Analytics & Social media impact on Healthcare
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Analytics & Social media impact on Healthcare
A view on how analytics and social media is used for shaping the healthcare industry
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Turning big data into better health outcomes

Turning big data into better health outcomes | Analytics & Social media impact on Healthcare |

Population health management is a multifaceted, many-layered endeavor that nevertheless has a common theme: the need for data and the ability to mine it for actionable information.


A broad spectrum of health care players -- individual providers, hospital systems, payers, local public health departments and federal agencies -- are all in some way addressing population health management. The approach involves identifying populations, assessing their disease status and developing appropriate responses, such as management programs for chronic diseases. Those activities require access to data -- and plenty of it.


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JAMA report looks at what drives healthcare analytics | Vital Signs | The healthcare business blog from Modern Healthcare

JAMA report looks at what drives healthcare analytics | Vital Signs | The healthcare business blog from Modern Healthcare | Analytics & Social media impact on Healthcare |

The big hope for proponents of computer-enabled predictive analytics in healthcare is to one day see it in widespread use, at the point of care, in actionable form, to aid in real-time clinical decisionmaking.

But broad use implementation of eHPA is still in its infancy, say the authors of “Implementing Electronic Health Care Predictive Analytics: Considerations and Challenges. ”

Their piece is one of a series of articles on various permutations of Big Data in the current issue of the healthcare policy journal Health Affairs.

“The term infancy is relative,” says the article's co-author Bin Xie, health services research manager with PCCI, a Dallas-based not-for-profit corporation spun out of the healthcare data analytics work done at Parkland Health & Hospital System. 

The decades-old Framingham risk model for cardiovascular events and the APACHE II scoring systems to gauge the acuity of ICU patients are both well known examples of predictive analytics systems, the authors point out. 

But very few risk prediction models targeting hospital readmissions had been incorporated into an electronic health record system for easy use and reference, according to a 2011 survey report, published in the Journal of the American Medical Association and cited in the Health Affairs article.

“There are already many implementations across many hospitals in the country and across the world,” Xie adds in an interview. “It could grow into a big, giant adult, so, when we compare it to its potential, it's still in its infancy.”

“We think in five to 10 years, it could really become a big thing in healthcare, especially when we address the difficulty of containing costs and improving the quality of care and the challenge of the growth in the number of senior citizens,” he said.

Just as government penalties for hospital readmissions captured the attention of many early implementers of eHPA efforts, “payment reform is one essential piece to drive this growth” in the future, Xie said.

Predictive analytics has four component parts, according to the authors—acquiring data, validating the risk-prediction model, applying it in a real-world setting and scaling up the model for broader use in a healthcare system. Their article focused on the latter two and the challenges of bringing them to fruition.

Among those challenges are setting up an appropriate oversight mechanism with the right balance between enough control to keep the program operating properly and also affording it enough breathing room to grow and respond to daily events, the authors said. Another is stakeholder engagement, which includes patient consent, particularly when the risk models are still in the early stages of development. 

“The first time you go out and experiment, you do need a rigorous framework of the patient's right to know, just as you do in research study,” Xie said. 

Other issues that data analytics program planners must address are data quality assurance, patient privacy protections, interoperability of the technology platform and transparency of the risk model. 

“Whenever possible, clinicians, in particular, need to be able to 'see into' a risk-prediction model and understand how it arrived at a certain prediction,” the authors advise.  Transparency builds needed trust in the model, and it might encourage “crowd sourcing” to improve the model or expand its use to other organizations or settings.

Follow Joseph Conn on Twitter: @MHJConn

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FDA Looks to Urge Companies to Tweet Drug Risks

FDA Looks to Urge Companies to Tweet Drug Risks | Analytics & Social media impact on Healthcare |

e FDA is looking into a new way to regulate drugs and medical devices—by using social media. The agency has drafted social media guidelines that would urge drug companies to use platforms such as Facebook, Twitter, and YouTube, to educate the public about the risks of their prescription drug or medical device.

The draft guidelines, which are currently under review by the agency, propose that companies be required to use the “character space constraints” on social media platforms such as Twitter to tweet the risks, along with the benefits, of a product. The guidelines also recommend that manufacturers include a link that takes readers to more information about the product. In the case of Twitter, that information should all be included in a single tweet.

The document states:

If a firm concludes that adequate benefit and risk information, as well as other required information, cannot all be communicated within the same character-space-limited communication, then the firm should reconsider using that platform for the intended promotional message.

If approved, the guidelines will become the first formal recommendation by the agency regarding manufacturers’ use of social media.

Lara can be reached at Follow Lara on Twitter: @BostonLara
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Digital Agenda for Europe

Find here the most recent analysis and data by country. A selection of key documents and graphs are shown about topics such as broadband, internet activity and skills, egovernment, e-health, ICT in schools, research and innovation, as well as other main indicators.  

Via Philippe Marchal
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Twelve innovative population health management projects

Twelve innovative population health management projects | Analytics & Social media impact on Healthcare |
As part of its Health Innovations Award program, twelve projects focusing on population health management, care coordination, interoperability, and financial and clinical analytics will receive up to $110 million in combined funding from the Department of Health and Human Services.  At the same time, HHS has made up to $730 million available to states seeking to transform their public and private health insurance structures to meet accountable care goals and encourage innovation across the healthcare delivery system.  “As a former governor, I understand the real sense of urgency states and local communities feel to improve the health of their populations while also reducing health care costs, and it’s critical that the many elements of health care in each state – including Medicaid, public health, and workforce training – work together,” outgoing HHS Secretary Kathleen Sebelius said.  “To help, HHS will continue to encourage and assist them in their efforts to transform health care.“These efforts will strengthen federal, state, and local partnerships, encourage broad stakeholder engagement, and capitalize on federal resources to ensure greater transformation of delivery of health care services,” added CMS Administrator Marilyn Tavenner.With an average award size of about $9 million, the twelve projects receiving funding in Round Two of the program include academic institutes, professional societies, and healthcare providers across the country.  Some of the recipients include:North Shore-LIJ Health System on Long Island, New York, which will use its $2.5 million award to help coordinate the care of patients with late-stage chronic kidney diseases by using patient education, home dialysis, depression screenings, and other population health management techniques to reduce costs and preempt medical errors.Icahn School of Medicine at Mount Sinai, awarded $9.6 million to test its Mobile Acute Care Team (MACT) as a way to integrate the hospital-at-home model to reduce unnecessary admissions.  The MACT team will help treat patients for acute needs at home while integrating community care providers and providing appropriate referrals to non-hospital-based services.The Association of American Medical Colleges was granted $7.1 million to test the scalability of electronic consults and referrals across five academic medical centers.  Integrated into its Epic EHR systems, the consortium will use standardized referral templates to ensure that patients receive the specialty help they need.Regents of the University of Michigan will use $6.3 million to implement the Michigan Surgical and Health Optimization Program (MSHOP), which allows for real-time risk stratification for patients undergoing abdominal surgery.  Over the next three years, the system will be implemented at 40 Michigan hospitals.
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How the patient portal is changing medical practice

How the patient portal is changing medical practice | Analytics & Social media impact on Healthcare |

"Medical practice has begun its inevitable journey toward this transformation when, unless an exam or a procedure is required, most medical questions and answers, as well as virtually all medication refills and renewals, appointment requests, interpretation and discussion of the implications of lab and imaging results will be conducted online rather than in the office.


The reimbursement system in the health care of the future will simply have to take this into account, as we slowly transition to a fee-for-service to a care management."

Via Andrew Spong
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Practices in West Cornwall to [pilot] allow local A&E & OOH GP service with access to patients’ GP records

Practices in West Cornwall to [pilot] allow local A&E & OOH GP service with access to patients’ GP records | Analytics & Social media impact on Healthcare |

Eight practices in West Cornwall will pilot a data sharing scheme using Microtest’s Guru to allow local A&E clinicians and the local out-of-hours GP service with access to patients’ GP records.

