted diagnosis and therapeutic strategy to the patient’s need, his environment and his way of life. In this research, an overview of applied methods on the management of diseases is presented. The most used methods are Artificial Intelligence methods such as machine learning and deep learning techniques which have improved diagnosis and prognosis efficiency.
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A hospital appointment system promotes information exchange between hospitals and patients. Because patients' no-show behaviour wastes medical resources and compromises the sustainability of medical services, it is of utmost importance to expound no-show behaviour and its determinants. From a multi-stage perspective, we identify channel convenience, waiting time, and expected technical quality as conscious determinants and no-show habit as an unconscious determinant and proposes a model for analysing patients' no-show behaviour. The proposed hypotheses are examined with data collected from a Chinese tertiary care public hospital's appointment system by binary logistic regression. The results show that channel convenience and expected technical quality are negatively related to no-show behaviour whereas the effects of waiting time and no-show habit on no-show behaviour are significantly positive. Meanwhile, the relationship between no-show habit and no-show behaviour is stronger for shorter waiting time or higher levels of channel convenience and expected technical quality. Our findings have implications for managing patients' no-show behaviour, improving hospital appointment system management, and providing sustainable medical services.
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Background: Missed appointments reduce the efficiency of the health care system and negatively impact access to care for all patients. Identifying patients at risk for missing an appointment could help health care systems and providers better target interventions to reduce patient no-shows. Objectives: Our aim was to develop and test a predictive model that identifies patients that have a high probability of missing their outpatient appointments. Methods: Demographic information, appointment characteristics, and attendance history were drawn from the existing data sets from four Veterans Affairs health care facilities within six separate service areas. Past attendance behavior was modeled using an empirical Markov model based on up to 10 previous appointments. Using logistic regression, we developed 24 unique predictive models. We implemented the models and tested an intervention strategy using live reminder calls placed 24, 48, and 72 hours ahead of time. The pilot study targeted 1,754 high-risk patients, whose probability of missing an appointment was predicted to be at least 0.2. Results: Our results indicate that three variables were consistently related to a patient's no-show probability in all 24 models: past attendance behavior, the age of the appointment, and having multiple appointments scheduled on that day. After the intervention was implemented, the no-show rate in the pilot group was reduced from the expected value of 35% to 12.16% (p value < 0.0001). Conclusions: The predictive model accurately identified patients who were more likely to miss their appointments. Applying the model in practice enables clinics to apply more intensive intervention measures to high-risk patients.
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Aims: Patient no-show is a recurrent problem in medical centers and, in conjunction with cancellation of appointments, often results in loss of productivity and excessive patient time to appointment. The purpose of this study was to develop a dynamic procedure for scheduling patients within an outpatient clinic where patients are expected to have multiple appointments, such as physical therapy, occupational therapy, primary care, and dentistry. Methods: This retrospective study involved the year 2014 de-identified patient records from an outpatient clinic affiliated with a large university hospital. A number of patient characteristics, appointment data, and historical attendance records were examined to determine if they significantly impacted patients who missed scheduled appointments (no-shows.) Patient attendance behaviors over multiple-appointments were examined to determine if their no-show and cancellation patterns differed from one appointment to the next. Decision tree analysis was applied to those predictors that significantly correlated with patient attendance behavior to assess the likelihood of a patient no-show. A sample dynamic appointment scheduling procedure that utilized different overbooking strategies for different appointment numbers was then developed. Computer simulation was used to assess the effectiveness of the dynamic procedure versus two other methods consisting of randomly assigned and uniformly assigned appointments. Results: The dynamic scheduling procedure resulted in increased scheduling efficiency through overbooking but with less than 5% risk of appointment conflicts (i.e., two patients showing at the same time), equating to approximately 0.16 conflicts per clinician per day. It also increased clinic utilization by about 6.7%. It consistently outperformed the other two methods with respect to the percentage of appointment conflicts. Limitations: The study is limited with respect to potential clinician cost increase resulting from possible appointment conflicts. A second limitation is that patients experiencing appointment conflicts might not wait for treatment, resulting in potential loss of revenue. A third limitation is that the model does not take into account patient satisfaction, nor the ethics of overbooking patients. Conclusions: A dynamic appointment scheduling procedure was developed using actual patient characteristics. The procedure resulted in creation of more efficient appointment schedules thereby increasing the clinic utilization.
