Technology (preventative apps like Apple Health and HealthKit; EHR, claims and reimbursement analytics; Physician Practice management etc.) will reinvent healthcare as we know it. I expect the healthcare transformation to start incrementally and develop slowly in sophistication. Though the early changes will appear clumsy and underwhelming, by 2030 they will seem obvious, inevitable and well beyond the changes we might envision today.
Why change? Consider this:
- Honeywell, a Fortune 100 technology and manufacturing company, needed to manage the ever-escalating cost of insuring its 130,000 employees and their dependents. Honeywell has reported that health care costs were growing approximately 8-10% per year.
- Self-insured employers like Wal-Mart want to make health care cost and quality information available to their 1.2 Million employees. Useful information that can be used by employees to select physicians based on how their rank, or how much they cost, resulting in savings for both the employee and the employer. Decision support enabler.
Historically, employers like Honeywell, Wal-Mart and their employees have not had access to comprehensive information about the cost and quality of care as they evaluate benefit designs across multiple health plans and treatment options.
In some cases, U.S health care providers and other market participants have actively resisted efforts by employers and others to obtain information about the costs and quality of health care services. Why? because opaqueness means money. UCSF researchers uncovered an enormous discrepancy in what different hospitals charge for the same procedure, ranging from a low of $1,529 to a high of $183,000. The median hospital charge was $33,611. The startling cost variation illustrates an inefficient system.
Despite this resistance, the health care industry generates extensive data that is relevant to determining the cost and quality of health care services. These data reside in myriad formats and disparate databases, without a common infrastructure, and have therefore been of limited value to employers and employees in controlling costs and improving outcomes.
In many cases, information relating to health care services has restrictions on its use, such as contractual agreements that some health plans and providers have historically entered into to not disclose price information. These factors make it challenging for employers and employees to use these data for the purposes of measuring cost and quality and making informed decisions. Read more
- IBM is moving to a private health exchange…Extend Health private exchange will be handling plan options for 110,000 IBM retirees
- Walgreens is moving employees to a Corporate Health Exchange. Of the 180,000 Walgreen employees eligible for healthcare insurance, 120,000 opted for coverage for themselves and 40,000 family members. Another 60,000 employees, many of them working part-time, were not eligible for health insurance.
- Trader Joe’s — decided to send some employees to the new public exchanges. Trader Joe’s has left coverage for three-quarters of its work force untouched but is giving part-time workers a contribution of $500 to buy policies. Because of the employees’ low incomes, the company says it believes many will be eligible for federal subsidies to help them afford coverage.
- Time Warner will direct retirees to an exchange to get health coverage
For the past year I have done strategy and implementation work in the employee Healthcare benefits and Private Exchange area. I wanted to share my insights into the massive structural changes taking place in health insurance. The move to patient-centered, consumer-driven, and value-based models is real.
Employee Health insurance in the U.S. is at the cusp of a major transition from an employer-driven payor model to a model directly involving many more employees and consumers. Private health insurance exchanges with a defined contribution approach represent a significant step in this journey. Also some clever risk shifting strategies are emerging where employers are moving part-time workers onto public exchanges.
The market size is enormous. Healthcare spending is forecasted to be ~$3.1 trillion in 2014, with $620 Bln of this paid by U.S. employers. In 2013, employers contributed 32% more in health care expenses than 2008.
A satisfying customer experience is the driver of any business’s revenue growth. Disney Theme Parks is no exception. Disney is executing a guest personalization strategy leveraging wearable computing (and analytics) to track, measure and improve the overall park experience. The ultimate goal is increase sales, return visits, word of mouth recommendations, loyalty and brand engagement across channels, activities, and time.
Wearable computing seems to be the next big thing. Many believe a new crop of gadgets — mostly worn on the wrist or as eyewear — will become a “fifth screen,” after TVs, PCs, smartphones, and tablets.
Wearables are already being used to monitoring vital signs, wellness and health. Devices like Fitbit, UP, Fuelband, Gear2 track activity, sleep quality, steps taken during the day. Consumers of all sorts — fitness buffs, dieters, and the elderly — have come to rely on them to capture and aggregate data.
What most people don’t understand is how powerful wearables (coupled with analytics) can be in designing new user experiences. Businesses thrive when they engage customers by creating a longitudinal view of each customer’s behavior. To understand the wearables use cases and potential we did a deep dive into a real-world application at Disney Theme Parks.
Wearable Computing at Disney: MyMagic+
Disney has been rolling out a new guest experience called MyMagic+ to the 30 million guests per year at the Walt Disney World Resort in Orlando.
