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Article

Digital Technologies for Public Health Services after the COVID-19 Pandemic: A Risk Management Analysis

by
Loredana Larisa Văduva (Ene)
1,
Ana-Maria Nedelcu
1,*,
Daniela Stancu (Zamfir)
1,
Cristinel Bălan
1,
Ioan-Matei Purcărea
1,
Mihaela Gurău
1 and
Daniel Alin Cristian
2,3
1
The Institute of National Economy, The Romanian Academy, Calea 13 Septembrie no. 13, sector 5, 050711 Bucharest, Romania
2
Department 10, General Surgery, Faculty of General Surgery, “Carol Davila” University of Medicine and Pharmacy Bucharest, 37 Dionisie Lupu Str., 020021 Bucharest, Romania
3
“Colțea” Clinical Hospital 1 Ion C. Brătianu Blvd., 030167 Bucharest, Romania
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(4), 3146; https://doi.org/10.3390/su15043146
Submission received: 26 December 2022 / Revised: 25 January 2023 / Accepted: 1 February 2023 / Published: 9 February 2023
(This article belongs to the Special Issue Public Health and Healthcare Services Technologies and Development in Post-COVID 19 Era)
Versions Notes

Abstract

:
Digitalization has become an important part of human lives that occurs in many fields, ranging from education to labor. Artificial intelligence is one of the most important disruptive technologies, which has produced massive changes in current medical practices, such as MRI, X-ray, and surgeries. AI-based surgeries present lower risks to patients and support medical specialists when it comes to burnout and more challenging operations, which can be more easily performed with the help of robots. The COVID-19 pandemic had a huge impact on healthcare systems due to the large number of patients that overburdened medical healthcare professionals and the medical capacities of hospitals. In this paper, we approach AI-based tools, which have a significant impact on various specializations in medicine under the form of robots, based on an extensive literature review. The research methods consist of a quantitative study conducted on a sample of 50 nurses with the purpose of assessing the awareness of nurses regarding digital technologies used in the medical field, focusing mainly on their capacity to classify digital technological risks that may occur in a public healthcare system. The results show that most of the respondents (62%) are aware of digital applications used in hospitals and are able to classify and manage the risks that may occur. After conducting our research, we found that nurses have a certain degree of reluctance when it comes to the introduction of digital technologies in the medical field.

1. Introduction

The COVID-19 pandemic can be traced back to 27 December 2019, at the Huanan Seafood Market in Hubei, the Provincial capital of Wuhan, as an array of unexplained pneumonia cases [1,2]. At that moment, it took the form of an epidemic, being frequently referred to as coronavirus and later being labeled as COVID-19, which expanded as a global-level pandemic [2,3]. The general public was not aware of coronavirus and could not prevent the spreading of the disease [2,4]. As the pandemic began in China, this was the first country to study the virus and take preventive measures against contagion, including “lockdowns”, quarantines, and wearing protective equipment [2,5].
The crisis generated by the SARS-CoV-2 virus exposed the rapid and cascading impact of worldwide materialization of a global risk. The high infection rates that had crossed borders, the expanding variants, and the increasing rates of contagion and mortality turned the pandemic into a planetary disaster and turned out to be more than a biological tragedy, with an impact on many sectors [2,6].
The SARS-CoV-2 virus has massively impacted the Asian and European continents and has led to the collapse of medical systems in several countries, such as Italy, given the large number of cases.
COVID-19 was designated a global health emergency on 30 January 2020, by the World Health Organization (WHO) [2,7].
Pakistan, China, and Russia were the countries with the best responses, through implementing emergency laws and policy practices against the COVID-19 pandemic [2,8]. The practices of these countries, through laws and policies, were recognized by the WHO [2,9].
The pandemic has cast doubt in plenty of nations on institutional efficiency in terms of dealing with unexpected phenomena, such as a global health crisis [2,10].
The differences between the policies of China and those of other states regarding the measures taken against COVID-19 generated many debates, as most governments opted for social lockdowns, whereas China mainly relied on voluntary suggestions [2,11].
The COVID-19 pandemic rapidly became the greatest global health crisis and legal reform since the influenza pandemic that had occurred in China, Pakistan, Russia, and other states [2].
The WHO boosted the establishment of legislation to grant administrations or institutions broad powers in order to take emergency actions [2,12]. The fighting tactics against the pandemic got more and more effective and, despite of the fact that the virus was still expanding, preventive and control measures in several countries were able to keep the contagion under control. China managed COVID-19 through a “containment target”, which comprised the total number of COVID-19 local transmissions [2,9].
This unprecedented global pandemic crisis has led to panic among people and severe restrictions in order to contain the spread of the disease. The risks surrounding the COVID-19 pandemic in healthcare systems involved the risk of contagion due to the large number of patients whom medical professionals came into contact every day. The COVID-19 pandemic caused colossal losses in both economic and social pillars when it came to human life losses and unprecedented changes in everyday life, but it also had an important role in spurring the digitalization of various industries [13].
In order to be able to benefit from new digital and remote service opportunities, which have emerged as a consequence of the COVID-19 pandemic, various organizations, including hospitals, have developed cloud-based IT infrastructures for remote contactless services, which has led to the emergence of new services, such as telemedicine, m-health, and e-learning, out of the need to contain the spread of the disease caused by the SARS-CoV-2 virus [14].
The two countries that were most impacted by the COVID-19 pandemic, China and Pakistan, will be the game changers in their regions through the China–Pakistan Economic Corridor (CPEC or Corridor), which will serve to enhance the socio-economic development of both countries [15,16].
According to several authors, including Baig et al. (2020) [15], digitalization will become the most compelling asset in the global value chain of the post-COVID-19 era, as the need for digital tools is expanding around the globe.

