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Jou nal o In o ma ion Sys ems Enginee ing and Managemen
2023, 8(1), 19556
e-ISSN: 2468-4376
h ps://www.jisem-jou nal.com/
Li e a u e Re iew
In luen ial Fac o s o Hospi al Managemen Ma u i y
Models in a pos -Co id-19 scena io – Sys ema ic
Li e a u e Re iew
Vanessa Be holdo Va gas1 *, Je e son de Oli ei a Gomes1, P iscila Co eia Fe nandes2, Rolando
Va gas Vallejos3 and João Vidal de Ca alho4
1 Depa men o Mechanical Enginee ing and Ae onau ics, Ins i u o Tecnológico de Ae onáu ica - ITA, São José dos Campos, B azil
2 Bio Enginee ing Labo a o y, Ins i u o Tecnológico de Ae onáu ica - ITA, São José dos Campos, B azil
3 Uni e sidade Fede al de Goiás, Goiânia – B azil
4 CEOS.PP, ISCAP, Poly echnic o Po o, Po o, Po ugal
*
Co esponding Au ho :
anessa b @i a.b
Ci a ion: Va gas, V. B., Gomes, J. D. O., Fe nandes, P. C., Vallejos, R. V., & Ca alho, J. V. D. (2023). In luen ial Fac o s o Hospi al Managemen
Ma u i y Models in a pos -Co id-19 scena io - Sys ema ic Li e a u e Re iew. Jou nal o In o ma ion Sys ems Enginee ing and Managemen , 8(1),
19556. h ps://doi.o g/10.55267/iad .07.12868
ARTICLE INFO
ABSTRACT
Recei ed: 30 Dec 2022
Accep ed: 24 Jan 2023
The impo ance o Ma u i y Models in heal hca e is p o en o suppo , moni o and di ec heal hca e o ganiza ions
o be e plan and execu e hei
in es men s, de elopmen s and p ocesses. In his wo k, wo li e a u e e iews we e
collec ed: one o hem ocuses on he iden i ica ion o he main ma u i y models de eloped in he heal h a ea, he
simila i ies and gaps be ween hem, iden i ying wha a e
he In luencing Fac o s o each model s udied, and he
o he is he iden i ica ion lessons lea ned o hospi al managemen du ing he Co id-
19 pandemic. Combining hese
wo lines o in es iga ion, i can be concluded ha , in o de o be e p epa e, adap
and make heal h sys ems mo e
esilien , i is undamen al ha u u e Ma u i y Models begin o map agili y in diagnosing diseases, scale o exams,
p ocess o hospi al disin ec ion and echnological in as uc u e, ocusing on ICTs such as ML, LMS, DL, Rob
o
Assis ance, Ac ua o s, Big Da a, Blockchain, Sma Wea ables, Deli e y D ones, A i icial In elligence, In e ne o
Things, Augmen ed Reali y, Vi ual Reali y, Senso s and Cloud Technology. These IFs a e iden i ied as gaps o
exis ing MMs in he sec o
. Allied o his, i is indica ed ha he u u e MMs conside expanding hei ocus in
supply chain, se ices and applica ions, moni o ing and, mainly, pa ien sa e y and ca e, gi en he impo ance ha
hese IFs demons a ed in coping wi h he pandemic.
Keywo ds: ma u i y model, heal hca e managemen , Co id-
19 lessons lea ned, esilience, pa ien sa e y,
echnology.
INTRODUCTION
Heal h ins i u ions oge he wi h go e nmen
o ganiza ions a e beginning o ealize ha he easons
associa ed wi h he inabili y o p ope ly manage heal h
p ocesses a e di ec ly ela ed o he limi a ions o echnological
in as uc u es and he ine iciency o hei managemen
(F eixo and Rocha, 2014; Sha ma, 2008). To ensu e ha plans
and ac ions ha e an impac , a moni o ing and e alua ion
p ocess is impo an (C uz e al., 2019). I coun ies measu e
and ack hei p og ess and ma u i y in digi al and
managemen heal h hen hey can iden i y key gaps o in o m
policy de elopmen , scale and sys ems in eg a ion and
in es ing in human esou ces and inancial capi al (Mechael and
Edelman, 2019).
In he las ou decades, se e al Ma u i y Models ha e been
p oposed, di e ing in he numbe o s ages, In luence Fac o s
(IF), and a eas o in e en ion (Rocha, 2011). Howe e , in he
heal h a ea, he in es iga ion o his p oblem is s ill in an
emb yonic phase, he Ma u i y Models o he heal h a ea a e s ill
poo ly consolida ed and p esen p oblems ha ha e no ye been
sol ed (Ca alho e al., 2019). Se e al s udies highligh he
impo ance o acing he challenge o inding adequa e models o
use in acili a ing, e alua ing, and measu ing he success a e o
p ojec s in heal h sys ems ( an Dyk and Schu e, 2013).
Va gas V. B. e al./ J INFORM SYSTEMS ENG, 8(1), 19556
2 / 10
This li e a u e e iew aims o consolida e he main
ma u i y models o he heal h a ea as well as hei
cha ac e is ics, le els, dimensions, he ela ionship, and gap
be ween hem. And hen, i will be possible o de elop b oade ,
asse i e, and need-o ien ed ma u i y models o global heal h
sys ems, con ex ualizing and including he needs ha he
Co id-19 pandemic has caused.
METHODOLOGY
Two li e a u e e iews we e collec ed: one on he heal h
Ma u i y Models and he o he one is abou he lessons lea ned
om hospi al managemen in he pandemic Co id-19 scena io.
The esea ch aimed o iden i y publica ions on he opic in
ecen yea s, as well as he no able seminal wo ks ha ga e ise
o hese ecen de elopmen s, o be e unde s and he
concep s and me hodologies ele an o he ield. The sea ches
we e ca ied ou in he da abases “Scopus”, “Web o Science”
and “PubMed”.
