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AN OVERVIEW OF AI ENABLED M-IOT WEARABLE
TECHNOLOGY AND ITS EFFECTS ON THE CONDUCT OF
MEDICAL PROFESSIONALS IN PUBLIC HEALTHCARE IN
PAKISTAN
Abdul Samad Dahri
Business Administration and Social Sciences
Mohammad Ali Jinnah University, Karachi, (Pakistan).
E-mail: dahriabdulsamad@gmail.com ORCID: https://orcid.org/0000-0003-4517-3493
Shaq-ur-Rehman Massan
QEC and Co-ordination
Mohammad Ali Jinnah University, Karachi, (Pakistan).
E-mail: srmassan@hotmail.com ORCID: https://orcid.org/0000-0001-6548-6513
Liaquat Ali Thebo
Department of Computing,
Mohammad Ali Jinnah University, Karachi, (Pakistan).
E-mail: liaqaut.ali@jinnah.edu ORCID: https://orcid.org/0000-0001-7097-5610
Recepción:
20/03/2020
Aceptación:
28/04/2020
Publicación:
15/06/2020
Citación sugerida:
Dahri, A. S., Massan, S.-U.-R., y Thebo, L. A.(2020). An overview of AI enabled M-IoT wearable technology and
its eects on the conduct of medical professionals in Public Healthcare in Pakistan. 3C Tecnología. Glosas de innovación
aplicadas a la pyme, 9(2), 87-111. http://doi.org/10.17993/3ctecno/2020.v9n2e34.87-111
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ABSTRACT
Interconnectivity of smart devices such as mobile technology adoption in healthcare holds humongous
impacts. Yet, medical professionals are reluctant to reap potential benets of technology and the reason
behind this phenomenon is ambiguous. This study aims to highlight current critical conditions in
public healthcare hospitals in Pakistan, and how IoT will add value in healthcare services eectiveness
through mobile computing and also to indicate current concepts that may add value in over-all smart
healthcare system. According to available information, study to on AI enabled M-IoT network-based
healthcare system specially in developing countries to address healthcare problems are rarely known.
This study empirically analyzed the factors that inuence IoT based smart healthcare devices adoption
in Pakistan. In understand the phenomenon, Partial Least Square Equation Model (PLS SEM) was
used to understand the relational inuence of performance expectance, eort expectance, and social
inuence over behavior to use through intention to use the technology supported by Unied Theory
of Acceptance Technology (UTAUT) assumptions. The results show that clinicians are reluctant to use
technology though the results also reveal that same clinicians have positive inuence of performance
and eort expectations on their intention to use technology that leads the actual behavior of using
the technology. Though, this research is among few to beacon upon urgent focus of public healthcare
management in developing countries. Yet, new research is lacking far behind to facilitate methods to opt
for M-IoT healthcare devices powered by AI.
KEYWORDS
Articial Intelligence (AI), Internet of Things (IoT), Technology proliferation, Unied Theory of
Acceptance Technology (UTAUT), Coronavirus (COVID-19), Healthcare.
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1. INTRODUCTION
The ongoing pandemic of corona virus has become one of the biggest threats to global economy and
especially healthcare. Healthcare is backbone to any nations’ development and growth (Samad, Memon,
& Kumar, 2020). It is always the healthcare facilities that serves when the state is faced with critical
public healthcare risk. Whether it be the eradication of Smallpox globally, Polio from USA, Cholera in
Asia claiming 100,000 deaths, Bubonic plague in China claiming 12 million deaths and Coronavirus
also known as ‘‘COVID-19’’outbreak in Wuhan district of China, which has become a global public
healthcare risk. According to Centers for Disease Control and Prevention, the Coronavirus (COVID-19)
was reported on 30th January 2020 in USA as well. With reference to ongoing spread of COVID-19
besides its exponential negative impact ranging from service, manufacturing, stock market and oil prices
to Gross Domestic Product of countries globally (see Figure 1) as well as it has become global health risk
by sickening more than 482,800 people and claimed 21,896 deaths in more than 171 countries globally.
