The remaining 4 dimensions address concrete methods to implement ie, how to use CCSs for health care and represent how-to knowledge. The service form and deployment model dimensions are consistent with the service and deployment models of CC, respectively [ 1 ]. They clarify the most basic operational principles of CCSs for health care organizations, which relate to principle knowledge.
The dimension service form contains 3 characteristics: The deployment model dimension indicates whether CCSs are deployed using a public, community, or private cloud. Because a hybrid cloud is, by definition, composed of 2 or more of the aforementioned deployment models, we do not define hybrid as an independent characteristic of the deployment model. Instead, our taxonomy represents a CCS with a hybrid deployment model by using 2 or more of the characteristics defined above. The targeted cloud advantage dimension describes the concrete cloud properties from which a health care organization can benefit.
This dimension highlights the effects of using CCSs and is also considered a type of principle knowledge. Ubiquity indicates that users can access the CCS from any location. Cost efficiency emphasizes the cost advantage brought by CCSs. Shareability refers to the ability of CCSs to enable the efficient exchange and sharing of data between different users, whereas interoperability denotes the ability of a CCS to smoothly integrate and operate with disparate systems and machines. Timeliness assesses how quickly CC is able to deliver services and related data to health care organizations real time vs not real time and thus relates to principle knowledge.
We define a CCS as real time if it is ready to process or transfer data at any time, such that the computational results and requested data are immediately available. The supported task dimension specifies the areas in which health care organizations use CCSs. This dimension highlights the manner in which CC supports health care and is deemed a type of how-to knowledge. Supported task includes 4 characteristics: Clinical refers to medical activities in health care organizations that are directly associated with patient diagnosis and treatment. Administrative denotes management or support tasks in health care organizations, such as patient registration, admission, and discharge.
Strategic represents tasks performed by management teams in health care organizations, such as strategic planning decisions, human resources management, and performance evaluations. Research represents all activities that are related to medical research. The user dimension relates to how-to knowledge and aggregates the possible user types of CCSs. This dimension differentiates between a patient who receives medical treatment at a health care organization, the medical staff health care professionals as well as administrators , and the family members of the patient.
Service delivery device refers to how-to knowledge because this dimension represents the types of client devices used to access the CCS. A CCS with an independent characteristic allows users to access services using any computer or mobile device. Adapted specifies that a CCS is compatible with different types of devices but operates more efficiently on a certain group of devices eg, mobile phones or tablets via technical adaptation to those devices eg, developing specialized applications for tablets or compressing data to accelerate data transfer for mobile phones.
Specialized represents those CCSs that can be accessed by only 1 or several designated groups of devices, such as authorized tablet computers, workstations in health care organizations, or specific medical devices. Finally, the patient data involvement dimension, which also relates to how-to knowledge, explains how patient-related data are used to deliver services. Internal indicates that a CCS uses patient data that are internally available to the health care organization for IT service delivery. External refers to a situation in which a CCS uses patient data collected from external sources, such as outside medical professionals or the patients themselves.
No involvement indicates that a CCS does not have access to patient data and thus does not use such data in IT service delivery. After completing all taxonomy development iterations, we classified all 50 CCSs that we identified during stage 1. Multimedia Appendix 4 presents the final classification results. In this section, we provide an example of how our taxonomy can be used to classify CCSs for health care organizations. This example examines a hospital decision support system for bed-patient assignments see C22, Multimedia Appendix 1.
Because this CCS addresses patient administration and assists hospital leadership in measuring and benchmarking hospital operations, it supports both administrative and strategic tasks. The CCS is delivered in the form of a software application and is hosted in a public cloud environment. The CCS does not operate in real time not real time. It is used by medical staff and is not device-specific independent. Finally, the patient data processed by the CCS are internal.