Literature Review

The healthcare industry had witnessed important developments, given the adaption required by the pandemic, as patients had to be remotely consulted. In this way, several innovations, such as contactless services, have been adopted [17].
Technology has become an important aspect of everyday medical practices, and its development is speeding at an unbelievable rate, predominantly in the domain of orthopedic surgery. Intraoperative output based on real-time navigation and robotic assistance has gained popularity in various areas [18,19]. Among the recent technological improvements, we mention 2D imaging, which has been replaced by virtual 3D imaging, and the introduction of partially or fully automated interactive digital preoperative planning in numerous developed countries [20,21,22].
In the medical world, a clear trend toward technology is evidenced, although orthopedic surgeons have honorably served their clinical craftsmanship, which they have acquired over many years of study and practice, but the global technological takeover can no longer be ignored. Mundane medical practices are provided with important opportunities from AI-based tools and computer-assisted applications, which can aid with the impressive amount of data that needs to be collected pre-, intra-, and post-operatively at a fast rate [18,23,24].
Computers and computer-related applications are dedicated to receiving, categorizing, and interpreting large data amounts, known as Big Data [23,25,26]. Artificial intelligence (AI) was introduced in 1956 by Prof. John McCarthy, with the goal of replicating human intelligence with the help of computers. Machine learning is one of the most important forms of artificial intelligence that uses computational algorithms to evolve, based on the lessons learned from previous experiences [27].
Artificial intelligence has come to the attention of researchers in recent years, with some AI-based tools found in everyday life, such as search engines, voice recognition software, and virtual assistants. In many medical specialties, AI has gained increased interest and promises personalized services for patient management, efficiency of medical practices, and improvement in current medical research and development capacity [28,29]. AI works in a logical way by analyzing and identifying patterns in a dataset that can be utilized to associate, meaning that the computer is provided with a training dataset [20,30], which is often a subset of a greater amount of known information.
Healthcare has been directly impacted by the COVID-19 pandemic, putting enormous pressure both on medical systems and on medical specialists. The healthcare field has found itself in need of change in order to keep up the pace with new requirements brought on by the pandemic, such as remote consultations of patients. In order to contain the spreading of the virus, new digital technologies, including telehealth, e-health, and mHealth, have been implemented within the medical field, with the purpose of providing specialized assistance through dedicated applications that would help monitor patients’ health without putting them at risk of infection.
New dedicated technologies used within the medical field not only come to the rescue of patients, but also to the rescue of medical specialists. Being one of the most researched technologies, artificial intelligence is under continuous development and has experienced impressive expansion in the medical field, especially in the imaging sector dedicated to specialties, such as orthopedics, radiology, ophthalmology, and dermatology. Algorithm-based medicine, which is still in its infancy, has become a reality that will grow in importance as researchers and experts in the field explore its potential. Although artificial intelligence has numerous benefits in the field of health, there are some issues of an ethical nature that must be addressed before a good social consolidation of the innovations it brings to a vital sector can be realized. From a broad perspective, artificial intelligence is often seen as the ability of machines to perform tasks that would normally be performed by humans [31,32].
AI-based technologies create a solid foundation for the digital revolution in healthcare and a framework for improving individual care and promoting innovation in dedicated research. According to Sullivan and Schweikart (2019) [33], one of the most important applications of AI technology is the use of algorithms in precision medicine. There are still many challenges in exploiting the full potential of AI in healthcare, such as regulations dedicated to the field, access to data, and technical and ethical issues attributed to the complexity of the algorithms used by artificial intelligence.
According to Larsen et al. (2016), [34] “telehealth” can be used as an umbrella term for all “tele-” labels that are used to denote the use of information and technology to support healthcare services. However, strictly speaking, telehealth is an expansion of telemedicine and can be viewed as a parent category that covers several areas. Telehealth has been comprehensively described by the WHO as “the provision of health care services where distance is a critical factor for all professionals in the field, by using ICT for the exchange of valid information” [35]. According to Otto et al. (2018) [36], the terms can be distinguished based on the involvement of healthcare providers: telemedicine is a service provided exclusively by physicians [37], and telehealth includes the distribution of services by all existing healthcare providers [38].
mHealth or mobile health is a subfield of eHealth, which represents the use of smart or mobile communication devices in order to deliver health and wellness services and information. According to Cortez et al. (2014) [39], mHealth services encompass the diagnosis and management of conditions and support for general health and well-being. The field of mHealth is expanding rapidly, as the global market of mobile devices is growing. Otto et al. (2018) [36] noted that mHealth is not a well-defined concept, and he emphasizes the definition suggested by Nacinovich (2011) [40] as “the use of mobile communications for health information and services”, which, unlike telemedicine, is “possible without direct involvement of medical service providers”.
eHealth is a recently emerged concept based on information and communication technology (ICT), which includes the use of digital products, services, or processes combined with an organizational change in health systems in order to improve public health and efficiency of healthcare delivery. The FDA (2016) [41] uses the term “eHealth” to describe a wide range of digital information tools, such as electronic health records (EHRs), computerized data entry mechanisms by an attending physician, and clinical decision support tools in diagnosis and treatment.
One of the first restrictions, which was adopted as a result of the ongoing pandemic, was to postpone or cancel medical appointments and surgeries that were not life threatening, in order to release some of the pressure that was put on the medical system due to an increased number of cases of contagion. This led to the use of digital technologies in virtual clinics that used telemedicine consultations and AI-based imaging, such as chest CT scans in order to detect post-COVID-19 lung complications. In this way, patients continued to receive specialized clinical care without overcrowding medical institutions.
The pandemic crisis has opened a door for the implementation of digital technologies, and, with public and government acceptance, these technologies could be used in chronic disease areas in the future. Some of the measures adopted by public health systems during the pandemic were the monitoring, surveillance, detection, and prevention of COVID-19. Risk management revolves around technology, economics, security, politics, culture, and law [41].
Contemporary technologies are able to manage fuel waste and generate effective filtering procedures, as well as state interference in environmental safety rules [41,42].
Since the last century, risk management has begun to raise interest within the healthcare field with the aim of helping organizations improve their reliability and performance [43].
Human activity and medical equipment can face risks, and this is why a vigorous risk management strategy has to be implemented within medical institutions in order to be able to classify and manage possibly occurring risks. Risk assessment involves the identification, classification, monitoring, and prediction of consequences.
Apart from the risks that concern medical activities and equipment, another risk that has to be taken into account is economic risk, which refers to the cost of implementing digital technologies. There is a risk that they may prove to be inefficient, and the investment made will be worthless. Digital technologies come to the aid of detecting, diagnosing, and treating COVID-19, as well as of other health problems related to the virus. Telemedicine consultations generate virtual clinics that reduce the physical crowding of patients in clinics and hospitals. Telemedicine can be defined as the utilization of ICT technologies in order to provide healthcare services, without being restricted by time and space [44].
ICU telemedicine provides 24/7 specialized care for critically ill patients, along with remote monitoring of other patients, which increases the quality and efficiency of healthcare [45]. Currently, telemedicine is used in various medical specialties, such as psychiatry [46], ophthalmology [47], dermatology [48], and neurology [49], with the potential of changing the way medical specialist healthcare is delivered to patients. Lark Health [50] and Buoy Health [51] are two medical chatbots powered by AI that can be used in interpreting individuals’ symptoms, while detecting COVID-19 contagion. Various types of medical imaging platforms have emerged worldwide as a result of the pandemic crisis, which have helped with diagnosing COVID-19 patients, and CT imaging has been the main tool used in confirming COVID-19 cases [52].
Taking into consideration the importance of the use of digital technologies in public healthcare units, as well as the risks that may occur, we considered it necessary to conduct a quantitative study with the purpose of identifying, analyzing, and highlighting the connection between the level of education of nurses, the level of digital technological applications used by nurses, and their capacity to classify risks that may arise in the medical field.