Though, before COVID-19, healthcare was in chronic crises mainly of doctor and nurse’s shortage,
burnout of physicians and high demand for enduring care (Meskó, Hetényi, & Győry, 2018). But global
healthcare systems were never in such a desperate condition ever before. In china for instance, hospitals
were maxed out and basic medical supplies such as gloves were empty and lead to shutdown of public
life at all levels (Wallace-Wells, 2020).
Therefore, for an eective healthcare system needs the availability, accessibility, acceptability and quality
of its healthcare workforce (World Health Organization, 2013). World Health Organization (WHO)
(2013) estimated problem of aged workforce with need-based healthcare workforce shortage globally by
17.4 million? Whereas, high frequency of patients and shortage of medical sta overloads physicians
while increasing burnout experience (Meskó et al., 2018).
As a result, lack of access to healthcare by masses and compromised quality in healthcare facilities is
common globally. According to Meara et al. (2015), 400 million people lack to one or more essential
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health services and 5 billion people do not have access to safe and aordable surgical and anesthesia
care when needed. According to Bernaert and Dimitrova (2017), at World economic, 9 billion people
will need healthcare services by 2050. While, staggering $ 142.6 billion were invested for developing
countries’ healthcare targets which no government could achieve. Moreover, global healthcare is $ 300
billion healthcare question that needs accurate spending. Since, 20% of healthcare spend is wasted
globally and top 15 countries waste $ 1100 to $ 1700 on an average. Which is above the average ($120/
person) spent on healthcare of more than 50 bottom countries (Bernaert & Akpakwu, 2018). According
to Malkani (2016) in this context, reported healthcare sector being ignored by the government and
policies and practices lack ecient implementation and virtually absent in case of Pakistan. Thus, new
approaches and eloquent work to ventilate vital healthcare services is the need of the hour.
Figure 1. Global Economic Growth Slowdown.
According to UN (2018) report, 90% of the global population lives in the rural areas, and this number
will reach to its peak by 2020. Where, life expectancy is worse, limited access to healthcare facilities, lack
of trained healthcare workers, transport diculty and so on, contribute to low quality of healthcare
among rural population (Strasser, Kam, & Regaldo, 2016). Besides, that skyrocketing costs, high priced
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drugs, hospital-acquired infections, and failure of to deliver healthcare boost adverse healthcare events.
Specially in developing countries like Pakistan where doctor to patient ratio is 1: 1300, doctor to nurse
is 1: 2.7 (Khan, 2019) nurse to patient ratio is 1:12411 patients (Zaidi, 2012), and only 22% of patients
are served through public hospitals (Solangi et al., 2017b)
Since, now a days, production, application and utilization of information technology demarks dierence
between developed and developing countries (Ajami, Ketabi, & Torabiyan, 2015). The workow of
hospital may vary from patient to patient, population problem, poor healthcare services and scarcity
of healthcare resources, where working conditions are exceptionally uncertain, unscheduled, and care
decentralized for instance COVID-19 pandemic, craves for the utilization of information technology
(Nazir et al., 2019).
The healthcare information technology (HIT) development holds potential to improve healthcare
quality and accuracy in emergency, safety, medical errors, eciency, and patient care (Rothenhaus
et al., 2009).Therefore, the immediate innovative intervention is needed in rural areas of developing
countries (Samad, Al-Athwari, & Hussain, 2019). Where, managers could use medical equipment and
new technological tools to improve patient safety and satisfaction (Birgani & Asadpoor, 2011).
Similarly, the development of hospital information systems accompanied with gradual advancement
of software, hardwires, and new methods in order to enhance agility and quality of healthcare services
is growing (Siamian et al., 2005). The increased population and scarcity of healthcare resources
necessitates the adoption of IoT serves the best solution in terms of cost and eciency (Tyagi, Agarwal,
& Maheshwari, 2016).