Penzance GP Dr Matthew Boulter, who is leading the project on behalf of NHS Kernow Clinical Commissioning Group, said the pilot comes from GPs’ frustrations at their patients being unnecessarily admitted to hospital due to a lack of information sharing.

“A GP puts in place what we thought were pretty detailed plans to avoid admittance, only to find out they’ve been admitted because the admitting physician didn’t know about the plans, and had no way to find out.”

Dr Boulter said allowing doctors and out-of-hours services to view a patient’s GP record can have an enormous benefit, reducing unnecessary admissions and costs to the healthcare system.

“Information is power – the more information you’ve got, the better decisions you can make.”

He said the CCG is aware of concerns about information governance and patient consent, and spent nine months developing an agreement for all the practices to agree to.

Each practice is able to dictate how much information it shares, while access is restricted to those on the local GP performers’ list with no temporary locums allowed to use it.

As part of the safeguards, the Guru system, which can also be used on mobile devices, has a consent screen that pops up when a user tries to access a patient’s records, asking them to confirm whether or not the patient has given their consent for the service.

Dr Boulter said the system includes an override option for access in emergency situations, but doctors who use this are required to fill in a free-text box justifying their access of the records.

Clinicians have read-only access to the records, and practice managers will get a weekly read-out with information about which patients’ records have been accessed by whom to ensure there are no abuses taking place.

Dr Boulter said the pilot is in the middle of going live, with doctors going through training before they receive their log-on details and use the service.

The trial will last for 12 months, with three-monthly audits taking place to consider feedback and make necessary changes so the pilot can continue past the trial’s end if it is a success.

“What we don’t want to do is have the system stop at 12 months while we navel-gaze and analyse the data.”

If the pilot is a success, Boulter said the CCG could look to extend access to ambulance services, Macmillan nurses and others who can benefit from the data sharing.


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NHS Lanarkshire use analytics apps for enhancing clinical operational efficiency

NHS Lanarkshire use analytics apps for enhancing clinical operational efficiency | Analytics & Social media impact on Healthcare |

NHS Lanarkshire has launched a range of analytics applications to give staff easier access to data.

The Scottish health board has developed the dashboards together with MicroStrategy to provide access to systems data about a range of departments across its three hospitals.

Alan Lawrie, the health board’s director of acute services, told EHI the applications interface with the board’s “relatively robust” clinical systems to provide easily viewable data to clinicians.

“With [the applications], we can get it easily rather than having to delve into the deeper, darker parts of the system.

“If you’re a busy doctor or nurse, you can sit in front of your computer and take a look with a couple of clicks.”

Lawrie said one of the applications is a module displaying real-time data from the hospitals’ emergency departments, allowing staff to adjust their focus as necessary.

“You can get a feel for what the heat of the department is and what’s happening on an hour-by-hour basis, right in front of you and in a very visual way.”

The board also has a suite of dashboards with planned care information like inpatient and outpatient bookings to help meet treatment targets, as well as a bed-management dashboard to show in real time how many beds are available in each ward and each hospital.

Lawrie said a ward dashboard with information from the previous month, such as staff sickness levels, complaints and compliments, provides senior charge nurses and ward teams with information about the quality of care.

The applications can be accessed by desktops, laptops and other mobile devices that are linked into the board’s network.

He said the applications have a “modest” cost, with a budget of about £100,000 for the A&E dashboard, while adding value to the existing systems.

“They’re an add-on, rather than duplicating what’s already there.”

Lawrie said the board started work on the emergency dashboard in 2012 before it went live in August 2013, and has received positive feedback from clinicians.

He said the board is planning to make the ward dashboard “a little more real-time” to improve the timeliness of the information.

One of its other major IT projects is its e-casenote electronic patient note project, which Lawrie said is part of the move towards a paperless system over the next 18 months.

Lawrie said the board is starting to back-scan a significant number of historical records and documents to be displayed within its clinical portal, which already includes data from some clinical systems.

The scanning includes basic optical character recognition to allow some search facilities, while the scanned documents will be placed in different sections for test results, correspondence and other categories.

Lawrie said the electronic clinical notes will go live at Hairmyres Hospital at the end of June, with the next phase of the project focused on moving information from GP systems into the portal.

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Patient Engagement Framework | NeHC | HIMSS

Patient Engagement Framework | NeHC | HIMSS | Analytics & Social media impact on Healthcare |

The Patient Engagement Framework is a model created to guide healthcare organizations in developing and strengthening their patient engagement strategies through the use of eHealth tools and resources.

The Framework is designed to assist healthcare organizations of all sizes and in all stages of implementation of their patient engagement strategies. This Framework can help your organization that treat patients as partners instead of just customers.

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Our health is in our hands (Wired UK)

Our health is in our hands (Wired UK) | Analytics & Social media impact on Healthcare |

This article was taken from the May 2014 issue of Wired magazine. Be the first to read Wired's articles in print before they're posted online, and get your hands on loads of additional content by subscribing online.

The healthcare world that most of us experience -- and the one that clinicians are traditionally incentivised to operate in -- has been one of "sick care", in which we focus our time and energies on treating diseases once they have appeared, or reached a point where they can no longer be ignored. The practice of medicine often resorts to a "reactive" state because the information which can be acquired from an individual, whether blood tests, vital signs, electrocardiograms or other measures, is incomplete at best, especially for the majority of us who spend most of our lives away from clinics and hospitals. Further compounding our reactive system is the fact that the ability to understand and make decisions from sporadic and fragmented health data (traditionally stored remotely in paper files) has primarily been left to interpretation by clinicians, doctors and consultants.


This paradigm, however, is on the cusp of change. We're beginning to shift from an era of intermittent, reactive health and medicine to one that is based on information, feedback and analytics. This will become proactive and continuous while engaging and empowering the individual (whether a healthy consumer or a patient), clinician and healthcare system. We are faced with many challenges, such as ageing populations, morbidity from high obesity rates and neurodegenerative disease, compounded in many parts of the world by a shortage of primary care physicians. Many of the digital devices and tools featured on the following pages will give us the opportunity to address many of these challenges for the consumer, patient and practitioner, as well as whoever is footing the bill, particularly as the incentives better align to reward prevention and early detection.

The convergence of faster, smarter, smaller, cheaper and interconnected technologies is accelerating exponentially. Devices are giving us new ways to measure, track, visualise, understand and optimise our bodies, health and wellbeing. The benefits could range from low-cost genetic sequencing to the layering of distributed mobile devices and sensors, wearables and implantables. The network of devices that makes up the internet of things could bring about the internet of the body.

The quantified-self movement began with leveraging basic consumer- and fitness-focused tools such as the Fitbit digital pedometer, but it is expanding to make use of a growing array of devices that can track metrics ranging from sleep patterns to brain waves. We are still in the era of 1.0 wearable sensors, but there are early signs of 2.0-era advances -- such as the Basis Watch (a fitness tracker which measures your movement, heart rate, sleep and perspiration), the Quanttus wristband and low-cost wearable patches (such as those from Vital Connect) which can transmit your electrocardiogram data, vital signs, posture and stress levels anywhere on the planet. This new generation of seamless and integrated devices -- and that's including Apple's long-rumoured iWatch -- will combine with mobile apps and secure APIs to connect your data to the cloud. Your healthcare system will be regularly "prescribed" for improving wellness, diagnosis and therapy.