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Background Practitioners and decision makers in the medical and insurance systems need knowledge on the relationship between work exposures and burnout. Many burnout studies – original as well as reviews - restricted their analyses to emotional exhaustion or did not report results on cynicism, personal accomplishment or global burnout. To meet this need we carried out this review and meta-analyses with the aim to provide systematically graded evidence for associations between working conditions and near-future development of burnout symptoms. MethodsA wide range of work exposure factors was screened. Inclusion criteria were: 1) Study performed in Europe, North America, Australia and New Zealand 1990–2013. 2) Prospective or comparable case control design. 3) Assessments of exposure (work) and outcome at baseline and at least once again during follow up 1–5 years later. Twenty-five articles met the predefined relevance and quality criteria. The GRADE-system with its 4-grade evidence scale was used. ResultsMost of the 25 studies focused emotional exhaustion, fewer cynicism and still fewer personal accomplishment. Moderately strong evidence (grade 3) was concluded for the association between job control and reduced emotional exhaustion and between low workplace support and increased emotional exhaustion. Limited evidence (grade 2) was found for the associations between workplace justice, demands, high work load, low reward, low supervisor support, low co-worker support, job insecurity and change in emotional exhaustion. Cynicism was associated with most of these work factors. Reduced personal accomplishment was only associated with low reward. There were few prospective studies with sufficient quality on adverse chemical, biological and physical factors and burnout. Conclusion While high levels of job support and workplace justice were protective for emotional exhaustion, high demands, low job control, high work load, low reward and job insecurity increased the risk for developing exhaustion. Our approach with a wide range of work exposure factors analysed in relation to the separate dimensions of burnout expanded the knowledge of associations, evidence as well as research needs. The potential of organizational interventions is illustrated by the findings that burnout symptoms are strongly influenced by structural factors such as job demands, support and the possibility to exert control.
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Background Implicit biases involve associations outside conscious awareness that lead to a negative evaluation of a person on the basis of irrelevant characteristics such as race or gender. This review examines the evidence that healthcare professionals display implicit biases towards patients. Methods PubMed, PsychINFO, PsychARTICLE and CINAHL were searched for peer-reviewed articles published between 1st March 2003 and 31st March 2013. Two reviewers assessed the eligibility of the identified papers based on precise content and quality criteria. The references of eligible papers were examined to identify further eligible studies. ResultsForty two articles were identified as eligible. Seventeen used an implicit measure (Implicit Association Test in fifteen and subliminal priming in two), to test the biases of healthcare professionals. Twenty five articles employed a between-subjects design, using vignettes to examine the influence of patient characteristics on healthcare professionals’ attitudes, diagnoses, and treatment decisions. The second method was included although it does not isolate implicit attitudes because it is recognised by psychologists who specialise in implicit cognition as a way of detecting the possible presence of implicit bias. Twenty seven studies examined racial/ethnic biases; ten other biases were investigated, including gender, age and weight. Thirty five articles found evidence of implicit bias in healthcare professionals; all the studies that investigated correlations found a significant positive relationship between level of implicit bias and lower quality of care. DiscussionThe evidence indicates that healthcare professionals exhibit the same levels of implicit bias as the wider population. The interactions between multiple patient characteristics and between healthcare professional and patient characteristics reveal the complexity of the phenomenon of implicit bias and its influence on clinician-patient interaction. The most convincing studies from our review are those that combine the IAT and a method measuring the quality of treatment in the actual world. Correlational evidence indicates that biases are likely to influence diagnosis and treatment decisions and levels of care in some circumstances and need to be further investigated. Our review also indicates that there may sometimes be a gap between the norm of impartiality and the extent to which it is embraced by healthcare professionals for some of the tested characteristics. Conclusions Our findings highlight the need for the healthcare profession to address the role of implicit biases in disparities in healthcare. More research in actual care settings and a greater homogeneity in methods employed to test implicit biases in healthcare is needed.
