Free Research Report on the State of Patient Experience in US Hospitals

Download Free Report from TCELab: Improving the Patient Experience

The Centers for Medicare & Medicaid Services (CMS) will be using patient feedback about their care as part of their reimbursement plan for acute care hospitals (see Hospital Value-Based Purchasing (VBP) program). The purpose of the VBP program is to promote better clinical outcomes for patients and improve their experience of care during hospital stays. Not surprisingly, hospitals are focusing on improving the patient experience (PX) to ensure they receive the maximum of their incentive payments.

Free Download of Research Report on the Patient Experience

I spent the past few months conducting research on and writing about the importance of patient experience (PX) in US hospitals. My partners at TCELab have helped me summarize these studies into a single research report, Improving the Patient Experience . As far as I am aware, these series of studies are the first to integrate these disparate US hospital data sources (e.g., Patient Experience, Health Outcomes, Process of Care, and Medicare spending per patient) to apply predictive analytics for the purpose of identifying the reasons behind a loyal patient base.

While this research is really about the entirety of US hospitals, hospitals still need to dig deeper into their own specific patient experience data to understand what they need to do to improve the patient experience. This report is a good starting point for hospitals to learn what they need to do to improve the patient experience and increase patient loyalty. Read the entire press release about the research report, Improving the Patient Experience.

Get the free report from TCELab by clicking the image or link below:

Download Free Report from TCELab: Improving the Patient Experience



Source by bobehayes

May 04, 17: #AnalyticsClub #Newsletter (Events, Tips, News & more..)


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A: The CLT states that the arithmetic mean of a sufficiently large number of iterates of independent random variables will be approximately normally distributed regardless of the underlying distribution. i.e: the sampling distribution of the sample mean is normally distributed.
– Used in hypothesis testing
– Used for confidence intervals
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How Big Data Analytics Can Help Track Money Laundering

Criminal and terrorist organizations are increasingly relying on international trade to hide the flow of illicit funds across borders. Big data analytics may be the key to tracking these financial flows.

or the past decade, governments around the world have established international anti-money laundering (AML) and counter-terrorist financing efforts in an effort to shut down the cross-border flow of funds to criminal and terrorist organizations. Their success has encouraged criminals to move their cash smuggling away from the financial system to the byzantine world of global trade. According to PwC US, big data analytics are becoming essential to tracking these activities.

It’s easy to understand why criminal and terrorist organizations would turn to the global merchandise export trade to hide the movement of their funds. It’s a classic needle in a haystack — an $18.3 trillion business formed of a “web of complexity that involves finance, shipping and insurance interests operating across multiple legal systems, multiple customs procedures, and multiple languages, using a set of traditional practices and procedures that in some instances have changed little for centuries,” PwC says.

Watching the Money Flow

There’s no real way to quantify how much money criminals are invisibly exchanging using this system. PwC notes that the Global Financial Integrity (GFI) research and advocacy organization estimates that 80 percent of illicit financial flows from developing countries are accomplished through trade-based money laundering (TBML), from more than $200 billion in 2002 to more than $600 billion in 2011. GFI believes more than $101 billion was illicitly smuggled into China in 2012 via over-invoicing, which is only one of the common TBML techniques.

“At its core, trade finance is an old-fashioned business,” the report says. “As other industries have adopted more technology- and data-driven infrastructures, trade finance has remained extremely document-intensive and paper-based, moored on a framework of instruments, systems, and practices that have proven their effectiveness and earned global trust over the generations.”

But they are also opaque, PwC says, making it extremely difficult for AML efforts to see what’s going on.

“For example, trade finance’s legacy procedures affect the relationship management aspect of AML, which includes know-your-customer (KYC) procedures and examination of customer documentation prior to transaction approval,” the report says. “In this paper-intensive environment, AML remains a largely manual procedure and thus prone to human error. It remains reliant upon established “red flag” checklists provided by regulators, in which transactions are manually reviewed by analysts, escalated should any concerns be raised, and then subjected to further manual review if wrongdoing is suspected.”

