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The healthcare industry in the GCC and around the world is undergoing fundamental transformation. Fueled by the skyrocketing cost of healthcare coupled with demand for better and more affordable care from citizens who are living longer and more patients suffering from chronic illnesses, healthcare actors including providers, payers and regulators are forced to rethink their care delivery model and explore opportunities to improve quality, contain cost and maximize efficiency. The GCC continues to spend more money on healthcare with expenditure projected to rise by an average of 6.2 percent a year between 2015 and 2018, to an estimated $48.3 billion, and government funding accounts for 65.8% of the total spending in Saudi Arabia and 68% of that in the UAE. With declining oil revenues driven by falling oil prices, improving healthcare efficiencies is becoming a national strategic priority. Leading the chart is shortage of well-trained healthcare professional in the GCC which is creating a large operational gap with around 20 qualified physicians for every 10,000 people, compared to approximately 27 doctors in the US and UK systems, and demand expected to soar 240% in 20 years according to a 2014 study. Clinical and administrative inefficiencies due to unnecessary medical procedures and inadequate utilization of resources are also affecting equitability and access to the healthcare system. Despite global and regional movement to reform healthcare and achieve substantial cost containment, the complex nature of the healthcare industry hinders the ability to realize noticeable reduction of unnecessary expenditure. This immense complexity confronting the healthcare industry will require smarter and more informed decisions to contain an increasingly disproportionate healthcare cost as required by market dynamics, increased governmental regulation, and today’s more demanding citizens.


Big Data Analytics has been the new game changer on all frontiers including health. Electronically linked hospitals, virtual clinics and connected patients are pumping enormous amount of health bits and bytes through digitally-connected environment and feeding into massive central data repositories of unprecedented memory and storage capacity. In the GCC, more healthcare institutions are embracing e-health as a strategic goal and going “digital”, and with new data emerging from EMR systems, IOT health apps, insurance claims and social media, the overarching vision of attaining smart health through data-powered health management decisions is now within reach. The right mix of enabling big-data technology, advanced data-science skills and sound governance model, together make up the analytics recipe to turn these voluminous data into valuable insights that can untangle the mystery of realizing total health value at the lowest possible cost. As an agent of transformation of many other industries, big data does come with the promise of changing the healthcare landscape and paving the way for newer and more innovative approaches to health. The following are three different areas and case studies where we foresee the role of analytics to be the “make or break” factor for payers, providers and policy makers to bring about desirable benefits to healthcare constituents.

Smart hospitals – Standardization of care and optimization of hospitals resources and staff allocation

Hospitals and healthcare organizations around the world are faced with the daunting and persistent challenge of “having to do more with less” by improving utilization and maximizing productivity. To do this, most hospital executives aim to standardize and streamline the delivery of care and view significant -and may be unintentional- variability as a major driver for the cost of care that jeopardizes a hospital’s competitiveness and diminishes its survival outlook in a world of value-based care. Aided by information, data, hospital management have the opportunity to obtain a deeper visibility and understanding of the operational aspects of their healthcare business, diagnose problematic areas and deficiencies and prescribe the right course of action.


The Emirate of Dubai, through the Dubai Health Authority, publishes its Annual Health Statistical Report with facts and figures concerning healthcare resources, usage, and utilization and shedding light on areas of strength and weakness of the health sector through a set of KPIs reflecting safety, quality and efficiency of service outcomes. As an example, in the 2013 report, an alarming observation centered on the sharp increase of hospitalization rate, or average length of stay (LOS) in one of Dubai’s hospitals from 1.0 days in 2012 to 4.1 days in 2013. Thought justifiable by possible expansion of clinical and surgical specialties requiring extensively longer care, the spike could signal abrupt deterioration of operating and clinical efficiency. LOS is a major key indicator of operational effectiveness, and shortening the average LOS by initiating care sooner in the care pathway has a multifold effect of 1) reducing the likelihood of adverse health events such as infections, 2) increasing the pace of care delivery by reducing the number of potential paths of care and 3) freeing up more beds to hospitalize more patients in the queue; all of which ultimately releases more working capital, lowers supply and operating costs and maximizes revenues. A recent study even suggested that an LOS that was longer or shorter than average was associated with increased risk of readmission caused by post-discharge complications. The LOS represents a cascade of encounters that includes, for example, patient admission to hospital, transfer to radiology department, follow up by consultant at bedside, and discharge from hospital by nurse and billing department. In a well-functioning system, this chain of activities take place in a synchronized fashion and with minimal delay between subsequent events. Operational breakdowns will potentially cause patients to be stuck waiting around for a process to be completed (e.g., test results or paperwork) which could prolong time in hospital at any given day. Analytics can be leveraged to mine historical data of patients’ trails of footprints within the healthcare facility, flag unwarranted LOS and trace root causes. Through time-series analysis, these efficiencies breakdowns within components of the chain can be predicted and operational bottlenecks leading to idle time for example can be accurately linked to their human, machine or process-driven attributes. The discovery of relations between events can reveal critical gap areas including potential skills deficiencies in the delivery of care, redundant processes that may lead to duplicate procedures and unnecessary tests, as well as wasted resources and inappropriate use of medical technology. Accordingly, analytics can inform performance optimization of HR, technology and allow hospital to better scale its resources to meet the changing needs of its patients particularly with the prominent trend of rising cases of chronic illnesses and heart diseases.