Devices such as the AliveCor Heart Monitor and low-cost handheld ultrasound technologies put measurements once consigned to an intensive-care unit into the hands of consumers and clinicians. The Qualcomm Tricorder XPRIZE has incentivised and spurred teams from around the world to develop consumer devices for home-based monitoring, connecting mobile diagnostics, artificial intelligence and beyond. One entrant, Scanadu Scout, a sensor designed by Yves Béhar, is due to come to market this year after crowdfunding was used to fund research and initial clinical trials.

By using these technologies and feedback loops, a physician, nutritionist, personal trainer or your social network can help you to be more accountable for your wellbeing. Privacy is critical of course, but leveraging the so-called Hawthorne effect (did you hit your 10,000 steps today?) can be a powerful way to implement behaviour change and adherence to medical regimens. We will all become more empowered, responsible for our own health with useful insights into our everyday wellness, disease prevention and disease management. We also have new ways to interact with our healthcare providers through digital checkups and telemedicine. Our data will also benefit biomedical research and others with similar conditions.

We now have the opportunity to make sense of the terabytes of data which each of us can generate every day. Artificial intelligence and our personal dashboards will lead to an era of predictive analytics.

With so much being tracked by so many devices, we will need to filter and integrate our personal data to the point where we aren't overwhelmed by it. Imagine a GPS system for your health: it knows your habits, your genomics and your goals, and can help you reach a target, whether that be to run a marathon, lose weight, manage hypertension or lower your risks for cancer.

Many challenges remain, not least from the regulatory bodies and insurers, which struggle to understand and leverage these fast-paced technologies. But having the ability to access and share user-generated health data can disrupt our often inefficient and error-prone healthcare systems and bring us to a new era -- one which can help us to reach our full potential as individuals.

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Merck puts big data to work to solve vaccine manufacturing concern

Merck puts big data to work to solve vaccine manufacturing concern | Analytics & Social media impact on Healthcare |

Big data analytics have become a common consideration in the R&D part of the pharmaceutical industry, allowing researchers to search and share remarkable amounts of data points. But Merck & Co. has turned to the process to solve a manufacturing problem and potentially save hundreds of millions of dollars in the process.


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How Walgreens plan to bring Big Data Analytics to Healthcare Clinics

How Walgreens plan to bring Big Data Analytics to Healthcare Clinics | Analytics & Social media impact on Healthcare |

Walgreens (NYSE: WAG) (Nasdaq: WAG) is expanding its relationship with  Inovalon Inc. a leading technology company to implement its patient assessment  tool and technology platform to support improvements in care quality and risk  score accuracy programs across more than 400 Healthcare Clinic at select  Walgreens locations.

The convergence of Inovalon’s data-driven patient assessment tool Electronic  Patient Assessment Solution Suite (ePASS®) and Healthcare Clinic at select  Walgreens creates a unique offering within the health plan and retail clinic  industry. With the implementation Inovalon’s analysis of more than 8.3 billion  medical events brings analytic insights to Healthcare Clinic programs.

“By integrating data analytics we can gain even deeper insights to help  improve patient care and ultimately outcomes” said Heather Helle divisional vice  president Healthcare Clinic. “We continue to expand the scope of services  capabilities and footprint at Healthcare Clinics. These types of innovative  solutions enable our nurse practitioners and physician assistants to play an  increasingly important role as part of a patient’s care team.”

Healthcare Clinic at select Walgreens improves members’ choice providing a  convenient community-based access point for member assessments versus the  traditional in-home model.

The combination of Inovalon’s advanced analytics and Healthcare Clinic’s  nurse practitioners and physician assistants as well as its laboratory and  immunization resources provides a superior solution to health plans ACOs and  integrated care delivery organizations seeking to achieve goals in improving  quality outcomes and risk score accuracy.

“Bringing advanced analytics to the point of care in real time is a powerful  benefit for patients being seen in today’s highly complex health care  environment” said Keith Dunleavy M.D. president and chief executive officer of  Inovalon. “We are proud to be working with Walgreens on this industry leading  initiative supporting its commitment to improve health care outcomes for  Healthcare Clinic partners and patients nationwide.”

Inovalon’s ePASS system delivers a patient assessment tool with  individualized predictive analytics to the point of care supporting advanced  insight and efficient resolution of gaps in quality care patient assessment  documentation and risk score accuracy. The risk score models of Medicare  Advantage Commercial Health Insurance Exchange and state managed Medicaid are  each supported within the ePASS system. Similarly the industry’s wide array of  quality outcomes programs including HEDIS® CMS Stars state Medicaid programs and  commercial accreditation requirements of NCQA and URAC are supported within the  platform provided at Healthcare Clinic at select Walgreens locations.

About Walgreens

As the nation's largest drugstore chain with fiscal 2013 sales of $72 billion  Walgreens ( vision is to be the first choice in health  and daily living for everyone in America and beyond. Each day Walgreens provides  more than 6 million customers the most convenient multichannel access to  consumer goods and services and trusted cost-effective pharmacy health and  wellness services and advice in communities across America. Walgreens scope of  pharmacy services includes retail specialty infusion medical facility and mail  service along with respiratory services. These services improve health outcomes  and lower costs for payers including employers managed care organizations health  systems pharmacy benefit managers and the public sector. The company operates  8200 drugstores in all 50 states the District of Columbia Puerto Rico and the  U.S. Virgin Islands. Take Care Health Systems is a Walgreens subsidiary that is  the largest and most comprehensive manager of worksite health and wellness  centers provider practices and in-store convenient care clinics with more than  750 locations throughout the country.

About Inovalon Inc.

Inovalon is a leading technology company that combines advanced data  analytics with highly targeted interventions to achieve meaningful impact in  clinical and quality outcomes utilization and financial performance across the  healthcare landscape. Inovalon’s unique achievement of value is delivered  through the effective progression of Turning Data into Insight and Insight into  Action®. Large proprietary datasets advanced integration technologies  sophisticated predictive analytics and deep subject matter expertise deliver a  seamless end-to-end platform of technology and nationwide operations that bring  the benefits of big data and large-scale analytics to the point of care. Driven  by data Inovalon uniquely identifies gaps in care quality data integrity and  financial performance – while also bringing to bear the unique capabilities to  resolve them. Touching more than 540000 physicians 220000 clinical facilities  and more than 140 million Americans this differentiating combination provides a  powerful solution suite that drives high-value impact improving quality and  economics for health plans ACOs hospitals physicians patients and researchers.  For more information visit


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A social pill for pharma

A social pill for pharma | Analytics & Social media impact on Healthcare |

Pharmaceutical companies are slow to board the social media bus, but the rest of the healthcare industry isn’t waiting around.


Online health information is readily available, and consumers have no reservations about tapping the Internet and social environments to find it.

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Lessons Learned: Bringing Big Data Analytics To Health Care

Lessons Learned: Bringing Big Data Analytics To Health Care | Analytics & Social media impact on Healthcare |

Big data offers breakthrough possibilities for new research and discoveries, better patient care, and greater efficiency in health and health care, as detailed in the July issue of Health Affairs. As with any new tool or technique, there is a learning curve.

Over the last few years, we, along with our colleagues at Booz Allen, have worked on over 30 big data projects with federal health agencies and other departments, including the National Institutes of Health (NIH), Centers for Disease Control (CDC), Federal Drug Administration (FDA), and the Veterans Administration (VA), along with private sector health organizations such as hospitals and delivery systems and pharmaceutical manufacturers.

While many of the lessons learned from these projects may be obvious, such as the need for disciplined project management, we also have seen organizations struggle with pitfalls and roadblocks that were unexpected in taking full advantage of big data’s potential.

Based on these experiences, here are some guidelines:

Acquire the “right” data for the project, even if it might be difficult to obtain.

We’ve found that many organizations, eager to get started on a big data project, often quickly gather and use the data that is the easiest to obtain, without considering whether it really goes to the heart of the specific health care problem they’re investigating. While this can speed up a project, the analytic results are likely to have only limited value.