Our taxonomy fulfills all predefined ending conditions after 14 development iterations. In particular, the fulfillment of 5 subjective ending conditions indicates high sufficiency of the taxonomy. We summarized these subjective ending conditions and provide a justification for the fulfillment of each condition in Multimedia Appendix 5. Notably, the subjective ending conditions describe the essential features of the derived taxonomy. By observing the taxonomy, which includes the classification results of CCSs for health care organizations, we obtained specific implications of CCSs for health care.
As demonstrated in Table 3 , these implications offer 2 types of challenges to our previous understanding of CC in health care: More importantly, as shown in Figure 3 , the summarized specificities suggest research opportunities with exemplary research questions, facilitating future research about this relevant phenomenon in health IT. Research opportunities for cloud computing in health care. Previous studies show that in a common context, PaaS and IaaS are as relevant as SaaS in the cloud market [ 40 ]; however, this result is challenged by CC in the context of health care type 1.
This is possibly because health care organizations expect to exploit the advantages of SaaS to the greatest extent and in a timely manner. For hospitals, cloud almost only means software as a service because many hospitals want to use them as off-the-shelf products. SaaS products that support medical areas are especially welcome because hospitals always expect to get immediate improvement from the cloud in their core business.
The lack of PaaS and IaaS in health care organizations indicates an insufficient state of CC in health care, which was confirmed by several interviewees ii08, i10, ii We want to develop our own SaaS, but there is just no specific PaaS for health care organizations. General PaaS are not enough. The need for PaaS in health care is not only because PaaS in general provides ready-to-use technical support for programmers but also because it has the potential to provide solutions to effectively fulfill industry-specific IT requirements. This is, for example, explained by an interviewee who was involved in developing a CCS for a hospital.
There were so many complex things we had to consider for hospitals. We kept wasting time on unnecessary meetings to find technical solutions. I dreamt of having a PaaS that could support us. Of course, there is more. Compliance is also a main topic. Hospitals ask over and over again whether our software is compliant with this or that. Further industry-specific IT requirements that can potentially be supported by a health care PaaS—constant demand on cutting-edge technologies, high health IT agility to meet changing medical requirements , the need for different domain-specific medical data structures, and support for industrial joint implementation activities eg, between government and hospital —were also mentioned by the interviewees.
For IaaS, previous research studies [ 42 ] and our interviewees both emphasized the strategic meaning i08 of IT infrastructure ie, critical information infrastructure for the health care industry and consequently the extremely high importance of IaaS i20 for health care organizations. Future research could focus on exploring the lack of PaaS and IaaS for health care. As revealed by our interview data, there is a particular need for research studies that systematically investigate specific requirements for health care that cannot be covered by PaaS and IaaS in a common context and thus a need to design and develop industry-specific PaaS and IaaS.
Previous studies have raised concerns about security and privacy as the Achilles heel of CC [ 43 ], which are main barriers for the adoption of health IT artifacts [ 44 , 45 ]. These concerns might be more severe for public clouds, whose infrastructures are accessible by many different users [ 46 ]. However, the dimension deployment model indicates that more than half of the investigated CCSs are based on public clouds, especially given that almost all of these CCSs involved patient data dimension: To this end, providing a high level of data security was regarded as a targeted cloud advantage in 10 of the identified CCSs, of which 6 were deployed on public clouds.
This challenges our understanding of CC in a general context type 1. Additionally, interoperability may also impede the adoption of CC in a general context [ 47 ]. For health care, however, our taxonomy demonstrates that increased interoperability is a benefit of CC. Security and interoperability are traditionally the most intractable challenges in health IT, and industry standards concerning IT security and interoperability in health care are evolving [ 9 ].
Cloud providers can devote resources to the implementation of industry standards or best practices that many hospitals cannot afford [ 4 ]. CC can thereby address security and interoperability issues in a more effective manner, which was confirmed by the interviewed experts ii04, ii07, i10, ii14, ii18, i Data security, interoperability Speaking of data security, using paper is also not safe, if you insist on saying a cloud is not safe. Moreover, researchers could focus on exploring the factors such as security and interoperability that have industry-specific impacts on cloud adoption in health care, in contrast to a general context.
