HEALTHCARE INTEGRATION BASED ON CLOUD COMPUTING

U.P.B. Sci. Bull., Seies C, Vol. 77, Iss. 2, 2015 ISSN 2286-3540 HEALTHCARE INTEGRATION BASED ON CLOUD COMPUTING Roxana MARCU 1, Dan POPESCU 2, Iulian DANILĂ 3 A high numbe of infomation systems ae available on the maket fo each hospital depatment with high pefomance esults but in healthcae we need access to data fom eveywhee at any time. This pape pesents an e-health solution based on healthcae infomation systems integation and cloud computing concept with focus on medical imaging depatment. Hybid cloud achitectue (public pivate) and a equest management application has been poposed and analysed in tems of waiting time defined as Quality of Sevice citeia (QoS). The sevice has been modelled using queuing theoy as two seially connected queues M/M/s and M/M/1. Keywods: cloud computing, healthcae, infomation system, integation, quality of sevice 1. Intoduction Healthcae infomation systems (HISs) ae consideed as one of the most complex and challenging field. Cuently ae available a lot of infomation systems specialized on each health aea: hospital management, phamacies, insuance companies, esouce management and the challenge is now to integate these systems in ode to access data when is needed having a minimum waiting time. Due to the high amount of data and the complexity of healthcae domain we will conside in this pape the adiology depatment as cental point of analysis of clinical and administative pocesses. Radiology in tems of infomation systems (RIS, PACS) has been descibed in [1] fom the clinical and opeational wokflow pespective. In [2] has been pesented a sevice oiented achitectue (SOA) based integation of adiology infomation system that intoduces an ERP system as cental point of management. The aim of this pape is to extend the pesented model in ode to obtain complete integation with all technologies including mobile communications fom the pespective of adiology data access and to analyse the esults in tems of waiting time pefomance. The pupose of this 1 PhD student, Faculty of Automatic Contol and Computes, Univesity POLITEHNICA Buchaest, Romania, e-mail: oxana.macu@cti.pub.o 2 Pofesso, Faculty of Automatic Contol and Computes, Univesity POLITEHNICA Buchaest, Romania, e-mail: dan_popescu_2002@yahoo.com 3 PhD student, Faculty of Automatic Contol and Computes, Univesity POLITEHNICA Buchaest, Romania, e-mail: iulian.danila@gmail.com

32 Roxana Macu, Dan Popescu, Iulian Danilă appoach is to convet an application into a numbe of components called sevices that can be shaed and eused. SOA Healthcae Infomation System integates a lage numbe of heteogeneous systems in a scalable solution (hadwae and softwae) based on HL7 standads and web sevices deliveing easonable esponse time unde the diffeent outpatient, inpatient and emegency system usage conditions [3]. Available infomation at the ight moment is the cental equiement fo healthcae theefoe a bette management of the esouces and contol of data is demanded. Repots show 35% of healthcae oganizations ae eithe implementing o opeating cloud computing in 2012 having as main concens in implementing cloud healthcae: secuity of data/applications (51%), pefomance of cloud sevices (36%) and integating cloud applications/ infastuctue with legacy systems (31%). Cuently this cloud technologies ae mostly limited to confeencing and collaboation (29%), compute powe (26%), office and poductivity suites (22%) [4]. Cloud computing technology is still new, but actually eseach esults descibe low pice and minimum esouces achitectues and low waiting time values fo data access[5]. 2. Backgound and elated wok A medical images stoage and etieval solution based on a DICOM Seve ove cloud computing using Windows Azue Cloud and SQL Azue is descibed in [6]. Authos concluded that using Platfom as a Sevice (PaaS) sevice model achitectue educes the management cost and incease secuity by the use of DICOM seve. A high level solution of shaing medical imaging ove cloud is pesented in [7]. Achitectue integates PACS system using a DICOM bidge oute and cloud povides to connect medical imaging modalities and to povide access to images to the end-use ove the Intenet. The achitectue demonstates secue, maintainable and fast setup solutions fo communication between hospitals as distinct/geogaphical dispesed entities. Secuity and pivacy as a equiement of a healthcae cloud platfom is one of the most analysed and discussed topics among eseaches. In [8] the authos pesent a hybid cloud infastuctue fo Electonic Health Recod (EHR) shaing and integation of medical data with focus on owneship of medical ecod. A mechanism of medical data potection is built up suppoting both nomal and emegency scenaio. The hybid cloud achitectue is veified by the authos fom a secuity pespective. Inceased stoage capabilities suppoted by a cloud based platfom confim one of the main concens that have been aised by most of the healthcae

