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January 19, 2023
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Smart Hospitals: Advancing Healthcare Through Technology
Smart hospitals are futuristic concepts that utilize existing technologies to advance health and improve communications
Highlights:
- Smart hospitals use technologies to facilitate the exchange of information between internal and external stakeholders
- Smart hospitals utilize various technologies, such as artificial intelligence and big data, the internet of things, wearable devices, patient and laboratory information systems, among others
- Smart hospitals use technology to improve process efficiency, utilize resources, improve patient outcomes, and prevent and monitor diseases
Introduction
A smart hospital is a hospital concept that uses a digital ecosystem consisting of various intelligent and self-learning technologies, like artificial intelligence (AI), to utilize time and effort and produce tangible results. This system connects patients, staff, and operations to learn from their interactions. As part of smart healthcare, smart hospitals utilize the P4 (prevent, participate, predict, and personalize) medicine approach. This concept involves a proactive rather than a reactive approach to individualized health. Although the implementation of this concept is yet to be realized, some of its components have been tested with promising outcomes.
Putting things into perspective
The concept of smart hospitals involves integrating innovative technology within the infrastructure of hospitals and with the staff that operates in them. This integrated system allows specialized computers to process data and information fed into these devices through back-and-forth communication with various hospital functions (1, 2). Once computers receive this information, they process it to provide real-time actionable outcomes, ranging from life-saving recommendations in the emergency room to optimizing the process of dispensing medications and resource utilization (3). In Moorfields Eye Hospital, London, United Kingdom, doctors developed and tested an AI system capable of diagnosing and recommending treatments for over 50 types of eye diseases with a 94% accuracy. The upcoming steps for this system involve clinical trials and regulatory approval (4, 5). Additionally, the dynamic system facilitates and improves the communication process between different members across all levels of the hospital (6). It is noteworthy to highlight that the concept of smart hospitals is not fully realized yet. However, the application of related technologies in certain hospital-related operations has been tested.
Technologies that can be utilized in smart hospitals
To better understand the technological tools used in smart hospitals, it is important to examine who will use and interact with them. Hospital staff usually consists of multiple participants, such as physicians, patients, nurses, pharmacists, management staff, researchers, and those responsible for daily operations (3, 6). Those members will interact at various levels, with people from different departments and functions and with patients and external suppliers (7).
To connect, collect, analyze, and derive results from all these functions, various technologies are required. Examples include the internet, AI and big data, cloud computing, the internet of things (IoT), and microelectronics (1, 3).
Hospital medical staff can also benefit from technologies embedded in various hospital functions to have a collective overview of all patient information. Examples of these include electronic medical record (EMR) systems, laboratory information management system (LIMS), and picture archiving and communication systems (PACS). Additionally, they can make use of devices that can assist in surgical operations (1, 3, 8).
With regard to supply chain and personnel management systems, technologies like radiofrequency identification (RFID) are implemented (3). The use of RFID has demonstrated tangible results in real-world settings. In one medical center, the cost of renting intravenous pumps dropped by almost 3.5 times after implementing the RFID system. Additionally, the system saved about 30 minutes per shift of staff time. In hospital inventory setting, RFID technology has helped reduce the number of purchased medical devices, therefore optimizing inventory and saving costs (9).
Patients are also part of the smart hospital system. Technologies directed towards them include wearable devices, diagnostic equipment, and others (3). A study examined the usability of a real-time remote cardiac monitoring system used to detect arrhythmia in patients with heart diseases. The system consisted of a sensor and a smartphone. Data revealed that the device provided results to the responsible cardiologist within 30-59 seconds upon detecting arrhythmia. This time is well ahead of the 4-6 minutes “golden period” suggested by the American Heart Association to save the patient (10).
Smart hospital technology to monitor, detect, and prevent diseases
The traditional process of disease detection and monitoring is time consuming. It involves collecting and analyzing data as separate processes, therefore risking the emergence of diseases that could otherwise be prevented. In smart hospitals, the dynamic ecosystem will analyze the inputs instantly to proactively detect patterns and present solutions to decision-makers (1, 3, 6). In a study that utilized smart technology, AI-based computed tomography analysis was effectively capable of screening and detecting COVID-19 pathogen in patients who contracted it (11).