In a general context, the use of CC is heavily motivated by short-term economic interests [ 48 ]. Research relying on this general understanding of CC claimed the low costs were the principle advantage of CC in health care [ 4 ]. Our research challenges the understanding of CC in a general context type 1 by revealing that when using CCSs, many health care organizations frequently must transfer large volumes of data to and from the cloud eg, medical images [ 49 ]. This can cause data transfer bottlenecks due to the obsolete network infrastructures currently in place at many health care organizations—a typical industry-specific IT issue i02, i08, i It is therefore not surprising that the interviewees were not convinced of the potential financial advantages of using CC in health care ii05, i07, i10, i However, our interviewees reported that in the long term, CC will reduce their general IT maintenance work i02, i24 and help them avoid possible IT reinvestments i Future research could therefore focus on re examining and explaining the economic results of using CCSs in health care organizations.
Moreover, researchers could focus on CC business processes or investment strategies in health care settings that enhance the short-term benefits for health care organizations. We recognize that most of the identified CCSs 36 of 50 support clinical tasks in health care organizations dimension: This observation challenges previous studies about traditional health IT type 2 , which have concluded that health care organizations primarily focus on the use of IT applications for administrative, strategic, or financial functions rather than clinical activities [ 50 ].
In clinical practice, even ordinary data analysis occasionally overwhelms traditional health IT with large volumes of data and complex analytical algorithms. A conservative but still well-recognized view of health IT is that medical staff are the main users of health IT applications [ 53 , 54 ], and many existing health IT applications are heavily physician-centered.
However, the evidence from our taxonomy challenges this view type 2 and implies a high potential of CC to realize patient-centeredness—a promising future direction for health IT [ 55 ]. Regarding the user dimension, we noticed that 8 identified CCSs included patients as their users, which is a premise of patient-centered health IT services. Additionally, several interviewees i02, ii08, i11 noted that CC innovatively involves patient family members to realize patient-centeredness, as did 2 identified CCSs C26, C An interviewee, whose hospital deploys a medical appointment CCS for patients, had this to say:.
Although we have to have more users and processes now, I believe CC can offer the necessary computer resources. Their children could help them Despite the potential of CC to support patient-centeredness, only a limited number of patient-centered CCSs were identified in this study. Future research could therefore focus on examining how CC supports patient-centeredness and on designing further CCSs that support it.
For health IT that supports clinical functions, physicians who are forced to adapt health care delivery processes to technologies are often unwilling to use it. Existing health IT research concluded that these devices are inherently subjected to limited computing capacity and are criticized as unsuitable for complex tasks, such as clinical work [ 58 ]. Future research could explore how CC overcomes the limitations of mobile or small devices in health care, which is a relevant but underinvestigated topic in health IT [ 58 ]. One major expected purpose of involving patient data in health IT is to employ the data as a means to link users or systems in different clinical areas and thereby facilitate their collaboration [ 59 ].
However, research generally highlights a lack of sufficient health IT applications that support collaboration [ 60 ]. Our taxonomy challenges this type 2 and reveals that CC has the potential to address this issue, as 21 of the 46 CCSs that involve patient data and support clinical areas possessed shareability or interoperability as an advantage dimension: However, these CCSs are not without limitations. Including patient data from different sources is the basis of collaboration in clinical activities [ 51 ]. The interviewees remarked that CCSs in health care organizations that have a collaboration purpose mostly focus on internal data exchanges which was also revealed by our taxonomy , although they believed that CC has the potential to also facilitate collaboration with external parties.
The timeliness dimension is another indicator for collaboration because it addresses how intensively data exchanges occur. However, for the 21 CCSs that supported clinical areas and possessed the shareability or interoperability characteristics, we found that only 8 enabled real-time data exchanges.