Healthcae integation based on cloud computing 33 oganizations. As discussed in [9] a cloud platfom that integates a cloud stoage laye coves local stoage and emote stoage using Intenet esulting in theoetical infinite stoage capacity. As descibed in [1] the pupose of this eseach is to conside the healthcae oganization as a business that fully integates clinical pocesses. Cloud sevice and deployment model is detemined based on the healthcae oganization stategy expected to be accomplish. In ode to validate this model we need to conside seveal sevices that ae included in the model. As a scope of this pape we build the model of accessing adiology data in an integated healthcae cloud platfom. In ode to achieve the highest value of quality in the sevice that is to be designed it is equied a stong analysis of the sevice system fom dedicated esouces and demands placed on the system pespective. Ideally in a healthcae infomation system a patient should neve wait fo a medical sevice but in this case we face a limited numbe of esouces (medical staff, modalities, waiting times etc.) and an uncontollable numbe of patients. Based on analyses of 141 papes pesented in [10] queuing theoy use in analysis of healthcae pocesses is feasible and ecommended fo QoS analysis fo both physical queues that ae ceated fo clinical sevice access o fo healthcae infomation system modelling pocesses. Queuing theoy is used to pedict the QoS measued in a vaiety of ways, as a function of both the demands on the system and the sevice capacity that has been allocated to the system [11]. 3. System achitectue based on cloud In this section, we popose as a solution to fully integate healthcae clinical and administative pocesses focusing on adiology depatment a cloud computing platfom that is to be used by seveal units (hospitals) and entities (depatments). As a deployment model we conside a hybid cloud fo the healthcae envionment, composed by seveal pivate clouds that ae epesenting each specific depatment (Radiology Pivate Cloud, Emegency Pivate Cloud) and a public cloud used to integate all depatments/hospitals and the thid paties. The pupose of the public cloud that is used fo unit integation is to avoid the mesh netwoks within the pivate clouds connections. As descibed in [5] the communications between the two public clouds will be held though HL7 CDA messages afte the link between the applications will be establish using an application defined in the public cloud. DICOM and HL7 ae successfully used to integate a lage numbes of applications fom vaious medical domains [12]. The adiology data will be stoed in the adiology pivate cloud and all depatments/ hospital can access medical patient data when is needed, the connection being established by the pivate cloud application. Fom the pespective of a netwok of

34 Roxana Macu, Dan Popescu, Iulian Danilă entities, defining the pivate clouds, with the scope of using infomation stoed in divese locations using a cental management application defined in the public cloud we popose the achitectue fom Fig.1. Fig.1. Healthcae pivate cloud achitectue In a healthcae envionment all patients ae equal but they ae split in diffeent pioity classes and this should be the base of modelling a healthcae sevice. Requests that ae linked to citical patients need to be handled fist even though thee ae othe equests fo the same type of data but the oveall waiting time fo each equest should be kept to minimum. Queues and queuing theoy in healthcae fo patient/esouce scheduling and data access epesents a challenge fo healthcae IT eseach domain due to the need of undestanding clinical and infomation pocesses fom sevice modelling and management pespective. In this pape we focus on data access due to adiology images that epesent a special type of data. Cloud achitectue poposed in Fig.1 consides a PACS database integated in a pivate cloud and a management application integated in a public cloud in ode to obtain full access to data fo any point. Requests fo medical images ae coming in the pesented hybid cloud achitectue fom at least 4 points: mobile infastuctue (mobile phones, tablets, laptops), hospital management system (ERP), adiology depatment (inten specific system o wokstations) and fom emegency depatment (wokstations). Anothe point of equest enty in the system can be consideed the medical staff that equests data fom home based on authentication data povided by the cloud povide.

