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Abstract

OBJECTIVES: Surgical wait times in Nova Scotia are at crisis levels, as patients are waiting too long on the wait list. This can lead to worsening illness course, and in some cases premature death. This project started with the objective of addressing the surgical waitlist crisis, as people in Nova Scotia are waiting months to years before receiving their life- saving surgeries. METHODS: This study involved obtaining a clinical dataset from an open data portal on the internet, loading the data into our analytical environment, exploring the data using analytical software, cleaning the data, determining feature variables (independent variables), and utilizing the data to train models for prediction of the outcome variable(s). R was utilized to perform exploratory, descriptive, and predictive statistical analysis on the surgical wait times dataset in Nova Scotia, for the time period 2014 to 2016. RESULTS: A bivariate multiple linear regression model was constructed with two dependent variables (consult_90th and surgery_90th), representing the 90th percentiles for each instance of a surgical specialty’s wait time, added together to give the combined surgical wait time and one independent variable. CONCLUSION: Although we can give an estimate of average wait times in Nova Scotia based on specialty from the model, only the waittimes from the following specialties are statistically significant: General Surgery, Dental, Opthalmology, Orthopaedic, and Otolaryngology (ENT) at 95% confidence. However, as the model is statistically significant with all the coefficients, then it can be assumed that the model can predict with 95% confidence the wait times from any specialty, even if the individual coefficient is not statistically significant.
Analysis of Surgical Wait Times in Nova Scotia
Carlo&Carandang
1
,&Gregory&Horne
2
,&William&Wells1,&Catherine&Stokes2
Corresponding&Author:&Carlo&Carandang,&MD&
Email:&carandangc@gmail.com&
ORCID:&0000-0002-2008-9642&
This project was supported by Saint Mary’s University, MSc Program in Computing & Data
Analytics and NSCC Institute of Technology
The statistical analysis and code for this study is located at this repository:
https://github.com/Carlo-Carandang/Nova_Scotia_Surgical_Wait_Times
&
1
Saint Mary’s University, MSc Program in Computing & Data Analytics, 923 Robie Street, Halifax, Nova Scotia,
Canada, B3H 3C3
2
NSCC, Institute of Technology Campus, 5685 Leeds Street, Halifax, Nova Scotia B3K 2T3
Analysis of Surgical Wait Times in Nova Scotia
Abstract
OBJECTIVES:!Surgical!wait!times!in!Nova!Scotia!are!at!crisis!levels,!as!patients!are!waiting!
too!long!on!the!wait!list.!This!can!lead!to!worsening!illness!course,!and!in!some!cases!
premature!death.!This!project!started!with!the!objective!of!addressing!the!surgical!waitlist!
crisis,!as!people!in!Nova!Scotia!are!waiting!months!to!years!before!receiving!their!life-
saving!surgeries.!!
METHODS:!This!study!involved!obtaining!a!clinical!dataset!from!an!open!data!portal!on!the!
internet,!loading!the!data!into!our!analytical!environment,!exploring!the!data!using!
analytical!software,!cleaning!the!data,!determining!feature!variables!(independent!
variables),!and!utilizing!the!data!to!train!models!for!prediction!of!the!outcome!variable(s).!
R!was!utilized!to!perform!exploratory,!descriptive,!and!predictive!statistical!analysis!on!the!
surgical!wait!times!dataset!in!Nova!Scotia,!for!the!time!period!2014!to!2016.!!
RESULTS:!A!bivariate!multiple!linear!regression!model!was!constructed!with!two!
dependent!variables!(consult_90th!and!surgery_90th),!representing!the!90th!percentiles!
for!each!instance!of!a!surgical!specialty’s!wait!time,!added!together!to!give!the!combined!
surgical!wait!time!and!one!independent!variable.!!
CONCLUSION:!Although!we!can!give!an!estimate!of!average!wait!times!in!Nova!Scotia!
based!on!specialty!from!the!model,!only!the!waittimes!from!the!following!specialties!are!
statistically!significant:!General!Surgery,!Dental,!Opthalmology,!Orthopaedic,!and!
