Sensing, communication, computation, and control technologies are facilitating smart healthcare to improve efficiency and effectiveness of medical treatment and care. This study focuses on the risk issues relevant with the adverse events where novel technical systems do not serve as expected. We discuss the unique challenges, define the scope of risk management in healthcare and review the state-of-art research on diverse topics under the framework widely used in risk management. Then, we present a systematic approach to identify the hazards to patients and other asset of interest in the perception, cyber communication, and execution of smart technologies and their operational contexts. We also investigate different methods for scenario, likelihood, and consequence analyses for specifying the risks of adverse events, and categorize the approaches of risk reduction, as the main strategy of treating risks of smart healthcare systems, into four groups of design, operation, organization, and legislation. At the last, the article proposes some research perspectives responding to the developing trend of smart healthcare.
AhadATahirMYau K-LA. 5G-based Smart healthcare network: Architecture, taxonomy, challenges and future research directions. IEEE Access2019; 7: 100747–100762.
2.
TianSYangWLe GrangeLM,et al.Smart healthcare: Making medical care more intelligent. Global Health J2019; 3: 62–65.
3.
HonanGPageAKocabasO,et al.Internet-of-everything oriented implementation of secure Digital Health (D-Health) systems. In 2016 IEEE Symp on Comput & Commun (ISCC), Messina, Italy, Jun 2016.
4.
BakerSBXiangWAtkinsonI. Internet of things for smart healthcare: Technologies, challenges, and opportunities. IEEE Access2017; 5: 26521–26544.
5.
SundaravadivelPKougianosEMohantySP, et al.Everything you wanted to know about smart health care: Evaluating the different technologies and components of the internet of things for better health. IEEE Consumer Electron Mag2018; 7: 18–28.
6.
SalahuddinMAAl-FuqahaAGuizaniM,et al.Softwarization of internet of things infrastructure for secure and smart healthcare. Comput2017; 50: 74–79.
7.
IslamSMRKwakDKabirMH,et al.The internet of things for health care: A comprehensive survey. IEEE Access2015; 3: 678–708.
8.
AlamMMMalikHKhanMI,et al.A survey on the roles of communication technologies in IoT-based personalized healthcare applications. IEEE Access2018; 6: 36611–36631.
9.
LiuSCLiXJiangYC,et al.Integrated learning approach based on fused segmentation information for skeletal fluorosis diagnosis and severity grading. IEEE Trans on Ind Inform2021; 17: 7554–7563.
10.
SinghAPPradhanNRLuhachAK, et al.A novel patient-centric architectural framework for blockchain-enabled healthcare applications. IEEE Trans on Ind Inform2021; 17: 5779–5789.
11.
CianchettiMLaschiCMenciassiA, et al.Biomedical applications of soft robotics. Nature Rev Mater2018; 3: 143–153.
12.
LeeISokolskyOChenS, et al.Challenges and research directions in medical cyber–physical systems. Proc of the IEEE2012; 100: 75–90.
13.
GuoJCTianSLXianH, et al.Cyber-physical healthcare system with blood test module on broadcast television network for remote cardiovascular disease (CVD) management. IEEE Trans on Ind Inform2021; 17: 3663–3670.
14.
SittigDSinghH. A new socio-technical model for studying health information technology in complex adaptive healthcare systems. Qual & Saf in Health Care2010; 19: i68–i74.
15.
RahamanAIslamMMIslamMR,et al.Developing IoT based smart health monitoring systems: A review. Revue D'Intelligence Artificielle2019; 33: 435–440.
16.
IslamMMRahamanAIslamMRDevelopment of smart healthcare monitoring system in IoT environment. SN Compu Sci2020; 1: 184–194.
17.
DangLMPiranMJHanD,et al.A survey on internet of things and cloud computing for healthcare. Electron2019; 8: 768.
MarxDASlonimAD. Assessing patient safety risk before the injury occurs: An introduction to sociotechnical probabilistic risk modelling in health care. Qual & Saf in Health Care2003; 12: ii33–ii38.
23.
RuncimanBMerryAWaltonM. Safety and ethics in healthcare: A guide to getting it right. Hampshire: Ashgate Publishing Limited, 2007.
24.
