Introduction
Artificial intelligence (Al) is gaining massive popularity in the healthcare sector, especially in the management of complex medical condition. Al involves the usage of complex software and algorithms capable of emulating human cognition to analyze complex medical data and generate the most probable conclusions without the need for direct human involvement (Russell 2). Technology in the healthcare sector has been evolving over the years.
When the use of physical files and paper was replaced with the digital data, it was a major milestone in this sector as it reduced time wastage, loss, or misplacement of files, the need to hire numerous employees, and the general cost of managing patients’ information. The introduction of artificial intelligence is seen as another major step towards using technology to improve the quality of services that medical institutions offer to their patients.
Dobrescu and Dobrescu explain that sometimes a doctor may face challenges trying to predict the outcome of a given medication on a patient suffering from a specific complex health problem (73). The doctor may have different options of treating the patient but may not have the capacity to predict the most appropriate one. Using Al, the doctor can feed detailed information about the patient’s condition into the machine and then conduct an analysis about the possible outcome when each of the options is used.
The machine would help to define the most appropriate treatment method that should be used to manage the condition of the patient. According to Young and Scitech, these complex machines currently offer more than the simple task of managing data (2). They are becoming instrumental in making critical medical decisions that eliminates cases of mistakes in this sector. In this paper, the researcher seeks to investigate the impact of artificial intelligence on the healthcare industry.
Objectives
Defining the aim and objectives of a study helps in determining the kind of data that should be collected from different sources. Emanuel explains that a clearly defined objective helps in ensuring that the researcher remains focused on the specific issues relating to the study (2282). The primary aim of the research is to investigate the impact of artificial inelegance in health care industry. The following are the objectives related to this aim of the study:
- To determine benefits of artificial intelligence to the healthcare industry;
- To identify negative impacts related to artificial intelligence in the healthcare sector;
- To identify and discuss challenges associated with implementing artificial intelligence in the healthcare sector;
- To discuss ways in which local healthcare institutions can embrace artificial intelligence as a way of improving their service delivery.
Methodology
The previous sections of the paper have provided background information and objectives that should be realized by the end of this study. In this section, the researcher seeks to explain the method that was used to collect and analyze relevant data from various sources. Askarzai et al. explain that when planning to conduct a research, it is important to develop an effective method that would help in finding the needed information (34).
The chosen method should be capable of addressing research aim and objectives. In this study, the researcher opted to use secondary data sources to inform the conclusion. It would have been desirable to collect data from primary sources. However, the limited time available made it difficult to sample participants and collect data from them in the form of face-to-face or phone interviews. As such, the study had to rely wholly on published sources.
When using secondary data, it is important to ensure that validity and reliability of data is not compromised (Wang and Pervaiz 310). The researcher should ensure that sources used are valid by analyzing the methods used to collect and process data. As such, the researcher considered it necessary to use peer-reviewed journals in the study. The reviewed journal articles are reliable because other scholars and industry experts have analyzed them to reaffirm their authenticity in addressing the issue under investigation (Askarzai et al. 39). The articles enabled the researcher to discuss applications of artificial intelligence in healthcare industry.
Analysis and Results
The concept of artificial intelligence has gained massive popularity in the healthcare sector. The articles reviewed in this paper reveal a growing popularity of Al in modern healthcare institutions. Doctors have come to appreciate the significance of using this advanced technology not only in managing data but also in making accurate predictions when offering a medical assistance to patients (Wong 1430). In this section, it is necessary to discuss findings made from the sources used in the study. This chapter will discuss the concept of artificial intelligence and the way it has gained popularity in the healthcare sector.
Artificial Intelligence
It is important to start by defining the concept of artificial intelligence and its relevance in a wide range of industries in general and in the healthcare sector specifically. Maddox defines AI as “a field of computer science that aims to mimic human intelligence with computer systems. This mimicry is accomplished through iterative, complex pattern matching, generally at a speed and scale that exceed human capability,” (31).