2. Materials and Methods

2.1. The Method of Conducting this Research

In order to understand the context and to answer these study problems, a questionnaire-based survey was conducted in a hospital in Bucharest. The survey was conducted between August and October 2022 among nurses in various fields of medicine, such as cardiology, urology, internal medicine, and day hospitalization, working at “Prof. Dr. Theodor Burghele” Clinical Hospital in Bucharest. Quantitative research was used to collect data from the targeted samples, which, in our case, were nurses, through a survey sampling method. The survey, as a structured tool, was used in order to perform a relevant case analysis for the present study on a sample size of 50 nurses. The objective of this quantitative study was to assess the awareness of nurses regarding digital technologies used in the medical field, mainly on their capacity to classify risks that may occur in a public healthcare system. The questionnaire was delivered in person, together with an informed consent form and details about the study. The questionnaire was developed using the NWI-R indicators applied by Slater et al. and Rao et al. [53,54], with the aim of capturing nurses’ perception of risk management regarding the introduction of digital technologies in public healthcare services. This research was carried out on a sample of 50 respondents. Each respondent completed a questionnaire with 20 close-ended questions specific to the field of activity of nurses. Out of the 20 questions, we chose to analyze the questions relevant for the purpose of this research. The questions concerned various aspects regarding the respondents, such as age and level of education, and included work-related questions in order to assess their perception of risk management in public healthcare services. The first section of the questionnaire envisaged structural indicators related to the respondents’ age, gender, net monthly income (including increments), and level of education in medical assistance. Data preprocessing, for the analysis and conversion of analog into digital responses, was performed by using Microsoft Excel. The second part of the questionnaire comprised 12 nursing-process items using a four-point Likert scale [55,56]. Taking into account the purpose of our research, the most important questions that helped us determine the perception of nurses when it came to risk management were chosen to be further analyzed. The questions aimed at the level of education of nurses, their ability to classify risks according to the severity and rate of occurrence, and their awareness level concerning digital applications used for risk management within hospitals. We also conducted a case study on the level of education of nurses and the applications they use for risk management in relation to their capacity to classify emerging risks, which analyzes the ability of nurses to assess and deal with risks that may occur within medical institutions when it comes to the introduction of digital tools.
The quantitative data were generated as a materialization of the research in the forms of tables (Table 1 presents the analysis of the level of education of nurses and the level of the applications used by them for risk management on the level of autonomy to classify the risks, and Table 2 presents the profile of the respondents).

2.2. Econometric Analysis

The purposes of an econometric analysis include the following: testing and (validation) of economic theories (in the sense of comparing theories, ideas, notions, and theoretical concepts with economic reality); estimation of the relationships between economic variables, including estimation of the parameters that the respective links assume, i.e., measuring the meaning, intensity, and stability of the links between the variables; identifying the characteristics of the data series; forecasting economic developments and behaviors; and analysis of economic policies (simulation of effects and evaluation of results) [57].
The equations of a model try to capture the links between endogenous and explanatory variables. Behavioral equations, defining equations, and accounting (equilibrium) equations can appear in an econometric model. Within a multifactorial linear regression model, the method of least squares is used to perform the calculations and obtain the results. The least squares method determines that regression equation for which the sum of the squares of the deviations between the recorded and the calculated data is the smallest possible, for the respective class of models. The problem that arises next is that of the extent to which the model can explain the evolution of the endogenous variable (in our case, (Yt) is the level of capacity of nurses to classify risks). In other words, how close the variables Y and Ŷ are. If the estimators are calculated by the method of least squares, and the linear regression equation also contains a free term, then the mean of variable Ŷ is equal to the mean of Y. The next step involves checking to what extent the variations in the two variables are similar.
The coefficient of determination (R2) is equal to the square of the linear correlation coefficient (Pearson) between the endogenous variables in the model and the estimated values of that variable if the estimators are calculated by the method of least squares. By way of definition, the coefficient of determination is a positive quantity (as a ratio of two square sums) and unitary (it evaluates the weight of a part in relation to the whole). If R2 is zero, the model does not explain the evolution of the analyzed phenomenon, and if it is one, then there is a perfect (functional) relationship between Y and X1t and between Y and X2t. The closer R2 is to one, the closer the model approximates the modeled economic process [58].

2.3. Regression Model

2.3.1. Estimation of Model Parameters

Given that (X1t) symbolizes the level of education of nurses, (X2t) is the level of the applications used for risk management by nurses, and (Yt) is the level of capacity of nurses to classify risks, the significance of the parameters in the bi-factor model of linear regression,
Yt = a0 + a1X1t + a2X2t + et
is as follows:
a0 represents the level of capacity of nurses to classify risks; if the level of education of nurses and the level of applications used for risk management by nurses remain unchanged, then we refer to the average level of education and the average interest in using applications.
a1 is the parameter that measures the intensity of the influence induced by the level of education of nurses on the level of capacity of nurses to classify risks.
a2 is the parameter that measures the intensity of the influence induced by the level of applications used for risk management by nurses on the level of capacity of nurses to classify risks.
et represents the random variable (errors) in the model.
The calculation of the estimators results in the following:
A ^ = 0.024 0.032 0.417

