Saturday, 3 December 2016

Digital Transformation helping to reduce patient's readmission

Digital Transformation is helping all the corners of life and healthcare is no exception.

Patients when discharged from the hospital are given verbal and written instructions regarding their post-discharge care but many of them get readmitted in 30 days due to various reasons. 

Over last 5 years this 30 days readmission rate is almost 19% with over 25 billions of dollars spent per year.

In October 2012 the Centers for Medicaid and Medicare Services (CMS) began penalizing hospitals with the highest readmission rates for health conditions like acute myocardial infarction (AMI), heart failure (HF), pneumonia (PN), chronic obstructive pulmonary disease (COPD) and total hip arthroplasty/total knee arthroplasty (THA/TKA).

Various steps to reduce the readmission:

·        Send the patient home with 30-day medication supply, wrapped in packaging that clearly explains timing, dosage, frequency, etc
·        Have hospital staff make follow-up appointments with patient's physician and don't discharge patient until this schedule is set up
·        Use Digital technologies like Big Data & IoT to collect vitals and keep up visual as well as verbal communication with patients, especially those that are high risk for readmission.
·        Kaiser Permanente & Novartis are using Telemedicine technologies like video cameras for remote monitoring to determine what's happening to the patient after discharge
·        Piedmont Hospital in Atlanta provides home care on wheels like case management, housekeeping services, transportation to the pharmacy and physician's office         
·        Use of Data Science algorithms to predict patients with high risk of readmission
·        Walgreens launched WellTransitions program where patients receive a medication review upon admission and discharge from hospital, bedside medication delivery, medication education and counseling, and regularly scheduled follow-up support by phone and online.
·        HealthLoop is a cloud based platform that automates follow-up care keeping doctors, patients and care-givers connected between visits with clinical information that is insightful, actionable, and engaging.
·        Propeller Health, a startup company in Madison has developed an app and sensors track medication usage and then send time and location data to a smartphone
·        Mango Health for iPhone and wearables like Apple Watch makes managing your medications fun, easy, and rewarding. App feature include: dose reminders, drug interaction info, a health history, and best of all - points and rewards, just for taking your medicines.

These emerging digital tools enable health care organizations to assess and better manage who is at risk for readmission and determine the optimal course of action for the patients. 

Such tools also enable patients to live at home, in greater comfort and at lower cost, lifting the burden on themselves and their families.

Digital is helping mankind in all ways !!

Saturday, 26 November 2016

Product recommendations in Digital Age

By 1994 the web has come to our doors bringing the power of online world at our doorsteps. Suddenly there was a way to buy things directly and efficiently online.

Then came eBay and Amazon in 1995....... Amazon started as bookstore and eBay as marketplace for sale of goods.

Since then, as Digital tsunami flooded, there are tons of websites selling everything on web but these two are still going great because of their product recommendations.

We as customers, love that personal touch and feeling special, whether it’s being greeted by name when we walk into the store, a shop owner remembering our birthday, helping us personally to bays where products are kept, or being able to customize a website to our needs. It can make us feel like we are single most important customer. But in an online world, there is no Bob or Sandra to guide you through the product you may like. This is where recommendation engines do a fantastic job.

With personalized product recommendations, you can suggest highly relevant products to your customers at multiple touch points of the shopping process. Intuitive recommendations will make every customer feel like your shop was created just for them.

Product recommendation engines can be implemented by collaborative filtering, content-
based filtering, or with the use of hybrid recommender systems.

There are various types of product recommendations:
           ·        Customers who bought this also bought - like Amazon
           ·        Best sellers in store – like HomeDepot
           ·        Latest products or arriving soon – like GAP
           ·        Items usually bought together – like Amazon
           ·        Recently views based on history – like Asos
           ·        Also buy at checkout – like Lego

There are many benefits that a product recommendation engine can do for digital marketing and it can go a long way in making your customers love your website and making it their favorite eCommerce site to shop for.

