MaaS (Model as a Service) might allow building and controlling shared healthcare Cloud-ready data, affording agile data design, economies of scale and maintaining a trusted environment and scaling security. With MaaS, models map infrastructure and allow controlling persistent storage and deployment audit in order to certify th at data are coherent and remain linked to specific storage. As a consequence, models allow to check where data is deployed and stored. MaaS can play a crucial role in supplying services in healthcare: the model containing infrastructure properties includes information to classify the on-premise data Cloud service in terms of data security, coherence, outage, availability, geo-location and to secure an assisted service deployment and virtualization.

Introduction
Municipalities are opening new exchange information with healthcare institutes. The objective is sharing medical research, hospital acceptance by pathology, assistance and hospitalization with doctors, hospitals, clinics and, of course, patients. This open data [6] should improve patient care, prevention, prophylaxis and appropriate medical booking and scheduling by making information sharing more timely and efficient. From the data management point of view it means the service should assure data elasticity, multi-tenancy, scalability, security together with physical and logical architectures that represent the guidelines to design healthcare services.

Accordingly, healthcare services in the Cloud must primarily secure the following data properties [2]:
-      data location;
-      data persistence;
-      data discovery and navigation;
-      data inference;
-      confidentiality;
-      availability;
-      on-demand data secure deleting/shredding [4] [5] [11] [12].

These properties should be defined during the service design and data models play the “on-premise” integral role in defining, managing and protecting healthcare data in the Cloud. When creating healthcare data models, the service is created as well and properties for confidentiality, availability, authenticity, authorization, authentication and integrity [12] have to be defined inside: here is how MaaS provides preconfigured service properties.

Applying MaaS to Healthcare – Getting Practice
Applying MaaS to design and deploy healthcare services means explaining how apply the DaaS (Database as a Service, see [2] and [4]) lifecycle to realize faster and positive impacts on the go-live preparation with Cloud services. The Use Case introduces the practices how could be defined the healthcare service and then to translate them into the appropriate guidelines. Therefore, the DaaS lifecycle service practices we are applying are [4]:

Take into account, healthcare is a dynamic complex environment with many actors: patients, physicians, IT professionals, chemists, lab technicians, researchers, health operators…. The Use Case we are introducing tries to consider the whole system. It provides the main tasks along the DaaS lifecycle and so how the medical information might be managed and securely exchanged [12] among stakeholders for multiple entities such as hospital, clinics, pharmacy, labs and insurance companies.

The Use Case
Here is how MaaS might cover the Use Case and DaaS lifecycle best practices integrate the above properties and directions:

Following, we apply MaaS’ properties (a subset) to the above healthcare Use Case. Per contra, Data Model properties (a subset) are applied along the DaaS lifecycle states:

The Outcome
MaaS defines service properties through which the DaaS process can be implemented and maintained. As a consequence, applying the Use Case through the introduced directions, the following results should be outlined.

Qualitative Outcomes:
1)    Healthcare actors share information on the basis of defined “on-premise” data models: models can be implemented and deployed using a model-driven paradigm;
2)    Data Models are standardized in terms of naming convention and conceptual templates (Pharma, Insurance, Municipality… and so on): in fact, models can be modified and updated with respect the knowledge they were initially designed;
3)    Storage and partitioning in the Cloud can be defined “a priori” and periodic audits can be set to certify that data are coherent and remain linked to specific sites;
4)    The users consult the information and perform 2 tasks:
4.1) try the (best) search and navigate the knowledge for personal and work activities;
4.2) give back information about user experience and practice/procedures that should be updated, rearranged, downsized or extended depending upon community needs, types of interaction, events or public specific situations.
5)    Models are “on-premise” policy-driven tools. Regulation compliance rules can be included in the data model. Changes on current compliance constraints means changes on the data model before it is deployed with the new version.

Quantitative Outcomes:
1)    Measurable and traceable costs reduction (to be calculated as a function of annual Cloud Fee, Resources tuning and TCO);
2)    Time reduction in terms of knowledge fast design, update, deployment, portability, reuse (to be calculated as a function of SLA, data and application management effort and ROI);
3)    Risk reduction accordingly to “on-premise” Cloud service design and control (to be calculated as a function of recovery time, chargeback on cost of applied countermeasures compared with periodical audit based upon model information).

Conclusion
MaaS might provide the real opportunity to offer a unique utility-style model life cycle to accelerate cloud data optimization and performance in the healthcare network. MaaS applied to healthcare services might be the right way to transform the medical service delivery in the Cloud. MaaS defines “on-premise” data security, coherence, outage, availability, geo-location and an assisted service deployment. Models are adaptable to various departmental needs and organizational sizes, simplify and align healthcare domain-specific knowledge combining the data model approach and the on-demand nature of cloud computing. MaaS agility is the key requirements of data services design, incremental data deployment and progressive data structure provisioning. Finally, the model approach allows the validation of service evolution. The models’ versions and configurations are a catalogue to manage both data regulation compliance [12] and data contract’s clauses in the Cloud among IT, Providers and Healthcare actors [9].

References
[1] N. Piscopo - ERwin^® in the Cloud: How Data Modeling Supports Database as a Service (DaaS) Implementations
[2] N. Piscopo - CA ERwin^® Data Modeler’s Role in the Relational Cloud
[3] D. Burbank, S. Hoberman - Data Modeling Made Simple with CA ERwin^® Data Modeler r8
[4] N. Piscopo – Best Practices for Moving to the Cloud using Data Models in the DaaS Life Cycle
[5] N. Piscopo – Using CA ERwin^® Data Modeler and Microsoft SQL Azure to Move Data to the Cloud within the DaaS Life Cycle
[6] N. Piscopo – MaaS (Model as a Service) is the emerging solution to design, map, integrate and publish Open Data http://cloudbestpractices.net/2012/10/21/maas/
[7] N. Piscopo - MaaS Workshop, Awareness, Courses Syllabus
[8] N. Piscopo - DaaS Workshop, Awareness, Courses Syllabus
[9] N. Piscopo – Applying MaaS to DaaS (Database as a Service ) Contracts. An intorduction to the Practice http://cloudbestpractices.net/2012/11/04/applying-maas-to-daas/
[10] N. M. Josuttis – SOA in Practice
[11] H. A. J. Narayanan, M. H. Güneş – Ensuring Access Control in Cloud Provisioned Healthcare Systems
[12] Kantara Initiatives -http://kantarainitiative.org/confluence/display/uma/UMA+Scenarios+and+Use+Cases

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