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BioMeanings

Seeing the Patterns in Space and Time

Blog/News

METRIC: Software to Measure Cancer Health Environment

9.9.14

BioMedware announces the award of a contract from the National Cancer Institute titled “METRIC Software to Measure Cancer Health Environment“. This is an SBIR Phase 1 contract with a 9 month performance period beginning September 12, 2014. The Principal Investigator for the project is BioMedware’s Chief Scientist, Pierre Goovaerts, supported by Andy Kaufmann and Robert Rommel as software engineers.

The goal of this project is to develop the first software specifically designed for the access, creation and visualization of environmental measures related to cancer health behavior and resources, providing: 1) a user-friendly interface for locating, accessing and importing data into the existing Geographical Information System, 2) automated homogenization of data layers to a common geography, 3) flexible construction of metrics to characterize the food, physical activity and health care access environments, 4) visualization, exploratory data analysis and ranking of the results to identify geographical disparities, and 5) efficient export of results into an Esri shapefile format, Excel workbooks and XML. Software architecture for the prototype will be built to extend and customize BioMedware’s SpaceStat platform. Software design will focus on providing a user-centered design to accomplish the workflow tasks described above.

Keep watching our site, we should have a project page up soon!

Drones, skyjacks, and business ecosystems

8.27.14

A business venue being explored by Amazon and smaller companies as well is delivery via drones. Interesting to see that the technology for skyjacking drones is already being pursued.  If you consider drone piracy to be a business model (and I guess it should be considered as one, even though there are legal and ethical considerations) then we are beginning to see the emergence of a business ecosystem.  Here, delivery by drones making possible a new business model — drone capture and piracy.  That is the idea behind a business ecosystem — innovations making possible the emergence of entirely new businesses, often building on the products of other businesses.

If you are interested in this sort of thing you might look at the following:

How to build a raspberry Pi quadcopter:  https:src=”//code.google.com/p/owenquad/

The Skyjack drone: src=”//www.slashgear.com/skyjack-based-on-raspberry-pi-is-a-drone-that-hijacks-other-drones-03307484/

CRCSI Lecture & Lunch Session with Dr. Geoffrey Jacquez

8.21.14

Friday 22nd August 2014
12:30pm – 2:00pm

(Perth, Australia) The CRCSI invite you to attend a short presentation from Geoffrey followed by discussion and networking with colleagues in the Health Program.

Geoffrey Jacquez has been the Science Director of the CRCSI Health Program for two years and is in Perth for a series of workshops and meetings. Geoffrey has over 20 years’ experience as an active researcher in cancer epidemiology and geography. He has been developing novel statistical methods for analysing case-control data for mobile individuals, and the assessment of space-time interaction for diseases with long latency in mobile populations. Most recently he has been exploring the challenges around the quantified self and crowd sourcing of the genome+, exposome and behavome.

Discussion Topic – “Project Us”
For the health-concerned who wishes to optimize or better their own or their family’s well-being, Project Us is a wearable device that will measure and quantify environmental level data to understand potential risk factors. Compared to Lapka, we will provide actionable recommendations to actively manage our users’ well-being. As a wearable device, the data will also be hyper-specific for each user, as opposed to existing static environmental detectors. We believe this will be a success because the environment is a major factor in maintaining good health and consumers have adopted wearable devices and mobile apps to understand and improve their wellness.

RSVP to Narelle Mullan, Health Program Manager on [email protected] or 0466 779 263

Venue
CRCSI WA Office
Unit 6, 12 Brodie Hall Drive
Technology Park, Bentley
Easy parking nearby

Light lunch will be provided

Announcing the Release of SpaceStat 4: software for the visualization, analysis, modeling and interactive exploration of spatiotemporal data

5.3.14
Download a Free 14-day evaluation of SpaceStat

SpaceStat 4.0 represents a major reworking of the underlying architecture of the application. Multithreading  has been introduced improving the performance of many methods. A LePace-Sage estimator for spatial-error and spatial-lag analyses has been added to the spatial regression method.

Based on customer feedback, we have designed feature enhancements in SpaceStat 4.0 that improve the appearance, functionality and performance of maps and graphs. You will also find that the extensive help documentation has been updated, revised and expanded.

Additionally, we’ve responded to your requests for a SpaceStat virtual class by adding a series of tutorials to our website. Each tutorial comes with a SpaceStat project designed to get you started working with a specific concept, and provides a landing page with a description, time estimate and associated project links.