Post 80s and 90s, Kevin Ashton in 1999 initiated term Internet of Things (IoT), referring as uniquely
identiable connected objects with radio-frequency identication (RFID) technology. IoT basically
observes interconnectivity of devices as data sources through existing internet infrastructure (Shaikh,
2019). IoT and technological advances in healthcare services to patients not only reduce errors, increase
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agility and accuracy in healthcare quality, but also, lowers costs through information integration
(Malliarou & Zyga, 2009). Therefore, it is worth acknowledging the role of every healthcare department
in hospital, recognize the importance of IoT integration in healthcare and to improve quality and reduce
cost of emergency department and related provisional units in hospitals (Mirhoseinie et al., 2014).
Recent developments in IoT unleashed unprecedented potential in business world (see Figure 1).
According to Global Forecast, IoT healthcare market is expected to grow from US $55.5 billion to US
$ 188.0 billion by 2024 at an annual compound growth rate of 27.6%. This is due to active patient
healthcare monitoring, patient centric management and high speed network technologies for IoT
connectivity (Singh, n.d.).
Figure 2. Source - Statista Research Department, Nov 14, 2019.
At present various tools and methods are used to quantify the healthcare performance. Accordingly Van
der Meulen (2017) reported a forecast that the IoT will connect 26 billion units till 2020. IoT brings
rich user experience, connectivity, reliability and smart healthcare services to patients (Islam et al., 2015),
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which leads to smart healthcare system (see Figure 2) composed of smart functionality, remote server,
and the network to remotely monitor patients (Yuehong et al., 2016). On a recent note, 76% healthcare
organizations believe that IoT will transform healthcare industry (Anurina, 2019).
Similarly, to address this problem, mobile technology provides grounds for IoT by using mobile phones,
IP connectivity, lower power consumption, security, apps, or through m-health care system (Nazir et al.,
2019). Mobile computing is new trend involved in many areas including healthcare providing quality
processing, storage, information and query to the users at remote geographical areas. Where, IoT serves
as an intelligent sensing technology that supports vitally in sending and receiving mobile medical data
(Ma et al., 2018).
Currently, most of the mobile-health applications are used by healthcare professionals for various tasks
(Elazhary, 2019). For instance, m-health and m-learning. Where, m-health apps are used for diagnosis of
diseases, drug references, and medical computations. Therefore, using M-IoT devices in healthcare will
reduce cost and improve eectiveness of healthcare system.
One of the most important use of m-health app is in PHS-personalized health system, where various
sensors in mobile such as gyroscope, accelerometer, altimeter, general packet radio service (GPRS), 4G
systems, global positioning system (GPS) and blue-tooth technology integrated with IoT environment
will collectively help monitor, diagnose, or even forecast health risks at a distance (Qi et al., 2017). Or
these sensors separately may be attached to body of patient (ankle, wrist, and chest) and collect data
through mobile app and sent it to respective department or doctor (Subasi et al., 2018).
The technological adoption in the healthcare paced the information ow between doctor and patient
pilling up twice every three years for which an estimated reading time for physician to remain up to
date is 29 hours straight which is impossible (Curioni-Fontecedro, 2017). This adds critical call for more
advanced computational power to smart devices used in healthcare system such as Articial Intelligence
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(AI) agents to enhance the predictiveness in healthcare workow (Bui, 2000), improve quality and lower
costs for patient care.
An AI enabled system uses sophisticated algorithm to ‘learn’ and extract useful information from a large
patient population to assist in making real-time inferences for healthcare outcomes. Moreover, there are
more than 97000 AI enables mobile healthcare (mHa) available on google play store and Apple store and
these would be downloaded by 500 million people globally till 2015 (Jahns & Houck, n.d.), while, 50% of
these apps will be downloaded in smart phones by 2017 (Siltala, 2013). This phenomenon progressively
has turned smartphones into medical kits for real-time healthcare monitoring for patient activities, early
predictability, disease screening, improved medication adherence (Alemdar & Ersoy, 2010) by medical
professional and minimized medical errors that are inevitable in human clinical practices (Pearson, 2011).
