Map of US Hospitals and their Patient Experience Ratings

Hospital RatingsHospitals are focusing on 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. Under the Hospital Value-Based Purchasing Program (beginning in FY 2013 for discharges occuring on or after October 1, 2012), CMS will make value-based incentive payments to acute care hospitals, based either on how well the hospitals perform on certain quality measures or how much the hospitals’ performance improves on certain quality measures from their performance during a baseline period.  The higher the score/greater the improvement, the higher the hospital’s incentive payment for that fiscal year.

Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS)

Patient feedback is being collected using a survey known as HCAHPS (Hospital Consumer Assessment of Healthcare Providers and Systems). HCAHPS (pronounced “H-caps“) is a national, standardized survey of hospital patients and was developed by a partnership of public and private organizations. I recently wrote about HCAHPS in a prior post. The survey asks a random sample of recently discharged patients about important aspects of their hospital experience. The data set includes patient survey results for over 3800 US hospitals on ten measures of patients’ perspectives of care.

The site indicates that the data files were updated in 5/30/2012. Based on HCAHPS reporting schedule, it appears the current survey data were collected from Q3 2010 through Q2 2011 and represent the latest publicly available patient survey data.

Map of US Hospitals and their Patient Experience Ratings

As consumers of healthcare, you need to understand how well hospitals are delivering a good patient experience. Using the HCAHPS data, I developed a map to help you easily identify and understand how your hospital ranks in patient experience. In the map below (see Figure 1), the colors are based on the patient advocacy index I created (average of top box scores for two questions: Overall hospital quality rating and Recommend hospital. In my prior analysis, this Patient Advocacy Index (PAI) had a reliability estimate (Cronbach’s alpha) of .95, suggesting that it is a reliable measure.

The colors for each hospital are based on their PAI (red = 0 – 20; purple = 21-40; yellow = 41-60; blue = 61-80; green = 81-100). If you click on one of the buttons, you will see detailed information about the patient experience metrics (if survey data were collected for that hospital) as well as response rates, sample sizes and other notes (if available). NOTE: Some hospitals do not have any ratings (those are typically red).

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Figure 1. Map of US Hospitals and their Patient Experience Ratings

Originally Posted at: Map of US Hospitals and their Patient Experience Ratings by bobehayes

Big data offers telcos new revenue streams

Telcos need to find ways to process and analyse large amounts of customer data quickly and in real-time in order to create new revenue streams and engage with customers effectively.

So said Rams Srinivasan, telco industry value engineer at SAP Middle East and North Africa, speaking yesterday at the SAP Forum in Sandton.

According to Srinivasan, telecommunications companies are struggling to break through and engage with their customers because of legacy systems that make it difficult to have a complete view of customers and deliver efficient service across multiple channels.

He explains telcos must adapt in order to attract and retain a new generation of customers who are highly informed, socially connected and very mobile.

Telcos can track conversations on social media to understand what customers are saying about their products and services, and take proactive measures to defend their brand image and reputation in real-time using analytics, said Srinivasan.

Operators are looking for new ways to increase revenues and profits – but few have shown the know-how needed to make the most of new digital channels, said Srinivasan.

According to a Frost & Sullivan report, with an increasingly saturated market, and declining traditional mobile voice and SMS revenues, operators are exploring new business models as part of their growth strategy.

Also, the declining cost of devices is driving the ecosystem to explore new possibilities through connected devices, adds Frost & Sullivan.

By analysing varied and unformatted digital data from digital channels like social media and through mobile phones, telcos can reveal new sources of business economic value and provide fresh insights into customer behaviour, said Srinivasan.

He pointed out operators need a strategy to accurately mine and analyse both structured and unstructured data. This will give them an opportunity to get deeper insights into customer behaviour, their service usage patterns and preferences in real-time.

“To be able to remain relevant and competitive in a world where technology giants like Google, Facebook and Apple are causing disruption, telcos need to engage with the customer in a better way by improving operational efficiency through data analytics.”

Today’s lifestyle demands continuous connectivity and excellence of service, leading to a highly competitive and diverse market, said Srinivasan. There are more digital connected devices than human beings and the numbers are growing.

Another new source of revenue for telcos is selling insights about customers to third parties, said Sherif Hamoudah, industry head, telecom, at SAP Africa, Middle East and Pakistan.