Healthcae integation based on cloud computing 35 In ode to collect all equests we popose a equest management application to build up the enty queue in the public cloud system. Each equest that is enteing in the cloud is linked to a patient with a cetain pioity class assigned fom 1 to. Based on the pioity class the application ceates j queues afte fist come fist seved (FCFS j ) pinciple and offe as input to the cloud infastuctue a FCFS queue composed by seial concatenation of all j queues in pioity ode: FCFS 1 ; FCFS 2 ; FCFS j. The algoithm is as follows: while TRUE do check equest_type = equest_data; case pioity when 1: add to FCFS 1 ; when 2 add to FCFS 2 ; when j: add to FCFS j ; end case. end while. Using Kendall notation fo queues we conside a model of a cloud system composed by two queues M/M/s enty queue and M/M/1 database pecede queue. Based on Buke s theoem and Jackson s theoem the system can be modeled as an open Jackson netwok in which the queues can be analyzed independently [13-15]. Consideing pobability that a equest fom M/M/s will ente in M/M/1 befoe database p o access diectly the database (1-p) we popose as mathematical model fo the cloud system two seially connected queues with aival ate λ fo M/M/s and λp fo M/M/1 (Fig.2.). FCFS λ(1-p) DB λ M/M/s λp M/M/1 Fig.2. Cloud sevice mathematical model: two seially connected M/M/s and M/M/1 queues M/M/s queue is to be analyzed as a pioity sevice with Poisson aivals λ, multiple seves s and identically distibuted sevice time μ fo all pioity classes [9]: μ = μ 1 = = μ (1)

36 Roxana Macu, Dan Popescu, Iulian Danilă Utilization atio fo a pioity class j is defined as cumulative utilization atio fo classes 1 though as: σ = ρ fo σ 0 k = 1 k 0 = λ ρ = and the μ In this case we conside the expected waiting time in the fist queue [9]: W (3) 0 Wq = fo σ 0 = 0 1 σ )(1 σ ) ( 1 whee W 0 is the expected time until one of the s seves is available consideing pobability that the system is busy: s (4) λ s W μ μ 0 = P0 s! sμ λ and s s 1 (5) 1 λ λ 0 = s μ μ sμ P + j= 0 j! s! sμ λ M/M/1 is defined as a non-peemptive queue is to be analysed as a pioity sevice with a single seve, Poisson aivals in each class and identically distibuted sevice time. The expected waiting time in the second queue [9]: W q ρ k k = 1 μ ( 1 σ 1 )(1 σ ), fo σ 0 = 0 The total waiting time in the system is to be calculated as: W total ( q1 q2 4. Pefomance analysis λ, s) = W ( λ, s) + W ( pλ,1) (7) In ode to analyze the poposed healthcae achitectue we focus on having the equied data at the ight moment of time defining this as QoS citeia. In tems of validating the cloud infastuctue we conside the vaiance of the waiting time of equests in the system using numeical calculations and (2) (6)

Healthcae integation based on cloud computing 37 simulations of the mathematical model inputs. Theefoe we conside thee pioity classes 1 (high), 2 (medium) and 3 (low) with a specific aival ate of equests in the system λ 1 = 5 equests/s, λ 2 = 10 equests/s and λ 3 = 15 equests/s and queue specific sevice time μ Q1 =20 equests/s and μ Q2 =100 equests/s. Futhe we will conside W 1 = waiting time fo class1 (high) patients, W 2 = waiting time fo class2 (medium) patients and W 3 = waiting time fo class3 (low) patients. Fo the fist queue that is ceated by the public cloud application (Request management application descibed in section 3) afte FCFS pinciple defined as M/M/s the waiting time in the queue is dependent on the numbe of seves that ae allocated fo the fist queue. Assuming the vaiable defined uppe (, λ, μ Q1 ), waiting time (W ) fo each pioity class is deceasing consideable if the numbe of seves, s, is inceasing (Table 1). Table 1 Waiting time fo M/M/s based on the numbe of seves No. of Class 1 Class 2 seves Class 3 (λ 3 =15) (λ 1 = 5) (λ 2 =10) 1 1666.66 5000 15000 2 79.36 333.33 818.18 3 4.02 30.30 98.03 4 0.18 2.57 11.78 5 0.00 0.19 1.29 Gaph plotting (Fig.3) shows that inceasing the numbe of seves significantly deceases the waiting time in the fist queue fo all pioity classes. Fig.3. Impact of inceasing the numbe of seves on waiting time fo the fist queue M/M/s A equest that is pocessed by the fist queue has the pobability p to be placed in the second queue. Consideing the stuctue of the fist queue (equests