The i s sea ch combined he keywo ds “ma u i y model”
and “heal h ca e” o “heal h” in he “Summa y” il e du ing
he pe iod om 2000 o 2022. A icles ha did no ha e hei
comple e s uc u e a ailable we e emo ed (278), he same o
a icles de eloped o o he a eas (48) and hose had
incomple e in o ma ion o analysis o duplica es (9). Finally,
six y-se en a icles we e selec ed and some (4) seminal wo ks
ha we e ci ed se e al imes among he six y-se en selec ed
a icles we e added.
Among he se en y-one a icles, wen y-nine ma u i y
models aimed a heal h assessmen s and ou , he o he s a e
li e a u e e iews on ma u i y models o s udies on models
al eady de eloped p e iously. Figu e 1 shows how his
classi ica ion o a icles ook place based on he PRISMA
Diag am.
PRISMA (P e e ed Repo ing I ems o Sys ema ic e iews
and Me a-Analyses) ocuses on ways in which au ho s can
ensu e anspa en and comple e epo ing o sys ema ic e iews
and me a-analyses (Libe a i e al., 2009). This app oach was
chosen so ha au ho s can mo e clea ly analyse, choose and
isualize he a ailable a icles.
The second sea ch combined he keywo ds "Co id-19",
"managemen " and "lea ning" in he "Summa y" il e . A u he
wel e s udies we e added, aken om a websi e sea ch engine
ha iden i ied he mos ele an and cu en websi es, his sea ch
was ca ied ou o ob ain mo e up- o-da e in o ma ion on a
subjec ha is ela i ely new. We emo ed he a icles ha had
closed access (47), which we e duplica ed in he da abases (19),
which add essed "dis ance lea ning" and "machine lea ning o
ano he a ea” (69), six y-six a icles we e also emo ed ha
add essed heal h lea ning in gene al (e.g., how o ea diabe es
and he elde ly in pandemic imes) because ou ocus is
managemen lea ning and o acili a e he s udy, hey we e
wi hd awn. Tha lea es wen y- wo a icles on he lessons
lea ned in he managemen o Co id-19. Figu e 2 shows how his
classi ica ion o a icles ook place based on he PRISMA
Diag am.
Figu e 1. PRISMA 2009 Flow Diag am o SLR Ma u i y Models
Va gas V. B. e al./ J INFORM SYSTEMS ENG, 8(1), 19556
3 / 10
Figu e 2. PRISMA 2009 Flow Diag am o SLR Co id
LITERATURE REVIEW
In his opic, we app oach he li e a u e e iew sepa a ely
in wo pa s in which hey occu ed: ma u i y models in he
hospi al a ea and lessons lea ned in he managemen o
hospi al sys ems in pandemic imes.
Ma u i y Models
Ma u i y Models ha e al eady p o en o be an impo an
suppo o o ganiza ions managemen , as hey allow o a
be e posi ioning o he o ganiza ion and help o ind he bes
solu ions o changes (Becke e al., 2009). Wa s Humph ey
i s published, in 1989, he Capabili y Ma u i y Model (CMM)
(Humph ey, 1989) he i s ma u i y model, was de eloped o
he ield o in o ma ion sys ems. The CMM was es ablished o
help o ganiza ions imp o e and augmen hei so wa e
p ocesses and has been ecognized as a s anda d Ma u i y
Model (Team CPD, 2000).
In his ega d, Ma u i y Models can be de ined, in
e olu iona y p og ess demons a ing a speci ic skill o in
mee ing a goal om an ea ly s age o a desi ed o no mally
occu ing inal s age (Me le , 2011). In addi ion, Ki ane and
colleagues (Ki ane e al., 2011) ci e ha Ma u i y Models a e
commonly used as a means o benchma king, sel -assessmen ,
change managemen and o ganiza ional lea ning. The e o e,
ma u i y models a e p esen ed o wo dis inc pu poses o use:
unde s anding how i ope a es and di ec ing how o change i in
a o o di e en o mo e use ul ways o ope a ing (Buckle,
2018). Acco ding o F ase and colleagues (F ase e al., 2002) all
ma u i y models sha e he p ope y o de ining a numbe o
dimensions a a ious s ages o ma u i y, wi h a desc ip ion o
cha ac e is ic pe o mance a a ious le els o g anula i y.
Me le (Me le , 2011) adds ha ma u i y le els gene ally ange
om h ee o six. Each ma u i y le el includes a checklis o
p og ess o he nex le el (We e ing and Ba enbu g, 2009).
Some Ma u i y Models aimed a heal h o ganiza ions we e
p oposed. These models ha e hei own speci ici ies ha
dis inguish hem om models om o he a eas: hey a e s ill a
an emb yonic s age o de elopmen (Me le , 2011; Rocha, 2011);
Ca alho (Ca alho e al., 2019) in his esea ch, he ound ha
models in he heal h a ea a e no comp ehensi e (i.e., hey
conside all a eas and subsys ems o heal h o ganiza ions); a e
poo ly de ailed (Ca alho e al., 2019); do no ha e he
cha ac e is ics o he ma u i y s ages s uc u ed by IF (Ca alho
e al., 2019); and do no ha e a p ope ly sys ema ized p ocess o
a gi en sys em o ad ance o a highe ma u i y (Ca alho e al.,
2015).
Twen y-nine ma u i y assessmen me hods in he heal h a ea
we e analyzed and hey could be syn hesized and compa ed.
These models a e desc ibed in Table 1.
