Feedback in the form of healthcare data analytics is one way to promote better preventative care and motivate physicians who are performing poorly.
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While almost two-thirds of organizations across the healthcare ecosystem have analytics strategies in place, our research shows that only a fifth are driving analytics adoption across the enterprise.
The IBM Institute for Business Value has been listening to what members of the healthcare ecosystem around the world have been saying about their experiences with analytics. We have surveyed 555 executives within the healthcare industry and are about to launch our latest point-of-view, Analytics across the ecosystem: A prescription for optimizing healthcare outcomes. This blog briefly explores just one of the aspects covered in the paper; ‘Importance of enabling organizational strategies with analytics’
The healthcare ecosystem is the convergence of otherwise separate entities, such as life sciences organizations, providers and payers, as well as social and government agencies. Going foreword, gaining and sharing meaningful insights from data across the entire healthcare ecosystem will be a necessity to correlate cost and quality of care. For example, increased interaction among providers, payers, life sciences organizations and patients can help reduce unplanned adverse events. Patients can benefit from more individualized care. Insights from analytics can facilitate continuous learning and promote quality improvement. However, organizations are still struggling with using advanced analytics for gaining such insights. Only 34% of our study’s respondents said they think in terms of analytics that can help gain actionable insight from data.
Enabling organizational strategies using analytics can lead to a significant impact. For example, in a recent IBM Institute for Business Value study about big data, the percentage of respondents in the healthcare and life sciences industries reporting a competitive advantage from analytics rose from 35% in 2010 to 72% in 2012, a 106% increase in two years.
To derive the most value, analytics must become an increasingly important factor in corporate strategy decisions. To position analytics accordingly, organizations must define the enabling analytics strategy, prioritize their roadmaps to address internal requirements and create strategies for future collaborative partnerships across the healthcare ecosystem. A comprehensive plan for governance is a foundational to drive adoption of any analytics strategy. High-level sponsorship of key analytics projects is an important success factor. The most effective analytics initiatives embed small, action-oriented analytics into key decision points of specific business processes that are used widely across the ecosystem. Metrics to measure success should be in place from day one and be tracked. To get the most out of these projects, organizations should focus on early insights that enable refinement of processes over time.
The point-of-view will explore this topic in further detail taking into context the requirements within the organization as well as across the entire ecosystem. Read the paper to learn more and discover the three areas of focus that can have a dramatic impact on your organization and entire ecosystem.
A recent article in the Harvard Business Review discusses the findings of a survey of senior executives across sectors and confirms that two-thirds of them report having big data in production, with 70 percent indicating that big data is of critical importance to their firms. Consumer oriented industries such as financial services are heavily represented in the survey, as are life sciences firms
As with most innovation and new technology programs, adoption rates and effectiveness will vary across sectors, and even within sectors. Typical factors influencing this dynamic are an industry sector’s historical approach to cutting edge technologies, focus (or lack thereof) on top-line oriented innovation vs cost reductions, and ROI considerations, just to name a few.
Even within Healthcare, the focus may be on R & D in pharma, claim expense control among payers, and clinical outcomes improvement in providers. Accordingly, stakeholders take widely varying approaches to investments in big data, and this consequently shapes their approaches towards the stewardship of enterprise-level data.Why is healthcare suboptimal in the use of available data?
A paper by the IMS Institute for Health examines this issue in great detail and outlines the tradeoffs that regulators and patients have to make for data to be leveraged meaningfully. The paper argues that longitudinal data, even in a non-identifiable form, can be extremely useful in accelerating research and supporting connected health initiatives, involving big data from multiple sources, including new sources such as wearables. However, there are several bottlenecks in harnessing all this data – these include the willingness of participants to share data, regulatory and privacy restrictions, the suitability and reliability of new data sources, and information security considerations.
The challenges related to interoperability of data are well-known, as are the privacy restrictions on the use of data. However, several workarounds are emerging, as market participants (belatedly) coalesce around common standards to enable data exchange and analysis across the healthcare ecosystem. Cloud-based models that are HIPAA compliant and secure, are becoming more and more accepted as enterprises recognize that in many cases, cloud environments can be even more secure than their own legacy environments.
However, even with all of this, the ability to derive value depends on an individual institution’s access to large amounts of data within and across health systems that enable benchmarking and pattern analysis. This is where the fragmented healthcare system comes up short. The big EHR vendors, despite their access to vast amounts of data, have been slow to take the lead. Initiatives such as health information exchanges (HIE) remain regional, with their own set of restrictions governed by the individual members of the exchange.
