The current approach to health care process automation has been predominantly based on a sectoral / enterprise perspective. Each enterprise is limited to one hospital and its own appendages; typically under the governance of a single organizational entity such as a hospital, a clinic or a diagnostic lab. The interoperability, re-configurability, upgradeability and affordability in automating their collaboration with other entities has been so grossly underestimated as a linear extension of their current processes, that such agencies are reeling under heavy spends to understand and keep pace with an ever changing landscape of technologies and products. Piece-meal, monolithic software solutions customized only to current needs, pose interfacing problems when the systems have to scale up and adapt readily to address newer health care delivery models and services.

Not only the governance of health systems differ in various demographics / states / countries, even within chosen governance unit, from a health system view point, there are only a few enterprises that host everything; from labs to operation theaters, pharmacy to transcriptions to insurance agents and lawyers within its campus. The majority is smaller enterprises providing subspecialties such as diagnostic labs, pharmacies, primary care centers, etc. who cannot afford significant investments in IT infrastructure and are willing to collaborate with other sub-specialties to provide comprehensive care to the citizen. From a payer’s perspective it is even worse, since the inefficiency in the whole system adds up in the bills either directly or indirectly, and that has to be reimbursed by the payer to keep the system going. From a patient’s perspective, the system often looks more like a spaghetti of silos in processes, entities, functions, faculty, products, standards, codes, practices, policies, etc, through which they have to pass in order to address their health problem. While the IT champions are proposing ubiquity, mobility, country-wide networks, citizen centric services, integrated disease surveillance, proactive care, etc. one does not see a single solution that addresses the payer, provider and consumer’s point of view together.

Digital Integrated Health Exchange (DIHE) considers care from a patient centric infrastructure extending across providers, payers and beneficiaries, collaborating electronically under strict medical, commercial and legal protocols in diverse settings. DIHE enables shielding away unnecessary clutter of information from the patient into separable, inter-operable, non-redundant modular units that can be reused and integrated in different ways and run concurrently to cater to the needs of various health care programs. This enables aggregation and distribution of resources, knowledge, skill and consumers to help amortize the cost over a larger population while improving access to care. However, there is significant legacy automation already lying inside individual entities, which is hard to replace given the investments that are already made by the providers. And yet, there are geographies where hardly any automation has happened; which will provide a leapfrog opportunity for sustainable adoption of ICT in the new paradigm. DIHE aims to interface legacy systems onto a standards based platform, brining them to a homogenous interaction pattern so that they can collaborate beyond their own campuses with other entities, faculty and patients.

The underlying problem in current health automation systems is very much due to the approach of developing monolithic solutions separately for each problem, with limited flexibility to alter / scale the system rapidly, commensurate with advances in knowledge, best practices and technology. Nature, on the other hand, has shown a different path in the designing of very complex organisms such as ourselves; by employing a Fractal based approach. We see a hierarchical organization, wherein some basic components (such as the cells), which are replicated to form groups (tissues), which are then replicated and grouped to form organs, which together form a whole living organism. This process repeats as higher organizations are built, in a “Fractal” pattern of replication, grouping, connecting in different patterns and orchestration of different workflows between them, to produce seemingly very different functionalities from the same building blocks, un-folding a whole new organism from the same elementary blocks. The integrated health care exchange (DIHE) adopts such an approach in constructing diverse health care programs out of common building blocks.

While in many other industry segments, the process of setting up “factories” to manufacture different products of a certain “class” by applying model based design and automated production, is an accepted process (such as in manufacturing cars, drugs or electronic chips), the concept is still in infancy when it comes to establishing “software factories” for health care segment that can manufacture different application from basic components. In the health segment today, most of software industry is busy employing scores of software programmers in building monolithic solutions for each problem, just like what it would be in developing a formula and manufacturing specific medicine separately for each patient! The approach should be to visualize the underlying commonalities and aggregate them into functional blocks and then produce the heterogeneities through a combination of such functional blocks. The question therefore is how do we break down a health system into its fundamental components and reassemble them rapidly in different ways to address the diverse needs of the market with minimal investment?