Moreover, healthcare data is broadly classied into non-AI and AI systems. Though, non-AI data uses
less complex computational process, is gradually replaced by AI enabled systems due their in-eciencies.
AI based platforms (applications) which are mostly hybrid in nature and involve AI Neural Networks
(ANNs), Fuzzy theory, and evolutionary algorithms (Sannino et al., 2019). For example, Dargazany,
Stegagno, and Mankodiya (2018) introduced the concept of wearable deep learning (WearDL) which
is unifying conceptual architecture inspired by human nervous system that oered inclusion of deep
learning, IoT, and wearable technologies. Where, brain was conceptualized as deep learning for cloud
computing and big data processing, the spinal cord as IoT for fog computing and big data transfer, and
the peripheral sensory and motor nerves as wearable technologies as edge devices for big data collection
(Sannino et al., 2019). Although, these techniques are theoretically sound yet lack potential practical
explorations.
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Figure 3. M-IoT Warble Smart Healthcare System.
Advantageously, over 85% of global population is under wireless signal (World Health Organization,
2013), 80% of them hold smartphones (Chaey, 2019), and in developing countries like Pakistan over
90% users have 2G internet (PTA annual report 2014-2015). These smartphones with m-health apps
enable patients to use healthcare applications to monitor their health indicators and diseases (Karaca
et al., 2019) and categorized into single condition apps which are developed for specialized diseases and
cluster condition applications which treat certain disease together. Based on literature, few of these
applications are discussed below:
Single condition Applications:
Glucose Level Sensing: This app is helpful for Diabetes patients whose glucose level sustains at higher
levels than normal. Through blood glucose monitoring system suggests best meal, exercise and medicine
time to the patient. Doctors may propose a noninvasive glucose measuring m-IoT method on actual
basis. In this method, sensor attached to patient body serves as IoT device and transmits real time
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information to respective department or doctor. This device is equipped with blood glucose collector,
smartphone, and a processor.
Blood Pressure Monitoring System: Blood pressure (PB) is force by heart to circulate blood in body. An
IoT medical device can assure monitoring of Bp, glucose level in blood, and any irregularity can also be
transmitted supported by IoT network.
Body Temperature Monitoring System: monitoring and maintaining body temperature is an important
element of healthcare. From m-IoT perspective, temperature varies from body to body yet gives accurate
readings and assists in infrared detection and RFID module.
Oxygen Saturation Monitoring System: device named pulse oximeter measures oxygen saturation level
in the blood. The use of IoT with pulse oximeter benets technology-based healthcare applications on
wrist. This is a low power, low cost, Bluetooth enabled device that connects with IoT network which
enables doctor or respective department to monitor patient remotely.
Electrocardiogram (ECG) Monitoring system: This device can display the ECG waves on the user. This
device generates specic ECG bio-signal reports of the patient and link this information to respective
user by integrating with IoT network. IOIO-OTG is micro controller that converts ECG analog signals
into digital data in binary numbers which can be monitored on IoT network. This device can be very
helpful for hospitals/ ED as it helps in detecting any anomalies in the patient health condition in advance
and reduces wait time at hospitals.
Cluster Condition Application:
Wheelchair Management System: keeping in view of elderly and disabled patients, smart wheelchairs
are recommended by health experts. These wheelchairs are enabling location, movement and status of
the user and links it with IoT network, helping respective users in monitoring patients.
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Rehabilitation System: IoT can help rehabilitation system regarding population growth and lack of
health expertise. Body sensor network can improve the abilities of the disabled person through IoT
networks that would enhance rehabilitation system. Number of rehabilitation system for example smart
city rehabilitation and integrated application system for prisoners (Islam et al., 2015).