The HBR article concludes that the vast majority of the problems in big data programs relate to people, not technology. Within organizations, effective use of data is stymied by management silos, and the disconnect between CIO's and the lines of business hampers the collaboration required for improving patient outcomes. Governance models within healthcare are evolving slowly, with the role of Chief Data Officer still relatively rare. The absence of centralized governance and the lack of collaboration weakens the data even further as individual groups choose to work with the limited data available within their silos.
The result of all this is an industry that operates sub-optimally at a system and a firm level across all of healthcare
So who bells the cat?
As technology continues to progress, the ability of healthcare to adopt and benefit from big data programs will continue to increase, and the focus has to inevitably turn to governance issues. As with sectors like retail and financial services, the solutions may emerge from disruptive forces outside the system. One obvious source of disruption is Silicon Valley, with its iconoclastic ways of approaching the status quo. Notwithstanding some regulatory challenges, most recently with insurance broking startup Zenefits, some of these startups will successfully disrupt incumbent players and their deeply entrenched interests in maintaining the status quo.
A second, unexpected, source of disruption seems to be big employers, especially the large companies that underwrite the costs of medical expenses for their employees. A group of twenty large employers, including the likes of American Express and Verizon, are forming a coalition to pool the medical claims information on their 4 million collective employees to identify opportunities for group purchasing and contracting that will put pressure on health plans and providers to reduce costs. These employers are motivated by multiple factors – controlling costs, retaining employees, and managing financial risk being some of them.
Frustration with the current state of healthcare, especially the large premium increases we have seen this year, will lead to some radical new thinking – which many of us crave, including this blogger. Sure, there will be lots of hand-wringing about new players playing fast and loose with patient data. At the same time, the fact is that the key to solving this bottleneck is in the hands of the incumbents in the healthcare system. If they choose not to use it, someone else will. Very soon.
This item first appeared in the Jan. 5 edition of Data Sheet, Fortune’s daily newsletter on the business of technology. Sign up here.
It is little wonder that legislators do not have the answers. The convergence between technology, life sciences and data is fast-moving, making categorisation and enforcement difficult. The world of wearable technology is not yet regulated. And with very little precedent, legal and commercial departments need experts with degrees in computing and biology. Engineering in the life sciences field is not new, but the internet has brought discovery much closer to the individual - and, with it, the need for doctors (and lawyers) who understand data algorithms.
Twitter also notes today that its new relationship with IBM was made possible by its acquisition of Gnip earlier this year, as it provided the enterprise-grade platform capable of delivering its 15 billion “social activities” created per day to Twitter partners, now including IBM.
Readers might enjoy previous post, “Next Hurdle For Medical Research: Capture and Integration Of Phenotype At Scale“
“Big data” and analytics applied to healthcare is a hot area of investment. This year alone, roughly $200 million of venture capital dollars has been allocated to the space (according to Rock Health). I’m here to tell you that these glory days are gone.
New startups are finding it difficult to differentiate from the swath of other companies unless they have something extremely novel.
While I still believe in the transformative power of data in healthcare (I’ve had two successful exits with analytics companies: RxAnte and Humedica), it’s increasingly clear to me that the current space is beginning to commoditize:A lot of companies are chasing the same targets. Large providers and insurers are increasingly finding it hard to choose amongst vendors — and as a result, pricing for pure analytics is decreasing.Most platforms provide extremely useful clinical insights, but rely on an end user that they don’t control to act. Hence these companies have no accountability for reducing the cost of healthcare and improving outcomes.Providers and payers are bombarded by different vendors every day. Some offer very elegant data visualization but not necessarily “new” or “better” data, and the underlying issues of accountability and pricing pressure remain.
That’s the bad news. Here’s the good news: There is still plenty of opportunity for startups in this space!
To hear more about the opportunities for startups and investors in healthcare, join VentureBeat at HealthBeat 2014, Oct. 27-28 in San Francisco,
where Mo Kaushal will be diving deeper into the topic.
Evolving policies around payment model reform (in which providers will be paid based on outcomes, not just services rendered) and meaningful use bonus payments for electronic medical record (EMR) adoption demonstrate that the healthcare industry is in the early days of transformation. Ongoing technology innovation, macroeconomics, and policy reactions will continue to accelerate the shift.