KingALFengLSokolskyO, et al.Assuring the safety of on-demand medical cyber-physical systems, In: Proc of the 1st Int Conf on Cyber-Physical Syst Netw and Appl. New York: IEEE, 2013, pp. 1–6.
25.
GrisposGGlissonWBChooKKR. Medical cyber-physical systems development: A forensics-driven approach, 2017 IEEE/ACM Int. Conf on Connected Health: Appl, Syst and Eng Technol (CHASE), Philadelphia, PA, Jul. 2017.
26.
DainS. Normal accidents: Human error and medical equipment design. Heart Surg Forum2002; 5: 254–257.
27.
ComdenSMarxDMurphy-CarelyM, et al.Using probabilistic risk assessment to model medication system failure in long-term care facilities. Adv in Patient Saf2005; 2: 395–408.
28.
SlakeyDPDavidsonI. Robotic surgery: An example of uncoupling the economics of technology. Health and Technol2019; 9: 25–29.
29.
NemethCWearsRWoodsD,et al.Minding the gaps: Creating resilience in health care. In: HenriksenKBattlesJBKeyesMAGradyML (eds) Advances in patient safety: New directions and alternative approaches. Rockville: Agency for Healthcare Research and Quality (US), 2008.
30.
ZaitsevaE. Reliability analysis methods for healthcare system, In: 3rd Int Conf on Human Syst Interact, Rzeszow, Poland, 2010.
31.
HabibzadehHNussbaumBHAnjomshoaF,et al.A survey on cybersecurity, data privacy, and policy issues in cyber-physical system deployments in smart cities. Sustain Cities and Soc2019; 50: 101660.
32.
DemirkanH. A smart healthcare systems framework. IEEE IT Professional2013; 15: 38–45.
JungMA. State-based Safety of Component-based Medical and Surgical Robot. PhD Thesis, Johns Hopkins University, USA, 2015.
35.
SomasundaramRThirugnanamM. Review of security challenges in healthcare internet of things. Wireless Netw2020; 27: 1–7.
36.
PerrowC. Normal accidents: Living with high-risk technologies. New York: Basic Books, 1984.
37.
LymberisA. Smart wearable systems for personalised health management: Current R&D and future challenges. 25th Annu Int Conf of the IEEE Eng in Med and Biol Soc, Cancun, Mexico, Sep 2003.
38.
PislaDCalinVBirlescuI,et al.Risk management for the reliability of robotic assisted treatment of non-resectable liver tumors. Appl Sci2020; 10: 52.
39.
ISO 14971:2019. Medical devices - application of risk management to medical devices.
40.
LinFWangASZhuangY,et al.Smart insole: A wearable sensor device for unobtrusive gait monitoring in daily life. IEEE Trans on Ind Inform2016; 12: 2281–2291.
41.
QiJYangPMinG,et al.Advanced internet of things for personalized healthcare systems: A survey. Pervasive and Mobile Comput2017; 41: 132–149.
42.
ARIA. Accident analysis of industrial automation. Lyon, France: French Ministry of Ecology, Sustainable Development and Energy, 2012.
43.
RodriguesMJPostolacheOCercasF. Physiological and behavior monitoring systems for smart healthcare environments: A review. Sensors2020; 20: 2186.
44.
McAdamsEGehinCMassotB, et al.The challenges facing wearable sensor systems. Studies in Health Technol and Inform2012; 177: 196–202.
45.
JhaDNMichalakPWenZY,et al.Multiobjective deployment of data analysis operations in heterogeneous IoT infrastructure. IEEE Trans on Ind Informs2020; 16: 7014–7024.
46.
Popentiu-VladicescuFAlbeanuG. Software reliability in the fog computing, In: 2017 Int Conf on Innov in Elect Eng and Comput Technol (ICIEECT), Karachi, Pakistan, Apr 2017.
47.
MahmoodMASeahWKG. Event reliability in wireless sensor networks. In: 7th Int Conf on Intell Sensors, Sens Netw and Inf Processing (ISSNIP), Adelaide, Australia, Dec, 2011.
48.
GawanmehA. Open Issues in Reliability, Safety, and Efficiency of Connected Health, In: IEEE 1st Int Conf on Connected Health: Appl, Syst and Eng Technol (CHASE), Washington, DC, Jun 2016.