For example, we worked with a federal agency experimenting with big data analytics to identify cases of perceived fraud, waste, or abuse. The program’s analysts focused on data they already had on hand and currently used to direct audit and investigation activity. We encouraged project staff to identify alternative data sources that might reveal important information about compliance history or “hotspots” for illegitimate activity.

We learned that historical case reports and online provider marketing materials were available and were a potentially valuable source for information to aid in fraud detection. However, the project analysts had decided it would take too long to incorporate that information and so had excluded it.

Many organizations – both inside and outside of health care – tend to stick with the data that’s easily accessible and that they’re comfortable with, even if it provides only a partial picture and doesn’t successfully unlock the value big data analytics may offer. But we have found that when organizations develop a “weighted data wish list” and allocate their resources towards acquiring high-impact data sources as well as easy-to-acquire sources, they discover greater returns on their big data investment.

Ensure that initial pilots have wide applicability.

Health organizations will get the most from big data when everyone sees the value and participates. Too often, though, initial analytics projects may be so self-contained that it is hard to see how any of the results might apply elsewhere in the organization.

We ran into this challenge when we helped a federal health agency experiment with big data analytics. The agency’s initial set of pilots focused on specific, computationally complex and storage-intensive challenges, such as reconfiguring a bioinformatics algorithm to run across a large cluster of processors and developing a data-capture approach to access and store data in real time from a laboratory instrument.

While each pilot solved a big data analytics challenge, the resulting capabilities did not provide examples that would be powerful enough to push transformational change across the organization, as the organizational leaders had hoped.

In subsequent pilots, we advised the agency to focus on less rigorous but more far-reaching pilots. In one project, the agency piloted an unstructured natural language processing and text search utility across a number of disparate data archives. In another project, we deployed a data platform that could rapidly generate millions of records of synthetic data for algorithm testing.

In each case, organizational decision-makers could more easily see the applicability and potential of big data analytics and more clearly understand the potential of big data to transform their organization.

Before using new data, make sure you know its provenance (where it came from) and its lineage (what’s been done to it).

Often in the excitement of big data, decision-makers and project staff forget this basic advice. They are often in a hurry to immediately start data mining efforts to search for unknown patterns and anomalies. We’ve seen many cases where such new data wasn’t properly scrutinized – and where supposed patterns and anomalies later turned out to be irrelevant or grossly misleading.

In one such case at a federal health agency, information contained in a data source suggested that there was a significant uptick in the number of less-experienced clinical investigators associated with a set of therapeutic areas. Project staff identified this as an important trend to aid in risk analysis for the agency and prepared to brief senior decision-makers.

However, when the findings were presented first to the administrator for the data source, he suspected that the trends might coincide with the roll-out of new address fields.

As a result of a data-field change, when new address information was added for an investigator, it didn’t append to the original file, but created an entirely new file – making it appear that there were many new investigators, when in fact the number of investigators had slightly decreased over time.

This scenario could have been avoided through an investigation and annotation of candidate data sources with provenance and lineage information prior to operational use. With big data analytic techniques, such details can be prospectively or retrospectively annotated to data records, indicating the prevailing process and data standard at the time of collection.

Then, data miners can leverage this factor in data mining efforts and predictive models to test whether the data-collection process is causing a significant effect in the outcome variable of interest.

Don’t start with a solution; introduce a problem and consult with a data scientist.

Unlike conventional analytics platforms, big data platforms can easily allow subject-matter experts direct access to the data, without the need for database administrators or others to serve as intermediaries in making queries. This provides health researchers with an unprecedented ability to explore the data – to pursue promising leads, search for patterns and follow hunches, all in real time. We have found, however, that many organizations don’t take advantage of this capability.

One federal health agency we worked with, for example, invested in big data analytics to enable network analysis of nodes in a supply chain. Instead of giving its subject-matter experts free rein to look for new and unexpected patterns, the agency stayed with the conventional approach, and simply provided canned business-intelligence reports and visualizations to the end-users.

Not surprisingly, the outputs of this approach disappointed organizational decision-makers in terms of generating new insights and value. We strongly encouraged the agency to make sure subject matter experts could have direct access to the data to develop their own queries and analytics.

Once this was provided, the user community rapidly grew, and there was an associated increase in new capability, training requests, and overall value for the organization.


Health organizations often build a big data platform, but fail to take full advantage of it. They continue to use the small-data approaches they’re accustomed to, or they rush headlong into big data, forgetting best practices in analytics.

It’s important to aim for initial pilots with wide applicability, a clear understanding of where one’s data comes from, and an approach that starts with a problem, not a solution. Perhaps the hardest task is finding the right balance.

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Big Hitters in Health: Social, HCP Comms and Big Data

Big Hitters in Health: Social, HCP Comms and Big Data | Analytics & Social media impact on Healthcare |

Digitally Sick are back and have taken the opportunity to look at the three big hitters for digital health in 2014: Social. HCP communications and big data (with the exception of mobile health which needs a pod of it's own).


Social media is now almost passé but over the last decade has revolutionised all aspects of pharma communications from patient support to clinical trials. In 2014 we are still struggling with how we should communicate with HCP's and finally big data, or data, is now the most exciting frontier in healthcare, what are the issues and how can this be leveraged by pharma?


Via Alex Butler
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Health Data's Future: 6 Paths to Health Data Maturity | HL7 Standards

Health Data's Future: 6 Paths to Health Data Maturity | HL7 Standards | Analytics & Social media impact on Healthcare |

Every year around the time of the health and government data extravaganza in Washington, Health DataPalooza, it’s reason to do an accounting of how far we’ve come in terms of accessing health data and using it as a foundation for value-based medicine. NPR says we have reached our “Awkward adolescence” (echoing Susannah Fox) with health data—lots of amazing things happening, but not a lot of impact.

Of course there’s plenty more work to do to make health data more accessible, more liquid and more private, but the progress since Health DataPalooza started less than 5 years ago is amazing, and we should take note, then come back to the paths forward.

This year, the big news was the FDA announced “OpenFDA”, available via an open API, with information on adverse drug events. Time will tell what the release will mean in terms of delivering health, but with over a thousand datasets now release by HHS alone, we are seeing a wave of new capability, even if data stories take time to tell.

Meanwhile, 80% of healthcare is now digitized, more than doubling from just a few years ago.

Samsung and Apple see the potential for accessing and harvesting health data and are moving into the fray to create personal health tracking hubs. There are many more examples showing that health data has, indeed, reached the limelight. Big health data, is now often called “the new oil”, and it’s already serving as a key resource in driving economics and powering countless new companies.

But that’s not what this post is about.

All this data is great, but it doesn’t take big data or rocket science to figure out what’s killing us, and how it might be prevented.

If you’d like to see what is killing us, check out this (small) data tool:

Stop the presses: It’s us.

You take smoking, diabetes, obesity, cardiovascular disease and alcohol out of the mix, and the vast majority of those in the developed world would live to 90+. For data to really have an impact on health, it’ll have to have an impact on us. Many of these disease are diseases of behavior. We can debate how difficult it is to change behavior, or what biochemistry, genetics or other factors drive behavior, but most of our health problems could be prevented by making different choices. Consumers are going to need to care about it and use it.

There are bright spots that this is possible. Engagement rates reach 70% among institutions who do it well, but it takes leadership.

The reorganization of the ONC without a consumer office doesn’t show a lot of confidence that they are going to lead the way.

How do we fix this?

We’re nearing the point where we’ll be able to capture someone’s vital signs every minute of every day via Samsung, Apple, and many others. Will all this measurement save us from ourselves? Can we truly get prevention, or do will we just get better at heading off problems at the last minute? While preventing heart attacks is great, as a new iWatch is rumored to do, it would be even better if we could fix the unhealthy state that makes them possible before a last-minute intervention is necessary.