This creates a world of opportunity of the healthcare analytics entrepreneur. Despite my concerns over traditional healthcare data analytics companies, there are a few areas I believe we need to accelerate.
Here are some of the criteria I believe will be required in the next generation of healthcare analytics companies:
1. New business models. For example, companies that are building out a service around a core piece of technology and that can deliver this service much more cost-effectively than any other incumbent competitor.
Navihealth is a great example of this. Navihealth’s core analytics uses patient function to predict the ideal setting upon discharge, coupled with a service model that helps optimize individual care in each post-acute facility. In other words, people in conjunction with the right technology targeting an at-risk business model has helped create a very unique value proposition to end customers.
2. Advanced, proprietary technologies. Beyond data analytics, next-generation artificial intelligence platforms will drive the next wave of innovation. New platforms must be able to ingest multi-source data and reveal novel insights that are actionable and not commoditized. These solutions will displace many current platforms as the data output becomes more valuable.
Vicarious is attacking the market for artificial intelligence by building a unified algorithmic architecture. Along the way, Vicarious has also secured investment from many of the biggest names in tech, including Mark Zuckerberg, Peter Thiel, Jeff Bezos, Jerry Yang, and Marc Benioff (to name a few). I’m also aware of some interesting examples of companies doing this in healthcare, but to my knowledge none of them has emerged from stealth — so watch this space!
3. High-value data sets that can’t be replicated. Companies that can provide proprietary data sets that can’t otherwise be easily obtained are increasingly setting a high bar for entry for new healthcare analytics startups.
This is one of the key reasons Optum acquired Humedica. Humedica is able to extract, standardize, and analyze millions of fully integrated clinical data versus just claims information.
As an investor, my dollars for healthcare analytics companies have already been invested, and it’s time for the next wave of innovation. For my money, healthcare entrepreneurs must focus on unique niches where little competition exists, and they must address those markets using differentiated technologies, data sets, and business models that target large problems.
Where do you see healthcare investment potential?
Mohit (Mo) Kaushal is a partner at Aberdare Ventures. He’s an MD MBA with extensive experience within clinical medicine, venture capital, and health policy. Prior to Aberdare, he was Chief Strategy Officer and EVP of Business Development at West Health, where he developed the West Health Investment Fund strategy and sourced and led investments. Prior to that he was the Director of Connected Health with the FCC, where he established the agency’s first dedicated health care team. He was also a member of the White House Health IT task force, a cross agency team focusing on implementing the technology aspects of Health Reform.HealthBeat — VentureBeat’s breakthrough health tech event — is returning on Oct 27-28 in San Francisco. This year’s theme is “The connected age: Integrating data, big & small.” We’re putting long-established giants of the health care world on stage with CEOs of the nation's most disruptive health tech companies to share insights, analyze trends, and showcase breakthrough products. Purchase one of the first 50 tickets and save $400!
The message seemed to resonate. In an informal, real-time poll of about 150 people in a breakout session about making analytics a strategic imperative, a slim majority said that their organization's analytics vendor is more important than its EHR vendor.
[See also: Health Catalyst shows Midas touch.]
It took a while to get to this point, despite strong clinician and administrator demand for data in recent years. "Data became cool at Mission Health," said CIO Jon Brown. Everyone asked for it but nobody did much with it, so nothing changed.
[See also: Partners and Health Catalyst join forces.]
A new report – 'Sustaining Universal Healthcare: Making Better Use of Information' – released today by Volterra Partners and EMC has outlined how data analytics and better use of information can improve the efficiency of healthcare delivery in the UK by up to 60%, with the potential to save the NHS between £16.5 billion and £66 billion per year.
The NHS is currently facing a £34 billion funding gap by 2020 and radical action is needed to preserve its core values of 'providing free healthcare for everyone'. The report recommends the use of information sharing and collaboration to deliver a proactive, personalised Wellness Model structure, focused on prevention.
'There has been considerable speculation that the NHS, as a universal healthcare institution, cannot continue in its current form,' Nick Bosanquet, Emeritus Professor of Health Policy at Imperial College and economist at Volterra Partners, commented.
'We need drastic change to cope with the increasing demands from an ageing population, chronic health conditions and emergency readmissions. The report estimates that with better informatics, cancer admissions could be reduced by 30%. This example and other predicted savings calculated in the study offer a clear path to delivering the Wellness Model.'