The definition identifies unique characteristics of Al that makes it a critical tool in different industries. The first major aspect of Al is that it mimics human intelligence using computer systems. As such, it is able to make rational decisions, as would a human being. The system is interactive, a factor that makes it to receive information and act upon it as would be appropriate (Choudhury and Kiciman 78). It has a unique capacity of matching complex patterns.
It would require a firm to hire a highly knowledgeable and experienced individual to accomplish such tasks. The quote also identifies another trait of Al as having a scale and speed that supersedes that of human beings (Meskó et al. 3). It means that a properly configured Al would accomplish complex tasks within a short time and with high degree of efficiency.
Artificial intelligence simulates processes of human intelligence with the help of computer systems. Bidgoli observes, “These processes include learning (the acquisition of information and rules for using the information), reasoning (using rules to reach approximate or definite conclusions), and self-correction,” (28). This definition identifies other unique characteristics of Al in terms of processes that it is capable of when properly configured.
Learning, which involves knowledge acquisition and understanding of rules, is one of the critical traits of artificial intelligence. Just like a human being, Al can learn about rules and regulations, new knowledge, and practices that should define its operations. Reasoning, which involves using the acquired knowledge and rules to arrive at the most desirable and rational conclusion, is the second process that Al is capable of in a wide range of contexts (Ivan and Velicanu 85).
Human beings can rely on this system to make the right decision, especially when presented with different options in an organizational context. Self-correcting is another process unique to this system. As the system gains new knowledge about an issue, it is capable to correct itself and embracing a better version on an issue.
The capabilities of artificial intelligence have made it an effective tool that companies are now using in a wide range of sectors. According to Burgess, Al first gained popularity in the industrial sector (2). It was seen as a tool that can help in the production sector by reducing the number of workers needed in the department. Many large manufacturing companies are currently using this technology to improve speed, cut down on cost, and enhance their production efficiency in the current competitive business environment (El-Hassoun et al. 220). The technology has also gained relevance in customer relations sector.
Dobrescu and Dobrescu note that robots are currently used to help communicate with customers in different contexts (77). The fact that they can be programmed to speak in several languages help in overcoming challenges that human beings often face. In this paper, the primary focus was to discuss the growing application of artificial intelligence in the healthcare industry. According to Young and Scitech, it took time for Al to be accepted in the healthcare sector (4). In the industrial sector, it is possible to experiment on machines as long as workers are assured of their protection. However, stakeholders in the medical sector wanted an assurance that the technology would not pose a threat to lives of patients and the medical staff.
Health Care in Organization
Healthcare sector in the Kingdom of Saudi Arabia has experienced major changes for the past several decades. According to Edmund, it was common to find cases where the wealthy businesspersons and top politicians would seek medical attention from western countries because they did not trust local medical institutions (19). However, that trend is slowly changing because of the investment that the government has put in the local healthcare sector. Landi argues that the growth of the healthcare sector depends on how much investment the government is willing to make (3).
The political leadership of the country realized that the economic growth depended on the physical well being of its citizens. It was necessary to find ways of improving the local healthcare sector as a way of strengthening the economy of the country.
Technology is increasingly becoming an important tool in the healthcare sector. Walston et al. note that in the past, medical records were kept in physical files in most of the hospitals around the world (244). This approach of managing data was prone to many challenges. It was common to find cases where files would be lost when they are needed the most. In other cases, they would be destroyed because of poor methods of storage.
Hospitals had to hire more workers to help in keeping records and it always took long to record patients’ information. However, the trend has been changing with the introduction of digital data management platforms. Most of the weaknesses of the traditional approach of keeping records were addressed (Murali and Sivakumaran 108). Although there are, some organizations that still use traditional methods of managing records, most institutions in Saudi Arabia have shifted to the use of digital databases. The new system is timesaving, requires a limited number of employees, and makes it easy for them to share critical data among the medical staff in different departments.
