Hitting newsstands in the United States of America and India simultaneously, A New Book–Planning and Designing Healthcare Facilities: A Lean, Innovative and Evidence based approach by Dr V.K. Singh and Prof. Paul Lillrank call for a fresh approach in designing new age hospitals in the wake of emerging new normal in the healthcare sector.

Authors say modern medicine is founded on the scientific pursuit for knowledge about the human body and its pathologies. Science builds theories and tests them against evidence. The scientific method should now be applied to the design of hospitals and health service systems. This is the rationale for evidence – based design.

According to authors, lean and innovation complement each other. Lean means creating more value for customers with fewer resources. Innovation is a process of translating an idea into goods or services that create value for which customer is ready to pay. The General Hospital is still a valid concept and is not going to disappear anytime soon, particularly in parts of the world where public health is poor, resources are scarce, and a majority of people is underserved. While visions are always welcome, the task at hand, and the theme of this book, is to improve on the Dominant Design, the standard General Hospital.

Evidence-based design (EBD) emphasizes credible evidence to influence design. It works on well-defined problems, applies a multidisciplinary perspective, involves users and customers, establishes quantitative performance measures for critical variables, and uses tests and simulations throughout the design process.

Prof. Lillrank along with Riikka-Leena Leskelä and Olli Tolkki say the hospital has traditionally been seen as a production site like a factory where flows of material, people, energy and information combine with fixed assets. In modern manufacturing, the factory has evolved into supply networks. In a similar vein, the modern hospital is seen as a node in a regional service system. A hospital design initiative should therefore begin with a master plan that defines the hospital’s place and role in a broader health service system.

Dr. Singh in a chapter on ‘Patient First, Functions Next and Design Later’ along with Mr. S K Biswas says the evolution of hospital design principles has gone through stages.

The first was ‘functions follow design’, as services had to be adapted to whatever structures were available. Next came ‘design follows functions’. Various professional groups lined out their requirements in terms of floor space and layout. More recently, the concept ‘Design follows first patients, then functions’ has been adopted. The emphasis now is on integrating the needs of patients, hospital functions, and functionaries in hospital design.

In a chapter, ‘Green Hospitals and Sustainable solution to Healthcare Facility’, Mr. Rajeev Boudhankar says as important parts of the modern urban landscape, hospitals must adopt environmentally friendly and sustainable designs and technologies. Green hospitals use energy, water, materials and land more efficiently than conventional buildings.

With more natural light and better air quality, green buildings contribute to improved health, comfort, and productivity. The LEED 2009 for Healthcare Green Building Rating System is a set of performance standards for certifying healthcare facilities.

In ‘Designing a Patient-centric Healthcare Facility Using Lean Methodology’, Mr. John Gallagher, Kim Chaney and Ron Kwon say 2P (Process Preparation) is a Lean design tool that helps to organize the flow of activity in a way that results in the least amount of waste. This chapter details how it worked when Concord Hillside Medical Associates, a multi-specialty group practice part of Harvard Vanguard Medical Associates near Boston, applied it in a major facility design project.

Likewise, in an article on ‘Creating safer healthcare environments using an evidence-based design process, Dr. Anjali Joseph, Ellen Taylor and Xiaobo Quan say a growing body of research shows that the healthcare built environment impacts safety outcomes such as infections, medication errors, falls, and staff injuries. Latent conditions that adversely impact patient safety are built into the physical environment during the planning, design, and construction phases. Design decisions should be proactively evaluated by engaging users from different disciplines such as infection control, nursing, risk managers, and environmental services. Emerging tools such as the Safety Risk Assessment (SRA) toolkit provide a structured way to apply evidence-based design to improve patient safety.

Dr. Singh and Dr. Biswas in another chapter on ‘Evidence Based Design in Hospitals – Theory to Implementation’ opine that the principles of Lean Healthcare support Evidence Based Design. Lean calls for the identification of all major stakeholders and specifying what they consider valuable. Stakeholder value can be grouped into the basic categories tangibility, reliability, responsiveness, assurance and empathy. When these requirements are not met, processes create waste.

EBD analyze the constraints that need to be addressed. The design process covers several stages, initial hypothetical design, process design, service design, and empirical design. These principles are detailed in a case in a hospital in Kolkata.

On Virtual Hospitals of Future, IT expert Sachin Gaur says the Information and Communication Technologies (ICT), particularly smart and wearable devices, have the potential to break constraints of time and location. Physical installations may turn virtual, and centralized services may be decentralized. Assessing the potential of new technologies, the CIMO -methodology of Evaluation Science can be employed. It asks the questions, what is the context in which technology is applied through what kinds of interventions, and which mechanisms are activated to produce which outcomes?