49.
ArfaouiAKribecheASenouciSM, et al.Game-based adaptive anomaly detection in wireless body area networks. Comput Netw2019; 1639: 106870.
50.
ZhouJCLegenzoffZYanX,et al.An ESD demonstrator system for evaluating the ESD risks of wearable devices. In: 39th Elect Overstress/Electrostatic Discharge Symp, Tuscon, AZ, Sep 2017.
51.
KumariATanwarSTyagiS, et al.Fog computing for Healthcare 4.0 environment: Opportunities and challenges. Comput & Elect Eng2018; 72: 1–13.
52.
PengLDhainiARHoPH. Toward integrated Cloud – Fog networks for efficient IoT provisioning: Key challenges and solutions. Future Gen Comput Syst2018; 88: 606–613.
53.
RamliMRBhardwajSKimDS. Toward reliable fog computing architecture for industrial internet of things. Elect and Electron Eng2019. doi: 10.20944/preprints201903.0096.v1
54.
TissaouiASaidiM. Uncertainty in IoT for smart healthcare: Challenges, and opportunities. In: JmaielMMokhtariMAbdulrazakBAloulouHKallelS (eds) The impact of digital technologies on public health in developed and developing countries. Cham: Springer, 2020, pp.12157.
55.
TangWZhangKZhangD,et al.Fog-enabled smart health: Toward cooperative and secure healthcare service provision. IEEE Commun Mag2019; 57: 42–48.
56.
ChallenRDennyJPittM,et al.Artificial intelligence, bias and clinical safety. BMJ Qual & Saf2019; 28: 231–237.
57.
XuYTranDTianY, et al.Analysis of cyber-security vulnerabilities of interconnected medical devices. In: 2019 IEEE/ACM Int Conf on Connected Health: Appl, Syst and Eng Technol (CHASE), Arlington, VA, Sep 2019.
58.
HasselgrenAKralevskaKGligoroskiD,et al.Block chain in healthcare and health sciences — A scoping review. Int J of Med Inform2020; 134: 104040.
59.
ISO 10993-1: 2018. Biological evaluation of medical devices part 1: Evaluation and testing within a risk management process.
60.
BordenLSJrKozlowskiPMPorterCR, et al.Mechanical failure rate of da vinci robotic system. Can J Urol2007; 14: 3499–3501.
61.
PislaruCSonteaVRaileanS. Assessing the safety of using incubators for newborns. Proc of the 4th Int Conf on Nanotechnol and Biomed Eng (ICNBME)2020; 77: 645–648.
62.
SoudersCPCatchpoleKHannemannA, et al.Flow disruptions in robotic-assisted abdominal sacrocolpopexy: Does robotic surgery introduce unforeseen challenges for gynecologic surgeons?Int Urogynecol J2019; 30: 2177–2182.
63.
ZaitsevaELevashenkoVRusinM. Reliability analysis of healthcare system, In: 2011 Federated Conf on Comput Sci and Inf Syst (FedCSIS), Szczecin, Poland, Sep 2011.
64.
WangXYZhangYY. Research on interactive interface of intelligent electronic medical testing equipment. Proc of Int Conf on Appl Math, Model and Statist Appl. (AMMSA)2017; 14: 410–413.
65.
BorieFMathonnetMDeleuzeA, et al.Risk management for surgical energy-driven devices used in the operating room. J of Visceral Surgery2018; 155: 259–264.
66.
bin AzmatSHanifMKZiaU,et al.Cognition based user interface design for healthcare systems. Int J of Comput Sci and Netw Secur2019; 19: 177–185.
67.
BenzkoJKrauseLJanßA,et al.Modular user interface design for integrated surgical workplaces. Biomed Eng2016; 61: 183–197.
68.
ClementeFFaiellaGRutoliG,et al.Critical failures in the use of home ventilation medical equipment. Heliyon2019; 5: e03034.
69.
SpreaficoCRussoDRizziC. A state-of-the-art review of FMEA/FMECA including patents. Comput Sci Rev2017; 25: 19–28.
70.