The concept of artificial intelligence in the medical sector is relatively new. According to Russell, only a handful of large hospitals in developed countries have embraced the new approach of offering medical attention to their patients (24). In Saudi Arabia, and many other developing economies in the region, the technology is not yet widespread. The ability of the medical staff to trust machines to conduct complex medical analysis requires some form of training and experience to generate the trust (Challener 16). When a mistake is made, the machine will not be blamed for it. The medical staff involved will have to bear the blame.
As such, the implementation of Al has been slow not only in the developing world but also in some of the developed countries. The need to ensure that patients’ lives are not put at risk is the main factor that has made some of these stakeholders to embrace a ‘go slow’ attitude towards embracing the new technology. Edmund believe that despite the existing fear and concerns towards the use of this technology in the healthcare sector, it holds the future of advanced medical treatment (18). Proper treatment of complex diseases such as cancer and HIV has proven elusive for decades. This technology offers hope in managing some of these complicated medical conditions.
The Impact of Artificial Inelegance in Health Care Industry
The use of artificial intelligence is promising to transform the healthcare sector at a time when the industry is under immense pressure to manage various lifestyle diseases and other major traditional complications. According to Emanet et al., doctors and other medical staff are facing a new problem in their line of duty, which is a highly informed society (12). In the past, it was rare for the majority of patients to question the approach that doctors took to offer the medication (Iqbal et al. 110).
That is no longer the case in the information era. People want to know why a decision was made, alternatives that were available, and why these options were considered inappropriate. In such an environment, these doctors cannot afford to make mistakes. They are aware that their actions and decisions would be subjected to questions to ensure that they are not subjecting their patients to any harm.
Al offers them a perfect opportunity to address these emerging concerns. The ability of this system to take information about patients’ conditions, process it based on the available treatment options, and come up with an appropriate approach needed to address it is one of the biggest benefits of using this technology. In this section, it is important to look at the various benefits of Al and negative effects, which are associated to it. The study also identifies challenges that medical experts and healthcare institutions may face when using it. The researcher also discusses ways in which local healthcare institutions in Saudi Arabia can embrace artificial intelligence as a way of improving their service delivery.
Benefits of Artificial Intelligence to the Healthcare Industry
Artificial intelligence offers a wide range of benefits to the healthcare industry. According to Tsang et al., one of the main benefits is the integration of information in a centralized database (4). In the past, doctors had to rely on paper files that needed to be moved physically from one office to the other. It would take long for physicians to have access to such files when attending to patients (Gao et al. 45). In some cases the records would be lost, misplaced, or destroyed, making it necessary for patients to redo some tests. Such procedures would not only be time consuming but also expensive to the patient and the healthcare institution.
Such problems have been eliminated by artificial intelligence technology. Once a patient’s data is taken at one point, it would be taken to the central database. As patient continues to receive medical attention, the historical records about the medication would be kept in the database. The physician can easily have access to the information without the need to make any physical movement. The physician would then make prescription about the medication needed. The pharmacist would then have access to information about the medication that should be given to the patient. Time spared by using this technology means that the institution can attend to a higher number of patients.
Increasing application of Al has made it for doctors to make unnecessary hospital visits when they should be getting adequate rest. Glaser explains that the system improves communication within an organization (3). It is easy to plan the staff to avoid cases where some of the staff is overworked while others do not spend enough time at work. Khan observe that it is a standard practice to recall a doctor to attend to an emergency after their official working hours if they are the only ones trusted with addressing the problem (11). Using Al, that doctor can work with other medical staff remotely to address the condition of the patient.
The doctor may not have to travel back to the hospital to offer the medical help needed. Al makes the work of these physicians easy and sometimes even enjoyable because they can afford to go for a holiday but still offer help to numerous patients from remote locations. Figure 1 below shows a medical team observing a patient going through diagnostics. Such observations can be made remotely without the doctor having to be physically present at the facility.