By leveraging the data stream, telecommunications companies can tailor their marketing campaigns to individual customers using location-based and social networking technologies, he said. Also, big data offers event-based marketing campaigns that use geolocation and social media, allowing differentiated response.

Big data offers telecom operators a real opportunity to gain a much more complete picture of their operations and customers, and to further their innovation efforts, he said.

Originally posted via “Big data offers telcos new revenue streams”

Source: Big data offers telcos new revenue streams

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?”

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

Linkage Analysis for Your VoC Program – Free Paper

Linkage Analysis for Your VoC Program
Linkage Analysis for Your VoC Program - click to download

Customer feedback provides useful information about the health of the customer relationship. Relationship and transactional surveys, commonly used to capture customer feedback, are used to assess and improve that health. While customer feedback metrics provide great value in and of themselves, when used with other types of business data, they can address meaningful business questions:

  • Are the customer feedback metrics predictive of future financial performance and business growth?
  • Do customers who report higher loyalty spend more than customers who report lower levels of loyalty?
  • Where do we set operational goals to ensure we maximize customer satisfaction?
  • Does employee training help improve the customer experience?

To answer these questions, companies look to a process called business linkage analysis. Download the free paper here.

Source: Linkage Analysis for Your VoC Program – Free Paper

Jun 08, 17: #AnalyticsClub #Newsletter (Events, Tips, News & more..)


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How to Create a Mind: The Secret of Human Thought Revealed


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Fix the Culture, spread awareness to get awareness
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:What is statistical power?
A: * sensitivity of a binary hypothesis test
* Probability that the test correctly rejects the null hypothesis H0H0 when the alternative is true H1H1
* Ability of a test to detect an effect, if the effect actually exists
* Power=P(reject H0|H1istrue)
* As power increases, chances of Type II error (false negative) decrease
* Used in the design of experiments, to calculate the minimum sample size required so that one can reasonably detects an effect. i.e: ‘how many times do I need to flip a coin to conclude it is biased?’
* Used to compare tests. Example: between a parametric and a non-parametric test of the same hypothesis



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Big data is a top business priority and drives enormous opportunity for business improvement. Wikibon’s own study projects that big data will be a $50 billion business by 2017.

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Why Your Company Should Use Data Science to Make Better Decisions

Data science is one of the most recent buzzwords that is gaining popularity in tech circles. With the number of job advertisements on the rise, one may think that data science and their professionals, data scientists, would become one of the most sought-after professionals in the technology job market over the next decade. Indeed, there are strong reasons to believe so, and surprisingly the cause won’t be the big companies, but the small and medium-sized ones, which are eager to find ways to interpret the data they now collect at an exponential rate.

Every company, big or small, is surrounded by data

Some people only relate data science with social networks like Facebook or Twitter; in general, networks with huge amounts of user information that is primarily used to deliver better advertisements. But that is only one side of the coin —albeit an important one— The democratization of the Internet has had two important consequences for almost any company, even if it is not related to technology:

  • Companies can now reach a greater number of potential customers from all over the world, with many different backgrounds and interests.
  • Mobile devices are always connected and are also extremely portable, which means that the number of transactions and customer interactions per product and service has increased substantially in recent years. One example of this is the mobile impact in e-commerce, and 2015 may be the year of Apple Pay.

How data influences decisions

Arguably one of the most important values that a company can have is its ability to make good decisions. This includes how to respond to the competition, how to plan future products and services, and so on. Data science specialists guide decisions by ensuring that the right questions are asked on data. Like Alice in “Alice in Wonderland”, if you ask the wrong questions on data, it does not matter which decision you take.

A data scientist uses math concepts to extract insights from data, but this is not limited to standard data mining that target users for marketing purposes or fraud detection, like banks have been doing for a long time. It is more like treating each internal or external business problem from a data perspective. For example, a company may discard a long project even before starting it if rigorous data analysis shows that it may not generate enough value. For this purpose, I think that a multidisciplinary team is needed with not only data scientists, but also economists, psychologists, and engineers.

To sum up, data science is here to help businesses in many ways. The first step an organization should take is to come up with a methodological way to extract insightful information from data. Once the organization has the specialists for this task, the next logical step is to close the gap between data and decision-making, by viewing each business case as a data problem.