On ‘Redefining Healthcare of Tomorrow in Smart City’, Dr Singh and Ms. Nimisha Singh say the three pillars of the smart city are-people, process, technology, and the information flows that bind them together as an optimized whole. The Smart Cities Mission under the leadership of Prime Minister Narendra Modi is an initiative towards urbanization. Smart cities require smart healthcare.

With the Internet of things (IoT), layers of smartness are being added to the hospitals, such as remote monitoring, chronic disease management, medication management, patient self-management, and workflow management.

In a Chapter titled ‘Delivering Inclusive Intelligent Healthcare’ by Innovative and Comprehensive e-Health System, Dr. Kuo Shou-Jen and Lai Chien-Wen aver that the chapter is based on a case study of Changhua Christian Healthcare System (CCH), Taiwan. CCH has been a pioneer in implementing sophisticated new technologies. The case emphasizes the importance of the combination of high tech with human touch.

In a chapter on ‘Planning Safe Hospitals’, Ms. Sushma Guleria says that the Hyogo Framework for Action 2005-2015 (HFA) spells out the challenge to substantially reduce the impact of disasters and to make risk reduction an essential component of development policies and programs. Risk reduction planning should be integrated into the health sector to make hospitals safe from disasters and strengthen their capacity to remain functional in disaster situations. Hospitals need to have disaster management plans and to evaluate their performance by implementing the Hospital Safety Index used widely to gather information for sound decision making.

In an article on ‘Designing Innovative Facilities: Contamination & Security Hazards at Hospitals’, Dr. Singh along with Dr. Raman Chawala emphasis on the danger of chemical, biological, radiological, nuclear and explosive (CBRNE) related terrorism which pose a contamination threat to healthcare institutions. Designing innovative resilience can provide long term and effective solutions by establishing a rigorous framework that can accelerate adaptation and ability to recover from any known and unknown contamination security and safety hazards.

‘Adapt or Obsolesce: The evolution of the Singapore Health System’, Matthew Saunders said Singapore is an advanced country of small size with a single party dominated political system. This has made it agile and able to respond quickly to changing circumstance in ways that differ from larger and more complex polities. Singapore faces the same challenges as other developing countries, including ageing society, non-communicable diseases and healthcare cost inflation, while it is well positioned to implement advanced technologies, such as electronic patient records and smart solutions. Singapore highlights the systemic nature of healthcare, where service production, facility design, finance, and regulation require innovative approaches to integration.

Dr. B. R. Shetty, Founder and Chairman, NMC Healthcare, Abu Dhabi, United Arab Emirates in his foreword said, “The book is a timely effort to discuss various concepts and tools to reduce delivery costs and maintain high quality by the means of planning a hospital with an eye on operations. It details experiences from around the globe. The authors strive to integrate several strains of thought: Lean, Innovation, Patient Centricity, and Evidence–based designs. This is what the healthcare industry needs.

Want to write for InnoHEALTH? send us your article at magazine@innovatiocuris.com

Read all the issues of InnoHEALTH magazine:
InnoHEALTH Volume 1 Issue 1 (July to September 2016) – https://goo.gl/iWAwN2
InnoHEALTH Volume 1 Issue 2 (October to December 2016) – https://goo.gl/4GGMJz
InnoHEALTH Volume 2 Issue 1 (January to March 2017) – https://goo.gl/DEyKnw
InnoHEALTH Volume 2 Issue 2 (April to June 2017) – https://goo.gl/Nv3eev
InnoHEALTH Volume 2 Issue 3 (July to September 2017) – https://goo.gl/MCVjd6
InnoHEALTH Volume 2 Issue 4 (October to December 2017) – http://amzn.to/2B2UMLw
InnoHEALTH Volume 3 Issue 1 (January to March 2018) – https://goo.gl/fksdQx
InnoHEALTH Volume 3 Issue 2 (April to June 2018) – https://goo.gl/grbtRo

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A new paradigm for use of machine intelligence in healthcare

Many national economies are on the brink of collapse under the burden of state-supported health insurance programs. For example in the US, a country that does not provide universal healthcare, the national debt is expected to double as a result of healthcare related liabilities over the next three decades. There is an even greater price to pay for the inefficiencies found in healthcare systems. When already overburdened systems are pushed to their limits, healthcare professionals are forced to make bad decisions.

