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Data Visualizations in Healthcare

A couple of weeks ago I attended HIMSS 2017 in Orlando (Healthcare Information and Management Systems Society), the largest annual Healthcare IT event in the US. One of the big tenets of the show was system interoperability. There are lots of different vendors, few standards, and vast amounts of data being collected. There is an emerging set of APIs (such as HL7 FHIR) to ensure data can be shared among systems and follows a patient properly through the various providers she encounters during her episodes of care.

Somewhat serendipitously I came across a booth which had large prints of beautiful data visualizations on it. The booth was from Arcadia Healthcare Solutions. Given my background on data visualization and my being employed at Rennova Health Technology Solutions and responsible for our healthcare IT products and services, I was drawn to interpret these prints. They are also featured in an online data gallery, which I encourage you to explore.

One of these visualizations created by Jeff Solomon is called “The Health IT Space“. It displays highly aggregated data from various EHR (Electronic Health Record) systems. From the gallery:

The Electronic Health Record is a data gold mine. Each patient you see generates millions of detailed records in real time that can be extracted and analyzed for improved predictive algorithms, increased operational efficiency, better care quality, and so much more.


These graphs are stylized Entity Relationship diagrams from seven different EHRs. Nodes are data tables, and edges are relationships between these tables inferred from shared attributes.


The color-highlighted nodes are referring to patient data. The size of the node corresponds to the amount of records in the respective table.


Again, from the gallery description:

In each cluster, the core patient entity – the nucleus around which the rest of the data revolve – can be identified by its contrasting color. The tables containing the bulk of the clinically and operationally valuable data tend to form clusters of large, interconnected nodes, while a larger number of satellite tables house system configurations and other low-volume metadata that has very few relationships to the nucleus.

At the large end of entire health systems, the graph starts to look very busy:


It is pretty amazing how much data is being aggregated into these graphs. Nearly 5,000 database tables – hence the black areas where there are too many dots to separate them at this resolution. A combined number of 18 billion records! Nearly 300,000 relationships between these tables (again, the lines are too numerous to be distinguishable).

The Health IT SpaceI find it somewhat humbling to review these graphs. Our own MedicalMime EHR falls into the small category by these standards. Major and Large EHRs are at least one, maybe two orders of magnitude larger and more complex.

Interpreting the large amount of data contained in the EHR opens up many ways to improve healthcare, both medically for the patient as well as operationally for the providers. Visualizations can help us to better understand patterns and trends which otherwise would remain hidden.

The entire big picture of the above visualization is indicated on the right. For a Hi-Res version please contact the friendly folks from Arcadia Health Solutions directly from their website.

Another big trend at HIMSS’17 was Artificial Intelligence. Machine learning and predictive analytics received a lot of attention. Solutions like IBM’s Watson Health promise to bring world-class expertise into ordinary physician practices through subscription to hosted specialty knowledge in the cloud – whether curated by scientists or machine-learned using statistical techniques from big data. Health Catalysts is an open platform to make machine learning techniques more accessible and bring them to small SW houses, not just the large companies with large R&D budgets. While certainly overhyped at the moment, acceptance for obtaining a “second medical opinion from the cloud / app” to improve clinical decisions is rising.

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Posted by on May 20, 2017 in Uncategorized


Digital Wages in the Gig Economy

Digital Wages in the Gig Economy

A small research team from the Oxford Internet Institute has recently issued a report based on a three year investigation into the worldwide geographies of the so-called Gig-Economy, online work which allows many talented people in the low and middle income countries of the world to compete on a global stage. From the Executive Summary:

Online gig work is becoming increasingly important to workers living in low- and middle-income countries. Our multi-year and multi-method research project shows that online gig work brings about rewards such as potential higher incomes and increased worker autonomy, but also risks such as social isolation, lack of work–life balance, discrimination, and predatory intermediaries. We also note that online gig work platforms mostly operate outside regulatory and normative frameworks that could benefit workers.