Va gas V. B. e al./ J INFORM SYSTEMS ENG, 8(1), 19556
4 / 10
Table 1. Syn hesis and compa ison o Ma u i y Models in he heal h a ea
Designa ion Me hod Heal h a ea Le els Re e ence
Elec onic Medical Reco d Adop ion
Model (EMRAM) Ma u i y Model EMR Resou ces 8 (HIMSS, 2005)
Manches e Pa ien Sa e y F amewo k
(MaPSaF) Ma u i y Model Pa ien sa e y 5 (Pa ke e al., 2006)
Quin eg a Ma u i y Model o
elec onic Heal hca e (eHMM) Ma u i y Model
Focus on con inuous
imp o emen o heal h p ocesses 7 (Sha ma, 2008)
IDC Heal hca e IT (HIT) Ma u i y
Model Ma u i y Model
De elopmen o in o ma ion
sys ems 5 (Dunb ack e al., 2008)
PACS Ma u i y Model (PMM) Ma u i y Model
Medical imaging communica ion
and a chi ing sys ems 5 (We e ing and Ba enbu g, 2009)
NHS In as uc u e Ma u i y Model
(NIMM) Ma u i y Model Technological in as uc u e 5
(NHS Connec ing o Heal h,
2011)
Hospi al Coope a ion Ma u i y Model
(HCMM) Ma u i y Model
O ganiza ional, s a egic and
echnical capabili ies 4 (Me le and Blondiau, 2012)
Heal h Usabili y Ma u i y Model
(UMM) Ma u i y Model
Elec onic medical eco d (EHR)
usabili y 5 (S agge s and Rodney, 2012)
Telemedicine Ma u i y Model (TMMM)
Ma u i y Model
Telemedicine
5
( an Dyk
and Schu e, 2013)
Heal hca e Analy ics Adop ion Model
(HAAM) Ma u i y Model Da a analysis 9 (Sande s e al., 2013)
Digi al Ma u i y Sel
-
Assessmen
(DMA) Ma u i y Index Digi al ans o ma ion
0 o 1400
(sco e) (NHS, 2013)
Con inui y o Ca e
Ma u i y Model
(CCMM) Ma u i y Model Pa ien ca e coo dina ion 8 (HIMSS, 2014)
Ou pa ien Elec onic Medical Reco d
(EMR) Adop ion Model (O-EMRAM) Ma u i y Model EMR o ou pa ien acili ies 8 (HIMSS, 2016a)
Digi al Imaging Adop ion Model
(DIAM) Ma u i y Model Medical images 8 (HIMSS, 2016b)
Adop ion Model o Analy ics Ma u i y
(AMAM) Ma u i y Model Analy ical esou ces 8 (HIMSS, 2016c)
Hospi al In o ma ion Sys em Ma u i y
Model (HISMM) Ma u i y Model
Hospi al in o ma ion sys ems
managemen 6 (Ca alho e al., 2019)
In as uc u e Adop ion Model
(INFRAM) Ma u i y Model
Heal hca e in as uc u e and
echnology esou ces 8 (HIMSS, 2018)
Ma u i y model o logis ical capabili ies
in home medical ca e se ices Ma u i y Model Logis ical capaci y 6 (Gu ié ez e al., 2018)
Clinically In eg a ed Supply Ou comes
Model (CISOM) Ma u i y Model Supply chain 8 (HIMSS, 2019)
In e ope abili y Ma u i y Model (IMM)
Ma u i y Model
In e ope abili y o heal h se ices
5
(Kou oubali
e al., 2019)
Heal h Ma u i y Assessmen Tool
(IS4H-MM 2.0) Ma u i y Index In o ma ion sys ems 5 (PAHO and WHO, 2019)
Global Digi al Heal h Index (GDHI)
Ma u i y Index
Digi al ans o ma ion
5
(Mechael and Edelman, 2019)
Ma u i y model o
echnology
pla o ms in he Sou h A ican
heal hca e con ex
Ma u i y Model Heal h pla o m 5 (Deale e al., 2019)
Ma u i y model o he applica ion o
social media in heal hca e Ma u i y Model Heal h social media capabili y 5 (Jami Pou and Ja a i, 2019)
Ma u i y model o Heal hca e Cloud
Secu i y (M²HCS) Ma u i y Model
Cybe secu i y in heal hca e
en i onmen s 4 (Akinsanya e al., 2019)
B azilian Digi al Heal h Index (BDHI)
Ma u i y Index
Digi al ans o ma ion
5
(C uz e al., 2019)
The
Knowledge Managemen Ma u i y
Model o Indonesian Hospi al Ma u i y Model
Knowledge managemen and
s a egy 8 (Ku niawan e al., 2019)
Digi al Ma u i y Index o Heal hca e
(DMI-H) Ma u i y Index Digi al ans o ma ion 4 (Folks, 2021)
Digi al
Ma u i y Index o Heal h
Ins i u ions (IMDIS) Ma u i y Index Digi al ans o ma ion 4 (Cos a and Ma in, 2021)
Twen y- h ee ma u i y models we e analyzed and six
ma u i y indexes. The ma u i y index is cha ac e ized by being
a code sys em (sco e, pe cen age, e c.) and is unde s ood as he
deg ee o (ac ual) ad ancemen and de ailing o he Ma u i y
Model.
Rega ding he numbe o s ages, hey anged om 4 o 9
s ages, and one o he me hods, as i is cha ac e ized as a ma u i y
index, only coun ed he sco e om 0 o 1400 (NHS, 2013). The
o he ma u i y indices ans o med he ob ained sco e in o le els,
as in a Ma u i y Model.
Va gas V. B. e al./ J INFORM SYSTEMS ENG, 8(1), 19556
5 / 10
All me hods a e mo e speci ic, add essing a sub-a ea o
heal h, he sub-a eas co e ed di e om each o he : digi al
ans o ma ion (5), in o ma ion sys ems (3), medical images
(2), Elec onic Medical Reco d (EMR) (2), pla o ms/social
media (2), pa ien sa e y (1), p ocesses (1), s a egies (1),
Elec onic Heal h Reco d (HER) (1), elemedicine (1), da a (1),
analy ical capabili ies (1), pa ien ca e (1), in as uc u e (1),
echnology in as uc u e (1), logis ics (1), supplies (1),
in e ope abili y (1), cybe secu i y (1), and knowledge
managemen (1).
