International Research Journal of Engineering and Technology (IRJET) e-ISSN:2395-0056
Volume: 12 Issue: 03 | Mar 2025 www.irjet.net p-ISSN:2395-0072
Ensuring Resilient Healthcare Systems: Predictive Failure Detection and Automation in Critical U.S. Health Infrastructure
Vijaybhasker Pagidoju, Saint Charles, MO USA
Lead Site Reliability Engineer /Architect, Centene Corporation USA ***
ABSTRACT: Healthcare cybersecurity is seeing a revolution as the Artificial Intelligence (AI) is being integrated into it and that is to protect hospitals, insurers, and other public health networks of digital attacks. The sophisticated cyberattacks of today emphasize the implementation of the real-time threat detection, anomaly prediction and its automatedresponsesystemswhicharepossiblewithAIdrivensecuritymechanisms.AnalysingAI’srolein strengthening national health infrastructure through AI’s capabilities like predictive analytics, encryption and compliance with regulationsuchasHIPAAandHITECHasthefocusofthispaper.Inaddition,itexplainstheproblemofAIadoption,suchas theprivacyissuesandadversarialattacks.Healthcaresystemscanprovidemoresecuresecurity,reducethelossofservice time,andprotecttheconfidentialityofpatientdatatoagreaterdegreebyadoptinganationalcyberresilienceframework basedonAI
KEYWORDS: Healthcare Automation, Predictive Monitoring, Fail Detection, Self-Healing systems, Cloud Healthcare, Large Language Models (LLMs), Chat GPT in Healthcare IT, System Reliability, Healthcare Cybersecurity, and Healthcare Cybersecurity.
I.INTRODUCTION
The impact of the digital transformation of healthcare has been digital transformation of healthcare, improving patient care, improving operational efficiency, and making healthcare more accessible. While it has also attracted an ever-rising numberofcyberthreatsagainsthealthcaresystems,includingransomware,databreaches,andphishingattacks.
The continuous evolving threats have rendered the traditional security measures ineffective to battle them. Healthcare infrastructureisnowstronglysafeguardedwiththehelpofAIasithasenableda level ofadvancedthreatdetection,real timesecurity monitoringandautomated incidentresponse.Towardthisend,this paperstudieshowAIcanbe leveraged forcybersecuritywithinnationalhealthsecurity,keepingdataprivate,beingelectronicallycompliant,andbeingresilient fromcyberattackswhileaddressingchallengesaroundimplementation
Fig.1PredictiveAnalyticsinHealthcare(RevealBI,2025)
International Research Journal of Engineering and Technology (IRJET) e-ISSN:2395-0056
Volume: 12 Issue: 03 | Mar 2025 www.irjet.net p-ISSN:2395-0072
II.US HEALTH INFRASTRUCTURE
System Failures
The U.S. healthcare system is the most extreme by virtue and with gaps in system that makes us susceptible to losses in safety, financial health, and operational continuity. With hospitals all across the nation strained by excessive patients, burnoutofhealthcareworkersandoverwhelmingshortageofmedicalsuppliesinthemidstofrecentpublichealthcrises suchastheCOVID19pandemic,somuchcouldonlybeaccomplished[1].
Thesefailureshaveturnedouttobeseriouslyhittingthepocketsashealthcarebillsskyrocketed,hospitalisationratesare increasing and problems with chronic diseases tend to continue longer because of service disruptions. Overall, overcrowdingintheemergencyroom(ER)notonlycauseslongerresponsetimes,butmortalityriskisalsoincreased.This also includes disruptions in the service outside hospitals since insurance systems are also being disrupted with their operation:delayedclaimsprocessingandfraudulentbillingaddfueltothecrisis.
If the performance of any segment within the healthcare is not bettered whether hospitals, insurance, the public health agenciesthentheperformanceoftheoverallhealthcaresystemhasachancetosuffer'cascadingeffect'andfail.However, predictivefailuredetectionandautomationwillneedtoreachthestrategiclevelsofnationalhealthcareresilienceunless thesethreatscontinue
Historical Failures
TheCOVID-19pandemicrevealedhowfragileourUShealthsystemisandhowanyunanticipatedsurgeindemandcanall of a sudden engulf medical facilities, disturb every necessary service and mainly hurt marginalized communities. As a resultoftheabsenceofanintegratedfederalresponseanddifferential,disaggregated,policiesatthestateandlocallevels, thiswasaccompaniedbytheformationoflogisticalbottleneckswhichreducedeffectivenessofcrisismanagement[1].