Otolaryngology!(ENT)!at!95%!confidence.!However,!as!the!model!is!statistically!significant!
with!all!the!coefficients,!then!it!can!be!assumed!that!the!model!can!predict!with!95%!
confidence!the!wait!times!from!any!specialty,!even!if!the!individual!coefficient!is!not!
statistically!significant.!
Surgical!wait!times!in!Nova!Scotia!are!at!crisis!levels,!as!patients!are!waiting!too!long!on!the!
wait!list.!This!can!lead!to!worsening!illness!course,!and!in!some!cases!premature!death.!
This!project!started!with!the!objective!of!addressing!the!surgical!waitlist!crisis,!as!people!in!
Nova!Scotia!are!waiting!months!to!years!before!receiving!their!life-saving!surgeries.!The!
Nova!Scotia!government!website!only!gives!numbers!about!wait!times!that!are!difficult!to!
interpret.!This!research!team!wanted!to!look!for!correlations!of!the!variables!in!the!surgical!
wait!times!dataset.!In!fact,!every!province!has!a!wait!times!dataset!that!is!updated!every!3!
months,!and!post!the!data!to!the!Canadian!Institute!for!Health!Information!public-facing!
website.!The!data!goes!back!to!the!beginning!of!the!recordings,!which!is!roughly!around!the!
early!2010s.!
The!data!set!for!surgical!wait!times!in!Nova!Scotia!has!a!dozen!columns!and!approximately!
7000!rows!when!the!dataset!was!obtained!for!the!years!2014!to!2016.!It’s!basically!an!
Excel!spreadsheet,!and!is!not!in!any!relational!database!tables.!It!maps!surgical!wait!times!
based!on!date,!hospital,!Health!Region!Zone,!specialty,!procedure,!and!doctor.!
Research!shows!that!surgical!wait!times!are!associated!with!significant!morbidity!and!
mortality!risk1–6.!Morbidity!risk!is!the!probability!of!the!illness!worsening,!while!mortality!
risk!is!the!probability!of!dying!from!the!illness.!It!is!widely!known!that!the!surgical!wait!
times!in!Nova!Scotia!are!at!crisis-levels!currently.!Patients!in!Nova!Scotia!are!waiting!too!
long!on!average!to!receive!their!procedure.!However,!it!is!not!known!how!the!data!can!
inform!the!improvement!of!services,!and!hence!morbidity!and!mortality.!Therefore,!it!is!
important!to!analyze!the!wait!times!data!using!advanced!statistical!and!data!analytics!to!
help!provide!more!insights!to!help!decrease!morbidity!and!mortality!from!waiting!for!
surgical!procedures.!
Although!the!most!urgent!surgeries!get!scheduled!first,!waiting!too!long!for!surgery!can!be!
detrimental!to!one’s!health!because!waiting!too!long!is!a!burden!on!the!patient!and!their!
family,!waiting!too!long!can!lead!to!death,!and!waiting!too!long!can!lead!to!worsening!of!the!
illness.!Indeed,!while!the!most!urgent!surgeries!get!scheduled!first,!waiting!too!long!for!
surgery!can!be!detrimental!to!one’s!health.!
Ultimately,!the!researchers!wanted!to!find!the!factors!associated!with!surgical!wait!times,!
so!that!new!insights!can!be!obtained!to!decrease!the!wait!times,!and!hence!save!lives!and!
prevent!illness!deterioration.!
Methods
This!study!involved!obtaining!a!clinical!dataset!from!an!open!data!portal!on!the!internet,!
loading!the!data!into!our!analytical!environment,!exploring!the!data!using!analytical!
software,!cleaning!the!data,!determining!feature!variables!(independent!variables),!and!
utilizing!the!data!to!train!models!for!prediction!of!the!outcome!variable(s).!
The!research!team!divided!the!work!into!the!following!tasks:!
Project!topic!selection!(C.C.,!G.H.,!W.W.,!C.S.)!
Dataset!selection!(C.C.,!W.W.)!
Data!cleaning!(C.C.,!G.H.)!
Exploratory!analysis!and!multiple!linear!regression!in!R!(G.H.,!W.W.)!
Multiple!linear!regression!and!polynomial!regression!in!Python!(C.C.!and!G.H.)!
Visualization!of!data!and!graphs!(G.H.)!
Research,!report!writing,!and!editing!(C.C.,!G.H.,!W.W.,!C.S.)!