TabibzadehMMuralidharanA. Reducing medication errors and increasing patient safety: Utilizing the fault tree analysis, In: AHFE 2017 Int Conf on Human Factors and Ergon in Healthcare and Med Devices, Los Angeles, CA, Jul 2017, 779: 207–218.
71.
JamshidiARahimiSAAit-kadiD, et al.A comprehensive fuzzy risk-based maintenance framework for prioritization of medical devices. Appl Soft Comput2015; 32: 322–334.
72.
LiuHCZhangLJPingYJ, et al.Failure mode and effects analysis for proactive healthcare risk evaluation: A systematic literature review. J of Eval in Clinical Practice2020; 26: 1320–1337.
73.
RogithDIyengarMSSinghH. Using fault trees to advance understanding of diagnostic errors. Joint Commission J on Qual and Patient Saf2019; 43: 598–605.
74.
IEC 61025:2006. Fault tree analysis (FTA).
75.
MaglogiannisIZafiropoulosE. Modeling risk in distributed healthcare information systems, In: 28th Int Conf of the IEEE Eng in Med and Biol Soc, New York, NY, Aug-Sep 2006.
76.
AbecassisZAMcElroyLMPatelRM,et al.Applying fault tree analysis to the prevention of wrong-site surgery. J of Surgical Res2015; 193: 88–94.
77.
JonasJADevonEPRonanJC, et al.Determining preventability of pediatric readmissions using fault tree analysis. J of Hospital Med2016; 11: 329–335.
HuqMSFraassBADunscombePB, et al.The report of task group 100 of the AAPM: Application of risk analysis methods to radiation therapy quality management. Med Physics2016; 43: 4209–4262.
80.
AlemzadehHChenDLewisA,et al.Systems-theoretic safety assessment of robotic telesurgical systems. In: KoornneefFVanGulijkC (eds) Computer safety, reliability and security. Cham: Springer, 2015, pp.213–227.
81.
PawlickiTSamostABrownDW,et al.Application of systems and control theory-based hazard analysis to radiation oncology. Med Physics2016; 43: 1514–1530.
82.
YamaguchiSThomasJ. A system safety approach for tomographic treatment. Saf Sci2019; 118: 772–782.
83.
RajihETholomierCCormierB, et al.Error reporting from the da vinci surgical system in robotic surgery: A Canadian multispecialty experience at a single academic centre. Can Urol Assoc J2017; 11: E197–E202.
84.
PatriarcaRDi GravioGCostantinoF,et al.Systemic safety management in anesthesiological practices. Saf Sci2019; 120: 850–864.
85.
BuchsNCVolonteFPuginF, et al.Augmented environments for the targeting of hepatic lesions during image-guided robotic liver surgery. J of Surgical Res2013; 184: 825–831.
86.
KohDHJangWSParkJW,et al.Efficacy and safety of robotic procedures performed using the da vinci robotic surgical system at a single institute in Korea: Experience with 10000 cases. Yonsei Med J2018; 59: 975–981.
87.
CooperMAIbrahimALyuH, et al.Underreporting of robotic surgery complications. J for Healthcare Qual2015; 37: 133–138.
88.
NaybourMT. Applying reliability engineering techniques to the process of community pharmacy dispensing, PhD thesis, University of Nottingham, UK, 2019.
89.
LyonsIBlandfordA. Safer healthcare at home: Detecting, correcting and learning from incidents involving infusion devices. Appl Ergonom2018; 67: 104–114.
90.
TeixeiraFCde AlmeidaCEHuqMS. Failure mode and effects analysis based risk profile assessment for stereotactic radiosurgery programs at three cancer centers in Brazil. Med Phys2016; 43: 171–178.
91.
LeeYCKimYHuynhJWY, et al.Failure modes and effects analysis for ocular brachytherapy. Brachytherapy2017; 16: 1265–1279.
92.
KölleKFougnerALLundteigenMA,et al.Risk analysis for the design of a safe artificial pancreas control system. Health and Technol2019; 9: 311–328.
93.
RusuIThomasTORoeskeJC,et al.Failure mode and effects analysis of linac-based liver stereotactic body radiotherapy. Med Phys2020; 47: 937–947.
94.
LiuZXNiXLiuYL,et al.Gastric esophageal surgery risk analysis with a fault tree and markov integrated model. Rel Eng & Syst Saf2011; 96: 1591–1600.