The use of smart technology does not stop at the screening and detection stages. It can be used to create personalized preventive approaches. Intelligent systems can utilize personal patient records to help devise protective strategies for medical conditions (3). In one study that used machine learning, the eating habits, lifestyle patterns, among other parameters, of 800 participants were fed into a system to help predict glucose levels after having a meal to create a personalized diet that prevents diabetes. Results of the study presented promising outcomes, as customized diets designed with the help of AI were found to modify the elevated levels of blood glucose after meals, hence preventing metabolic consequences (12).
Smart hospital technology in diagnosis and treatment processes
Since the AI can manage a large volume of data, it can be implemented in the decision-making process from diagnosis to treatment (1, 13). AI is not only capable of analyzing the data, but the system can learn from the results (3). This leads to improved perceived outcomes on both spectrums of the clinical process and the benefits extend to both the patient and the physician. In reality, AI, through machine learning, has been tested in several clinical settings and was found to provide accuracy in diagnosing medical conditions. Additionally, results have highlighted that the system’s accuracy exceeded that of expert physicians (1, 5). A study found that AI-enabled diagnosis of eye diseases, compared to the collective diagnosis of 5 experts, yielded an accuracy of over 95% (5). Another example of clinical decision-making AI with broad applicability is IBM Watson (3, 14).
Once the smart hospital system has assisted in the diagnosis process, it will analyze patient information and present patient-tailored solutions to produce the best outcomes (14). For example, a patient diagnosed with cancer can have “smart” therapeutic agents assigned that specifically target cancerous cells. The same patient would be, with the help of AI, monitored in real-time while receiving treatment to ensure that targeting of the cancer cells is optimal (1). The use of technology in smart hospitals also extends to the surgery room, where robotic surgical devices assist surgeons in performing operations, both remotely and on-site. Examples of these robotics include the Da Vinci system and the Flex Robotic System, among others (1, 6). In other areas such as patient care, robotic rehabilitation devices have shown promising results in helping people with mobility-related conditions (15,16).
Health management through the technology of smart hospitals
Despite all technological advances, the rate of chronic diseases is rising. Patients who suffer from these lifelong conditions may not be compliant with what their doctors prescribe for them (1, 3). In smart hospitals; patients remain connected to the digital ecosystem in a way that allows their doctors to monitor their condition and determine treatment effectiveness and compliance (1, 3, 7). In a study by Labovitz et al., the use of AI to improve medication adherence in stroke patients via mobiles has been tested. Results revealed that AI improved medication adherence by up to 67%, as reflected by plasma drug concentration (17). One of the perks of the digital ecosystem is that it allows patient health monitoring with wearable or implantable devices (3, 7). An example of this technology is an implanted pacemaker, which feeds information for physicians to help them monitor their patients (18).
Smart hospitals and research
Smart hospitals with research centers can utilize technologies like AI in various stages of research ranging from drug screening to patient selection in clinical trials (1, 3). In drug development, AI has been used to develop a medication for obsessive-compulsive disorder known as DSP-1181. This molecule is currently undergoing phase I trials (19).
According to literature, about 1 in every five cancer studies fail to complete enrollment. The use of AI can support in this area (20). In a study by Beck et al., the use of Watson AI was found to reduce screening time to select participants in 3 breast cancer trials by 78%, with an accuracy reaching up to 99.4% (20).
Conclusion
Smart hospitals are futuristic concepts that utilize existing technologies to advance health and improve communications and operations. Despite presenting an excellent opportunity to advance healthcare, these intelligent systems have shortcomings. Concerns about these systems include personal and system security, cultural acceptability, results reliability and decreased human interaction. Additional system-related reasons include the need for guidelines that dictate these systems' framework and the high costs of creating such a fully integrated infrastructure. The pros and cons of such a system need to be carefully considered before implementation.
References:
1. Tian S, Yang W, Le Grange JM, Wang P, Huang W, Ye Z. Smart healthcare: making medical care more intelligent. Global Health Journal. 2019;3(3):62-5.