With that in mind, here are my wishes and a few predictions for the next phase of health care and health technology (now forever linked) and the road to solving health care with health data:

1. We need to create tools that can actually measure and impact behavior on what goes into us, not just stats on where we are and how we’re moving.

At the end of the day, we’re going to need to measure and provide feedback on input on intake as much as output. We’ll need to not only sense motion and vital signs but also what we’re putting into our bodies in terms of food, drink and chemicals, and start to change it. There has been work on tooth sensors to measure intake and Apple and others appear to be working on hydration sensors. It’ll be exciting to see developments in these areas in the coming years.

2. We need to better understand what drives metabolic disease. Metabolism-related killers are becoming our primary killers, but many normal weight people, in addition to obese people, die of metabolic disease. There’s still a lot we don’t understand about prevention and the disease. Yet metabolic disorders such as diabetes are taking an ever-greater toll and half the country will be at risk for diabetes by 2020. That’s a lot of suffering, a lot of death, and an enormous cost.

3. We need to prepare for the fight of a generation. Metabolic diseases are killing us in ever-larger numbers. The more we measure what’s driving costs, as we collect more and more Health Data, we’re going to run straight into a very big wall of conclusion: sugar is killing us.

With the release of FedUp, the idea of sugar as a culprit for our health care woes is starting to hit the mainstream.  If the fight against control of tobacco was tough (and by no means won), the fight against sugar will be 10x harder.

4. We need to correlate outcomes and environment. That means we need to understand what networks behavior of the health care system.  We’ll learn a lot from the 125,000 people who die per year from not adhering to their medications. Why aren’t they taking them on time? What’s preventing people from treating themselves?

For that we need to understand things at a systems level and better correlate with the social determinants of health. As Atul Gawande pointed out, yet again, at health DataPalooza, the overall vulnerability of a population is what’s drives our biggest health costs. The intersection of socio-economic/social determinants and network behavior will help us solve major hotspots, major sources of cost and suffering.

5. This one might be obvious, but we need to be better at predicting with data. EHRs like their name implies, are records, focused on the past. We need electronic health systems that are predictive. Apple and Samsung or others will do it, and they appear to be correctly focused on a new kind of technology for the new business model of health care, focused on risk spread among all players (and value place on prediction).

Dave Chase, CEO of Avado,  now part of WebMD, issued a stern warning to healthcare providers and their approach to healthIT on Susannah Fox’s blog:

“Just as it was easy to dismiss Google, craiglist, ebay, groupon, foursquare, facebook, etc. so too are the Iora Healths, Caremores, HealthCare Partners, Edison Health, One Medical, Surgery Center OK, Paladina Health, etc. ,but their value proposition is compelling. All of those players are deploying health IT in a radically different way than incumbents. Those orgs and their supporting technology take it for granted that patients are a core member of the care team, have access to their data and generally are using IT for competitive advantage.”

6. We need better rules on ownership and rights around health data, we could start with a Health Data Bill of Rights. In the consumer space, the rise of Snapchat and Whatsapp are indicative of a rise in the awareness and need for privacy. In health care, it will take time, but as health data gets “consumerized” with Apple and Samsung entering the fray, I predict the needs will become more and more apparent.

We need to work on rules and awareness to make health data more private and at the same time more easily exchanged. I don’t know exactly what that will look like but I, like many others, get the sense the answer may come through the blockchain. Fred Wilson at Union Square Ventures sees it as driving the next big investment cycle, after social and now mobile. He says, “our 2014 fund will be built during the blockchain cycle”. More on that in an upcoming post.

What do you think? What do we need to solve health care with health data?

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Patient engagement – we need to live it, not talk it - Health Foundation

Patient engagement – we need to live it, not talk it - Health Foundation | Analytics & Social media impact on Healthcare |

Patient engagement is much talked about. Thankfully. However, casting my mind back 20 years when organising The King’s Fund’s Promoting Patient Choice conference, I recall having no difficulty in finding two doctors to get up in front of the 300+ audience and argue that ‘patients should do what they’re told’.

Some healthcare professionals may still think and act like that, but few, if any, would now get up on a stage to proclaim it in public. I’d suggest that’s quite a far way to come in 20 years in terms of reversing medical paternalism.

But it’s not far enough.

The call for patient-centred care, 6Cs, experience based design, co-production, collective and individual participation, patient experience, etc, etc, has never been louder. That’s great for those of us who have worked in the field for a long time. And even more importantly for patients, for staff and for the system (as the evidence overwhelmingly proves). But it’s also frustrating.

Why? Because in some senses, the gap between this louder rhetoric and real practice has got wider. Whether it’s self-managing my own two long-term conditions, being an informal carer for two mental health service users in Salford, or caring for my own loved ones (resulting in me being in three different hospitals over the last six days), it’s just not happening on the ground. Or at least the ground I’m treading on.

I have experienced no care plans, no patient decision aids, no health coaching, motivational interviewing nor appreciative enquiry. There are some leaflets. There is some communication. But this is mainly transmission. A protocol to get through. A monologue not a dialogue. A mouth, sometimes a rushed smile, big hands but no ears. A heart that too often seems so deeply buried that, when one does appear, it’s sadly the exception not the rule.

As a clinician and management consultant, the full potential of ‘individual participation’ (to coin the phrase within NHS England’s laudable Transforming Participation Guidance) has yet to move beyond a few well-intentioned early adopters.

So how do we get better – both patients and the people/systems that treat them?

It’s actually really easy. And that’s what makes it so hard.

It’s simply about becoming people again. It starts with basic things like ‘Hello, my name is...’ (hats off to Kate Granger). Names are part of what make us human. The most successful encounters over my past six days as a carer/parent/patient started when the staff members introduced themselves. When you say your name, you usually smile. This personal information and associated positive body language gets the whole conversation off to a great start.

Next, the caregiver asks the patient and also the relative carer, ‘How are you doing?’ Next, ‘Is there anything you need?’ Then they go on to outline what they’re there to do (hopefully with the patient/carer’s blessing/‘informed consent’).

But this is where things fall down. Staff know what the process is. But too often – far too often – patients and carers simply don’t know what’s supposed to happen next. They know how to make a cup of tea, they know how to make retail choices. But they don’t know what the stages are in their care journey/pathway/experience. And no one has told them.

So they’re left in the dark with no or very limited expectations: How long am I supposed to wait here? What’s happening next? They said they’d come back and tell me what I have wrong. Without setting expectations, patients don’t have a benchmark to know what is good or bad, what’s right or wrong, what’s too long a wait or just right. No wonder 64% of the nearly 1 million patients reported in the December 2013 GP–Patient Survey state they are as involved in decisions as they want to be; yet only 3% have a care plan. They don’t know what they don’t know.

Staff, therefore, have to think of patients as people and not just body parts (the MI in bed 6; the liver in bed 12, and so on). Soft stuff – such as dignity, respect and compassion – is important. But we need to go deeper and better our understanding of what makes our patients tick. We need to reflect (and record?) the following with regards to our patients:

health beliefs – is diabetes just the 'touch of sugar' that Granny had, or the disease that took Dad too early?are medicines miracle cures, or poisons foisted on us by a profiteering, disease-mongering pharmaceutical industry?what’s their level of health literacy eg when telling a patient to ‘choose a hepatologist’ – what is choice, what’s a hepatologist, does the data exist and is it presented in a digestible format?what’s their level of motivation – can they bothered with health or are they too busy with even more basic needs such as food, shelter and warmth?

Without insight into and measurement of what our patients are really thinking and capable of, and helping them set expectations, we’re firing blanks at the ‘self-management’ target with our eyes closed. We’ll talk it, but we won’t live it. And neither will our patients.