The Need to Share Data

This state of affairs is exacerbated by a number of factors, especially the lack of data sharing between customs, tax and legal authorities and a tendency to rely on AML procedures designed to target cash smuggling and financial system misuse. Instead, PwC says, authorities need to develop targeted TBML responses that focus on data sharing and text and data analytics.

So what exactly does TBML look like? Common TBML techniques include the following:

Under-invoicing. The exporter invoices trade goods at a price below the fair market price. This allows the exporter to effectively transfer value to the importer, as the payment for the trade goods will be lower than the value the importer receives when reselling the goods on the open market.
Over-invoicing. This technique is much the same as the first, except in reverse. The exporter invoices trade goods at a price above the fair market value, allowing the importer to transfer value to the exporter.
Multiple invoicing. With this technique, a money launderer or terrorist financier issues multiple invoices for the same international trade transaction, justifying multiple payments for the same shipment. “Payments can originate from different financial institutions, adding to the complexity of detection, and legitimate explanations can be offered if the scheme is uncovered (e.g., amendment of payment terms, payment of late fees, etc.),” the report explains.
Over- and under-shipment. In some cases, the parties simply overstate or understate the quantities of goods shipped relative to the payments sent or received. PwC calls out an extreme example of this, known as “phantom shipping,” in which no goods are exchanged at all, but shipping and customs documents are processed as normal.
False description of trade goods. With this technique, money launderers misrepresent the quality or type of trade goods. For instance, they might replace an expensive item listed on the invoice and customs documents with an inexpensive item.
Informal money transfer systems (IMTS). These networks have, in many cases, been co-opted by criminals and terrorists. PwC points to Colombia’s Black Market Peso Exchange (BMPE) as a prime example. Established by Colombian businesses trying to get around Colombia’s restrictive currency exchange policies, the BMPE allows users to sell dollars to a broker, who then trades them for Pesos to a legitimate Colombian business that needs hard U.S. currency to purchase goods for shipment to South America. It’s not just Colombian drug traffickers repatriating their profits either; PwC notes that similar systems exist around the world, including the hawalahundi system on the Indian sub-continent and others in Venezuela, Argentina, Brazil and Paraguay.

What Can Big Data Do?

So how can big data analytics help organizations find these illicit transactions in an $18.3 trillion haystack? Well, for one, the sea of documents generated by this activity — the commercial invoices, bills of lading, insurance certificates, inspection certificates, certificates of origin and more — that make it so difficult to see what’s truly happening may also be the point of vulnerability.

“A global, one-stop solution to TBML is highly unlikely,” PwC says. “The most effective solution would involve the imposition of bank-like compliance requirements on all organizations that trade internationally. But while this would create transparency across transactions, it would also create a massive layer of red tape that would adversely impact the preponderance of traders and related parties who are engaged in legitimate activity. The largely unquantifiable nature of the TBML problem makes it difficult to justify such an intrusive, expensive and vastly complicated solution. Short of global regulation, we have global analytics.”

In other words, automating anti-TBML monitoring — extracting and analyzing in-house and external data, both structured and unstructured — is of critical importance.

PwC believes such a program must properly align across key business areas and incorporate automated processes using a variety of advanced techniques, including:

Text analytics. The capability to extract data from text files in an automated fashion can unlock a massive amount of data that can be used for transaction monitoring.
Web analytics and Web-crawling. These tools can systematically scan the web to review shipment and custom details and compare them against corresponding documentation.
Unit price analysis. This statistic-driven approach uses publicly available data and algorithms to detect if unit prices exceed or fall far below global and regional established thresholds.
Unit weight analysis. This technique involves searching for instances where money launderers are attempting to transfer value by overstating or understating the quantity of goods shipped relative to payments.
Network (relationship) analysis of trade partners and ports. Enterprise analytics software tools can identify hidden relationships in data between trade partners and ports, and between other participants in the trade lifecycle. They can also identify potential shell companies or outlier activity.

International trade and country profiling analysis. An analysis of publicly available data may establish profiles of the types of goods that specific countries import and export, flagging outliers that might indicate TBML activity.

Thor Olavsrud

Orginally posted via “How Big Data Analytics Can Help Track Money Laundering”

Source: How Big Data Analytics Can Help Track Money Laundering by anum

The Modern Day Software Engineer: Less Coding And More Creating

Last week, I asked the CEO of a startup company in Toronto, “How do you define a software engineer?”.