38 Roxana Macu, Dan Popescu, Iulian Danilă ae pocessed depended on pioity class) we ae inteested on how is influenced the waiting time of the lowest pioity classes by the aival ate of the highest pioity class. Fo these analyses the nominal aival ate fo pioity class 1 was multiplied by 1.5. Assuming the vaiable defined in the beginning of the section (, λ, μ Q1 ) and multiplying λ 1 by 1.5, waiting time (W ) fo each pioity class is inceasing (Table 2). Table 2 Waiting times fo all pioity classes based on utilization atio 3 ρ 10 Class 3 (λ 3 = 15) (base) Class 1 (λ 1 = 5) Class 2 (λ 2 = 10) Class 3 (λ 3 = 15) 0.05 0.52 1.85 5.04 5.04 0.07 0.81 2.29 5.83 5.04 0.11 1.26 3.04 7.22 5.04 0.16 2.03 4.42 9.85 5.04 0.25 3.38 7.30 15.65 5.04 In case of significantly incease of the aival ate fo class 1, W 2 and W 3 ae exponentially inceased (Fig.4) but it can be seen that the influence on W 2 is less than W 3 theefoe the quality of the system is not damatically affected since we ae focusing on highest pioity classes. Fig.4. Impact of inceasing aival ate fo class 1 on class 3 and class 2 waiting times Assuming the vaiable defined in the beginning of the section (, λ, μ Q1 ) and the aival ate fo class 2 is inceased the total waiting time (W 1 ) fo pioity

Healthcae integation based on cloud computing 39 class 1 is not affected and the waiting time fo pioity class 3 (W 3 ) is inceasing exponentially (Table 3). Table 3 Waiting times fo pioity class 2 and 3 having a vaiable aival ate fo pioity class 1 3 ρ 10 Class 3 base (λ 3 = 15) Class 1 (λ 1 = 5) Class 2 (λ 2 = 10) Class 3 (λ 3 = 15) 0.1 0.52 1.85 5.04 126.05 0.15 0.52 2.63 6.73 126.05 0.22 0.52 3.99 10.19 126.05 0.33 0.52 6.65 18.97 126.05 0.50 0.52 13.19 54.18 126.05 Fig.5 shows that the impact of inceasing the aival ate of class 2 is not affecting W 1 but is damatically affecting W 3 in case this is inceased uncontollable. Fig.5. Impact of inceasing aival ate fo class 2 on class 3 waiting times Assuming the vaiable defined in the beginning of the section (, λ, μ Q1 ) the total waiting time (W ) fo each pioity class having the pobability p [0,1] fo eaching the second queue, is not significant affected by the incease of pioity class 1 aival ate (Table 4). Incease of waiting time W 3 could affect system pefomance theefoe the total waiting time needs to be analyzed in ode to decide if a the equests need to be handled depending also on the execution time.

40 Roxana Macu, Dan Popescu, Iulian Danilă Table 4 Total waiting time fo all pioity classes No. of seves Class 1 (λ 1 = 5) Class 2 (λ 2 = 10) Class 3 (λ 3 = 15) 1 16.90 51.21 153.55 2 1.02 4.54 11.73 3 0.27 1.51 4.53 4 0.23 1.23 3.66 5 0.23 1.21 3.56 6 0.23 1.21 3.55 Having the pobability p fo each equest to hit the second queue the pefomance of the system in tems of total waiting time is impotant to be analyzed (Fig.6). Fig.6. Impact of inceasing numbe of seves in Q 1 on total waiting time of the equests in the system W t (s) = W M/M/s (s) + W M/M/1 We would like to emphasize that even though inceasing the aival ates fo the highest pioity classes exponentially inceases the waiting time of the lowest pioity classes the total waiting time in the system is not affected since thee is a pobability p that the equests will be placed in the second queue. 5. Conclusions In this pape we poposed a cloud computing based infastuctue fo healthcae envionments with focus on accessing adiology data. Using the cloud computing technology healthcae is impoving consideably in tems of data