Smart insurers – Preventing and controlling health fraud, waste and abuse practice

Fraud losses drains insurers’ finances and undermine their ability to offer competitive rates to policyholders driving higher premiums and increasing the average cost of healthcare to citizens.  In the US, it is estimated that insurance fraud strips $30 billion from the industry each year and costing policyholders an estimated $200-$300 a year in additional premiums. In the UAE, losses from health insurance fraud and abuse are approximated at around one billion dollars according to a 2011 study which estimated that misuse accounts for 30 percent of the nation’s healthcare costs. In response to fraud, public insurers are setting centralized fraud bureaus, such as the US National Insurance Crime Bureau (NICB), for the purpose of crafting effective regulations to combat and deter fraud practices.  Similarly, insurance companies are also establishing fraud intelligence units to detect, predict and prevent fraudulent claims.

Health fraud has many faces that are well-known to insurers that have suffered one way or another from its consequences. In its more innocent forms, patients sometime opt to bypass primary care providers and directly approach specialists under the misconception that it makes for a more effective care. Under a more dreadful form of fraud, patients may falsify information on claims to receive unlawful reimbursements. Other common cases of members-driven fraud include identity-theft and doctors shopping. In the latter, patients seek to obtain the same care from multiple clinicians to obtain controlled or recreational drugs that can then be sold in the black market. Physicians performing diagnosis and treatment may purposely assign an erroneous code and bill at a higher-paying service than was performed, a practice referred to as up-coding. Pharmacies and physicians can jointly engage in preferential and selective referrals and unnecessary prescriptions for exchanging kickbacks and incentives. By mining historical claim and billing data, insurers have the opportunity to apply a variety of data mining and predictive analytics tools such as anomaly detection to flag suspicious claims, profile the perpetrators and identify the ones with the highest likelihood of saving or cost recovery. Network analysis, a powerful tool useful for modeling relationships between entities in claims, can be used for identifying organized fraud activities and fraudsters’ rings. In addition, by developing predictive scoring models, not only can insurers avoid paying fraudulent claims at first notification of loss but they can also check new policies for connections to a historical incidents to avoid fraud proliferation. Insurers should strive integrate analytics in their broad risk management strategy and should work with regulators to close financial and administrative loopholes that are draining healthcare finance.

Smart Governments – Informing public health programs and national health campaigns

Citizens are increasingly accessing information and services through government health websites and are in turn voicing out their interests, needs and concerns on local healthcare environment and online health services through social media platforms. A recent study has shown that 25 of every 100 US citizens actively seek advice/information from government agencies regarding a health or safety issue. In the GCC where the internet penetration rates is poised to reach 66.82% by 2017, government health services are also becoming more accessible. The compelling challenge for government health agencies is how to engage and address citizens appropriately, and then use this interaction to create health value constructively and innovatively and to control the cost variable in the health equation.

The recent outbreak of diseases around the world such as Ebola in Africa and MERS (Middle East respiratory syndrome coronavirus) in the Middle East yielded an overwhelming proliferation of user-generated content, such as blogs and tweets data, in structured, unstructured and semi-structured format, as well as video and audio content. Unstructured and free-style text data when analyzed through NLP (Natural Language Processing) can reveal a multitude of contextual information (e.g., topics, ideas), logical and lexical semantics and even emotional undertones (e.g., sarcasm, excitement, disappointment). When combined with location-enabled data from mobile sources, community-based sentiments and population-level behavioral pattern can be sensed before, during and after the onslaught of a virus spread. For instance, a study has been conducted by an MIT-based group of researchers to model the spread of the Ebola virus and to forecast response to future incidents by comparing data from hospitals, social media and news to data from hospital records about incidences of the disease. According to their study, overreactions to the outbreak of a disease often cause people to panic and flee potentially spreading the disease to new areas and resulting in adverse but avoidable health, social and economic consequences with severe losses in money, resources and lives. Guided by these insights, government health agencies can counteract the media coverage and tailor their online and social media campaigns with accurate, targeted and actionable tips and recommendations. In the GCC where epidemics such as MERS continue to pose a significant threat, social media analytics has to be front and centered to health strategies and should alter the way governments think about disease prevention and management. In general, social media analytics can enable GCC governments to listen to the public regarding common health and safety concerns, possible misinterpretations of government messages, health myths that could be dangerous, or even to understand common health concerns discussed in demographic groups and social media communities that can help prioritize national health agendas. The enablement of the new generation of a comprehensive smart health depends on the ability to mine the web and social media to guide the development of clinical infrastructure and E-health services, respond to emerging health trends, threats and sentiment with the appropriate response aligned with the national health strategy and to maintain proactive role in creating preventive health and supporting citizens healthy life-style programs.


Health inefficiencies are attributed to many factors but can fundamentally be summed up in the absence of the right depth-level of intelligence to match the complex tasks of achieving clinical and operational effectiveness, enhanced member/patient satisfaction, while improving financial and administrative performance. While fulfilling these goals may seem more like playing a multi-dimensional chess, big data analytics has a big and central role to play in navigating through the complex space of health uncertainties, unlocking the value within clinical, financial and sentiment data and bring data-driven decisions in sight of health providers, payers and regulators. Big data 2.0, with “predictive capabilities” being a major differentiator, comes with the promise of changing the healthcare landscape and paving the way for newer and more innovative approach to healthcare. In the GCC, big data phenomenon remains a relatively new concept still finding its way into the construct of “typical” health organizations and government institutions. It may take a long time before the culture of decision-making is analytically transformed and data-driven discoveries are embedded into key organizational processes. Nevertheless, achieving a quantum leap towards analytics maturity will require a new breed of leaders who will place analytics at the core of their organizational strategic mission and then employ the right analytics resources to prioritize and resolve top challenges related to health inefficiencies. The true benefits realized across financial and health dimensions will not only be felt short-term but will trickle downstream creating sustainable impacts at multiple levels in the public health continuum, and will ultimately reveal accurate views of how healthcare is delivered today and prescribing ways of how it can best be delivered tomorrow most effectively and efficiently on the long term.