Acco ding o Table 2, all he IFs con empla ed in hese
wen y-nine ma u i y models in he heal h a ea we e compiled
and how many imes hey we e ci ed, among he s udied
models.
Table 2. Syn hesis o he IFs men ioned in he MM heal hca e
IFs MM Heal hca e Numbe o
pape ci ed
Da a managemen and analysis 9
Technological in as uc u e 8
Se ices and applica ions 8
S a egic managemen 7
In o ma ion secu i y 7
Managemen and ope a ional s uc u e 6
In eg a ion and alignmen 6
Compe ence and p o essional aining 5
Financial managemen 5
Go e nance and o ganiza ional s uc u e 5
In as uc u e 5
S anda diza ion 5
Quali y and ime o he p ocess and se ice 5
P edic i e analy ics and decision suppo 4
Human e o managemen
4
Inno a ion 4
P oduc i i y 4
Engagemen wi h he pa ien 3
Supply chain managemen 3
Knowledge managemen 3
In o ma ion echnology and managemen 3
Ma ke ing 3
S a and wo kload 3
Legal, poli ical and e hical conce ns
3
Se ices p o ision 3
Pa ien sa e y 3
Sys ems au oma ion 2
O ganiza ional cul u e 2
Human ac o s design 2
EMR/EHR 2
Moni o ing and con ol 2
Social business 2
Abili y o lea n 1
Sa e y c i ical communica ions 1
D ug managemen and op imiza ion 1
In e ope abili y 1
Feedback me hods 1
PACS 1
Resea ch and design 1
Redundancy 1
Sus ainabili y 1
T ans e o ca e 1
T aining and compe ence 1
Telemedicine 1
Usabili y 1
NOTE: Simila IFs in he wo sea ches a e highligh ed in yellow.
Co id-19 Managemen Lea nings
A he end o 2019 and in i s mon hs o 2020, e e y coun y
on he plane was impac ed in some way by Co id-19 and, on
Ma ch 11, 2020, he Wo ld Heal h O ganiza ion (WHO) decla ed
a pandemic. Mos coun ies a ound he wo ld epo ed
signi ican inc eases in Co id-19 cases du ing he mon h o
Ma ch and mos coun ies ha e es ic ed mo emen and
implemen ed social dis ancing ules, as well as closing public
buildings, schools, shops, and o he places o wo ship social
ga he ing (Newbu e al., 2020).
The pandemic caused se e e damage o socioeconomic and
global phenomena such as commodi y p ices, emi ances, ade,
ou ism, signi ican job losses, and d as ically lowe wages (Zhao
e al., 2021). In he heal h sec o , which was one o he mos
a ec ed du ing he pandemic, Mish a and colleagues men ions
he lack o s anda dized models in a pandemic si ua ion, anging
om hospi alized pa ien ca e o local esiden heal h
managemen in e ms o moni o ing, e alua ion o diagnosis
and medica ions (Mish a e al., 2021).
To de elop a s anda dized model in a pandemic si ua ion, i
is necessa y o unde s and wha should be moni o ed and he
lessons lea ned om his scena io. In his way, six een segmen s
(i.e., keywo ds o sec o s) ha we e ci ed by au ho s in wen y-
wo publica ions ha analyzed he lea ning ha he Co id-19
pandemic le us in ela ion o hospi al managemen and he
numbe o imes hey we e ci ed we e conglome a ed, ha is,
among he wen y- wo publica ions, how many s udies ci ed
each segmen . These segmen s a e also named “In luencing
Fac o ” om now on. These ac o s a e shown in he Table 3.
Table 3. Syn hesis o IFs ci ed in Co id-19 a icles
IFs Co id-19 Numbe o pape ci ed
Technology 16
Supply chain 6
Se ices and applica ions 5
Moni o ing 5
Diagnosis/ es s 4
Pa ien sa e y 3
A endance 3
Communica ion 3
Disin ec ion 2
Inno a ion 2
Counseling and aining 1
Da a 1
EMR 1
Financial managemen 1
Medicines 1
S anda diza ion 1
NOTE: Simila IFs in he wo sea ches a e highligh ed in yellow.
Rega ding Technologies, in Table 4 was men ioned which
echnologies speci ically he s udies men ioned:

Va gas V. B. e al./ J INFORM SYSTEMS ENG, 8(1), 19556
6 / 10
Table 4. Syn hesis o echnologies speci ied in he s udies o
Co id-19
Technologies (co id-19)
Machine lea ning (ML)
Lea ning managemen sys em (LMS)
Deep lea ning (DL)
Robo assis ance
Ac ua o s
Big da a
Blockchain
Sma wea able de ices
Deli e y d ones
A i icial in elligence
In e ne o hings
Augmen ed eali y
Vi ual eali y
Senso s
Telemedicine
Cloud echnology
NOTE: Simila IFs in he wo sea ches a e highligh ed in
yellow.
A compa ison was made o he in luencing ac o s in he
wo li e a u e sea ches and hose ha we e simila we e
highligh ed.
RESULTS
A he end o he li e a u e e iew on he se en y-one
a icles co esponding o ma u i y models in he heal h a ea, i
can be analyzed ha none o he wen y-nine models analyzed
co e s he heal h ecosys em, i e o he wen y-nine models a e
ocused on digi al ans o ma ion, h ee on in o ma ion
sys em, wo on medical images, EMR, pla o ms/social media,
and only one o pa ien sa e y, p ocesses, s a egies, HER,
elemedicine, da a, analy ical capabili ies, pa ien ca e,
in as uc u e, echnology in as uc u e, logis ics, supplies,
in e ope abili y, cybe secu i y, and knowledge managemen .