Initially,!the!data!had!to!be!cleaned,!as!there!were!many!missing!values.!This!was!
performed!manually,!as!the!column!and!rows!were!populated!haphazardly,!and!therefore!
had!to!inspect!and!note!the!missing!values,!row!by!row!(12!columns!and!about!7000!rows).!
Subsequently,!business!and!clinical!rules!were!followed!to!fill!in!missing!values,!utilizing!
the!clinical!domain!expertise!of!one!of!the!authors!(C.C.)!who!had!practiced!as!a!physician!
in!Nova!Scotia!for!several!years.!As!an!example!of!missing!values,!each!of!the!surgical!
procedures!had!to!be!classified!into!the!surgical!specialty!to!which!it!belonged,!as!many!of!
the!rows!were!missing!this!information.!In!addition,!there!were!missing!values!for!hospital,!
Health!Region!Zone,!procedure!performed!by!surgeon,!and!specialty!of!the!surgeon.!Any!
missing!values!not!obtainable!within!the!dataset!were!scraped!from!the!internet!to!
populate!the!missing!values,!such!as!hospital!information!and!information!on!the!surgeon.!
Once!the!dataset!was!cleaned!and!missing!values!were!replaced!with!inferred!values!from!
domain!expertise!and/or!web!scraping,!exploratory!statistical!analysis!was!performed!on!
the!surgical!wait!times!dataset!in!Nova!Scotia.!The!research!team!was!trying!to!determine!if!
there!were!any!correlations!between!the!numerous!independent!variables!(i.e.!specialty,!
period,!facility,!etc.)!and!the!dependent!variable!(surgical!wait!times).!This!exploratory!
analysis!was!the!beginning!of!an!ongoing,!in-depth!analysis!of!the!Nova!Scotia!Surgical!Wait!
Times,!to!help!to!improve!patient!care!through!data!analysis!and!statistical!modelling.!
R!was!utilized!to!perform!exploratory,!descriptive,!and!predictive!statistical!analysis!on!the!
surgical!wait!times!dataset!in!Nova!Scotia,!for!the!time!period!2014!to!2016.!
Analytical Environment
Software
Data!analyses,!visualizations,!and!production!of!this!report!were!undertaken!using!
software!with!the!specifications!detailed!below.!!
Anaconda!v0.0.0!
Jupyter!Notebook!v0.0.0!
pdfTeX!(TeXlive!2015)!v3.14159265-2.6-1.40.16!
Pandoc!v1.16.0.2!
R!v3.4.4!
R!Studio!v1.1.383!
R!Markdown!v2!
Hardware
Data!analyses,!visualizations,!and!production!of!this!report!were!undertaken!using!
hardware!with!the!specifications!detailed!below:!!
Apple6MacBook6Air!Processor:!1.6!GHz!Intel!Core!i5;!Number!of!Processors:!1;!Total!
Number!of!Cores:!2;!L2!Cache!(per!Core):!256!KB;!L3!Cache:!3!MB;!Memory:!8!GB!1600!
MHz!DDR3;!Operating!System:!Apple!Mac!OS!X!v10.0!
Apple6Macbook6Pro!Processor:!3.1!GHz!Intel!Core!i7;!Number!of!Processors:!1;!Total!
Number!of!Cores:!2;!L2!Cache!(per!Core):!256!KB;!L3!Cache:!3!MB;!Memory:!16!GB!1857!
MHz!DDR3;!Graphics:!Intel!Iris!Graphics!6100!1536!MB;!Operating!System:!Apple!Mac!OS!X!
v10.13!
Hewlett6Packard6Spectre613!Processor:!2.5GHz!Intel!Core!i7;!Number!of!Processors:!1;!
Total!Number!of!Cores:!4;!Memory:!8!GB!DDR3;!Graphics:!Intel!HD!Graphics!520;!Operating!
System:!Xubuntu!Linux!v16.04!LTS!
Acquire the Surgical Wait Times Data
The!surgical!wait!times!data!was!obtained!from!the!Nova!Scotia!Government!Open!Data!
Portal.!Here!are!the!steps!to!acquire!the!dataset:!
1) Navigate!to!the!Open!Data!Portal!
2) Select!the!Data!Catalogue!