> See also: The NHS is sick- big data could be the cure
The report exposes the gap between the NHS and other industries in its use of data analytics and technology. The lack of electronic records, predictive analytics, collaboration and effective monitoring of patient and treatment outcomes, in addition to personalised care, is leading to failures and financial inefficiencies that are unsustainable in the long-term.
There are pockets of excellence across the UK where data analytics has been effectively employed to deliver better quality of care for patients. If these examples were implemented nationally this would result in savings of £840 million per year due to a reduction in A&E attendances; £200 million per year through reduced complications due to diabetes; £126 million per year through better care management for patients with Chronic Obstructive Pulmonary Disease (COPD); Up to £32 million per year through the reduction of readmission rates and £5 billion of savings in staff time through more efficient working practices.
> See also: Medway Hospital seeks big savings as software connects it to NHS spine
Scotland has used informatics technology to provide an integrated care model for the treatment of diabetes. This collaborative data-driven project has yielded impressive results with the incidence of lower extremity amputation decreasing by 30% over four years and major amputations falling by 40.7%. The report estimates that if the same system was implemented in England, it could result in 1,775 fewer amputations, saving the NHS £37 million per year.
> See also: Monitoring the health of NHS IT
The report identifies a number of recommendations to enhance patient care, including speeding up the accessibility of data and communicating the benefits to patients and GPs ahead of time to build trust and buy in, collaboration at a local level with health institutions and academia, investment in appropriate skills in the health workforce to handle and use data effectively, and a change in culture within the Department of Health to drive a real shift to the Wellness Model, rather than just using data to improve performance management
Apple's foray into mHealth, given its reported upcoming iWatch device, its moves into electronic health record technology and the development of its HealthKit platform, will have a dramatic impact on healthcare and advance mHealth like few other initiatives, according to a report at Product Design & Development.
Via Alex Butler
Watson, the IBM system that won “Jeopardy,” has shown promise in answering some kinds of questions. Now the company sees a broader role, a bit like the deductions that helped its namesake’s famous partner solve fictional crimes.
The company on Thursday is announcing advances in the technology and the availability of what it calls IBM’s Watson Discovery Advisor, a cloud service that it says can help research teams analyze vast troves of data to come up with new research ideas.
IBM is also pointing to a peer-reviewed case study to back up its claims. It describes how a tool based on Watson–developed at Baylor College of Medicine in Houston–was able to sort through about 70,000 scientific papers for relevant data about a particular protein and generate hypotheses that could be tested by scientists.
Watson is a collection of algorithms and software that runs on IBM’s Power line of servers, available for customers to use from its own data centers. Its components are designed to derive meaning more human language, and learn from data and other observations as opposed to being explicitly programmed to carry out instructions.
The company hasn’t generated a lot of measurable revenue from Watson so far, but it is betting that the extension of the technology from answering questions to generating hypotheses should help.
“Discovery is a lot more subtle,” said John Gordon, vice president in IBM’s Watson group. “You are trying to find connections.”
In the study, biologists and data scientists using the technology were able to identify proteins that modify p53, a protein related to many cancers. But the broader point was to show the potential of letting computers analyze data and make useful suggestions about it, amid a flood of research being generated by companies and other institutions.
“The literature is immense,” said Olivier Lichtarge, a professor of molecular and human genetics at Baylor who was the principal investigator on the study. “It is very difficult for any researcher to thoroughly master.”
Gordon said a related motivation is that many research dead-ends generate data that ends up on the cutting room floor. With the aid of Watson, companies could better mine that private information and combine it with scientific data in the public domain.
One company studying such possibilities to evaluate medications and treatments is Johnson & Johnson, IBM said.
But the company sees applications beyond the health realm, including making automated suggestions based on financial, legal, energy and intelligence-related information, IBM said.
Wen is an attending physician and director of patient-centered care research in the Department of Emergency Medicine at George Washington University. She is the author of"When Doctors Don't Listen: How to Avoid Misdiagnoses and Unnecessary Care," and founder of Who's My Doctor, a project to encourage transparency in medicine. On Twitter:
Amazon’s Dash buttons will allow you re-order your Tide… or Dash, with one click.
Use big data wisely by automating tasks, personalizing treatment and improving communication between physicians and patients.
In our information-infused age, health care organizations can use big data to improve quality of care and outcomes for patients, especially those with chronic conditions. Equally important, they can harness the power of big data to prevent diseases and improve wellness initiatives.