One of the eye-catching and very information rich visualizations comes from a related Blog post by the “Connectivity, Inclusion, and Inequality Group” called “Uneven Geographies of Digital Wages“.


Dollar Inflow and Median Wage by Country

The cartogram depicts each country as a circle and sizes each country according to dollar inflow to each country during March 2013 (on the freelance work platform, rebranded in 2015 to Upwork). The shading of the inner circle indicates the median hourly rate published by digital workers in that country. The graphic broadly reveals that median wages are, perhaps unsurprisingly low in developing countries and are significantly higher in wealthier countries.

Another Blog post on the geographies of online work adds several more visualizations (based on 2013 data, so a bit dated by now). For instance, one world map highlights the relationship between supply and demand. It distinguishes between countries with a positive balance of payment (i.e. countries in which more work is sold than bought) and countries with a negative balance of payment (countries in which more work is bought than is sold). The figure more clearly delineates the geography of supply and demand: with much of the world’s demand coming from only a few places in the Global North.


Balance of payments

Another very interesting and dense visualization is a connectogram (see our previous post on Connectograms and the Circos tool) demonstrating the highly international trade in the online Gig-Economy: 89% of the trade measured by value happened between a client and a contractor who are in different countries. The network therefore attempts to illustrate the entirety of those international flows in one graph. It depicts countries as nodes (i.e. circles) and volumes of transactions between buyers and sellers in those countries as edges (i.e. the lines connecting countries). Country nodes are shaded according to the world region that they are in and sized according to the number of buyer transactions originating in them. Edges are coloured according to the flow of services: with the line shaded as the colour of the originating/selling region. Edges are also weighted according to the total volume of trade.


The Geographic Network of Sales

We see not just a complex many-to-many relationship of international trade, but also the large role that a few geographic relationships take (in particular, India and the Philippines selling to the United States).

Back to the Executive Summary of the above report:

The report’s central question is whether online gig work has any development potentials at the world’s economic margins. Its motive is to help platform operators to improve their positive impact, to help workers to take action to improve their situations, and to prompt policy makers and stakeholders interested in online gig work to revisit regulation as it applies to workers, clients, and platforms in their respective countries.

It is interesting to see these marketplaces evolve, in terms of the international, distributed nature, issues such as taxation, intermediation, opportunities and risks. There are also entirely new forms of social networks forming, based on blockchain powered token systems convertible into crypto-currencies (such as Steem). The core concept here is to eliminate not just geographical distance, but also risks from exchange rate fluctuations and predatory intermediaries. It remains to be seen to what degree this can act as a counterweight to technology-induced increasing inequality.


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Posted by on March 26, 2017 in Industrial, Socioeconomic



10 years of BI Magic Quadrant

Every February the Gartner group publishes its Magic Quadrant (MQ) report on the Business Intelligence segment. As covered in previous years (2012, 2013, 2014), its centerpiece is a 2-dimensional quadrant of the Vision (x-axis) vs. Execution (y-axis) space. When this year’s report came out about 3 weeks ago, it completed a decade worth of data (each year from 2008 to 2017) on about 20-25 companies each year. Here is the latest 2017 picture (with trajectory from previous year):


I have collected the MQ position data in a simple Google Docs spreadsheet here. The usual disclaimers are worth repeating:

  • Gartner does not publish the x,y coordinates as they caution against using them directly for interpretation.
  • To approximate the data, I screen-scraped them from images revealed by Google search, which introduces both inaccuracies and the possibility of (my) clerical transcription error.
  • Changes in the x,y coordinate for one company from one year to the next are a combination of how that company evolved as well as how Gartner’s formula (also not published) may have changed. For example, from 2015 to 2016 many companies “deteriorated” in the ranking as can be seen from the following graphic:


  • It is unlikely that so many companies deteriorate in their execution in unison. More likely, the formula changed and shifted the evaluation landscape upwards, meaning companies that stayed the same on the previously used factors now slipped downwards. (I read somewhere that Gartner wanted to have only 3 companies in the leader quadrant – an instance of curve-fitting if you will.) Whatever the reason, this shift removed all but three companies from the upper rectangle on execution.
  • That said, relative changes between companies in the same year are still meaningful, as they are all graded on the same formula.