2. Jang Y, Ryoo I, Kim S. Smart Hospital Sensor Network Deployment for Mobile and Remote Healthcare System. Sensors. 2021;21(16):5514.
3. Holzinger A, Röcker C, Ziefle M. Smart health: open problems and future challenges: Springer; 2015.
4. De Fauw J, Ledsam JR, Romera-Paredes B, Nikolov S, Tomasev N, Blackwell S, et al. Clinically applicable deep learning for diagnosis and referral in retinal disease. Nature medicine. 2018;24(9):1342-50.
5. Lee D, Yoon SN. Application of artificial intelligence-based technologies in the healthcare industry: Opportunities and challenges. International Journal of Environmental Research and Public Health. 2021;18(1):271.
6. Thakare V, Khire G. Role of emerging technology for building smart hospital information system. Procedia Economics and Finance. 2014;11:583-8.
7. Gomez-Sacristan A, Rodriguez-Hernandez MA, Sempere V. Evaluation of quality of service in smart-hospital communications. Journal of Medical Imaging and Health Informatics. 2015;5(8):1864-9.
8. Evans RS. Electronic health records: then, now, and in the future. Yearbook of medical informatics. 2016;25(S 01):S48-S61.
9. Roper KO, Sedehi A, Ashuri B. A cost-benefit case for RFID implementation in hospitals: adapting to industry reform. Facilities. 2015.
10. Kakria P, Tripathi NK, Kitipawang P. A real-time health monitoring system for remote cardiac patients using smartphone and wearable sensors. International journal of telemedicine and applications. 2015;2015.
11. Gozes O, Frid-Adar M, Greenspan H, Browning PD, Zhang H, Ji W, et al. Rapid ai development cycle for the coronavirus (covid-19) pandemic: Initial results for automated detection & patient monitoring using deep learning ct image analysis. arXiv preprint arXiv:200305037. 2020.
12. Zeevi D, Korem T, Zmora N, Israeli D, Rothschild D, Weinberger A, et al. Personalized nutrition by prediction of glycemic responses. Cell. 2015;163(5):1079-94.
13. Wang S, Summers RM. Machine learning and radiology. Medical image analysis. 2012;16(5):933-51.
14. Somashekhar SP, Sepúlveda MJ, Puglielli S, Norden AD, Shortliffe EH, Kumar CR, et al. Watson for Oncology and breast cancer treatment recommendations: agreement with an expert multidisciplinary tumor board. Annals of Oncology. 2018;29(2):418-23.
15. Bostelman R, Albus J. Robotic patient transfer and rehabilitation device for patient care facilities or the home. Advanced Robotics. 2008;22(12):1287-307.
16. Borggraefe I, Schaefer JS, Klaiber M, Dabrowski E, Ammann-Reiffer C, Knecht B, et al. Robotic-assisted treadmill therapy improves walking and standing performance in children and adolescents with cerebral palsy. european journal of paediatric neurology. 2010;14(6):496-502.
17. Labovitz DL, Shafner L, Reyes Gil M, Virmani D, Hanina A. Using artificial intelligence to reduce the risk of nonadherence in patients on anticoagulation therapy. Stroke. 2017;48(5):1416-9.
18. López-Liria R, López-Villegas A, Leal-Costa C, Peiró S, Robles-Musso E, Bautista-Mesa R, et al. Effectiveness and Safety in Remote Monitoring of Patients with Pacemakers Five Years after an Implant: The Poniente Study. International journal of environmental research and public health. 2020;17(4):1431.
19. Burki T. A new paradigm for drug development. The Lancet Digital Health. 2020;2(5):e226-e7.
20. Beck JT, Rammage M, Jackson GP, Preininger AM, Dankwa-Mullan I, Roebuck MC, et al. Artificial intelligence tool for optimizing eligibility screening for clinical trials in a large community cancer center. JCO clinical cancer informatics. 2020;4:50-9.