Decision-making is another area that artificial intelligence is promising to transform the medical sector. Nicholson and Scitech state that sometimes it may not be easy to predict an appropriate medication that may have the right impact on a patient (11). It is common to find cases where physicians fail to agree on the appropriate approach that a patient suffering from a complex disease should be treated. Each physician makes base decision on knowledge and past experiences.
In cases where the patient needs an urgent medical attention, chances of survival would be significantly reduced if quick decisions were not made. Ma et al. observe that making a quick decision does not guarantee the best results (244). The decision should be optimal in addressing a specific concern. Al makes it possible to address such challenges in the health sector. The medical staff involved in offering the service only needs to feed the information into the Al and allow it to process the possible outcome of each of the medical options available. In less than two minutes, it would present the staff with the most optimal option to manage the condition of the patient.
Artificial intelligence is expected to transform the approach that is taken in clinical trials. Currently, it forces medical researchers to experiment on human beings after conducting several tests on animals, especially the rats, in a laboratory setting (Pauleen and William 2).
The problem is that some of these clinical trials may have unforeseen consequences on human beings. Some of the patients who have in the past agreed to be part of clinical trials have had their conditions worsen (Maddox 32). Such are the challenges that Al seeks to address. Instead of using human beings to test effectiveness of a drug or a new method of medication, researchers can use simulation by coding the right data into the system. Al will process the data and present a precise result of the possible outcome when used on specific patients. In doing so, it eliminates risks that patients may be subjected to if they have to take part in the clinical trials.
Saama Technologies is an example of the leading laboratories currently using Al to process data in their clinical tries (Neves et al. 22). The institution has made massive investment into this sector as a way of making it possible to conduct tests without jeopardizing lives of people. According to Aljuaid et al., the fact that Al eliminates the use of human beings in conducting clinical trials, medical researchers are at liberty to come up with numerous ideas of managing complex diseases such as cancer (8). The system offers a platform where these researchers can afford to be creative without taking any risk at all. Oliveira et al. believe that such creativity can help in developing new and effective treatment methods (1010). Several mistakes will be made along the way, but finally something new and beneficial will be discovered.
The physicians’ wellbeing is another area that Al is addressing. According to a report released by Stanford Medicine, burnout costs the institution over $ 7.75 million every year (Oliveira 13). Some of its medical staff spends a lot of time at work, making it impossible for them to perform properly the following day. Stanford Medicine is only one institution that is recording close to eight million dollars in annual loss because of this problem.
The cost could run into billions of dollars if all medical institution in the country were to be surveyed, which shows the severity of the problem. Reduced performance of the medical staff because of burnout poses a massive threat to patients. Al promises to address the problem of burnout among the medical staff. The speed with which this technology undertakes its tasks means that doctors do not have to spend a lot of time on a single patient. They can therefore, complete their daily tasks in time so that they can have time to rest (Shah 407). As explained above, they also are less likely to be summoned back to the hospital when they go home because they can use Al to offer the urgent help needed. Figure 2 below shows an intelligent environment created by Al.
One of the greatest benefits of Al in the healthcare sector is the reduced cost of operation. According to Zaman et al., many healthcare institutions spend a lot in administrative tasks that require a high number of human resources (80). The new technology reduces the number of people needed to do these tasks by over 90%. It also eliminates the need to purchase consumables such as papers and pens. Alraga explains that although the initial investment may be high, the long-term use of the system cuts costs of operation significantly (100). Costs associated with duplication of work or mistakes are effectively eliminated when using such systems in a given hospital. The healthcare institution can afford to use the resources to improve other areas of operation.
Improved client satisfaction is another factor that cannot be underestimated when discussing benefits of artificial intelligence in the healthcare sector. According to Jiang et al., one of the issues that patients and their loved ones find annoying when they visit a hospital is the time they have to wait to get medical attention (2). Some patients have lost their lives waiting to be attended to, especially in the crowded public hospitals (Aljuaid et al. 7).