Naturally, it is of interest to study the current leaders – Microsoft, Tableau and Qlik. Here is the dynamic evolution of these three competitors MQ positions over the last decade as GIF:

mq_leaders_allFrom the entire trajectory (left) one can see that all of them have been leaders for many years.

Tableau joined the leader board in Feb-2013 from the status as challenger. It went public in May-2013 (ticker symbol DATA) and has grown into a company with close to $1B in annual revenue and > 3000 employees. It has had particularly consistent ratings on Execution scores since then. Many of the visualization metaphors it has introduced are commoditized by now, with a desktop designer tool for both Windows and Mac, a robust server product as well as a free public cloud-based option. For any company of such size it is a challenge to grow fast enough and it needs to both stay ahead of the competition as well as diversify into adjacent markets. Its stock price reached lofty heights of $127 (roughly $10B market cap) by mid-2015, but saw a drop to ~$80 by year-end 2015 and then cut in half one month later ($41 on Feb-1, 2016), from where it has only modestly recovered to around $52. Most SW products nowadays are offered as a service, which Tableau still hasn’t transitioned to as much as others have. That said, it’s Aug-2016 hiring of Adam Selipsky from Amazon Web Services indicates this transition and focus on Tableau Online and scale.

Microsoft is in a unique position for many reasons: It has a very healthy and diverse product portfolio across Windows, Office, Server, Cloud, and others. Most of these help build out a complete BI stack, helping it in the Vision dimension. Furthermore, it can subsidize the development of a large product and offer it free to capture market share. Unlike Tableau, the Power BI price point is near zero, which has helped it acquire a large community of developers, which in turn provides a growing gallery of solutions and plug-in visualization components. Lastly, Power BI is very well integrated with products such as Excel, SharePoint and SQL Server. Many enterprises already invested in the Microsoft stack will find it very easy to leverage the BI functionality.

I don’t have personal experience with QlikView, but enjoy reading on Andrei Pandre’s visualization Blog about it. Qlik was always a bit different, focusing on complex analytics more than mainstream tooling, and it having been taken private in Mar-2016 seems to have reduced its leadership status.

Another Blog I quite enjoy reading is (such as the 2017 article on the BI MQ by author Bruno Aziza).

I would summarize various factors influencing the BI solutions over the last few years:

  • Visualization tools and galleries – BI tools have reached a high level of maturity around the  generation of dashboards with interconnected components as well as complex interactive visuals such as treemaps or animated bubble charts. Composing the visual presentation is often the smallest part of a BI project, with proper data-mining often taking an order of magnitude more effort and resources.
  • ETL Commoditization – the need to support data-wrangling as part of the solution, not a mix of add-on tools. Microsoft’s SSIS, Tableau’s Maestro, Alteryx Designer Tools, etc.
  • Hybrid Cloud and On-Premise solution – Most enterprises want a combination of some (often historically invested) On-Premise data store / analytics capabilities with newer (typically subscription-based) Cloud-based services.
  • Big Data abilities and Stream processing – Need to integrate popular data visualization tools (Excel, Tableau, Power BI, QlikView) with big data platforms such as Hadoop. Furthermore, ability to analyze data as it is ingested in real-time without the time-consuming post-processing for dimensional analysis (data cubes)
  • Predictive Analytics and Machine Learning – Move focus from reporting (past, what happened?) via alerting (present, what’s happening?) to predicting (future, what will happen?)


Two weeks ago I attended the HIMSS’17 conference in Orlando (HIMSS = Healthcare Information Management System Society). I was particularly interested in the Clinical and Business Intelligence track and exhibitors in that space. My overall impression is that adoption of BI tools in Healthcare is still somewhat limited, with the bigger operational challenges around system interoperability and data exchanges, as well as adoption of digital tools (tablets, portals, Electronic Health Record, etc.) by patients, physicians, and providers.