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Highlights:
- Smart hospitals use technologies to facilitate the exchange of information between internal and external stakeholders
- Smart hospitals utilize various technologies, such as artificial intelligence and big data, the internet of things, wearable devices, patient and laboratory information systems, among others
- Smart hospitals use technology to improve process efficiency, utilize resources, improve patient outcomes, and prevent and monitor diseases
Introduction
A smart hospital is a hospital concept that uses a digital ecosystem consisting of various intelligent and self-learning technologies, like artificial intelligence (AI), to utilize time and effort and produce tangible results. This system connects patients, staff, and operations to learn from their interactions. As part of smart healthcare, smart hospitals utilize the P4 (prevent, participate, predict, and personalize) medicine approach. This concept involves a proactive rather than a reactive approach to individualized health. Although the implementation of this concept is yet to be realized, some of its components have been tested with promising outcomes.
Putting things into perspective
The concept of smart hospitals involves integrating innovative technology within the infrastructure of hospitals and with the staff that operates in them. This integrated system allows specialized computers to process data and information fed into these devices through back-and-forth communication with various hospital functions (1, 2). Once computers receive this information, they process it to provide real-time actionable outcomes, ranging from life-saving recommendations in the emergency room to optimizing the process of dispensing medications and resource utilization (3). In Moorfields Eye Hospital, London, United Kingdom, doctors developed and tested an AI system capable of diagnosing and recommending treatments for over 50 types of eye diseases with a 94% accuracy. The upcoming steps for this system involve clinical trials and regulatory approval (4, 5). Additionally, the dynamic system facilitates and improves the communication process between different members across all levels of the hospital (6). It is noteworthy to highlight that the concept of smart hospitals is not fully realized yet. However, the application of related technologies in certain hospital-related operations has been tested.
Technologies that can be utilized in smart hospitals
To better understand the technological tools used in smart hospitals, it is important to examine who will use and interact with them. Hospital staff usually consists of multiple participants, such as physicians, patients, nurses, pharmacists, management staff, researchers, and those responsible for daily operations (3, 6). Those members will interact at various levels, with people from different departments and functions and with patients and external suppliers (7).
To connect, collect, analyze, and derive results from all these functions, various technologies are required. Examples include the internet, AI and big data, cloud computing, the internet of things (IoT), and microelectronics (1, 3).
Hospital medical staff can also benefit from technologies embedded in various hospital functions to have a collective overview of all patient information. Examples of these include electronic medical record (EMR) systems, laboratory information management system (LIMS), and picture archiving and communication systems (PACS). Additionally, they can make use of devices that can assist in surgical operations (1, 3, 8).
With regard to supply chain and personnel management systems, technologies like radiofrequency identification (RFID) are implemented (3). The use of RFID has demonstrated tangible results in real-world settings. In one medical center, the cost of renting intravenous pumps dropped by almost 3.5 times after implementing the RFID system. Additionally, the system saved about 30 minutes per shift of staff time. In hospital inventory setting, RFID technology has helped reduce the number of purchased medical devices, therefore optimizing inventory and saving costs (9).
Patients are also part of the smart hospital system. Technologies directed towards them include wearable devices, diagnostic equipment, and others (3). A study examined the usability of a real-time remote cardiac monitoring system used to detect arrhythmia in patients with heart diseases. The system consisted of a sensor and a smartphone. Data revealed that the device provided results to the responsible cardiologist within 30-59 seconds upon detecting arrhythmia. This time is well ahead of the 4-6 minutes “golden period” suggested by the American Heart Association to save the patient (10).
Smart hospital technology to monitor, detect, and prevent diseases
The traditional process of disease detection and monitoring is time consuming. It involves collecting and analyzing data as separate processes, therefore risking the emergence of diseases that could otherwise be prevented. In smart hospitals, the dynamic ecosystem will analyze the inputs instantly to proactively detect patterns and present solutions to decision-makers (1, 3, 6). In a study that utilized smart technology, AI-based computed tomography analysis was effectively capable of screening and detecting COVID-19 pathogen in patients who contracted it (11).
The use of smart technology does not stop at the screening and detection stages. It can be used to create personalized preventive approaches. Intelligent systems can utilize personal patient records to help devise protective strategies for medical conditions (3). In one study that used machine learning, the eating habits, lifestyle patterns, among other parameters, of 800 participants were fed into a system to help predict glucose levels after having a meal to create a personalized diet that prevents diabetes. Results of the study presented promising outcomes, as customized diets designed with the help of AI were found to modify the elevated levels of blood glucose after meals, hence preventing metabolic consequences (12).