95.
Komar. Fuzzy fault tree analysis for patient safety risk modeling in healthcare under uncertainty. Appl Soft Comput2015; 37: 942–951.
96.
AlmulhemA. Threat modeling for electronic health record systems. J of Med Syst2012; 36: 2921–2926.
97.
KimDWChoiJYHanKH. Medical device safety management using cybersecurity risk analysis. IEEE Access2020; 8: 115370–115382.
98.
StrielkinaAKharchenkoVUzunD. Availability models for healthcare IoT systems: Classification and research considering attacks on vulnerabilities, In: 2018 IEEE 9th Int Conf on Depend Syst, Services and Technol (DESSERT), Kyiv, Ukraine, May 2018.
99.
YasarMSAlemzadehH. Real-time context-aware detection of unsafe events in robot-assisted surgery. In: 2020 50th Annu IEEE/IFIP Int Conf on Depend Syst and Netw (DSN), Valencia, Spain, Jul 2020.
100.
IEC 62551:2013. Analysis techniques for dependability - Petri net techniques.
101.
SobrinhoAPerkusichAda SilvaLD,et al.Towards medical device certification a colored Petri nets model of a surface electrocardiography device. In: Proc of 40th Annu Conf of the IEEE Ind Electron Soc, New York: IEEE, 2014, 2645–2651.
102.
ZhuWWDingXYLanXT,et al.Time petri net-based proactive risk management for rehabilitation care in the smart hospital. Basic & Clinical Pharma & Toxic2021; 128: 83.
103.
MaglogiannisIZafiropoulosEPlatisA, et al.Risk analysis of a patient monitoring system using Bayesian network modeling. J of Biomed Inform2006; 39: 637–647.
104.
ZoulloutiBAmgharMSbitiN. Risk analysis of operating room using the fuzzy Bayesian network model. Int J of Eng2017; 30: 66–74.
105.
KazemiRMoslehADierksM. A hybrid methodology for modeling risk of adverse events in complex health-care settings. Risk Anal2017; 37: 421–440.
106.
LiMLiuZXLiXP, et al.Dynamic risk assessment in healthcare based on Bayesian approach. Rel Eng & Syst Saf2019; 189: 327–334.
107.
FengJCuiNQWangZY, et al.Bayesian Network meta-analysis of the effects of single-incision laparoscopic surgery, conventional laparoscopic appendectomy and open appendectomy for the treatment of acute appendicitis. Exp and Therapeutic Med2017; 14: 5908–5916.
108.
KhanSULoneANTalluriS,et al.Efficacy and safety of mechanical versus manual compression in cardiac arrest - A Bayesian network meta-analysis. Resuscitation2018; 130: 182–188.
JonesRDespotouG. Transparent representation of safety activities in a patient care pathway. In: 13th IEEE Conf on Ind Electron and Appl (ICIEA 2018), Wuhan, China, 2018, 2192–2197.
111.
CicottiGCoronatoA. Towards a probabilistic model checking-based approach for medical device risk assessment, In: Proc of 2015 IEEE Int Symp on Med Meas and Appl, New York: IEEE, 2015, 180–185.
112.
CulwickMDMerryAFClarkeDM,et al.Bow-tie diagrams for risk management in anaesthesia. Anaesth Intensive Care2016; 44: 712–718.
113.
ReedSRemenyte-PrescottRReesB. Effect of venepuncture process design on efficiency and failure rates: A simulation model study for secondary care. Int J of Nursing Stud2017; 68: 73–82.
114.
MortensenJDVasavadaANMerryweatherAS. The inclusion of hyoid muscles improve moment generating capacity and dynamic simulations in musculoskeletal models of the head and neck. PLoS ONE2018; 13: e0199912.
115.
OliveiraCCda SilvaJM. Fault diagnosis in highly dependable medical wearable systems. J of Electron Testing: Theory and Appl2016; 32: 467–479.
116.
OliverI. An approach to combining medical device fault analysis with trusted computing forensics, In: 2020 IEEE Int Conf on Big Data, Virtual, Atlanta, 2020, 1831–1837.
117.