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Aim To explore the quantitative and qualitative literature on the impact of nurse‐led post‐discharge telephone follow‐up (TFU) call interventions on patient outcomes. Background Adverse patient outcomes such as post‐discharge problems, premature contact with health systems, inability to self‐manage conditions, and hospital readmissions all have a financial impact on health care systems as well as the health and well‐being, and satisfaction of patients. Design A mixed‐study systematic review. Review Methods A systematic search of CINAHL, Ebsco, PubMed, Quest and Cinch‐Health databases was undertaken using the key terms “nurs*”, “nurse‐led”, “nurse initiated”, “discharge”, “hospital”, “telephone”, “follow‐up”, and “telephone follow‐up” to identify relevant original peer‐reviewed studies published between 2010 and 2016. Ten articles were selected for inclusion. The selected papers were critically appraised. A sequential explanatory approach with a convergent synthesis was used to report findings following PRISMA guidelines. Results The findings demonstrate that nurse‐led TFU interventions have the potential to improve patient outcomes. The studies suggest patient satisfaction with TFU is one of the strongest positive outcomes from the interventions. However, the results do not support improvement in patient readmission or mortality. Conclusions Of the 10 studies reviewed, only two were methodologically strong limiting the conclusions that can be drawn from the current research on this topic. Telephone follow‐up interventions improve patient satisfaction, and have the potential to meet patient information and communication needs, improve self‐management and follow‐up appointment attendance and reduce post‐discharge problems. Further research is required to explore patients’ perceptions of the most useful content of TFU calls, the efficacy of TFU calls, and nurses’ perceptions and experiences of conducting TFU interventions. Relevance to clinical practice When conducted by a nurse, these interventions have the potential to enhance post‐discharge care to patients and meet care needs. Patients perceive TFU as acceptable and are satisfied with this form of post‐discharge care. This article is protected by copyright. All rights reserved.
Article
Patient arrivals at the Emergency Department (ED) of hospitals has an unpredictable behaviour. So that, adequate forecasting of this process can serve a management baseline to better allocate ED human resources and medical equipment. In this paper, a multi-method patient arrival forecasting outline for EDs is developed. The methods followed within this study include single methods as Linear Regression (LR), Autoregressive Integrated Moving Average (ARIMA), Artificial Neural Network (ANN), Exponential Smoothing (ES) and hybrid methods as ARIMA-ANN and ARIMA-LR. As the subject of the study, a private hospital ED case in Turkey is carried out. Data of ED patient arrivals for the year of 2016 was used to set up models. Forecasting performance of the multi-method outline was measured using mean absolute percentage error. The ARIMA-ANN hybrid model is shown to outperform in terms of forecasting accuracy. In order to contribute to the current knowledge, this paper is a novel attempt of applying these methods to model ED patient arrivals and making an overall assessment among them. The results can be used to aid in strategic decision-making on ED staffing and scheduling policy planning in response to predictable arrival variations.
Article
No-show appointments significantly impact the functioning of healthcare institutions, and much research has been performed to uncover and analyze the factors that influence no-show behavior. In spite of the growing body of literature on this issue, no synthesis of the state-of-the-art is presently available and no systematic literature review (SLR) exists that encompasses all medical specialties. This paper provides a SLR of no-shows in appointment scheduling in which the characteristics of existing studies are analyzed, results regarding which factors have a higher impact on missed appointment rates are synthetized, and comparisons with previous findings are performed. A total of 727 articles and review papers were retrieved from the Scopus database (which includes MEDLINE), 105 of which were selected for identification and analysis. The results indicate that the average no-show rate is of the order of 23%, being highest in the African continent (43.0%) and lowest in Oceania (13.2%). Our analysis also identified patient characteristics that were more frequently associated with no-show behavior: adults of younger age; lower socioeconomic status; place of residence is distant from the clinic; no private insurance. Furthermore, the most commonly reported significant determinants of no-show were high lead time and prior no-show history.