Smart hospital technology in diagnosis and treatment processes
Since the AI can manage a large volume of data, it can be implemented in the decision-making process from diagnosis to treatment (1, 13). AI is not only capable of analyzing the data, but the system can learn from the results (3). This leads to improved perceived outcomes on both spectrums of the clinical process and the benefits extend to both the patient and the physician. In reality, AI, through machine learning, has been tested in several clinical settings and was found to provide accuracy in diagnosing medical conditions. Additionally, results have highlighted that the system’s accuracy exceeded that of expert physicians (1, 5). A study found that AI-enabled diagnosis of eye diseases, compared to the collective diagnosis of 5 experts, yielded an accuracy of over 95% (5). Another example of clinical decision-making AI with broad applicability is IBM Watson (3, 14).
Once the smart hospital system has assisted in the diagnosis process, it will analyze patient information and present patient-tailored solutions to produce the best outcomes (14). For example, a patient diagnosed with cancer can have “smart” therapeutic agents assigned that specifically target cancerous cells. The same patient would be, with the help of AI, monitored in real-time while receiving treatment to ensure that targeting of the cancer cells is optimal (1). The use of technology in smart hospitals also extends to the surgery room, where robotic surgical devices assist surgeons in performing operations, both remotely and on-site. Examples of these robotics include the Da Vinci system and the Flex Robotic System, among others (1, 6). In other areas such as patient care, robotic rehabilitation devices have shown promising results in helping people with mobility-related conditions (15,16).
Health management through the technology of smart hospitals
Despite all technological advances, the rate of chronic diseases is rising. Patients who suffer from these lifelong conditions may not be compliant with what their doctors prescribe for them (1, 3). In smart hospitals; patients remain connected to the digital ecosystem in a way that allows their doctors to monitor their condition and determine treatment effectiveness and compliance (1, 3, 7). In a study by Labovitz et al., the use of AI to improve medication adherence in stroke patients via mobiles has been tested. Results revealed that AI improved medication adherence by up to 67%, as reflected by plasma drug concentration (17). One of the perks of the digital ecosystem is that it allows patient health monitoring with wearable or implantable devices (3, 7). An example of this technology is an implanted pacemaker, which feeds information for physicians to help them monitor their patients (18).
Smart hospitals and research
Smart hospitals with research centers can utilize technologies like AI in various stages of research ranging from drug screening to patient selection in clinical trials (1, 3). In drug development, AI has been used to develop a medication for obsessive-compulsive disorder known as DSP-1181. This molecule is currently undergoing phase I trials (19).
According to literature, about 1 in every five cancer studies fail to complete enrollment. The use of AI can support in this area (20). In a study by Beck et al., the use of Watson AI was found to reduce screening time to select participants in 3 breast cancer trials by 78%, with an accuracy reaching up to 99.4% (20).
Conclusion
Smart hospitals are futuristic concepts that utilize existing technologies to advance health and improve communications and operations. Despite presenting an excellent opportunity to advance healthcare, these intelligent systems have shortcomings. Concerns about these systems include personal and system security, cultural acceptability, results reliability and decreased human interaction. Additional system-related reasons include the need for guidelines that dictate these systems' framework and the high costs of creating such a fully integrated infrastructure. The pros and cons of such a system need to be carefully considered before implementation.
References:
1. Tian S, Yang W, Le Grange JM, Wang P, Huang W, Ye Z. Smart healthcare: making medical care more intelligent. Global Health Journal. 2019;3(3):62-5.
2. Jang Y, Ryoo I, Kim S. Smart Hospital Sensor Network Deployment for Mobile and Remote Healthcare System. Sensors. 2021;21(16):5514.
3. Holzinger A, Röcker C, Ziefle M. Smart health: open problems and future challenges: Springer; 2015.
4. De Fauw J, Ledsam JR, Romera-Paredes B, Nikolov S, Tomasev N, Blackwell S, et al. Clinically applicable deep learning for diagnosis and referral in retinal disease. Nature medicine. 2018;24(9):1342-50.