3) Search!‘surgical!wait!times’!
4) Select!Surgical!Wait!Times!
5) Select!the!Export!option!
6) Select!CSV!
7) Save!the!data!file!to!the!project!subdirectory!
(nova_scotia_wait_times/data_analysis/data).!
8) Load!the!data!from!a!tab-separated-values!formatted!file!with!column!headers!
Wrangle the Data
Once!the!data!was!loaded,!the!feature!names!of!the!data!set!were!determined:!
[1]!Period![2]!Specialty![3]!Procedure![4]!Provider!
[5]!Zone![6]!Facility![7]!Year![8]!Quarter!
[9]!Consult_Median![10]!Consult_90th![11]!Surgery_Median![12]!Surgery_90th!
The!dataset!contained!6843!observations!and!12!features.!Viewing!a!sample!of!the!dataset,!
it!was!determined!that!multiple!variables!were!categorical,!and!the!wait!times!were!the!
only!continuous!variables.!Here!are!the!definitions!of!the!12!features!in!the!dataset:!
Feature!
Definition!
Period!
Time!period!
Specialty!
Surgical!specialty!
Procedure!
Surgical!procedure!
Provider!
Surgeon!
Zone!
Healthcare!Zone,!1!to!4!
Facility!
Hospital!
Year!
Year!
Quarter!
Quarter!(3-months)!
Consult_Median!
Maximum!time!that!50%!of!patients!recently!waited!for!consult!
Consult_90th!
Maximum!time!that!90%!of!patients!recently!waited!for!consult!
Surgery_Median!
Maximum!time!that!50%!of!patients!recently!waited!for!surgery!
Surgery_90th!
Maximum!time!that!90%!of!patients!recently!waited!for!surgery!
The!surgical!specialty!types!were!parsed!as!follows:!specialty,!all!specialties,!cardiac,!
dental,!general,!neurosurgery,!obstetrics/gynaecology,!ophthalmology,!oral!and!
maxillofacial,!oral!maxillofacial,!orthopaedic,!otolaryngology!(ent),!plastic,!thoracic,!
urology,!vascular.!In!total,!there!are!15!surgical!specialty!types!and!159!surgical!procedure!
types.!
Next!was!the!scatterplots!of!the!features!paired!against!one!another:!
Figure.!Features!as!pairs!in!scatterplots.!
!
The!exploratory!analysis!gave!us!an!initial!signal!that!specialty!may!be!correlated!with!wait!
times,!as!the!scatterplots!of!the!various!combinations!of!the!variables!showed!that!the!
specialties!showed!a!‘banded’!pattern!when!it!was!paired!and!plotted!versus!wait!times,!
and!some!specialties!appeared!to!have!greater!variation!in!wait!times!than!others.!The!
‘banded’!pattern!occurred!due!to!the!specialty!variable!being!categorical!(discrete!
datapoints).!Because!of!this!banded!pattern,!specialty!was!chosen!as!the!independent!
variable,!and!consult!and!surgery!times!were!chosen!as!the!dependent!variable.!Therefore,!
after!this!exploration!of!the!dataset!for!features!to!study,!only!observations!with!surgical!
procedure!type!‘all’!were!extracted:!
feature!
missing_count!
nonmissing_count!
consult_90th!
12!
284!
consult_median!
12!
284!
facility!
296!
0!
period!
0!
296!
procedure!
0!
296!
provider!
0!
296!
quarter!
296!
0!
specialty!
0!
296!
surgery_90th!
0!
296!
surgery_median!
0!
296!
year!
296!
0!
zone!
296!
0!
The!minimum,!maximum,!average,!standard!deviation,!and!total!combined!wait!days!by!
consultation!and!surgery!specialty.!Average!wait!days!is!the!median!number!of!days!on!the!
surgical!wait!list:!
minimum!
maximum!
average!
sigma!
total!
observations!
66!
198!
157!
49!
702!
5!
148!
1032!
327!
319!
7006!
16!
65!
2234!
177!
298!
14432!
56!
155!
949!
252!
236!
3081!
10!
64!
882!
199!
149!
9573!
41!
115!
2875!
392!
497!
16779!
33!
171!
620!
421!
159!
4332!
11!
162!
1365!
662!
318!
26539!