New LeSage-Pace Estimator

A LeSage-Pace estimator for spatial-error and spatial-lag analyses has been added to the spatial regression method.

LePaceSage2“I am involved in developing and applying multiple regression models for the mass valuation of residential real estate properties. Modelers such as me are always seeking to find improved model accuracy. The spatial regression models in SpaceStat are of particular interest. The addition of the LeSage-Pace output makes it easier to compare to other methods. Incidentally the Spatial Error Model has been the best performer among all models I have tested lately. It is featured in a book I have written on spatio-temporal methods in mass appraisal to be published June 2014. Also thanks to BioMedware for making this change to the product.”

Richard A. Borst, PhD
Tyler Technologies, Inc.

Recently Published Research using SpaceStat…

Spatial Relationship Quantification between Environmental, Socioeconomic and Health Data at Different Geographic Levels

Int. J. Environ. Res. Public Health 2014, 11(4), 3765-3786; doi:10.3390/ijerph110403765
Authors: Mahdi-Salim Saib, Julien Caudeville, Florence Carre, Olivier Ganry, Alain Trugeon  and Andre Cicolella

“We used Spacestat to evaluate relationships between spatial data collected at different geographic scales. Spacestat is easy-to-use and provides powerful tools that make possible spatial data processing, exploratory analysis, and the quantification of spatial relationships in environmental health research. Spacestat is extraordinarily useful for stakeholders seeking to prioritize prevention actions in the context of environmental inequalities reduction.”

Julien Caudeville
French National Institute for Industrial Environment and Risks (INERIS)
Parc Technologique Alata, BP 2, 60550 Verneuil-en-Halatte, France

Space-time clusters of breast cancer using residential histories: A Danish case control study

BMC Cancer.2014, 14:255.  DOI: 10.1186/1471-2407-14-255
Authors: Nordsborg Baastrup Rikke, Meliker R Jaymie, Ersbøll Kjær Annette, Jacquez M Geoffrey, Poulsen Harbo Aslak, Raaschou-Nielsen  Ole

Background

A large proportion of breast cancer cases are thought related to environmental factors. Identification of specific geographical areas with high risk (clusters) may give clues to potential environmental risk factors. The aim of this study was to investigate whether clusters of breast cancer existed in space and time in Denmark, using 33 years of residential histories.

Methods

We conducted a population-based case–control study of 3138 female cases from the Danish Cancer Registry, diagnosed with breast cancer in 2003 and two independent control groups of 3138 women each, randomly selected from the Civil Registration System. Residential addresses of cases and controls from 1971 to 2003 were collected from the Civil Registration System and geo-coded. Q-statistics were used to identify space-time clusters of breast cancer. All analyses were carried out with both control groups, and for 66% of the study population we also conducted analyses adjusted for individual reproductive factors and area-level socioeconomic indicators.

Results

In the crude analyses a cluster in the northern suburbs of Copenhagen was consistently found throughout the study period (1971–2003) with both control groups. When analyses were adjusted for individual reproductive factors and area-level socioeconomic indicators, the cluster area became smaller and less evident.

Conclusions

The breast cancer cluster area that persisted after adjustment might be explained by factors that were not accounted for such as alcohol consumption and use of hormone replacement therapy. However, we cannot exclude environmental pollutants as a contributing cause, but no pollutants specific to this area seem obvious.

Download a Free 14-day evaluation of SpaceStat

Dr. Jacquez recently participated in the Swedish Collaboration on Health Roundtable

4.12.14

Source: CRCSI  Newsletter 

Representatives from the CRCSI were invited by Future Position X to attend the GeoLife Research Program Roundtable in Gavle, Sweden.  The group participated in an intense series of discussions and meetings over four days to advance the program planning and the formation of collaborative partnerships with GeoLife.

The GeoLife program aims to integrate health, information, public infrastructure and knowledge with fundamental GIS technology to become a driver of economic growth and sustainable innovation and has significant Swedish funding for the coming ten years.  Several CRCSI proposals are under consideration within the program with decisions to be made in coming months.

The CRCSI attendees included CEO Peter Woodgate, Tarun Weeramanthri (Chair CRCSI Health Program Board and Executive Director of the Public Health Division, WA), Mike Ridout Director Stakeholder Engagement, Health Program Science Directors Clive Sabel and Geoff Jaquez,  plus affiliate member Dan Goldberg and Research Investment Committee member Mike Goodchild. Future Position X very kindly funded the travel.