EmakhuJAguwaCMonplaisirL, et al.Failure type prediction in software-related medical device recalls. In: Proc of the 2020 IISE Annu Conf, Virtual, Nov 2020, 628–633.
118.
LakkamrajuPAnumukondaMChowdhurySR. Improvements in accurate detection of cardiac abnormalities and prognostic health diagnosis using artificial intelligence in medical systems. IEEE Access2020; 8: 32776–32782.
119.
HametPTremblayJ. Artificial intelligence in medicine. Metabolism2017; 69: S36–S40.
120.
CorciovaCAndritoiDCiorapR. Elements of risk assessment in medical equipment, In: 8th Int Symp on Adv Topics in Elect Eng (ATEE), Bucharest, Romania, May 2013.
121.
LiuYL. Safety barriers: Research advances and new thoughts on theory, engineering and management. J of Loss Prev in the Process Ind2020; 67: 104260.
122.
FeiBNgWSChauhanS, et al.The safety issues of medical robotics. Rel Eng & Syst Saf2001; 73: 183–192.
123.
KazanzidesP. Safety design for medical robots, In: 31st Annu Int Conf of the IEEE EMBS, Minneapolis, MN, Sep 2009.
124.
LussierBChatilaRIngrandF,et al.On fault tolerance and robustness in autonomous systems. In: 3rd IARPIEEE/RAS-EURON Joint Workshop on Tech Challenges for Depend Robots in Human Environ, Manchester, UK, Sep 2004.
125.
GuptaRTanwarSTyagiS, et al.Tactile-internet-based telesurgery system for Healthcare 4.0: An architecture, research challenges, and future directions. IEEE Netw2019; 33: 22–29.
126.
XingLD. Cascading failures in internet of things: Review and perspectives on reliability and resilience. IEEE Internet of Things J2020; 8: 44–64.
127.
WolffJKleinZKrishnanSM. Improving medical product design by analyzing failures. In: 5th Int Conf of Educ, Res and Innov, Madrid, Spain, 2012, 2074–2080.
128.
IadanzaEPennatiDManettiL,et al.FMECA Design analysis: Risk management for the manufacture of a CBCT scanner. IEEE Access2019; 7: 181546–181564.
129.
SchippersJMSeidelM. Operational and design aspects of accelerators for medical applications. Phys Revi Accel and Beam2015; 18: 034801.
130.
JetleyRPurushothaman IyerRJonesP. A formal methods approach to medical device review. Comput2006; 39: 61–67.
131.
MatAMasliA. SOFL-based approach for requirements analysis of brain tumor treatment system. Proc of Int Conf on Comput Inf Sci2012; 2: 885–888.
132.
KwiatkowskaMLea-BanksHMereacreA, et al.Formal modelling and validation of rate-adaptive pacemakers. In: Proc of IEEE Int Conf on Healthcare Inform, 2014, 23–32.
WongSTsuiKChinK, et al.A simulation study to achieve healthcare service quality improvement in accident & emergency department, In Proc of IEEE Int Conf on Qual and Rel, 2011, 259–263.
135.
MahfoudHAbdellahEEl BiyaaliA. Dependability-based maintenance optimization in healthcare domain. J of Qual in Maint Eng2018; 24: 200–233.
136.
BahreiniRDoshmangirLImaniA. Influential factors on medical equipment maintenance management in search of a framework. J of Qual in Maint Eng2019; 25: 128–143.
137.
ChanTHde VericourtFBesbesO. Contracting in medical equipment maintenance services: An empirical investigation. Manag Sci2019; 65: 1136–1150.
138.
TaghipourSBanjevicDJardineAKS. Prioritization of medical equipment for maintenance decisions. J of the Oper Res Soc2011; 62: 1666–1687.
139.
Hernandez-LopezLAPimentel-AguilarABOrtiz-PosadasMRAn index to prioritize the preventive maintenance of medical equipment. Health Technol (Berl)2020; 10: 399–403.
140.
MaktoubianJAnsariK. An IoT architecture for preventive maintenance of medical devices in healthcare organizations. Health and Technol2019; 9: 233–243.
141.
ShamaylehAAwadMAbdullaAO. Criticality-based reliability-centered maintenance for healthcare. J of Qual in Maint Eng2020; 26: 311–334.