A very important first step is to unwind the current spaghetti into interdependent layers focusing on discrete aspects of health system, with each layer consisting of a stack of reusable automation components. Following a “fractal” approach, a discrete workflow implemented at a given layer itself becomes a component for the layer that consumes its service. Following a “Service Oriented Architecture” model, each component provides specific “Services” according to a published set of SLAs using specific protocols, to other components. By dissecting the inherent hierarchy in composition of a health care program, it will become easyto address challenges and design issues at each layer independently to a large extent. This approach also allows the modeling and simulation to be done at each layer without having to dwelling into functionalities of the layers beneath.

The Rapidly evolving Technology landscape

Hardware landscape

As with any IT intensive program, Health Information and Communication Technology programs consumes a lot of (proprietary) hardware devices, like computers, communication, biomedical etc. Given the fact that none of the hardwares are specifically built for the Health Care system; they have their own “constraints” around their usage. The total cost associated with ownership of such “general purpose” computing platforms is hardly reflected in its MRP, which is the only vector visible to the owner. The ownership cost is intimately linked with the users own ability to do preventive and long term technical servicing and maintenance.

Optimizing “purpose” with platform is in itself a complicated exercise because there is no common bridge between the knowledge of hardware intelligence and clinical services. The cost optimization model under the long term sustenance plan will fill in this gap by bringing in, “buy as you need”, and “integrate as you scale” health services.

It is also important that the “content” presented is optimized based on the platform. For example, whatever is presented on a lowend cell phone (which has the maximum user base) may be much less than what can be presented on a laptop. But what is the most essential aspect that needs to be presented even on a cell phone? This is based on the context in which it is being presented and the role of the consumers like doctors, patients etc. These aspects are missing in today’s optimized solutions, ignoring the gross wastage of available bandwidth and hardware platforms.

Network landscape

While communication networks are evolving from Wide Area Network (WAN) to Local Area Networks (LAN) to Personal Area Network (PAN) to Body Area Network (BAN), the standards are influenced mainly by the nature of traffic from entertainment, stock markets etc. But healthcare information lags behind in being mugged to the infrastructure even though the nature of healthcare requires 100% reliability of the network. The landscape seems to be heading towards adopting OFC/WIMAX/3G as preferred WAN; WIFI as preferred LAN; Blue tooth as preferred PAN and ZIGBEE as preferred BAN. This combination enables aggregation of traffic and amortization of cost in a hierarchical topology that can provide ubiquitous connectivity. Only in remote and difficult terrains could satellite find a place, as the associated costs do not promote affordability. In this dynamic landscape, the cost amortization plan needs to be carefully worked out in a feasible yet, optimal manner, while plans for scaling need to be inbuilt. Legacy systems already in place cannot be ignored in building a healthcare network.

This necessarily will have to be “managed” under an existing, plus future roadmap and hence a “hybrid” mode of technology adoption needs to be implemented. The industry is already generating a number of “adaptors” that help bridge this interoperability gap between legacy implementations and news additions.

Software landscape

Software engineering evolved from building basic automation application packages to Enterprise wide Life Cycle Products. Today in the post internet world, software applications are a conglomerate of various “open source and non open source technology frameworks”. The Health Care system today, in any part of the world is grappling with the word “integrated” and struggling to come out of “legacies”. With no software intensive resources within its faculty, the adoption path of software has to be planned in a way that is primarily need driven and flexible.

While Health IT standards like HL7 v 3.0 and DICOM 3.0 are intersecting and coming together as IHE to enable seamless flow of care data, the current stage is full of turbulence. The PPP model therefore provides for dynamic capacity, capabilities, educative adoption of software, implementation and above all, meaningful assimilation.

Biomedical devices landscape

The wonder days of “standalone” clinical devices is giving rapid way to the connected devices paradigm. More and more clinical devices are being connected to a network of computing platforms and can be accessed from distant locations reliably.

Not only the costs associated are exorbitant when Bio Medical interfaces are rigidly tied to a network or a computing platform, but its economic usage is also limited. The emerging group of connected devices provides the ability to have its cost amortized over a large user group population and enables cost of Health Care services to decrease.

In conclusion, DIHE believes that in the “personalization of care” and “management of disease & wellness”, the ability to “rapidly reconfigure” the value driven integration of these diverse knowledge components will help optimize cost and therefore maximize bottom line for all “collaborating stakeholders”. This “transparent interdependence” on “shared value drivers” will ultimately drive long term sustainability of health system ICT adoption and transformation programs of the “appropriate kind” across societies.

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Related December 2007