The quantified self and crowd sourcing of the genome+, exposome and behavome: Perspective and call for action

1.28.14

by Geoffrey M. Jacquez1,2 and Robert Rommel2

1.  Department of Geography, State University of New York at Buffalo, Buffalo, NY

2. BioMedware, Ann Arbor MI

Introduction: Perhaps one of the greatest challenges and limitations in environmental health and epidemiology is that of measurement of individual health outcomes, their causes, and correlates.  Data on risk factors and exposures are often measured with imperfect instruments such as surveys that are inherently inaccurate and subject to recall and other bias.  At present, biomarkers may provide reasonable estimates of exposures, but can be difficult to obtain and are available for only a small number of compounds.  Ideally, epidemiologists and exposure assessment scientists would have timely information regarding measurements of individual-level physiology and ambient environment (e.g. at the human boundary layer), the specific times and locations where these measurements were collected, and in what settings.

We are at the beginning of a revolution in measurement that has the potential to transform environmental health and epidemiology (Swan 2013).  We argue this transformation will require new ways of thinking about data sharing, consent, privacy and confidentiality, and the formation of an organization to foster the use of personal, crowd-sourced data for the common good.  We begin with a working definition of the genome+, exposome and behavome, and examples of how technology is revolutionizing their measurement.  Next, we consider technology trends in the quantifiable self, and where these will lead in the near future.  We predict these trends will culminate in a new era for epidemiology and environmental health provided mechanisms are established to foster sharing of individual information from these new data streams.  We close with a call for action to form an organization charged with governance, data security, and mechanisms for data sharing, with the ultimate mission of advancing epidemiology, the environmental health sciences, and human health.

Genome+, exposome and behavome:  Consider a conceptual model of three important determinants of health (Figure 1).  Both illness and well-being are treated as core outcomes, whose expression is influenced by the genome+, exposome and behavome.  An individual’s “Genome +” is comprised of their genome (genetic composition), regulome (which controls gene expression), proteome (their compliment of amino acids and proteins) and metabalome (the basis of metabolism and homeostasis).   Together, these constitute a good portion of an individual’s biological makeup.  The exposome is defined as the totality of exposures over a person’s life course (Wild 2005). We define the behavome as the totality of an individual’s health-related behaviors over their life course.  Wild’s definition of the exposome included behavioral determinants of exposure; we treat the behavome separately to clarify the role of human behavior in mediating the exposome (through health behaviors such as smoking, exercise, diet and so on), as well as interactions between the exposome and the genome+ (for example, many behaviors are now recognized to have a genetic component, such as a predilection to alcohol and substance abuse).  These determinants of human health act through place, defined as the geographic, environmental, social and societal milieus experienced over a person’s life course.

Technology trends in measurement of the genome+, exposome and behavome: Measurement of the genome+, exposome and behavome is an enormous challenge.  Nonetheless, the last few years have seen major advances in measurement.  We now provide a few examples of such advances, before considering implications of these technology trends for environmental health and epidemiology.

Measurement of the genome+:  Continued improvements in sequencing technology are dramatically reducing the cost of sequencing individual genomes. In 2000 the Human Genome Project was completed, having sequenced the first whole human genome, at a cost in excess of USD$2 billion (Davies 2010). In 2012 the 1000 genomes project released their phase 1 sequencing data (Pybus et al. 2014).  This project is first to sequence the genomes of over 1,000 individuals, sampled to document human genetic variation across 25 populations from around the globe (The_1000_Genomes_Project_Consortium 2012). When it began, costs for fully sequencing an entire genome were high, and as a result only a portion of each genome was sequenced.  But the cost of whole genome sequencing continues to drop, and the USD$1,000 whole sequence genome is now available (Hayden 2014). In medical practice and research whole genome sequencing is posing ethical challenges regarding the amount of information to disclose to the individual, especially given incomplete knowledge of the genetic basis of disease (Yu et al. 2013).  Nonetheless, we expect whole genome sequencing for individuals to soon be a commodity available for USD$100 or less.  That sequence data will support viable business models is being proven out by companies such as 23andme (https:src=”//www.23andme.com/; see src=”//www.isogg.org/wiki/List_of_personal_genomics_companies for a list of personal genomics companies), which offer partial sequencing using saliva samples to explore ancestral origins and disease risks.  These dramatic reductions in cost of measurement are also occurring in the exome, epigenome, and other constituents of the genome+  (Zentner and Henikoff 2012, Weinhold 2012, Meissner 2012, Mefford 2012).  It is clear that measurements of the genome+ will soon be widely and inexpensively available, and will be incorporated into individual electronic health records, notwithstanding the informatics and ethical challenges posed by their integration (Kho et al. 2013, Flintoft 2014, Tarczy-Hornoch et al. 2013, Hazin et al. 2013).