So lets’ go back to being people. To being honest, caring and communicative. Simple, eh?

Mark is a clinician, management consultant and patient advocate,

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Five things the NHS must learn about empowering patients

Five things the NHS must learn about empowering patients | Analytics & Social media impact on Healthcare |

Half of all patients in hospital say they aren't as involved in their care as much as they would like. This figure hasn't improved in a decade.

There's been a lot of talk from recent governments about giving more power to patients. The latest commitment is that the NHS will get "dramatically better" at involving people in their own care, but the change so far has been anything but dramatic.

Despite all the structural overhauls to the NHS there has been very little change in the areas that matter most: how involved people feel in the big decisions about their care, and whether patients' voices are heard when things go wrong, or are ignored as we have seen in several high profile scandals.

I recently led a coalition of MPs and peers concerned about health to look at what we could learn from other countries that were trying to solve this problem. Across over 100 examples we looked at, five key lessons stood out.

Knowledge is power

In Denmark, everyone has the ability to see and interact with their medical records online. This gives people the power to really understand their health and treatment. Giving British patients this ability needn't mean another huge national IT project. In Malawi all patients carry hard copies of their records. We already do this for maternity care – why not other areas too?

Make shared decision-making the easy choice for clinicians

Many clinicians are cautious about sharing decision-making more. They worry that empowered patients will be more demanding rather than more independent. Partnership with patients needs to be the easy choice, which means making consultations smarter rather than longer. Massachusetts general allows doctors in the hospital and community to prescribe decision support tools for patients to use at home to decide which treatment is best for them.

Invest in supporting carers

For many people with long-term health problems, family members provide the vast majority of the care they receive. Giving these carers the skills to support their loved ones at home is a great investment in quality of life, and in affordable healthcare. A chain of hospitals in India has come up with a great solution – when vulnerable patients are admitted, their main carer can go on a short course at the hospital to learn the skills to look after them at home. They then get to practise these skills on the ward before the patient is discharged.

Groups of patients are a powerful asset

When patients come together they can be a powerful force for improving their own health and that of others. In Uganda a large proportion of HIV/Aids care is delivered by groups of patients working to help their peers understand and manage their condition. We've seen what peer support and education can do in the UK for years through the work of networks like Alcoholics Anonymous and Weight Watchers. What other problems could be tackled by people power in this way?

Listen to what patients have to say

Patient stories have enormous power to challenge and change the status quo. Mothers' perspectives are at the heart of a global initiative called Respectful Maternity Care. Countries including Nigeria and Nepal are inspiring and informing midwives using women's stories to improve the experience of childbirth for thousands. The approach has already been successful in one NHS maternity unit that was struggling to tackle serious failures in care.

We can't directly cut and paste any of these overseas examples into the NHS. Nonetheless, they do help us to think bigger and bolder about making real change happen at long last.

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How Healthcare CIOs View the Data Analytics Landscape

How Healthcare CIOs View the Data Analytics Landscape | Analytics & Social media impact on Healthcare |

Nearly all provider CIO respondents in a recent survey believe data analytics will play a big role in succeeding with accountable care and other value-based healthcare initiatives. But while 42% say they have a flexible and scalable analytics plan, more than three-quarters report only moderate or minimal commitment to integrating analytics into practice.

The April survey from eHealth Initiative and the College of Information Management Executives got responses from 98 provider organizations--35% delivery systems, 27% hospitals, 14% academic medical centers and 9% community health centers/clinics. Only four respondents were not running analytics at the time of the survey.


Seventy-two percent of responding providers extract data from more than 10 platforms or interfaces--some more than 100--with EHR and billing/financial data still by far the most common. But data also comes from patient-generated sources such as portals and health risk assessments (45%), unstructured text (39%), remote monitoring devices (29%), health information exchanges (22%), mobile applications (11%) and genomic data (7%).

Still Young and Learning

Analytics remain in the early stages of maturity. Traditional common uses of analytics continue at high levels. These include quality improvement (93%), revenue cycle management, (91%), resource utilization (81%), and population health management (79%). Respondents use descriptive analytics that mine for historical or retrospective analysis at a 94% rate. Only 68% use predictive analytics to forecast outcomes, trends or performance, and this is mostly done on a monthly or quarterly basis. One-third use prescriptive analytics with sophisticated models to optimize performance and recommend specific actions. On 20% of respondent’s analytics operations regularly integrate and coordinate at an institutional level.

 New Obstacles

Trained staff to collect/process/analyze data, along with interoperability and cost, have been common barriers to implementing an effective analytics program. Survey respondents report new challenges are emerging. These include access to external data beyond proprietary networks; cost-prohibitive work required to clean/validate/integrate external data when available, lack of funding or return on investment, increased regulations on data use and patient privacy. “These trends suggest the critical need for strategic planning in implementing analytics, no matter how large or small,” according to a report of survey results.


Consumer engagement has started to make its mark on data analytics initiatives. Two-thirds of respondents use analytics to support engagement with the primary focus being on patient satisfaction. But few organizations also are applying analytics to consumer strategies such as personalized communication and services, acquisition and retention of consumers, or targeted behavioral change programs.

A report on survey findings, “The Landscape of Data & Analytics in Healthcare” is available here.

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Big data, analytics in healthcare has expanded from business intelligence and revenue-cycle management to clinical care.

Big data, analytics in healthcare has expanded from business intelligence and revenue-cycle management to clinical care. | Analytics & Social media impact on Healthcare |

In this age of big data, analytics in healthcare has expanded frombusiness intelligence and revenue-cycle management to clinical care.

For example, health insurer WellPoint is branching out from simply looking for gaps in coding, thanks to a combination of better data and more advanced algorithms. Now, the company can look for gaps in care as well, Patrick McIntyre, the company's senior vice president for healthcare economics, explained last week at SAS Institute's 11th annual executive conference on health analytics.

About 2.5 million of WellPoint's 37 million enrollees have insurance tied to some sort of value-based reimbursement model, McIntyre said, and the Indianapolis-based payer shares reports with providers whenever there is risk-sharing. "We use analytics and reporting to create economies of scale for all of the provider communities we work with," he said.

Patrick McIntyreWellPoint performs both retrospective analysis of claims – a more traditional form of data mining – as well as proactive analysis of care gaps. This helps the company coach providers on better coding and service delivery.

Health insurers, of course, historically have been met with mistrust and suspicion when they reach out to members and providers. With their vast data collections, that is changing. "There isn't a magic bullet, but it's really bound in trust," McIntyre said.

"We need to provide the right care to the right patient at the right time," he added. "I think analytics is going to be the differentiator."

Making sense of the unstructured
Mark Pitts, VP for enterprise informatics data and analytics at Highmark Health, the new, Pittsburgh-based parent company of Allegheny Health Network and health insurer Highmark, offered similar sentiments. As someone with years of experience at payers, a "primary challenge" for Pitts has been how to influence individual behavior to promote better health and save money.

Today, with the advent of "text analytics," organizations like Highmark can make sense of vast stores of unstructured data, not just information entered in a discrete format. (Pitts called this the "bag of words" method.)

According to Pitts, computers now can look for "term concurrence" across multiple documents to search out patterns, such as evidence of patient dissatisfaction, according to Pitts, so people don't have to flip through hundreds of pages in hopes of stumbling across something meaningful. "Have machines find things," he said.

Allegheny is getting ready to bring this technology to the provider side. For example, Pitts said, the length of a nurse's progress note often correlates with illness severity. By paying attention to patients with particularly detailed notes, the health system might be able to prevent medication errors, escalation of acuity and even hospital readmissions, he suggested.