In a startling example, during Hurricane Katrina, doctors and nurses at New Orleans’ Memorial Hospital found themselves incapable of making even simple decisions. In her Pulitzer Prize-winning coverage, Sheri Fink reported how patients deemed the least likely to survive, were injected with a lethal combination of drugs — even as the evacuation was already on its way.

This chilling anecdote sheds light on a greater problem underpinning many of the biggest threats hurting human society and the eco-system of our planet.

We humans are not equipped for making good decisions under stress. Positive outcomes across a multitude of fields, including healthcare, largely depend on the ability to make good decisions under unpredictable and stress inducing conditions. We, humans, are not only bad at making decisions under pressure but are poorly equipped for making any rational decisions at all.

Daniel Kahneman, in his Nobel Prize winning work on decision making, explains how after years of deliberation leading to a single decision, the person making the decision might still completely ignore the entire process of deliberation and instead make a decision driven by emotions in a whim of the moment.

An Age of Automated Decision Making

As the information age is about the focus on automating processes related to access to information, the next age, the age of “decisioning”, will focus on automation of processes related to decision making. Whereas humans are excellent in pattern detection and pattern making, an essential requirement for creating intelligent computer systems, computers are strong in making decisions where processing of facts is of vital importance. This almost magical ability of computing systems to process information includes the capability to identify and utilize extremely subtle connections between otherwise seemingly disconnected pieces of information, a feat us humans could never do on our own with the kind of precision and scale even a simple computer system can. In no other field, rational decisions are of such vital importance as in healthcare.

Not only healthcare is the most significant economic liability for many nations, but it is also the only field of practice affecting people’s everyday lives that can be considered truly as “life and death” matter. It is, therefore, the area where the human society most desperately needs help.

Computer-aided decision systems can be categorized into three evolutionary stages, each with a corresponding quality of results, and the requirement for a level of human involvement.

Whereas up until a few years ago, most systems were still ‘descriptive’ with some examples of ‘predictive’ decision- making support systems, in the last two years the developments in the field of machine intelligence have for the first time made the dream of prescriptive expert systems realistic. Recent advancements in both open source software and commercial hardware have paved the way for rapid prototyping of ideas that promise to revolutionize the way decisions are made across a multitude of fields, including healthcare.

Examples of these advancements include deep learning platforms such as Google’s TensorFlow and Keras, unstructured data processing innovations such as word vectorization, and Nvidia’s data processing focused GPU product-line.

Regardless of the tremendous promise and the recent hype surrounding machine intelligence, some significant concerns remain without any serious attention. While machine intelligence innovators would like to focus on showcasing “what’s possible,” before introducing new ideas and processes into the healthcare apparatus, it is far more important to ask “what could go wrong”.

Morality and Healthcare Algorithms

Every machine intelligence solution can be reduced into two aspects; the data inputs available for the solution, and the means by which the solution process those inputs. These two act as the causes for the result the system provides. Algorithms underpin every decision a computer system makes, regardless of the kind of system it is. The unique feature in this regard, of modern machine intelligence systems, such as those based on neural networks and the deep learning method, is that humans cannot audit them. It is for this reason that we can get surprising results us humans could not arrive at without working with machine intelligence, and it is important to understand that surprising results may also be negative results. In many cases, adverse results in the healthcare context arise from causes that were set years or even decades before, and as of today, it is virtually impossible to establish true causality in such cases. This means that it would be almost impossible to know if a given machine intelligence solution is contributing positively in the long term or just driving shortterm efficiency. This will hold true at least for the next 100 years, or as long as it takes to understand causality in results that take decades to mature.

When in the 1980’s game theory-based principles were widely introduced in the western healthcare context, nobody predicted the consequences. For example, while nurses and doctors were incentivized to meet certain productivity quotas such as the number of days spent in the ICU, mortality rates of patients skyrocketed as a result of ICU beds being more available. In effect, a quota scheme is an example of a simple algorithm and can be used to highlight the danger that comes with introducing machine intelligence into healthcare.

This applies in particular in countries with universal healthcare and poorly performing national economy. Under such conditions, humans that make the decisions about the use of machine intelligence in the national healthcare system, are under tremendous stress. Not only their decisions affect individual patients’ lives but also have the potential for changing the destiny of an entire nation. It is very hard to see how under such conditions, non-experts, being bombarded with endless hype by self-proclaimed machine intelligence experts, would be able to make the right decisions.

In fact, government officials, healthcare professionals, nor computer and data scientists, are formally trained in morality and often lack even the most basic understanding of ontology, epistemology, and formal logic. The three legs of the stool on which rational decisions sit. As a result, as we have seen through examples in financial markets, online advertising, and other early embracers of algorithmic decision making, we end up with so-called greedy algorithms that optimize towards a given end ruthlessly without caring about anything else.