She replied, “Someone who makes sh*t work”;

This used to be all you needed. If your online web app starts to crash, hire a software engineer to fix the problem.

If your app needs a new feature, hire a software engineer to build it (AKA weave together lines of code to make sh*t work).

We need to stop referring to an engineer as an ‘engineer’. CEOs of startups need to stop saying ‘we need more engineers’.

The modern day ‘engineer’ cannot simply be an engineer. They need to be a renaissance person; a person who is well versed in multiple aspects of life.

Your job as a software engineer cannot be to simply ‘write code’. That’s like saying a Canadian lawyer’s job is to speak English.

English and code are means of doing the real job: Produce value that society wants.

So, to start pumping out code to produce a new feature simply because it’s on the ‘new features list’ is mindless. You can’t treat code as a means itself.

The modern day engineer (MDE) needs to understand the modern day world. The MDE cannot simply sit in a room alone and write code.

The MDE needs to understand the social and business consequences of creating and releasing a product.

The MDE cannot leave it up to the CEOs and marketers and business buffs to come up with the ‘why’ for a new product.

Everyone should be involved in the ‘why’, as long they are in the ‘now’.

New frameworks that emphasis less code and more productivity are being released every day, almost.

We are slowly moving towards a future where writing code will be so easy that it would be unimpressive to be someone who only writes code.

In the future Google Translate will probably add JavaScript and Python (and other programming languages) to their list of languages. Now all you have to do is type in English and get a JavaScript translation. In fact, who needs a programming language like JavaScript or Python when you can now use English to directly tell a computer what to do?

Consequently, code becomes a language that can be spoken by all. So, to write good code, you need to be more than an ‘engineer’. You need to be a renaissance person and a person who understands the wishes, wants, emotions and needs of the modern day world.

Today (October 22nd, 2015), I was at a TD Canada Trust networking event designed for ‘tech professionals’ in Waterloo ON, Canada. The purpose of this event was to demo new ‘tech’ (the word has so many meanings nowadays) products to young students and professionals. The banking industry is in the process of a full makeover, if you didn’t know. One of the TD guys, let’s call him Julio, was telling me a little summary of what TD was (and is) trying to do with its recruitment process.

Let me give you the gist of what he said:

“We have business professionals (business analysts, etc) whose job is to understand the 5 W’s of the product. Also, we have engineers/developers/programmers who just write code. What we are now looking for is someone who can engage with others as well as do the technical stuff.”

His words were wise, but I was not sure if he fully understood the implications of what he was talking about. This is the direction we have been heading for quite some time now, but it’s about time we kick things up a notch.

Expect more of this to come.
Expect hybrid roles.
Expect it become easier and easier to write code.
Expect to be valued for your social awareness paired with your ability to make sh*t work.

Perhaps software tech is at the beginning of a new Renaissance era.

*View the original post here*

Twitter: @nikhil_says


Originally Posted at: The Modern Day Software Engineer: Less Coding And More Creating by nbhaskar

Apr 27, 17: #AnalyticsClub #Newsletter (Events, Tips, News & more..)


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Adoption of analytics tools and capabilities has not yet caught up to industry standards. Talent has always been the bottleneck towards achieving the comparative enterprise adoption. One of the primal reason is lack of understanding and knowledge within the stakeholders. To facilitate wider adoption, data analytics leaders, users, and community members needs to step up to create awareness within the organization. An aware organization goes a long way in helping get quick buy-ins and better funding which ultimately leads to faster adoption. So be the voice that you want to hear from leadership.


Q:Explain the difference between “long” and “wide” format data. Why would you use one or the other?
A: * Long: one column containing the values and another column listing the context of the value Fam_id year fam_inc

* Wide: each different variable in a separate column
Fam_id fam_inc96 fam_inc97 fam_inc98

Long Vs Wide:
– Data manipulations are much easier when data is in the wide format: summarize, filter
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Customer Loyalty and Goal Setting

All companies who use customer loyalty surveys strive to see increases in their customer loyalty scores. Improving customer loyalty has been shown to have a positive impact on business results and long-term business success. Toward that end, executives implement various company-wide improvements in hopes that improvements in customer loyalty scores will follow.