Thismeansthatitbecameone ofthe worst hitcountriesinthe world, the U.S.havingseenovera million deaths. During theopioidcrisis,thepandemiconlyjackedupexistingvulnerabilitiestoincreaseoverdoserelatedemergencydepartment (ED)visitswhilesimultaneouslydecreasingoverallEDadmissions[2].
Analysisofthepublichealthemergencydatabasesfoundthatalthoughinfectiousdiseaseoutbreakswerethemajordriver of the healthcare system failure recognized by the United States, the widespread disruption in addiction treatment and social support networks increased opioid-related ED visits by 28.5%, illustrating the reach of healthcare system failure beyond infectious disease outbreak to other public health emergencies. Such failures highlight the imperative of a nationwidepredictivemonitoringsystemtoidentifyandinhibittherunningofsystemicbreakdownsfromhappening.Yet, theU.S.healthcaresystemhasyettoadequatelypreparefortheadversitythatliesahead,whetherinspiredbypandemics, cyberattacksorstressontheinfrastructure
International Research Journal of Engineering and Technology (IRJET) e-ISSN:2395-0056
Volume: 12 Issue: 03 | Mar 2025 www.irjet.net p-ISSN:2395-0072
Nationwide Strategy
RecentcrisesexposeustovulnerabilitieswhichshowtheneedforanationalstrategytomaketheU.S.healthcaresystem resilient.Forexample,thereisanexistingtransformationalsolutionforrealtimeriskassessment;earlyfailuredetection; self-healingattheearlystageforoperationcontinuity
The U.S. could anticipate and prevent systemic failures in hospital networks, public health infrastructure, and insurance systemswiththeuseofAIsoastorealizepatientcare.AnAIbasedhealthcaresurveillancesystemcouldservetowardsthe objectiveofearlyintervention,ERmanagement,resourceallocationanddisastermanagementonanationalscale.
Automationinhealthadministration alsoincludesthe reductionofinefficienciesin claimsprocessing, reductionof fraud and provision of uninterrupted service. As national healthcare failure has significant consequences, policymakers should investinpredictivehealthcaretechnologytoprotectthepeoplefromfuturecrises.
While AI driven resilience measures will safeguard patient safety, they will also strengthen the economic survival of the health care sector and underscore that the country’s critical medical services will continue to be accessible to all Americans,evenwhenconfrontedwiththekindofunprecedentedchallengesthatthecurrentenvironmentpresents
III.NATIONAL HEALTHCARE
Prediction Analytics
By using artificial intelligence (AI) and machine learning (ML), the infrastructure of the healthcare is becoming the predictor of system failure before it happens and that is securing proactive risk mitigation and operational stability. As pointedoutbyOguguaetal.(2024),predictiveanalyticshavesignificantlyadvancedindiseasecontrolandcontrolchronic diseasemanagement,somethingthatAIcanrevolutionizehealthcareoperations[3].
Usingreal timedata analysisandpattern recognition,AImodelscanforeseeaccessingpointsurgesinpatientloadinthe hospital and that of critical infrastructure breakdown like equivalent equipment. Through machine learning algorithms trainedonhistoricalandrealtimedata,anomaliesthatsignalpotentialdisruptionsinhospitalsandpublichealthagencies getidentifiedandpotentialrisksarerespondedtopre-eminently
Fig.2USHealthcaresystem(ISPOR,2025)
International Research Journal of Engineering and Technology (IRJET) e-ISSN:2395-0056
Volume: 12 Issue: 03 | Mar 2025 www.irjet.net p-ISSN:2395-0072
For example, AI based diagnostic tools can identify early warning signs of failures in the dynamic system in case if emergency departments (EDs) run short of staff or there is not enough left of medical resource, immediately start a contingency plan to shield bad results. Predictive models help in pharmaceutic supply chains to optimize the inventory management, which eliminates the medication shortages risk or over stock, which might cause financial losses and inefficiencies.
