How will data affect your healthcare?
Written by: Mariam Mirza
Art by: Cheng-Yu (Kou)
Our society is experiencing a technology and data-driven revolution unlike any seen before. Healthcare systems increasingly refer to complex algorithms to save the most lives, process resources more efficiently, and ensure the best outcome for patients. But in this struggle to serve the masses versus the few, could some be left out of this life-saving health provision? Who, and what, is getting lost in the number-crunching?
There is a distinct link between our place in the socioeconomic ladder and our health: populations at the lower end are less likely to achieve optimal health. Big Data is a double-edged sword; it provides tremendous insight to reduce health disparities, yet can also widen the gap without the concerted action and engagement of minority groups. When patients are admitted to hospital, data should inform not only the most suitable medical intervention, but also address their likelihood of recovery and staying healthy upon release. However, challenges of ethics, privacy, inadequate data or access to said data, can remove the benefits of precision medicine (improved diagnosis, treatment and prevention) from those that need it most.
Our very own Jeremy Bentham’s utilitarian belief in providing the greatest good for the greatest number may seem like the best way to minimize suffering. However, prioritization of finite resources in such a way can inadvertently put a price tag on each individual life. Impossible ethical decisions instigate debates on budget allocation to mental health, foreign aid, or choosing between providing treatment for a rare debilitating disease versus a community-wide service. Algorithms can make this process easier, but at the frontline are healthcare professionals who treat patients – not data points.
With comparisons drawn between the privatised healthcare services of the US and the UK’s NHS universal health care system (always in the midst of more cuts), the direct impact on health access is not limited to high-income (HIC), but also low-income countries (LIC). Worst case scenario: Big Data could be an expensive distraction driven by HICs focusing on disease-specific outcomes, which are largely useless to countries who most need data access. Minority groups, the homeless and others without a political voice, may lose out.
Alongside poor data governance and monitoring, there may be nothing to prevent private companies exploiting or leaking information, which can also threaten the safety of individuals.
The UN developed 17 Sustainable Development Goals (SDG) to build upon agreed principles, covering a broad range of social development issues; a step in the right direction, but not by any means easily acted upon. The first target for SDG #3 is reducing maternal mortality to under 70 deaths per 100,000 live births. In Guatemala, women at childbirth are five times more likely to die than in the US, despite having good clinical resources. The gap in health outcomes between city and indigenous Maya populations reveals a wider problem, where language and logistics restrict access to life-saving healthcare.
Conversely, China has remarkably reduced maternal mortality rates tenfold over four decades. Female discrimination, malnutrition, absence of family planning education, and lack of obstetric care were identified as major factors. With data supporting the most successful strategies it is easier to see the bigger picture.
In 2014, the UN publication ‘A World That Counts’ focused on the positive outcomes achievable by Big Data with informed, reflective and effective stewardship of data. This has been championed by monitoring the spread of epidemics using geodatabases and mobile phone traces. Since 2000, a dramatic reduction in malaria, and an increase in preventive efforts has been led by the Against Malaria Foundation (AMF), distributing 14 million insecticide-treated nets throughout Africa at only $3 per net.
Asking the right questions about data brings evidence-based answers. Most start with asking what the biggest killer is and how to fight it. In 2013, over three million people died from ischaemic stroke, three times the deaths from malaria. However, deaths from strokes are mainly in the over 70s, whereas malaria is a primary killer of children. While stroke is the ‘bigger killer’, over half a million children under five years died of malaria – each child losing an entire lifetime. It is not simply about the volume of death tolls, but also the life lost as a result.
Global health is not just about saving the most lives, but changing the conditions of life so it becomes worth living. Dr. Chris Murray, director of the Institute for Health Metrics and Evaluation (IHME) created the metric of the Disability Adjusted Life Year (DALY). It incorporates not just survival, but the quality of living, and is the first step towards calculating the Global Burden of Disease (GBD) – a population-wide snapshot of world health. Such parameters provide the perfect platform to identify the areas where more data is needed. Collaboration with non-profit organizations, academia, industry and governments can drive forward innovative solutions, such as mobile applications informing preventative measures for disease eradication, or identifying charities with the most human impact as opposed to clever marketing.
In an ideal world for the altruist, every health care intervention from screening programmes to vaccines, would be processed by a supercomputer to fund projects producing the most bang for their buck. However, data does not create meaning or intention – we do. While we may be living longer, the future of meaningful healthcare still has great disparities in who enjoys the benefits. If we can balance the numbers with critical thinking and ethical reasoning, the potential to significantly improve healthcare for millions of people is well within our grasp.
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