One common method for improving performance is goal setting. There is a plethora of research on the effectiveness of goal setting in improving performance. In the area of customer satisfaction, what typically occurs is that management sees that their customer loyalty score is 7.0 (on a 0-10 scale) at the start of the year. They then set a customer loyalty goal of 8.0 for the end of the fiscal year. What happens at the end of the year? The score remains about 7.0. While their intentions are good, management does not see the increases in loyalty scores that they set out to attain. What went wrong? How can this company effectively use goal setting to improve their customer loyalty scores?

Here are a few characteristics of goals that improve the probability that goals will improve performance:

Specific. Goals need to be specific and clearly define what behaviors/actions are going to be taken to achieve the goal and in what time-frame or frequency these behaviors/actions should take place. For example, a goal stating, “Decrease the number of contacts with the company a customer needs to resolve an issue” does little to help employees focus their efforts because there is no mention of a rate/frequency associated with the decrease. A better goal would be, “Resolve customer issues in three or fewer contacts.”

Measurable. A measurement system needs to be in place to track/monitor progress toward the goal. The measurement system is used to determine whether the goal has been achieved and provides a feedback loop to the employees who are achieving the goal.

A common problem with using customer loyalty scores as the metric to track or monitor improvements is that satisfaction goals are still vague with respect to what the employees can actually do to impact satisfaction/loyalty scores. Telling the technical support department that the company’s customer loyalty goal is 8.0 provides no input on how that employee can affect that score. A better measure for the technical support department would be “satisfaction with technical support” or other technical support questions on the survey (e.g., “technical support responsiveness,” technical support availability”). We know that satisfaction with technical support is positively related to customer loyalty. Using these survey questions for goal setting has a greater impact on changing their behaviors compared to using vague loyalty questions. Because satisfaction with technical support is related to customer loyalty, improvements in technical support satisfaction should lead to improvements in loyalty scores.

An even better measure would be to use operational metrics for goal setting. The company must first identify the key operational metrics that are statistically related to customer satisfaction/loyalty. This process involves in-depth research via linkage analysis (e.g., linking satisfaction scores with operational measures such as hold time, turnaround time, and number of transfers) but the payoffs are great; once identified, the customer-centric operational metrics can be used for purposes of goal setting.

Difficult but attainable. Research has shown that difficult goals lead to better performance compared to goals that are easy. Difficult goals focus attention to the problem at hand. Avoid setting goals, however, that are too difficult and, consequently, not achievable. One way to set difficult and attainable goals is to use historical performance data to determine the likelihood of achieving different performance levels.

Relevant. Goals for the employees should be appropriate for the employees’ role; can the employee impact the goal? Additionally, the goal should be relevant to both the employee and the organization. Holding employees to be responsible for goals that are outside of their control (e.g., technical support representatives being responsible for product quality) is unfair and can lead to low morale.

Accepted (or mutually set). For goal setting to increase performance, employees should be allowed to participate in setting their goals. Goals that are not accepted by the recipient are not likely to be internalized and motivating. A good approach would be to get employees involved early in the process of goal setting. Let them help in identifying the problem, selecting (or understanding) the key measures to track, and setting the goal.


The following are key characteristics of effective goals:

  • Specific
  • Measurable
  • Difficult but attainable
  • Relevant
  • Accepted (or mutually set)

Goal setting can be an effective management tool. Incorporating this methodology can build a customer-centric culture by ensuring employees’s behaviors are guided by measures that matter to the customer.

Source: Customer Loyalty and Goal Setting

Two Underutilized Heroes of Data & Innovation: Correlation & Covariance

Two Underutilized Heroes of Data & Innovation: Correlation & Covariance
Two Underutilized Heroes of Data & Innovation: Correlation & Covariance

Yes, Data driven innovation is fun and it gets most done in less. But let’s talk about a math that is not as much known as it should be in the enterprise world. Correlation & Covariance are two such values that are most underutilized and have the tendency to cause maximum impact and disruption to any complicated business model.