Healthcare Solutions Using Smartphones: Currently, smartphones are equipped with sensors and
electronic control applications. In the healthcare eld smartphones provided real-support and monitoring
and communication between patient and respective department or physician. Few of smartphone
m-healthcare apps include; blood pressure watches which is smart wrist band connected with smartphone
and gathers blood pressure data. Another one is, heart rate monitors that measures and records heart
related data. Health assist is also m-health app that keeps record of bloom-health app that keeps record
of blood pressure, heart rate, body temperature and other designated physical activities.
Regrettably, in general, it is observed in the healthcare sector that usage of technology by doctors across
the nation for various medical and investigative techniques, is not best tted with technological healthcare
framework globally (Solangi et al., 2017a). Moreover, negative attitude towards technology adoption is
also observed among healthcare professional which directs signicant concerns towards acceptance and
ecient use of technology (Mitzner et al., 2010). Followed by Sa, Thiessen, and Schmailzl (2018) who
reported medical professionals avoided using technologic fearing mass control by management. Since,
negative attitude of medical professionals using technological devices in healthcare may delay fruits of
m-IoT in healthcare system.
Similarly, various problems are linked with future structure of healthcare for embracing m-IoT
innovations, specially getting doctors and healthcare services on standby for global uncertain calamities
such as COVID-19. Indirect Emergency Healthcare (IEH) for instance, keeping these conditions on
hand, a dedicated service called indirect emergency healthcare oers varying solutions to these situations
including information availability, alter information, post-accident action, and record keeping. These
problems require novel investigation to establish m-IoT smart healthcare system for remote patient
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assistance, monitoring, early diagnosis, and early treatment, especially in less privileged areas of
developing nations.
Therefore, this research leads to utilize standard constructs of technology acceptance model (TAM) to
understand the relevance and attainability of m-IoT based smart healthcare system. The technological
acceptance model (TAM) is grounded on the idea of social psychology use as a gage to illustrate and
asses the behavior of users to utilize innovation. TAM is used for best quality level (Bagozzi, 2007). The
rened version of TAM is Unied theory of acceptance and use of Technology (UTAUT) which has
been quite eective to measure the factors that determine use behavior of technology in the healthcare
consumers (Venkatesh et al., 2003). The UTAUT theory is inclusion of Innovation Diusion Theory
(IDF), Theory of Planned Behavior (TPB), Social Cognitive Theory (SCT), Motivation Model (MM),
Theory of Reasoned Action (TRA), Model of Personal Computer Utilization (MPCU), and TAM itself
to better understand the acceptance and usage of new technology.
Since, UTAUT model proved valuable framework to understand behavioral aspect of technological
acceptance and utilization in dierent cultures over short period of time. The UTAUT model measures
the behavior (BU) of technology through behavioral intention (BI) of technology use inuenced by four
determinants namely, performance expectancy (is the degree of worth performing a task to achieve set
goals), eort expectancy (is the ease associated with use), social inuence (social importance linked with
new system utilization), and facilitating conditions (one’s believe of technological and organizational
infrastructure exists for support) (Venkatesh, Thong, & Xu, 2016). Specially, Pakistan has under-
developed technical and infrastructure support system (Kurji, Premani, & Mithani, 2016). Based on
UTAUT theory to examine its relevance and practicability in the eld of M-IoT healthcare system. Since,
in healthcare technological adoption behavior is at individual level, therefor, performance expectancy,
eort expectancy, and social inuence determinants are used based on UTAUT to understand behavioral
intention towards utilization of M-IoT in public and private healthcare sector.