2.3.2. Interpretation of Estimation Results

If the level of education of nurses and the level of applications used for risk management by nurses remain unchanged, at the mean value, then we anticipate an increase in the level of capacity of nurses to classify risks by 0.024 percentage points (pp).
Increasing the level of education of nurses by 1% has the effect of increasing the level of capacity of nurses to classify risks by 0.032 pp, and increasing the level of applications used for risk management by nurses by 1% is associated with an increase in the level of capacity of nurses to classify risks by 0.417 pp.
b.
Adjustment accuracy
We estimate the accuracy of the match starting from the coefficient of determination R2 and the corrected coefficient of determination.
The estimated demand value (Ŷ) is calculated as follows:
Ŷ = XÂ
and the values of the residual variable are calculated as follows:
u = Y − Ŷ
The coefficient of determination is calculated according to the following relationship:
R 2 = 1 − ∑ u t 2 ∑ Y t − Y ¯ 2
where Ȳ is the mean of the endogenous variable.
Since the coefficient of determination increases when additional explanatory variables are added to the model, even if they are not significant, the adjusted coefficient of determination is calculated as follows:
R ¯ 2 = 1 − n − 1 n − k − 1 1 − R 2
In the calculations, we used the average of the variable Y, which is denoted Ȳ and is calculated as follows:
Y ¯ = ∑ Y t n = 1.83 50 = 0.04
The coefficient of determination is calculated according to the following relationship:
R 2 = 1 − ∑ u t 2 ∑ Y t − Y ¯ 2 = 1 − 0 . 0017 0.0025 = 0.32
The adjusted coefficient of determination is calculated according to the following relationship:
R ¯ 2 = 1 − n − 1 n − k − 1 1 − R 2 = 1 − 50 − 1 50 − 2 − 1 1 − 0.32 = 0.2911

3. Results

The regression model presented above explains 32.00% of the variation in the mean of the ROE profit evolution (R2 = 0.32).
c.
The covariance matrix of estimators
The covariance matrix of estimators is calculated as follows:
V(Â) = (su)2(X′X)−1
where (su)2 is the selection variance of the residuals and is calculated from the following relationship:
s u 2 = ∑ u t 2 n − k − 1 = 0 . 0017 47 = 0.000036
The estimation of the parameters in the multifactorial regression equation is based, as in the unifactorial case, on a series of assumptions regarding the form of the dependence between the variables, the explanatory variable, and the deviation variable.
From the calculations, the theoretical assumptions underlying the linear multifactorial model are the following:
I-1M: linearity of the model. The model is linear in the sense that whichever record (Yt, X1t, X2t, …, Xkt) is selected, the form of the link between Ytm, the explanatory variables Xkt, and the deviation variable is linear.
I-2M: the hypotheses regarding the explanatory variables are as follows:
  • The number of observations is greater than the number of parameters.
  • Each exogenous variable has nonzero but finite variance.
  • There is no linear relationship between two or more explanatory variables (absence of collinearity).
  • Exogeneity: the explanatory variables are not correlated with the errors in the regression equation. Variant: the explanatory variables are not random, but they have fixed values when the selection is repeated.
The assumptions of I-2Mc and I-2Md can be written while being concentrated as follows: matrix X is of the rank k + 1. If the last assumption is not respected, it means that there is a linear relationship between the columns of matrix X and, consequently, between the columns of matrix X′X. That is, X′X is singular, and then the relationship of the estimators calculated by using the least squares method does not make sense since there is no (X′X)−1.
The assumptions related to the deviation variable (to the errors) are similar to those presented for the unifactorial linear regression model:
I-3M: Assumptions regarding errors
  • et errors have zero mean.
  • et errors have constant dispersion whatever t is (errors are not heteroscedastic).
  • et errors are independent (not autocorrelated).
  • et errors are normally distributed.
When it comes to the profile of the 50 respondents, in the gender category, 90% of the respondents were females, while 10% of the respondents were males. In the age category, most of the respondents (58%) were between the ages of 36 and 50, 22% of the respondents were over the age of 50, and 20% were between the ages of 20 and 35. Considering the category of level of education, 56% of the respondents had graduated with a post-secondary education, while 30% had graduated from college, and only 14% of the respondents had graduated with a form of advanced education, i.e., either a Master’s degree or a PhD degree. In the monthly net income category, almost the entire sample of respondents (92%) had incomes between RON 3000 and 5000, and only 8% had incomes over RON 5000.
The purpose of this research is to identify, analyze, and highlight the link between the level of education of nurses, the level of applications used by nurses, and their capacity to classify the risks arising from an activity that has been carried out.
The survey-based questionnaire has helped us better understand the level of medical risks from the perception of nurses when it comes to the introduction of digital technologies. As shown below, we analyzed three of the most relevant questions applied in the survey in order to further conduct our study.
According to the first analyzed question, which purpose was to assess the respondents’ level of education in the field of medical assistance, all of the 50 respondents (100%) answered college graduate, and none of them responded with PhD, Master’s degree, post-secondary studies, and high school or post-secondary studies. This study was easier to conduct as all of the respondents have the same educational level. This shows that the respondents are more eager to work than to continue their studies, either out of financial or personal reasons.
For the second question, which purpose was to assess the respondents’ ability regarding risk classification based on severity and frequency of occurrence, most of the respondents answered completely true (36), followed by rather true (9), completely untrue (4), and rather not true (1). This shows that 72% of the respondents (36 out of 50) are able to classify risks based on their severity and occurrence frequency. A total of 18% (9 out of 50) of the respondents considered it rather true, meaning that they are also able to classify risks, although in a lower manner than the first (36) respondents. A total of 8% (4 out of 50) of the respondents answered completely untrue, meaning they consider that they are not able to classify risks, and 2% (1 out of 50) chose the answer option of rather not true.
According to the third analyzed question regarding the respondents’ awareness on the utilization of digital technologies for risk management in hospitals, 40% (20 out of 50) answered completely true, 24% (12 out of 50) answered rather not true, 22% (11 out of 50) answered rather true, and 14% (7 out of 50) chose completely untrue as their answer option. This shows that most of the respondents (20 out of 50) are aware of the digital technologies being used within hospitals for the purpose of assessing risk management, while 11 out of 50 are also aware but in a lower manner, 12 out of 50 are unaware, and 7 out of 50 are completely unaware of the utilization of digital tools within the medical field.