Realizing that guests were arriving with smartphones and tablets in hand and expecting access to more information, Disney started the MyMagic+ initiative to provide a next generation experience. The overarching goal of MyMagic+ is to provide a much more personalized friction-free vacation at various theme parks, even down to characters knowing your name.
Disney is following in the steps of Harrah’s (now Caesars Entertainment) Total Rewards program that provided an integrated experience for gamblers across nearly 40 resorts and casinos. Loyal spenders were rewarded with innumerable entertainment options, enticing special offers, free hotel rooms, and different ways to redeem credits.
How does MyMagic+ work?
A key element of MyMagic+ is MagicBand. MagicBands is a ultra-personalization experience. These brightly colored bands link with online profiles for each visiting family member, and can be scanned at park kiosks to access advance ride bookings, receive customer service, and pay for all the stuff your kids want to buy.
The key to a great experience is being predictive in terms of context. For instance, while wearing her MagicBand, a young lady who loves Disney princesses might be approached by her favorite of the park’s life-size characters and be greeted by name.
Disney extracts and integrates all the information about the guest from all the park siloed data systems. as well as from external sources. This allows them to create a longitudinal view of each guest’s behavior over channels, activities and time.
Sophisticated pattern-detection science is applied against the 360-degree view to extract each guest’s behavioral predictors – like early warning on guest/family fading, real-time park experience dynamics (via feedback), and each guest sensitivity to specific promotions. The objective is to turn these signals into individuated recommendations served via customer marketing systems.
Technology behind MagicBand
According to Disney, each waterproof MagicBand contains an HF Radio Frequency device and a transmitter which sends and receives RF signals through a small antenna inside the MagicBand and enables it to be detected at short-range touch points throughout Walt Disney World Resort. MagicBands can also be read by long-range readers and used to deliver personalized experiences, as well as provide information that helps us improve the overall experience.
The next version of MagicBand might have much more computing built into it. If they go the Android route…Google has announced an SDK aimed at making Android, more palatable for small devices. Android apparently was consuming more battery. Samsung tried using Android for the Galaxy Gear, its smart watch, and the results were not so great. It couldn’t last very long without a recharge. For the Gear 2 Samsung dropped Android in favor of Tizen, its own operating system. I won’t be surprised if Apple and Disney team up in a few years around this.
Another day, another data breach. Just received another “We’re sorry you got hacked”…letter.
This is the fifth letter I have received in the past 3 months: Forbes.com, Target, Neiman Marcus, credit card company and a previous employer. What is going on?
Why aren’t firms investing in beefing up their predictive ability to spot the cyber-security intrusion threats? What’s taking them so long to identify? Why is the attack signature – sophisticated, self-concealing malware – so difficult to spot? Do firms need to invest in NSA PRISM type threat monitoring capabilities?
The three impediments to discovering and following up on attacks are:
- Volume, velocity and variety – Not collecting appropriate security data
- Immaturity and not identifying relevent event context (event correlation)
- lack of system awareness and vulnerability awareness
Obviously… where there is pain…there is opportunity for entrepreneurs see below – data from IBM). There is a growing focus on big data use case for security analytics after all the breaches we are seeing. General Electric announced it had completed a deal to buy Wurldtech, a Vancouver-based cyber-security firm that protects big industrial sites like refineries and power plants from cyber attacks.
Here are three recent examples that I was personally affected by – Forbes, Target, Neiman Marcus.
The days of business as usual are over. Data generation costs are falling everyday. The cost of collection and storage is also falling. The speed of insight-to-action business requirement is increasing.
The bottleneck is clearly shifting from transaction processing to Analytics & Insight-driven “sense-and-respond” Action.
Here are just a few examples of “data-to-insight” as well “insight-to-action” analytics at work:
- Target predicts customer pregnancy from shopping behavior, thus identifying prospects to contact with offers related to the needs of a newborn’s parents.
- Tesco (UK) issues 100 million personalized coupons annually at grocery cash registers across 13 countries. Predictive analytics increased redemption rates by a factor of 3.6.
- Netflix (Cinematch, Max) predicts which movies you will like based on what you watched.
- Life insurance companies calculate the likelihood an elderly insurance policy holder will die within 18 months in order to trigger end-of-life counseling.
- Con Edison predicts energy distribution cable failure, updating risk levels that are displayed on operators’ screens three times an hour in New York City.