Growing use of electronic health records plays a big role in building up health care data sets. As of last year, nearly six in 10 hospitals had adopted at least a basic EHR system, according to the Office of the National Coordinator for Health Information Technology, and 93 percent had a certified EHR system in place. The National Ambulatory Medical Care Survey also showed that in 2013, close to 80 percent of office-based physicians used some type of EHR system. These systems play host to structured and unstructured data — including a mountain of physician notes — for millions of patients across the country, all of it ripe for use in analytics to help improve and personalize treatments as well as in predictive modeling to better understand where risks lie.Doing the Right Thing
A recent conversation I had with Blackford Middleton, M.D., chief informatics officer at Vanderbilt University Medical Center, made it plain that leading-edge institutions want to build clinical decision support systems that exploit big data — an organization's high-volume, high-variety, high-velocity information assets. The goal, as the good doctor put it: "to ensure the right things happen for the right patient at the right time."
Vanderbilt University Medical Center is taking on some big data–infused efforts to improve the quality of care it renders under the new value-based health care services model. At the same time, it wants to use big data to reduce unnecessary services.
"We don't need to do thousands of hospital and ambulatory care tests that don't impact care outcomes. Do more of the right ones and fewer of the wrong ones and you will lower costs and increase the value of care provided," Middleton says. Those same results can be achieved when health care systems analyze big data to help a patient avoid a single emergency clinical visit, for example, or an inappropriate medication that has adverse effects.
A big part of getting to this end, though, is moving to a continuous care model that extends the doctor-patient connection beyond scheduled visits. Good continuous care is dependent on more regular infusions of patient data than the updates generated during visits — it requires engaging the patient to be a more active participant in his or her own health care.
It also means investing in communications and other technologies so patients can respond more easily to between-appointment recommendations such as changes in treatment plans. And, it requires coming up with solutions to ensure that the data obtained can be put to use effectively. Such extensions of the connection between the health care team and the patient should translate into successful outcomes and mitigate excessive care costs.The Road to Better Health Care
Here are five ways your health care organization can work with patients, and with their data, to build a better care model:
Make the patient a responsible member of the health care team. In some respects, it's easier than ever to do so, given the range of smartphone apps, fitness wristbands, smart watches and other tools to record exercise, food intake, weight and blood pressure and transmit it to health care providers. Maybe, suggests Middleton, health care providers should take a closer look at ways to use these tools to make such data contributions into a gamelike experience "to maximize the health care productivity function for each patient."
For example, perhaps patients with high blood pressure conditions can accumulate points for every reading they transmit, and once they reach a certain number, exchange them for a coupon for a healthy smoothie at their next doctor visit. Understanding how various measures consistently trend over time, after all, can be important for doctors treating patients with chronic conditions such as diabetes, hypertension, obesity and even depression.
Think ahead to a holistic approach to data acquisition and analysis. In addition to driving a continuous care model with the help of patients' self-reported information on structured data points, other data — perhaps in unstructured format — can be included in the patient-care analytics mix. As Middleton points out, social, cultural and environmental factors play a big part in people's health and can provide important context for health care management. Commentary on social networks, employee wellness program information and community health statistics — all are venues that might provide useful data related to these parts of patients' lives.
As Middleton says, "There are a lot of issues to be wrestled with," such as getting patients to authorize access by providers to their private information. "But, I think it will come. I think patients will be willing to trade off what's viewed as privacy against what they may view as bringing convenience or value to them."
There is an opportunity, as well, to combine a better clinical understanding of patients with an already robust financial understanding of those same individuals, based on the data stored within health care organizations' administrative systems. Incorporating patient receivables data into health care analytics processes, for example, can help a health care system to understand if it has been successful at effectively engaging patients in their own health care.
Remove the noise. While there is value to be gained from adopting electronic medical records, health care organizations also find that the current generation often floods them with too much undifferentiated information. It's a work in progress for many hospitals to synthesize and distill from that flood the essential signals that pertain to the care of a particular condition for a particular patient. And, as data come in from more sources, perhaps in different formats, the synthesis and distilling challenges are poised to grow.
Vanderbilt is taking steps to meet aspects of the challenge of highlighting important EHR data with projects like Pharmacogenomic Resource for Enhanced Decisions in Care and Treatment, or PREDICT. PREDICT works so that automatic point-of-care decision support is launched when a certain drug is prescribed for individuals whose EHR data indicate that they are at risk for variant genotypes. "Personalization becomes very real," Middleton says.