4. Discussion

The adoption of remote health technologies has led to the widening of the “digital division” in Western civilizations, not only due to the lack of access (first-level digital division), but also due to the lack of use (second-level digital division) [59]. The main benefits brought by eHealth solutions are (1) clinical gains through more effective treatment and (2) safe treatment and efficiency gains through better organization of the medical system [60].
The sources of barriers influencing eHealth adoption in common practices include a lack of awareness of remote monitoring; anxiety about accountability for the data generated; system design; regulatory standards; and increasing demand for services, education, and patient empowerment [61]. An AI-based medical system shows benefits in the process of orthopedic clinical diagnosis and treatment. As bone tumors are difficult to diagnose and prognose due to their biological behavioral changings and high local recurrence, AI is very useful to detect and help with making treatment decisions. With the aid of Big Data analytical ability and the learning ability of artificial intelligence, diagnostic procedures can be effectively solved, instead of manual analyzing hundreds of variables.
The survey-based questionnaire illustrates several relevant conclusions on the ability of nurses to assess risks in the case of the adoption of digital technologies in public healthcare services. Based on the questionnaire, when it comes to the question, “How aware are you of the digital applications used for risk management in hospitals?” it was found that most of the respondents (62%) are aware of the digital applications used in hospitals and are able to categorize and manage the risks that may occur.
For the question, “Are you able to classify the risks depending on their severity and frequency of occurrence?” most of the respondents answered completely true (36), followed by rather true (9), completely untrue (4), and rather not true (1). This shows that 72% of the respondents (36 out of 50) are able to classify risks based on their severity and occurrence frequency, while 18% (9 out of 50) answered rather true, which means that they are also able to classify risks, although in a lower manner than the first (36) respondents. A total of 8% (4 out of 50) of the respondents answered completely untrue, meaning they consider that they are not able to classify risks, and 2% (1 out of 50) chose the answer option of rather not true.
For the question, “How aware are you of the digital applications used for risk management in hospitals?” 40% (20 out of 50) answered completely true, 24% (12 out of 50) answered rather not true, 22% (11 out of 50) answered rather true, and 14% (7 out of 50) chose completely untrue as the answer option. This shows that most of the respondents (20 out of 50) are aware of the digital technologies being used within hospitals for the purpose of assessing risk management, while 11 out of 50 are also aware, albeit in a lower manner. However, 12 out of 50 are unaware and 7 out of 50 are completely unaware of the utilization of digital tools within the medical field.
This illustrates that less than half of the respondents are aware of the digital tools being used in the medical field with the aim of mitigating and solving risks that may occur within medical institutions when it comes to patients and healthcare specialists. Our study shows that most of the nurses are able to classify risks and are aware of the digital technologies implemented in hospitals for the purpose of risk management. However, due to the lack of knowledge on digitalization, mainly on digital solutions that can bring benefits to medical specialists and patients, nurses might be reluctant to adopt them. The limitations of our study are related to the fact that the respondents belong to a single hospital; thus, in the future, we will extend this research to other health units. Additionally, the analysis on perceived capacity can be conducted according to different medical specialties.
Following this research, it is noticed that most of the respondents (36) considered that they were able to classify risks based on their severity and occurrence frequency. Most probably, this aspect is given to the fact that not all of the research community is familiar with digital tools, and many show a degree of reluctance to technological change. This is the reason why, in the future, nurses and medical specialists should become more specialized with emerging digital tools used within the medical field by being exposed to specialty literature and by practicing on digital tools, such as virtual assistants and robots.
The limited number of respondents (50 nurses) does not allow us to extrapolate the results to the entire research community. It should also be taken into account that only a limited number of variables were analyzed in this study. The existence of more important variables is possible, and this is a reason why follow-up studies should take other variables into account.

5. Conclusions

The pandemic has led to sudden changes in markets, a large number of job losses, and a growing digital division. All of these have an important impact on the global population. A clinical risk consists of the probability of a patient suffering an adverse, and even involuntary, event, due to the medical assistance received during hospitalization, which could lead to prolonged hospitalization, health deterioration, disability, or even death. Patient safety represents the foremost dimension of healthcare quality. Clinical risk management consists of a set of complex actions taken in order to identify situations that may be harmful to patients, through the intervention or non-intervention of medical teams and the adoption of measures that allow risk assumption with the purpose of ensuring patients’ safety within the health system. Clinical risks can be divided into two categories: voluntary and involuntary. A voluntary clinical risk is when a physician performs an intervention with a known risk in order to increase the performance of the treatment. An involuntary risk represents an exposure to risks that appear as a result of errors or to unknown risks about which there is no information.
After conducting our research, we found out that nurses have a certain degree of reluctance when it comes to the introduction of digital technologies in the medical field. However, the respondents prove that they are able to identify, classify, and, thus, deal with emerging risks in their field of activity. Most of the respondents are not familiar with digital tools in the medical field, given the novelty of the domain.
Digital applications represent an important aspect in the daily life of medical specialists by simplifying their jobs and helping them in precision operations and mitigating the risk of human errors.
In conclusion, digital tools introduced in the medical field show great importance both for medical specialists and patients, and they will increase in popularity as research and development continue. Most of the nurses are able to identify and classify the medical risks that may occur and efficiently deal with them.
The post-COVID-19 medical systems of several countries have shown great technological improvements, given the necessity of change brought on by the pandemic crisis. New technologies, such as as e-Health, mHealth, and telemedicine, have emerged with the purpose of taking off the pressure that has been put on the medical systems during the pandemic. The large number of infections has almost led to the collapse of the medical systems in several countries, leading to the emergence of remote medical services and consultations.

6. Limitations of the Research

The study has been conducted on a sample of 50 nurses, which may not be sufficient enough for a clear picture of the capacity of nurses to classify risks and their awareness of digital technologies implemented in hospitals for the purpose of risk management. A study with a larger sample has to be conducted in order to better understand the perception of nurses regarding digitalization in the public health sector and their ability to classify emerging risks in their field of activity.

Author Contributions

Conceptualization, L.L.V., A.-M.N., D.S., C.B., I.-M.P., M.G. and D.A.C.; methodology, L.L.V., A.-M.N., D.S., C.B., I.-M.P., M.G. and D.A.C.; software, L.L.V., A.-M.N., D.S., C.B., I.-M.P., M.G. and D.A.C.; validation, L.L.V., A.-M.N., D.S., C.B., I.-M.P., M.G. and D.A.C.; formal analysis, L.L.V., A.-M.N., D.S., C.B., I.-M.P., M.G. and D.A.C.; investigation, L.L.V., A.-M.N., D.S., C.B., I.-M.P., M.G. and D.A.C.; resources, L.L.V., A.-M.N., D.S., C.B., I.-M.P., M.G. and D.A.C.; data curation, L.L.V. and A.-M.N.; writing—original draft preparation, L.L.V., A.-M.N., D.S., C.B., I.-M.P., M.G. and D.A.C.; writing—review and editing, L.L.V., A.-M.N., D.S., C.B., I.-M.P., M.G. and D.A.C.; visualization, L.L.V., A.-M.N., D.S., C.B., I.-M.P., M.G. and D.A.C.; supervision, L.L.V., A.-M.N., D.S., C.B., I.-M.P., M.G. and D.A.C.; project administration, L.L.V., A.-M.N., D.S., C.B., I.-M.P., M.G. and D.A.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

This study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of “Prof. Dr. Theodor Burghele” Clinical Hospital (protocol code 7371/24.08.2022).