Near real-time physiological measurement is expanding rapidly in clinical medicine as well as in the burgeoning “wearables” marketplace.  In 2012 Qualcomm established their Tricorder XPrize (src=”//www.qualcommtricorderxprize.org/), with the goal of creating a wireless handheld device that monitors and diagnoses a patient’s health conditions using personal health metrics.  The top three entries are to be announced in May, 2014. Wearable and implantable wireless sensors for healthcare monitoring include cancer detection, glucose monitoring, seizure warning, cardiac rate and rhythm, and heart attack detection, among others (Darwish and Hassanien 2011).  Google is now testing a glucose monitor incorporated into a contact lens (Landen 2014), and “smart” t-shirts for monitoring pulse, respiration and stress levels will soon be on the market (src=”//www.omsignal.com/).  Activity sensors such as those from Jawbone and fitbit are currently available, priced at around USD$100, and provide a record of daily activity (e.g. steps taken, distance traveled, time asleep).  Data from wearable sensors is already being used to monitor physical activity levels in pediatric patients (Yan et al. 2014).  At the 2014 Consumer Electronics Show wearables were prominent and recognized as an emerging market segment (Rowinski 2014).  While still small, the digital health market segment received $1.9B in venture funding in 2013, and posted 39% growth.  It has more than doubled since 2011 (RockHealth 2014).  Even so, the marketplace is nascent and fragmented, it is unclear what will succeed and what will not, and we have only an imperfect understanding of what people will adopt (Figure 2).  It seems reasonable, however, to assume that wearables as a technology will rapidly evolve and that their adoption and use will expand quickly.  This potentially poses a great opportunity for measurement in the environmental and health sciences.

Measurement of the exposome:   The challenge for quantification of an individual’s exposome is measurement of the ambient environment at the human boundary layer – the epidermis, mouth, mucosa, and nasal passages – where contaminants and pathogens enter the body (Balshaw and Kwok 2012).  This requires wearable sensors integrated into clothing (e.g. smart shirts, pants and shoes), jewelry and bracelets, or wearable on the lapel ( see (Windmiller and Wang 2013) for a review).   Recognizing their importance for quantification of the exposome, the National Institutes of Health has funded several initiatives to develop such “environmental tricorders” (see for example src=”//grants.nih.gov/grants/guide/rfa-files/RFA-ES-09-005.html). Environmental tricorders are already on the market, although the environmental factors they monitor are somewhat limited, including Volatile Organic Compounds, dust, light, sound, ionizing radiation, carbon dioxide and others.  Examples include products from Valarm and Sensorcon, among others.   The Knight foundation recently funded prototyping of the “Global Sensor Web”, whose objective is to create an online platform for aggregating geo-tagged data sets from public and personal data sources (src=”//www.knightfoundation.org/grants/201347663/), although these are not necessarily data from wearable sensors.  As noted below, there currently is a distance between sensors of sufficient quality, accuracy and precision to be of immediate use in environmental epidemiology, and the low-cost sensors currently being adopted in the consumer marketplace.