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Performance
Expectancy
Effort
Expectancy
Social Influence
Behavior
Intention
Behavior to Use
2. MATERIAL AND METHODS
Population for this study comprised of over 479 (Bureau of statistics planning & development department
government of Sindh, 2016), including dentists, physicians, and gynecologist and surgeons in Sindh
public hospitals, out of which 9 medical professionals were unclear on using wearables M-IoT healthcare
devices. Remaining population (who understood m-health applications and IoT wearable devices) of 470
healthcare professionals following Krejcie and Morgan (1970) model for a minimum sample of 214 was
selected for data collection on random bases. The study uses questionnaire of acceptance of technology
adapted from previously validated instrument by Cimperman, Makovec, and Trkman (2016) as this
scale specically addresses home telehealth devices, to focus on M-IoT applications on determinants of
behavioral intention to use M-IoT technology. It comprises of one part for participants’ demographics
and second part including 15 items for UTAUT determinants for performance expectancy (PE), eort
expectancy (EE), and social inuence (SI) on behavioral of use (BU) of using new technology, through
behavioral intention (BI), where the answers were recorded on 7-point frequency Liker scale. Since,
incomplete responses were screened, the nal sample for analysis was of 185 responses that belonged
to age group of 20 to 40 years mainly female (74%) and male (36%). They were graduates (68%) and
undergraduates (42%), with work experience ranging from 2 years to 40 years in eld of Sindh province
of Pakistan.
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3. RESEARCH FINDINGS
3.1. RESULTS OF MEASUREMENT MODEL
For measurement model, convergent validity and discriminant validity values were evaluated.
Convergent validity
Convergent validity is the degree of latent variables correlated with other variables items (Hair et al.,
2016). Following Henseler et al. (2014) factor loadings, average variance extracted (AVE) and composite
reliability (CR) were assessed. Further, factor loading threshold of 0.6 was achieved (see Figure 1
outer loadings and t-values), for AVE values were above 0.5, and CR values were also above 0.7 on
recommended threshold by Chin (1998); Nunnally and Bernstein (1994); and Hair et al., (2011) (see
Table 1).
Discriminant Validity
Discriminant validity is simply the distinctiveness among the constructs. Following Henseler, Ringle,
Sarstedt (2015) Hetero-Trait-Mono-Trait (HTMT) ratio of correlation was evaluated. For HTMT
threshold values should be below 0.9 recommended by Gold and Arvind Malhotra (2001) along with
condence interval for better signicance assessment which should be less than 1 (Henseler et al., 2015).
Table 1 reveals achievement of all suggested criterion for discriminant validity.
Table 1. Hetero-trait-mono-trait (HTMT), CR, AVE (N=185).
Variable PE EE SI BI BU CR AVE
PE 1 0.821 0.641
EE 0.042 1 0.844 0.66
SI 0.057 0.047 1 0.798 0.611
BI 0.012 0.048 0.055 1 0.804 0.638
BU 0.081 0.078 0.057 0.039 1 0.884 0.617
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Note: Performance Expectancy (PE), Eort Expectancy (EE), Social Inuence (SI), Behavioral intention
(BI), and Behavioral of Use (BU).
4. MEASURES AND METHODS
Hair et al. (2016) recommended R2, standard beta, t-values via bootstrapping procedure on 5000
samples for predictive relevance Q2 and the eect size f2. Moreover, condence interval values were
also taken into consideration which ensures the condence of same response from same sample of target
population as revealed in Table 2 below.
Table 2. Results of Structural model Assessment.
Path Beta St. Dev T Stats P Values R2 f2 Q2
PE -> BI 0.459 0.112 4.098 0.000 0.524 0.267 0.139
EE -> BI 0.126 0.023 5.478 0.001 0.494 0.248 0.238
SI -> BI -0.013 0.068 0.195 0.849 0.152
BI-UB 0.326 0.07 4.639 0.000 0.251
PE-BI-UB 0.096 0.029 3.288 0.001
EE-BI-UB 0.121 0.034 3.567 0.002
SI-BI-BU 0.035 0.016 2.187 0.001
Table 2 above, reveals that PE is signicantly related to BI (b=0.459, t=4.098, p=0.000), EE is signicantly
related to BI (b=0.126, t=5.478, p=0.001), SI is signicantly related to BI (b=-0.013, t=0.195, p=0.849),
BI is signicantly related to UB (b=0.326, t=4.639, p=0.195). BI mediated between PE and UB (b=0.096,
t=3.288, p=0.000), BI mediated between EE and UB (b=0.096, t=3.288, p=0.001), and BI mediated
between SI and BU (b=0.035, t=2.187, p=0.001).