First, a quick high level math primer (picked from Wiki): In probability theory and statistics, the mathematical descriptions of covariance and correlation are very similar.[1][2] Both describe the degree of similarity between two random variables or sets of random variables.
Correlation refers to any of a broad class of statistical relationships involving dependence.
Whereas, Covariance is a measure of how much two random variables change together. If the greater values of one variable mainly correspond with the greater values of the other variable, and the same holds for the smaller values, i.e., the variables tend to show similar behavior, the covariance is positive.[1] In the opposite case, when the greater values of one variable mainly correspond to the smaller values of the other, i.e., the variables tend to show opposite behavior, the covariance is negative. Anyways, over with the math talk, you could find more information by searching for covariance & correlations and if you are not blown away by it’s capabilities, do take out some extra time for reading about cross-correlation & cross covariance. You will get into the world of predictive modeling and so much more savvy stuff that you could do with these two interesting and powerful concepts.

On a traditional note, a company is analytically as smart as the analytics team it entails. But, on an interesting note, it does not have to be like this. A smarter business model like utilizing correlation & covariance on your captured data could do the heavy lifting for you and help you focus on the areas that are really causing some significant impact to your business. As you must have already read, by definition covariance and correlation can help you understand relationship between 2 random sets of data.

What is happening in most of the companies that I spoke with is that most of us have utilized this math while handling known sets of data within the periphery of a project. For an example, a project data and its variables can be correlated together for finding any hidden relations. If these relationships are not determined, it could cost your businesses a significant impact. If you are not at this yet, stop reading now and get your correlation & covariance mojo active at the least within the projects.

If your organization is already doing it within projects, you are part of that savvy organization which takes success and failures of their projects too seriously for them to be left with professionals. Now, you might need to ask, what next. Where is the next big wave? Innovation is the next big thing that is riding on the data that correlation/covariance could provide your organization. How about doing it within different projects, departments, silos etc. Consider for a case where one project is impacting the other. So, one tiny dependency on a remote department could cause a significant impact to totally unrelated department in the business.

Yes, you guessed right, we are talking about a big-data problem, or may be one of the biggest big-data problems for your organization.

Correlation and covariance have the power to identify those hidden relationships that you would have never guessed existed and then helps you find the extent of their dependency. How much one variable varies with the other. Once you have a model in place to comb your organization’s data for any correlations and thereby finding their covariance, you would understand how much one event is linked to other and by what degree. This would help your business identify high impact areas that you could then map to high performance. All you need to do is understand if the identified relationship is known or unknown. If it’s known, yes, you have validated that sometimes world is as sane as you expect it to be, and If not, wallah, you just identified a potential area to investigate and worry about, to make sure all relationships in your business are accounted for.

If data combing is done properly for any possible correlations and covariance, you could assure nothing will ever fall through the crack again. Your radar will always pick potential areas as soon as their relationship is established. And yes, that will save your business some cash and help it run optimally.

So, to do a quick recap:
1. Make sure you understand what correlation/covariance is, and for added bonus, read about cross correlation & cross covariance.
2. Make sure your project or projects in your company are leveraging correlation/covariance in finding hidden dependencies that could jeopardize the success of your project.
3. Make sure, you have big-data setup that could help connect data across various projects, departments & business units for finding possible correlations and their covariance.
4. Make sure you have right triggers, alarms and action plan setup for investigating any identified relationships further.
5. Make sure you have an automated system that combs the business data and help identifies possible cracks in real time.

If you are done with those 5 steps, your business is destined for consistent improvements and sustained data driven innovations.

And yes, as I always rant, you don’t have to do it in-house. Probably, for better business sense, get it made outside and then once it is validated, bring it in-house. All you need is a good data analytics/visualization platform that could take any number of structured and un-structured data and find correlations between them.

Originally Posted at: Two Underutilized Heroes of Data & Innovation: Correlation & Covariance

Apr 20, 17: #AnalyticsClub #Newsletter (Events, Tips, News & more..)


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Q:What is cross-validation? How to do it right?
A: It’s a model validation technique for assessing how the results of a statistical analysis will generalize to an independent data set. Mainly used in settings where the goal is prediction and one wants to estimate how accurately a model will perform in practice. The goal of cross-validation is to define a data set to test the model in the training phase (i.e. validation data set) in order to limit problems like overfitting, and get an insight on how the model will generalize to an independent data set.