Through the integration of AI into predictive health care monitoring, system failure situations do not become reactive issues, but rather are augmented by intelligent automation of proactivity for both patient care as well as institutional reliability
Early Warning Systems
Early warning systems powered by AI that integrate with the ER, medical supply, or cyber threats greatly enhance the national healthcare resilience by predicting ER surges, shortages of medical supply, and cyber threats By analyzing historical and real time patient admission data, machine learning models predict peak emergency room congestion and thushospitalscandeployadditionalstaffandresourcesduringtimewhenitislikelytobeneeded.
Through predictive modelling, Mbanugo and Unanah (2025) criticize AI for the optimization of resource distribution so that healthcare providers can better handle the needs of patients with efficiency [4]. Just as in pharmaceutical inventory systems, different AI approaches can forecast supply chain disruption caused by trends in pharmaceutical and medical equipmentdemandstomisstheprobabilityofshortagesthatmightinterferewithpatientcare.
TheyareAIpoweredcybersecuritytools,thatpre-emptthecyber-attackssuchasransomwareaimedatpatient’sdataand hospitaloperations,thatfindvulnerabilitiesinthehealthcareITsystems.UnanahandMbanugo(2025)emphasizethefact thatdigitalhealthrecordsandAI-drivenCRMuseisontherise,andinturnmakingdatasecurityanessentialchallenge[4].
Using AI based cyber security framework helps strengthen hospitals against cyberspace data breach and operational disturbances, to guard patient information and institutional integrity. The value that AI driven early warning system brings to the resilience of the US healthcare system is through proactively tackling ER surges, medical supply shortages, andcyberthreats
National-Level Implementation
RealtimemonitoringofhealthinfrastructureiscriticalfornationalAIpoweredsurveillancenetworkandforcoordinated responsetoallkindsofthreatsandsystemicfailures.NationalhealthagenciescantakeadvantageofthepowerofAIand
Fig.3AIinHealthcare(GrandViewResearch,2022)
International Research Journal of Engineering and Technology (IRJET) e-ISSN:2395-0056
Volume: 12 Issue: 03 | Mar 2025 www.irjet.net p-ISSN:2395-0072
big data analytics to build interrelated networks of AI that watch over real time hospital performance, medical supply chain,andpatientflow.
Inhisstudy,Oguguaetal.(2024)havehighlightedthetransformativepotentialofpredictiveanalyticsinpublichealthas it can help in improving disease control measures by means of data and insights [3]. This concept can be expanded to the areaofhealthcareinfrastructure:bybringinghospitaldata,insuranceclaims,aswellaspublichealthinformationtogether inthenationalsystem,patternscanbeidentifiedthatanticipatecrisesforimmediateintervention.
Suchasystemwillalsobebeneficialinmakingthehealthcaredeliverymoreequitableasitwillrevealsuchareasofleast resources wherethereis more likelyinsufficient healthcaredeliveryunder the present system. Additionally,asanalyzed byUnanahandMbanugo(2025),AI-basedCRMplatformscanfurtherimprovehealthcareproviderpatientengagementby providingpersonalizedcarerecommendationsforthepatients,henceenhancingpatients’outcomes[4].
Despite this, the challenges that national implementation will have to face will involve regulatory challenges such as compliancewithHIPAAaswellasethicalAIusageinhealthcaredecisionmaking.TheimplementationofstandardizedAI governance policies will be required to provide transparency, accuracy and fairness in the usage of AI in the practice of healthcare.TheU.S.caninvestinAIpowerednationalsurveillancenetworkstobetterfortifyitshealthcareinfrastructure todealwithdisruptionstoavoidlessresponsiveandmorefragilesystemwhenfacinganotherpublichealthcrisis.
Autonomous System Recovery
The concept of self-healing automation in healthcare is AI driven technologies for the detection of system failures, automatic resolution of issues and also for continuous operation without human intervention. With hospitals and public health networks ever more dependent on digital infrastructure, autonomous recovery mechanisms need never to be greater.