Real time is crucial for effective data exchanges and the resulting collaboration in clinical processes ii06, i08, i11, i Collaboration [based on data exchanges] should not only take place but also in a real-time manner. A delay of important data for even a few minutes could be fatal for clinical activities. Future research should therefore strive to improve CCSs for collaboration in clinical activities due to the currently still insufficient state of CCSs as well as general health IT [ 51 , 60 ] for supporting collaboration.
Moreover, researchers could also investigate how CC supports collaboration in areas other than clinical settings in health care. For health IT research, our contributions are threefold. In particular, our taxonomy targets principle and how-to knowledge to systematically conceptualize the concept of CC for health care settings. Thus, the derived dimensions and characteristics of the taxonomy highlight the aspects of CC that are most relevant to health care.
We thereby contribute to closing the gap between an insufficient conceptual understanding of CC and the actual phenomenon in practice for health care. Second, our taxonomy suggests 7 specificities that subvert and thus challenge our previous understanding of CC in a general context or of traditional health IT. These specificities advance the understanding of CC in health care. Third, we derived concrete research opportunities for health IT see Multimedia Appendix 6 for a summary.
As presented at the beginning, health IT researchers have been interested in the development of single CC applications or data security topics. For both topics, we provide suggestions that guide future research eg, to focus on developing CCSs that enable collaboration in health care or even create new opportunities and directions eg, to focus on inherently increased, instead of decreased, IT security in health care by using CC.
In addition, we noticed that research topics on CC are by nature broad and diverse, which should not be limited to the development of CC applications and IT security, as in current health care settings, but can include more areas such as its business perspective [ 61 , 62 ], its adoption by organizations [ 63 , 64 ], user awareness and acceptance [ 65 , 66 ], and its certification [ 67 - 69 ].
The proposed research directions in this study are a step toward facilitating research on CC in health care settings. For health IT practice, the derived taxonomy can be applied to investigate CCSs for health care organizations on 2 different levels. On a macro level, the classification of available CCSs in a certain health IT market using the taxonomy can serve as an indicator of the current state of these CCSs. Cloud providers or policy makers could, for example, suggest new CCSs that address possible market gaps eg, PaaS for hospitals. On a micro level, health care organizations could apply the taxonomy to understand an individual CCS.
A main limitation of this research is that our data focused on health care organizations that are hospitals and clinics, as implied by the literature review search string and by the interview questions. This is because hospitals and clinics are not only the backbone of the health care industry [ 70 ] but also representative IT consumers in health care [ 71 ]. We therefore expected that a taxonomy derived from hospitals and clinics would provide more generally valid insights into CC for health care settings. Research that focuses on CC in more specific health care settings eg, nursing homes could employ our taxonomy as a starting point.
We suggest that such research use the proposed dimensions and characteristics as a checklist to investigate CC. Our work relied on data from 24 expert interviews, which does not necessarily guarantee that all CCSs for health care organizations from practice were discovered. However, the selection of our interviewees ensured a wide spectrum of knowledge about CC in health care in Asia, Western Europe, and the United States, which represent the main CC health care markets.
Future research could also include niche CC markets to further verify and improve our taxonomy. By relying on perspectives from a taxonomy for CCSs for health care organizations, we provide a solid conceptual cornerstone for research about CC in health care; moreover, the suggested specificities of CC for health care and the related future research opportunities will serve as a valuable roadmap.
Conflicts of Interest: None declared. National Center for Biotechnology Information , U. J Med Internet Res. Published online Jul Author information Article notes Copyright and License information Disclaimer. Corresponding author.
Corresponding Author: Ali Sunyaev ude. Originally published in the Journal of Medical Internet Research http: This is an open-access article distributed under the terms of the Creative Commons Attribution License https: The complete bibliographic information, a link to the original publication on http: Abstract Background Cloud computing is an innovative paradigm that provides users with on-demand access to a shared pool of configurable computing resources such as servers, storage, and applications.