Informed Consent Statement

Informed consent was obtained from all subjects involved in this study.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Normale, D. Novel human virus? Pneumonia cases linked to seafood market in China stir concern. Science 2020, 24, aba7672. [Google Scholar] [CrossRef]
  2. Diaz, J. Coronavirus Was In U.S. Weeks Earlier Than Previously Known, Study Says. NPR. 2020. Available online: https://www.npr.org/sections/coronavirus-live-updates/2020/12/01/940395651/coronavirus-was-in-u-s-weeks-earlier-than-previously-known-study-says (accessed on 23 October 2022).
  3. Aldhahri, M.; Alghamdi, R. Awareness of COVID-19 Before and After Quarantine Based on Crowdsourced Data From Rabigh City, Saudi Arabia: A Cross-Sectional and Comparative Study. Front. Public Health 2021, 9, 632024. [Google Scholar] [CrossRef] [PubMed]
  4. Qi, F.; Hu, L. Including people with disability in the COVID-19 outbreak emergency preparedness and response in China. Disabil. Soc. 2020, 35, 848–853. [Google Scholar] [CrossRef]
  5. Khaskheli, M.B.; Wang, S.; Hussain, R.Y.; Butt, M.J.; Yan, X.; Majid, S. Global law, policy, and governance for effective prevention and control of COVID-19: A comparative analysis of the law and policy of Pakistan, China, and Russia. Front. Public Health 2023, 10, 1035536. [Google Scholar] [CrossRef]
  6. Mahmood, M.T. The main concerns of the Pakistan’s taxation policy and effectiveness of the legal reforms introduced by the government from independence till 2020. Int. JL Mgmt. Human 2021, 4, 317. [Google Scholar]
  7. World Health Organization. Statement on the Second Meeting of the International Health Regulations (2005) Emergency Committee Regarding the Outbreak of Novel Coronavirus (2019-nCoV) Geneva: World Health Organization (2020). Available online: https://www.who.int/news/item/30-01-2020-statement-on-the-second-meeting-of-the-international-health-regulations-(2005)-emergency-committee-regarding-the-outbreak-of-novel-coronavirus-(2019-ncov) (accessed on 2 October 2022).
  8. Iqbal, M.; Ma, J.; Ullah, Z.; Ahmad, N.; Ibrahim, M.; Waqas, M.; Ahmad, M. Identifying Lockdown Relaxation Strategies and Policy Implications to Fight against COVID-19: Medical Experts Perspective from Pakistan. Soc. Work. Public Health 2022, 37, 609–630. [Google Scholar] [CrossRef] [PubMed]
  9. Papageorgiou, M.; Melo, D.D.S.N.D. China as a Responsible Power Amid the COVID-19 Crisis: Perceptions of Partners and Adversaries on Twitter. Fudan J. Humanit. Soc. Sci. 2022, 15, 159–188. [Google Scholar] [CrossRef]
  10. Zhu, G.; Chou, M.; Tsai, C. Lessons Learned from the COVID-19 Pandemic Exposing the Shortcomings of Current Supply Chain Operations: A Long-Term Prescriptive Offering. Sustainability 2020, 12, 5858. [Google Scholar] [CrossRef]
  11. Bicchieri, C.; Fatas, E.; Aldama, A.; Casas, A.; Deshpande, I.; Lauro, M.; Parilli, C.; Spohn, M.; Pereira, P.; Wen, R. In science we (should) trust: Expectations and compliance across nine countries during the COVID-19 pandemic. PLoS ONE 2021, 16, e0252892. [Google Scholar] [CrossRef]
  12. Lazarus, J.V.; Ratzan, S.C.; Palayew, A.; Gostin, L.O.; Larson, H.J.; Rabin, K.; Kimball, S.; El-Mohandes, A. A global survey of potential acceptance of a COVID-19 vaccine. Nat. Med. 2021, 27, 225–228. [Google Scholar] [CrossRef]
  13. Kim, S. Kim South Korea Bets on ‘Untact’ for the Post-Pandemic Economy Bloomberg Businessweek. 2020. Available online: https://www.bloomberg.com/news/articles/2020-06-10/south-korea-untact-plans-for-the-post-pandemic-economy (accessed on 15 October 2022).
  14. Xiao, Y.; Fan, Z. 10 Technology Trends to Watch in the COVID-19 Pandemic World Economic Forum April. 2020. Available online: https://www.weforum.org/agenda/2020/04/10-technology-trends-coronavirus-covid19-pandemic-robotics-telehealth/ (accessed on 20 October 2022).
  15. Baig, A.; Hall, B.; Jenkins, P.; Lamarre, E.; McCarthy, B. The COVID-19 Recovery will be digital: A Plan for the First 90 Days. McKinsey Digit. 2020, 14. Available online: https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-covid-19-recovery-will-be-digital-a-plan-for-the-first-90-days (accessed on 25 December 2022).
  16. Lee, S.; Lee, D. Lessons learned from battling COVID-19: The Korean experience. Int. J. Environ. Res. Public Health 2020, 17, 7548. [Google Scholar] [CrossRef]
  17. Bini, S.A. Artificial intelligence, machine learning, deep learning, and cognitive computing: What do these terms mean and how will they impact health care? J. Arthroplast. 2018, 33, 2358–2361. [Google Scholar]
  18. Haeberle, H.; Helm, J.M.; Navarro, S.; Karnuta, J.M.; Schaffer, J.L.; Callaghan, J.J.; Mont, M.A.; Kamath, A.F.; Krebs, V.E.; Ramkumar, P.N. Artificial Intelligence and Machine Learning in Lower Extremity Arthroplasty: A Review. J. Arthroplast. 2019, 34, 2201–2203. [Google Scholar]
  19. Helm, J.M.; Swiergosz, A.M.; Haeberle, H.S.; Karnuta, J.M.; Schaffer, J.L.; Krebs, V.E.; Spitzer, A.I.; Ramkumar, P.N. Machine Learning and Artificial Intelligence: Definitions, Applications, and Future Directions. Curr. Rev. Musculoskelet. Med. 2020, 13, 69–76. [Google Scholar]
  20. Myers, T.G.; Ramkumar, P.N.; Ricciardi, B.F.; Urish, K.L.; Kipper, J.; Ketonis, C. Artificial intelligence and Orthopaedics: An introduction for clinicians. J. Bone Joint Surg. Am. 2020, 102, 830–840. [Google Scholar] [CrossRef] [PubMed]
