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#PositiveImpact: big data in health

Our #PositiveImpact blog explores emerging themes in the design and delivery of Positive Impact. Each month, our global experts and guests share reflections on the intentional creation of enduring social and economic value. In this month's blog, Farley Cleghorn, Palladium’s Global Health Director, explores the potential for a more effective relationship between healthcare, corporate social responsibility, and business. He asks the all important question: how can big data services help the public and private sectors transform healthcare services and create Positive Impact?

Big data and big data analytics can help prevent epidemics and develop better cures for diseases.

Why does it matter?
Big data can change the nature of healthcare. With big data and big data analytics we are poised to help prevent epidemics, develop better cures for diseases, make health care delivery more efficient and cost effective and galvanize prevention and health promotion, keeping people healthier for much longer.

For example, when social media and search engine data is consolidated with the data from individual and facility health activities, we can predict seasonal flu a week to 10 days before patients start showing up in doctors’ offices.
When it comes to big data analytics, wearable technologies such as fitbits, watches and even fabric will make the upload of body functions as simple as smiling.

Big potential
Health is fraught with data minefields – data is personal and owned by the individual yet each interaction with the health delivery system generates a sizeable record over time. Linking these records provides the individual with a lifetime medical record that is a powerful personal tool to manage one’s health long before disease occurs.

When the data from multiple individuals is combined and consolidated even more powerful tools emerge from the data. Group and population trends can be discerned and followed. Predictions and forecasting are enabled and better resource allocation facilitated.

Surveys can be extracted from the data without the need for field work. Issues of representativeness would become obsolete since such a large proportion of any population would already be included. Real time changes could be monitored and we could determine shifts in attention and resourcing.

Extracting value from Big Data
Data (bits and bytes) become information (with potential value) which becomes knowledge (has value in itself); knowledge applied in an enterprise becomes business analytics and knowledge applied for our common good is wisdom. The big question is how do we extract value from big data?

Companies are gearing up to monetize the big data available primarily to sell more technology solutions or to drive over-the-counter sales of health products. This leaves the individual consumer in a wild west where they are unsure what to trust in the marketplace. Governments, which usually have the statutory responsibility for the delivery of health services or at least to regulate this delivery, are too slow and ponderous to effectively manage this process. In some parts of the world governments are trying to leapfrog these issues by mandating a “smart” environment that allows for the unimpeded flow of health and other information.

There is, however, a missing element here. Where is the convener for this conversation that can accommodate all the potential players – including individuals, governments, employers, philanthropists and entrepreneurs?

Palladium can play that convener role – bringing together multiple stakeholders across the public and private sectors to create enduring economic and social value in the world of big data. Over the past 15 years we have developed and grown out our Informatics Practice that operates primarily in the health space. Combined with our analytical, modelling and forecasting skills we are positioned to deliver big data services to governments and the private sector around the world.

Big data in action
Two of our projects - the USAID Health and Education Plus (HEP+) project in Guatemala and Kenya Health Management Information Systems (HMIS) - illustrate how big data and big data analytics can help transform healthcare services.

In Kenya, we worked to support Kenya’s National Health Information Systems strategy through the implementation of electronic medical records and integration of health-related systems. Health data was integrated from across multiple platforms, with the end goal being to improve data visibility and promote data use for decision making. Across counties, we partnered with devolved county governments to scale up electronic medical records, particularly using the system IQCare. During the project lifetime we trained 1,968 healthcare workers in IQCare, resulting in enhanced system and data use.

Similarly in Guatemala, big data and analytics have helped to transform health decision making and efficiency. In developing a Health Indicators system, part of Guatemala’s National Social Indicators System (SNIS), health sector decision making has become far more streamlined. The portal provides information related to health outbreaks, such as Dengue, diarrhea and malaria, and predicts chances of outbreaks for surrounding communities. In addition, it provides information on budgets and expenses, as well as availability of provisional supplies and medicines by location. Not only is the data portal accessible to all public health workers in Guatemala, it is open to the general public.

For more on Palladium’s Positive Impact work follow us on Twitter, LinkedIn, Facebook and at #PositiveImpact.


About the author

#PositiveImpact December contributor: Farley Cleghorn is Palladium's Global Director of Health.

Dr. Farley R. Cleghorn, M.D., MPH serves as Global Director of Health at Palladium. He has 20 years of HIV/AIDS research and programmatic experience in the Caribbean and the developing world, where his focus has been on the epidemiology, natural history, and pathogenesis of HIV/AIDS and related infections. He has made many original contributions to the field of human retrovirology, particularly in the areas of HTLV-I epidemiology and the modeling of HTLV-I oncogenesis (a model for cancer causation), as well as the epidemiology and natural history of HIV-1 infection.