Examples: leave-one-out cross validation, K-fold cross validation

How to do it right?

the training and validation data sets have to be drawn from the same population
predicting stock prices: trained for a certain 5-year period, it’s unrealistic to treat the subsequent 5-year a draw from the same population
common mistake: for instance the step of choosing the kernel parameters of a SVM should be cross-validated as well
Bias-variance trade-off for k-fold cross validation:

Leave-one-out cross-validation: gives approximately unbiased estimates of the test error since each training set contains almost the entire data set (n?1n?1 observations).

But: we average the outputs of n fitted models, each of which is trained on an almost identical set of observations hence the outputs are highly correlated. Since the variance of a mean of quantities increases when correlation of these quantities increase, the test error estimate from a LOOCV has higher variance than the one obtained with k-fold cross validation

Typically, we choose k=5 or k=10, as these values have been shown empirically to yield test error estimates that suffer neither from excessively high bias nor high variance.


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Bob Hayes to Address Vovici Vision 2010 Users Conference, May 10-12, 2010

Dulles, VA – November 2, 2009 – Vovici, the leading provider of survey software and enterprise feedback management (EFM) solutions, will hold its user conference, Vision 2010, May 10-12, 2010 in Reston, Virginia.

Vision 2010 will bring together feedback management leaders and experts across multiple industries to participate in compelling educational sessions, training, and peer networking opportunities. Among the confirmed keynote presenters will be three customer loyalty luminaries:

  • Jeb Dasteel, Chief Customer Officer of Oracle (NASDAQ: ORCL)
  • Jeanne Bliss, author of I Love You More Than My Dog and Chief Customer Officer
  • Bob Hayes, Ph. D., author of Beyond the Ultimate Question and recognized loyalty expert

“At Oracle, executive leadership is relentlessly focused on listening to customers and prioritizing feedback to drive customer strategy at all levels,” said Dasteel. Dasteel has been with Oracle for 11 years, five of which have been spent running Oracle’s Global Customer Programs and as CCO for the last year. Dasteel was named the 2009 Chief Customer Officer of the Year at the first Chief Customer Officer Summit.

Jeanne Bliss spent 25 years in the role of Chief Customer Officer at Lands’ End, Allstate, Microsoft, Mazda and Coldwell Banker. Today her firm, CustomerBLISS consults around the world, teaching and guiding companies and leaders how to wrap their business around customer relationships and business prosperity. “Leading companies understand the importance of listening to customers, using feedback to deliver an experience with impact, and creating a lasting bond,” noted Bliss. Her first book, Chief Customer Officer (Jossey-Bass, 2006), was based on 25 years of reporting to the CEOs of five major corporations.

Bob Hayes, Ph.D., is the president and founder of Business Over Broadway. He is a recognized expert in customer satisfaction and loyalty measurement, and has conducted survey research for enterprise companies, including Siebel Systems, Oracle, Agilent Technologies, and Cisco Systems. “There are key ingredients to a successful customer feedback program. Adoption of these elements is critical to improving both customer relationship management and customer loyalty, and Vision 2010 will offer a great opportunity to learn how to accomplish these,” said Hayes.

To register for Vision 2010, please visit:

“Vovici is the Voice of the Customer platform that is helping Fortune 500 companies to emotionally connect to customers,” said Greg Stock, chairman and CEO of Vovici. “We are very excited to bring this amazing group together to share insights and proven methodologies that actually achieve higher level business objectives and make the customer’s vision a reality.”

Source: Bob Hayes to Address Vovici Vision 2010 Users Conference, May 10-12, 2010 by bobehayes

Every step you take: Who owns our mobile health data?

Gadgets that track your steps, sleeping and heart rate could help us live longer and cut national healthcare costs by billions – or so we are told.

Microsoft has just launched its first wearable health gadget, the Band, in the US ahead of its global launch.

Similar products from Samsung and Google are already on the market and early next year the much-hyped Watch from Apple will go on sale.

Millions of us are going to be having our most intimate bodily functions monitored by these gadgets, creating more health data than has ever existed before.