Only wo o he models men ioned conside di e en weigh s
o he IF p esen ed (Ca alho e al., 2019b), (Cos a and Ma in,
2021).
The IF add essed in hese models a e summa ized, om he
highes numbe o ci a ions o he lowes : Managemen and
da a analysis, Technological in as uc u e, Se ices and
applica ions, S a egic managemen , In o ma ion secu i y,
Managemen and ope a ional s uc u e, In eg a ion and
alignmen , Compe ence and p o essional aining, Financial
managemen , Go e nance and o ganiza ional s uc u e,
In as uc u e, S anda diza ion, Quali y and ime o he
p ocess and se ice, P edic i e analysis and decision suppo ,
Managemen o human ailu e, Inno a ion, P oduc i i y,
In ol emen pa ien ca e, Supply chain managemen ,
Knowledge managemen , In o ma ion echnology and
managemen , Ma ke ing, People and wo kload, Legal, poli ical
and e hical conce ns, Se ice deli e y, Pa ien sa e y, Sys ems
au oma ion, O ganiza ional cul u e, Human Fac o s Design,
EMR/HER, Moni o ing and Con ol, Social Business, Lea ning
Abili y ende , Sa e y C i ical Communica ions, Medica ion
Managemen and Op imiza ion, In e ope abili y, Feedback
Me hods, PACS, Resea ch and Design, Redundancy,
Sus ainabili y, Ca e T ans e , T aining and Compe ence,
Telemedicine and Usabili y.
The e is a g ea emphasis on da a managemen and analysis,
echnological in as uc u e and se ices and applica ions.
Howe e , in a sec o whe e he ocus should be pa ien ca e, he
o ganiza ion's sa e y cul u e, and quali y o ca e, we ha e only
ew ci a ions: Quali y and p ocess ime (5), Human e o
managemen (4), Engagemen wi h he pa ien (3), Pa ien sa e y
(3), Human ac o s design (2), Sa e y c i ical communica ions (1).
In addi ion, none o he models conside ed any IF in
endemic/pandemic con ol si ua ions.
I we analyze he mos ci ed IF in he wen y- wo a icles
co esponding o he lessons lea ned in managing hospi al
sys ems in a pos -Co id-19 scena io, he ollowing s and ou :
Supply chain, Se ices and applica ions, Moni o ing,
Diagnosis/ es s, Pa ien sa e y, Ca e, Communica ion,
Disin ec ion, Inno a ion, Counseling and T aining, Da a,
Elec onic Medical Reco d (EMR), Financial Managemen ,
Medicines, S anda diza ion and; Technologies such as: Machine
Lea ning (ML), Online Lea ning (LMS), Deep Lea ning (DL),
Robo Assis ance, Ac ua o s, Big Da a, Blockchain, Sma
Wea able De ices, Deli e y D ones, A i icial In elligence,
In e ne o Things, Augmen ed Reali y, Vi ual Reali y, Senso s,
Telemedicine, Cloud echnology.
When compa ed he IF chosen in he heal h Ma u i y Models
wi h he IF highligh ed as impo an o be e alua ed in a
pandemic scena io, we ealize ha he main di e ence is exac ly
in he speci ic cha ac e is ics o a pandemic such as: Diagnoses,
Tes s, Disin ec ion, bu , in addi ion, he g ea di e ence ocuses
on he echnologies in ol ed in he heal hca e indus y,
echnologies such as Machine Lea ning (ML), Online Lea ning
(LMS), Deep Lea ning (DL), Robo Assis ance, Ac ua o s, Big
Da a, Blockchain, Sma Wea ables, Deli e y D ones, A i icial
In elligence, In e ne o Things, Augmen ed Reali y , Vi ual
eali y, Senso s and Cloud Technology.
These echnologies, called ICTs (In o ma ion and
Communica ion Technologies) p omo e he digi iza ion and
in e connec i i y o p ocesses, p oduc s, se ices and people
(Koh e al., 2019). I s applica ion in heal hca e ga e ise o he
e m Heal hca e 4.0 (H4.0) (Thuemmle and Bai, 2017). The
concep o H4.0 is a con inuous bu dis up i e p ocess o
ans o ming he en i e heal h alue chain, om he p oduc ion
o medicines and medical equipmen , hospi al and non-hospi al
ca e, heal h logis ics, heal hy li ing en i onmen , inancial
sys ems and social (Pang e al., 2018) as well as assis ing in he
implemen a ion o di e en public heal h in e en ions, such as
disease su eillance, ou b eak esponse, and heal h sys ems
managemen (Wo ld Heal h O ganiza ion, 2021). I s objec i e is
o p o ide high-quali y heal h se ices mo e e icien ly and o
dec ease cos s and esou ce u iliza ion (Al-Ja oodi e al., 2020).
Use echnology o ans o m he heal hca e sys em is an
impo an s a egy in he cu en pandemic si ua ion (Mish a e
al., 2021). Acco ding o da a om he S a e o Digi al Heal h
epo , om he US ma ke in elligence pla o m CB Insigh s,
Va gas V. B. e al./ J INFORM SYSTEMS ENG, 8(1), 19556
7 / 10
global in es men in digi al heal h eached a eco d in 2021,
eaching he ma k o US$ 57.2 billion, an inc ease o 79%
compa ed o he p e ious yea . In 2020, global in es men s in
his a ea o aled US$ 32 billion (Folha Vi ó ia, 2022).