38!
136!
1081!
390!
258!
11910!
25!
151!
738!
372!
186!
5598!
15!
73!
449!
179!
134!
1307!
6!
61!
819!
219!
170!
6002!
22!
112!
685!
307!
242!
2151!
6!
Rows!with!missing!values!were!removed!before!training!the!prediction!model.!
Visualise the Data
After!wrangling!the!data,!the!data!was!visualized:!
Figure!1.!
!
Figure!1!shows!the!total!wait!time!in!person!years,!grouped!by!surgical!specialty.!Cardiac!
surgery!has!the!lowest!value,!while!orthopaedic!has!the!highest.!
Figure!2.!
!
Figure!2!shows!the!median!total!wait!time!in!days,!grouped!by!surgical!specialty.!Cardiac!
surgery!has!the!lowest!value,!while!orthopaedic!has!the!highest.!
Figure!3.!
!
Figure!3!shows!the!wait!time!distribution!in!days,!grouped!by!surgical!specialty.!Notice!
cardiac!surgery!and!thoracic!surgery!at!the!lower!end,!and!orthopaedic!at!the!higher!end.!
Figure!4.!
!
Figure!4!shows!the!histogram!of!the!frequencies!of!the!wait!times.!Most!people!are!waiting!
between!100!to!200!days!for!their!surgical!procedure.!
Figure!5.!
!
Figure!5!shows!the!histogram!of!the!frequencies!of!the!wait!times,!grouped!by!consultation!
wait!times!and!surgery!wait!times.!Most!people!are!waiting!between!50!days!and!200!days!
for!a!consulation,!and!between!50!to!250!days!for!a!surgical!procedure.!
It!was!determined!from!the!dataset!that!the!total!wait!time!to!receive!a!surgical!procedure!
is!the!sum!of!the!consultation!wait!time!and!the!surgical!wait!time.!The!domain!expert!who!
had!worked!in!the!system!(C.C.)!for!several!years!interpreted!the!total!wait!time!for!a!
surgical!procedure!as!the!sum!of!the!following!wait!times:!
*!Wait!time!to!see!a!family!doctor,!who!will!determine!if!a!surgery!consultation!is!needed!
(family!doctor!wait!time)!*!Wait!time!to!consult!with!a!surgeon!(consult!wait!time)!*!Wait!
time!for!the!surgical!procedure!(surgery!wait!time)!
So!the!total!wait!time!to!receive!a!surgical!procedure!is!as!follows:!
Total Wait Time = Family Doctor Wait Time + Consult Wait Time + Surgery Wait Time
But!this!dataset!is!missing!the!Family!Doctor!Wait!Time,!so!the!estimated!wait!times!for!
surgical!procedures!in!this!analysis!is!missing!the!Family!Doctor!Wait!Time.!Therefore,!any!
estimated!prediction!of!wait!times!from!this!study!is!an!underestimate!of!the!real!wait!
time.!
Results
Build the Model
Prior!to!building!the!statistical!model!the!baseline!factor!was!set!to!‘general!surgery’!
instead!of!the!default!‘cardiac!surgery’!to!determine!the!impact,!if!any,!on!the!linear!
regression!model!with!regards!to!the!null!hypothesis.!
A!bivariate!multiple!linear!regression!model!was!constructed!with!two!dependent!
variables!(consult_90th!and!surgery_90th),!representing!the!90th!percentiles!for!each!
instance!of!a!surgical!specialty’s!wait!time,!added!together!to!give!the!combined!surgical!
wait!time!and!one!independent!variable.!
A summary of the multiple linear regression model is as follows:
Call:!lm(formula!=!specialty90!~!specialty)!
Residuals:!
Min!
1Q!
Median!
3Q!
Max!
-536.39!
-144.57!
-66.16!
70.76!
2366.55!
Coefficients:!
Estimate!Std.!
Error!
t!value!
Pr(>!
(Intercept)!
257.71!
38.80!
6.643!
1.68e-10!***!
specialtycardiac!
-117.31!
135.51!
-0.866!
0.387417!
specialtydental!
180.16!
82.30!
2.189!
0.029447!*!
specialtyneurosurgery!
50.39!
99.67!
0.506!
0.613607!
specialtyobstetrics/gynaecology!