The new system speeds up the process, which means that it would take a significantly shorter duration to attend to patients. The new technology also promises improved quality of services to these patients. Al makes accurate predictions about the outcome of a given approach of medication. It means that patients are assured of high quality medical services. Success rates are significantly high when using this technology to manage complex medical problems because of the improved accuracy.
Negative Impacts Related To Artificial Intelligence in the Healthcare Sector
The artificial intelligence has numerous benefits to the healthcare sector, as discussed above, and it is evident that it holds the future to the medical industry. However, Schünemann explains that it has potential threats that should not be ignored when analyzing its relevance and application (28). One of the greatest concerns of using Al in medical application is that modern algorithms are only as effective as data used in training them. It means that if mistakes were made when training them on how to handle a patient, then the machine would be less effective. Hakawi et al. observe that just like human beings, Al is capable of learning and self-correcting (47).
It means that weaknesses in the system caused by incomplete or poor data during the training process can be eliminated by providing new information to make the system more efficient. The problem is that there may be casualties in the process of learning from experience.
The computer system used in offering medical attention to patients requires serious attention to detail when entering data. Schünemann explains that a physician would need to enter patient’s data into the system so that the machine can process it and determine the appropriate medical treatment that should be used (28). The computer concept of garbage-in garbage-out holds true when using artificial intelligence in the healthcare sector (Davenport 95). As such, when the physician fails to make accurate entries into the system, the patient may start receiving inappropriate medication. Physicians are encouraged to be precise when making their entries.
They are encouraged to countercheck their entries before allowing the system to process it. Unlike a human being, the machine may fail to realize that it is making a mistake, which may result into serious injuries on the patient.
Data privacy is another major negative impact that scholars have identified when it comes to the use of Al in the healthcare sector. Cyber-insecurity is becoming a major concern to different industries around the world (Oliveira 17). Techno-savvy criminals and ill-intentioned individuals are currently using their skills to hack into databases and systems for selfish interests. The fact that these criminals can manipulate the system and influence the medical services offered to a patient by manipulating the data is a major concern (Bidgoli 27). In the past, such possibilities were extremely remote.
It would require such a criminal to use force to gain physical access to a healthcare institution to achieve such ill-intended gains. However, they can now have access to critical data by hacking into the database. Healthcare institutions are encouraged to have strong firewall that can protect internal data from being accessed by such criminals (Emanuel 2281). It is also necessary to have back-up databases just in case the current one is attacked by cybercriminals.
Patient’s privacy is one of the cardinal ethical requirements that medical staff have to observe at all times. In a traditional context, physicians had to ensure that their files are kept in safe cabinets out of reach for third parties. However, that is no longer the case when using the digital data platforms (Landi 1). The security of the data is no longer in the hands of the physician. Cybercriminals can use sophisticated systems to gain unauthorized entry into the system and steal sensitive patient’s information.
They can do whatever they like with the information after retrieving it. Healthcare institutions can face lawsuits if patients’ information is made public. The trust between the affected patients and the medical staff who offered them services may also be lost. These doctors may pay for mistakes they did not commit because of the limitations of the system.
Discussion & Managerial Implications
The analysis of data obtained from secondary sources has identified the impact of artificial intelligence in the healthcare sector. It is important to discuss challenges associated with Al to understand the managerial implications. Managers need to understand what is expected of them as they implement this technology. Artificial intelligence also has some specific challenges, besides the negative impacts discussed above, that makes it difficult for some hospitals in the developing countries to embrace it. Addressing these challenges may make it easy for local institutions in Saudi Arabia to embrace the technology. One of the main challenges is technological limitations.