Healthcae integation based on cloud computing 41 access. This has been analysed using queuing theoy poviding a mathematical model composed by two systems (M/M/s and M/M/1) based on which a numeical simulation has been done in ode to validate the model in tems of quality of sevice. The pefomance analysis in tems of waiting time has shown that the poposed achitectue significantly impoves the quality of the healthcae sevices. Having an integated healthcae infastuctue will save physical space and impove the efficiency of the medical staff. Costs of the poposed infastuctue ae not highe than anothe infastuctue since the medical units will only ent the infastuctue to stoe medical data as they need. Also, the poposed achitectue pefomance is incompaable in tems of quality with a taditional non-integated infomational envionment. Even though the medical unit uns an infomation system, if this is not integated in an oveall healthcae infastuctue the waiting times fo data access ae vey high. The hybid cloud (public and pivate) ensues the secuity of data and communications between depatments due too pivate cloud technology and messages that ae used in the public cloud. Futue wok will include design and analysis of a esouce scheduling sevice in cloud. R E F E R E N C E S [1] R. Macu, D. Popescu, PACS as legacy system fo Healthcae ERP, in Poc. Intenational Wokshop "Fosteing Innovation in Healthcae Sevices", Mach 14-15, 2012 Başov, Romania, Ed. Caol Davila, Bucuesti, pp.7-12 [2] R. Macu, D. Popescu, Integate adiology in an ERP system SAP case study, in Poc. 20th Telecommunications foum TELFOR 2012, Sebia, Belgade, Novembe 20-22, 2012, pp. 1649-1652 [3] T.-H. Yang, Y. Sun, F. Lai, A Scalable Healthcae Infomation System Based on a Seviceoiented Achitectue, in Jounal of Medical Systems, vol. 35, no.3, June 2011, pp.391-407 [4] ***CDW S 2013, State of The Cloud Repot, http://www.cdwnewsoom.com, 2013 [5] O.-S. Lupse, M. M. Vida, L. Stoicu-Tivada, Cloud Computing and Inteopeability in Healthcae Infomation System, in Poc. The Fist Intenational Confeence on Intelligent Systems and Applications, INTELLI 2012, Fance, Apil 29, 2012, pp. 81-85 [6] A. Umamakeswai, N. Vijayalakshmi, T. Renugadevi, Stoage and etieval of medical Images using Cloud Computing, in Jounal of Atificial Intelligence, vol. 5, no.4, pp. 207-213 [7] L. B. Silva, Shaing medical imaging ove the cloud sevices, in Intenational Jounal of Compute Assisted Radiology and Sugey, vol. 8, no. 3, May 2013, pp. 323-333 [8] Y.-Y. Chen, J.-C. Lu, J.-K. Jan, A Secue HER System Based on Hybid Clouds, in Jounal of Medical Systems, vol. 36, no.5, Octobe 2012, pp.3375-3384 [9] B. Castoiu, Cloud SaaS Infastuctue, in U.P.B. Sci. Bull., Seies C, vol. 73, no. 2, 2011, pp. 89-102 [10] C. Lakshmi, S. A. Iye, Application of queueing theoy in health cae: A liteatue eview, in Opeations Reseach fo Health Cae, vol. 2, no. 1 2, Mach June 2013, pp. 25-39 [11] M. Daskin, Sevice Science, John Wiley&Sons, Inc., 2010 [12] F. Moldoveanu, S.- A. Cistescu, An agent-oiented and sevice-oiented achitectue in medicine, in U.P.B. Sci. Bull., Seies C, vol. 75, no. 1, 2013, pp. 3-16

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