Farzad Mostashari, MDNext wave for Kaiser data

Like Highmark, Kaiser Permanente is both healthcare system and health plan. That massive organization's EHR contains something in the neighborhood of 10 petabytes of data, according to Terhilda Garrido, VP for health IT transformation and analytics, making it ripe for big data technologies.

Garrido wants to gather "patient-reported outcomes" after each encounter, including what patients say on social media, data pulled from medical devices and patient satisfaction ratings. "That, for us, represents the next wave of the continuum," she said.

Physicians can order questionnaires through KP's My Health Manager patient portal. "It's a little clunky," Garrido said, but noted that it is the first stage of what probably will be a long effort. Later, there may be auto-collection of data from mobile and home-based devices, adding another way to track and measure outcomes.

Mostashari on gauging outcomes
If Kaiser is successful, former national health IT coordinator Farzad Mostashari, MD, would be pretty happy.
Graham Hughes, MD"Most of which determines our outcomes isn't what happens in our office," Mostashari, visiting fellow of the Engelberg Center for Health Care Reform at the Brookings Institution in Washington, said during the opening keynote of the SAS event.

"One of the things that drove me crazy in medicine is that I never got any feedback," Mostashari said. According to the former national coordinator, "99.999 percent of the time, we have no idea what we get" for all the money spent on healthcare.

SAS Chief Medical Officer Graham Hughes, MD, echoed some of these sentiments. "Ninety-nine percent of patient care takes place outside of traditional care settings," he said, emphasizing the importance of collecting and analyzing data from patients' everyday lives to close gaps in care and personalize treatments.

"Maybe we start to think of every disease as a rare disease?" Hughes wondered aloud as he discussed the potential of Big Data to help individuals make healthy lifestyle choices outside the sterile, controlled environment of a hospital or clinic.

"We're starting to get past the PowerPoint page and into some real situations," Hughes said.


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#Healthcare Reforms That Work, an interesting example

#Healthcare Reforms That Work, an interesting example | Analytics & Social media impact on Healthcare |

Diseases like diabetes, heart disease, cancer, chronic kidney disease, and stroke are on the rise in both the developed and the developing world, and they have a few things in common.

First, they are responsible for contributing a large chunk of patients into the health care system, especially in developed countries like the US. In fact such diseases are the cause of death in more than 4 out of 5 cases in the US. Second, these diseases are non-communicable, and are caused by poor diet, lack of exercise, and unhealthy lifestyle rather than bacteria, viruses and other micro-organisms.

Third and most importantly, unlike communicable diseases where the incidence rates and overall costs are on the decline, these conditions are posing a heavier burden on health care systems every year. All of our new policies and efforts to provide affordable health care could pale in comparison to the exponentially rising costs associated with these diseases and their demographics. In light of this increasing burden, what can we do?

One area where we’ve begun to make some inroads is with chronic kidney disease (or CKD for short).  CKD is a condition that affects 1 in 10 Americans over 20 and costs the US health care system almost $100 Billion a year. In the US, a little over 1 % of Medicare patients have advanced renal impairment, and yet nearly 8 % of the total Medicare budget is spent on treating them. Today nearly 2 million people in the world are kept alive by dialysis. CKD is closely linked with other conditions such as hypertension, diabetes and heart disease. It disproportionately affects the poor, and is expected to worsen in developing countries at an alarming rate—though the incidence and prevalence of CKD is increasing at an alarming rate in every part of the world. This disease is poorly understood and very few people take measures to prevent it, though CKD is preventable and its progression can be slowed by simple strategies and lifestyle changes.

The good news is that we’ve already begun to transform costs for CKD.  Five innovative reforms have been found to help reduce costs without compromising on the quality of health care services:

Decentralization of responsibilities: Most dialysis centers today are trying to provide drug management, laboratory services, and vascular surgery management in addition to dialysis service under one roof. This bundling of care allows each center to function independently and to provide a one stop solution to all patient needs. In 2010, provision for bundled care was included both in thePatient protection and Affordable Care Act and in the Affordable Health Care for America Act. Bundling services together discourages unnecessary care, and encourages better coordination across providers which can lead to better quality of service at a lower overall cost.

Transparency: In a country like India, the incidence and prevalence of kidney disease is still unknown today. The lack of such data makes it impossible for any country to allocate resources effectively. In other words, cost containment cannot be achieved without cost estimation first. Unlike India, the UK has been devoting significant effort towards the maintenance of the UK Renal Registrywhich discloses all outcome measures including patient survival for kidney disease population. This approach has helped UK become one of the most cost-effective and one of the best providers of kidney care in the developed world today.

Incentives: After a decent level of transparency is established in any system, it becomes relatively easy to evaluate its performance and suggest remedies to improve it or maybe even reward good performance. In some hospitals in Europe, an incentive program is being discussed to encourage good performance. For example, a hospital in Italy has suggested a model which advocates higher use of Peritoneal Dialysis (or PD for short, a form of home dialysis which is cheaper than traditional hospital dialysis). Published literature indicates that PD is at least $ 15,000 to $ 20,000 cheaper than traditional hospital dialysis per year and also offers similar if not better quality of life. If hospitals decide to raise the percentage of patients on PD from 12% (the European average), to an easily achievable 40%, it could lead to a considerable amount of savings without any compromise in quality of care.

Patient participation: Nowadays, patients want to be in control of their own therapy and want to know more about it. They want to be given the choice of how care should be provided and what services they will receive. Taking all this into account, it is of paramount importance that the system gives patients the right to choose. A number of successful evaluations have been carried out in the US and the UK where patients were given their own personal budget to allocate, rather than a standard menu of services. These evaluations have shown that such an approach not only makes the patient feel more satisfied but also overall expenditure falls, since clinicians are less conservative than patients when it comes to spending money on health care.

Another innovation that has contributed to the improvement of patient participation is telemedicine. Today, there are home dialysis patients who are remote monitored by the doctor, and as result the patients spend less time in the hospital, less money on drugs required to treat complications, and most of all enjoy a higher quality of life. These patients are more “in control” of their lives.

A focus on prevention: Chronic kidney disease is actually divided into 5 distinct stages, and the costs associated with each stage are different. A study in 2004 revealed that costs continuously increase as we progress from stage I to stage IV. This is clear proof that a far greater sum of money can be saved by health care systems if they can reach patients at an earlier stage. Currently the US only spends less than 4 cents on preventive measures for every dollar spent on health care. It might help if more resources are allocated in educating the public on how to avoid diseases like chronic kidney disease, like emphasizing how to eat and exercise right.

If we can apply what we’ve learned here to the other big four diseases, we might be able to stem the rising costs even as the incidence of these diseases continues to rise. But we have to keep innovating. In all, the need is for innovative policy, new perspective and a more holistic approach to providing health care. The five reforms arising from CKD management can possibly be a powerful tool in achieving that goal.

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Healthcare data analytics landscape changing rapidly

Healthcare data analytics landscape changing rapidly | Analytics & Social media impact on Healthcare |

Nearly half of healthcare organizations responding to a new survey say they  are experiencing a positive return on investment in data analytics and reporting  technology.

The survey, by TCS Healthcare Technologies in conjunction with the Case  Management Society of America and the American Board of Quality Assurance and  Utilization Review Physicians, found the landscape changing quickly from similar  measures taken in 2008 and 2010.

Forty-six percent reported positive ROI, compared with 14 percent who  reported a negative return, according to an announcement

Thirty percent of respondents reported stratifying healthcare information to  promote population-based screening, or to identify candidates for case  management. Meanwhile, just 25 percent reported using predictive modeling  applications, while 35 percent reported doing so two years ago.

Excel (39 percent), Crystal Reports (20 percent) and Access (17 percent)  remain the most widely used applications.