5. Lee D, Yoon SN. Application of artificial intelligence-based technologies in the healthcare industry: Opportunities and challenges. International Journal of Environmental Research and Public Health. 2021;18(1):271.
6. Thakare V, Khire G. Role of emerging technology for building smart hospital information system. Procedia Economics and Finance. 2014;11:583-8.
7. Gomez-Sacristan A, Rodriguez-Hernandez MA, Sempere V. Evaluation of quality of service in smart-hospital communications. Journal of Medical Imaging and Health Informatics. 2015;5(8):1864-9.
8. Evans RS. Electronic health records: then, now, and in the future. Yearbook of medical informatics. 2016;25(S 01):S48-S61.
9. Roper KO, Sedehi A, Ashuri B. A cost-benefit case for RFID implementation in hospitals: adapting to industry reform. Facilities. 2015.
10. Kakria P, Tripathi NK, Kitipawang P. A real-time health monitoring system for remote cardiac patients using smartphone and wearable sensors. International journal of telemedicine and applications. 2015;2015.
11. Gozes O, Frid-Adar M, Greenspan H, Browning PD, Zhang H, Ji W, et al. Rapid ai development cycle for the coronavirus (covid-19) pandemic: Initial results for automated detection & patient monitoring using deep learning ct image analysis. arXiv preprint arXiv:200305037. 2020.
12. Zeevi D, Korem T, Zmora N, Israeli D, Rothschild D, Weinberger A, et al. Personalized nutrition by prediction of glycemic responses. Cell. 2015;163(5):1079-94.
13. Wang S, Summers RM. Machine learning and radiology. Medical image analysis. 2012;16(5):933-51.
14. Somashekhar SP, Sepúlveda MJ, Puglielli S, Norden AD, Shortliffe EH, Kumar CR, et al. Watson for Oncology and breast cancer treatment recommendations: agreement with an expert multidisciplinary tumor board. Annals of Oncology. 2018;29(2):418-23.
15. Bostelman R, Albus J. Robotic patient transfer and rehabilitation device for patient care facilities or the home. Advanced Robotics. 2008;22(12):1287-307.
16. Borggraefe I, Schaefer JS, Klaiber M, Dabrowski E, Ammann-Reiffer C, Knecht B, et al. Robotic-assisted treadmill therapy improves walking and standing performance in children and adolescents with cerebral palsy. european journal of paediatric neurology. 2010;14(6):496-502.
17. Labovitz DL, Shafner L, Reyes Gil M, Virmani D, Hanina A. Using artificial intelligence to reduce the risk of nonadherence in patients on anticoagulation therapy. Stroke. 2017;48(5):1416-9.
18. López-Liria R, López-Villegas A, Leal-Costa C, Peiró S, Robles-Musso E, Bautista-Mesa R, et al. Effectiveness and Safety in Remote Monitoring of Patients with Pacemakers Five Years after an Implant: The Poniente Study. International journal of environmental research and public health. 2020;17(4):1431.
19. Burki T. A new paradigm for drug development. The Lancet Digital Health. 2020;2(5):e226-e7.
20. Beck JT, Rammage M, Jackson GP, Preininger AM, Dankwa-Mullan I, Roebuck MC, et al. Artificial intelligence tool for optimizing eligibility screening for clinical trials in a large community cancer center. JCO clinical cancer informatics. 2020;4:50-9.
Article reviewed by
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Dr. Ana Baroni MD. Ph.D.
Scientific & Medical Advisor
Quality Garant
Ana has over 20 years of consultancy experience in longevity, regenerative and precision medicine. She has a multifaceted understanding of genomics, molecular biology, clinical biochemistry, nutrition, aging markers, hormones and physical training. This background allows her to bridge the gap between longevity basic sciences and evidence-based real interventions, putting them into the clinic, to enhance the healthy aging of people. She is co-founder of Origen.life, and Longevityzone. Board member at Breath of Health, BioOx and American Board of Clinical Nutrition. She is Director of International Medical Education of the American College of Integrative Medicine, Professor in IL3 Master of Longevity at Barcelona University and Professor of Nutrigenomics in Nutrition Grade in UNIR University.
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