- Linkedin (People you may know, Jobs you may like, Groups you may be interested in)
This slide from IBM’s Investor Briefing summarizes the data-driven transformation underway in most businesses. IBM like others is seeing that almost every company in a quest for growth has business initiatives like (1) Identifying root causes of customer attrition & developing retention strategy; or (2) Collecting data and constantly improving the efficacy of multi-channel marketing campaigns and customer targeting;
Better/Faster/Cheaper Analytics Execution
Industrialization of analytics is the new buzzword. Overcoming the jumble of point solutions is a non-trivial challenge in a big firm. Disparate vendors, disparate capabilities, different interfaces, all acquired over a long period of time.
To meet demand for faster/better/cheaper innovation around analytics, CFOs and CIOs are rethinking their silo’d sourcing strategies, fragmented tech budgets aligned against one-off projects, and are looking at new ways of doing things via out-tasking, IT outsourcing and business process outsourcing their Analytics and Data Science functions.
The “should we or shouldn’t we outsource data science” discussion is heating up in board-rooms and executive suites as analytics becomes core to the firm, C-level execs have to consolidate efforts for delivering the same services to different groups within an organization.
“Google, Facebook are really big data companies, not software companies. They collect data, process it and sell it back with value added extensions. They don’t have better algorithms. They simply have more data.” — Anonymous
Data is valuable. Data is plentiful. Data is complex. Data is in flux. Data is fast moving. Capturing and managing data (Cloud, On-Premise, Hybrid IT) is challenging. It’s a paradox of the information age. The glut of information that bombards us daily too frequently obscures true insight.
Help people uncover, see, understand and visualize data presents a broad and momentous market opportunity….call this user-driven discovery. Take for instance, Facebook (like Amazon.com) builds a custom Web page every time you visit. It pores over all the actions your friends have taken—their postings, photos, likes, the songs they listen to, the products they like—and determines in milliseconds which items you might wish to see, and in what order. Is this the future for every firm…..
The opportunity is simply getting bigger by the day. Every customer interaction is generating a growing trail of data (“data exhaust”). Every machine that services the customer is generating data. Every conversation, transaction, engagement, touchpoint location, offer, response is a potential digital bread-crumb of opportunity.
Now let’s flip the context. A typical mobile user check their phone interface 150 times a day for updates. A Gen Y or Millenial user obviously much more than a Gen X user. The consumption patterns for information are changing continuously. Facebook style real-time updates which were revolutionary 5 years ago seem outdated in the mobile world. We live in an “attention deficit economy” where attention is the new basis for competition. The firms that create the evolving experience using data which can grab/hold your attention will attract marketing and ad $$s.
As a result, the buzz and hype around data…small data, big data, machine data, social data, mobile data, wearables data….is relentless. As a result there are a lot of new initiatives and companies. I have been asked repeatedly by a lot of entrepreneurs and strategy teams about analytics market size and opportunity size. Product and services firms are also interested in opportunity sizing as they create new offerings in the data rich world.
I thought i would share a mashup of industry and market sizing data i have collected so far.
- How big is the overall market for Analytics, Big Data?
- How big is the market for Digital Customer Interaction or Engagement?
- How big is the market for Mobile and Social Intelligence?
- How big is the market for Wearables?
- What is growing fast, faster and fastest?
All good questions as services firms think about digital strategy, analytics and future state. You always want to be in the “hot” area… selling is easier, valuations are richer, revenue growth percentages exponential.
Self-tracking, Seamless Engagement and Personal Efficiency improvement’s new frontier is Personalized Big Data and Digital Health. This is really becoming a viable idea around wearable and sensor computing and the basis for new data platform wars.
The new platforms for digital life or data driven life – that collect, aggregate and disseminate — will cover a wide range of new User Experience (UX) use cases and end-points… medical devices, sensor-enable wristwear, headset/glasses, tech-sensitive clothing. All of them are going to collect a lot of data, low latency analytics, and enable data visualization. Several new firms are entering the activity tracker market LG (Life Band Touch), Sony (the Core), Garmin (Vivofit), Glassup, Pebble, JayBird Reign etc.
Data collection is just one piece of the solution. The foundation for personalized big data is Descriptive and Predictive Analytics. Ok…What do i next? what is the suggestion? in the form of predictive search (automated deduction or augmented reality).
How do i discover useful patterns, analyze, visualize, share, query and mobilize the collected data? A wide range of start-ups – Cue, reQall, Donna, Tempo AI, MindMeld, Evernote, Osito, and Dark Sky – and big companies like Apple, Google and Samsung are working on predictive apps — aimed at enabling new robo-assistants that act as personal valets, anticipating what you need before you ask for it.