142.
ZhangQ. Case study of cost benefits of condition based maintenance used in medical devices. In: 2013 Annu Rel and Maint Symp (RAMS), Orlando, FL, 2013.
143.
ShamaylehAAwadMFarhatJ. Iot based predictive maintenance management of medical equipment. J of Med Syst2020; 44: 72.
144.
SalihA. Optimizing the maintenance strategy through predicting remaining useful life (RUL) for critical medical equipment based on degradation analysis. In: BallAGelmanLRaoBKN (eds) Proc of 32nd int congr and exhib on cond monit and diagnostic eng manag. 244529. London: Springer, May 2020, pp. 1483–1494.
145.
WangBS. Medical equipment maintenance: Management and oversight. San Rafael: Morgan & Claypool, 2012.
146.
ValaSChemwenoPPintelonL, et al.A risk-based maintenance approach for critical care medical devices: A case study application for a large hospital in a developing country. Int J of Syst Assur Eng and Manag2018; 9: 1217–1233.
147.
ElseddawyAZKandilAH. Selection of appropriate maintenance strategy for medical equipment. In: 9th Cairo Int Biomed Eng. Conf (CIBEC), Cairo, Egypt, Dec 2018.
148.
HouriaZBBesbesMElaoudB,et al.Maintenance strategy selection fro medical equipments using fuzzy multiple criteria decision making approach. In: CIE45, Metz, France, 2015, pp. 28–30.
149.
McKeonLMOswaksJDCunninghamPD. Safeguarding patients: Complexity science, high reliability organizations, and implications for team training in healthcare. Clinical Nurs Specialist2006; 20: 298–304.
150.
KaralisEBarberyG. The common barriers and facilitators for a healthcare organization becoming a high reliability organization. Asia Pacific J of Health Manag2018; 13: i05.
151.
BabyarJ. Direct reliability: Strategies to revolutionize healthcare. J of Public Health2020; 28: 89–95.
152.
WheelerDS. Organization-wide approaches to patient safety. Innov and Entrep in Health2015; 2: 49–57.
153.
ChassinMRLoebJM. The ongoing quality improvement journey: Next stop, high reliability. Health Aff2011; 30: 559–568.
154.
RileyWDavisSEMillerKK, et al.A model for developing high-reliability teams. J Nurs Manag2010; 18: 556–563.
155.
AmalbertiRAuroyYBerwickD, et al.Five system barriers to achieving ultrasafe health care. Ann of Internal Med2005; 142: 756–764.
156.
MelloMMBoothmanRCMcDonaldT, et al.Communication-and resolution programs: The challenges and lessons learned from six early adopters. Health Aff2014; 33: 20–29.
157.
LuxtonDDKaylRAMishkindMC. Mhealth data security: The need for HIPAA-compliant standardization. Telemed and e-Health2012; 18: 284–288.
158.
IEC 60601-1-6:2020. Medical electrical equipment—Part 1-6: General requirements for safety—collateral standard: Usability: Analysis, test and validation of human factors compatibility.
159.
IEC 80001-1:2010. Application of risk management for IT-networks incorporating medical devices — Part 1: Roles, responsibilities and activities.
160.
ISO 27799:2016. Health informatics — Information security management in health using ISO/IEC 27002.
161.
HoAQuickO. Leaving patients to their own devices? Smart technology, safety and therapeutic relationships. BMC Med Ethics2018; 19: 18.
162.
MacraeC. Governing the safety of artificial intelligence in healthcare. BMJ Qual & Saf2019; 28: 495–498.
163.
WoodsDD. Four concepts for resilience and the implications for the future of resilience engineering. Rel Eng & Syst Saf2015; 141: 5–9.
164.
NanCSansaviniG. A quantitative method for assessing resilience of interdependent infrastructures. Rel Eng & Syst Saf2017; 157: 35–53.
165.
MerelliEPaolettiNTeseiL. Adaptability checking in complex systems. Sci of Comput Program2016; 115–116: 23–46.
166.
Corral-AceroJMargaraFMarciniakM, et al.The ‘digital twin’ to enable the vision of precision cardiology. Eur Heart J2020; 41: 4556–4564.