Let the data inform task automation. As more information becomes part of the health care equation, health care professionals can spend more time doing what they love: engaging in the hands-on care part of the equation while letting data analytics drive task completion.
For example, it is possible to have data from EHRs, sensors and wearable medical devices feed into the health care provider's patient engagement system and trigger just-in-time patient engagement communications specific to that individual. That way, a health care worker doesn't have to, for example, manually monitor incoming data to direct a response to a spike in a patient's glucose levels. Rather, business rules can be created to assess the level of urgency and take the appropriate next step, such as scheduling a screening appointment if the rise in glucose levels was minimal.
Better facilitate feedback from providers to patients.Health care communications infrastructures have unfortunately lagged behind those found in many other sectors. The ability to acquire and analyze more data to drive best-evidence and best-experience recommendations in a continuous care model can't be underestimated.
But the promise of technologies such as wearable devices and EHRs is negated if providers can't deliver their findings — or any information, advice or encouragement — to patients using the communications pathways a patient prefers, whether that's via Web portals or personalized, automated emails, voice mails or text messages. According to the TeleVox Healthy World Research Initiative, for example, close to 50 percent of patients prefer email for communications about patient care between visits, followed by text messaging at 31 percent.
Sadly, when patients disengage, health care organizations risk recurrence of events that might otherwise have been avoided. Says Middleton, "Patients' preferences and utilities impact to a great degree how we see them engage in care or pursue healthy outcomes and behavior."
We know there will be obstacles to overcome in adopting new ways of working with patients and data to turn the vision of continuous care into a reality. But we also know that we can't fail — not if we are to create a more healthful populace without further driving up health care costs.
Though designed to improve care by sharing data from various sources, poor interoperability remains a huge barrier to accountable care organizations (ACOs), according to a survey from Premier and the eHealth Initiative.
In fact, all 62 ACOs responding to the online poll reported that access to data from external sources was a challenge for their organization.
"Even when ACOs have successfully adopted and merged HIT systems, they aren't able to effectively leverage data and analytics to derive value out of their investments," Keith J. Figlioli, Premier's senior vice president of healthcare informatics and member of the Office of the National Coordinator's Health IT Standards Committee, said in an announcement.
Those problems could stymie cost and quality improvements going forward.
Among the findings:
88 percent of the ACOs face significant obstacles in integrating data from disparate sources83 percent report challenges integrating technology analytics into workflowInteroperability of disparate systems is a significant challenge for 95 percent of organizationsAt least 90 percent of respondents cited the cost and return on investment of HIT as a key barrier to further implementationsAs ACOs pull data from more sources, they also report lower abilities to leverage their HIT infrastructure to support care coordination, patient engagement, population health management and quality measurement
These organizations have technology in place to improve clinical quality, the most common being electronic health records (86 percent), disease registry (74 percent), data warehouse (68 percent), clinical decision support (58 percent), and health information exchange (44 percent.)
However, the technology for distance-based medicine was less common. Only 38 percent had secure messaging, 36 percent had referral-management tools, 34 percent provided phone-based telemedicine and 26 percent had video-based telemedicine. That raises concern about rural ACOs' ability to leverage health IT to effectively manage remote populations, the authors said.
A previous eHealth Initiative survey found ACOs have made little progress in boosting their health IT capabilities in the past year. Seventy-six percent of respondents in that poll did not participate in a health information exchange at an enterprise, community or state level.
A study from Johns Hopkins, meanwhile, found robust analytics infrastructure that reports data in real time to care teams key to success for ACOs.
General Electric – a literal powerhouse of a corporation involved in virtually every area of industry, has been laying the foundations of what it grandly calls the Industrial Internet for some time now.
But what exactly is it? In this post I will try and give a basic overview of the ideas which they are hoping will transform industry, and how it’s all built around big data.
If you’ve heard about the Internet of Things which I’ve written about previously, a simple way to think of the industrial internet is as a subset of that, which includes all the data-gathering, communicating and analysis done in industry.
In essence, the idea is that all the separate machines and tools which make an industry possible will be “smart” - connected, data-enabled and constantly reporting their status to each other in ways as creative as their engineers and data scientists can devise.
This will increase efficiency by allowing every aspect of an industrial operation to be monitored and tweaked for optimal performance, and reduce down-time – machinery will break down less often if we know exactly the best time to replace a worn part.