Objective We aim to generate insights into the concept of cloud computing for health IT research. Methods We employed a 2-stage approach in developing a taxonomy of cloud computing services for health care organizations. Results Our taxonomy is composed of 8 dimensions and 28 characteristics that are relevant for cloud computing services in health care organizations. Conclusions By relying on perspectives from a taxonomy for cloud computing services for health care organizations, this study provides a solid conceptual cornerstone for cloud computing in health care. Introduction Background and Objective Cloud computing CC is an innovative paradigm that provides users with on-demand access to a shared pool of configurable computing resources such as servers, storage, and applications [ 1 ].
Our research focuses on the following research questions RQs: What are the relevant properties of CC for service delivery to health care? What are the specific meanings of these properties for health care? Methods Overview We employed a 2-stage approach to develop a taxonomy of CCSs for health care organizations. Open in a separate window. Figure 1. Literature Review To obtain data for the development of our taxonomy, we followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses framework [ 33 ] and performed a review of the literature on CC in health care organizations.
Expert Interviews To gather knowledge that could inform the development of the taxonomy from practice, we conducted 24 semistructured expert interviews, as listed in Table 1. Table 1 Overview of interviewees. Taxonomy Development For the taxonomy development, we chose the method proposed by Nickerson et al [ 32 ], which provides a systematic taxonomy development approach for IT objects and is well acknowledged in the domain of health IT [ 38 , 39 ]. Results Dimensions and Characteristics Our taxonomy of CCSs for health care organizations is composed of 8 dimensions and 28 characteristics see Table 2 for overview.
Table 2 Taxonomy of cloud computing services for health care organizations. Dimension Characteristics Principle knowledge Service form Software, platform, infrastructure Deployment model Public, private, community Targeted cloud advantage Scalability, elasticity, ubiquity, cost efficiency, shareability, interoperability, security Timeliness Real time, not real time How-to knowledge Supported task Clinical, administrative, strategy, research User Patient, medical staff, family member Service delivery device Independent, adapted, specialized Patient data involvement Internal, external, no involvement.
Classification and Evaluation After completing all taxonomy development iterations, we classified all 50 CCSs that we identified during stage 1. Table 3 Specificities of cloud computing for health care. Figure 3. Specificity 1: Interviewee i Specificity 2: Cloud Computing Brings More Data Security and Interoperability to Health Care Previous studies have raised concerns about security and privacy as the Achilles heel of CC [ 43 ], which are main barriers for the adoption of health IT artifacts [ 44 , 45 ]. CC is safe. The problem is how to make people believe that.
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Specificity 3: Specificity 4: Specificity 5: Cloud Computing Supports Patient-Centeredness A conservative but still well-recognized view of health IT is that medical staff are the main users of health IT applications [ 53 , 54 ], and many existing health IT applications are heavily physician-centered. An interviewee, whose hospital deploys a medical appointment CCS for patients, had this to say: Specificity 6: Specificity 7: Contributions For health IT research, our contributions are threefold.
Limitations and Conclusions A main limitation of this research is that our data focused on health care organizations that are hospitals and clinics, as implied by the literature review search string and by the interview questions. Multimedia Appendix 1 Overview of identified cloud computing services.
Cloudlet-Based Mobile Cloud Computing for Healthcare Applications - IEEE Conference Publication
Click here to view. Multimedia Appendix 2 Overview of interview questions. Multimedia Appendix 3 Taxonomy development iterations. Multimedia Appendix 4 Taxonomy of cloud computing services for health care organizations. Multimedia Appendix 6 Future research directions. Footnotes Conflicts of Interest: References 1. Mell P, Grance T. NIST definition of cloud computing. National Institute of Standards and Technology; Paul M, Das A. Provisioning of healthcare service in cloud.
Information and Communication Technology. Springer; Quwaider M, Jararweh Y. Multi-tier cloud infrastructure support for reliable global health awareness system. Simulation Modelling Pract Theory. Kuo AM.
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