According to Walston et al., implementing this system requires medical institutions to have advanced technology in data management and treatment of patients (248). Some of these technologies are yet to spread beyond developed economies because of their complexity and associated costs (Murali and Sivakumaran 107). It means that it might take some time for local healthcare institutions to have the capacity to automate their systems in a way that can sustain Al application.
Limited number of experts in the country is another challenge that may slow the pace at which Al might be applied in the local medical sector. It takes time to equip medical doctors and other staff with skills needed to operate these machines (Alraga 99). They need to conduct accurate configuration of these systems and learn to make accurate data entry. They also have to know how to monitor the progress made by the system in processing patients’ data or in offering help.
When they detect any mistake, they have to know how to stop the system so that they can take the manual control of the entire process. The majority of local medical staff in the Kingdom of Saudi Arabia has not had the opportunity to work with Al. They have limited capacity to treat patients using the system, which means that the country will need to invest in training them before considering the ability of introducing the concept.
Fear of job loss is an issue that may complicate the ability of local healthcare institutions to implement this technology. According to Davenport, it is true that when Al is introduced, the need for human resource may drop, as most of the tasks will be automated (96). Some staff members may feel that their job security is threatened, and as such, may make an effort to frustrate successful implementation of such a system. It is necessary to find ways of addressing such concerns among local medical staff to ensure that they do not resist the implementation of the new technology. Using Kurt Lewin’s change model shown in figure 3 below may help.
As shown in the figure, the process should take place in three stages. The first stage is unfreezing, where the management would explain the relevance of the technology to the firm and prepare all stakeholders for the change. All their fears and concerns should be addressed at this stage. The second step is to introduce change, which in this case involves introducing the new system in the organization. The stage is to refreeze, where stakeholders are helped to adapt to the new system.
Initial cost of installation is often an issue among institutions that have embraced this new technology in their operations. It may require a healthcare facility to make a significant investment in purchasing the machines needed to facilitate such a system. It is also very costly to train the staff so that they can understand how to operate under the new system. The high cost has made Al out of reach to many institutions facing financial challenges. Aljuaid et al. observe that these costs are likely to drop as the technology become common in different parts of the world (7).
Limitations
When conducting this study, the researcher encountered a few challenges worth discussing at this stage. The biggest challenge that the researcher faced is the limited time within which the study had to be completed. It was desirable for the researcher to visit some of the local clinics to determine the level of knowledge and application of Al, if any, to help understand the progress that local stakeholders have made. However, that was not possible because of the limited time. As Wang and Pervaiz observe, academic researchers are ethically required to deliver their reports within a specified period set by the school or the lecturer (309).
As such, the researcher considered it necessary to use secondary data instead. To enhance the validity and reliability of the study, the researcher used a high number of peer-reviewed journal articles. Most of these articles were published within the past five years. Having current articles helps in ensuring that the information obtained from them is recent and relevant to the study.
Conclusions & Recommendations
Artificial intelligence has gained massive popularity in various industries because of its effectiveness. In the healthcare sector, the technology is currently used to help in diagnostics and processing of patients’ data to determine the most effective method of treatment that should be used. The current trend shows that this technology also promises to help in dealing with complex medical conditions such as cancer. The study has identified numerous benefits that this technology has if it is applied in the right way. However, it is important to appreciate the fact that it has some weaknesses that should not be ignored when using it.
The research has also identified challenges that may make it difficult to apply this technology in some of the developing economies such as in the Kingdom of Saudi Arabia. Given the significance of this technology, local stakeholders should consider the following recommendations to help in the implementation of Al in the local healthcare sector:
- The government of Saudi Arabia needs to make substantial investment in the healthcare sector to make it possible for the relevant stakeholders to explore Al technologies.
- The local medical staff needs to get proper training on the application of Al in the healthcare sector. They need to work closely with experts from the western countries.
- They need to understand their new role under this technology instead of fearing it. Fear may affect implementation of the technology locally.
- The Saudi society may need some sensitization for them to understand the relevance of Al in modern medical sector.
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