Data is behind this transformation, specifically the new tools that technology is giving us to record and analyze every aspect of a machine’s operation. And GE is certainly not data poor – according to Wikipedia its 2005 tax return extended across 24,000 pages when printed out.
And pioneering is deeply engrained in its corporate culture – being established by Thomas Edison, as well as the first private company in the world to own its own computer system, in the 1960s.
So of all the industrial giants of the pre-online world, it isn’t surprising they are blazing a trail into the brave new world of big data.
GE generates power at its plants which is used to drive the manufacturing that goes on in its factories, and its financial divisions enable the multi-million transactions involved when they are bought and sold. With fingers in this many pies, it’s clearly in the position to generate, analyze and act on a great deal of data.
Sensors embedded in their power turbines, jet engines and hospital scanners will collect the data – it’s estimated that one typical gas turbine will generate 500Gb of data every day. And if that data can be used to improve efficiency by just 1% across five of their key sectors that they sell to, those sectors stand to make combined savings of $300 billion.
With those kinds of savings within sight, it isn’t surprising that GE is investing heavily. In 2012 they announced $1 billion was being invested over four years in their state of the art San Ramon, California analytics centre, in order to attract pioneering data talent to lay the software foundations of the Industrial Internet.
In aviation, they are aiming to improve fuel economy, maintenance costs, reduction in delays and cancellations and optimise flight scheduling – while also improving safety.
Abu Dhabi-based Etihad Airways was the first to deploy their Taleris Intelligent Operations technology, developed in partnership with Accenture.
Huge amounts of data are recorded from every aircraft and every aspect of ground operations, which is reported in real-time and targeted specifically to recovering from disruption, and returning to regular schedule.
And last year it launched its Hadoop based database system to allow its industrial customers to move its data to the cloud. It claims it has built the first infrastructure which is solid enough to meet the demands of big industry, and works with its GE Predictivity service to allow real-time automated analysis. This means machines can order new parts for themselves and expensive downtime minimized – GE estimates that its contractors lose an average of $8 million per year due to unplanned downtime.
Green industries are benefitting too – its 22,000 wind turbines across the globe are rigged with sensors which stream constant data to the cloud, which operators can use to remotely fine-tune the pitch, speed, and direction the blades are facing, to capture as much of the energy from the wind as possible.
Each turbine will speak to others around it, too – allowing automated responses such as adapting their behaviour to mimic more efficient neighbours, and pooling of resources (i.e wind speed monitors) if the device on one turbine should fail.
Their data gathering extends into homes too – millions are fitted with their smart meters which record data on power consumption, which is analyzed together with weather and even social media data to predict when power cuts or shortages will occur.
GE has come further and faster into the world of big data than most of its old-school tech competitors. It’s clear they believe the financial incentive is there – chairman and CEO Jeff Immelt estimates that they could add $10 trillion to $15 trillion to the world’s economy over the next two decades. In industry, where everything including resources is finite, efficiency is of utmost importance – and GE are demonstrating with the Industrial Internet that they believe big data is the key to unlocking its potential.
The increased use of data analytics for speedier clinical trials, more efficient regulation, and better population health management is at the top of the priorities list for the Food and Drug Administration (FDA) in the next four years, according to its latest FDA Strategic priorities report. Analytics will be the foundation of several of the FDA’s ongoing initiatives, including reducing tobacco use, developing new methods of testing medical products and conducting clinical trials, and identifying health consequences of certain products or behaviors.“An increasingly global and complex marketplace, rapidly evolving technologies, and emerging areas of science are having a major impact on FDA’s mission to promote and protect the public health,” the report says. “FDA must tackle these new challenges expeditiously, as we continue to meet our core responsibilities. Working collaboratively with our international regulatory partners, we will continue efforts towards international harmonization and regulatory convergence. We achieve this by using smart regulatory approaches to streamline and modernize our regulatory programs and minimize regulatory uncertainty for industry, while protecting and maximizing public health and safety.”In order to accomplish its four core goals of oversight, access, informed decision-making and organization excellence, the FDA will rely on regulatory science, globalization, safety and quality, smart regulation, and financial stewardship. At the heart of these strategies will be the use of analytics to inform standards for product development, distribution, and safety, as well as ways to detect consumer safety issues, risks, and adverse events.“FDA is already taking concrete steps to advance safety and quality across the Agency,” the report added. “For example, the Case for Quality Initiative, which includes a voluntary compliance improvement program pilot, promotes medical device quality. The planned Office of Pharmaceutical Quality will highlight and consolidate quality principles and review throughout the drug lifecycle.”The Case for Quality Initiative will include a voluntary compliance improvement pilot program for medical devices, which “aims to reduce the risk of patient harm by helping manufacturers identify and deploy quality-related design and production practices,” the brief explains. The Agency will also work to improve understanding of regulatory requirements and provide guidance to healthcare stakeholders and consumers through new rules and standards.“If biomedical science is to deliver on its promise, scientific creativity and effort must also focus on improving the medical product development process itself, with the explicit goal of robust development pathways that are efficient and predictable and result in products that are safe, effective, and available to patients,” the report concludes.
