Informing a Medical Data Revolution

Written by Dr. P. Niclas Broer, M.D. Reconstructive Surgeon at Bogenhausen Teaching Hospital, Technical University Munich and Sabrina Juran, M.Sc., Technical Specialist: Data and Research at United Nations Population Fund.

More and better data are important for health care outcomes and can make a difference to and for people’s health, especially for those at the margins of society. Health care provides a great opportunity for the data revolution, both because it involves a lot of data and will involve even more as wearable devices gain steam, and because the medical industry is open to new technological adoption.

Why are we talking about a medical data revolution?

First, because of the sheer amount of data that are available and the great supply of timely information through pharmaceutical medical databases, digitized patient records, etc. Open-data initiatives by governments, such as U.S. opendata.gov, including data from clinical trials and from patients covered under public insurance programs, provide information on health-care to the public and to academics for in-depth research.

Second, with current technological advances, data capture and analysis are becoming cheaper and easier. A major benefit in health care is that data can be better linked at a low cost, since data for a single patient may come from various health care providers, hospitals, laboratories, and physician offices. Advances in information technology such as electronic medical records (EMR), GPS, mobile applications, wearables and machine learning are enabling detailed capture of structured data about patients, providing a more granular view of diseases and treatment. Other technological advances, including biological research, genomics, and imaging, allow the medical industry to better understand many diseases.

In this technological context, we are more and more moving toward an evidence-based medicine. Instead of relying on medical judgment when making decisions, clinical data can be systematically reviewed before making a treatment decisions based on the best available information.

Aggregating individual data sets into big-data algorithms often provides the most robust evidence, since nuances in subpopulations (such as the presence of patients with gluten allergies) may be rare that they are not readily apparent in small samples.

Fiscal concerns are also certainly driving the data revolution in health care. For example, in the U.S. in 2013, health-care expenses represented 17.6 per cent of U.S. GDP, nearly $600 billion more than the expected benchmark for a nation of the United States’ size and wealth.[1] New opportunities must be found to improve health outcomes – where can investments at the margins result in better patient outcomes? Where can this marginal investment lead to saving money for the health care system?

In order to provide disincentives for overutilization of health services, there has been a shift from fee-for-service compensation to risk-sharing arrangements, such as prevention, chronic disease management, and prioritizing measurable outcomes. Under such schemes, medical stakeholders have greater incentives to compile and exchange information. For example, careful study of data can give insights into who is likely to get sick, thereby enabling preventive treatment.

Further, health systems need to be linked up around the patient. In the case of a cancer patient, different care providers work with a specific patient. New technology enables linking information at low cost and much more efficiently. Innovation related to new technologies and new forms of co-operation between the public and private sectors could help to leapfrog statistical capacity building at all levels.

While many diseases are well characterized and understood, in the future, new technologies with high precision, such as wearables, could detect diseases earlier and taking into account the individual patient. The health sector is an obvious target for big data; Apple and other smartwatch OEMs have fitness-tracking functions included. Some devices can take patient monitoring to a new level.

In the long run, McKinsey estimated that “even a few simple interventions can have an enormous impact when scaled up”. In the “right living” pathway, for instance, they estimated that aspirin use by those at risk for coronary heart disease, combined with early cholesterol screening and smoking cessation, could reduce the total cost of their care by more than $30 billion. While these actions have been encouraged for some time, big data now enables the faster identification of high-risk patients, more effective interventions, and closer monitoring.

While the role for medical stakeholders will become clearer, the more we advance in this field, an important responsibility will be the capture, management and storage of data – consumer data, general big data, behavioural data and environmental data.

Medical data analytics need to make sense of big data with the right tools. For this, data producers need to work closer with data users and data analysts. Medical stakeholders need to go beyond medicine to explore what can be learned and added from other areas, such as development, biology etc. New pathways will open up as new data become available, which will foster a feedback loop. For instance, treatment could change if new data suggest that the standard protocol for a particular disease for a certain patient does not produce the optimal results.

A further need is to advocate for clinical leadership, so that physicians are inspired to apply this new thinking. Medical stakeholders will only benefit from big data if they take a more holistic, patient-centred approach to value, one that focuses equally on health-care spending and treatment outcomes.

Challenges to be overcome include concerns about patient confidentiality. Although new technologies can anonymize patient information from records being transported into large databases, medical stakeholders must be vigilant and watch for potential problems as more information becomes public.

Realizing a medical data revolution will require setting the right incentives to support coordination between different stakeholders within health care systems. Recognizing the value of big data and being willing to act on its insights will require a fundamental shift in mind-set from data producers and users alike, one that may prove difficult to achieve.

[1] This estimate is based on a measure developed by McKinsey, “estimated spending according to wealth,” which is derived from a regression analysis of income and spending data from other countries in the OECD. It estimates how much a given country would be expected to spend on health care based on per capita GDP.

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