Howe e , signi ican challenges emain ega ding how he
echnology is deployed, moni o ed, and managed in p ac ice
(Newbu e al., 2020). Ba ie s o a digi al ans o ma ion in
heal hca e a e o en no echnological. Ha old F. Wol ,
P esiden and CEO o he Heal hca e In o ma ion and
Managemen Sys ems Socie y (HIMSS), a global no - o -p o i
o ganiza ion, conside s a shi in cul u e o be he bigges
hu dle in he indus y's digi al ans o ma ion. Simila ly, he
business consul ing i m McKinsey ound ha he h ee
ba ie s o ackling he digi al age mos men ioned by leade s
in he pha maceu ical and medical echnology indus y we e
cul u e and mindse , o ganiza ional s uc u e, and go e nance
(Jones e al., 2019).
When we alk abou he adop ion o echnologies in heal h,
we canno o ge o hink abou pa ien sa e y, which is a
pa icula challenge, wi h mo e han wo decades o esea ch
and sa e y ini ia i es o de ine he p oblem, bo h na ionally
and globally. I he e is a echnological ansi ion in heal h
sys ems wi h undisciplined and chao ic p ocesses (Gonçal es
e al., 2011; Gonçal es and Rocha, 2012) we will ha e a g ea e
p obabili y o medical e o and unce ain ies in pa ien sa e y.
Pa ien sa e y was men ioned in only 3 analyzed ma u i y
models, howe e , due o i s impo ance o a heal hca e
o ganiza ion, he au ho s emphasize he need o deba e and
include i mo e in managemen ools.
Jus i ying he need o in es in pa ien sa e y, some
wo ying da a a e shown: medical e o has become he hi d
leading cause o dea h in No h Ame ica, behind hea disease
and cance (CDC/Na ional Cen e o Heal h S a is ics, 2022;
Maka y and Daniel, 2016); a oidable e o s a e esponsible o
app oxima ely 50,000 dea hs pe yea (Na eh e al., 2005); he
Ins i u e o Medicine epo also es ima ed ha 44,000 o 98,000
people die each yea in US hospi als om po en ially
p e en able ad e se e en s; he Canadian Ad e se E en s
S udy e ealed ha pa ien s we e ha med by ad e se e en s a
a a e o 7.5% o hospi aliza ions,8 wi h 36.9% o hese e en s
conside ed highly p e en able; Maka y and colleagues
(Maka y and Daniel, 2016) es ima es ha 251,454 people su e
p e en able dea hs each yea in he US, ha ansla es o 688
daily dea hs in he US heal h ca e sys em, o pu ha numbe
in pe spec i e, ha 's equi alen o wo jumbo je plane c ashes
e e y day (Snowdon, 2022). Added o ha , B bo o ic and
colleagues (B bo o ić e al., 2022) e ealed ha , du ing he
Co id-19 pandemic, se e al pa ien sa e y issues we e
iden i ied.
Some ac o s ha con ibu e o human e o we e
highligh ed by Snowdon (Snowdon, 2022): EMRs a e no
enough, he e is e y li le “line o sigh ” o “ anspa ency” in
mos heal h sys ems. Repo ing o ad e se e en s is
inadequa e, i.e., e idence o he p e alence o oo cause o
ad e se e en s in clinical se ings such as p ima y ca e, long-
e m ca e, o home-based ca e is lacking, he e o e, p o ide
eams o heal h sys em leade s ha e limi ed abili y o p e en
e en s when hey a e no measu ed, epo ed o sha ed om one
clinical se ing o ano he , in addi ion o dis us ing he
me hodology o e iewing medical eco ds o de e mine he
na u e and p e alence o ad e se e en s. Lack o epo ing
s anda diza ion also con ibu es o ailu e, as he e a e ew
oppo uni ies o lea n om mis akes o ad e se e en s wi hin
sys ems o o ganiza ions. The lack o da a analysis, o iden i y
isks o link da a o esul s o allow o aceabili y. And las ly,
ad e se e en s ha simply should ne e happen in heal hca e,
like gi ing he w ong medica ion ha esul s in pe manen
damage o dea h.
Fu he mo e, a special impo ance o he supply chain is
highligh ed and men ioned in six a icles on heal h managemen
lea ning. The p ocu emen and supply o c ucial heal h p oduc s
in he ea ly s ages o he Co id-19 eme gency was chao ic
(Ha land e al., 2021). Policy make s ha e been wo king ha d o
mobilize he mo emen o essen ial goods and se ices gi en
hei impo ance in con aining he pandemic. I signi ies he
impo ance o es ablishing and main aining logis ics and supply
chain managemen (LSCM) ope a ions, bo h du ing con ainmen
and beyond (Illahi and Mi , 2021). Mo e g anula da a sugges s
ha N95 espi a o s and disposable ace masks accoun ed o he
la ges sha e o impo s in July-Oc obe 2020 (35% and 25%,
espec i ely), ollowed by non-disposable ace masks (16%).
A e he ini ial su ge, da a shows ha impo s o ace masks
ha e s abilized a a ound $ 350-450 million pe mon h
h oughou 2021, sligh ly abo e p e-c isis le els. The da a also
poin o a di e si ica ion o supplie s. While China accoun ed o
abou 94% o he alue o disposable masks impo ed by he
Uni ed S a es in July 2020, he o igin o disposable masks has
di e si ied wi h he eme gence o new supplie s: Mexico, Ko ea
and o he coun ies accoun ed o 11%, 5% and 4 %, espec i ely,
o US ace mask impo s in No embe 2021. Analysis con i ms
ha global ade and supply chains ha e played a key ole in
helping coun ies gain access o p oduc s (OECD, 2022).