Healthcare services can be crippled by cybersecurity threats, technical failures and IT infrastructure breakdowns, which mayresultinlifethreateningconsequences.Nifakosetal.(2021)statethatcyberattackswhichincludesransomwareand socialengineeringhaveexposedhealthcareITvulnerabilitiesandthereforethereisanurgentneedforautomatedsystems inprotectingandrescuingsecurity[5].
Application ofAI powered self-healing network can overcomethesechallenges bysensingthesystemhealthcontinually, identifying anomalies and pre executing pre-emptive fix before the failure causes a disruption of critical operations. Hospitals that use AI automation could detect the predicted failures and reroute the network traffic; if no redundancy serversareavailable,thehospitalcanautomaticallyswitchovertobackupserversorjustdisabletraffictemporarilyincase ofcyberattacks.
Fig.4PatientMonitoringSystem(MDPI,2024)
IV.NATIONAL STRATEGY
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In a public health network, self-healing AI can monitor the transmission of medical data and detect any failures that can damagepatientrecords.Instead,theyhelpachievetheminimumdowntimeandimprovethereliabilityofservices,thereby creatingahealthcareecosysteminwhichthesystemisoperatingdespiteextremeconditions.
Although, for the implementation to be successful there needs to be a robust infrastructure with comprehensive risk assessments, and cybersecurity awareness training for healthcare professionals to mitigate human vulnerabilities. Through AI based automation integration into hospital and public health networks, the healthcare providers can greatly improvesystem’sresilienceandensurecontinuousservicedelivery
Insurance Administration
In insurance and healthcare administration, AI driven failover mechanisms are used, which enable the quickly switching from the failed system to the backup system in the event of a failure. As the healthcare industry, numerous pieces of sensitivedatasuchaspatientrecordsandbillinginformationarehandled,makingsystemfailureamatterofhighrisk.As Vieira et al.(2023)pointout,digital solutionshavea keyroleinreducinghealthinequitiesandcontinuingcaredelivery, havingeconomicruralareasscarcehealthcareresources[6].
In this context, AI powered failover systems can be important in keeping service accessible and minimizing the sort of disruptionthatparticularlyhurtsthevulnerable.
Failover mechanisms driven through the use of AI rely on the predictive analytics of identifying risks and the ability to automatefailoverprocessespriortodisruptionofprocesses.AIcanmonitorrealtimeclaimsprocessing,detectfraudulent activities when processing claims in the insurance sector and reroute operations automatically to secure backup servers ontheoccurrenceofcyber-attacksorsystemfailures.
In healthcare administration, also, AI driven automation might prevent technical downtime from preventing access to patient appointment systems, medical billing, and electronic health records (EHR). It not only relieves regulatory bottlenecks;itincreasespatientexperienceanddecreasesserviceoutagesthatcanincurfinanciallosses.
BelowisthetableillustratingtheimpactofAI-poweredlearnsensitiveIToperationstomaintaincontinuityofhealthcare:
Fig.5AIRobotsinhospitals(Anolytics,2020)
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These statistics indicate that when it comes to AI driven failover mechanisms the downtime is greatly reduced, so that essential services in healthcare and insurance can continue. Despite that, while AI automation makes the system more robust,therearesomechallengesthatfollow,suchashowtoremaincompliantwithregulations(forexample,HIPAA)and dealissuesrelatedtoalgorithmicdecisionmakinginethicalterms.AIpoweredfailovercanonlybemaximallybenefitedif coupledwithhumanoversight,continuousmonitoringandstrictcybersecuritypolicies.
Nationwide AI Adoption
To realize a self-healing automation, a nationwide AI adoption strategy is required. Within the United States, hospitals, insurersandpublichealthagenciesusedisjointedITprocessesandservices.
Consequently, such standardizing of the AI driven automation can facilitate an establishment of an interconnected and self-healinghealthcareecosystemwiththepotentialoftherealtimeresponsivenesstotheirsystemfailures,cyberthreats and the administrative inefficiency. Vieira et al. (2023) demonstrate how digitally enabled healthcare can mitigate these systemic disparities by leveraging AI driven automation of healthcare provision, as it is an equalizing element for the access to healthcare in underserved areas [6]. Through the integration of AI in the national healthcare policies implemented by the government, data exchange between care providers becomes seamless and the process for crisis responseisalsoeasier.