Users cited the importance of dashboard and visualization capabilities,  naming among their priorities the ability to manipulate reports and data  presented and to view trends for individual patients and for large sets of  data.

Applications for population health management that integrate claims and  clinical data are key to the success of accountable care organizations, an IDC Health  Insights report found recently, saying many organizations have found that  relying on EHR information alone isn't enough.

While tools that help organizations with case management have been  touted for their ability to improve care, as New Jersey-based primary-care  practice Vanguard Medical Group experienced, it's not all about the technology.  A Kaiser Permanente study found readmission-prediction software wasn't accurate enough for  it to replace manual review of cases.


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There are ways to do analytics right; 'If you are just about grabbing data, you will be on a data grab forever.'

There are ways to do analytics right; 'If you are just about grabbing data, you will be on a data grab forever.' | Analytics & Social media impact on Healthcare |

With apologies to Internet meme-makers everywhere, analytics experts have a message for healthcare providers trying to get their heads around business and clinical intelligence: "Big data, you're doing it wrong."
So much attention and energy have been put toward "big data" in the last couple of years, for perfectly understandable reasons. For example, health systems collectively have spent billions of dollars installing EHRs in recent years. "They want to get their value," says Cora Sharma, analytics analyst for Chilmark Research, a Cambridge, Mass.-based health IT research firm.
They certainly see a lot of potential in the data. A March poll from MeriTalk and EMC found that 63 percent of healthcare executives in the federal government believe that big data will improve population health management. Similar numbers show that advanced analytics would "significantly improve patient care" and make it easier to deliver preventive care in the Military Health System and Veterans Health Administration.


But so few have proper goals and strategies for their data, according to Graham Hughes, MD, chief medical officer of business analytics firm SAS, based in Cary, N.C.
"They're looking to accumulate data and how to get data in," Hughes says. In his opinion, this is a faulty course of action. "It's not about the data. It's about how you're going to manage it."
The focus, according to Hughes, should be on information management, including data governance, stewardship and quality. "If you are just about grabbing data, you will be on a data grab forever."
Optum, the IT and analytics division of UnitedHealth Group, published a white paper in February that corroborated this belief, particularly when it comes to clinical analytics.
"It may sound impressive to say that your organization has access to terabytes of patient information, but without robust technology and smart people to manipulate it, that data is simply words and numbers without context," researchers point out in the white paper.

"Raw data from claims or from an EMR database are not suitable for analysis. Turning raw data into usable information requires preparation, including normalization and validation. Only then can an organization gain trustworthy insights from the information and put it to use in maximizing patient care, reducing risk and strengthening a business's bottom line,” they add.
Hughes says that organizations have been spending too much time and money on enterprise data warehouses, which he sometimes refers to as "data landfills." The repository is not as important as the location of the data, according to Hughes. "An EDW isn't where data goes to die. An EDW is a staging point for analytics," he says. "An EDW needs to be easy for clinicians to understand and interpret, and also needs to interoperate with and push data back out to other systems," Hughes continues. Too many organizations wrongly assume that data should get moved to the analytics software, he says. "Modern analytics run directly from transactional systems," so there is no need to replicate the data in every situation.
"It's where the data is being moved to [that] makes it actionable," he says.
Indeed, Hughes notes that analytics historically have been seen as retrospective reviews, but data stores are so great now that predictive analytics are now possible, even if much of the data remains unstructured. "Analytics is now about providing actionable insights back into workflow," in close to real-time if necessary, he says.
"Sometimes this is done in too fragmented a fashion," Hughes says, a reflection of the "best-of-breed" strategy of years past. Organizations bring in pieces of analytics technology every time they see a new "shiny object," he says.
"To me, this feels where we were with clinical systems in the late '80s and '90s," Hughes says. He suggests thinking about it not as "niche buying," but rather as a strategic, enterprise-wide investment.
The MeriTalk-EMC study found that only a third of federal healthcare executives had invested in technology to optimize data processing, and less than 20 percent said their agency was "very prepared" to manage big data. In the private sector, according to Sharma, early adopters such as Intermountain Healthcare, Kaiser Permanente and Partners HealthCare are farther along than most, but she worries about smaller, less-tightly-integrated organizations. "There's no kind of off-the-shelf software out there for them," Sharma says. So they turn to the best-of-breed strategy.

A severe shortage of analytics pros makes navigating this landscape all the more difficult, according to Hughes. "It's also a mistake to think you can staff up on this easily," he says.
Hughes suggests managing data in the cloud, through a vendor or looking for "self-service solutions" that provide expertise. "You need clinical analysts to create models, and let the system be smart enough to give good recommendations," Hughes says.
Sharma views the lack of qualified data engineers as an opportunity for payers; Aetna and United are among those insurance companies that have been beefing up their analytics divisions lately. "They're basically offering ACOs in a box," Sharma says. However, she adds, "Providers are still more reluctant to work with payers, for a lot of reasons."
Thus, big data in healthcare remains fraught with pitfalls.

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6 Steps For A Sustainable Approach To R&D Through Big Data

6 Steps For A Sustainable Approach To R&D Through Big Data | Analytics & Social media impact on Healthcare |

By Todd Skrinar, principal in the Advisory Life Sciences practice and Thaddeus Wolfram, manager in the Advisory Life Sciences practice, Ernst & Young LLP

Near the end of 2013, many in the life sciences industry were looking for clear evidence that the FDA was willing to work with industry to get more needed drugs to patients. Eyes were focused on the “scorecard” of new drugs approved, which for the first eight months of 2013 reached 18."




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The changing face of analytics in Healthcare

The changing face of analytics in Healthcare | Analytics & Social media impact on Healthcare |

The way in which information is used by healthcare organizations is in the midst of a revolution, shaped by emergent analytical techniques that change the value proposition. These new analytics can help drive fundamental improvements in patient health, complementing the traditional requirements for information to address cost management and resource utilization.

The ability to capture, integrate, and store healthcare “big data” sets are now tangible capabilities that are available to the healthcare sector. The connection, at an individual patient level, of electronic medical records, medications usage, lab results, demographics, care management activities, and potentially streaming data from ever-present medical and fitness devices, results in a complex data set, but delivers one that is focused on building an integrated view of the patient and their environment. This new found availability is supporting the development and proliferation of new analytic tools and processes that are designed to learn from the relevant patient collective experience and apply within the personalized context of the individual patient. Understanding the whys and hows of a patient achieving specific outcomes can be derived by combining the personal characteristics of the patient with the longitudinal engagement with multiple parts of the health system. Analytics therefore help improve not just the body of medical knowledge (i.e. exploiting real world evidence), but can also be the enabler for applying this knowledge directly to the individual and the intervention required.

These new analytics and the ability to analyze structured and unstructured data underpin emerging cognitive computing systems that learn and interact naturally. These systems are trained by using artificial intelligence and machine learning algorithms and establish a mechanism for assimilation of experience in the creation of enhanced medical knowledge.  One great example is the collaboration between IBM Watson and Memorial Sloan-Kettering Cancer Center to help fight cancer with evidence-based diagnosis and treatment suggestions.

Taking advantage of this evolution in analytics not only prepares, but can active lead organizations to the value-based, outcomes-driven delivery demanded of participating healthcare organizations of tomorrow.

Many health organizations have now identified and put into practice an analytics strategy but fewer are driving analytics into the forefront across the enterprise. The IBM Institute for Business Value recently engaged with key constituents of the healthcare ecosystem around the world to understand their experience with analytics, surveying over 500 executives within the healthcare industry. To read more about this exciting study, read the paper, Analytics across the ecosystem A prescription for optimizing healthcare outcomes

Richard Baxter's curator insight, January 30, 2014 7:20 AM

Thanks for sharing Marsha - fascinating opportunities by harnessing data what not to like!