I did see specialty solution providers such as Dimensional Insight. While impressive, their approach seems decidedly old school and traditional. I doubt that any company can sustain a lead in this space by maintaining a focus on their proprietary core technology (such as their Diver platform / data-cube technology). Proper componentization, standard interface support (such as HL7 FHIR) and easy-to-integrate building blocks will win broader practical acceptance than closed-system proprietary approaches.

There are some really interesting systems being applied to healthcare such as IBM’s Watson Health or the new platform from Health Catalyst. But that is a story for another Blog post…

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Posted by on March 5, 2017 in Industrial


Magic Quadrant Business Intelligence 2014

Over the last two years we have posted some visualization and interpretation of Gartner’s Magic Quadrant Analysis on BI companies. The previous articles in 2012 and 2013.

A Blog reader contacted me about the 2014 update; he sent me the {x,y} coordinate data for 2014 and so it was relatively straightforward to update the public Tableau workbook for it. Here is the image of all 29 companies with their changes from 2013 to 2014:

Gartner’s Magic Quadrant for Business intelligence, changes from 2013 to 2014

Gartner’s Magic Quadrant for Business intelligence, changes from 2013 to 2014

With the slider controls for Execution and Vision as well as the changes thereof, it is easy to filter the dashboard interactively. For example, there were a dozen companies who improved in their execution score (moving up in the quadrant):

Subset of companies who improved execution over the last year.

Subset of companies who improved execution over the last year.

Most of the companies improving their execution are niche players, with SAP being the only leader improving its execution score.

Most of the leaders improved in their vision score (moving right in the quadrant), including Tableau, QlikTech, Tibco and SAS.

Subset of companies who improved vision over the last year.

Subset of companies who improved vision over the last year.


7 companies, most of them leaders, lost ground on both execution and vision (moving to the bottom-left):

Companies who lost ground on both execution and vision in 2014

Companies who lost ground on both execution and vision in 2014


Lastly, I have updated the Public Tableau workbook with the Magic Quadrant as originally published in 2012 with the data for 2013 and 2014. (Click here for the interactive drawing.)

Public Tableau workbook with 7 years of BI Magic Quadrant data.

Public Tableau workbook with 7 years of BI Magic Quadrant data.

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Posted by on September 28, 2014 in Industrial


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Apple’s Health App and HealthKit – Platforms for Next Gen Healthcare?

Business Analytics 3.0

mobile-applicationsGame on….I think we just witnessed a big next generation leap in Healthcare Data and Analytics.  Apple jumped into the health information business on June 2, 2014, launching both a new health app and a cloud-based health information platform with IOS 8.

The new App, called simply “Health”, will collect a number of body metrics including blood pressure, heart rate, and stats on diet and exercise.  Health will constantly monitor key health metrics (like blood sugar or blood pressure), and if any of them begin to move outside the healthy range, the app can send a notification to the user’s doctor.

The Health app will share all its information with a new cloud platform called “HealthKit.” The new health cloud platform is designed to act as a global repository for all the user’s health information. It will accept data collected by a variety of third-party devices and apps. For instance Nike is now working to…

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Posted by on June 2, 2014 in Medical


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Visualizing Voting Preferences for World Values

The other day I listened to a presentation by Melinda Gates prepared for the United Nations to deliver an update about progress towards the Millennium Development Goals (MDG). The eight goals of the MDG had been embraced by the UN back in 2005 for the time target of 2015. So it is reasonable to see whether the world is on track to reach each of these eight goals. To summarize, from the MDG Wikipedia page:

  1. Eradicating extreme poverty and hunger
  2. Achieving universal primary education
  3. Promoting gender equality and empowering women
  4. Reducing child mortality rates
  5. Improving maternal health
  6. Combating HIV/AIDS, malaria, and other diseases
  7. Ensuring environmental sustainability
  8. Developing a global partnership for development

A good listing of reports, statistics and updates can be found on the UN website here.