Measurement of the behavome:  We think of the behavome as separate from Wild’s exposome, as health behaviors are key mediators of exposures.  Behavioral recognition methods for assessing what an individual is doing has for decades been an important topic of health research.  With the advent of sensors in residences, health care facilities, and wearable on patients, the issue of multisensor data fusion for activity recognition has become an important topic.   These technologies are already being deployed and assessed in nursing home and assisted living facilities. Recent research has demonstrated these methods can identify risky behaviors with good accuracy and low deployment costs (Palumbo et al. 2013).  The “internet of things” including smart homes, smart cars and smart workplaces, is in the early phase of what many predict to be explosive growth (Ashton 2009).  In 2008 the number of devices on the Internet exceeded the number of people, and in 2020 will exceed 50 billion devices (Swan 2012). Information on when, where and how we use appliances, electronic devices, machinery and environmental  controls in home and workplace settings, and while commuting, have yet to be used to quantify the behavome.  The value of near real-time data on ambient temperatures and how often and when we use the refrigerator may have enormous value for quantifying, for example, personal energy budgets.  A variety of different approaches for assessing health behaviors have been suggested using technologies such as inertial sensors, Global Positioning System, smart homes, Radio Frequency IDentification and others. Most promising is the sensor fusion approach that combines data from several sensors simultaneously (Lowe and ÓLaighin 2014).  To our knowledge technologies such as Google Glass have yet to be used for capturing video images to chronicle dietary intake and other health-related activities.  Other potential applications include quantification of personal energy budgets, individual walkability (e.g. (Mayne et al. 2013)), and documentation of other personalized environmental metrics.  Once health-related behaviors are known, the possibility of using gamification (Whitson 2013) and other approaches to encourage salubrious behaviors become possible  (Schoech et al. 2013).

Where will these measurement trends lead?  At present there are two domains for measuring the quantified self, the high-end approach focused on measurement accuracy and precision, and the quantified self as a commodity that is focused on capturing the consumer market (refer to Figure 3).  We see the possibility of a future convergence and emergence of low-cost sensors of sufficient quality to support a common good – high quality environmental health research, made possible by volunteered information provided by enlightened citizen scientists.  But achievement of this goal likely will require the establishment of appropriate mechanisms of data sharing, oversight and governance, and the creation of a user community of sufficient market mass to influence the development of COTS sensors of sufficient quality to support research in environmental health and epidemiology.

What benefits might be realized?  There is a growing recognition that new ways of measuring ourselves require new ways of understanding what “normal” means (McFedries 2013).  We know amazingly little about the ambient environments individuals experience through the course of their daily lives.  Similarly, we know very little about the local environments experienced by the members of local communities across the US and around the world.  How much temporal and spatial variability is there in air quality at the human boundary layer for individuals in diverse communities?  What are the exposure profiles of children as they move about their daily lives in our neighborhoods and schools?  A national baseline environmental assessment, incorporating data on the quantified self collected by citizen scientists, may begin to address such questions.  Important issues will need to be addressed regarding data quality, data sharing, sampling design and governance, but these do not appear to be insurmountable  (Goldberg et al. 2013).

A call for action.  It seems reasonable to the authors to assume the technology trends in figure 3 will indeed result in more accurate measurement of individual-level physiology, exposures and genetics at decreased costs.  In fact, the commoditization of the quantifiable self is rapidly taking place, as demonstrated by products such as Google Glass, fitbit, the advent of environmental tricorders from sensorcon, Valarm and others.  The data collected by these technologies is a highly valued business asset, and as such is not likely to be shared with research scientists for advancing epidemiology and public health. This balkanization of data for measuring the quantifiable self may be ameliorated by appealing directly to the individual to act both in their own self interest and also for the common good.  We must provide an alternative for the highly vested, motivated citizen scientist, so they may choose to share their personal measurements for the good of all.  This will require the formation of an organization charged with governance, data sharing, data security, and oversight of research and data use, with the overall mission of advancing human health.  Rather than a balkanization of measurements on the quantifiable self into isolated information silos under the control of corporations, we envision the enlightened sharing of such personal measurements that will lead to safer neighborhoods, workplaces and communities.  We believe our professional organizations, the AAAS, Sigma Xi, ISEE, and the AAG, are best positioned to take a leadership role in addressing this need.  We encourage readers to consider bringing this to the attention of their professional organizations.

Figure 1. Schematic of relationships between the genome+, behavome, exposome and human health.

 

Figure 2. Schematic of a survey by Forrester Research, Inc., assessing adoption preferences for wearable devices.
Figure 2. Schematic of a survey by Forrester Research, Inc., assessing adoption preferences for wearable devices.
Figure 3. Technology trends in the quantified self.
Figure 3. Technology trends in the quantified self.