Article
Introduction: Integrated care is becoming very crucial in healthcare management due to the increase of chronic non-communicable diseases (NCD) globally. Therefore, this paper aimed to review and examine how applicable and transferable quality referral factors were to the Saudi health system. Methods: Two searches were completed to (1) identify quality referral factors and (2) to identify the Saudi literature relevant to the referral system. This paper employed a knowledge-to-action framework, and assessed quality factors of referrals for their applicability and transferability in the Saudi context using Wang's literature-based method. Results: The quality referral factors introduced were safe, timely, effective, efficient, equitable, patient centred, structured referral letter, dissemination of referral guidelines, central computerised system, and inclusion criteria. These factors could be applicable and transferrable to the Saudi setting with a consideration of 13 factors. The 13 factors include organizational structure, social acceptability/norms, availability of resources and skills, magnitude of health issues, and characteristics of the target population. Conclusion: Although deficits are present in the current Saudi referral system, the applicability and transferability of the quality referral factors are attainable in Saudi Arabia. From an international perspective, this review shows 10 quality factors that are significant in the process of referral system for chronic NCD.
Article
The US healthcare sector is among leading globally in the incorporation of advanced technologies in its operations. This study evaluated existing data to understand how technological advancement in the US healthcare sector has impacted the cost of healthcare services and the patient satisfaction. The study was based on a quantitative analysis of 24 existing studies selected from various electronic databases. The results indicate a significant increase in the cost of healthcare service due to technological adoption and other factors. Increase in cost due to technological adoption is evident is the area of diagnosis (63%, P = 0.002) and patient monitoring (51%, P = 0.021). A significantly higher percentage, was found, of patients that believe the adoption of advanced technologies leads to improved quality in diagnostic procedures (67%, P = 0.042), monitoring (79%, P = 0.004), and data keeping (85%, P = 0.032). Strategies need to be developed to manage costs associated with technological adoption while ensuring the delivery of quality services.
Article
Background: Physician burnout in the United States has reached epidemic proportions and is rising rapidly, although burnout in other occupations is stable. Its negative impact is far reaching and includes harm to the burned-out physician, as well as patients, coworkers, family members, close friends, and healthcare organizations. Objective: The purpose of this review is to provide an accurate, current summary of what is known about physician burnout and to develop a framework to reverse its current negative impact, decrease its prevalence, and implement effective organizational and personal interventions. Data sources: I completed a comprehensive MEDLINE search of the medical literature from January 1, 2000, through December 28, 2016, related to medical student and physician burnout, stress, depression, suicide ideation, suicide, resiliency, wellness, and well-being. In addition, I selectively reviewed secondary articles, books addressing the relevant issues, and oral presentations at national professional meetings since 2013. Study selection: Healthcare organizations within the United States were studied. Results: The literature review is presented in 5 sections covering the basics of defining and measuring burnout; its impact, incidence, and causes; and interventions and remediation strategies. Conclusions: All US medical students, physicians in training, and practicing physicians are at significant risk of burnout. Its prevalence now exceeds 50%. Burnout is the unintended net result of multiple, highly disruptive changes in society at large, the medical profession, and the healthcare system. Both individual and organizational strategies have been only partially successful in mitigating burnout and in developing resiliency and well-being among physicians. Two highly effective strategies are aligning personal and organizational values and enabling physicians to devote 20% of their work activities to the part of their medical practice that is especially meaningful to them. More research is needed.