Sample Vote for 6 of 16 MDG choices

Sample Vote for 6 of 16 MDG choices

At the end of Melinda’s presentation is a link to a UN global survey on the MDG goals after 2015. I took this survey and found the visualization of voting results quite interesting. First, one is asked to select six out of a list of sixteen (6 of 16) goals which one thinks have the highest impact for a future better world. (The survey methodology is described in more detail here.) Here is a sample vote:

A nice touch is that for each of the sixteen goals there is a different color and when you check that goal, one of the sixteen areas on the stylized globe is filled with that color. Personal data such as name is optional, but some demographic information is required, including age, gender, educational level and country. Next, one can look at a summary of all currently tallied votes and compare them interactively to ones own vote (checkmarks on the right).


It is perhaps not surprising that I voted very similar to others in similar demographic cohorts.

  • Country: I picked five of the Top five goals like all other voters living in the US. I included ‘Political freedoms’ in my top six, which in the US only ranks 11th.
  • Age: I shared five of the Top six goals with people in my age group (world-wide). The one I did not check was ranked 4th (Better job opportunities). When you mouse over one of the goals, the display changes to highlight this goal in all columns:
Interactive Vote Analysis with highlighted goal

Interactive Vote Analysis with highlighted goal

  • Gender: Here I picked four of the Top five goals (did not include the ‘Better job opportunities’).
  • Education: I voted very similar to people with very high HDI (Human Development Index, a visualization of which we covered in a previous post) with five of the Top six.

From the above, it seems somewhat surprising that voters in the US did not ascribe a higher value to ‘Better job opportunities’, given how much economic values and topics like unemployment seem to dominate the media. That said, these votes should be a reflection about which goals are most valuable for making the world a better place – not just your own home country. Worldwide it seems that other, more fundamental goals are judged by voters in the US to be more important than ‘Better Job opportunities’.

Another chart on the results page is showing a heat map of the world countries based on how many votes have been submitted. I thought it was interesting that Ghana had submitted about twice as many votes as all of the US, and Nigeria about 7x as many. The country with most voters at this time is India, but not far ahead of Nigeria.


A fairly useless dynamic animation in this map is a map pin drop of four people who voted similarly to me. I found this too anecdotal to be of any real interest and downright annoying that I couldn’t turn it off. and just focus on the vote heat map. For example, the total number of votes should be displayed in the Legend. I vaguely remember that it was several hundred thousand from 194 countries prior to starting the survey, but couldn’t get that data to display again without clicking on the Vote Again:


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Posted by on September 21, 2013 in Education, Medical, Scientific


Personal Analytics with the Suunto Ambit

Suunto Ambit

Half a year ago my wife bought me the Suunto Ambit multi-function sport watch and heart rate monitor. It is a fantastic device, with very precise GPS, lots of add-on functionality and an interesting online portal and community.

There is some configuration and setup involved, such as pairing the Ambit with your heart rate belt and in the case of cycling with a cadence pod. You charge the batteries by plugging it into a USB port, which is also the way how you upload the data form the device to your computer or a website.

While the device itself and its programmability is quite advanced, I want to focus here on the associated online portal called where you can upload and visualize all your data for free – and share it with friends or the community if you’re so inclined. This amount of personal data collecting and analyzing is a fairly recent phenomenon, often referred to as Personal Analytics.

Each recorded session with the Suunto can be uploaded and classified into one of many sports, such as hiking, cycling, basketball, or indoor exercise. Each session is called a move, and with the portal you can collectively visualize all your moves. The current theme at has a black background with mostly orange bars and charts. One of the first controls to organize your moves is either a list or a calendar control.

Calendar Control for Moves

Calendar Control for Moves

This already gives you a good overview over the type of sports activities and the distribution over weekday and weekends. A summary display is available in various forms, such as the following simple bar charts.