Unlike healthcare professionals, these algorithms are not afraid of losing their livelihood and reputation as a result of making the wrong decision that ends up hurting people.

While a healthcare facility or a professional working in one, could be sued for damages, in the case of machine intelligence systems, liabilities in the healthcare context have so far not been defined.

Security in Healthcare Systems

In the light of the recent events regarding ransomware, and the rapid growth in its popularity as a cybercrime tool, it does not seem too far-fetched that in the near-future entire hospitals will be targeted and held for ransom. Indeed many hospitals had already become victims of ransomware as a consequence of passive global or national attacks. In the recent WannaCrypt ransomware attack individual medical devices were rendered temporarily useless after being infected.

Siemens released multiple warnings about its healthcare devices being possibly vulnerable to WannaCrypt. Beau Woods, deputy director of Cyber Statecraft Initiative at the Atlantic Council said that it was likely that many important medical devices such as MRIs and other crucial computer-aided systems were rendered temporarily useless by the attack.

These examples show how healthcare organizations and their technology partners are currently incapable of securing important systems. Whereas in the human operated healthcare apparatus, the devastation is so easy to create, in a highly automated machine intelligence based healthcare apparatus, the problem would be significantly amplified.

Here too, regarding security and healthcare systems, we have to seriously consider the implications of the machine intelligence ideas that are adopted today, regarding the threat landscape over the next few decades.

For example, current cryptographic methods are all based on so-called key exchange cryptography. Any sudden improvement in computation power, for instance in the form of quantum computing becoming practical, will lead to an immediate collapse of the key exchange based security paradigm. In a machine intelligence dominated healthcare apparatus, collapse of the key exchange based security paradigm has the potential for leading to the greatest human travesty in the history of the world. In the absence of serious discussion about such longer-term threats, it will not be possible to make the right decisions regarding decision-making automation and wider adoption of machine intelligence based expert systems in healthcare.

The Importance of Human Touch

Sometime in the distant future, we may be able to completely automate certain key aspects of healthcare, such as triage management. Even then it is of significant importance to not lose sight of the essence of healthcare; taking care of people. Taking care of people is one part taking care of their physical body, and one part taking care of their feelings.

In a completely machine intelligence and robotics based triage management approach it would be hard for patients to feel that they are being taken care of in the way they feel when an actual doctor is treating them.

On the other hand, in a very short period of time, the doctors would lose their ability to deal with the basic day-to-day taking care of patients that still today keep them overworked.

Because algorithms don’t feel anything, for example, empathy, it is also likely that as specialist doctors increasingly get their inputs from those algorithms, they become further distanced from the human aspect that some argue is necessary for healing practice. In this light, perhaps it’s more reasonable for the allopathic medicine to seek intelligence from its eastern counterparts such as TCM, TTM, and Ayurveda, as opposed to seeking it from machines. Machines that ultimate base their decisions on the combination of the information they are receiving and the algorithms that process the information. Both the information and the algorithms being a product of, and therefore limited by, the people who produce them.

Combining the instruments and other marvels of the western symptomatic healthcare approach with the more holistic but in some cases inferior Eastern practices, have the potential for driving significant change within the healthcare system as we know it today.

In terms of specific machine intelligence implementations, as the point-of-view presented in this article clearly shows, the focus should be on long-term macro effects of such implementations, as opposed to focusing on short-term and micro context.

Perhaps in a future world where the western and eastern medicinal practices are better integrated and institutionalized into a new era of taking care of patients, we will be better equipped to handle the challenges that come with machine intelligence focused healthcare.

Want to write for InnoHEALTH? send us your article at  magazine@innovatiocuris.com

Read all the issues of InnoHEALTH magazine:
InnoHEALTH Volume 1 Issue 1 (July to September 2016) – https://goo.gl/iWAwN2 
InnoHEALTH Volume 1 Issue 2 (October to December 2016) – https://goo.gl/4GGMJz 
InnoHEALTH Volume 2 Issue 1 (January to March 2017) – https://goo.gl/DEyKnw 
InnoHEALTH Volume 2 Issue 2 (April to June 2017) – https://goo.gl/Nv3eev
InnoHEALTH Volume 2 Issue 3 (July to September 2017) – https://goo.gl/MCVjd6
InnoHEALTH Volume 2 Issue 4 (October to December 2017) – http://amzn.to/2B2UMLw

Connect with InnovatioCuris on:
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Twitter: https://twitter.com/innovatiocuris
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