“Although FDA’s primary responsibility is to review the safety and effectiveness of new medical products developed by industry, the Agency is also committed to assisting product developers in translating discoveries in basic science into new therapies that will save lives and improve health care.”
Our last healthcare blog, Meaningful Use: A Valuable Asset, focused on utilizing Meaningful Use and theHITECH Act as a means to effect positive change. We left off with the recommended shift to healthcare consumerism and provider relationship management.
To support these ideas, we need a new emphasis on provider and physician relationship management with a goal of targeted patient acquisition. We will address that in our next post Patient Acquisition Through Physician Relationship Management so stay tuned!
(The writer is the CEO of EdgeVerve Systems_
Population health and the increasing retail and consumer-minded approach are arguably the biggest shifts in the healthcare industry today, and they show little sign of letting up anytime soon.
“No matter the form it takes (and it’s not always M&A), a smart partnership can strengthen any organization’s appeal to retail consumers.”
While Novartis’ recent partnership with Google and its longtime relationship with Proteus have indicated that the pharma company has an interest in digital health, a page on the company’s website, added this summer, lays out its broad vision and explicit interest in mobile health specifically. The company even has a mobile health strategy lead, Michele Angelaccio, who holds the title of Associate Director US Mobile Health Strategy at Novartis Pharmaceuticals.
“We have a unique understanding of the challenges doctors and patients are facing, and can help guide startups in building and testing proposed solutions,” Angelaccio says in the piece. “Partnering with these health technologists is the cornerstone of our mobile health strategy. It will continue to propel us forward as an innovator and it is the means by which mHealth will help us to meet our customers’ needs and solve some of the business challenges we’re facing.”
In the post, Novartis highlights tracking and monitoring of patients as one of the biggest opportunities in mobile health. They mention the now-discontinued VaxTrak, for instance, as well asPodhaler Pro, an inhaler training app for cystic fibrosis patients.
Novartis currently has 13 iPhone apps in the Apple App Store, nine of which are patient or consumer-facing. The list includes two games, “Sickel Cell Iron Invaders” and “Marley’s World” which are designed to teach players about Sickle Cell disease and Multiple Sclerosis, respectively. It also includes MyNetManager and Clinical Trial Seek, two apps that launched last March.
The article also discusses a 2013 digital health challenge sponsored by Novartis, and ultimately won by home monitoring startup Sense.ly. They add that work is continuing to build on Sense.ly’s platform, which is set to come out of beta later this year.
Novartis’s interest in tracking as the primary vehicle for making the most of mobile health opportunities is displayed by the deals the company has been involved with over the last few years. It sponsored some major trials with Proteus Digital Health, a company that aims to track patients with ingestible sensors embedded in pills. This year, Novartis has also partnered with TicTrac to help multiple sclerosis patients engage in self-tracking and, in a high profile deal, signed on tolicense Google’s smart contact lens to help people with diabetes track their blood glucose levels.
The article concludes, however, with the suggestion that the company is getting ready to go beyond just tracking to technologies that “could reach the market in the near future, including some that enable patients to undergo testing, diagnosis and treatment remotely.” Perhaps the company’s interest in Sense.ly, which reaches out to homebound patients with a virtual clinical avatar, points to the sorts of technology Novartis is pursuing.
“Through solutions like these, we intend to make a major change in the way care is delivered, and increase access to health services,” Angelaccio said.
Social Proof is important to your business but many aren’t familiar with the term. It is also known as Social Influence and basically, it boils down to people looking for cues on what to do based on what others are doing. Although we pride ourselves on individuality, we are find comfort in numbers and groups and often look to what others are doing for assurance that we are on the track.