|By Cloud Ventures||
|December 10, 2012 04:54 PM EST||
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.
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  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 :
- data location;
- data persistence;
- data discovery and navigation;
- data inference;
- on-demand data secure deleting/shredding    .
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  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  and ) 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 :
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  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:
|Objective||To facilitate services to healthcare users and to improve exchange information experience among stakeholders. The Use Case aims to reduce costs of services by rapid data designing, updating, deployment and to provide data audit and control. To improve user experience with healthcare knowledge.|
|Description||Current costs of data design, update and deployment are expensive and healthcare information (clinical, pharmaceutical, prevention, prophylaxis…) is not delivered fast enough based upon user experience;
Costs for hospitalization and treatments information should be predictable based upon user experience and interaction.
|Actors||Clinical and Research Centres;
Healthcare Institute/Public Body (Access Administrators);
Healthcare Institute/Public Body (Credentials, Roles Providers);
IT Operations (Cloud Providers, Storage Providers, Clinical Application Providers).
|Requirements||Reducing costs and rapidly delivering relevant data to users, stakeholders and healthcare institutes;
Enabling decision making information to actors who regularly need access   to healthcare services but lack the scale to exchange (and require) more dedicated services and support;
Fast supporting and updating healthcare data to users due to large reference base with many locations and disparate applications;
Ensuring compliance and governance directions are currently applied, revised and supervised;
Data security, confidentiality, availability, authenticity, authorization, authentication and integrity to be defined “on-premise”.
|Pre-processing and post-processing||Implementing and sharing data models;
Designing data model properties according to private, public and/or hybrid Cloud requirements;
Designing “on-premise” of the data storage model;
Modeling data to calculate “a priori” physical resources allocation;
Modeling data to predict usage “early” and to optimize database handling;
Outage is covered by versions and changes archived based on model partitioning;
Content discovery assists in identifying and auditing data to restore the service to previous versions and to irrecoverably destroying the data, if necessary, is asked by the regulations.
|Included and extended use case||Deployment is guided from model properties and architecture definition;
Mapping of data is defined and updated, checking whether the infrastructure provider has persistence and finding out whether outages are related to on-line tasks;
Deploying and sharing are guided from model properties and architecture definition.
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:
DaaS Lifecycle States
Healthcare Data Model Properties
|Data Location||Create Data Model
Model Archive and Change
Deploy and Share
|Data models contain partitioning properties and can include data location constraints. User tagging of data (a common Web 2.0 practice, through the use of clinic user-defined properties) should be managed. Support to compliant storage for preventative care data records should be provided|
|Data persistence||Create Data Model
Model Archive & Change
|For any partition, sub-model, or version of models, data model has to label and trace data location. Model defines a map specifying where data is stored (ambulatory care, clinical files have different storages). Providers persistence can be registered. Data discovery can update partition properties to identify where data is located|
|Data inference||Create Data Model||Data model has to support inference and special data aggregation: ambulatory might inference patient’s insurance file. All inferences and aggregations are defined, updated and tested into the model|
|Confidentiality||Create Data Model
Populate, Use and Test
|Data model guides rights assignment, access controls, rights management, and application data security starting from data model. As different tenants (hospitals, clinics, insurance companies and pharmacies) access the data, users and tenants should be defined inside the model. Logical and physical controls have to be set|
|High availability||Deploy and Share
Model Archive and Change
|Data model and partitioning configuration together with model changes and versions permits mastering of a recovery scheme and restoration when needed. Data inventory (classified by Surgery, Radiology, Cardiology, for example) vs discovery have to be traced and set.|
|Fast updates at low cost||Create Data Model
Generate Schema/Update Data Model
|Data reverse and forward engineering permits change management and version optimization in real-time directly on data deployed properties|
|Multi-database partitioning||Create Data Model
Deploy and Share
|Bi-directional partitioning in terms of deployment, storage, and evolution through model versioning has to be set. Multi-DBMS version management helps in sharing multi-partitioning deployments: for example, Insurance and Surgery by Patient, normally are partitioned and belong to different tenants vs different databases|
|Near-zero configuration and administration||Create Data Model
Generate Schema/Update Data Model
|Data models cover and contain all data properties including scripts, stored procedures, queries, partitions, changes and all configuration and administration properties. This means administrative actions decrease to leave more time for data design and update (and deployment). Regulation compliance can be a frequent administration task: models ensure that healthcare compliance and governance is currently aligned|
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.
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.
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).
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  and data contract’s clauses in the Cloud among IT, Providers and Healthcare actors .
 N. Piscopo - ERwin® in the Cloud: How Data Modeling Supports Database as a Service (DaaS) Implementations
 N. Piscopo - CA ERwin® Data Modeler’s Role in the Relational Cloud
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 N. Piscopo – Best Practices for Moving to the Cloud using Data Models in the DaaS Life Cycle
 N. Piscopo – Using CA ERwin® Data Modeler and Microsoft SQL Azure to Move Data to the Cloud within the DaaS Life Cycle
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 N. Piscopo - DaaS Workshop, Awareness, Courses Syllabus
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