Influential Factors for Hospital Management Maturity Models in a post-Covid-19 scenario - Systematic Literature Review
Abstract
The importance of Maturity Models in the health area was proven to support, monitor and direct health organizations to better plan and execute to their investments and developments. In this work, two reviews of the literature were collected: one of them focuses on identifying the main maturity models developed in the health area, the similarities, and gaps between them, identifying which are the Influencing Factors and, the other one, is to identify the lessons learned during the Covid-19 pandemic. In a pandemic scenario, the health sectors demonstrated the importance of the resilience, in which health systems had to adapt abruptly, considering physical structures; professional management; patient safety; supply chain and; technologies. Technologies, played an essential role to mitigating the pressure that health systems faced due to the increase in health costs, growth of chronic diseases, population aging, population’s expectation for more personalized health and, added to that, the confrontation of Covid-19 pandemic. In this sense, we identified the lack of maturity models that address the adversities that occurred during the Covid-19 pandemic in health systems for better hospital management and avoid the pressure to which they could be subjected again.
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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.
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