The e o e, o ob ain a g ea e p obabili y o success and
imp o emen in he managemen o heal h sys ems, using he
lessons lea ned in dealing wi h he Co id-19 pandemic, i can be
assumed ha o ganiza ions should in es in imp o emen s in
he supply chain, se ices and applica ions, moni o ing and,
mainly, echnologies in o de o pa e he way o H4.0. In
addi ion, issues ha a ec pa ien sa e y and ca e should be
p io i ized. Howe e , o make hese imp o emen s possible, i is
essen ial ha all p ocesses a e o ganized, moni o ed,
s anda dized, documen ed and e alua ed. Fo ha , i is
sugges ed he use o managemen ools ha will help in his
p ocess, such as, Ma u i y Models. Wi h he use o his ool,
heal hca e o ganiza ions will be able o accele a e hei
digi iza ion p ocess, assess a speci ic aspec o digi al ma u i y
and speci ic sec o s, and be guided owa ds he bes s a egic
decisions.
Below is p esen ed Figu e 3, in which he IFs o he wo se s
and hei common in e sec ion in he Venn Diag am we e
ga he ed. In his way, i acili a es he in e p e a ion o he I s ha
a e missing om he MM in he heal h a ea, as lea ned du ing
Co id-19.
Va gas V. B. e al./ J INFORM SYSTEMS ENG, 8(1), 19556
8 / 10
Figu e 3. Venn Diag am IFs MMs and IFs Co id-19
CONCLUSION
Ma u i y Models in di e en sec o s aims o suppo
o ganiza ions o iden i y hei cu en si ua ion in he
echnology adop ion p ocess, and many ail o connec wi h he
nex s ep, which would be he de elopmen o ac ion plans. I
has al eady been p o en by au ho s ha he p ocess o digi al
ans o ma ion in heal h mus be guided by assessmen
me hods and Ma u i y Models. When we can measu e and
assess he ma u i y o heal h sys ems, we will be able o ha e
be e con ol and di ec ion o hospi als managemen o be e
in es in esou ces, echnologies and be e manage people.
Thus, wen y-nine me hods o assessing ma u i y in heal h
we e analyzed. In his li e a u e e iew, i was possible o
obse e he di e en suba eas o which ma u i y models we e
de eloped o enhance he measu emen o hese dimensions
and, consequen ly, hei ma u i y. The Ma u i y Models s udied
in he li e a u e, in he heal h a ea, a e speci ic o suba eas o
some sec o s, mos o hem a e ocused on digi al
ans o ma ion. The e is a lack o Ma u i y Models ha conside
he heal h sys em as a whole and e alua e i b oadly, wi h a
global ision, which would be ex emely impo an o be e
managemen o heal h o ganiza ions. In addi ion, he e is a lack
o ma u i y models ha ocus on pa ien well-being and sa e y,
which should be he main conce n o a sec o ha deals di ec ly
wi h his poin . In addi ion, mos MMs conside all FIs o be o
equal impo ance when e alua ing he de ined p ocess and, as
is known, i is impo an o de ine hei di e ences and
impo ance o a mo e accu a e e alua ion.
Wi h he Co id-19 pandemic, i is possible o highligh he
di icul y ha all heal h sys ems aced, such as he lack and
ealloca ion o heal h p o essionals, lack o equipmen , lack o
sa e y ma e ials, logis ical di icul ies in eloca ing equipmen
and ma e ials, s anda dized models o aid managemen , agili y
in he p ocess o de eloping e ec i e d ugs o he disease,
de elopmen o echnologies o ack he disease and p e en i s
sp ead, ine ec i e cul u e abou echnologies, pa ien sa e y,
men al heal h o heal h wo ke s and o he gene al popula ion.
Finally, we poin ed ha global heal h sys ems we e no
p epa ed, i.e., hey we e no well eady o deal wi h he
ad e si ies ha occu ed du ing he pandemic. To p o e his
ac , he li e a u e e iew ca ied ou , in wen y- wo a icles,
ha had as hei main heme he lessons lea ned abou
managemen o hospi al sys ems du ing he Co id-19
pandemic.
C ossing hese wo lines o in es iga ion, we can conclude
ha , in o de o be e p epa e, adap and make heal h sys ems
mo e esilien , i is undamen al ha he Ma u i y Models ha
will be de eloped a e Co id-19 ake in o accoun hese aspec s
ha we e mapped in he e ision o he a p esen ed li e a u e o
lessons lea ned in hospi al managemen , and ha no Ma u i y
Model, so a , has con empla ed. In his way, we sugges ha
u u e Ma u i y Models conside mapping he agili y o disease
diagnosis, examina ion scale, hospi al disin ec ion p ocess and
echnological in as uc u e, wi h a ocus on ICTs, capable o
p omo ing digi iza ion and in e connec i i y o p ocesses,
p oduc s, se ices and people , such as ML, LMS, DL, Robo
Assis ance, Ac ua o s, Big Da a, Blockchain, Sma Wea ables,
Va gas V. B. e al./ J INFORM SYSTEMS ENG, 8(1), 19556
9 / 10
Deli e y D ones, A i icial In elligence, In e ne o Things,
Augmen ed Reali y, i ual eali y, senso s and cloud
echnology.
Allied o his, al hough some MMs al eady add ess hese
issues, i is indica ed ha he au ho s conside expanding hei
ocus on supply chain, se ices and applica ions, moni o ing
and, mainly, on pa ien sa e y and ca e, gi en he impo ance
ha hese IFs demons a ed in coping wi h he pandemic.
Fo u u e li e a u e e iews and esea ch on Ma u i y
Models in he heal h a ea, a deepe s udy on he needs obse ed
du ing Co id-19, i s suba eas and IF ha should be highligh ed
is ecommended. In addi ion, ield esea ch in hospi als is
ecommended because his wo k, in ac , only collec ed da a
om he li e a u e ha we ha e oday.
ACKNOWLEDGEMENTS
This wo k is inanced by Po uguese na ional unds h ough
FCT - Fundação pa a a Ciência e Tecnologia, unde he p ojec
UIDB/05422/2020. This s udy was also inanced by he
Coo denação de Ape eiçoamen o de Pessoal de Ní el Supe io
- B asil (CAPES) - Finance Code 001.