Research sensors, such as those funded by grants from NIH and Qualcomm’s Xprize challenge, strive for accuracy and high quality, but are expensive. The emergence of the wearables marketplace is resulting in sensors as commodities, COTS (commercial off the shelf) sensors that are relatively inexpensive but not necessarily of sufficient quality (e.g. accuracy and precision of measurement) to support environmental health research. We see a trend towards higher quality, low cost sensors (Future bubble) that may be suited for baseline environmental and population-level exposure assessment.
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Davies, K. 2010. The $1,000 Genome: The Revolution in DNA Sequencing and the New Era of Personalized Medicine. New York: Simon and Schuster, Inc.
Flintoft, L. (2014) Phenome-wide association studies go large. Nature Reviews Genetics, 15.
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Our Changing World: Soil Mapping (Podcast – Radio New Zealand)

12.10.13

Landcare Research scientists are using mobile spectroscopy to analyse soil, allowing geostatisticians to create better soil maps.

Dr. Pierre Goovaerts, from BioMedware, explained how the complex mathematical theory of geostatistics is being used to better understand soil science and create better soil maps. You can view a New Zealand soil map here.

Geostatistics short course – Perth, Western Australia

10.25.13

In conjunction with CRCSI and WALIS Forum 2013, Dr. Goovaerts will conduct a short course November 6th and 7th.

The course will increase your understanding of geostatistics and its application to several disciplines. After completion of this course you will be well prepared to import, visualize and analyse your own data in a space-time information system.

The course is designed to be practical with lectures, examples and exercises to provide skills and encourage participation.

read more >>

A commentary on the Behavome and Genetic GIS

9.5.13

Recently, I coined the term “Behavome” as the totality of an individual’s behaviors that mediate exposures (the exposome) and gene expression (the genome). This construct matters because it largely defines the determinants of human health.

Figure 1
Figure 1

This schematic representation of genetic geographic information science (Genetic GIS) captures the three primary determinants of human health, the genome +, behavome and exposome. Health has two facets, illness and well being. An individual’s biology is represented by the “Genome +”, comprised of their genome (genetic composition), regulome (which controls gene expression), proteome (their compliment of amino acids and proteins) and metabalome (the basis of metabolism and homeostasis). The environments they experience, which is represented as the exposome, is defined as the totality of exposures over the life course (Wild 2005).

Finally, the totality of an individual’s health behaviors over the life course is represented by the behavome, which mediates the exposome and interactions between the exposome and the genome +. These determinants of human health act through place, defined as the geographic, environmental, social and societal milieus experienced over a person’s life course. This synthesis is referred to as genetic geographic science, or genetic GIS.

Thoughts from Austin: NAACCR Annual Meeting

6.12.13

This week I am attending the meetings of the North American Association of Central Cancer Registries that is being held in Austin, Texas. The topic of this year’s conference is “Thinking big, the future of cancer surveillance”, and I’m involved in two activities. The first was a series of workshops that occurred on Saturday and Sunday titled “Evaluation of Homomorphic Cryptography for Geospatial Studies with Human Subjects”. This workshop was convened as part of a grant funded by National Library of Medicine that is evaluating the feasibility of using homomorphic cryptography to accelerate the pace of research and discovery for studies that use human subjects data. “Homomorphic” means mathematical operations can be conducted on encrypted data (e.g. in the encrypted space), greatly reducing the risk to privacy of confidential data.

My co-organizer, Dr. Khaled El Emam of Privacy Analytics and the University of Ottawa e-health laboratory were very happy with the recommendations that came out of the working group. These are being written up as a BioMedware report to the National Library of Medicine, and will be available in our Publications when they are ready. But here is a preview of some of the “low-hanging fruit” that homomorphic cryptography may make possible.

First, increased data security greatly enhances data sharing, and hence participation in all manner of activities where data sharing plays an important role. It turns out a key bugaboo in the processing of disease registry data is deduplication; the removal of duplicate data records that may appear in several data bases. This arises, for example, when snowbirds flit between Michigan and Florida, yet have records of cancer tumor treatment in both States. The data providers must be very satisfied that the potential for unintentional release of their highly confidential patient records is absolutely minimal, meaning, in practice, that two data providers may be reluctant to share data to search for record duplicates. Homomorphic encryption solves this by having deduplication take place in the encrypted space – hence even if the data security is breached the records appear as complete gibberish.

Second, increased data sharing means data aggregation across data providers becomes far less of a concern. Hence activities that involve pooling data, such as determining the number of cases anticipated in projected enrollment reports for NIH grant applications, suddenly becomes very easy.
Other opportunities were identified – keep checking back for our release of the workshop report!

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