Why do these machines help us stay fit and more importantly what happens to all that information we are generating and sharing?

Tim Cook introducing the Apple Watch
Apple will soon follow Microsoft and Google into the mobile health device market

Massive market

Before the giants of the tech world realised that wearable, health-focused gadgets were the new big thing the market was already thriving.

In March the European Commission published its green paper on mobile health, which contained some mind-boggling statistics.

It suggests that 97,000 apps are on sale in the mobile health sector, which includes tracking apps but also apps that help patients make appointments and keep track of medication.

It predicts that by 2017 more than 1.5 billion people around the world will be using these apps, generating total revenues of £14.5bn ($23bn).

In the EU alone it is estimated that these apps and gadgets could reduce health costs by £77.5bn (99bn euros).

Sector pioneers

Most of the growth has come from start-ups that saw the potential early and now face a competitive onslaught from the big technology companies.

Five years ago French firm Withings launched its wireless scales – the device feeds data back to you, by plotting a graph of your weight over time.

“It started with the scales because we thought that was the one dimension that would make sense for people to track,” Julien De Preaumont, chief marketing officer at Withings, says.

“The first rule of data is to make people aware of their health to make them realise how their weight is evolving.

black wireless scales by Withtings
The wireless scales by Withings uses data visualisation to help dieters lose weight

“The curve reveals the impact of life changes, it will show how a divorce, a diet or a new job will affect your weight.”

After the scales took off, Withings launched wearable gadgets that track your movement, heart rate, blood pressure and sleep.

The company maintains that the data it collects belongs to the user only.

But it has published reports revealing the most obese cities in France and the US, as well as another study showing sleep patterns across Europe.

Withings says this does not compromise the privacy of the individual user’s data because it is aggregated and anonymised.

Business games

While Withings has grown to be a global business, US firm Fitbit has also seen its business thrive beyond its borders.

Founded in 2007 Fitbit offers wireless scales, wearable devices that monitor movement, heart rate, sleep and blood pressure, and is evangelical about the motivating power of targets and data on our health.

Fitbit also offers companies its gadgets and software for corporate use.

Its “corporate wellness” scheme started in the US and companies can use the scheme to get a rebate on their taxes.

A screengrab from a Fitbit challenge
Games and challenges can be used to motivate people to compete against each other

Clients so far include blue-chip multinationals such as BP and Time Warner.

Employees can sign up and different divisions can compete against each other over the number of steps taken or stairs climbed.

“The key is to make the product sticky,” says Gareth Jones from Fitbit, and the key to that is gamification.

“Our software incorporates challenges like daily showdowns and weekend warriors which motivate people and keep them coming back.”

But should employees be worried about sharing their every movement, 24 hours a day with a corporate scheme?

“We don’t have data about this, it’s very much a choice of the individual as to whether they sign in for the programme. We see the result of that as purely the people who agree to participate and the people who don’t,” says Mr Jones.

“We might share with the corporate administrator information that 50 people have been invited and 45 have said yes. How the company uses that information is up to the company.”

‘In the hands of the people’

The potential of all the data that is now being collected is huge, both for business and for public health bodies.

Imagine going to the doctor and being able to show them how much exercise you do, how much sleep you get and your blood pressure for the last year.

While the insurance industry is using mobile applications for arranging appointments and giving health information, they are yet to fully embrace the use of wearable devices and the data they collect, though it is a development that could completely change their business as many research papers suggest.

Meanwhile the use of the data for medical research is also a long way off.

Professor John Newton from Public Health England would like to see a more joined-up approach.

“We’ve got the world of apps, a huge investment from the technology companies, but the healthcare sector hasn’t made the link,” he says.

“If you were able to make the link between a hospital service like a diabetic clinic with a patient’s mobile phone data, they could tell immediately whether that person’s diabetes was going out of control.”

His message is clear: “Put the data into the hands of the people who can use it to make a difference.”

Like all the new data that is being recorded and analysed the possibilities are massive but the ethical and privacy issues surrounding our personal information will not go away quickly.

Originally posted via “Every step you take: Who owns our mobile health data?”

Originally Posted at: Every step you take: Who owns our mobile health data?