Moreover, PE had eect size of 0.267on BI, EE had eect size of 0.248on BI, SI had eect size of 0.152
on BI, and BI had eect size of 0.251 on BU.
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In addition to that, Stone-Geisser (Stone, 1974; Geisser, 1974) test for predictive relevance by blindfolding
procedure for goodness-of-t was followed as shown in Table 2. These values were found above zero
(0.139) for BI and (0.238) for BU. which according to Henseler, Ringle, and Sinkovics (2009) shows
model had good predictiveness as bearing value above 0 “Zero”.
5. DISCUSSION
Aim of this study was to address the phenomenon of technology adaptability in the light of AI enabled
IoT warble devices eectiveness for adoptability behavior through UTAUT assumptions particularly in
context of developing countries. And the overall model was found to have 52% variance explained by
acceptance determinants and 42% of variance explained intentional use on actual use of technology
which is 10% reduced due to social inuence that aected negatively over behavior to use the technology.
Moreover, the study reveals the mediating eect of intention to use the technology between determinants
and behavior to use the AI enabled m-health applications for wearable IoT devices technology in
healthcare.
The behavior to use technology was positively inuenced by the performance expectancy and eort
eciency. Whereas, the social inuence negatively aects the behavioral intention of technology
adoption. These eects were further carried by the behavioral intention to actual behavior of using the
technology. It was noticed that social inuence in developing countries was not supportive and that also
reduced the actual behavior to use the technology by 10% in local context of Pakistan. This highlights
the fact that in social structure of Pakistan, technology is either less trusted and still rely on physical
method of getting treatment for diseases or diagnoses.
6. CONCLUSION
The aim of this study was to elaborate technological advancement to enable eciency of existing
healthcare services specially in developing countries. This study also reviews the potential areas that are
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enhanced in terms of technology adoption. Since, the technology holds the future of any organization,
healthcare practitioners were reluctant towards technology adoption. Thus, this study focuses on
important elements that might hinder clinician’s technology adoption behavior.
Findings of this study beacon the direct eect of UTAUT determinants on behavioral intention of
medical professionals to use new technology in healthcare system. Findings also revealed the usefulness of
UTAUT model to test the behavioral intention towards M-IoT use and provides additional contribution
in the literature in the role of experience and signicance behavioral role in new technological adoptions.
There are certain limitations that may attract new research as this study gather viewpoint of only clinicians
and focusses only public healthcare sector which might less likely to adopt technology. Further, social
and cultural dimensions must be included to better asses behavior of patients and medical professionals
towards technology adoption.
Typically, in developing countries like Pakistan, hospital-acquired infections are themselves a big killer.
Thus, hospital management could deploy devices that monitor medical professional within hospital
and patients out of hospital vicinities. Such as M-IoT based hygiene monitoring system could save
million patients, time of doctors, and eciently manage the resources. This can be reected in current
COVID-19 pandemic, where global system in all respects has collapsed, specically the healthcare sector.
And failure of healthcare is currently approximated to failure of the state machinery.
Therefore, the future of healthcare, even in emergency department, performance typically relies
on connectivity of smart devices over the internet, and the transfer of information is crucial for any
developed as well as developing countries like Pakistan. This research beacons the AI enabled M-IoT
adoption in healthcare to benet masses with high accuracy, eectiveness and eciency.
ACKNOWLEDGEMENT
The authors wish to thank Hazrat Manzoor Hussain (RA).
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