-24.23!
59.68!
-0.406!
0.685083!
specialtyophthalmology!
250.74!
63.71!
3.935!
0.000106!***!
specialtyoral!maxillofacial!
136.10!
95.75!
1.421!
0.156338!
specialtyorthopaedic!
440.68!
61.02!
7.222!
5.19e-12!***!
specialtyotolaryngology!(ent)!
218.69!
69.83!
3.131!
0.001930!**!
specialtyplastic!
115.49!
84.41!
1.368!
0.172388!
specialtythoracic!
-39.88!
124.72!
-0.320!
0.749385!
specialtyurology!
15.10!
73.05!
0.207!
0.836358!
specialtyvascular!
100.79!
124.72!
0.808!
0.419727!
Signif.!codes:!0!|!***!0.001!|!**!0.01!|!*!0.05!|!‘.’!0.1!|!‘’!1!
Residual!standard!error:!290.3!on!271!degrees!of!freedom!
Multiple!R-squared:!0.2383,!Adjusted!R-squared:!0.2046!
F-statistic:!7.066!on!12!and!271!DF,!p-value:!3.522e-11!
From the above summary of the model, the following statistical measures were extracted:
degrees!of!freedom:!13!and!271!
p-value!of!the!model:!3.522271910^{-11}!
residual!standard!error:!290.3315348!
F-statistic:!7.0658736!
F-critical:!1.7583858!
An analysis of variance (ANOVA) of the linear regression model was used to validate the
model:
Analysis!of!Variance!Table!
Response:!specialty90!
Measure!
Df!
Sum!Sq!
Mean!Sq!
F!value!
Pr(>F)!
specialty!
12!
7147193!
595599!
7.0659!
3.522e-11!***!
Residuals!
271!
22843240!
84292!
!
!
Interpretation
The!residual!standard!error!(standard!error!of!the!estimate)!is!290.33,!where!it!is!the!
standard!deviation!of!the!variation!of!observations!around!the!regression!line!(it!is!the!
standard!deviation!of!the!regression!model).!So!waitimes!average!can!be!estimated!as!+-
2(290.33)!=!580.66.!As!this!value!is!large!when!compared!to!the!average!waittimes!in!the!
sample,!then!the!variation!of!observed!y!values!from!the!regression!line!is!also!large.!For!
future!research,!we!should!look!for!other!variables!which!can!explain!more!of!the!variation!
in!waittimes!(ie.!costs!for!procedures,!facilities!funding,!staffing!levels,!etc.).!
R^2!is!0.2383,!but!we!do!have!to!utilize!the!adjusted!R^2,!as!we!have!multiple!independent!
variables.!The!adjusted!R^2!is!0.2046,!where!20.46%!of!the!variation!in!waittimes!is!
explained!by!the!variation!in!specialty,!taking!into!account!the!sample!size!and!number!of!
independent!variables.!
Next,!we!want!to!determine!if!the!model!is!significant.!Hypothesis!testing!is!set!up!as!
follows:!
Hypotheses: H0: ( 1 ) = ( 2 ) = … = ( k ) = 0 (no linear relationship)
HA: at least one ( i ) ≠ 0 (at least one independent variable affects y)
F statistic = (SSR/k)/(SSE/(n-k-1))
Since!F!=!7.07!is!in!the!rejection!region!(it!is!greater!than!the!F(critical)!=!1.76),!we!reject!
the!null!hypothesis!at!alpha!=!0.05,!and!accept!the!alternative!hypothesis!that!at!least!one!
independent!variable!affects!total!surgical!wait!times!at!95%!confidence.!According!to!the!
model!the!H0!(null!hypothesis)!should!be!rejected!in!favour!of!HA!(alternative!hypothesis).!
We!determine!that!the!model!is!significant.!
In!addition,!we!conclude!that!the!regression!model!does!explain!a!significant!portion!of!the!
variation!in!waittimes.!This!conclusion!is!also!confirmed!by!the!F-statistic!with!a!p-value!=!
3.522271910^{-11},!and!therefore!it!is!less!than!the!alpha!value!of!0.05,!and!we!can!also!
conclude!from!this!p-value!that!the!model!does!explain!a!significant!portion!of!the!variation!
in!waittimes.!