Advantages of product recommendations:
·        Increased conversion rate
·        Increased order value due to cross-sell
·        Better customer loyalty
·        Increased customer retention rates
·        Improved customer experience

Application of Data Science to analyze the behavior of customers to make predictions about what future customers will like. Big Data along with machine learning and artificial intelligence are the key to product recommendations.

Understanding the shopper’s behavior on different channels is also a must in personalizing the experience. Physical retail, mobile, desktop and e-mails are the main sources of information for the personalization engines

Amazon was the first player in eCommerce to invest heavily on product recommendations. Its recommendation system is based on a number of simple elements: what a user has bought in the past, which items they have in their virtual shopping cart, items they’ve rated and liked, and what other customers have viewed and purchased. Amazon has used this algorithm to customize the browsing experience & pull returning customers. This has increased their sale by over 30%.

Yahoo, Netflix, Yahoo, YouTube, Tripadvisor, and Spotify are other famous sites taking advantage of the recommender systems. Netflix ran a famous 1 million dollars competition from 2006 till 2009 to improve their recommendation engine.

Many commercial product recommendation engines are available today such as Monetate, SoftCube, Barilliance, Strands etc.

Ultimately most important goal for any eCommerce platform is to convert visitors into paying customers. Today the customer segmentation era as gone and its hyper- personalization

Product recommendations are extremely important in digital age !!

Saturday, 19 November 2016

What is Deep Learning ?

Remember how you started recognizing fruits, animals, cars and for that matter any other object by looking at them from our childhood? 

Our brain gets trained over the years to recognize these images and then further classify them as apple, orange, banana, cat, dog, horse, Toyota, Honda, BMW and so on.

Inspired by these biological processes of human brain, artificial neural networks (ANN) were developed.  Deep learning refers to these artificial neural networks that are composed of many layers. It is the fastest-growing field in machine learning. It uses many-layered Deep Neural Networks (DNNs) to learn levels of representation and abstraction that make sense of data such as images, sound, and text

Why ‘Deep Learning’ is called deep? It is because of the structure of ANNs. Earlier few decades back, neural networks were only 2 layers deep as it was not feasible to build larger networks. Now with big data platforms we can have neural networks with 10+ layers.

Using multiple levels of neural networks in Deep Learning, computers now have the capacity to see, learn, and react to complex situations as well or better than humans.

Normally data scientists spend lot of time in data preparation – feature extraction or selecting variables which are actually useful to predictive analytics. Deep learning does this job automatically and make life easier.

Many technology companies have made their deep learning libraries as open source:
  • Google’s Tensorflow
  • Facebook open source modules for Torch
  • Amazon released DSSTNE on GitHub
  • Microsoft released CNTK, its open source deep learning toolkit, on GitHub

Today we see lot of examples of Deep learning around:
  • Google Translate is using deep learning and image recognition to translate not only voice but written languages as well. 
  • With CamFind app, simply take a picture of any object and it uses mobile visual search technology to tell you what it is. It provides fast, accurate results with no typing necessary. Snap a picture, learn more. That’s it.
  • All digital assistants like Siri, Cortana, Alexa & Google Now are using deep learning for natural language processing and speech recognition
  • Amazon, Netflix & Spotify are using recommendation engines using deep learning for next best offer, movies and music
  • Google PlaNet can look at the photo and tell where it was taken
  • DCGAN is used for enhancing and completing the human faces
  • DeepStereo: Turns images from Street View into a 3D space that shows unseen views from different angles by figuring out the depth and color of each pixel
  • DeepMind’s WaveNet is able to generate speech which mimics any human voice that sounds more natural than the best existing Text-to-Speech systems
  • Paypal is using H2O based deep learning to prevent fraud in payments
Till now, Deep Learning has aided image classification, language translation, speech recognition and it can be used to solve any pattern recognition problem, and all of it is happening without human intervention.

Deep learning is a disruptive Digital technology that is being used by more and more companies to create new business models.

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