AnationwideAIhealthcarestrategywouldincorporatethedeploymentofAIenabledself-healingmechanismsinhospitals, insuranceprovidersandpublichealthagenciesmanagingthemajorsystemswiththecapabilityofautonomousrecovery Security in cybersecurity should be one of the pillars of this strategy because cyber-attack on healthcare institutions is increasing.AccordingtoNifakosetal.(2021),policiesandrequirementsformandatoryAIthreatsdetectionandautomatic mitigationshouldbeenforced[5]
The following are the prioritized measures that should be implemented to constitute a national scale of a self-healing healthcareinfrastructure:
Standardization: Set AIs as protocols for hospitals, insurers and public health systems alike to transform into unifiedautomationprotocols
Fig.6AdaptationofAmericanstoAItoolsinhealthcare(EnterpriseAppsToday,2023)
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Cybersecurity: SomeAIpoweredthreatdetection,recoverofthesystemandfailovercapabilityclearlyneedtobe mandated.
Interoperability: Itensureseasyexchangeofdatawithhealthproviders,insurersandgovernmentagencies
Compliance: Identifythe processfor howto develop AI governanceframework to deal with privacy,ethics,and orientationtowardcompliancewithinregulatorydirections.
Collaboration: Work withtheAItechnologycompanies andhealthcareinstitutionstofosterthe governmentfor nationalwideadoption
Thetransitiontothefullself-healinghealthcaresysteminvolvessubstantialinvestment,butinthelongrun,thesebenefits of the transition (improved reliability, reduced operations cost and better patient outcomes) far exceed the initial cost. With AI-driven automation on a national scale, the U.S. healthcare system can get closer to reaching a time where downtime,inefficiencies,andcyberthreatsbecomeobsoleteandhealthcarequalitydeliveryisnolongeranissue
V.CYBERSECURITY AND AI
Cyber Threat Detection
Digitisationofhealthcaredeliveryhasgrowntheattacksurfaceofcybercriminalsandhospitals,insurersaswellaspublic health databases are the natural targets of cyber-attacks. Security for the modern day has moved to an AI driven cybersecurity and it has emerged as essential defense mechanism when offering real time threat detection, anomaly predictionandautomatedincidentresponse.
Securitymeasuresbasedon AIsuchasmachinelearningalgorithmanddeeplearningmodel canusemassivedatasetsto detectfromransomware,phishingandadvancedpersistentthreats(APTs)[7]inaccordancewithArefin&Simcox(2024). However,theseAImodelsremainsuperiorcomparedtotraditionalsecuritymeasureswheretraditionalrulesareconfined orsomepartyhastomonitoreveryaspectofthecyberspaceregularly,bothofthesecasesarenevergoodapproachestoa securitycapability.
CentralizedandfederatedtransferlearningisshowntoimproveAIbasedcyberthreatsdetectionbyChakrabortyetal.[8]. Intheproposedmodel(basedonaCentralizedMulti-SourceTransferLearning(CMTL)algorithm)attackslikeDistributed DenialofService(DDoS),malwareandinjectionattacksareeffectivelydetectedandclassified.
It enhances data transmission efficiency with security by interweaving cloud computing and the Edge of Things (EoT) shape. The intact attack patterns that artificial intelligence processes are faster and more accurate than human analysts andconsequentlyreduceresponsetimetocybercrimesbypotentialpatients’sensitivedata.
We have adversarial attacks, privacy issues and high cost of implementation, all things that need to be addressed by AI basedsecuritysystems.Currently,researchintofederatedlearningandquantumresistantencryptionisongoing,however, AI’s role in healthcare cybersecurity will increase and will be used to help boost the national health infrastructure resilienceagainstdigitalthreats.