What do you think? Do you think investing in data science gives companies a competitive advantage? Do you think data science can be applied to decision-making inside an organization? Join the comments below.

Daniel_Marti¦ünDaniel is a software engineer at Fon and PhD student at the Artificial Intelligence and Software Engineering department of Complutense University of Madrid. His research interests are computer vision and machine learning. He is specially interested in the applications of the emerging field of deep learning.

You can reach him at LinkedIn, or Twitter.


Originally posted via “Why Your Company Should Use Data Science to Make Better Decisions”

Originally Posted at: Why Your Company Should Use Data Science to Make Better Decisions by anum

The Data Analytics of Halloween


On All Hallows Eve, little yellow Minions, pint-size Yodas, and pretty pink princesses–not to mention ghosts, goblins, rock stars, witches, superheroes, and cowboys–will roam through the neighborhoods in search of bite-size pieces of sugary goodness.

It’s All About the Force (and the Minions)

The Minions as well as Yoda and his friends from “Star Wars” are among the top costumes that adults, children, and even pets will be donning on Halloween, according to the National Retail Federation’s (NRF) 2015 Halloween Consumer Top Costumes Survey. Prosper Insights & Analytics conducted the poll of 6,754 consumers from September 1-8, 2015.

More than 1.8 million children will trick or treat dressed as “Star Wars” characters, while 1 million will choose to become Minions, those dungaree- and goggle-wearing loyal yellow servants.

“As we’ve seen for several years, Hollywood and pop culture both have a tremendous impact on how adults and their children decide to dress the part each Halloween, and it’s evident some of the biggest newsmakers of the year will be out in full force this fall,” says NRF President and CEO Matthew Shay.

Princesses and Witches Rule, Too

As for the princess costumes–they top the kids’ costume list for the 11th year in row. In fact, about 3.2 million little tikes will be living out their favorite fairytales dressed in stunning silky gowns with sparkling jewels. Another 2 million children will become their favorite Disney “Frozen” characters–Anna, Elsa, and that adorable little snow guy, Olaf.

And for the 11th year in a row the witch costume came in as the top adult costume, with more than 4.3 million adults going with the old standby. “Star Wars” characters are also big with the grown ups, ranking 5th for adults this year after tying for 12th last year.

Han Solo, Princess Leia, Yoda! Oh, my!

According to the NRF, 1.4 million adults will channel Han Solo, Princess Leia, Yoda, or other beloved “Star Wars” characters this Halloween. Animal characters (2.4 million), “Batman” characters (2 million) and zombies (1.9 million) round out the top five adult costumes. And if all that wasn’t scary enough, 774,000 adults are planning to dress as their favorite political characters this year.

About one in 10 of the lovers of all things Halloween (12.9%) will dress their furry friends in costumes this year. Of the 80% of those surveyed who have already chosen their pets’ costumes, 10.6% have decided to turn Kitty and Spot into little round pumpkins. A few pet owners plan to outfit their four-legged friend in “Star Wars” (8th on the list) or Minion costumes (11th).

The NRF estimates that 68 million Americans will dress up this Halloween and another 20 million pet owners will dress up their pets. And total spending for the holiday will hit $6.9 billion, including an average of $27.33 spent on each costume.

“It’s easier than ever for consumers to find creative Halloween costumes given the popularity of Pinterest and Instagram and the immediate access to pop culture trends,” says Pam Goodfellow, an analyst at Prosper Insights & Analytics. “It’s always a nice surprise to see what tops the lists each year and to see just how creative people will get when it comes to their own and even their pets’ costumes.”

Here is a great infographic from the Opensky Merchant Community Blog that shows this data from the National Retail Federation:

halloween infographic The Data Analytics of Halloween–2015 Costume Edition

Infographic sourced from:
View original post HERE.

Originally Posted at: The Data Analytics of Halloween

Jun 01, 17: #AnalyticsClub #Newsletter (Events, Tips, News & more..)


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Data aids, not replace judgement
Data is a tool and means to help build a consensus to facilitate human decision-making but not replace it. Analysis converts data into information, information via context leads to insight. Insights lead to decision making which ultimately leads to outcomes that brings value. So, data is just the start, context and intuition plays a role.


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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Every person in the world having more than 215m high-resolution MRI scans a day.

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