REFERENCES
Al-Ja oodi, J., Mohamed, N., AbuKhousa, E., (2020). Heal h 4.0:
On he Way o Realizing he Heal hca e o he Fu u e.
IEEE Access.
h ps://doi.o g/10.1109/ACCESS.2020.3038858
Becke , J., Knacks ed , R., Pöppelbuß, J., (2009). De eloping
Ma u i y Models o IT Managemen . Business &
In o ma ion Sys ems Enginee ing 1, 213–222.
h ps://doi.o g/10.1007/s12599-009-0044-5
B bo o ić, O., B bo o ić, H., H ain, L., (2022). The COVID-19
Pandemic C isis and Pa ien Sa e y Cul u e: A Mixed-
Me hod S udy. In J En i on Res Public Heal h 19.
h ps://doi.o g/10.3390/ije ph19042237
Buckle, P., (2018). Ma u i y Models o Sys ems Thinking.
Sys ems 6. h ps://doi.o g/10.3390/sys ems6020023
Ca alho, J. V., Rocha, Á., an de We e ing, R., & Ab eu, A.
(2019). A Ma u i y model o hospi al in o ma ion
sys ems. Jou nal o Business Resea ch, 94, 388-399.
Ca alho, J. V., Rocha, Á., & Ab eu, A. (2019b). Ma u i y
assessmen me hodology o HISMM-hospi al
in o ma ion sys em ma u i y model. Jou nal o medical
sys ems, 43(2), 1-11.
Ca alho, J. V., Rocha, Á., & Vasconcelos, J. (2015). Towa ds an
encompassing ma u i y model o he managemen o
hospi al in o ma ion sys ems. Jou nal o medical
sys ems, 39(9), 1-9.
CDC/Na ional Cen e o Heal h S a is ics, (2022). Leading
Causes o Dea h [WWW Documen ]. URL
h ps://www.cdc.go /nchs/ as a s/leading-causes-o -
dea h.h m (accessed 3.1.22).
Cos a, C.G.A. da, Ma in, H. de F., (2021). Desen ol imen o de
um mé odo pa a a aliação de ma u idade digi al de
ins i uições de saúde, J. Heal h In o m.
C uz, T., Lopes, P., Pisa, I., (2019). B azilian Digi al Heal h Index
BDHI - A aliação da ma u idade da saúde digi al no
B asil [WWW Documen ]. Uni e sidade Fede al de São
Paulo. URL h ps://saude360.uni esp.b /p oje o/bdhi
(accessed 2.9.22).
Fai banks, R.J., Wea s, R.L., Woods, D.D., Hollnagel, E., Plsek,
P., Cook, R.I., (2014). Resilience and esilience
enginee ing in heal h ca e, in: Join Commission Jou nal
on Quali y and Pa ien Sa e y. Join Commission
Resou ces, Inc., pp. 376–383.
h ps://doi.o g/10.1016/S1553-7250(14)40049-7
Folha Vi ó ia, (2022). In es imen os em ecnologia na saúde êm
eco de depois da pandemia. R7 Cidades.
F ase , P., Moul ie, J., G ego y, M., (2002). The use o ma u i y
models/g ids as a ool in assessing p oduc de elopmen
capabili y, in: IEEE In e na ional Enginee ing
Managemen Con e ence. IEEE, pp. 244–249.
h ps://doi.o g/10.1109/IEMC.2002.1038431
F eixo, J., Rocha, Á., (2014). A qui e u a de In o mação de
Supo e à Ges ão da Qualidade em Unidades
Hospi ala es. Ibe ian Jou nal o In o ma ion Sys ems and
Technologies. h ps://doi.o g/10.17013/ is i.14.1-15
Gonçal es, J., Sil ei a, A., Rocha, A., (2011). A pla o m o s udy
he quali y o li e in oncology pa ien s. In J In Sys
Change Manag 5, 209.
h ps://doi.o g/10.1504/IJISCM.2011.044501
Gonçal es, J.J., Rocha, Á.M., (2012). A decision suppo sys em
o quali y o li e in head and neck oncology pa ien s.
Head Neck Oncol 4, 3. h ps://doi.o g/10.1186/1758-3284-
4-3
Ha land, C.M., Knigh , L., Pa ucco, A.S., Lynch, J., Telgen, J.,
Pe e s, E., Tá ai, T., Fe k, P., (2021). P ac i ione s’
lea ning abou heal hca e supply chain managemen in
he COVID-19 pandemic: a public p ocu emen
pe spec i e. In e na ional Jou nal o Ope a ions and
P oduc ion Managemen 41, 178–189.
h ps://doi.o g/10.1108/IJOPM-05-2021-0348
Humph ey, W.S., (1989). Managing he So wa e P ocess. .
Illahi, U., Mi , M.S., (2021). Main aining e icien logis ics and
supply chain managemen ope a ions du ing and a e
co ona i us (COVID-19) pandemic: lea ning om he
pas expe iences. En i on De Sus ain 23, 11157–11178.
h ps://doi.o g/10.1007/s10668-020-01115-z
Jones, G.L., da Pe e , Z., Ru e , K.-A., da Somau oo, A., (2019).
P omo ing an o e due digi al ans o ma ion in
heal hca e. McKinsey Insigh s.
Ki ane, F., Mul ey, A., Campion, M., (2011). De elopmen O a
F amewo k Ma u i y Model o he Con inued Quali y
Imp o emen o a Locally Cus omised Clinical
In o ma ion Sys em Used in C i ical Ca e Medicine, in:
P oceedings o he In e na ional Con e ence on Heal h
In o ma ics. SciTeP ess - Science and and Technology