  21. Beyaz, S. A brief history of artificial intelligence and robotic surgery in orthopaedics & traumatology and future expectations. Jt. Dis. Relat. Surg. 2020, 31, 653–655. [Google Scholar]
  22. Wu, D.; Liu, X.; Zhang, Y.; Chen, J.; Tang, P.; Chai, W. Research and application of artificial intelligence based three-dimensional preoperative planning system for total hip arthroplasty. Zhongguo Xiu Fu Chong Jian Wai Ke Za Zhi 2020, 34, 1077–1084. [Google Scholar]
  23. Karnuta, J.M.; Haeberle, H.S.; Luu, B.C.; Roth, A.L.; Molloy, R.M.; Nystrom, L.M.; Piuzzi, N.S.; Schaffer, J.L.; Chen, A.F.; Iorio, R.; et al. Artificial Intelligence to Identify Arthroplasty Implants From Radiographs of the Hip. J. Arthroplast. 2020, 36, S290–S294.e1. [Google Scholar] [CrossRef]
  24. Fontana, M.A.; Lyman, S.; Sarker, G.K.; Padgett, D.E.; MacLean, C.H. Can machine learning algorithms predict which patients will achieve minimally clinically important differences from Total joint arthroplasty? Clin. Orthop. Relat. Res. 2019, 477, 1267–1279. [Google Scholar]
  25. Karnuta, J.M.; Churchill, J.L.; Haeberle, H.S.; Nwachukwu, B.U.; Taylor, S.A.; Ricchetti, E.T.; Ramkumar, P.N. The value of artificial neural networks for predicting length of stay, discharge disposition, and inpatient costs after anatomic and reverse shoulder arthroplasty. J. Shoulder Elb. Surg. 2020, 29, 2385–2394. [Google Scholar]
  26. Deo, R.C. Machine Learning in Medicine. Circulation 2015, 132, 1920–1930. [Google Scholar] [CrossRef] [PubMed]
  27. Panchmatia, J.R.; Visenio, M.R.; Panch, T. The role of artificial intelligence in orthopaedic surgery. Br. J. Hosp. Med. 2018, 79, 676–681. [Google Scholar] [CrossRef]
  28. Tack, C. Artificial intelligence and machine learning|applications in musculoskeletal physiotherapy. Musculoskelet. Sci. Pr. 2018, 39, 164–169. [Google Scholar] [CrossRef]
  29. Shah, R.F.; Bini, S.A.; Martinez, A.M.; Pedoia, V.; Vail, T.P. Incremental inputs improve the automated detection of implant loosening using machine-learning algorithms. Bone Jt. J. 2020, 102-B, 101–106. [Google Scholar] [CrossRef]
  30. Davenport, T.; Kalakota, R. The potential for artificial intelligence in healthcare. Future Health J. 2019, 6, 94–98. [Google Scholar] [CrossRef]
  31. Chung, J.; Zink, A. Hey Watson, Can I Sue You for Malpractice? Examining the Liability of Artificial Intelligence in Medicine. Forthcom. Asia-Pac. J. Health Law Policy Ethics 2017. Available online: https://ssrn.com/abstract=3076576 (accessed on 2 October 2022).
  32. Sullivan, H.R.; Schweikart, S.J. Are Current Tort Liability Doctrines Adequate for Addressing Injury Caused by AI? AMA J. Ethics 2019, 21, E160–E166. [Google Scholar] [CrossRef]
  33. Larsen, S.B.; Sørensen, N.S.; Petersen, M.G.; Kjeldsen, G.F. Towards a shared service centre for telemedicine: Telemedicine in Denmark, and a possible way forward. Health Inform. J. 2016, 22, 815–827. [Google Scholar] [CrossRef]
  34. Botrugno, C. Towards an ethics for telehealth. Nurs. Ethics 2017, 26, 357–367. [Google Scholar] [CrossRef]
  35. Otto, L.; Harst, L.; Schlieter, H.; Wollschlaeger, B.; Richter, P.; Timpel, P. Towards a Unified Understanding of eHealth and Related Terms–Proposal of a Consolidated Terminological Basis. In Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies—HEALTHINF, Funchal, Madeira, Portugal, 1 January 2018; pp. 533–539. [Google Scholar] [CrossRef]
  36. The World Health Report. Conquering suffering, enriching humanity. World Health Forum 1997, 18, 248–260.
  37. The World Health Report. Health Systems Financing: The Path to Universal Coverage, Technical Document. 2010. Available online: https://www.who.int/publications/i/item/9789241564021 (accessed on 1 October 2022).
  38. Cortez, N.G.; Cohen, I.G.; Kesselheim, A.S. FDA Regulation of Mobile Health Technologies. N. Engl. J. Med. 2014, 371, 372–379. [Google Scholar] [CrossRef] [PubMed]
  39. Nacinovich, M. Defining mHealth. J. Commun. Health 2011, 4, 1–3. [Google Scholar] [CrossRef]
  40. FDA. Final Guidance- General Wellness: Policy for Low Risk Devices. FDA Webinar. 2016. Available online: https://www.fda.gov/medical-devices/workshops-conferences-medical-devices/webinar-final-guidance-general-wellness-policy-low-risk-devices-september-1-2016. (accessed on 17 October 2022).
  41. Khaskheli, M.B.; Wang, S.; Yan, X.; He, Y. Innovation of the Social Security, Legal Risks, Sustainable Management Practices and Employee Environmental Awareness in The China–Pakistan Economic Corridor. Sustainability 2023, 15, 1021. [Google Scholar] [CrossRef]
  42. Hadi, N.U.; Batool, S.; Mustafa, A. CPEC: An Opportunity for a Prosperous Pakistan or Merely a Mirage of Growth and Development. Dialogue Pak. 2018, 13, 296–311. [Google Scholar]
  43. Card, A.J.; Ward, J.R.; Clarkson, P.J. Trust-Level Risk Evaluation and Risk Control Guidance in the NHS East of England. Risk Anal. 2013, 34, 1469–1481. [Google Scholar] [CrossRef] [PubMed]
  44. Lei, J.; Guan, P.; Gao, K.; Lu, X.; Chen, Y.; Li, Y.; Meng, Q.; Zhang, J.; Sittig, D.F.; Zheng, K. Characteristics of health IT outage and suggested risk management strategies: An analysis of historical incident reports in China. Int. J. Med. Inform. 2014, 83, 122–130. [Google Scholar]
  45. Santiago, I. Trends and Innovations in Biosensors for COVID-19 Mass Testing. ChemBioChem 2020, 21, 2880–2889. [Google Scholar] [CrossRef]