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Fig.7Digitalhealthcarecyberthreats(ResearchGate,2019)
National Regulations
The regulations state that EHRs must be protected by stringent data privacy measures in order to keep the electronic health records of patients in protection against unauthorized access. Security audits are also automated by AI to ensure patientdataishiddenandencryptedwhilekeepingitsafe;vulnerabilitiesaredetectedinrealtime,andsecurityisensured [7].
The ability to enforce real time access control measures and implement the advanced encryption techniques is in one of thegoodsidesofAI.Thesemachinelearningalgorithmswill thenanalyzetheuser behaviorpatternsandreportunusual anomalousbehavior,whichwillpreventunauthorizedaccessbeforeabreach.NotonlydoesAI-poweredsecuritysystems aid in the automated response of incidents to contain the compromised data as soon as possible and alert of potential breaches,butalsohelpmeetthecomplianceregulations[8].
Nevertheless,implementingofAIwithindataprivacymanagementdoesnotcomewithoutethicalandtechnicalchallenges. Hurdlesincludecomponentthatautomaticallyrespondsbasedonthecontextoftheexistingconversationandthatare'out ofscope'forhumanagents(ortasksappropriateforAI,butnotavailabletohumanagents)wheneverpossible.Also,there is extensive testing, and constant monitoring of compliance between AI driven security systems and legacy healthcare databases.
However,withconstantimprovementinAI withinthe boundaryoffederatedlearning,and decentralized encryption,the future of secured health information is really reflective. Incorporating AI into national regulatory frameworks can allow healthcareinstitutionstoestablishdefencesthatarekeepingwithpatients’trustandtransparency
Cyber Resilience
In response to the growing cyber-attacks on the nation’s health infrastructure, governments should put together a comprehensive cyber resilience plan driven by AI based defences. An AI’s predictive analytics skills give healthcare organizations the ability to predict impending nightmare before they happen, reducing system downtime and assuring continuoushealthcareservice.
Chakrabortyetal.(2023)statethatcyberresilienceisposedusingAIvia multisourcetransfer learningandfederatedAI modelsandrealtimecyberthreatdetectionandmitigation[8].Themainadvantageoftheseframeworksisintheabilityto share the threat intelligence among the areas within the healthcare institutions, without compromising data privacy, a major factor in preventing the huge attacks which cost a lot during the tenders from these institutions, not only in healthcarebutineducationandothersectorstoo.
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One of the main elements of a national cyber resilience strategy is AI based failover mechanisms that keep critical healthcare systems operational during cyber incident. If the primary network of a hospital is compromised, AI driven automatedfailureoversystemstransfertaskstosecurebackupenvironmentspreservingserviceinterruption.
Furthermore, security orchestration using AI aids incident response at national health networks by automating the processandminimizingitsinterventionandresponsetime.ButanAIenablednationalcyberresilienceframeworkcannot beexecutedwithoutproperinvestmentintraining,modernizinginfrastructureandcollaborationsbetweenthepublicand private.ItmeansthatcontinuousstealthdevelopmentofnewthreatintelligenceisrequiredforAImodelstostayeffective againstnewcyberthreats.
Furthermore, ethics and AI policy must be put in place that will prevent the use of biased decisions in cybersecurity. A national cyberresiliencestrategywitha well-structured AIincorporatedwill allowhealthcareinstitutionstobettertheir ability to detect, prevent and recover from cyber-attacks on a national scale and ensure security and continuity of healthcareservices.
VIII.CONCLUSION
Someofthenationalhealthinfrastructureisindangerfromgrowingcyberthreats,andAIdrivencybersecuritysolutions are necessary to protect. By using AI powered threats detection, encryption, and automatic failover mechanisms healthcareinstitutions canincreasethesecurity, minimizedowntimeand be compliantwiththe requirementsset by the regulationHIPAAandHITECH.
But algorithmic bias, integration cost along with adversarial attacks are the challenges that have to be catered by AI to facilitateitsfullest. Afeatured“national cyber resilienceframework”thatinvolves theuseof AIto guardhealthcaredata andserviceswithrulesofsecuritymeasure,sharingoftheintelligenceandregulatorygatherings.The long-termsecurity andstabilityofhealthcare,whilethreatscontinuetoevolve,willrelyupontherolethatAIwillplay
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