Summary information about heart rate zones

Summary information about heart rate zones

Another display format summarizes your selected moves, such as all moves in a particular month together with commensurate calorie consumption and breakdown of hours by type of move.

Moves Summary Display

Moves Summary Display

You can now select either a single or multiple moves (or group by the type of move) and display more information about this particular move. Note the x-axis can be set to display either distance or time and one can zoom in on any part of the entire recorded move. One can alos overlay multiple measures in the same chart by selecting more than one factor, although I find this to lead to very busy and confusing charts.

Graph and BarChart details per move

Graph and BarChart details per move

There are many individual measurements available for display, some based on individual sensors (like heart rate or GPS location or temperature or altitude / air pressure), others based on calculations and estimates (such as speed, recovery parameter “R&R”, EPOC or VO2).


Of particular interest to me as a cyclist is the ability to overlay the GPS-track on a Google map. Not only is it a very detailed recording of the route, but it is color-coded based on the currently selected measure. For example, the color-range shows the heart rate in the same colors as the above bar charts. One can clearly see where one is just warming up at the beginning (low heart rate, green color) or where one is riding up “into the red”, i.e. towards the limits of one’s own heart rate. Selecting points along the route displays some information about that particular point of the ride.

One interesting feature would be a time-geo correlation of any portable photo camera when taking pictures along the ride. Based on synchronized time one could then easily geo-code the photos even without any GPS capability within the camera itself.

The Suunto Ambit can do a lot more, including customizing the display mode and storing your configurations in so-called apps. One idea I have for this is to display an estimate of the total calorie consumption for a known route when continuing at the current pace (but I haven’t played with the programming yet). The Ambit seems to be particularly well suited to hiking, mountain biking or skiing due to its altimeter; however I don’t get to leverage that in flat Florida. Only the few bridges over the Intracoastal waterway show up as bumps in the vertical – with the corresponding acceleration of the heart rate on the uphill side.

One of the downsides is the fact that the heart rate sensor worn around the chest does not work in the water. Hence any swimming in the Ocean or the pool can not be measured precisely. (I replace the measurements with estimates.) And sure enough, just recently Suunto announced the new Ambit 2, which overcomes this limitation. Such is the world of new electronic toys, that the half-life of their innovation is getting shorter and shorter.

Bubble Chart of set of rides

Bubble Chart of set of rides

Measures in Bubble Chart

Measures in Bubble Chart

One last chart I wanted to point out is the flexible bubble chart. Shown above is a selection of all my rides in the first half of 2013 (47 rides minus two outliers, very long rides which would have changed the scale and compressed the rest of the chart). This gives a good feel for the distribution and variance of personal rides over a longer period of time – from the quick half hour duration to the more typical rides of a good 2 hours. Note that one can select any of about 30 measures in any of the three drop-down boxes (X-Axis, Y-Axis, Bubble-Size).

One side-effect of measuring and visualizing so many moves is that we find some interesting differences in our respective exercise habits and corresponding energy consumption. While I burn most of my calories on the bicycle, my wife gets more exercise out of indoor circuit exercises and Yoga than I do. For me, after literally decades of recreational cycling, I can raise my heart rate to much higher levels for extended periods of time on the bike compared to indoor circuit exercises. In a way that is not surprising, given the strength and oxygen consumption of the large leg muscles compared to smaller shoulder and arm muscles. But I would not have expected the difference to be so pronounced and could not have quantified it nearly as precisely as without such personal analytics.

It can be expected that the field of healthcare and personal analytics will converge and provide much more personalized data and insight into the specific life of any patient. Medical indicators like heart rate, blood sugar, blood pressure or factors like exercise and diet will become much more quantifiable and individually tracked over time. The hope is that this will also lead to better, more personal and generally more preventive care and medical treatments to any personal condition.

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Posted by on June 30, 2013 in Recreational


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