  46. Hollander, J.E.; Carr, B.G. Virtually Perfect? Telemedicine for COVID-19. N. Engl. J. Med. 2020, 382, 1679–1681. [Google Scholar] [CrossRef]
  47. Kern, C.; Fu, D.J.; Kortuem, K.; Huemer, J.; Barker, D.; Davis, A.; Balaskas, K.; A Keane, P.; McKinnon, T.; A Sim, D. Implementation of a cloud-based referral platform in ophthalmology: Making telemedicine services a reality in eye care. Br. J. Ophthalmol. 2019, 104, 312–317. [Google Scholar] [CrossRef]
  48. Wolf, J.A.; Moreau, J.F.; Akilov, O.; Patton, T.; English, J.C.; Ho, J.; Ferris, L.K. Diagnostic Inaccuracy of Smartphone Applications for Melanoma Detection. JAMA Dermatol. 2013, 149, 422–426. [Google Scholar] [CrossRef] [PubMed]
  49. Grossman, S.N.; Han, S.C.; Balcer, L.J.; Kurzweil, A.; Weinberg, H.; Galetta, S.L.; Busis, N.A. Rapid implementation of virtual neurology in response to the COVID-19 pandemic. Neurology 2020, 94, 1077–1087. [Google Scholar] [CrossRef] [PubMed]
  50. Digital Disease Management & Prevention Platform. Lark Health. 2020. Available online: https://lark.com/ (accessed on 30 November 2020).
  51. Symptom Checker, Check Your Symptoms in Real Time. Buoyhealth.com. 2020. Available online: https://www.buoyhealth.com/symptom-checker/ (accessed on 30 November 2020).
  52. Wu, J.; Pan, J.; Teng, D.; Xu, X.; Feng, J.; Chen, Y.-C. Interpretation of CT signs of 2019 novel coronavirus (COVID-19) pneumonia. Eur. Radiol. 2020, 30, 5455–5462. [Google Scholar] [CrossRef] [PubMed]
  53. Yang, W.; Yan, F. Patients with RT-PCR-confirmed COVID-19 and Normal Chest CT. Radiology 2020, 295, E3. [Google Scholar] [CrossRef]
  54. Slater, P.; O’Halloran, P.; Connolly, D.; McCormack, B. Testing of the Factor Structure of the Nursing Work Index-Revised. Worldviews Evid.-Based Nurs. 2010, 7, 123–134. [Google Scholar] [CrossRef]
  55. Rao, A.D.; Kumar, A.; McHugh, M.; Rao, R.A.D.; Kumar, C.A.; McHugh, J.M. Better Nurse Autonomy Decreases the Odds of 30-Day Mortality and Failure to Rescue: Nurse Autonomy. J. Nurs. Sch. 2016, 49, 73–79. [Google Scholar] [CrossRef]
  56. Communication Research Measures. In Communication Studies; Routledge: New York, NY, USA, 2009; ISBN 978-0-415-87146-4.
  57. University of Lincoln. (n.d.). Guides: MASH Guide: Home. Available online: https://guides.library.lincoln.ac.uk/c.php?g=110730 (accessed on 10 October 2022).
  58. Jula, D.; Jula, N.-M. Econometrie, 3rd ed.; Publisher Mustang: București, Romania, 2019; pp. 21, 24, 39, 41, 86, 87. ISBN 978-606-652-207-6. [Google Scholar]
  59. Griebel, L.; Enwald, H.; Gilstad, H.; Pohl, A.-L.; Moreland, J.; Sedlmayr, M. eHealth literacy research—Quo vadis? Inform. Healh Soc. Care 2017, 43, 427–442. [Google Scholar] [CrossRef]
  60. Kolasa, K.; Kozinski, G. How to Value Digital Health Interventions? A Systematic Literature Review. Int. J. Environ. Res. Public Health 2020, 17, 2119. [Google Scholar] [CrossRef]
  61. Diaz-Skeete, Y.; Giggins, O.M.; McQuaid, D.; Beaney, P. Enablers and obstacles to implementing remote monitoring technology in cardiac care: A report from an interactive workshop. Health Inform. J. 2019, 26, 2280–2288. [Google Scholar] [CrossRef] [Green Version]
Table
Table 1. Analysis of the influences of the level of education of nurses and the level of applications used by nurses for risk management on the level of capacity of nurses to classify risks that may arise.
Table 1. Analysis of the influences of the level of education of nurses and the level of applications used by nurses for risk management on the level of capacity of nurses to classify risks that may arise.
No. ans.tLevel of Education of NursesLevel of Applications Used by NursesLevel of Applications Used for Risk Management by NursesLevel of Capacity of Nurses to Classify Risks
ttX1tX2tYtYt EstimationUtUt2(Yt-Ymedium)2
110.030.040.040.0414−0.00140.000.00
…20.010.040.040.0408−0.00080.000.00
50500.010.020.030.0324−0.00240.000.00
SUM1.041.461.831.830.000.00170.0025
MEAN0.020.030.04
Table
Table 2. Profile of the respondents.
Table 2. Profile of the respondents.
CategoryFrequencyPercentage (%)
GenderMale510
Female4590
Age20–35 2020
36–50 2958
Over 50 1122
Level of education in the field of medical assistanceCollege graduate1530
Post-secondary education2856
Advanced education714
Monthly net incomeRON 3000–5000 4692
Over RON 500048
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Văduva, L.L.; Nedelcu, A.-M.; Stancu, D.; Bălan, C.; Purcărea, I.-M.; Gurău, M.; Cristian, D.A. Digital Technologies for Public Health Services after the COVID-19 Pandemic: A Risk Management Analysis. Sustainability 2023, 15, 3146. https://doi.org/10.3390/su15043146

AMA Style

Văduva LL, Nedelcu A-M, Stancu D, Bălan C, Purcărea I-M, Gurău M, Cristian DA. Digital Technologies for Public Health Services after the COVID-19 Pandemic: A Risk Management Analysis. Sustainability. 2023; 15(4):3146. https://doi.org/10.3390/su15043146

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Văduva (Ene), Loredana Larisa, Ana-Maria Nedelcu, Daniela Stancu (Zamfir), Cristinel Bălan, Ioan-Matei Purcărea, Mihaela Gurău, and Daniel Alin Cristian. 2023. "Digital Technologies for Public Health Services after the COVID-19 Pandemic: A Risk Management Analysis" Sustainability 15, no. 4: 3146. https://doi.org/10.3390/su15043146

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