An!analysis!of!variance!(ANOVA)!of!the!linear!regression!model!was!used!to!validate!the!
model.!For!example,!the!degrees!of!freedom,!sum!of!squares,!and!mean!squared!can!easily!
be!retrieved!for!both!the!speciality!and!residuals!parameters,!and!the!F-statistic!for!the!
specialty!parameter.!The!values!of!the!F-statistic!(7.0658736)!and!F-critical!(1.7880147)!
indicate!the!H0!(null!hypothesis)!should!be!rejected!in!favour!of!HA!(alternative!
hypothesis).!
Next,!we!extract!the!coefficients!so!the!linear!regression!model!equation!can!be!
constructed.!Here!is!the!prediction!model:!
waittimes = 257.71 - 117.31(Cardiac Surgery) + 180.16(Dental) + 50.39(Neurosurgery) -
24.23(Obstetrics/Gynaecology) + 250.74(Opthalmology) + 136.10(Oral Maxillofacial) +
440.68(Orthopaedic) + 218.69(Otolaryngology (ENT)) + 115.49(Plastic Surgery) -
39.88(Thoracic Surgery) + 15.10(Urology) + 100.79(Vascular Surgery)
General!Surgery!is!the!default,!where!all!the!other!specialty!variables!are!0.!The!model!
predicts!the!following!for!average!wait!times!by!specialty:!
Although!we!can!give!an!estimate!of!average!wait!times!in!Nova!Scotia!based!on!specialty!
from!the!model,!only!the!waittimes!from!the!following!specialties!are!statistically!
significant:!General!Surgery,!Dental,!Opthalmology,!Orthopaedic,!and!Otolaryngology!(ENT)!
at!95%!confidence.!However,!as!the!model!is!statistically!significant!with!all!the!
coefficients,!then!it!can!be!assumed!that!the!model!can!predict!with!95%!confidence!the!
wait!times!from!any!specialty,!even!if!the!individual!coefficient!is!not!statistically!
significant.!
Conclusion
In!this!project,!we!started!with!the!analysis!for!specialty!vs!wait!times,!as!the!exploratory!
analysis!gave!us!an!initial!signal!that!specialty!may!be!correlated!with!wait!times,!as!the!
scatterplots!of!the!various!combinations!of!the!variables!showed!that!the!specialties!
showed!a!‘banded’!pattern!when!it!was!paired!and!plotted!versus!wait!times,!and!some!
specialties!appeared!to!have!greater!variation!in!wait!times!than!others.!The!‘banded’!
pattern!occurred!due!to!the!specialty!variable!being!categorical!(discrete!datapoints).!
We!used!multiple!linear!regression!for!specialty!vs.!wait!times,!and!found!that!our!model!
explained!20%!of!the!variation!in!the!dependent!variable!(wait!times).!Although!we!can!
give!an!estimate!of!average!wait!times!in!Nova!Scotia!based!on!specialty!from!the!model,!
only!the!wait!times!from!the!following!specialties!are!statistically!significant:!General!
Surgery,!Dental,!Opthalmology,!Orthopaedic,!and!Otolaryngology!(ENT)!at!95%!confidence.!
However,!as!the!model!is!statistically!significant!with!all!the!coefficients,!then!it!can!be!
assumed!that!the!model!can!predict!with!95%!confidence!the!wait!times!from!any!
specialty,!even!if!the!individual!coefficient!is!not!statistically!significant.!
In!summary,!our!model!for!specialty!vs.!total!wait!times!(consult!plus!surgery!wait!times)!
was!statistically!significant,!as!indicated!by!our!F-statistic!with!a!p!<!0.05.!This!F-statistic!
analysis!was!performed!with!all!of!the!specialties!included!in!the!model,!without!backward!
elimination.!
!
!
References
1.!Coughlin!SMP,!Guidolin!K,!Fortin!D,!Frechette!E,!Malthaner!R,!Inculet!R.!Is!it!safe!to!wait?!
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Thorac!Surg.!2014;98(5):1564–70.!!
3.!Kulkarni!GS!AP!Urbach!DR.!Longer!wait!times!increase!overall!mortality!in!patients!with!
bladder!cancer.!J!Urol.!2009;182(4):1318–24.!!
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